system

Abstract

An object of the system according to the embodiment is to realize a realistic customer simulation using the VR technology and the generated AI.SOLUTION: A system according to an embodiment includes VR technology, a generative AI, and persona settings. The VR technology performs customer simulation using the VR technology. The generation AI automatically generates a customer by using the generation AI and generates a response. The persona setting sets the persona of the customer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has limited means for improving the quality of customer simulations, leaving room for improvement in terms of recreating more realistic situations.

[0005] The system according to the embodiment aims to realize a realistic customer simulation using VR technology and generative AI. [Means for solving the problem]

[0006] The system according to the embodiment includes VR technology, a generation AI, and persona setting. The VR technology performs customer simulation using VR technology. The generation AI uses the generation AI to automatically generate customers and generate responses. The persona setting sets a customer persona. [Effects of the Invention]

[0007] The system according to the embodiment can realize a realistic customer simulation using VR technology and generative AI. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The customer simulation system according to an embodiment of the present invention uses VR technology to perform customer simulations, and a generation AI automatically generates customers and generates responses. This allows the customer simulation system to efficiently train employees and provide higher quality services.

[0029] A customer simulation system according to an embodiment includes VR technology, a generation AI, and persona setting. The VR technology performs customer simulation. For example, a user can wear a VR headset and interact with customers in a virtual space. The generation AI automatically generates customers and generates responses. For example, the generation AI generates customers based on pre-defined personas and generates appropriate answers to user questions. The persona setting sets the customer's persona. For example, various types of customers can be simulated by setting attributes such as age, gender, occupation, and personality. This allows customer simulation to be performed by combining VR technology and the generation AI.

[0030] VR technology can change a customer's movements and facial expressions in real time. For example, VR technology uses motion capture technology to change a customer's movements in real time. For example, it can reproduce in real time a customer's movements such as raising their hand, walking, or sitting. It also uses facial recognition technology to change a customer's facial expression in real time. For example, it can reproduce in real time a customer's expressions such as smiling, being surprised, or being angry. It also uses AI technology to change a customer's movements and facial expressions in real time. For example, it can generate a customer's reaction in real time based on the user's speech and actions. This allows the customer's movements and facial expressions to change in real time, achieving a simulation that is closer to reality.

[0031] Generative AI can analyze a user's movements and speech content and provide appropriate feedback in real time. For example, generative AI uses sensors built into a VR headset to analyze a user's movements. For example, it analyzes the user's hand and head movements and provides appropriate feedback. It also introduces voice recognition technology to analyze the user's speech content. For example, it converts what the user says into text and generates a customer response based on that content. It also uses AI technology to analyze a user's movements and speech content. For example, it learns the user's behavioral patterns and speech content and provides appropriate feedback in real time. This allows for more effective training by analyzing a user's movements and speech content and providing appropriate feedback in real time.

[0032] VR technology can be applied to a variety of industries. For example, in the medical industry, VR technology can simulate conversations with patients in a VR space. For example, doctors can be trained to listen to patients' symptoms and make appropriate diagnoses. In the education industry, VR technology can simulate conversations between teachers and students in a VR space. For example, teachers can be trained to answer students' questions. In the entertainment industry, VR technology can simulate conversations with characters in a VR space. For example, a situation can be recreated in which a character in a game interacts with a user. This makes it possible to apply VR technology to a wide range of industries, enabling a wide range of simulations.

[0033] VR technology can be used to develop systems that can perform simulations using devices. For example, VR technology uses AR glasses to perform simulations that overlay customers on real space. For example, it recreates a situation in which a user interacts with a customer in a real store. Also, an app that performs customer simulations can be developed using smartphones. For example, a customer can be displayed on the smartphone screen and a conversation can take place. Also, a system that performs customer simulations can be developed using tablets. For example, a customer can be displayed on the tablet screen and a conversation can take place. This will broaden the range of use by developing systems that can perform simulations on devices other than VR headsets.

[0034] Generative AI can learn from a customer's past behavioral history and generate more consistent responses. For example, generative AI can learn from a customer's past behavioral history and generate consistent responses. For example, it can generate responses based on the products a customer has purchased in the past and the details of their inquiries. In addition, the customer's behavioral history can be stored in a database, and the generative AI can generate responses based on that data. For example, it can generate responses based on the stores a customer has visited in the past and the services they have used. In addition, generative AI can learn from a customer's past behavioral history and generate responses based on past behavioral patterns. For example, it can generate responses based on the questions and requests a customer has made in the past. In this way, the system can learn from a customer's past behavioral history and generate more consistent responses, achieving a realistic simulation.

