system

Abstract

The system according to this embodiment aims to realize realistic customer service role-playing in a virtual space. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, and a control unit. The analysis unit analyzes the user's speech patterns or responses to customer inquiries. The generation unit generates facial expressions and eye movements of a person playing the role of a customer based on the information analyzed by the analysis unit. The control unit performs customer service role-playing in a virtual space based on the information generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there was a problem that it was difficult to reproduce real customer reactions in customer service role-playing within a virtual space.

[0005] The system according to the embodiment aims to realize real customer service role-playing within a virtual space.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a generation unit, and a control unit. The analysis unit analyzes the user's speech patterns or responses to customer inquiries. The generation unit generates facial expressions and eye movements of a person playing the role of a customer based on the information analyzed by the analysis unit. The control unit performs customer service role-playing in a virtual space based on the information generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can realize realistic customer service role-playing in a virtual space. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The customer service role-playing system according to an embodiment of the present invention is a system that performs customer service role-playing in a virtual space. This system uses a generative AI to conduct a conversational exchange, and the generative AI instantly judges and generates even the smallest details of the customer's facial expressions and eye movements. The customer can be a real person obtained through a full-body scan or a character created by the generative AI. The virtual space can be, for example, an immersive space, but other virtual spaces with similar functions can also be used. First, the user enters the virtual space and begins customer service role-playing. The generative AI analyzes the user's speech and responses to the customer's questions and generates appropriate conversation. Furthermore, the generative AI instantly generates the customer's facial expressions and eye movements. As a result, the user can have a realistic customer service experience in the virtual space. For example, when the user says "Welcome," the generative AI generates a smile for the customer and has them respond with "Hello." Also, when the user explains a product, the generative AI generates small details of the customer's facial expressions and movements, such as moving their eyes with interest. As a result, the user can obtain an experience close to actual customer service. This system allows users to have a realistic customer service experience regardless of location or time. Furthermore, the difficulty level of the customer service scenarios can be freely adjusted, making it accessible to a wide range of users, from beginners to advanced. Additionally, by implementing this system, other customer service companies can conduct role-playing exercises that closely resemble real-world experiences, contributing to the improvement of their employees' skills. In this way, the customer service role-playing system can provide users with a realistic customer service experience.

[0029] The customer service role-playing system according to this embodiment comprises an analysis unit, a generation unit, and a control unit. The analysis unit analyzes the user's speech and responses to customer inquiries. The analysis unit analyzes the user's statements using, for example, a generation AI and generates appropriate responses. The generation unit generates facial expressions and eye movements of a person playing the role of a customer based on the information analyzed by the analysis unit. The generation unit generates facial expressions and eye movements of a person playing the role of a customer in real time using, for example, a generation AI. The control unit performs customer service role-playing in a virtual space based on the information generated by the generation unit. The control unit controls customer service in the virtual space using, for example, a generation AI. As a result, the customer service role-playing system can analyze the user's speech and responses and provide a realistic customer service experience in a virtual space.

[0030] The analysis unit analyzes the user's vocabulary and responses to customer inquiries. Specifically, the analysis unit uses speech recognition technology to convert the user's utterances into text data, and then analyzes that text data using natural language processing technology. The generating AI has been trained in advance on a large amount of customer service data to understand the content of the user's utterances and generate appropriate responses that are appropriate to the context. For example, if a user says, "Please tell me about this product," the analysis unit analyzes that utterance and generates a response that provides detailed information about the product. Furthermore, the analysis unit also analyzes the user's tone of voice, speed, and emotion, and uses this information to generate more natural and appropriate responses. As a result, the analysis unit can analyze the user's utterances with high accuracy and generate appropriate responses in real time.

[0031] The generation unit generates facial expressions and eye movements of the customer character based on the information analyzed by the analysis unit. Specifically, the generation AI determines what kind of facial expressions and eye movements the customer character should exhibit based on the user's statements and the analysis results. For example, if the user asks, "How do I use this product?", the generation unit generates an expression of interest from the customer character and eye movements that make it appear as if they are looking at the user. The generation AI can generate natural movements in real time by utilizing pre-trained facial expression and eye movement data. Furthermore, the generation unit can dynamically change the facial expressions and eye movements of the customer character in response to the user's reactions. In this way, the generation unit provides a realistic customer service experience in a virtual space, allowing users to practice in a situation close to actual customer service scenarios.

[0032] The control unit performs customer service role-playing in a virtual space based on information generated by the generation unit. Specifically, the control unit controls the movements and dialogue of the character in the virtual space and manages the interaction with the user in real time. The generation AI dynamically changes the scenario in the virtual space in response to the user's statements and actions, providing a more realistic customer service experience. For example, if the user asks, "How many colors does this product come in?", the control unit will have the person playing the customer respond, "This product comes in three colors: red, blue, and green," and simultaneously control the display of the product. Furthermore, the control unit can record the user's progress and learning outcomes, collecting data to provide feedback later. In this way, the control unit effectively manages customer service role-playing in the virtual space and helps users acquire skills that will be useful in actual customer service situations.

[0033] The customer service role-playing system includes an acquisition unit that acquires full-body scan data. The acquisition unit acquires full-body scan data, for example, using a 3D scanner. The acquisition unit can also perform a full-body scan using image data, for example. The acquisition unit can also perform a full-body scan using multiple cameras, for example. By acquiring full-body scan data, it is possible to recreate a real person in a virtual space. Full-body scan data includes, but is not limited to, 3D scan data and image data. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input data acquired by a 3D scanner into a generation AI, which can analyze the data and generate a person in the virtual space.

[0034] The customer service role-playing system includes a data collection unit that collects data to generate detailed facial expressions and eye movements of a person playing the role of a customer. The data collection unit can, for example, use a camera to collect facial expression data of the person playing the role of a customer. The data collection unit can also, for example, use a sensor to collect eye movements. The data collection unit can also, for example, use a microphone to collect audio data. By collecting data to generate detailed facial expressions and eye movements of the person playing the role of a customer, a more realistic customer service experience can be provided. Detailed facial expressions and eye movements include, but are not limited to, smiles, eyebrow movements, and eye movements. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input facial expression data acquired by the camera into a generation AI, which can analyze the data to generate facial expressions and eye movements.

[0035] The customer service role-playing system includes a setting unit for setting the difficulty level of customer service. The setting unit sets the difficulty level of customer service according to the user's skill level, for example. The setting unit can set difficulty levels for beginners, intermediate users, and advanced users, for example. The setting unit can also adjust the difficulty level according to the user's request, for example. This makes the system usable by a wide range of users, from beginners to advanced users, by setting the difficulty level of customer service. The difficulty levels of customer service include, for example, beginner, intermediate, and advanced users, but are not limited to these examples. Some or all of the above processing in the setting unit may be performed using, for example, AI, or not using AI. For example, the setting unit can input the user's skill level into a generating AI, and the generating AI can analyze the data to set the optimal difficulty level.

[0036] The generation unit can generate facial expressions and eye movements of a person playing the role of a customer using a generation AI. For example, the generation unit can generate facial expressions of a person playing the role of a customer using a generation AI. The generation unit can also generate eye movements using a generation AI, for example. The generation unit can also generate actions of a person playing the role of a customer using a generation AI, for example. This makes it possible to generate facial expressions and eye movements of a person playing the role of a customer in real time by using a generation AI. The generation AI includes, but is not limited to, deep learning models and generative opposite networks (GANs). Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input facial expression data into the generation AI, and the generation AI can analyze the data to generate facial expressions and eye movements.

[0037] The control unit can control customer service in the virtual space using generative AI. The control unit can, for example, use generative AI to control customer service in the virtual space. The control unit can also, for example, use generative AI to control customer service scenarios. The control unit can also, for example, use generative AI to control the movement of characters in the virtual space. This allows for real-time control of customer service in the virtual space using generative AI. Generative AI includes, but is not limited to, deep learning models and generative opposite networks (GANs). Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input customer service scenarios into the generative AI, and the generative AI can analyze the data to control customer service in the virtual space.

