system

Abstract

We provide the system. [Solution] A means for generating customer models for users to interact within a virtual space, A configuration means for constructing a dialogue scenario based on customer characteristic information set by the user, An execution means for executing a simulation in which the user and the customer model interact within the virtual space, An evaluation means for analyzing and evaluating the response during the aforementioned simulation, A means for presenting feedback to the user based on the aforementioned evaluation, A means for accumulating data obtained during the aforementioned simulation and using it to improve future automated response systems, 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 method for controlling a persona chatbot, which is 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, while improving the quality of customer service has been emphasized, effective training means for employees have been demanded. However, conventional training methods have problems such as difficulty in reproducing the actual customer service scene and variations in the training results of employees due to time and place constraints. In addition, since feedback on customer service skills is not sufficiently provided, the learning effect is limited. An object of the invention is to provide a system that solves such problems and efficiently and effectively provides training for customer service using a virtual space.

Means for Solving the Problems

[0005] The system according to the present invention includes a generation means for generating a customer model for user interaction in a virtual space. The generation means includes a configuration means for constructing a dialogue scenario based on customer characteristic information set by the user, and further includes an execution means for executing a simulation in which the user and the customer model interact in the virtual space. The system also has an evaluation means for analyzing and evaluating responses during the simulation, and includes a presentation means for presenting feedback to the user based on the evaluation. In addition, it provides a storage means for accumulating data obtained during the simulation and using it to improve future automated response systems. In this way, the present invention makes it possible to implement training to improve employees' customer service skills without the constraints of time and place.

[0006] A "user" refers to an individual who operates the system in a virtual space to receive customer service training.

[0007] A "virtual space" refers to a digital environment that mimics the real world, where users can simulate interactions with customer models.

[0008] A "customer model" is a virtual customer generated based on customer characteristic information set by the user.

[0009] "Generation means" refers to a system component that has the function of creating the necessary customer model based on user input.

[0010] "Customer characteristic information" refers to attribute information such as age, gender, occupation, and interests that users use when determining customer scenarios.

[0011] "Configuration means" refers to the system's function of creating specific scenarios to facilitate smooth interaction between the customer model and the user.

[0012] "Execution means" refers to the system's functionality for conducting interactive simulations with the generated customer model within a virtual space.

[0013] "Evaluation means" refers to the function of a system that analyzes the user's responses during a simulation and performs an evaluation based on that analysis.

[0014] "Presentation method" refers to a function that allows the system to display simulation evaluation results and feedback to the user.

[0015] "Storage method" refers to a system function that saves data obtained from simulations and uses it for future system improvements. [Brief explanation of the drawing]

[0016] [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]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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.

[0020] 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.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] The customer service training system in a virtual space according to the present invention utilizes various computer technologies to enable users to have a realistic customer service experience in a VR environment. This system consists of a user, a server, and a terminal, and each element works in cooperation with the others.

[0038] 1. User actions

[0039] First, the user logs into the system using a terminal. Through the interface on the terminal, the user inputs customer characteristic information necessary for virtual customer service. This information includes the customer's age, occupation, interests, etc. After the user inputs the information, the terminal sends it to the server.

[0040] 2. Processing on the server

[0041] Based on the received customer characteristics information, the server activates a generative AI to generate an appropriate customer model. This customer model, based on the specified persona, enables realistic interaction within the virtual space. The generated model is then sent by the server to the terminal and deployed into the VR environment.

[0042] 3. Run the simulation

[0043] The terminal displays the received customer model in the virtual space and prepares it for user interaction. The user experiences a simulated customer service interaction using VR controllers and voice input. The system controls the customer model's actions and responses based on scenarios provided by the server.

[0044] 4. Evaluation and Feedback

[0045] As the simulation progresses, the server analyzes and evaluates user responses in real time. The evaluation results are saved along with the analysis data and provided to the user as feedback after the simulation is complete. This feedback includes both positive aspects and areas for improvement in the user's responses.

[0046] 5. Data accumulation and utilization

[0047] The server stores data from the entire simulation, which is then used for later analysis and further improvements to the AI. This stored data will also contribute to research and development aimed at automating customer service tasks in the future and improving the accuracy of AI responses.

[0048] In this form, the system of the present invention provides a powerful tool for users to efficiently and effectively acquire customer service skills in a virtual space. By improving the quality and effectiveness of customer service training, it is expected to lead to an overall improvement in service.

[0049] The following describes the processing flow.

[0050] Step 1:

[0051] The user logs into the system using a terminal. The user enters customer characteristics information (age, occupation, interests, etc.) into the interface, and the terminal sends this information to the server.

[0052] Step 2:

[0053] The server activates a generation AI based on the customer characteristics information it receives, and generates a customer model suitable for the specified persona. The server then sends the generated model to the terminal.

[0054] Step 3:

[0055] The device displays the received customer model in the virtual space, preparing the user for interaction. The device then starts the VR environment, readying the user to begin the experience.

[0056] Step 4:

[0057] Users interact with customer models using VR controllers and voice input. They experience simulated customer service according to a scenario they select.

[0058] Step 5:

[0059] The server analyzes the user's responses in real time during the simulation. Based on the responses, it collects evaluation data on the user's customer service skills.

[0060] Step 6:

[0061] After the simulation is complete, the server compiles the evaluation results and generates feedback to provide to the user. The server sends the generated feedback to the terminal, and the user checks the results.

[0062] Step 7:

[0063] The server stores simulation data in a database. This stored data will be used to improve AI response accuracy and system performance in the future.

[0064] (Example 1)

[0065] 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."

[0066] This invention aims to provide users with a realistic dialogue experience in a virtual space and to effectively improve their customer service skills. Conventional customer training systems have problems with the versatility of dialogue scenarios and the accuracy of user feedback, and often fail to adequately reproduce real-world customer service scenes, making practical training difficult.

[0067] 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.

[0068] In this invention, the server includes means for generating a customer model using artificial intelligence based on customer characteristic information input by the user, means for a user terminal to receive the generated customer model and introduce it into a virtual environment, and means for executing a simulation in which the user interacts with the customer model within the virtual environment. This enables the user to have a highly customized conversational experience based on individual customer characteristics and to acquire more practical and useful customer service skills.

[0069] "Generative artificial intelligence" is a technology that generates models and algorithms for performing specific tasks based on input data.

[0070] A "customer model" is a virtual character that can be simulated in a virtual environment based on specific customer characteristics.

[0071] A "virtual environment" is a three-dimensional simulation space created by a computer, a space that users can experience interactively.

[0072] "Customer characteristic information" refers to data including customer age, occupation, interests, etc., and is used to generate customer models.

[0073] "Simulation" is the process of running user and customer model interactions within a virtual environment.

[0074] "Real-time analysis" is a technology that processes information the moment it is acquired and immediately provides analysis results.

[0075] "Feedback" refers to evaluations and advice based on user performance obtained during the simulation.

[0076] This invention is a system that enables users to obtain a realistic customer service experience using virtual reality, and is realized through the collaboration of a server, a terminal, and a generative AI.

[0077] First, the user logs into the system using a terminal. The terminal has a specific input / output interface and provides the user with a means to access the virtual environment. The user inputs customer characteristic information, such as the customer's age, occupation, and interests, through the terminal's interface. The input information is then sent to the server by the terminal.

[0078] The server generates a customer model using generative AI based on the received customer characteristics information. The generative AI processes the data and generates a virtual character that matches the specified persona. This process includes natural language processing and machine learning techniques. The generated customer model is sent from the server to the terminal and deployed into the virtual reality space.

[0079] The terminal displays the received customer model in the virtual space, enabling the user to interact with it. The user can interact within the virtual space using a VR controller or voice input. The system controls the customer model's responses based on scenarios provided by the server.

[0080] To give a concrete example, when a user uses a virtual reality system to simulate restaurant service, they interact with customers of a specified age group and interests. In this case, they might use a prompt like this: "Please create a virtual space for a restaurant service simulation targeting a customer who is a university student in their 20s and interested in Italian food."

[0081] This configuration allows the system to provide users with an efficient and effective customer service training environment. The system improves users' communication skills and promotes quality improvements in customer service operations.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] Users log in to the system using their terminal. Authentication is performed by entering their user ID and password, and access is granted. This prepares the user to begin their individual training session.

[0085] Step 2:

[0086] The user inputs customer characteristics information to be used in the virtual space through the terminal interface. This input data includes the customer's age, occupation, and interests. The terminal organizes this data and sends it to the server via a secure protocol. The output at this stage is a data package containing customer characteristics information.

[0087] Step 3:

[0088] The server receives customer characteristic information from the terminal as input and activates the generating AI. The AI ​​uses natural language processing and machine learning algorithms to generate the optimal customer model. The data processing in this process is the process of converting characteristic information into model parameters suitable for virtual dialogue. The generated customer model is sent to the terminal as server output.

[0089] Step 4:

[0090] The terminal receives a customer model from the server as input and deploys it into the virtual space. Using dedicated VR software, the model is displayed in the user's visual space. The output is an interactive virtual simulation environment that the user can experience through a VR device.

[0091] Step 5:

[0092] Users interact with customer models in a virtual space using VR controllers and voice input. User actions and responses are recorded in real time and sent to a server. This allows user feedback to be collected as data.

[0093] Step 6:

[0094] The server analyzes the collected user responses as input. Through an algorithm, the appropriateness of the responses and the corresponding skills are automatically evaluated. The evaluation results are quantified and stored as detailed analysis data. The output is feedback information presented to the user, including areas for improvement and strengths.

[0095] Step 7:

[0096] The server stores all data during the simulation. This dataset is saved to be used for future improvements to AI algorithms and the development of new training scenarios. This data accumulation enables continuous system improvement.

[0097] (Application Example 1)

[0098] 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."

[0099] In today's service industry, staff are required to be able to meet the diverse needs of customers, but there is a lack of environments where practical skills can be efficiently acquired. This leads to problems such as inconsistencies in customer service quality and decreased learning effectiveness. In particular, there is a lack of realistic training utilizing virtual environments, and there is a need for efficient and flexible acquisition of customer service skills.

[0100] 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.

[0101] In this invention, the server includes generation means for generating a person model for the user to interact with within a virtual environment, configuration means for configuring a dialogue scenario based on customer characteristic information set by the user, and execution means for performing a simulated execution in which the user and the person model interact within the virtual environment. This enables practical training tailored to a variety of customer scenarios.

[0102] A "user" is an individual or group that interacts with a character model within a virtual environment.

[0103] A "virtual environment" is a computer-generated space that is different from the real world and is experienced by users using head-mounted displays or similar devices.

[0104] A "character model" refers to a character generated by AI that interacts with the user within a virtual environment.

[0105] "Generation means" refers to a method or system for generating a human model within a virtual environment based on user input information.

[0106] "Customer characteristic information" refers to attribute information of customers interacting in a virtual environment, such as age, occupation, and interests.

[0107] A "dialogue scenario" is a designed situation or setting in which a character model and a user interact within a virtual environment.

[0108] "Execution means" refers to the mechanism or method for conducting a simulated dialogue between the generated character model and the user.

[0109] "Evaluation means" refers to a system or method that analyzes and evaluates user reactions during simulated execution.

[0110] "Presentation means" refers to a method or device used to show evaluation results or areas for improvement to the user.

[0111] A "storage method" refers to a system or method for storing data obtained during simulated execution and using it to improve the system in the future.

[0112] A "head-mounted display" is a display device worn on the head by a user to visually experience a virtual environment.

[0113] A "control device" is a system that uses an artificial intelligence model to control the behavior of a human model within a virtual environment.

[0114] The system for realizing this invention consists of a series of hardware and software components for enabling interactive training within a virtual environment. First, the user wears a head-mounted display and logs into the system using a terminal with a dedicated application installed. This terminal inputs customer characteristic information set by the user and sends it to the server.

[0115] The server activates an AI model based on the received customer characteristics information, generating a persona model for interacting with the user within a virtual environment. This persona model simulates realistic conversations based on a specific persona. The generated model is delivered from the server to the user via a head-mounted display.

[0116] Users engage in simulated conversations with a human model according to dialogue scenarios. This process is coordinated by a control system, which controls the AI ​​model's behavior. User responses are evaluated in real time, and feedback is provided on areas that need improvement. This allows users to experience diverse customer scenarios in a virtual environment and improve their skills.

[0117] This system also stores data to enable progressive learning. Specifically, data obtained during simulations is saved using storage methods and used to improve the response system in the future. Data analysis tools such as Python and pandas are utilized in this process. As an example scenario, a prompt such as "As a customer of a bicycle toolkit, please ask questions about its usage and convenience" can be provided.

[0118] In this way, users can efficiently and effectively learn customer service skills and improve their practical response abilities within a virtual environment.

[0119] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0120] Step 1:

[0121] The user logs into the system by operating a terminal. The input here is the user's authentication information. The terminal processes this, verifies the user's identity, and approves the login. The output indicates that the user's access rights have been authenticated and the system is available for use.