[0035] The generative AI can collect user reactions to customer responses as feedback, continuously improving the accuracy of its responses. For example, the generative AI can collect user reactions to customer responses as feedback and use this as learning data for the generative AI. For example, it can record responses that satisfied or dissatisfied users. It can also develop a system that collects user reactions in real time and improves the response accuracy of the generative AI. For example, it can analyze reactions from the user's facial expressions and voice and collect this as feedback. The generative AI can also learn from user reactions and continuously improve its response accuracy. For example, it can adjust the content and tone of the response based on user feedback. In this way, it can collect user reactions to customer responses as feedback and continuously improve the response accuracy of the generative AI, achieving more effective training.

[0036] The generation AI can automatically generate roles other than customers. For example, the generation AI automatically generates the roles of colleagues and superiors. For example, it recreates a situation in which a user works together with colleagues to progress a project. The generation AI also automatically generates the role of a customer. For example, it recreates a situation in which a user negotiates business with a customer. The generation AI can also automatically generate multiple roles simultaneously. For example, it recreates a situation in which a user holds a meeting with a colleague and superior. In this way, by automatically generating roles other than customers, a wider variety of situations can be recreated.

[0037] Generative AI can generate responses that correspond to different languages ​​and cultures. For example, generative AI can learn data from different languages ​​and generate responses in multiple languages. For example, it can support languages ​​such as English, French, and Chinese. Generative AI can also learn data from different cultures and generate responses that correspond to the culture. For example, it can generate responses based on the customs and manners of different cultures. Furthermore, generative AI can generate responses that correspond to different languages ​​and cultures in order to build an international training system. For example, it can recreate international business situations. This allows it to build an international training system by generating responses that correspond to different languages ​​and cultures.

[0038] Persona setting can generate a customer's backstory and detailed profile. Persona setting, for example, generates a customer's backstory. For example, it sets the customer's past experiences, hobbies, family composition, etc. Persona setting is also used to generate a detailed customer profile. For example, it sets the customer's occupation, age, personality, etc. in detail. Furthermore, based on the persona setting, a customer's backstory and detailed profile are generated to recreate more realistic situations. For example, it sets a situation in which a customer has a specific problem. In this way, a customer's backstory and detailed profile are generated to recreate more realistic situations.

[0039] An interface that can be dynamically changed can be provided for persona settings. For example, an interface that can be dynamically changed can be provided for persona settings. For example, the customer's age or personality can be changed during training. Furthermore, a system that can adjust persona settings in real time can be developed to flexibly change situations during training. For example, the customer's occupation or background can be changed. Furthermore, an interface that can dynamically change persona settings can be provided to flexibly change situations during training. For example, the customer's problems or requests can be changed. In this way, by providing an interface that can dynamically change persona settings, situations can be flexibly changed during training.

[0040] Persona settings can be applied to different work situations. For example, persona settings can be applied to emergency response situations, such as a situation where a customer has an urgent problem. Persona settings can also be applied to team meeting situations, such as a situation where a customer participates as a team member. Persona settings can also be applied to presentation situations, such as a situation where a customer receives a presentation. By applying persona settings to different work situations, a wide range of training is possible.

[0041] Persona settings can be automatically optimized based on the user's past training history. For example, a system is developed that analyzes the user's past training history and automatically optimizes persona settings. For example, training is focused on situations that the user is not good at. Furthermore, persona settings are dynamically adjusted based on the user's training history. For example, a persona is set based on situations that the user has experienced in the past. Furthermore, a system is developed that optimizes persona settings based on the user's training history. For example, the difficulty of the persona is adjusted according to the user's skill level. In this way, more effective training is achieved by automatically optimizing persona settings based on the user's past training history.

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

[0043] The customer simulation system can also analyze a user's past training data and provide an individually customized training plan. For example, it can focus on training situations that the user found difficult in the past. It can also dynamically adjust the training content based on the user's progress. This allows it to provide optimal training according to the user's skill level.