[0038] The analysis unit can analyze the user's past conversation history and select the optimal analysis method. For example, the analysis unit can use the user's past vocabulary and tone to determine the optimal analysis method for the generating AI. The analysis unit can also extract specific patterns from the user's past conversation history, and the generating AI can adjust the analysis method accordingly. The analysis unit can also analyze the user's past conversation history and the generating AI can select the most effective analysis method. This allows for the selection of the optimal analysis method by analyzing the user's past conversation history, thereby generating more natural conversations. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the user's past conversation history into the generating AI, which can then analyze the data and select the optimal analysis method.

[0039] The analysis unit can improve the accuracy of its analysis by considering the user's speech speed and tone during the analysis process. For example, if the user speaks quickly, the generation AI can improve the accuracy of the analysis and generate a quick response. The analysis unit can also adjust the accuracy of the analysis by having the generation AI adjust the accuracy of the analysis by having the generation AI adjust the accuracy of the analysis by having the user speak calmly and generate a natural response. The analysis unit can also analyze the user's speech speed and tone in real time, and have the generation AI generate the optimal response. This allows for the generation of more appropriate responses by considering the user's speech speed and tone. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's speech speed and tone into the generation AI, which can then analyze the data and generate the optimal response.

[0040] The analysis unit can prioritize obtaining highly relevant analysis results by considering the user's geographical location information during analysis. For example, if the user is in a specific region, the generating AI will prioritize obtaining analysis results related to that region. The analysis unit can also, for example, use the user's current location to obtain the most relevant analysis results. The analysis unit can also, for example, analyze the user's geographical location information in real time, and the generating AI can obtain the optimal analysis results. This allows for the provision of more relevant analysis results by considering the user's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into the generating AI, which can then analyze the data and prioritize obtaining highly relevant analysis results.

[0041] The analysis unit can analyze a user's social media activity during analysis and obtain relevant analysis results. For example, the analysis unit can use a generating AI to obtain relevant analysis results from a user's social media activity. The analysis unit can also use a generating AI to obtain optimal analysis results by analyzing a user's past social media posts. The analysis unit can also use a generating AI to obtain relevant analysis results by analyzing a user's social media activity in real time. This allows for the provision of more relevant analysis results by analyzing a user's social media activity. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input a user's social media activity into a generating AI, which can then analyze the data and obtain relevant analysis results.

[0042] The generation unit can improve the accuracy of its generation by referring to the past facial expression data of the person playing the customer during the generation process. For example, the generation unit uses the past facial expression data of the person playing the customer to generate the optimal facial expression using its generation AI. The generation unit can also analyze the past facial expression patterns of the person playing the customer to improve the accuracy of its generation AI. The generation unit can also refer to the past facial expression data of the person playing the customer to generate a more natural facial expression using its generation AI. This allows for the generation of more natural facial expressions by referring to the past facial expression data of the person playing the customer. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the past facial expression data of the person playing the customer into its generation AI, which can then analyze the data to generate the optimal facial expression.

[0043] The generation unit can customize facial expressions and eye movements during generation by considering environmental information within the virtual space. For example, the generation unit's generating AI can customize facial expressions and eye movements based on lighting and background within the virtual space. The generation unit can also adjust facial expressions and eye movements to match the movements of other characters within the virtual space. For example, the generation unit can analyze environmental information within the virtual space in real time, and the generating AI can generate optimal facial expressions and eye movements. This allows for the generation of more appropriate facial expressions and eye movements by considering environmental information within the virtual space. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input environmental information within the virtual space into the generating AI, which can then analyze the data to generate optimal facial expressions and eye movements.

[0044] The generation unit can generate optimal facial expressions and eye movements by considering the geographical location information of the person playing the customer during generation. For example, if the person playing the customer is in a specific region, the generation AI will generate facial expressions related to that region. The generation unit can also generate the most appropriate facial expressions based on the current location of the person playing the customer. The generation unit can also analyze the geographical location information of the person playing the customer in real time, and the generation AI will generate optimal facial expressions and eye movements. This allows for the generation of more appropriate facial expressions and eye movements by considering the geographical location information of the person playing the customer. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the geographical location information of the person playing the customer into the generation AI, and the generation AI can analyze the data to generate optimal facial expressions and eye movements.

[0045] The generation unit can analyze the social media activity of the person playing the customer during generation and generate relevant facial expressions and eye movements. For example, the generation unit can use the customer's social media activity to generate relevant facial expressions using the generation AI. The generation unit can also analyze the customer's past social media posts and generate the most appropriate facial expressions using the generation AI. The generation unit can also analyze the customer's social media activity in real time and generate relevant facial expressions and eye movements using the generation AI. This allows for the generation of more relevant facial expressions and eye movements by analyzing the customer's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the customer's social media activity into the generation AI, which can analyze the data and generate relevant facial expressions and eye movements.

[0046] The control unit can select the optimal control method by referring to past customer service data during control. For example, the control unit can have the generating AI select the optimal control method based on past customer service data. The control unit can also, for example, analyze past customer service patterns and have the generating AI select the most effective control method. The control unit can also, for example, refer to past customer service data and have the generating AI select a natural control method. This allows for the selection of the optimal control method by referring to past customer service data, thereby providing more natural customer service. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input past customer service data into the generating AI, which can then analyze the data and select the optimal control method.

[0047] The control unit can customize customer service scenarios during control by taking into account environmental information within the virtual space. For example, the control unit can have the generating AI customize customer service scenarios based on lighting and background within the virtual space. The control unit can also have the generating AI adjust customer service scenarios in accordance with the movements of other characters within the virtual space. The control unit can also analyze environmental information within the virtual space in real time, allowing the generating AI to provide the optimal customer service scenario. This allows for the provision of more appropriate customer service scenarios by taking environmental information within the virtual space into consideration. Some or all of the above-described processes in the control unit may be performed using AI, or not using AI. For example, the control unit can input environmental information within the virtual space to the generating AI, which can then analyze the data to provide the optimal customer service scenario.

[0048] The control unit can control the optimal customer service scenario during control, taking into account the user's geographical location information. For example, if the user is in a specific region, the control unit can generate a customer service scenario related to that region using AI. The control unit can also, for example, have the AI ​​generate the most appropriate customer service scenario based on the user's current location. The control unit can also, for example, analyze the user's geographical location information in real time, and have the AI ​​generate the optimal customer service scenario. This allows for the provision of a more appropriate customer service scenario by taking the user's geographical location information into consideration. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's geographical location information into the AI, and the AI ​​can analyze the data to provide the optimal customer service scenario.

[0049] The control unit can analyze the user's social media activity during control and control relevant customer service scenarios. For example, the control unit can use a generating AI to provide relevant customer service scenarios based on the user's social media activity. The control unit can also analyze the user's past social media posts and have the generating AI provide the optimal customer service scenario. The control unit can also analyze the user's social media activity in real time and have the generating AI provide relevant customer service scenarios. This allows for the provision of more relevant customer service scenarios by analyzing the user's social media activity. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's social media activity into a generating AI, which can then analyze the data and provide relevant customer service scenarios.

[0050] The acquisition unit can select the optimal acquisition method by referring to the user's past scan data when acquiring a full-body scan. For example, the acquisition unit can select the optimal acquisition method based on the user's past scan data. The acquisition unit can also, for example, analyze the user's past scan patterns and select the most effective acquisition method. The acquisition unit can also, for example, refer to the user's past scan data and select a natural acquisition method. This allows for the selection of the optimal acquisition method by referring to the user's past scan data, resulting in the acquisition of more natural scan data. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past scan data into a generating AI, which can then analyze the data and select the optimal acquisition method.

[0051] The acquisition unit can select the optimal acquisition method when acquiring a full-body scan, taking into account the user's current posture and movements. For example, the acquisition unit can analyze the user's current posture in real time and select the optimal acquisition method. The acquisition unit can also analyze the user's movements in real time and select the most effective acquisition method. The acquisition unit can also select a natural acquisition method, taking into account the user's posture and movements. This allows for the acquisition of more appropriate scan data by considering the user's current posture and movements. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the user's current posture and movements into a generating AI, which can then analyze the data and select the optimal acquisition method.

[0052] The acquisition unit can prioritize acquiring highly relevant scan data by considering the user's geographical location information when acquiring a full-body scan. For example, if the user is in a specific region, the acquisition unit will prioritize acquiring scan data related to that region. The acquisition unit can also prioritize acquiring the most relevant scan data based on the user's current location. The acquisition unit can also analyze the user's geographical location information in real time and prioritize acquiring the most suitable scan data. This allows for the acquisition of more relevant scan data by considering the user's geographical location information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's geographical location information into a generating AI, which can then analyze the data and prioritize acquiring highly relevant scan data.