[0122] Step 2:

[0123] The user inputs customer characteristics information using the terminal's interface. This input includes the customer's age, occupation, interests, etc. The terminal receives this information, organizes the data, and sends it to the server. The output is the structured customer characteristics data sent to the server.

[0124] Step 3:

[0125] The server generates a person model using a generative AI model based on the received customer characteristic information. In this process, the AI ​​analyzes the input characteristic information and creates an appropriate customer persona. The output is a customized person model for use within the virtual environment.

[0126] Step 4:

[0127] The server sends the generated character model to the terminal. The input here is the character model data generated on the server, which the terminal receives and prepares to display in the virtual environment. The output is the character model available on the terminal.

[0128] Step 5:

[0129] The user wears a head-mounted display and begins a simulated conversation with a human model in a virtual environment. The input is a human model provided by the server, and includes voice commands spoken by the user. The terminal analyzes the voice input, and the AI ​​model controls the human model's responses. The output is an interactive dialogue between the user and the human model.

[0130] Step 6:

[0131] The server evaluates the user's responses in real time during the simulated dialogue. The input here is the user's response data, and the server applies an algorithm to evaluate its appropriateness. The output is improvement feedback data based on the evaluation.

[0132] Step 7:

[0133] The server presents evaluation results and feedback to the user. The input is real-time analyzed evaluation data, which the terminal provides to the user, indicating areas for improvement. The output is information conveyed to the user as specific feedback.

[0134] Step 8:

[0135] The server stores data collected during simulated dialogue. Input consists of various data obtained during the dialogue, which the server stores in a structured format. Output is entries into a database that can be used for future system improvements.

[0136] 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.

[0137] This invention provides a system that recognizes user emotions in real time and uses that information to provide more effective customer service training. This system includes a user, a server, a terminal, and an emotion recognition engine, all of which work together.

[0138] 1. User actions

[0139] The user logs into the system via a terminal and prepares to interact with the customer model in a virtual space. The user enters customer characteristic information to be used in training into the terminal. This information is sent from the terminal to the server.

[0140] 2. Processing on the server

[0141] Based on the transmitted customer characteristics information, the server uses a generative AI to generate a customer model based on a specific persona. The generated model is sent to the terminal and then placed in a virtual space.

[0142] 3. Emotion Recognition Engine

[0143] The device is equipped with an emotion recognition engine that determines the user's emotional state from their voice and body movements. This data is sent to the server in real time and used to improve customer model responses and scenario settings.

[0144] 4. Run the simulation

[0145] The user starts a simulation in a virtual space using a device. The user interacts with a generated customer model using VR controllers and voice input. An emotion recognition engine monitors the user's emotions and adjusts the customer model's responses accordingly based on that information.

[0146] 5. Evaluation and Feedback

[0147] The server analyzes the user's responses and emotional data recorded during the experience and provides an appropriate evaluation. This evaluation result is provided to the user as feedback, indicating areas for improvement in training. For example, it identifies situations where the user reacted emotionally and advises on appropriate countermeasures.

[0148] 6. Data accumulation and utilization

[0149] The data accumulated across the entire system will be used to improve the accuracy of AI responses and enhance emotion recognition technology in the future. This will enable us to continue providing users with a more natural and effective virtual customer service experience.

[0150] In this configuration, the system of the present invention can provide advanced customer service training that incorporates emotion recognition, thereby improving employees' customer service skills and customer interaction abilities.

[0151] The following describes the processing flow.

[0152] Step 1:

[0153] The user logs into the terminal and enters customer characteristic information through the interface. The terminal then sends this information to the server.

[0154] Step 2:

[0155] Based on the customer characteristics information received by the server, the generation AI is activated to generate a customer model based on the specified persona. The server then sends the generated model to the terminal.

[0156] Step 3:

[0157] The terminal displays the customer model it received in the virtual space, preparing the user to begin the conversation.

[0158] Step 4:

[0159] The emotion recognition engine installed in the device analyzes the user's voice and body movements in real time to determine their emotional state. The device then sends the recognized emotion data to a server.

[0160] Step 5:

[0161] The server dynamically adjusts the customer model's response based on emotional data. For example, if the server determines that the user is irritated, the customer model will respond in a calming manner.

[0162] Step 6:

[0163] Users interact with a customer model using VR controllers and voice input to experience a simulated customer service interaction.

[0164] Step 7:

[0165] The server analyzes the user's responses and sentiment data during the simulation and makes an appropriate evaluation. The server then sends the evaluation results to the terminal to present them to the user as feedback.

[0166] Step 8:

[0167] The terminal displays the evaluation results to the user, clearly indicating the strengths and areas for improvement in customer service.

[0168] Step 9:

[0169] The server stores all simulation data, which will be used for future system improvements and AI model training.

[0170] (Example 2)

[0171] 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".

[0172] Conventional virtual reality dialogue systems have struggled to provide an effective learning experience that dynamically reflects the user's emotional state. This has resulted in problems such as unrealistic user feedback and insufficient improvement of customer service skills. This invention aims to provide an effective and natural experience for the user by analyzing the user's emotions in real time and dynamically adjusting the dialogue content within the virtual space based on that analysis.

[0173] 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.

[0174] In this invention, the server includes means for generating a character model generated by an information processing device for users to interact in a virtual space, means for configuring a dialogue simulation based on attribute information set by the user, and means for executing a simulated experience in which the user and the character model interact in the virtual space. This makes it possible to provide dynamic dialogue content that reflects the user's emotional state.

[0175] An "information processing device" is a device or system that processes input data and generates results according to a specific purpose.

[0176] A "character model" is a virtual character created within a virtual space based on specific settings and attributes, for the purpose of interacting with the user.

[0177] "Attribute information" refers to data that represents specific characteristics or features set by the user, and is used to build dialogue simulations.

[0178] A "simulated experience" is an interactive experience designed to recreate real-world situations within a virtual space.

[0179] "Evaluation means" refers to methods or devices for analyzing data obtained during a simulated experience and evaluating the user's responses and emotional state.

[0180] A "display device" is a device used to visually present the results and information of a computer system to a user.

[0181] A "storage device" is a digital or physical recording medium used to store acquired data and prepare it for future use or analysis.

[0182] "Emotion recognition means" refers to technologies and devices that analyze a user's emotions from their voice and actions and acquire information in real time.

[0183] This invention provides a customer service training system that recognizes user emotions in real time and responds accordingly. The system consists of a user, a terminal, and a server.

[0184] The user first logs into the system using a terminal. During this process, the user enters attribute information. This attribute information includes customer characteristics and hobbies, and is used to build a simulated experience within the virtual space.

[0185] The server generates a character model for use in the virtual space using a generative AI model based on attribute information received from the terminal. For example, a general-purpose AI specializing in natural language generation can be used as the generative AI. The server then sends the generated character model to the terminal, allowing the user to begin the simulated experience in the virtual space.

[0186] The device is equipped with emotion recognition technology that analyzes the user's emotions in real time from their voice and actions. This data is sent to a server and used to dynamically adjust the responses of the human model during the simulated experience.

[0187] An example of a prompt message is: "You are a male salesperson in your 40s. Your hobby is golf, and you enjoy talking about hobbies with clients." Based on this information, the system can provide the user with a personalized simulated experience.

[0188] With this configuration, the present invention can provide practical and dynamic training to improve employees' customer service capabilities. Through simulated experiences in a virtual space, users can learn and receive feedback in situations closer to reality, enabling them to acquire skills efficiently.

[0189] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0190] Step 1:

[0191] Users log in to the system via a terminal and enter the necessary attribute information. This information includes, for example, the customer's age, occupation, and hobbies. This data is an important element for personalizing the simulated experience in the virtual space. The entered data is sent from the terminal to the server.

[0192] Step 2:

[0193] The server receives attribute information sent from the terminal. Based on the received data, it uses a generative AI model to generate a human model for use in the virtual space. Specifically, the generative AI analyzes the input data and constructs a human model with the corresponding features. The generated model is then sent to the terminal.

[0194] Step 3:

[0195] The terminal places a human model received from the server into the virtual space. At this time, the emotion recognition system is activated and prepares to analyze the user's voice and actions in real time. The tone and speed of the user's voice, as well as their body movements, are the targets of emotion recognition.

[0196] Step 4:

[0197] The user begins a simulated experience in a virtual space through their device. Using a VR headset and controllers, the user interacts with a generated character model. Emotion recognition measures analyze the user's facial expressions and movements and adjust the character model's responses accordingly. This process is performed in real time, providing the user with dynamic responses.

[0198] Step 5:

[0199] The server collects user actions and emotional data recorded during the simulated experience. Based on the collected data, it performs analysis to evaluate the quality of responses and identify areas for improvement. This analysis includes response speed, accuracy, and emotional appropriateness. The analysis results are sent to the terminal as an evaluation report.

[0200] Step 6:

[0201] The terminal receives evaluation reports sent from the server and provides feedback to the user. This feedback highlights successes and areas for improvement, allowing the user to prepare for the next simulation. The collected data is stored through memory and used to improve the system in the future.

[0202] (Application Example 2)

[0203] 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".

[0204] In modern industrial sectors, efficient collaboration between automated equipment and human workers is essential. Especially in factories, while automation by machines is advancing, the emotions and stress levels of human workers can still impact work efficiency. Therefore, there is a need for a system that appropriately monitors the emotional state of workers in the work environment and allows automated systems to adjust their responses as needed. However, current systems lack sufficient interactive adjustments based on such emotional states, thus necessitating improvements to the work environment.

[0205] 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.

[0206] In this invention, the server includes means for generating a customer model for user interaction within a virtual environment, means for configuring a dialogue scenario based on characteristic information set by the user, and recognition means for receiving voice and facial expression data in real time and analyzing the emotional state. This makes it possible to recognize the stress and emotional changes felt by the worker in real time and dynamically adjust the response and support content of the automated device.

[0207] "Means for generating customer models for user interaction within a virtual environment" refers to a process equipped with the functionality to automatically generate digital customer models for user interaction within a virtual environment, based on characteristics set by the user.

[0208] "Means for constructing dialogue scenarios based on characteristic information" refers to a process that constructs dialogue scenarios within a virtual environment based on customer characteristic information set by the user, and adjusts the dialogue to proceed according to that scenario.

[0209] "Recognition means for receiving voice and facial expression data in real time and analyzing emotional state" refers to an analysis process that acquires the user's voice and facial expression data in real time and uses that data to determine the user's emotional state.

[0210] A "server" is a core computing device that centrally processes information sent by users and distributes the generated information to user terminals.

[0211] "Adjustment methods" refer to the process of appropriately modifying the customer model's responses based on the emotional information obtained, thereby optimizing the user experience.

[0212] To implement this invention, the entire system consists of a user terminal, a server, an emotion recognition device, and a generative AI model.

[0213] The server receives characteristic information from the user terminal and generates a customer model based on it. By using the generated AI model, an appropriate customer model is created for interacting with the user in a virtual environment. At that time, the server receives audio and video data transmitted from the terminal and analyzes the emotional state in real time. TENSORFLOW® is used for emotion recognition, identifying emotions from audio and facial expression data.

[0214] The user terminal uses smart glasses or a head-mounted display as an interface, enabling operation within a virtual environment. This allows the user to interact with a generated customer model, and the emotional state information collected during this interaction is sent to the server. This information forms the basis for the system to adjust the customer model's responses according to the user's emotional state.

[0215] A specific use case involves factory workers wearing smart glasses to monitor their stress levels in real time during work. If a worker experiences stress, the system adjusts its response based on that information so that an automated system can provide appropriate assistance. An example of a prompt used in this case would be: "Based on emotion recognition data, generate a response instructing the robot on how to assist the worker in a stressful situation."

[0216] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0217] Step 1:

[0218] The user logs into the virtual environment via a terminal and enters characteristic information. This information includes settings for the customer model with which they interact in the virtual environment. The entered information is structured on the terminal and becomes data sent to the server.

[0219] Step 2:

[0220] The server sends the received characteristic information to the generating AI model. Based on this information, the generating AI model creates a customer model for use in the virtual environment, which the server receives. The customer model includes dialogue patterns and personas, and the generation results are sent to the user terminal.

[0221] Step 3:

[0222] The terminal places the received customer model into a virtual environment and initiates interaction with the user. Here, the user's voice and video data are captured in real time and analyzed by an emotion recognition system. This data is acquired using smart glasses or a head-mounted display.

[0223] Step 4:

[0224] The server receives analysis data from the emotion recognition system and evaluates the user's emotions. Using features obtained from voice and facial expressions, it estimates the emotional state and adjusts the customer model's response based on the results. Machine learning algorithms are applied to this analysis.

[0225] Step 5:

[0226] Based on the user's emotions, the server returns an adjusted response to the user's terminal. The terminal uses this information, and the customer model in the virtual environment performs an appropriate response. The user receives feedback through the interaction and evaluates their own response.