[0053] The acquisition unit can analyze the user's social media activity when acquiring a full-body scan and prioritize the acquisition of relevant scan data. For example, the acquisition unit can prioritize the acquisition of relevant scan data from the user's social media activity. The acquisition unit can also analyze the user's past social media posts and prioritize the acquisition of optimal scan data. The acquisition unit can also analyze the user's social media activity in real time and prioritize the acquisition of relevant scan data. This allows for the acquisition of more relevant scan data by analyzing the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's social media activity into a generating AI, which can analyze the data and prioritize the acquisition of relevant scan data.

[0054] The data collection unit can select the optimal data collection method by referring to past data collection data during data collection. For example, the data collection unit can select the optimal data collection method based on past data collection data. The data collection unit can also select the most effective data collection method by analyzing past data collection patterns. For example, the data collection unit can select a natural data collection method by referring to past data collection data. This allows for more effective data collection by selecting the optimal data collection method by referring to past data collection data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection data into a generating AI, which can then analyze the data and select the optimal data collection method.

[0055] The data collection unit can select the optimal data collection method by considering the user's current situation and environment when collecting data. For example, the data collection unit can analyze the user's current situation in real time and select the optimal data collection method. For example, the data collection unit can also analyze the user's environment in real time and select the most effective data collection method. For example, the data collection unit can select a natural data collection method by considering the user's situation and environment. This allows for more appropriate data collection by considering the user's current situation and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current situation and environment into a generating AI, which can then analyze the data and select the optimal data collection method.

[0056] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also prioritize the collection of the most relevant data based on the user's current location. The data collection unit can also analyze the user's geographical location information in real time and prioritize the collection of the most suitable data. This allows for the collection of more relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0057] The data collection unit can analyze the user's social media activity during data collection and prioritize the collection of relevant data. For example, the data collection unit can prioritize the collection of relevant data from the user's social media activity. The data collection unit can also analyze the user's past social media posts and prioritize the collection of the most relevant data. The data collection unit can also analyze the user's social media activity in real time and prioritize the collection of relevant data. This allows for the collection of more relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI, which can then analyze the data and prioritize the collection of relevant data.

[0058] The setting unit can select the optimal difficulty setting by referring to past customer service data during the setting process. For example, the setting unit can use past customer service data to have the generating AI select the optimal difficulty setting. The setting unit can also analyze past customer service patterns and have the generating AI select the most effective difficulty setting. The setting unit can also refer to past customer service data and have the generating AI select a natural difficulty setting. This allows for the selection of the optimal difficulty setting by referring to past customer service data, thereby providing a more effective customer service experience. Some or all of the above-described processes in the setting unit may be performed using AI, for example, or without AI. For example, the setting unit can input past customer service data into the generating AI, which can then analyze the data and select the optimal difficulty setting.

[0059] The setting unit can select the optimal difficulty setting while considering the user's current skill level. For example, the setting unit can analyze the user's current skill level in real time, and the generating AI can select the optimal difficulty setting. The setting unit can also, for example, consider the user's skill level and have the generating AI select the most effective difficulty setting. The setting unit can also, for example, analyze the user's skill level in real time and have the generating AI select a natural difficulty setting. This allows for the provision of a more appropriate difficulty setting by considering the user's current skill level. Some or all of the above-described processes in the setting unit may be performed using AI, for example, or without AI. For example, the setting unit can input the user's skill level into the generating AI, and the generating AI can analyze the data and select the optimal difficulty setting.

[0060] The settings unit can determine the optimal difficulty level by considering the user's geographical location information during the setup process. For example, if the user is in a specific region, the generating AI will provide a difficulty level setting relevant to that region. The settings unit can also, for example, use the user's current location to provide the most appropriate difficulty level setting using the generating AI. The settings unit can also, for example, analyze the user's geographical location information in real time, and the generating AI will provide the optimal difficulty level setting. This allows for the provision of a more appropriate difficulty level setting by considering the user's geographical location information. Some or all of the above-described processes in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can input the user's geographical location information into the generating AI, which can then analyze the data to provide the optimal difficulty level setting.

[0061] The settings unit can analyze the user's social media activity during setup and set relevant difficulty levels. For example, the settings unit can use a generating AI to provide relevant difficulty levels based on the user's social media activity. The settings unit can also analyze the user's past social media posts and have the generating AI provide the optimal difficulty level. The settings unit can also analyze the user's social media activity in real time and have the generating AI provide relevant difficulty levels. This allows for the provision of more relevant difficulty levels by analyzing the user's social media activity. Some or all of the above-described processes in the settings unit may be performed using AI, or not. For example, the settings unit can input the user's social media activity into a generating AI, which can then analyze the data and provide relevant difficulty levels.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The customer service role-playing system may include a voice analysis unit that analyzes the tone and volume of the user's voice. For example, if the user is speaking in a high-pitched voice, the voice analysis unit can generate a response that matches the tone of that voice using a generating AI. For example, if the user is speaking in a low-pitched voice, the voice analysis unit can also generate a response that matches the tone of that voice using a generating AI. For example, if the user's voice volume is high, the voice analysis unit can also generate a response that matches the volume of that voice using a generating AI. This allows for more natural conversation by generating responses according to the tone and volume of the user's voice. Some or all of the above processing in the voice analysis unit may be performed using AI, for example, or without AI. For example, the voice analysis unit can input user voice data into a generating AI, which can analyze the data and generate an optimal response.

[0064] A customer service role-playing system may include a gesture analysis unit that analyzes user gestures. For example, if the user raises their hand, the gesture analysis unit can generate a response corresponding to that gesture using a generating AI. The gesture analysis unit can also generate a response corresponding to if the user points their finger. For example, if the user shakes their head, the gesture analysis unit can generate a response corresponding to that gesture using a generating AI. This enables more interactive conversations by generating responses according to the user's gestures. Some or all of the above-described processes in the gesture analysis unit may be performed using AI, for example, or without AI. For example, the gesture analysis unit can input user gesture data into a generating AI, which can analyze the data and generate an optimal response.

[0065] A customer service role-playing system may include a behavioral analysis unit that analyzes the user's past behavioral history. The behavioral analysis unit can, for example, analyze what customer service scenarios the user has chosen in the past, and a generative AI can suggest the optimal scenario. The behavioral analysis unit can also, for example, analyze what responses the user has given in the past, and a generative AI can generate the optimal response. The behavioral analysis unit can also, for example, analyze what gestures the user has used in the past, and a generative AI can suggest the optimal gesture. This allows for a more personalized customer service experience by analyzing the user's past behavioral history. Some or all of the above-described processes in the behavioral analysis unit may be performed using AI, for example, or without AI. For example, the behavioral analysis unit can input user behavioral data into a generative AI, which can analyze the data to generate the optimal scenario and responses.

[0066] A customer service role-playing system may include a feedback collection unit that collects real-time user feedback. For example, the feedback collection unit can collect in real time how the user felt about a scenario, and a generating AI can adjust the scenario based on that feedback. The feedback collection unit can also collect how the user felt about a particular response, and the generating AI can adjust the response based on that feedback. Furthermore, the feedback collection unit can collect how the user felt about a particular gesture, and the generating AI can adjust the gesture based on that feedback. This allows for the provision of a more appropriate customer service experience by collecting real-time user feedback. Some or all of the above-described processes in the feedback collection unit may be performed using AI, or not. For example, the feedback collection unit can input user feedback data into a generating AI, which can analyze the data to generate optimal scenarios and responses.