[0227] Step 6:

[0228] The server stores emotional data and response results collected during the interaction in a database. This information will be used for future system improvements and to enhance the accuracy of the generative AI model.

[0229] 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.

[0230] 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 those described above. 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 shown 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.

[0231] 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.

[0232] [Second Embodiment]

[0233] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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).

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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".

[0245] The customer service training system in a virtual space according to the present invention utilizes various computer technologies to enable users to have a realistic customer service experience in a VR environment. This system consists of a user, a server, and a terminal, and each element works in cooperation with the others.

[0246] 1. User actions

[0247] First, the user logs into the system using a terminal. Through the interface on the terminal, the user inputs customer characteristic information necessary for virtual customer service. This information includes the customer's age, occupation, interests, etc. After the user inputs the information, the terminal sends it to the server.

[0248] 2. Processing on the server

[0249] Based on the received customer characteristics information, the server activates a generative AI to generate an appropriate customer model. This customer model, based on the specified persona, enables realistic interaction within the virtual space. The generated model is then sent by the server to the terminal and deployed into the VR environment.

[0250] 3. Run the simulation

[0251] The terminal displays the received customer model in the virtual space and prepares it for user interaction. The user experiences a simulated customer service interaction using VR controllers and voice input. The system controls the customer model's actions and responses based on scenarios provided by the server.

[0252] 4. Evaluation and Feedback

[0253] As the simulation progresses, the server analyzes and evaluates user responses in real time. The evaluation results are saved along with the analysis data and provided to the user as feedback after the simulation ends. This feedback includes both positive aspects and areas for improvement in the user's responses.

[0254] 5. Data accumulation and utilization

[0255] The server stores data from the entire simulation, which is then used for later analysis and further improvements to the AI. This stored data will also contribute to research and development aimed at automating customer service tasks in the future and improving the accuracy of AI responses.

[0256] In this form, the system of the present invention provides a powerful tool for users to efficiently and effectively acquire customer service skills in a virtual space. By improving the quality and effectiveness of customer service training, it is expected to lead to an overall improvement in service.

[0257] The following describes the processing flow.

[0258] Step 1:

[0259] The user logs into the system using a terminal. The user enters customer characteristics information (age, occupation, interests, etc.) into the interface, and the terminal sends this information to the server.

[0260] Step 2:

[0261] The server activates a generation AI based on the customer characteristics information it receives, and generates a customer model suitable for the specified persona. The server then sends the generated model to the terminal.

[0262] Step 3:

[0263] The device displays the received customer model in the virtual space, preparing the user for interaction. The device then starts the VR environment, readying the user to begin the experience.

[0264] Step 4:

[0265] Users interact with customer models using VR controllers and voice input. They experience simulated customer service according to a scenario they select.

[0266] Step 5:

[0267] The server analyzes the user's responses in real time during the simulation. Based on the responses, it collects evaluation data on the user's customer service skills.

[0268] Step 6:

[0269] After the simulation is complete, the server compiles the evaluation results and generates feedback to provide to the user. The server sends the generated feedback to the terminal, and the user checks the results.

[0270] Step 7:

[0271] The server stores simulation data in a database. This stored data will be used to improve AI response accuracy and system performance in the future.

[0272] (Example 1)

[0273] 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."

[0274] This invention aims to provide users with a realistic dialogue experience in a virtual space and to effectively improve their customer service skills. Conventional customer training systems have problems with the versatility of dialogue scenarios and the accuracy of user feedback, and often fail to adequately reproduce real-world customer service scenes, making practical training difficult.

[0275] 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.

[0276] In this invention, the server includes means for generating a customer model using artificial intelligence based on customer characteristic information input by the user, means for a user terminal to receive the generated customer model and introduce it into a virtual environment, and means for executing a simulation in which the user interacts with the customer model within the virtual environment. This enables the user to have a highly customized conversational experience based on individual customer characteristics and to acquire more practical and useful customer service skills.

[0277] "Generative AI" is a technology that generates models or algorithms for performing specific tasks based on the input data.

[0278] "Customer model" is a virtual character that can be simulated based on specific customer characteristics in a virtual environment.

[0279] "Virtual environment" is a three-dimensional simulation space generated by a computer, a space that users can interactively experience.

[0280] "Customer characteristic information" is data that includes a customer's age, occupation, interests, etc., and is information used for generating a customer model.

[0281] "Simulation" is a process of executing the interaction between a user and a customer model within a virtual environment.

[0282] "Real-time analysis" is a technology that processes information at the moment it is acquired and immediately outputs the analysis results.

[0283] "Feedback" is an evaluation or advice based on the user's performance obtained during the simulation.

[0284] The present invention is a system for a user to obtain a realistic customer service experience using virtual reality, which is realized by the cooperation of a server, a terminal, and generative AI.

[0285] First, the user uses the terminal to log in to the system. The terminal has a specific input / output interface and provides the user with a means of accessing the virtual environment. The user inputs customer characteristic information, such as the customer's age, occupation, interests, etc., through the interface of the terminal. The input information is transmitted by the terminal to the server.

[0286] The server generates a customer model using a generative AI based on the received customer characteristic information. The generative AI processes data and generates virtual characters that match the specified personas. This process includes natural language processing and machine learning techniques. The generated customer model is transmitted from the server to the terminal and introduced into the virtual reality space.

[0287] The terminal displays the received customer model in the virtual space and makes it interactive for the user. The user can interact in the virtual space through a VR controller or voice input. The system controls the response of the customer model based on the scenario provided by the server.

[0288] For a specific example, when the user conducts a restaurant customer service simulation using a virtual reality system, the user interacts with customers having the specified age group and interests. In this case, for example, the following prompt sentence is used: "Generate a virtual space for conducting a restaurant customer service simulation for customers in their 20s who are interested in Italian cuisine."

[0289] With such a configuration, this system can provide an efficient and effective customer service training environment for the user. This system improves the user's interaction skills and promotes the improvement of quality in customer service operations.

[0290] The flow of the specific process in Example 1 will be described using FIG. 11.

[0291] Step 1:

[0292] The user logs in to the system using the terminal. Authentication is performed by entering the user ID and password, and access is permitted. As a result, the user is ready to start an individual training session.

[0293] Step 2:

[0294] The user inputs customer characteristics information to be used in the virtual space through the terminal interface. This input data includes the customer's age, occupation, and interests. The terminal organizes this data and sends it to the server via a secure protocol. The output at this stage is a data package containing customer characteristics information.

[0295] Step 3:

[0296] The server receives customer characteristic information from the terminal as input and activates the generating AI. The AI ​​uses natural language processing and machine learning algorithms to generate the optimal customer model. The data processing in this process is the process of converting characteristic information into model parameters suitable for virtual dialogue. The generated customer model is sent to the terminal as server output.

[0297] Step 4:

[0298] The terminal receives a customer model from the server as input and deploys it into the virtual space. Using dedicated VR software, the model is displayed in the user's visual space. The output is an interactive virtual simulation environment that the user can experience through a VR device.

[0299] Step 5:

[0300] Users interact with customer models in a virtual space using VR controllers and voice input. User actions and responses are recorded in real time and sent to a server. This allows user feedback to be collected as data.

[0301] Step 6:

[0302] The server analyzes the collected user responses as input. Through an algorithm, the appropriateness of the responses and the corresponding skills are automatically evaluated. The evaluation results are quantified and stored as detailed analysis data. The output is feedback information presented to the user, including areas for improvement and strengths.

[0303] Step 7:

[0304] The server accumulates all data during the simulation. This dataset is saved for use in future improvements to AI algorithms and the development of new training scenarios. The accumulation of data enables continuous system improvement.

[0305] (Application Example 1)

[0306] 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".

[0307] In the modern customer service industry, staff are required to be able to respond to various customer requests, but there is a lack of an environment where practical skills can be efficiently acquired. As a result, variations in customer service quality and a decline in learning effects have become problems. In particular, there is a lack of realistic training using virtual environments, and there is a need to acquire efficient and flexible customer service skills.

[0308] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means respectively.

[0309] In this invention, the server includes a generating means for generating a human model for the user to interact within a virtual environment, a configuring means for configuring an interaction scenario based on the customer characteristic information set by the user, and an executing means for executing a simulated execution of the interaction between the user and the human model within the virtual environment. Thereby, practical training along various customer scenarios becomes possible.

[0310] The "user" is an individual or group that interacts with a human model within a virtual environment.

[0311] The "virtual environment" is a computer-generated space different from the real world, and is a space that the user experiences using a head-mounted display or the like.

[0312] A "character model" refers to a character generated by AI that interacts with the user within a virtual environment.

[0313] "Generation means" refers to a method or system for generating a human model within a virtual environment based on user input information.

[0314] "Customer characteristic information" refers to attribute information of customers interacting in a virtual environment, such as age, occupation, and interests.

[0315] A "dialogue scenario" is a designed situation or setting in which a character model and a user interact within a virtual environment.

[0316] "Execution means" refers to the mechanism or method for conducting a simulated dialogue between the generated character model and the user.

[0317] "Evaluation means" refers to a system or method that analyzes and evaluates user reactions during simulated execution.

[0318] "Presentation means" refers to a method or device used to show evaluation results or areas for improvement to the user.

[0319] A "storage method" refers to a system or method for storing data obtained during simulated execution and using it to improve the system in the future.

[0320] A "head-mounted display" is a display device worn on the head by a user to visually experience a virtual environment.

[0321] A "control device" is a system that uses an artificial intelligence model to control the behavior of a human model within a virtual environment.

[0322] The system for realizing this invention consists of a series of hardware and software components for enabling interactive training within a virtual environment. First, the user wears a head-mounted display and logs into the system using a terminal with a dedicated application installed. This terminal inputs customer characteristic information set by the user and sends it to the server.

[0323] The server activates an AI model based on the received customer characteristics information, generating a persona model for interacting with the user within a virtual environment. This persona model simulates realistic conversations based on a specific persona. The generated model is delivered from the server to the user via a head-mounted display.

[0324] Users engage in simulated conversations with a human model according to dialogue scenarios. This process is coordinated by a control system, which controls the AI ​​model's behavior. User responses are evaluated in real time, and feedback is provided on areas that need improvement. This allows users to experience diverse customer scenarios in a virtual environment and improve their skills.

[0325] This system also stores data to enable progressive learning. Specifically, data obtained during simulations is saved using storage methods and used to improve the response system in the future. Data analysis tools such as Python and pandas are utilized in this process. As an example scenario, a prompt such as "As a customer of a bicycle toolkit, please ask questions about its usage and convenience" can be provided.

[0326] In this way, users can efficiently and effectively learn customer service skills and improve their practical response abilities within a virtual environment.

[0327] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0328] Step 1:

[0329] The user logs into the system by operating a terminal. The input here is the user's authentication information. The terminal processes this, verifies the user's identity, and approves the login. The output indicates that the user's access rights have been authenticated and the system is available for use.

[0330] Step 2:

[0331] The user inputs customer characteristics information using the terminal's interface. This input includes the customer's age, occupation, interests, etc. The terminal receives this information, organizes the data, and sends it to the server. The output is the structured customer characteristics data sent to the server.

[0332] Step 3:

[0333] The server generates a person model using a generative AI model based on the received customer characteristic information. In this process, the AI ​​analyzes the input characteristic information and creates an appropriate customer persona. The output is a customized person model for use within the virtual environment.

[0334] Step 4:

[0335] The server sends the generated character model to the terminal. The input here is the character model data generated on the server, which the terminal receives and prepares to display in the virtual environment. The output is the character model available on the terminal.

[0336] Step 5:

[0337] The user wears a head-mounted display and begins a simulated conversation with a human model in a virtual environment. The input is a human model provided by the server, and includes voice commands spoken by the user. The terminal analyzes the voice input, and the AI ​​model controls the human model's responses. The output is an interactive dialogue between the user and the human model.

[0338] Step 6:

[0339] The server evaluates the user's responses in real time during the simulated dialogue. The input here is the user's response data, and the server applies an algorithm to evaluate its appropriateness. The output is improvement feedback data based on the evaluation.

[0340] Step 7:

[0341] The server presents evaluation results and feedback to the user. The input is real-time analyzed evaluation data, which the terminal provides to the user, indicating areas for improvement. The output is information conveyed to the user as specific feedback.

[0342] Step 8:

[0343] The server stores data collected during simulated dialogue. Input consists of various data obtained during the dialogue, which the server stores in a structured format. Output is entries into a database that can be used for future system improvements.

[0344] 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.

[0345] This invention provides a system that recognizes user emotions in real time and uses that information to provide more effective customer service training. This system includes a user, a server, a terminal, and an emotion recognition engine, all of which work together.

[0346] 1. User actions

[0347] The user logs into the system via a terminal and prepares to interact with the customer model in a virtual space. The user enters customer characteristic information to be used in training into the terminal. This information is sent from the terminal to the server.

[0348] 2. Processing on the server

[0349] Based on the transmitted customer characteristics information, the server uses a generative AI to generate a customer model based on a specific persona. The generated model is sent to the terminal and then placed in a virtual space.

[0350] 3. Emotion Recognition Engine

[0351] The device is equipped with an emotion recognition engine that determines the user's emotional state from their voice and body movements. This data is sent to the server in real time and used to improve customer model responses and scenario settings.

[0352] 4. Run the simulation

[0353] The user starts a simulation in a virtual space using a device. The user interacts with a generated customer model using VR controllers and voice input. An emotion recognition engine monitors the user's emotions and adjusts the customer model's responses accordingly based on that information.

[0354] 5. Evaluation and Feedback

[0355] The server analyzes the user's responses and emotional data recorded during the experience and provides an appropriate evaluation. This evaluation result is provided to the user as feedback, indicating areas for improvement in training. For example, it identifies situations where the user reacted emotionally and advises on appropriate countermeasures.

[0356] 6. Data accumulation and utilization

[0357] The data accumulated across the entire system will be used to improve the accuracy of AI responses and enhance emotion recognition technology in the future. This will enable us to continue providing users with a more natural and effective virtual customer service experience.

[0358] In this configuration, the system of the present invention can provide advanced customer service training that incorporates emotion recognition, thereby improving employees' customer service skills and customer interaction abilities.

[0359] The following describes the processing flow.

[0360] Step 1:

[0361] The user logs into the terminal and enters customer characteristic information through the interface. The terminal then sends this information to the server.

[0362] Step 2:

[0363] Based on the customer characteristics information received by the server, the generation AI is activated to generate a customer model based on the specified persona. The server then sends the generated model to the terminal.

[0364] Step 3:

[0365] The terminal displays the customer model it received in the virtual space, preparing the user to begin the conversation.

[0366] Step 4:

[0367] The emotion recognition engine installed in the device analyzes the user's voice and body movements in real time to determine their emotional state. The device then sends the recognized emotion data to a server.

[0368] Step 5:

[0369] The server dynamically adjusts the customer model's response based on emotional data. For example, if the server determines that the user is irritated, the customer model will respond in a calming manner.

[0370] Step 6:

[0371] Users interact with a customer model using VR controllers and voice input to experience a simulated customer service interaction.

[0372] Step 7:

[0373] The server analyzes the user's responses and sentiment data during the simulation and makes an appropriate evaluation. The server then sends the evaluation results to the terminal to present them to the user as feedback.

[0374] Step 8:

[0375] The terminal displays the evaluation results to the user, clearly indicating the strengths and areas for improvement in customer service.

[0376] Step 9:

[0377] The server stores all simulation data, which will be used for future system improvements and AI model training.

[0378] (Example 2)

[0379] 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".

[0380] Conventional virtual reality dialogue systems have struggled to provide an effective learning experience that dynamically reflects the user's emotional state. This has resulted in problems such as unrealistic user feedback and insufficient improvement of customer service skills. This invention aims to provide an effective and natural experience for the user by analyzing the user's emotions in real time and dynamically adjusting the dialogue content within the virtual space based on that analysis.

[0381] 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.

[0382] In this invention, the server includes means for generating a character model generated by an information processing device for users to interact in a virtual space, means for configuring a dialogue simulation based on attribute information set by the user, and means for executing a simulated experience in which the user and the character model interact in the virtual space. This makes it possible to provide dynamic dialogue content that reflects the user's emotional state.

[0383] An "information processing device" is a device or system that processes input data and generates results according to a specific purpose.

[0384] A "character model" is a virtual character created within a virtual space based on specific settings and attributes, for the purpose of interacting with the user.

[0385] "Attribute information" refers to data that represents specific characteristics or features set by the user, and is used to build dialogue simulations.

[0386] A "simulated experience" is an interactive experience designed to recreate real-world situations within a virtual space.

[0387] "Evaluation means" refers to methods or devices for analyzing data obtained during a simulated experience and evaluating the user's responses and emotional state.

[0388] A "display device" is a device used to visually present the results and information of a computer system to a user.

[0389] A "storage device" is a digital or physical recording medium used to store acquired data and prepare it for future use or analysis.

[0390] "Emotion recognition means" refers to technologies and devices that analyze a user's emotions from their voice and actions and acquire information in real time.

[0391] This invention provides a customer service training system that recognizes user emotions in real time and responds accordingly. The system consists of a user, a terminal, and a server.

[0392] The user first logs into the system using a terminal. During this process, the user enters attribute information. This attribute information includes customer characteristics and hobbies, and is used to build a simulated experience within the virtual space.

[0393] The server generates a character model for use in the virtual space using a generative AI model based on attribute information received from the terminal. For example, a general-purpose AI specializing in natural language generation can be used as the generative AI. The server then sends the generated character model to the terminal, allowing the user to begin the simulated experience in the virtual space.

[0394] The device is equipped with emotion recognition technology that analyzes the user's emotions in real time from their voice and actions. This data is sent to a server and used to dynamically adjust the responses of the human model during the simulated experience.

[0395] An example of a prompt message is: "You are a male salesperson in your 40s. Your hobby is golf, and you enjoy talking about hobbies with clients." Based on this information, the system can provide the user with a personalized simulated experience.

[0396] With this configuration, the present invention can provide practical and dynamic training to improve employees' customer service capabilities. Through simulated experiences in a virtual space, users can learn and receive feedback in situations closer to reality, enabling them to acquire skills efficiently.

[0397] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0398] Step 1:

[0399] Users log in to the system via a terminal and enter the necessary attribute information. This information includes, for example, the customer's age, occupation, and hobbies. This data is an important element for personalizing the simulated experience in the virtual space. The entered data is sent from the terminal to the server.

[0400] Step 2:

[0401] The server receives attribute information sent from the terminal. Based on the received data, it uses a generative AI model to generate a human model for use in the virtual space. Specifically, the generative AI analyzes the input data and constructs a human model with the corresponding features. The generated model is then sent to the terminal.

[0402] Step 3:

[0403] The terminal places a human model received from the server into the virtual space. At this time, the emotion recognition system is activated and prepares to analyze the user's voice and actions in real time. The tone and speed of the user's voice, as well as their body movements, are the targets of emotion recognition.

[0404] Step 4:

[0405] The user begins a simulated experience in a virtual space through their device. Using a VR headset and controllers, the user interacts with a generated character model. Emotion recognition measures analyze the user's facial expressions and movements and adjust the character model's responses accordingly. This process is performed in real time, providing the user with dynamic responses.

[0406] Step 5:

[0407] The server collects user actions and emotional data recorded during the simulated experience. Based on the collected data, it performs analysis to evaluate the quality of responses and identify areas for improvement. This analysis includes response speed, accuracy, and emotional appropriateness. The analysis results are sent to the terminal as an evaluation report.

[0408] Step 6:

[0409] The terminal receives evaluation reports sent from the server and provides feedback to the user. This feedback highlights successes and areas for improvement, allowing the user to prepare for the next simulation. The collected data is stored through memory and used to improve the system in the future.

[0410] (Application Example 2)

[0411] 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."

[0412] In modern industrial sectors, efficient collaboration between automated equipment and human workers is essential. Especially in factories, while automation by machines is advancing, the emotions and stress levels of human workers can still impact work efficiency. Therefore, there is a need for a system that appropriately monitors the emotional state of workers in the work environment and allows automated systems to adjust their responses as needed. However, current systems lack sufficient interactive adjustments based on such emotional states, thus necessitating improvements to the work environment.

[0413] 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.

[0414] In this invention, the server includes means for generating a customer model for user interaction within a virtual environment, means for configuring a dialogue scenario based on characteristic information set by the user, and recognition means for receiving voice and facial expression data in real time and analyzing the emotional state. This makes it possible to recognize the stress and emotional changes felt by the worker in real time and dynamically adjust the response and support content of the automated device.

[0415] "Means for generating customer models for user interaction within a virtual environment" refers to a process equipped with the functionality to automatically generate digital customer models for user interaction within a virtual environment, based on characteristics set by the user.

[0416] "Means for constructing dialogue scenarios based on characteristic information" refers to a process that constructs dialogue scenarios within a virtual environment based on customer characteristic information set by the user, and adjusts the dialogue to proceed according to that scenario.

[0417] "Recognition means for receiving voice and facial expression data in real time and analyzing emotional state" refers to an analysis process that acquires the user's voice and facial expression data in real time and uses that data to determine the user's emotional state.

[0418] A "server" is a core computing device that centrally processes information sent by users and distributes the generated information to user terminals.

[0419] "Adjustment methods" refer to the process of appropriately modifying the customer model's responses based on the emotional information obtained, thereby optimizing the user experience.

[0420] To implement this invention, the entire system consists of a user terminal, a server, an emotion recognition device, and a generative AI model.

[0421] The server receives characteristic information from the user's terminal and generates a customer model based on it. By using the generated AI model, an appropriate customer model is created for interacting with the user in a virtual environment. In doing so, the server receives audio and video data transmitted from the terminal and analyzes the emotional state in real time. TensorFlow is used for emotion recognition, identifying emotions from audio and facial expression data.

[0422] The user terminal uses smart glasses or a head-mounted display as an interface, enabling operation within a virtual environment. This allows the user to interact with a generated customer model, and the emotional state information collected during this interaction is sent to the server. This information forms the basis for the system to adjust the customer model's responses according to the user's emotional state.

[0423] A specific use case involves factory workers wearing smart glasses to monitor their stress levels in real time during work. If a worker experiences stress, the system adjusts its response based on that information so that an automated system can provide appropriate assistance. An example of a prompt used in this case would be: "Based on emotion recognition data, generate a response instructing the robot on how to assist the worker in a stressful situation."

[0424] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0425] Step 1:

[0426] The user logs into the virtual environment via a terminal and enters characteristic information. This information includes settings for the customer model with which they interact in the virtual environment. The entered information is structured on the terminal and becomes data sent to the server.

[0427] Step 2:

[0428] The server sends the received characteristic information to the generating AI model. Based on this information, the generating AI model creates a customer model for use in the virtual environment, which the server receives. The customer model includes dialogue patterns and personas, and the generation results are sent to the user terminal.

[0429] Step 3:

[0430] The terminal places the received customer model into a virtual environment and initiates interaction with the user. Here, the user's voice and video data are captured in real time and analyzed by an emotion recognition system. This data is acquired using smart glasses or a head-mounted display.

[0431] Step 4:

[0432] The server receives analysis data from the emotion recognition system and evaluates the user's emotions. Using features obtained from voice and facial expressions, it estimates the emotional state and adjusts the customer model's response based on the results. Machine learning algorithms are applied to this analysis.

[0433] Step 5:

[0434] Based on the user's emotions, the server returns an adjusted response to the user's terminal. The terminal uses this information, and the customer model in the virtual environment performs an appropriate response. The user receives feedback through the interaction and evaluates their own response.

[0435] Step 6:

[0436] The server stores emotional data and response results collected during the interaction in a database. This information will be used for future system improvements and to enhance the accuracy of the generative AI model.

[0437] 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.

[0438] 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 those described above. 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 shown 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.

[0439] 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.

[0440] [Third Embodiment]

[0441] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0442] 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.

[0443] 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).

[0444] 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.

[0445] 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.

[0446] 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).

[0447] 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.

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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".

[0453] The customer service training system in a virtual space according to the present invention utilizes various computer technologies to enable users to have a realistic customer service experience in a VR environment. This system consists of a user, a server, and a terminal, and each element works in cooperation with the others.

[0454] 1. User actions

[0455] First, the user logs into the system using a terminal. Through the interface on the terminal, the user inputs customer characteristic information necessary for virtual customer service. This information includes the customer's age, occupation, interests, etc. After the user inputs the information, the terminal sends it to the server.

[0456] 2. Processing on the server

[0457] Based on the received customer characteristics information, the server activates a generative AI to generate an appropriate customer model. This customer model, based on the specified persona, enables realistic interaction within the virtual space. The generated model is then sent by the server to the terminal and deployed into the VR environment.

[0458] 3. Run the simulation

[0459] The terminal displays the received customer model in the virtual space and prepares it for user interaction. The user experiences a simulated customer service interaction using VR controllers and voice input. The system controls the customer model's actions and responses based on scenarios provided by the server.

[0460] 4. Evaluation and Feedback

[0461] As the simulation progresses, the server analyzes and evaluates user responses in real time. The evaluation results are saved along with the analysis data and provided to the user as feedback after the simulation ends. This feedback includes both positive aspects and areas for improvement in the user's responses.

[0462] 5. Data accumulation and utilization

[0463] The server stores data from the entire simulation, which is then used for later analysis and further improvements to the AI. This stored data will also contribute to research and development aimed at automating customer service tasks in the future and improving the accuracy of AI responses.

[0464] In this form, the system of the present invention provides a powerful tool for users to efficiently and effectively acquire customer service skills in a virtual space. By improving the quality and effectiveness of customer service training, it is expected to lead to an overall improvement in service.