[0067] A customer service role-playing system may include a feedback analysis unit that analyzes the user's past feedback. The feedback analysis unit can, for example, analyze what kind of feedback the user has provided in the past, and a generative AI can propose the optimal scenario. The feedback analysis unit can also, for example, analyze what kind of feedback the user has provided in response to what kind of responses in the past, and a generative AI can generate the optimal response. The feedback analysis unit can also, for example, analyze what kind of feedback the user has provided in response to what kind of gestures in the past, and a generative AI can propose the optimal gesture. This allows for a more personalized customer service experience by analyzing the user's past feedback. Some or all of the above processing in the feedback analysis unit may be performed using AI, for example, or without AI. For example, the feedback analysis unit can input user feedback data into a generative AI, which can analyze the data to generate the optimal scenario and response.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The analysis unit analyzes the user's language and responses to customer inquiries. For example, the analysis unit uses generative AI to analyze the user's statements and generate appropriate responses. Step 2: The generation unit generates the facial expressions and eye movements of the person playing the customer based on the information analyzed by the analysis unit. The generation unit generates the facial expressions and eye movements of the person playing the customer in real time, for example, using a generation AI. Step 3: The control unit performs customer service role-playing in the virtual space based on the information generated by the generation unit. The control unit controls customer service in the virtual space, for example, using a generation AI.

[0070] (Example of form 2) The customer service role-playing system according to an embodiment of the present invention is a system that performs customer service role-playing in a virtual space. This system uses a generative AI to conduct a conversational exchange, and the generative AI instantly judges and generates even the smallest details of the customer's facial expressions and eye movements. The customer can be a real person obtained through a full-body scan or a character created by the generative AI. The virtual space can be, for example, an immersive space, but other virtual spaces with similar functions can also be used. First, the user enters the virtual space and begins customer service role-playing. The generative AI analyzes the user's speech and responses to the customer's questions and generates appropriate conversation. Furthermore, the generative AI instantly generates the customer's facial expressions and eye movements. As a result, the user can have a realistic customer service experience in the virtual space. For example, when the user says "Welcome," the generative AI generates a smile for the customer and has them respond with "Hello." Also, when the user explains a product, the generative AI generates small details of the customer's facial expressions and movements, such as moving their eyes with interest. As a result, the user can obtain an experience close to actual customer service. This system allows users to have a realistic customer service experience regardless of location or time. Furthermore, the difficulty level of the customer service scenarios can be freely adjusted, making it accessible to a wide range of users, from beginners to advanced. Additionally, by implementing this system, other customer service companies can conduct role-playing exercises that closely resemble real-world experiences, contributing to the improvement of their employees' skills. In this way, the customer service role-playing system can provide users with a realistic customer service experience.

[0071] The customer service role-playing system according to this embodiment comprises an analysis unit, a generation unit, and a control unit. The analysis unit analyzes the user's speech and responses to customer inquiries. The analysis unit analyzes the user's statements using, for example, a generation AI and generates appropriate responses. The generation unit generates facial expressions and eye movements of a person playing the role of a customer based on the information analyzed by the analysis unit. The generation unit generates facial expressions and eye movements of a person playing the role of a customer in real time using, for example, a generation AI. The control unit performs customer service role-playing in a virtual space based on the information generated by the generation unit. The control unit controls customer service in the virtual space using, for example, a generation AI. As a result, the customer service role-playing system can analyze the user's speech and responses and provide a realistic customer service experience in a virtual space.

[0072] The analysis unit analyzes the user's vocabulary and responses to customer inquiries. Specifically, the analysis unit uses speech recognition technology to convert the user's utterances into text data, and then analyzes that text data using natural language processing technology. The generating AI has been trained in advance on a large amount of customer service data to understand the content of the user's utterances and generate appropriate responses that are appropriate to the context. For example, if a user says, "Please tell me about this product," the analysis unit analyzes that utterance and generates a response that provides detailed information about the product. Furthermore, the analysis unit also analyzes the user's tone of voice, speed, and emotion, and uses this information to generate more natural and appropriate responses. As a result, the analysis unit can analyze the user's utterances with high accuracy and generate appropriate responses in real time.

[0073] The generation unit generates facial expressions and eye movements of the customer character based on the information analyzed by the analysis unit. Specifically, the generation AI determines what kind of facial expressions and eye movements the customer character should exhibit based on the user's statements and the analysis results. For example, if the user asks, "How do I use this product?", the generation unit generates an expression of interest from the customer character and eye movements that make it appear as if they are looking at the user. The generation AI can generate natural movements in real time by utilizing pre-trained facial expression and eye movement data. Furthermore, the generation unit can dynamically change the facial expressions and eye movements of the customer character in response to the user's reactions. In this way, the generation unit provides a realistic customer service experience in a virtual space, allowing users to practice in a situation close to actual customer service scenarios.

[0074] The control unit performs customer service role-playing in a virtual space based on information generated by the generation unit. Specifically, the control unit controls the movements and dialogue of the character in the virtual space and manages the interaction with the user in real time. The generation AI dynamically changes the scenario in the virtual space in response to the user's statements and actions, providing a more realistic customer service experience. For example, if the user asks, "How many colors does this product come in?", the control unit will have the person playing the customer respond, "This product comes in three colors: red, blue, and green," and simultaneously control the display of the product. Furthermore, the control unit can record the user's progress and learning outcomes, collecting data to provide feedback later. In this way, the control unit effectively manages customer service role-playing in the virtual space and helps users acquire skills that will be useful in actual customer service situations.

[0075] The customer service role-playing system includes an acquisition unit that acquires full-body scan data. The acquisition unit acquires full-body scan data, for example, using a 3D scanner. The acquisition unit can also perform a full-body scan using image data, for example. The acquisition unit can also perform a full-body scan using multiple cameras, for example. By acquiring full-body scan data, it is possible to recreate a real person in a virtual space. Full-body scan data includes, but is not limited to, 3D scan data and image data. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input data acquired by a 3D scanner into a generation AI, which can analyze the data and generate a person in the virtual space.

[0076] The customer service role-playing system includes a data collection unit that collects data to generate detailed facial expressions and eye movements of a person playing the role of a customer. The data collection unit can, for example, use a camera to collect facial expression data of the person playing the role of a customer. The data collection unit can also, for example, use a sensor to collect eye movements. The data collection unit can also, for example, use a microphone to collect audio data. By collecting data to generate detailed facial expressions and eye movements of the person playing the role of a customer, a more realistic customer service experience can be provided. Detailed facial expressions and eye movements include, but are not limited to, smiles, eyebrow movements, and eye movements. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input facial expression data acquired by the camera into a generation AI, which can analyze the data to generate facial expressions and eye movements.

[0077] The customer service role-playing system includes a setting unit for setting the difficulty level of customer service. The setting unit sets the difficulty level of customer service according to the user's skill level, for example. The setting unit can set difficulty levels for beginners, intermediate users, and advanced users, for example. The setting unit can also adjust the difficulty level according to the user's request, for example. This makes the system usable by a wide range of users, from beginners to advanced users, by setting the difficulty level of customer service. The difficulty levels of customer service include, for example, beginner, intermediate, and advanced users, but are not limited to these examples. Some or all of the above processing in the setting unit may be performed using, for example, AI, or not using AI. For example, the setting unit can input the user's skill level into a generating AI, and the generating AI can analyze the data to set the optimal difficulty level.

[0078] The generation unit can generate facial expressions and eye movements of a person playing the role of a customer using a generation AI. For example, the generation unit can generate facial expressions of a person playing the role of a customer using a generation AI. The generation unit can also generate eye movements using a generation AI, for example. The generation unit can also generate actions of a person playing the role of a customer using a generation AI, for example. This makes it possible to generate facial expressions and eye movements of a person playing the role of a customer in real time by using a generation AI. The generation AI includes, but is not limited to, deep learning models and generative opposite networks (GANs). Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input facial expression data into the generation AI, and the generation AI can analyze the data to generate facial expressions and eye movements.

[0079] The control unit can control customer service in the virtual space using generative AI. The control unit can, for example, use generative AI to control customer service in the virtual space. The control unit can also, for example, use generative AI to control customer service scenarios. The control unit can also, for example, use generative AI to control the movement of characters in the virtual space. This allows for real-time control of customer service in the virtual space using generative AI. Generative AI includes, but is not limited to, deep learning models and generative opposite networks (GANs). Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input customer service scenarios into the generative AI, and the generative AI can analyze the data to control customer service in the virtual space.

[0080] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit can improve the accuracy of the analysis using a generative AI to produce a more accurate response. For example, if the user is relaxed, the analysis unit can also adjust the accuracy of the analysis using a generative AI to produce a more natural conversation. For example, if the user is excited, the analysis unit can also adjust the accuracy of the analysis using a generative AI to produce a response in an appropriate tone. In this way, by adjusting the accuracy of the analysis according to the user's emotions, a more appropriate response can be produced. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI, which can analyze the data to adjust the accuracy of the emotion-based analysis.