[0465] The following describes the processing flow.

[0466] Step 1:

[0467] The user logs into the system using a terminal. The user enters customer characteristics information (age, occupation, interests, etc.) into the interface, and the terminal sends this information to the server.

[0468] Step 2:

[0469] The server activates a generation AI based on the customer characteristics information it receives, and generates a customer model suitable for the specified persona. The server then sends the generated model to the terminal.

[0470] Step 3:

[0471] The device displays the received customer model in the virtual space, preparing the user for interaction. The device then starts the VR environment, readying the user to begin the experience.

[0472] Step 4:

[0473] Users interact with customer models using VR controllers and voice input. They experience simulated customer service according to a scenario they select.

[0474] Step 5:

[0475] The server analyzes the user's responses in real time during the simulation. Based on the responses, it collects evaluation data on the user's customer service skills.

[0476] Step 6:

[0477] After the simulation is complete, the server compiles the evaluation results and generates feedback to provide to the user. The server sends the generated feedback to the terminal, and the user checks the results.

[0478] Step 7:

[0479] The server stores simulation data in a database. This stored data will be used to improve AI response accuracy and system performance in the future.

[0480] (Example 1)

[0481] 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."

[0482] This invention aims to provide users with a realistic dialogue experience in a virtual space and to effectively improve their customer service skills. Conventional customer training systems have problems with the versatility of dialogue scenarios and the accuracy of user feedback, and often fail to adequately reproduce real-world customer service scenes, making practical training difficult.

[0483] 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.

[0484] In this invention, the server includes means for generating a customer model using artificial intelligence based on customer characteristic information input by the user, means for a user terminal to receive the generated customer model and introduce it into a virtual environment, and means for executing a simulation in which the user interacts with the customer model within the virtual environment. This enables the user to have a highly customized conversational experience based on individual customer characteristics and to acquire more practical and useful customer service skills.

[0485] "Generative artificial intelligence" is a technology that generates models and algorithms for performing specific tasks based on input data.

[0486] A "customer model" is a virtual character that can be simulated in a virtual environment based on specific customer characteristics.

[0487] A "virtual environment" is a three-dimensional simulation space generated by a computer, a space that users can experience interactively.

[0488] "Customer characteristic information" refers to data including customer age, occupation, interests, etc., and is used to generate customer models.

[0489] "Simulation" is the process of running user and customer model interactions within a virtual environment.

[0490] "Real-time analysis" is a technology that processes information the moment it is acquired and immediately provides analysis results.

[0491] "Feedback" refers to evaluations and advice based on user performance obtained during the simulation.

[0492] This invention is a system that enables users to obtain a realistic customer service experience using virtual reality, and is realized through the collaboration of a server, a terminal, and a generative AI.

[0493] First, the user logs into the system using a terminal. The terminal has a specific input / output interface and provides the user with a means to access the virtual environment. The user inputs customer characteristic information, such as the customer's age, occupation, and interests, through the terminal's interface. The input information is then sent to the server by the terminal.

[0494] The server generates a customer model using generative AI based on the received customer characteristics information. The generative AI processes the data and generates a virtual character that matches the specified persona. This process includes natural language processing and machine learning techniques. The generated customer model is sent from the server to the terminal and deployed into the virtual reality space.

[0495] The terminal displays the received customer model in the virtual space, enabling the user to interact with it. The user can interact within the virtual space using a VR controller or voice input. The system controls the customer model's responses based on scenarios provided by the server.

[0496] To give a concrete example, when a user uses a virtual reality system to simulate restaurant service, they interact with customers of a specified age group and interests. In this case, they might use a prompt like this: "Please create a virtual space for a restaurant service simulation targeting a customer who is a university student in their 20s and interested in Italian food."

[0497] This configuration allows the system to provide users with an efficient and effective customer service training environment. The system improves users' communication skills and promotes quality improvements in customer service operations.

[0498] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0499] Step 1:

[0500] Users log in to the system using their terminal. Authentication is performed by entering their user ID and password, and access is granted. This prepares the user to begin their individual training session.

[0501] Step 2:

[0502] The user inputs customer characteristics information to be used in the virtual space through the terminal interface. This input data includes the customer's age, occupation, and interests. The terminal organizes this data and sends it to the server via a secure protocol. The output at this stage is a data package containing customer characteristics information.

[0503] Step 3:

[0504] The server receives customer characteristic information from the terminal as input and activates the generating AI. The AI ​​uses natural language processing and machine learning algorithms to generate the optimal customer model. The data processing in this process is the process of converting characteristic information into model parameters suitable for virtual dialogue. The generated customer model is sent to the terminal as server output.

[0505] Step 4:

[0506] The terminal receives a customer model from the server as input and deploys it into the virtual space. Using dedicated VR software, the model is displayed in the user's visual space. The output is an interactive virtual simulation environment that the user can experience through a VR device.

[0507] Step 5:

[0508] Users interact with customer models in a virtual space using VR controllers and voice input. User actions and responses are recorded in real time and sent to a server. This allows user feedback to be collected as data.

[0509] Step 6:

[0510] The server analyzes the collected user responses as input. Through an algorithm, the appropriateness of the responses and the corresponding skills are automatically evaluated. The evaluation results are quantified and stored as detailed analysis data. The output is feedback information presented to the user, including areas for improvement and strengths.

[0511] Step 7:

[0512] The server stores all data during the simulation. This dataset is saved to be used for future improvements to AI algorithms and the development of new training scenarios. This data accumulation enables continuous system improvement.

[0513] (Application Example 1)

[0514] 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."

[0515] In today's service industry, staff are required to be able to meet the diverse needs of customers, but there is a lack of environments where practical skills can be efficiently acquired. This leads to problems such as inconsistencies in customer service quality and decreased learning effectiveness. In particular, there is a lack of realistic training utilizing virtual environments, and there is a need for efficient and flexible acquisition of customer service skills.

[0516] 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.

[0517] In this invention, the server includes generation means for generating a person model for the user to interact with within a virtual environment, configuration means for configuring a dialogue scenario based on customer characteristic information set by the user, and execution means for performing a simulated execution in which the user and the person model interact within the virtual environment. This enables practical training tailored to a variety of customer scenarios.

[0518] A "user" is an individual or group that interacts with a character model within a virtual environment.

[0519] A "virtual environment" is a computer-generated space that is different from the real world and is experienced by users using head-mounted displays or similar devices.

[0520] A "character model" refers to a character generated by AI that interacts with the user within a virtual environment.

[0521] "Generation means" refers to a method or system for generating a human model within a virtual environment based on user input information.

[0522] "Customer characteristic information" refers to attribute information of customers interacting in a virtual environment, such as age, occupation, and interests.

[0523] A "dialogue scenario" is a designed situation or setting in which a character model and a user interact within a virtual environment.

[0524] "Execution means" refers to the mechanism or method for conducting a simulated dialogue between the generated character model and the user.

[0525] "Evaluation means" refers to a system or method that analyzes and evaluates user reactions during simulated execution.

[0526] "Presentation means" refers to a method or device used to show evaluation results or areas for improvement to the user.

[0527] A "storage method" refers to a system or method for storing data obtained during simulated execution and using it to improve the system in the future.

[0528] A "head-mounted display" is a display device worn on the head by a user to visually experience a virtual environment.

[0529] A "control device" is a system that uses an artificial intelligence model to control the behavior of a human model within a virtual environment.

[0530] The system for realizing this invention consists of a series of hardware and software components for enabling interactive training within a virtual environment. First, the user wears a head-mounted display and logs into the system using a terminal with a dedicated application installed. This terminal inputs customer characteristic information set by the user and sends it to the server.

[0531] The server activates an AI model based on the received customer characteristics information, generating a persona model for interacting with the user within a virtual environment. This persona model simulates realistic conversations based on a specific persona. The generated model is delivered from the server to the user via a head-mounted display.

[0532] Users engage in simulated conversations with a human model according to dialogue scenarios. This process is coordinated by a control system, which controls the AI ​​model's behavior. User responses are evaluated in real time, and feedback is provided on areas that need improvement. This allows users to experience diverse customer scenarios in a virtual environment and improve their skills.

[0533] This system also stores data to enable progressive learning. Specifically, data obtained during simulations is saved using storage methods and used to improve the response system in the future. Data analysis tools such as Python and pandas are utilized in this process. As an example scenario, a prompt such as "As a customer of a bicycle toolkit, please ask questions about its usage and convenience" can be provided.

[0534] In this way, users can efficiently and effectively learn customer service skills and improve their practical response abilities within a virtual environment.

[0535] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0536] Step 1:

[0537] The user logs into the system by operating a terminal. The input here is the user's authentication information. The terminal processes this, verifies the user's identity, and approves the login. The output indicates that the user's access rights have been authenticated and the system is available for use.

[0538] Step 2:

[0539] The user inputs customer characteristics information using the terminal's interface. This input includes the customer's age, occupation, interests, etc. The terminal receives this information, organizes the data, and sends it to the server. The output is the structured customer characteristics data sent to the server.

[0540] Step 3:

[0541] The server generates a person model using a generative AI model based on the received customer characteristic information. In this process, the AI ​​analyzes the input characteristic information and creates an appropriate customer persona. The output is a customized person model for use within the virtual environment.

[0542] Step 4:

[0543] The server sends the generated character model to the terminal. The input here is the character model data generated on the server, which the terminal receives and prepares to display in the virtual environment. The output is the character model available on the terminal.

[0544] Step 5:

[0545] The user wears a head-mounted display and begins a simulated conversation with a human model in a virtual environment. The input is a human model provided by the server, and includes voice commands spoken by the user. The terminal analyzes the voice input, and the AI ​​model controls the human model's responses. The output is an interactive dialogue between the user and the human model.

[0546] Step 6:

[0547] The server evaluates the user's responses in real time during the simulated dialogue. The input here is the user's response data, and the server applies an algorithm to evaluate its appropriateness. The output is improvement feedback data based on the evaluation.

[0548] Step 7:

[0549] The server presents evaluation results and feedback to the user. The input is real-time analyzed evaluation data, which the terminal provides to the user, indicating areas for improvement. The output is information conveyed to the user as specific feedback.

[0550] Step 8:

[0551] The server stores data collected during simulated dialogue. Input consists of various data obtained during the dialogue, which the server stores in a structured format. Output is entries into a database that can be used for future system improvements.

[0552] 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.

[0553] This invention provides a system that recognizes user emotions in real time and uses that information to provide more effective customer service training. This system includes a user, a server, a terminal, and an emotion recognition engine, all of which work together.

[0554] 1. User actions

[0555] The user logs into the system via a terminal and prepares to interact with the customer model in a virtual space. The user enters customer characteristic information to be used in training into the terminal. This information is sent from the terminal to the server.

[0556] 2. Processing on the server

[0557] The server uses a generative AI to generate a customer model based on the transmitted customer characteristics information, creating a customer model based on a specific persona. The generated model is sent to the terminal and then placed in a virtual space.

[0558] 3. Emotion Recognition Engine

[0559] The device is equipped with an emotion recognition engine that determines the user's emotional state from their voice and body movements. This data is sent to the server in real time and used to improve customer model responses and scenario settings.

[0560] 4. Run the simulation

[0561] The user starts a simulation in a virtual space using a device. The user interacts with a generated customer model using VR controllers and voice input. An emotion recognition engine monitors the user's emotions and adjusts the customer model's responses accordingly based on that information.

[0562] 5. Evaluation and Feedback

[0563] The server analyzes the user's responses and emotional data recorded during the experience and provides an appropriate evaluation. This evaluation result is provided to the user as feedback, indicating areas for improvement in training. For example, it identifies situations where the user reacted emotionally and advises on appropriate countermeasures.

[0564] 6. Data accumulation and utilization

[0565] The data accumulated across the entire system will be used to improve the accuracy of AI responses and enhance emotion recognition technology in the future. This will enable us to continue providing users with a more natural and effective virtual customer service experience.

[0566] In this configuration, the system of the present invention can provide advanced customer service training that incorporates emotion recognition, thereby improving employees' customer service skills and customer interaction abilities.

[0567] The following describes the processing flow.

[0568] Step 1:

[0569] The user logs into the terminal and enters customer characteristic information through the interface. The terminal then sends this information to the server.

[0570] Step 2:

[0571] Based on the customer characteristics information received by the server, the generation AI is activated to generate a customer model based on the specified persona. The server then sends the generated model to the terminal.

[0572] Step 3:

[0573] The terminal displays the customer model it received in the virtual space, preparing the user to begin the conversation.

[0574] Step 4:

[0575] The emotion recognition engine installed in the device analyzes the user's voice and body movements in real time to determine their emotional state. The device then sends the recognized emotion data to a server.

[0576] Step 5:

[0577] The server dynamically adjusts the customer model's response based on emotional data. For example, if the server determines that the user is irritated, the customer model will respond in a calming manner.