[0081] The analysis unit can analyze the user's past conversation history and select the optimal analysis method. For example, the analysis unit can use the user's past vocabulary and tone to determine the optimal analysis method for the generating AI. The analysis unit can also extract specific patterns from the user's past conversation history, and the generating AI can adjust the analysis method accordingly. The analysis unit can also analyze the user's past conversation history and the generating AI can select the most effective analysis method. This allows for the selection of the optimal analysis method by analyzing the user's past conversation history, thereby generating more natural conversations. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the user's past conversation history into the generating AI, which can then analyze the data and select the optimal analysis method.

[0082] The analysis unit can improve the accuracy of its analysis by considering the user's speech speed and tone during the analysis process. For example, if the user speaks quickly, the generation AI can improve the accuracy of the analysis and generate a quick response. The analysis unit can also adjust the accuracy of the analysis by having the generation AI adjust the accuracy of the analysis by having the generation AI adjust the accuracy of the analysis by having the user speak calmly and generate a natural response. The analysis unit can also analyze the user's speech speed and tone in real time, and have the generation AI generate the optimal response. This allows for the generation of more appropriate responses by considering the user's speech speed and tone. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's speech speed and tone into the generation AI, which can then analyze the data and generate the optimal response.

[0083] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated user emotions. For example, if the user is tense, the generation AI may prioritize displaying important analysis results. If the user is relaxed, the generation AI may also prioritize displaying detailed analysis results. If the user is excited, the generation AI may also prioritize displaying rapid analysis results. This allows for the prioritization of more important information by determining the priority of analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into the generation AI, which can analyze the data and determine the priority of analysis results based on emotions.

[0084] The analysis unit can prioritize obtaining highly relevant analysis results by considering the user's geographical location information during analysis. For example, if the user is in a specific region, the generating AI will prioritize obtaining analysis results related to that region. The analysis unit can also, for example, use the user's current location to obtain the most relevant analysis results. The analysis unit can also, for example, analyze the user's geographical location information in real time, and the generating AI can obtain the optimal analysis results. This allows for the provision of more relevant analysis results by considering the user's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into the generating AI, which can then analyze the data and prioritize obtaining highly relevant analysis results.

[0085] The analysis unit can analyze a user's social media activity during analysis and obtain relevant analysis results. For example, the analysis unit can use a generating AI to obtain relevant analysis results from a user's social media activity. The analysis unit can also use a generating AI to obtain optimal analysis results by analyzing a user's past social media posts. The analysis unit can also use a generating AI to obtain relevant analysis results by analyzing a user's social media activity in real time. This allows for the provision of more relevant analysis results by analyzing a user's social media activity. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input a user's social media activity into a generating AI, which can then analyze the data and obtain relevant analysis results.

[0086] The generation unit can estimate the user's emotions and adjust the facial expressions and eye movements generated based on the estimated user emotions. For example, if the user is nervous, the generation AI can generate a relaxed facial expression for the person playing the role of a customer. For example, if the user is relaxed, the generation AI can also generate a natural facial expression for the person playing the role of a customer. For example, if the user is excited, the generation AI can also generate an interesting facial expression for the person playing the role of a customer. This allows for a more realistic customer service experience by adjusting facial expressions and eye movements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI, which can analyze the data and adjust facial expressions and eye movements based on the emotions.

[0087] The generation unit can improve the accuracy of its generation by referring to the past facial expression data of the person playing the customer during the generation process. For example, the generation unit uses the past facial expression data of the person playing the customer to generate the optimal facial expression using its generation AI. The generation unit can also analyze the past facial expression patterns of the person playing the customer to improve the accuracy of its generation AI. The generation unit can also refer to the past facial expression data of the person playing the customer to generate a more natural facial expression using its generation AI. This allows for the generation of more natural facial expressions by referring to the past facial expression data of the person playing the customer. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the past facial expression data of the person playing the customer into its generation AI, which can then analyze the data to generate the optimal facial expression.

[0088] The generation unit can customize facial expressions and eye movements during generation by considering environmental information within the virtual space. For example, the generation unit's generating AI can customize facial expressions and eye movements based on lighting and background within the virtual space. The generation unit can also adjust facial expressions and eye movements to match the movements of other characters within the virtual space. For example, the generation unit can analyze environmental information within the virtual space in real time, and the generating AI can generate optimal facial expressions and eye movements. This allows for the generation of more appropriate facial expressions and eye movements by considering environmental information within the virtual space. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input environmental information within the virtual space into the generating AI, which can then analyze the data to generate optimal facial expressions and eye movements.

[0089] The generation unit can estimate the user's emotions and determine the priority of facial expressions and eye movements to generate based on the estimated user emotions. For example, if the user is tense, the generation AI will prioritize generating relaxed facial expressions. If the user is relaxed, the generation AI may also prioritize generating natural facial expressions. If the user is excited, the generation AI may also prioritize generating interesting facial expressions. This allows for the generation of more appropriate facial expressions by prioritizing facial expressions and eye movements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input user emotion data into the generation AI, which can analyze the data to determine the priority of facial expressions and eye movements based on the emotions.

[0090] The generation unit can generate optimal facial expressions and eye movements by considering the geographical location information of the person playing the customer during generation. For example, if the person playing the customer is in a specific region, the generation AI will generate facial expressions related to that region. The generation unit can also generate the most appropriate facial expressions based on the current location of the person playing the customer. The generation unit can also analyze the geographical location information of the person playing the customer in real time, and the generation AI will generate optimal facial expressions and eye movements. This allows for the generation of more appropriate facial expressions and eye movements by considering the geographical location information of the person playing the customer. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the geographical location information of the person playing the customer into the generation AI, and the generation AI can analyze the data to generate optimal facial expressions and eye movements.

[0091] The generation unit can analyze the social media activity of the person playing the customer during generation and generate relevant facial expressions and eye movements. For example, the generation unit can use the customer's social media activity to generate relevant facial expressions using the generation AI. The generation unit can also analyze the customer's past social media posts and generate the most appropriate facial expressions using the generation AI. The generation unit can also analyze the customer's social media activity in real time and generate relevant facial expressions and eye movements using the generation AI. This allows for the generation of more relevant facial expressions and eye movements by analyzing the customer's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the customer's social media activity into the generation AI, which can analyze the data and generate relevant facial expressions and eye movements.

[0092] The control unit can estimate the user's emotions and adjust the customer service scenario in the virtual space based on the estimated user emotions. For example, if the user is nervous, the control unit can have the generating AI provide a relaxed scenario. For example, if the user is relaxed, the control unit can have the generating AI provide a natural scenario. For example, if the user is excited, the control unit can have the generating AI provide an interesting scenario. This allows for the provision of more appropriate scenarios by adjusting the customer service scenario according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input user emotion data into the generating AI, and the generating AI can analyze the data to adjust the customer service scenario based on the emotions.

[0093] The control unit can select the optimal control method by referring to past customer service data during control. For example, the control unit can have the generating AI select the optimal control method based on past customer service data. The control unit can also, for example, analyze past customer service patterns and have the generating AI select the most effective control method. The control unit can also, for example, refer to past customer service data and have the generating AI select a natural control method. This allows for the selection of the optimal control method by referring to past customer service data, thereby providing more natural customer service. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input past customer service data into the generating AI, which can then analyze the data and select the optimal control method.

[0094] The control unit can customize customer service scenarios during control by taking into account environmental information within the virtual space. For example, the control unit can have the generating AI customize customer service scenarios based on lighting and background within the virtual space. The control unit can also have the generating AI adjust customer service scenarios in accordance with the movements of other characters within the virtual space. The control unit can also analyze environmental information within the virtual space in real time, allowing the generating AI to provide the optimal customer service scenario. This allows for the provision of more appropriate customer service scenarios by taking environmental information within the virtual space into consideration. Some or all of the above-described processes in the control unit may be performed using AI, or not using AI. For example, the control unit can input environmental information within the virtual space to the generating AI, which can then analyze the data to provide the optimal customer service scenario.