[0578] Step 6:

[0579] Users interact with customer models using VR controllers and voice input to experience simulated customer service.

[0580] Step 7:

[0581] The server analyzes the user's responses and sentiment data during the simulation and makes an appropriate evaluation. The server then sends the evaluation results to the terminal to present them to the user as feedback.

[0582] Step 8:

[0583] The terminal displays the evaluation results to the user, clearly indicating the strengths and areas for improvement in customer service.

[0584] Step 9:

[0585] The server stores all simulation data, which will be used for future system improvements and AI model training.

[0586] (Example 2)

[0587] 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."

[0588] Conventional virtual reality dialogue systems have struggled to provide an effective learning experience that dynamically reflects the user's emotional state. This has resulted in problems such as unrealistic user feedback and insufficient improvement of customer service skills. This invention aims to provide an effective and natural experience for the user by analyzing the user's emotions in real time and dynamically adjusting the dialogue content within the virtual space based on that analysis.

[0589] 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.

[0590] In this invention, the server includes means for generating a character model generated by an information processing device for users to interact in a virtual space, means for configuring a dialogue simulation based on attribute information set by the user, and means for executing a simulated experience in which the user and the character model interact in the virtual space. This makes it possible to provide dynamic dialogue content that reflects the user's emotional state.

[0591] An "information processing device" is a device or system that processes input data and generates results according to a specific purpose.

[0592] A "character model" is a virtual character created within a virtual space based on specific settings and attributes, for the purpose of interacting with the user.

[0593] "Attribute information" refers to data that represents specific characteristics or features set by the user, and is used to build dialogue simulations.

[0594] A "simulated experience" is an interactive experience designed to recreate real-world situations within a virtual space.

[0595] "Evaluation means" refers to methods or devices for analyzing data obtained during a simulated experience and evaluating the user's responses and emotional state.

[0596] A "display device" is a device used to visually present the results and information of a computer system to a user.

[0597] A "storage device" is a digital or physical recording medium used to store acquired data and prepare it for future use or analysis.

[0598] "Emotion recognition means" refers to technologies and devices that analyze a user's emotions from their voice and actions and acquire information in real time.

[0599] This invention provides a customer service training system that recognizes user emotions in real time and responds accordingly. The system consists of a user, a terminal, and a server.

[0600] The user first logs into the system using a terminal. During this process, the user enters attribute information. This attribute information includes customer characteristics and hobbies, and is used to build a simulated experience within the virtual space.

[0601] The server generates a character model for use in the virtual space using a generative AI model based on attribute information received from the terminal. For example, a general-purpose AI specializing in natural language generation can be used as the generative AI. The server then sends the generated character model to the terminal, allowing the user to begin the simulated experience in the virtual space.

[0602] The device is equipped with emotion recognition technology that analyzes the user's emotions in real time from their voice and actions. This data is sent to a server and used to dynamically adjust the responses of the human model during the simulated experience.

[0603] An example of a prompt message is: "You are a male salesperson in your 40s. Your hobby is golf, and you enjoy talking about hobbies with clients." Based on this information, the system can provide the user with a personalized simulated experience.

[0604] With this configuration, the present invention can provide practical and dynamic training to improve employees' customer service capabilities. Through simulated experiences in a virtual space, users can learn and receive feedback in situations closer to reality, enabling them to acquire skills efficiently.

[0605] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0606] Step 1:

[0607] Users log in to the system via a terminal and enter the necessary attribute information. This information includes, for example, the customer's age, occupation, and hobbies. This data is an important element for personalizing the simulated experience in the virtual space. The entered data is sent from the terminal to the server.

[0608] Step 2:

[0609] The server receives attribute information sent from the terminal. Based on the received data, it uses a generative AI model to generate a human model for use in the virtual space. Specifically, the generative AI analyzes the input data and constructs a human model with the corresponding features. The generated model is then sent to the terminal.

[0610] Step 3:

[0611] The terminal places a human model received from the server into the virtual space. At this time, the emotion recognition system is activated and prepares to analyze the user's voice and actions in real time. The tone and speed of the user's voice, as well as their body movements, are the targets of emotion recognition.

[0612] Step 4:

[0613] The user begins a simulated experience in a virtual space through their device. Using a VR headset and controllers, the user interacts with a generated character model. Emotion recognition measures analyze the user's facial expressions and movements and adjust the character model's responses accordingly. This process is performed in real time, providing the user with dynamic responses.

[0614] Step 5:

[0615] The server collects user actions and emotional data recorded during the simulated experience. Based on the collected data, it performs analysis to evaluate the quality of responses and identify areas for improvement. This analysis includes response speed, accuracy, and emotional appropriateness. The analysis results are sent to the terminal as an evaluation report.

[0616] Step 6:

[0617] The terminal receives evaluation reports sent from the server and provides feedback to the user. This feedback highlights successes and areas for improvement, allowing the user to prepare for the next simulation. The collected data is stored through memory and used to improve the system in the future.

[0618] (Application Example 2)

[0619] 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."

[0620] In modern industrial sectors, efficient collaboration between automated equipment and human workers is essential. Especially in factories, while automation by machines is advancing, the emotions and stress levels of human workers can still impact work efficiency. Therefore, there is a need for a system that appropriately monitors workers' emotional states in the work environment and allows automated systems to adjust their responses as needed. However, current systems lack sufficient interactive adjustments based on such emotional states, thus necessitating improvements to the work environment.

[0621] 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.

[0622] In this invention, the server includes means for generating a customer model for user interaction within a virtual environment, means for configuring a dialogue scenario based on characteristic information set by the user, and recognition means for receiving voice and facial expression data in real time and analyzing the emotional state. This makes it possible to recognize the stress and emotional changes felt by the worker in real time and dynamically adjust the response and support content of the automated device.

[0623] "Means for generating customer models for user interaction within a virtual environment" refers to a process equipped with the functionality to automatically generate digital customer models for user interaction within a virtual environment, based on characteristics set by the user.

[0624] "Means for constructing dialogue scenarios based on characteristic information" refers to a process that constructs dialogue scenarios within a virtual environment based on customer characteristic information set by the user, and adjusts the dialogue to proceed according to that scenario.

[0625] "Recognition means for receiving voice and facial expression data in real time and analyzing emotional state" refers to an analysis process that acquires the user's voice and facial expression data in real time and uses that data to determine the user's emotional state.

[0626] A "server" is a core computing device that centrally processes information sent by users and distributes the generated information to user terminals.

[0627] "Adjustment methods" refer to the process of appropriately modifying the customer model's responses based on the emotional information obtained, thereby optimizing the user experience.

[0628] To implement this invention, the entire system consists of a user terminal, a server, an emotion recognition device, and a generative AI model.

[0629] The server receives characteristic information from the user's terminal and generates a customer model based on it. By using the generated AI model, an appropriate customer model is created for interacting with the user in a virtual environment. In doing so, the server receives audio and video data transmitted from the terminal and analyzes the emotional state in real time. TensorFlow is used for emotion recognition, identifying emotions from audio and facial expression data.

[0630] The user terminal uses smart glasses or a head-mounted display as an interface, enabling operation within a virtual environment. This allows the user to interact with a generated customer model, and the emotional state information collected during this interaction is sent to the server. This information forms the basis for the system to adjust the customer model's responses according to the user's emotional state.

[0631] A specific use case involves factory workers wearing smart glasses to monitor their stress levels in real time during work. If a worker experiences stress, the system adjusts its response based on that information so that an automated system can provide appropriate assistance. An example of a prompt used in this case would be: "Based on emotion recognition data, generate a response instructing the robot on how to assist the worker in a stressful situation."

[0632] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0633] Step 1:

[0634] The user logs into the virtual environment via a terminal and enters characteristic information. This information includes settings for the customer model with which they interact in the virtual environment. The entered information is structured on the terminal and becomes data sent to the server.

[0635] Step 2:

[0636] The server sends the received characteristic information to the generating AI model. Based on this information, the generating AI model creates a customer model for use in the virtual environment, which the server then receives. The customer model includes dialogue patterns and personas, and the generated results are sent to the user terminal.

[0637] Step 3:

[0638] The terminal places the received customer model into a virtual environment and initiates interaction with the user. Here, the user's voice and video data are captured in real time and analyzed by an emotion recognition system. This data is acquired using smart glasses or a head-mounted display.

[0639] Step 4:

[0640] The server receives analysis data from the emotion recognition system and evaluates the user's emotions. Using features obtained from voice and facial expressions, it estimates the emotional state and adjusts the customer model's response based on the results. Machine learning algorithms are applied to this analysis.

[0641] Step 5:

[0642] Based on the user's emotions, the server returns an adjusted response to the user's terminal. The terminal uses this information, and the customer model in the virtual environment performs an appropriate response. The user receives feedback through the interaction and evaluates their own response.

[0643] Step 6:

[0644] The server stores emotional data and response results collected during the interaction in a database. This information will be used for future system improvements and to enhance the accuracy of the generative AI model.

[0645] 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.

[0646] 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 those described above. 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 shown 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.

[0647] 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.

[0648] [Fourth Embodiment]

[0649] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0650] 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.

[0651] 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).

[0652] 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.

[0653] 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.

[0654] 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).

[0655] 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.

[0656] 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.

[0657] 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.

[0658] 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.

[0659] 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.

[0660] 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.

[0661] 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".

[0662] The customer service training system in a virtual space according to the present invention utilizes various computer technologies to enable users to have a realistic customer service experience in a VR environment. This system consists of a user, a server, and a terminal, and each element works in cooperation with the others.

[0663] 1. User actions

[0664] First, the user logs into the system using a terminal. Through the interface on the terminal, the user inputs customer characteristic information necessary for virtual customer service. This information includes the customer's age, occupation, interests, etc. After the user inputs the information, the terminal sends it to the server.

[0665] 2. Processing on the server

[0666] Based on the received customer characteristics information, the server activates a generative AI to generate an appropriate customer model. This customer model, based on the specified persona, enables realistic interaction within the virtual space. The generated model is then sent by the server to the terminal and deployed into the VR environment.

[0667] 3. Run the simulation

[0668] The terminal displays the received customer model in the virtual space and prepares it for user interaction. The user experiences a simulated customer service interaction using VR controllers and voice input. The system controls the customer model's actions and responses based on scenarios provided by the server.

[0669] 4. Evaluation and Feedback

[0670] As the simulation progresses, the server analyzes and evaluates user responses in real time. The evaluation results are saved along with the analysis data and provided to the user as feedback after the simulation ends. This feedback includes both positive aspects and areas for improvement in the user's responses.

[0671] 5. Data accumulation and utilization

[0672] The server stores data from the entire simulation, which is then used for later analysis and further improvements to the AI. This stored data will also contribute to research and development aimed at automating customer service tasks in the future and improving the accuracy of AI responses.

[0673] In this form, the system of the present invention provides a powerful tool for users to efficiently and effectively acquire customer service skills in a virtual space. By improving the quality and effectiveness of customer service training, it is expected to lead to an overall improvement in service.

[0674] The following describes the processing flow.

[0675] Step 1:

[0676] The user logs into the system using a terminal. The user enters customer characteristics information (age, occupation, interests, etc.) into the interface, and the terminal sends this information to the server.

[0677] Step 2:

[0678] The server activates a generation AI based on the customer characteristics information it receives, and generates a customer model suitable for the specified persona. The server then sends the generated model to the terminal.

[0679] Step 3:

[0680] The device displays the received customer model in the virtual space, preparing the user for interaction. The device then starts the VR environment, readying the user to begin the experience.

[0681] Step 4:

[0682] Users interact with customer models using VR controllers and voice input. They experience simulated customer service according to a scenario they select.

[0683] Step 5:

[0684] The server analyzes the user's responses in real time during the simulation. Based on the responses, it collects evaluation data on the user's customer service skills.

[0685] Step 6:

[0686] After the simulation is complete, the server compiles the evaluation results and generates feedback to provide to the user. The server sends the generated feedback to the terminal, and the user checks the results.

[0687] Step 7:

[0688] The server stores simulation data in a database. This stored data will be used to improve AI response accuracy and system performance in the future.

[0689] (Example 1)

[0690] 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".

[0691] This invention aims to provide users with a realistic dialogue experience in a virtual space and to effectively improve their customer service skills. Conventional customer training systems have problems with the versatility of dialogue scenarios and the accuracy of user feedback, and often fail to adequately reproduce real-world customer service scenes, making practical training difficult.

[0692] 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.

[0693] In this invention, the server includes means for generating a customer model using artificial intelligence based on customer characteristic information input by the user, means for a user terminal to receive the generated customer model and introduce it into a virtual environment, and means for executing a simulation in which the user interacts with the customer model within the virtual environment. This enables the user to have a highly customized conversational experience based on individual customer characteristics and to acquire more practical and useful customer service skills.

[0694] "Generative artificial intelligence" is a technology that generates models and algorithms for performing specific tasks based on input data.