[0095] The control unit can estimate the user's emotions and determine the priority of customer service scenarios based on the estimated user emotions. For example, if the user is nervous, the control unit can have the generative AI prioritize providing a relaxed scenario. For example, if the user is relaxed, the control unit can have the generative AI prioritize providing a natural scenario. For example, if the user is excited, the control unit can have the generative AI prioritize providing an interesting scenario. This allows for the provision of more appropriate scenarios by prioritizing customer service scenarios according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI or not using AI. For example, the control unit can input user emotion data into the generative AI, which can analyze the data to determine the priority of customer service scenarios based on emotions.

[0096] The control unit can control the optimal customer service scenario during control, taking into account the user's geographical location information. For example, if the user is in a specific region, the control unit can generate a customer service scenario related to that region using AI. The control unit can also, for example, have the AI ​​generate the most appropriate customer service scenario based on the user's current location. The control unit can also, for example, analyze the user's geographical location information in real time, and have the AI ​​generate the optimal customer service scenario. This allows for the provision of a more appropriate customer service scenario by taking the user's geographical location information into consideration. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's geographical location information into the AI, and the AI ​​can analyze the data to provide the optimal customer service scenario.

[0097] The control unit can analyze the user's social media activity during control and control relevant customer service scenarios. For example, the control unit can use a generating AI to provide relevant customer service scenarios based on the user's social media activity. The control unit can also analyze the user's past social media posts and have the generating AI provide the optimal customer service scenario. The control unit can also analyze the user's social media activity in real time and have the generating AI provide relevant customer service scenarios. This allows for the provision of more relevant customer service scenarios by analyzing the user's social media activity. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's social media activity into a generating AI, which can then analyze the data and provide relevant customer service scenarios.

[0098] The acquisition unit can estimate the user's emotions and adjust the timing of the full-body scan acquisition based on the estimated user emotions. For example, if the user is tense, the acquisition unit can perform a full-body scan at a relaxed time. For example, if the user is relaxed, the acquisition unit can also perform a full-body scan at a natural time. For example, if the user is excited, the acquisition unit can also perform a full-body scan at an appropriate time. By adjusting the timing of the full-body scan acquisition according to the user's emotions, more appropriate scan data can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's emotion data into the generative AI, and the generative AI can analyze the data and adjust the timing of the full-body scan acquisition based on the emotions.

[0099] The acquisition unit can select the optimal acquisition method by referring to the user's past scan data when acquiring a full-body scan. For example, the acquisition unit can select the optimal acquisition method based on the user's past scan data. The acquisition unit can also, for example, analyze the user's past scan patterns and select the most effective acquisition method. The acquisition unit can also, for example, refer to the user's past scan data and select a natural acquisition method. This allows for the selection of the optimal acquisition method by referring to the user's past scan data, resulting in the acquisition of more natural scan data. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past scan data into a generating AI, which can then analyze the data and select the optimal acquisition method.

[0100] The acquisition unit can select the optimal acquisition method when acquiring a full-body scan, taking into account the user's current posture and movements. For example, the acquisition unit can analyze the user's current posture in real time and select the optimal acquisition method. The acquisition unit can also analyze the user's movements in real time and select the most effective acquisition method. The acquisition unit can also select a natural acquisition method, taking into account the user's posture and movements. This allows for the acquisition of more appropriate scan data by considering the user's current posture and movements. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the user's current posture and movements into a generating AI, which can then analyze the data and select the optimal acquisition method.

[0101] The acquisition unit can estimate the user's emotions and determine the priority of scan data to acquire based on the estimated user emotions. For example, if the user is tense, the acquisition unit may prioritize acquiring scan data of relaxed facial expressions. For example, if the user is relaxed, the acquisition unit may also prioritize acquiring scan data of natural facial expressions. For example, if the user is excited, the acquisition unit may also prioritize acquiring scan data of interesting facial expressions. By prioritizing scan data according to the user's emotions, more appropriate scan data can be acquired. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's emotion data into a generative AI, which can analyze the data and determine the priority of scan data based on emotions.

[0102] The acquisition unit can prioritize acquiring highly relevant scan data by considering the user's geographical location information when acquiring a full-body scan. For example, if the user is in a specific region, the acquisition unit will prioritize acquiring scan data related to that region. The acquisition unit can also prioritize acquiring the most relevant scan data based on the user's current location. The acquisition unit can also analyze the user's geographical location information in real time and prioritize acquiring the most suitable scan data. This allows for the acquisition of more relevant scan data by considering the user's geographical location information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's geographical location information into a generating AI, which can then analyze the data and prioritize acquiring highly relevant scan data.

[0103] The acquisition unit can analyze the user's social media activity when acquiring a full-body scan and prioritize the acquisition of relevant scan data. For example, the acquisition unit can prioritize the acquisition of relevant scan data from the user's social media activity. The acquisition unit can also analyze the user's past social media posts and prioritize the acquisition of optimal scan data. The acquisition unit can also analyze the user's social media activity in real time and prioritize the acquisition of relevant scan data. This allows for the acquisition of more relevant scan data by analyzing the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's social media activity into a generating AI, which can analyze the data and prioritize the acquisition of relevant scan data.

[0104] The data collection unit can estimate the user's emotions and adjust the data collection method based on the estimated user emotions. For example, if the user is tense, the data collection unit can collect data in a relaxed environment. If the user is relaxed, the data collection unit can also collect data in a natural environment. If the user is excited, the data collection unit can also collect data in an appropriate environment. By adjusting the data collection method according to the user's emotions, more appropriate data can be collected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into a generative AI, which can analyze the data and adjust the data collection method based on emotions.

[0105] The data collection unit can select the optimal data collection method by referring to past data collection data during data collection. For example, the data collection unit can select the optimal data collection method based on past data collection data. The data collection unit can also select the most effective data collection method by analyzing past data collection patterns. For example, the data collection unit can select a natural data collection method by referring to past data collection data. This allows for more effective data collection by selecting the optimal data collection method by referring to past data collection data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection data into a generating AI, which can then analyze the data and select the optimal data collection method.

[0106] The data collection unit can select the optimal data collection method by considering the user's current situation and environment when collecting data. For example, the data collection unit can analyze the user's current situation in real time and select the optimal data collection method. For example, the data collection unit can also analyze the user's environment in real time and select the most effective data collection method. For example, the data collection unit can select a natural data collection method by considering the user's situation and environment. This allows for more appropriate data collection by considering the user's current situation and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current situation and environment into a generating AI, which can then analyze the data and select the optimal data collection method.

[0107] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is tense, the data collection unit may prioritize collecting relaxed data. For example, if the user is relaxed, the data collection unit may also prioritize collecting natural data. For example, if the user is excited, the data collection unit may also prioritize collecting interesting data. This allows for the collection of more appropriate data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can analyze the data and determine the priority of data based on emotions.

[0108] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also prioritize the collection of the most relevant data based on the user's current location. The data collection unit can also analyze the user's geographical location information in real time and prioritize the collection of the most suitable data. This allows for the collection of more relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0109] The data collection unit can analyze the user's social media activity during data collection and prioritize the collection of relevant data. For example, the data collection unit can prioritize the collection of relevant data from the user's social media activity. The data collection unit can also analyze the user's past social media posts and prioritize the collection of the most relevant data. The data collection unit can also analyze the user's social media activity in real time and prioritize the collection of relevant data. This allows for the collection of more relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI, which can then analyze the data and prioritize the collection of relevant data.

[0110] The settings unit can estimate the user's emotions and adjust the difficulty level of customer service based on the estimated emotions. For example, if the user is nervous, the generating AI may set the difficulty level of customer service to a low level. For example, if the user is relaxed, the generating AI may set the difficulty level of customer service to a medium level. For example, if the user is excited, the generating AI may set the difficulty level of customer service to a high level. In this way, by adjusting the difficulty level of customer service according to the user's emotions, a more appropriate level of customer service experience can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can input user emotion data into the generating AI, and the generating AI can analyze the data and adjust the difficulty level of customer service based on emotions.