[0695] A "customer model" is a virtual character that can be simulated in a virtual environment based on specific customer characteristics.

[0696] A "virtual environment" is a three-dimensional simulation space generated by a computer, a space that users can experience interactively.

[0697] "Customer characteristic information" refers to data including customer age, occupation, interests, etc., and is used to generate customer models.

[0698] "Simulation" is the process of running user and customer model interactions within a virtual environment.

[0699] "Real-time analysis" is a technology that processes information the moment it is acquired and immediately provides analysis results.

[0700] "Feedback" refers to evaluations and advice based on user performance obtained during the simulation.

[0701] This invention is a system that enables users to obtain a realistic customer service experience using virtual reality, and is realized through the collaboration of a server, a terminal, and a generative AI.

[0702] First, the user logs into the system using a terminal. The terminal has a specific input / output interface and provides the user with a means to access the virtual environment. The user inputs customer characteristic information, such as the customer's age, occupation, and interests, through the terminal's interface. The input information is then sent to the server by the terminal.

[0703] The server generates a customer model using generative AI based on the received customer characteristics information. The generative AI processes the data and generates a virtual character that matches the specified persona. This process includes natural language processing and machine learning techniques. The generated customer model is sent from the server to the terminal and deployed into the virtual reality space.

[0704] The terminal displays the received customer model in the virtual space, enabling the user to interact with it. The user can interact within the virtual space using a VR controller or voice input. The system controls the customer model's responses based on scenarios provided by the server.

[0705] To give a concrete example, when a user uses a virtual reality system to simulate restaurant service, they interact with customers of a specified age group and interests. In this case, they might use a prompt like this: "Please create a virtual space for a restaurant service simulation targeting a customer who is a university student in their 20s and interested in Italian food."

[0706] This configuration allows the system to provide users with an efficient and effective customer service training environment. The system improves users' communication skills and promotes quality improvements in customer service operations.

[0707] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0708] Step 1:

[0709] Users log in to the system using their terminal. Authentication is performed by entering their user ID and password, and access is granted. This prepares the user to begin their individual training session.

[0710] Step 2:

[0711] The user inputs customer characteristics information to be used in the virtual space through the terminal interface. This input data includes the customer's age, occupation, and interests. The terminal organizes this data and sends it to the server via a secure protocol. The output at this stage is a data package containing customer characteristics information.

[0712] Step 3:

[0713] The server receives customer characteristic information from the terminal as input and activates the generating AI. The AI ​​uses natural language processing and machine learning algorithms to generate the optimal customer model. The data processing in this process is the process of converting characteristic information into model parameters suitable for virtual dialogue. The generated customer model is sent to the terminal as server output.

[0714] Step 4:

[0715] The terminal receives a customer model from the server as input and deploys it into the virtual space. Using dedicated VR software, the model is displayed in the user's visual space. The output is an interactive virtual simulation environment that the user can experience through a VR device.

[0716] Step 5:

[0717] Users interact with customer models in a virtual space using VR controllers and voice input. User actions and responses are recorded in real time and sent to a server. This allows user feedback to be collected as data.

[0718] Step 6:

[0719] The server analyzes the collected user responses as input. Through an algorithm, the appropriateness of the responses and the corresponding skills are automatically evaluated. The evaluation results are quantified and stored as detailed analysis data. The output is feedback information presented to the user, including areas for improvement and strengths.

[0720] Step 7:

[0721] The server stores all data during the simulation. This dataset is saved to be used for future improvements to AI algorithms and the development of new training scenarios. This data accumulation enables continuous system improvement.

[0722] (Application Example 1)

[0723] 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".

[0724] In today's service industry, staff are required to be able to meet the diverse needs of customers, but there is a lack of environments where practical skills can be efficiently acquired. This leads to problems such as inconsistencies in customer service quality and decreased learning effectiveness. In particular, there is a lack of realistic training utilizing virtual environments, and there is a need for efficient and flexible acquisition of customer service skills.

[0725] 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.

[0726] In this invention, the server includes generation means for generating a person model for the user to interact with within a virtual environment, configuration means for configuring a dialogue scenario based on customer characteristic information set by the user, and execution means for performing a simulated execution in which the user and the person model interact within the virtual environment. This enables practical training tailored to a variety of customer scenarios.

[0727] A "user" is an individual or group that interacts with a character model within a virtual environment.

[0728] A "virtual environment" is a computer-generated space that is different from the real world and is experienced by users using head-mounted displays or similar devices.

[0729] A "character model" refers to a character generated by AI that interacts with the user within a virtual environment.

[0730] "Generation means" refers to a method or system for generating a human model within a virtual environment based on user input information.

[0731] "Customer characteristic information" refers to attribute information of customers interacting in a virtual environment, such as age, occupation, and interests.

[0732] A "dialogue scenario" is a designed situation or setting in which a character model and a user interact within a virtual environment.

[0733] "Execution means" refers to the mechanism or method for conducting a simulated dialogue between the generated character model and the user.

[0734] "Evaluation means" refers to a system or method that analyzes and evaluates user reactions during simulated execution.

[0735] "Presentation means" refers to a method or device used to show evaluation results or areas for improvement to the user.

[0736] A "storage method" refers to a system or method for storing data obtained during simulated execution and using it to improve the system in the future.

[0737] A "head-mounted display" is a display device worn on the head by a user to visually experience a virtual environment.

[0738] A "control device" is a system that uses an artificial intelligence model to control the behavior of a human model within a virtual environment.

[0739] The system for realizing this invention consists of a series of hardware and software components for enabling interactive training within a virtual environment. First, the user wears a head-mounted display and logs into the system using a terminal with a dedicated application installed. This terminal inputs customer characteristic information set by the user and sends it to the server.

[0740] The server activates an AI model based on the received customer characteristics information, generating a persona model for interacting with the user within a virtual environment. This persona model simulates realistic conversations based on a specific persona. The generated model is delivered from the server to the user via a head-mounted display.

[0741] Users engage in simulated conversations with a human model according to dialogue scenarios. This process is coordinated by a control system, which controls the AI ​​model's behavior. User responses are evaluated in real time, and feedback is provided on areas that need improvement. This allows users to experience diverse customer scenarios in a virtual environment and improve their skills.

[0742] This system also stores data to enable progressive learning. Specifically, data obtained during simulations is saved using storage methods and used to improve the response system in the future. Data analysis tools such as Python and pandas are utilized in this process. As an example scenario, a prompt such as "As a customer of a bicycle toolkit, please ask questions about its usage and convenience" can be provided.

[0743] In this way, users can efficiently and effectively learn customer service skills and improve their practical response abilities within a virtual environment.

[0744] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0745] Step 1:

[0746] The user logs into the system by operating a terminal. The input here is the user's authentication information. The terminal processes this, verifies the user's identity, and approves the login. The output indicates that the user's access rights have been authenticated and the system is available for use.

[0747] Step 2:

[0748] The user inputs customer characteristics information using the terminal's interface. This input includes the customer's age, occupation, interests, etc. The terminal receives this information, organizes the data, and sends it to the server. The output is the structured customer characteristics data sent to the server.

[0749] Step 3:

[0750] The server generates a person model using a generative AI model based on the received customer characteristic information. In this process, the AI ​​analyzes the input characteristic information and creates an appropriate customer persona. The output is a customized person model for use within the virtual environment.

[0751] Step 4:

[0752] The server sends the generated character model to the terminal. The input here is the character model data generated on the server, which the terminal receives and prepares to display in the virtual environment. The output is the character model available on the terminal.

[0753] Step 5:

[0754] The user wears a head-mounted display and begins a simulated conversation with a human model in a virtual environment. The input is a human model provided by the server, and includes voice commands spoken by the user. The terminal analyzes the voice input, and the AI ​​model controls the human model's responses. The output is an interactive dialogue between the user and the human model.

[0755] Step 6:

[0756] The server evaluates the user's responses in real time during the simulated dialogue. The input here is the user's response data, and the server applies an algorithm to evaluate its appropriateness. The output is improvement feedback data based on the evaluation.

[0757] Step 7:

[0758] The server presents evaluation results and feedback to the user. The input is real-time analyzed evaluation data, which the terminal provides to the user, indicating areas for improvement. The output is information conveyed to the user as specific feedback.

[0759] Step 8:

[0760] The server stores data collected during simulated dialogue. Input consists of various data obtained during the dialogue, which the server stores in a structured format. Output is entries into a database that can be used for future system improvements.

[0761] 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.

[0762] This invention provides a system that recognizes user emotions in real time and uses that information to provide more effective customer service training. This system includes a user, a server, a terminal, and an emotion recognition engine, all of which work together.

[0763] 1. User actions

[0764] The user logs into the system via a terminal and prepares to interact with the customer model in a virtual space. The user enters customer characteristic information to be used in training into the terminal. This information is sent from the terminal to the server.

[0765] 2. Processing on the server

[0766] The server uses a generative AI to generate a customer model based on the transmitted customer characteristics information, creating a customer model based on a specific persona. The generated model is sent to the terminal and then placed in a virtual space.

[0767] 3. Emotion Recognition Engine

[0768] The device is equipped with an emotion recognition engine that determines the user's emotional state from their voice and body movements. This data is sent to the server in real time and used to improve customer model responses and scenario settings.

[0769] 4. Run the simulation

[0770] The user starts a simulation in a virtual space using a device. The user interacts with a generated customer model using VR controllers and voice input. An emotion recognition engine monitors the user's emotions and adjusts the customer model's responses accordingly based on that information.

[0771] 5. Evaluation and Feedback

[0772] The server analyzes the user's responses and emotional data recorded during the experience and provides an appropriate evaluation. This evaluation result is provided to the user as feedback, indicating areas for improvement in training. For example, it identifies situations where the user reacted emotionally and advises on appropriate countermeasures.

[0773] 6. Data accumulation and utilization

[0774] The data accumulated across the entire system will be used to improve the accuracy of AI responses and enhance emotion recognition technology in the future. This will enable us to continue providing users with a more natural and effective virtual customer service experience.

[0775] In this configuration, the system of the present invention can provide advanced customer service training that incorporates emotion recognition, thereby improving employees' customer service skills and customer interaction abilities.

[0776] The following describes the processing flow.

[0777] Step 1:

[0778] The user logs into the terminal and enters customer characteristic information through the interface. The terminal then sends this information to the server.

[0779] Step 2:

[0780] Based on the customer characteristics information received by the server, the generation AI is activated to generate a customer model based on the specified persona. The server then sends the generated model to the terminal.

[0781] Step 3:

[0782] The terminal displays the customer model it received in the virtual space, preparing the user to begin the conversation.

[0783] Step 4:

[0784] The emotion recognition engine installed in the device analyzes the user's voice and body movements in real time to determine their emotional state. The device then sends the recognized emotion data to a server.

[0785] Step 5:

[0786] The server dynamically adjusts the customer model's response based on emotional data. For example, if the server determines that the user is irritated, the customer model will respond in a calming manner.

[0787] Step 6:

[0788] Users interact with customer models using VR controllers and voice input to experience simulated customer service.

[0789] Step 7:

[0790] The server analyzes the user's responses and sentiment data during the simulation and makes an appropriate evaluation. The server then sends the evaluation results to the terminal to present them to the user as feedback.

[0791] Step 8:

[0792] The terminal displays the evaluation results to the user, clearly indicating the strengths and areas for improvement in customer service.

[0793] Step 9:

[0794] The server stores all simulation data, which will be used for future system improvements and AI model training.

[0795] (Example 2)

[0796] 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".

[0797] Conventional virtual reality dialogue systems have struggled to provide an effective learning experience that dynamically reflects the user's emotional state. This has resulted in problems such as unrealistic user feedback and insufficient improvement of customer service skills. This invention aims to provide an effective and natural experience for the user by analyzing the user's emotions in real time and dynamically adjusting the dialogue content within the virtual space based on that analysis.

[0798] 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.

[0799] In this invention, the server includes means for generating a character model generated by an information processing device for users to interact in a virtual space, means for configuring a dialogue simulation based on attribute information set by the user, and means for executing a simulated experience in which the user and the character model interact in the virtual space. This makes it possible to provide dynamic dialogue content that reflects the user's emotional state.

[0800] An "information processing device" is a device or system that processes input data and generates results according to a specific purpose.

[0801] A "character model" is a virtual character created within a virtual space based on specific settings and attributes, for the purpose of interacting with the user.

[0802] "Attribute information" refers to data that represents specific characteristics or features set by the user, and is used to build dialogue simulations.

[0803] A "simulated experience" is an interactive experience designed to recreate real-world situations within a virtual space.

[0804] "Evaluation means" refers to methods or devices for analyzing data obtained during a simulated experience and evaluating the user's responses and emotional state.

[0805] A "display device" is a device used to visually present the results and information of a computer system to a user.

[0806] A "storage device" is a digital or physical recording medium used to store acquired data and prepare it for future use or analysis.