[0111] The setting unit can select the optimal difficulty setting by referring to past customer service data during the setting process. For example, the setting unit can use past customer service data to have the generating AI select the optimal difficulty setting. The setting unit can also analyze past customer service patterns and have the generating AI select the most effective difficulty setting. The setting unit can also refer to past customer service data and have the generating AI select a natural difficulty setting. This allows for the selection of the optimal difficulty setting by referring to past customer service data, thereby providing a more effective customer service experience. Some or all of the above-described processes in the setting unit may be performed using AI, for example, or without AI. For example, the setting unit can input past customer service data into the generating AI, which can then analyze the data and select the optimal difficulty setting.

[0112] The setting unit can select the optimal difficulty setting while considering the user's current skill level. For example, the setting unit can analyze the user's current skill level in real time, and the generating AI can select the optimal difficulty setting. The setting unit can also, for example, consider the user's skill level and have the generating AI select the most effective difficulty setting. The setting unit can also, for example, analyze the user's skill level in real time and have the generating AI select a natural difficulty setting. This allows for the provision of a more appropriate difficulty setting by considering the user's current skill level. Some or all of the above-described processes in the setting unit may be performed using AI, for example, or without AI. For example, the setting unit can input the user's skill level into the generating AI, and the generating AI can analyze the data and select the optimal difficulty setting.

[0113] The settings unit can estimate the user's emotions and determine the priority of customer service difficulty settings based on the estimated user emotions. For example, if the user is nervous, the settings unit may prioritize providing a relaxed difficulty setting through the generating AI. For example, if the user is relaxed, the settings unit may also prioritize providing a natural difficulty setting through the generating AI. For example, if the user is excited, the settings unit may also prioritize providing an interesting difficulty setting through the generating AI. This allows for the provision of more appropriate difficulty settings by determining the priority of difficulty settings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the settings unit may be performed using AI, for example, or not using AI. For example, the settings unit can input user emotion data into the generating AI, and the generating AI can analyze the data to determine the priority of customer service difficulty settings based on emotions.

[0114] The settings unit can determine the optimal difficulty level by considering the user's geographical location information during the setup process. For example, if the user is in a specific region, the generating AI will provide a difficulty level setting relevant to that region. The settings unit can also, for example, use the user's current location to provide the most appropriate difficulty level setting using the generating AI. The settings unit can also, for example, analyze the user's geographical location information in real time, and the generating AI will provide the optimal difficulty level setting. This allows for the provision of a more appropriate difficulty level setting by considering the user's geographical location information. Some or all of the above-described processes in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can input the user's geographical location information into the generating AI, which can then analyze the data to provide the optimal difficulty level setting.

[0115] The settings unit can analyze the user's social media activity during setup and set relevant difficulty levels. For example, the settings unit can use a generating AI to provide relevant difficulty levels based on the user's social media activity. The settings unit can also analyze the user's past social media posts and have the generating AI provide the optimal difficulty level. The settings unit can also analyze the user's social media activity in real time and have the generating AI provide relevant difficulty levels. This allows for the provision of more relevant difficulty levels by analyzing the user's social media activity. Some or all of the above-described processes in the settings unit may be performed using AI, or not. For example, the settings unit can input the user's social media activity into a generating AI, which can then analyze the data and provide relevant difficulty levels.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The customer service role-playing system may include a voice analysis unit that analyzes the tone and volume of the user's voice. For example, if the user is speaking in a high-pitched voice, the voice analysis unit can generate a response that matches the tone of that voice using a generating AI. For example, if the user is speaking in a low-pitched voice, the voice analysis unit can also generate a response that matches the tone of that voice using a generating AI. For example, if the user's voice volume is high, the voice analysis unit can also generate a response that matches the volume of that voice using a generating AI. This allows for more natural conversation by generating responses according to the tone and volume of the user's voice. Some or all of the above processing in the voice analysis unit may be performed using AI, for example, or without AI. For example, the voice analysis unit can input user voice data into a generating AI, which can analyze the data and generate an optimal response.

[0118] A customer service role-playing system may include a gesture analysis unit that analyzes user gestures. For example, if the user raises their hand, the gesture analysis unit can generate a response corresponding to that gesture using a generating AI. The gesture analysis unit can also generate a response corresponding to if the user points their finger. For example, if the user shakes their head, the gesture analysis unit can generate a response corresponding to that gesture using a generating AI. This enables more interactive conversations by generating responses according to the user's gestures. Some or all of the above-described processes in the gesture analysis unit may be performed using AI, for example, or without AI. For example, the gesture analysis unit can input user gesture data into a generating AI, which can analyze the data and generate an optimal response.

[0119] The customer service role-playing system may include a facial expression analysis unit that analyzes the user's facial expressions. For example, if the user smiles, the facial expression analysis unit can generate a response corresponding to that expression using a generating AI. For example, if the user shows a surprised expression, the facial expression analysis unit can also generate a response corresponding to that expression using a generating AI. For example, if the user shows a confused expression, the facial expression analysis unit can also generate a response corresponding to that expression using a generating AI. This allows for more emotionally rich conversations by generating responses according to the user's facial expressions. Some or all of the above processing in the facial expression analysis unit may be performed using AI, for example, or without AI. For example, the facial expression analysis unit can input the user's facial expression data into a generating AI, which can then analyze the data and generate the optimal response.

[0120] A customer service role-playing system may include a behavioral analysis unit that analyzes the user's past behavioral history. The behavioral analysis unit can, for example, analyze what customer service scenarios the user has chosen in the past, and a generative AI can suggest the optimal scenario. The behavioral analysis unit can also, for example, analyze what responses the user has given in the past, and a generative AI can generate the optimal response. The behavioral analysis unit can also, for example, analyze what gestures the user has used in the past, and a generative AI can suggest the optimal gesture. This allows for a more personalized customer service experience by analyzing the user's past behavioral history. Some or all of the above-described processes in the behavioral analysis unit may be performed using AI, for example, or without AI. For example, the behavioral analysis unit can input user behavioral data into a generative AI, which can analyze the data to generate the optimal scenario and responses.

[0121] A customer service role-playing system can estimate a user's emotions and dynamically change the customer service scenario based on those emotions. For example, if a user is nervous, the generative AI can provide a relaxing scenario. If the user is relaxed, the generative AI can also provide a more challenging scenario. If the user is excited, the generative AI can also provide a scenario that maintains that excitement. This allows for a more appropriate customer service experience by dynamically changing the scenario according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the system may be performed using AI or not. For example, the system can input user emotion data into the generative AI, which can analyze the data and dynamically change the scenario based on the emotions.

[0122] A customer service role-playing system may include a feedback collection unit that collects real-time user feedback. For example, the feedback collection unit can collect in real time how the user felt about a scenario, and a generating AI can adjust the scenario based on that feedback. The feedback collection unit can also collect how the user felt about a particular response, and the generating AI can adjust the response based on that feedback. Furthermore, the feedback collection unit can collect how the user felt about a particular gesture, and the generating AI can adjust the gesture based on that feedback. This allows for the provision of a more appropriate customer service experience by collecting real-time user feedback. Some or all of the above-described processes in the feedback collection unit may be performed using AI, or not. For example, the feedback collection unit can input user feedback data into a generating AI, which can analyze the data to generate optimal scenarios and responses.

[0123] A customer service role-playing system can estimate a user's emotions and provide feedback based on those emotions. For example, if a user is nervous, the generative AI can provide advice to help them relax. If the user is relaxed, the generative AI can also provide feedback to help them maintain that state. If the user is excited, the generative AI can also provide feedback to help them control their excitement. This allows for a more appropriate customer service experience by providing feedback according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processes described above in the system may be performed using AI or not. For example, the system can input user emotion data into a generative AI, which can then analyze the data and provide emotion-based feedback.

[0124] A customer service role-playing system may include a feedback analysis unit that analyzes the user's past feedback. The feedback analysis unit can, for example, analyze what kind of feedback the user has provided in the past, and a generative AI can propose the optimal scenario. The feedback analysis unit can also, for example, analyze what kind of feedback the user has provided in response to what kind of responses in the past, and a generative AI can generate the optimal response. The feedback analysis unit can also, for example, analyze what kind of feedback the user has provided in response to what kind of gestures in the past, and a generative AI can propose the optimal gesture. This allows for a more personalized customer service experience by analyzing the user's past feedback. Some or all of the above processing in the feedback analysis unit may be performed using AI, for example, or without AI. For example, the feedback analysis unit can input user feedback data into a generative AI, which can analyze the data to generate the optimal scenario and response.

[0125] The customer service role-playing system can estimate the user's emotions and adjust the training pace based on those emotions. For example, if the user is nervous, the generative AI can slow down the training pace. If the user is relaxed, the generative AI can maintain a normal training pace. If the user is excited, the generative AI can speed up the training pace. This allows for a more appropriate training experience by adjusting the training pace according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the system may be performed using AI or not. For example, the system can input user emotion data into the generative AI, which can analyze the data and adjust the training pace based on the emotions.

[0126] The customer service role-playing system can estimate the user's emotions and customize the training content based on those emotions. For example, if the user is nervous, the generative AI can provide training that helps them relax. If the user is relaxed, the generative AI can also provide training to maintain that state. If the user is excited, the generative AI can also provide training to maintain that excitement. This allows for a more appropriate training experience by customizing the training content according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the system may be performed using AI or not. For example, the system can input user emotion data into the generative AI, which can then analyze the data to customize the training content based on the emotions.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The analysis unit analyzes the user's language and responses to customer inquiries. For example, the analysis unit uses generative AI to analyze the user's statements and generate appropriate responses. Step 2: The generation unit generates the facial expressions and eye movements of the person playing the customer based on the information analyzed by the analysis unit. The generation unit generates the facial expressions and eye movements of the person playing the customer in real time, for example, using a generation AI. Step 3: The control unit performs customer service role-playing in the virtual space based on the information generated by the generation unit. The control unit controls customer service in the virtual space, for example, using a generation AI.

[0129] 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.

[0130] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0131] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12. The generation unit is implemented by the control unit 46A of the smart device 14. The control unit is implemented by the specific processing unit 290 of the data processing device 12. The acquisition unit acquires full-body scan data using the camera 42 of the smart device 14. The collection unit collects data on facial expressions and eye movements using the camera 42 and microphone 38B of the smart device 14. The setting unit sets the difficulty level of customer service using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] 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.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] 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.

[0140] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12. The generation unit is implemented by the control unit 46A of the smart glasses 214. The control unit is implemented by the specific processing unit 290 of the data processing device 12. The acquisition unit acquires full-body scan data using the camera 42 of the smart glasses 214. The collection unit collects data on facial expressions and eye movements using the camera 42 and microphone 238 of the smart glasses 214. The setting unit sets the difficulty level of customer service using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] 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.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] 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.

[0156] 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.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] 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.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12. The generation unit is implemented by the control unit 46A of the headset terminal 314. The control unit is implemented by the specific processing unit 290 of the data processing device 12. The acquisition unit acquires full-body scan data using the camera 42 of the headset terminal 314. The collection unit collects data on facial expressions and eye movements using the camera 42 and microphone 238 of the headset terminal 314. The setting unit sets the difficulty level of customer service using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0168] 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.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0171] 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.

[0172] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0173] 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.

[0174] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] 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.

[0179] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12. The generation unit is implemented by the control unit 46A of the robot 414. The control unit is implemented by the specific processing unit 290 of the data processing device 12. The acquisition unit acquires full-body scan data using the camera 42 of the robot 414. The collection unit collects data on facial expressions and eye movements using the camera 42 and microphone 238 of the robot 414. The setting unit sets the difficulty level of customer service using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0182] 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.

[0183] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0184] 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.

[0185] 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.

[0186] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0187] 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."

[0188] 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.

[0189] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0198] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0199] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0200] (Note 1) An analysis unit that analyzes the user's language or responses to customer inquiries, A generation unit generates facial expressions and eye movements of a person playing the role of a customer based on the information analyzed by the aforementioned analysis unit, The system includes a control unit that performs customer service role-playing in a virtual space based on the information generated by the generation unit. A system characterized by the following features. (Note 2) It includes an acquisition unit that acquires body scan data. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a data collection unit that collects data to generate detailed facial expressions and eye movements of the person playing the role of a customer. The system described in Appendix 1, characterized by the features described herein. (Note 4) It has a setting section for adjusting the difficulty level of customer service. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is The AI ​​generates facial expressions and eye movements of the person playing the role of a customer. The system described in Appendix 1, characterized by the features described herein. (Note 6) The control unit, Generative AI controls customer service within a virtual space. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, Analyze the user's past conversation history and select the optimal analysis method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by taking into account the user's speech speed and tone. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, the system prioritizes obtaining highly relevant analysis results by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the user's social media activity is analyzed, and relevant analytical results are obtained. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the facial expressions and eye movements generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, the accuracy of the generation is improved by referencing past facial expression data of the person playing the customer. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, facial expressions and eye movements are customized by considering environmental information within the virtual space. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and determines the priority of facial expressions and eye movements to generate based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the system considers the geographical location of the person playing the customer to generate the optimal facial expressions and eye movements. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, the social media activity of the person playing the customer is analyzed, and relevant facial expressions and eye movements are generated. The system described in Appendix 1, characterized by the features described herein. (Note 19) The control unit, It estimates the user's emotions and adjusts the customer service scenario in the virtual space based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The control unit, During control, the system selects the optimal control method by referring to past customer service data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The control unit, During control, the customer service scenario is customized by considering environmental information within the virtual space. The system described in Appendix 1, characterized by the features described herein. (Note 22) The control unit, The system estimates the user's emotions and prioritizes customer service scenarios based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The control unit, During control, the system takes the user's geographical location into consideration to control the optimal customer service scenario. The system described in Appendix 1, characterized by the features described herein. (Note 24) The control unit, During control, the system analyzes the user's social media activity and controls the relevant customer service scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 25) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of full-body scan acquisition based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The acquisition unit is, When acquiring a full-body scan, the system selects the optimal acquisition method by referring to the user's past scan data. The system described in Appendix 2, characterized by the features described herein. (Note 27) The acquisition unit is, When acquiring a full-body scan, the optimal acquisition method is selected considering the user's current posture and movements. The system described in Appendix 2, characterized by the features described herein. (Note 28) The acquisition unit is, It estimates the user's emotions and determines the priority of scan data to acquire based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The acquisition unit is, When acquiring a full-body scan, the system prioritizes acquiring highly relevant scan data by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 30) The acquisition unit is, When acquiring a full-body scan, the system analyzes the user's social media activity and prioritizes the acquisition of relevant scan data. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned collection unit is We estimate the user's emotions and adjust the data collection method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned collection unit is During data collection, the optimal collection method is selected by referring to previously collected data. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned collection unit is When collecting data, the optimal collection method is selected considering the user's current situation and environment. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and prioritizes the collection of relevant data. The system described in Appendix 3, characterized by the features described herein. (Note 37) The setting unit is, The system estimates the user's emotions and adjusts the difficulty level of customer service based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 38) The setting unit is, During setup, past customer service data is referenced to select the optimal difficulty level. The system described in Appendix 4, characterized by the features described herein. (Note 39) The setting unit is, During setup, the system selects the optimal difficulty setting, taking into account the user's current skill level. The system described in Appendix 4, characterized by the features described herein. (Note 40) The setting unit is, The system estimates the user's emotions and determines the priority of customer service difficulty settings based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 41) The setting unit is, During setup, the game adjusts the difficulty level to be optimal, taking into account the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 42) The setting unit is, During setup, the system analyzes the user's social media activity and sets appropriate difficulty levels. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An analysis unit that analyzes the user's language or responses to customer inquiries, A generation unit generates facial expressions and eye movements of a person playing the role of a customer based on the information analyzed by the aforementioned analysis unit, The system includes a control unit that performs customer service role-playing in a virtual space based on the information generated by the generation unit. A system characterized by the following features.

2. It includes an acquisition unit that acquires body scan data. The system according to feature 1.

3. It includes a data collection unit that collects data to generate detailed facial expressions and eye movements of the person playing the role of a customer. The system according to feature 1.

4. It has a setting section for adjusting the difficulty level of customer service. The system according to feature 1.

5. The generating unit is The AI ​​generates the facial expressions and eye movements of the person playing the role of a customer. The system according to feature 1.

6. The control unit, Using generated AI to control customer service within a virtual space. The system according to feature 1.

7. The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit, Analyze the user's past conversation history and select the optimal analysis method. The system according to feature 1.

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