[0807] "Emotion recognition means" refers to technologies and devices that analyze a user's emotions from their voice and actions and acquire information in real time.

[0808] This invention provides a customer service training system that recognizes user emotions in real time and responds accordingly. The system consists of a user, a terminal, and a server.

[0809] The user first logs into the system using a terminal. During this process, the user enters attribute information. This attribute information includes customer characteristics and hobbies, and is used to build a simulated experience within the virtual space.

[0810] The server generates a character model for use in the virtual space using a generative AI model based on attribute information received from the terminal. For example, a general-purpose AI specializing in natural language generation can be used as the generative AI. The server then sends the generated character model to the terminal, allowing the user to begin the simulated experience in the virtual space.

[0811] The device is equipped with emotion recognition technology that analyzes the user's emotions in real time from their voice and actions. This data is sent to a server and used to dynamically adjust the responses of the human model during the simulated experience.

[0812] An example of a prompt message is: "You are a male salesperson in your 40s. Your hobby is golf, and you enjoy talking about hobbies with clients." Based on this information, the system can provide the user with a personalized simulated experience.

[0813] With this configuration, the present invention can provide practical and dynamic training to improve employees' customer service capabilities. Through simulated experiences in a virtual space, users can learn and receive feedback in situations closer to reality, enabling them to acquire skills efficiently.

[0814] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0815] Step 1:

[0816] Users log in to the system via a terminal and enter the necessary attribute information. This information includes, for example, the customer's age, occupation, and hobbies. This data is an important element for personalizing the simulated experience in the virtual space. The entered data is sent from the terminal to the server.

[0817] Step 2:

[0818] The server receives attribute information sent from the terminal. Based on the received data, it uses a generative AI model to generate a human model for use in the virtual space. Specifically, the generative AI analyzes the input data and constructs a human model with the corresponding features. The generated model is then sent to the terminal.

[0819] Step 3:

[0820] The terminal places a human model received from the server into the virtual space. At this time, the emotion recognition system is activated and prepares to analyze the user's voice and actions in real time. The tone and speed of the user's voice, as well as their body movements, are the targets of emotion recognition.

[0821] Step 4:

[0822] The user begins a simulated experience in a virtual space through their device. Using a VR headset and controllers, the user interacts with a generated character model. Emotion recognition measures analyze the user's facial expressions and movements and adjust the character model's responses accordingly. This process is performed in real time, providing the user with dynamic responses.

[0823] Step 5:

[0824] The server collects user actions and emotional data recorded during the simulated experience. Based on the collected data, it performs analysis to evaluate the quality of responses and identify areas for improvement. This analysis includes response speed, accuracy, and emotional appropriateness. The analysis results are sent to the terminal as an evaluation report.

[0825] Step 6:

[0826] The terminal receives evaluation reports sent from the server and provides feedback to the user. This feedback highlights successes and areas for improvement, allowing the user to prepare for the next simulation. The collected data is stored through memory and used to improve the system in the future.

[0827] (Application Example 2)

[0828] 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".

[0829] In modern industrial sectors, efficient collaboration between automated equipment and human workers is essential. Especially in factories, while automation by machines is advancing, the emotions and stress levels of human workers can still impact work efficiency. Therefore, there is a need for a system that appropriately monitors workers' emotional states in the work environment and allows automated systems to adjust their responses as needed. However, current systems lack sufficient interactive adjustments based on such emotional states, thus necessitating improvements to the work environment.

[0830] 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.

[0831] In this invention, the server includes means for generating a customer model for user interaction within a virtual environment, means for configuring a dialogue scenario based on characteristic information set by the user, and recognition means for receiving voice and facial expression data in real time and analyzing the emotional state. This makes it possible to recognize the stress and emotional changes felt by the worker in real time and dynamically adjust the response and support content of the automated device.

[0832] "Means for generating customer models for user interaction within a virtual environment" refers to a process equipped with the functionality to automatically generate digital customer models for user interaction within a virtual environment, based on characteristics set by the user.

[0833] "Means for constructing dialogue scenarios based on characteristic information" refers to a process that constructs dialogue scenarios within a virtual environment based on customer characteristic information set by the user, and adjusts the dialogue to proceed according to that scenario.

[0834] "Recognition means for receiving voice and facial expression data in real time and analyzing emotional state" refers to an analysis process that acquires the user's voice and facial expression data in real time and uses that data to determine the user's emotional state.

[0835] A "server" is a core computing device that centrally processes information sent by users and distributes the generated information to user terminals.

[0836] "Adjustment methods" refer to the process of appropriately modifying the customer model's responses based on the emotional information obtained, thereby optimizing the user experience.

[0837] To implement this invention, the entire system consists of a user terminal, a server, an emotion recognition device, and a generative AI model.

[0838] The server receives characteristic information from the user's terminal and generates a customer model based on it. By using the generated AI model, an appropriate customer model is created for interacting with the user in a virtual environment. In doing so, the server receives audio and video data transmitted from the terminal and analyzes the emotional state in real time. TensorFlow is used for emotion recognition, identifying emotions from audio and facial expression data.

[0839] The user terminal uses smart glasses or a head-mounted display as an interface, enabling operation within a virtual environment. This allows the user to interact with a generated customer model, and the emotional state information collected during this interaction is sent to the server. This information forms the basis for the system to adjust the customer model's responses according to the user's emotional state.

[0840] A specific use case involves factory workers wearing smart glasses to monitor their stress levels in real time during work. If a worker experiences stress, the system adjusts its response based on that information so that an automated system can provide appropriate assistance. An example of a prompt used in this case would be: "Based on emotion recognition data, generate a response instructing the robot on how to assist the worker in a stressful situation."

[0841] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0842] Step 1:

[0843] The user logs into the virtual environment via a terminal and enters characteristic information. This information includes settings for the customer model with which they interact in the virtual environment. The entered information is structured on the terminal and becomes data sent to the server.

[0844] Step 2:

[0845] The server sends the received characteristic information to the generating AI model. Based on this information, the generating AI model creates a customer model for use in the virtual environment, which the server then receives. The customer model includes dialogue patterns and personas, and the generated results are sent to the user terminal.

[0846] Step 3:

[0847] The terminal places the received customer model into a virtual environment and initiates interaction with the user. Here, the user's voice and video data are captured in real time and analyzed by an emotion recognition system. This data is acquired using smart glasses or a head-mounted display.

[0848] Step 4:

[0849] The server receives analysis data from the emotion recognition system and evaluates the user's emotions. Using features obtained from voice and facial expressions, it estimates the emotional state and adjusts the customer model's response based on the results. Machine learning algorithms are applied to this analysis.

[0850] Step 5:

[0851] Based on the user's emotions, the server returns an adjusted response to the user's terminal. The terminal uses this information, and the customer model in the virtual environment performs an appropriate response. The user receives feedback through the interaction and evaluates their own response.

[0852] Step 6:

[0853] The server stores emotional data and response results collected during the interaction in a database. This information will be used for future system improvements and to enhance the accuracy of the generative AI model.

[0854] 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.

[0855] 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 those described above. 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 shown 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.

[0856] 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 robot 414.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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."

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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 this memory.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] The following is further disclosed regarding the embodiments described above.

[0876] (Claim 1)

[0877] A means for generating customer models for users to interact within a virtual space,

[0878] A configuration means for constructing a dialogue scenario based on customer characteristic information set by the user,

[0879] An execution means for executing a simulation in which the user and the customer model interact within the virtual space,

[0880] An evaluation means for analyzing and evaluating the response during the aforementioned simulation,

[0881] A means for presenting feedback to the user based on the aforementioned evaluation,

[0882] A means for accumulating data obtained during the aforementioned simulation and using it to improve future automated response systems,

[0883] A system that includes this.

[0884] (Claim 2)

[0885] The system according to claim 1, wherein the generation means transmits customer characteristic information from a user terminal to a server, and the server delivers the generated customer model to the user terminal.

[0886] (Claim 3)

[0887] The system according to claim 1, wherein the evaluation means includes an algorithm that analyzes the user's response in real time and determines the appropriateness of the response.

[0888] "Example 1"

[0889] (Claim 1)

[0890] A means for generating a customer model using artificial intelligence based on customer characteristic information entered by the user,

[0891] The user terminal provides means for receiving the generated customer model and deploying it to the virtual environment,

[0892] A means for executing a simulation in which the user interacts with the customer model within the virtual environment,

[0893] A means for analyzing and evaluating user responses obtained during the aforementioned simulation,

[0894] A means for presenting the aforementioned evaluation results to the user as feedback,

[0895] A means for accumulating data during the aforementioned simulation and using it to improve future dialogue algorithms,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, wherein the generation of the customer model includes sending customer characteristic information from the user terminal to a server, generating a model on the server, and providing the result to the user terminal.

[0899] (Claim 3)

[0900] The system according to claim 1, wherein the evaluation means includes using an algorithm for processing user responses in real time and determining their appropriateness.

[0901] "Application Example 1"

[0902] (Claim 1)

[0903] A means for generating a character model for users to interact with within a virtual environment,

[0904] A configuration means for constructing a dialogue scenario based on customer characteristic information set by the user,

[0905] An execution means for performing a simulated execution in which the user and the human model interact within the virtual environment,

[0906] An evaluation means for analyzing and evaluating the response during the simulated execution,

[0907] A presentation means for presenting improvement information to the user based on the aforementioned evaluation,

[0908] A means for accumulating information obtained during the aforementioned simulated execution and using it to improve the automated response system in the future,

[0909] A display device that provides a virtual environment using a head-mounted display,

[0910] Including a control device that uses an artificial intelligence model to simulate human behavior,

[0911] A system that includes this.

[0912] (Claim 2)

[0913] The system according to claim 1, wherein the generation means transmits customer characteristic information from a user device to an information processing device, and the information processing device distributes a person model generated to the user device.

[0914] (Claim 3)

[0915] The system according to claim 1, wherein the evaluation means includes a calculation process that analyzes the user's response in real time and determines the appropriateness of the response.

[0916] "Example 2 of combining an emotion engine"

[0917] (Claim 1)

[0918] A means for generating a person model generated by an information processing device for users to interact in a virtual space,

[0919] Means for configuring a dialogue simulation based on attribute information set by the user,

[0920] A means for executing a simulated experience in which the user and the character model interact within the virtual space,

[0921] An evaluation means using a computing device that analyzes and evaluates responses during the simulated experience,

[0922] A presentation means using a display device that presents opinions to the user based on the aforementioned evaluation,

[0923] A storage means for accumulating information obtained during the aforementioned simulated experience and using it to improve future automated response systems,

[0924] An emotion recognition means for analyzing the user's emotions in real time based on voice and actions, and for adjusting the response of the person model,

[0925] A system that includes this.

[0926] (Claim 2)

[0927] The system according to claim 1, wherein the generating means transmits attribute information via an input device to a computer system, and the computer system delivers the generated person model to the user terminal.

[0928] (Claim 3)

[0929] The system according to claim 1, wherein the evaluation means includes a procedure for recording and analyzing the user's response and emotional data, and comprises a calculation means for determining the appropriateness of the response.

[0930] "Application example 2 when combining with an emotional engine"

[0931] (Claim 1)

[0932] A means for generating customer models for users to interact with in a virtual space,

[0933] A configuration means for constructing a dialogue scenario based on characteristic information set by the user,

[0934] An execution means for executing a simulation in which the user and the customer model interact within the virtual space,

[0935] A recognition system that receives voice and facial expression data in real time and analyzes emotional states,

[0936] An adjustment means for adjusting the customer model's response based on the emotional information acquired by the recognition means,

[0937] An evaluation means for analyzing and evaluating the response during the aforementioned simulation,

[0938] A means for presenting feedback to the user based on the aforementioned evaluation,

[0939] A means for accumulating data obtained during the aforementioned simulation and using it to improve the automated response system in the future,

[0940] A system that includes this.

[0941] (Claim 2)

[0942] The system according to claim 1, wherein the generation means transmits characteristic information from the user device to a central processing unit, and the central processing unit distributes the customer model generated to the user device.

[0943] (Claim 3)

[0944] The system according to claim 1, wherein the evaluation means includes rules for analyzing the user's response and emotional state in real time and determining the appropriateness of the response. [Explanation of Symbols]

[0945] 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 for generating customer models for users to interact within a virtual space, A configuration means for constructing a dialogue scenario based on customer characteristic information set by the user, An execution means for executing a simulation in which the user and the customer model interact within the virtual space, An evaluation means for analyzing and evaluating the response during the aforementioned simulation, A means for presenting feedback to the user based on the aforementioned evaluation, A means for accumulating data obtained during the aforementioned simulation and using it to improve future automated response systems, A system that includes this.

2. The system according to claim 1, wherein the generation means transmits customer characteristic information from a user terminal to a server, and the server delivers the generated customer model to the user terminal.

3. The system according to claim 1, wherein the evaluation means includes an algorithm that analyzes the user's response in real time and determines the appropriateness of the response.

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