[0044] The customer simulation system can also provide simulation scenarios for different industries. For example, in the medical industry, it can simulate interactions with patients, in the education industry, it can simulate interactions between teachers and students, and in the entertainment industry, it can simulate interactions with characters. This allows for training that is tailored to a wide range of industries.

[0045] The customer simulation system can also collect user feedback in real time and use it to improve the system. For example, users can fill out a questionnaire after completing a simulation, and the system's accuracy can be improved based on that feedback. It can also collect user reactions in real time and adjust the system's response. This makes it possible to utilize user feedback to provide more effective training.

[0046] The customer simulation system can also analyze the content of the user's speech and provide appropriate feedback. For example, if the user does not use appropriate language, it can suggest appropriate language to use. Also, if the user does not provide an appropriate answer to a question, it can provide guidance on how to respond appropriately. This can improve the user's speaking skills and provide more effective training.

[0047] The customer simulation system can also learn the user's behavioral patterns and provide individually customized feedback. For example, it can provide feedback based on the user's past behavioral patterns. It can also dynamically adjust the training content according to the user's behavioral patterns. This allows it to provide optimal training based on the user's behavioral patterns.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: VR technology simulates a customer. For example, a user can wear a VR headset and interact with a customer in a virtual space. Step 2: The generative AI automatically generates a customer and generates a response. For example, the generative AI generates a customer based on a pre-defined persona and generates an appropriate answer to the user's question. Step 3: Set up a persona for your customers. For example, you can simulate various types of customers by setting attributes such as age, gender, occupation, and personality.

[0050] (Example 2) The customer simulation system according to an embodiment of the present invention uses VR technology to perform customer simulations, and a generation AI automatically generates customers and generates responses. This allows the customer simulation system to efficiently train employees and provide higher quality services.

[0051] A customer simulation system according to an embodiment includes VR technology, a generation AI, and persona setting. The VR technology performs customer simulation. For example, a user can wear a VR headset and interact with customers in a virtual space. The generation AI automatically generates customers and generates responses. For example, the generation AI generates customers based on pre-defined personas and generates appropriate answers to user questions. The persona setting sets the customer's persona. For example, various types of customers can be simulated by setting attributes such as age, gender, occupation, and personality. This allows customer simulation to be performed by combining VR technology and the generation AI.

[0052] VR technology can change a customer's movements and facial expressions in real time. For example, VR technology uses motion capture technology to change a customer's movements in real time. For example, it can reproduce in real time a customer's movements such as raising their hand, walking, or sitting. It also uses facial recognition technology to change a customer's facial expression in real time. For example, it can reproduce in real time a customer's expressions such as smiling, being surprised, or being angry. It also uses AI technology to change a customer's movements and facial expressions in real time. For example, it can generate a customer's reaction in real time based on the user's speech and actions. This allows the customer's movements and facial expressions to change in real time, achieving a simulation that is closer to reality.

[0053] Generative AI can analyze a user's movements and speech content and provide appropriate feedback in real time. For example, generative AI uses sensors built into a VR headset to analyze a user's movements. For example, it analyzes the user's hand and head movements and provides appropriate feedback. It also introduces voice recognition technology to analyze the user's speech content. For example, it converts what the user says into text and generates a customer response based on that content. It also uses AI technology to analyze a user's movements and speech content. For example, it learns the user's behavioral patterns and speech content and provides appropriate feedback in real time. This allows for more effective training by analyzing a user's movements and speech content and providing appropriate feedback in real time.

[0054] The generation AI can use the emotion estimation function to generate customer responses that correspond to the user's emotional state. For example, the generation AI uses the emotion estimation function to estimate the user's emotional state from their facial expressions and voice. For example, if the user is smiling, the customer will respond with a smile. AI technology is also used to generate customer responses that correspond to the user's emotional state. For example, if the user is angry, the customer will respond calmly. The emotion estimation function also monitors the user's emotional state in real time and generates customer responses based on that data. For example, if the user is surprised, the customer will show a surprised expression. This allows for more emotional training by generating customer responses that correspond to the user's emotional state.

[0055] VR technology can be applied to a variety of industries. For example, in the medical industry, VR technology can simulate conversations with patients in a VR space. For example, doctors can be trained to listen to patients' symptoms and make appropriate diagnoses. In the education industry, VR technology can simulate conversations between teachers and students in a VR space. For example, teachers can be trained to answer students' questions. In the entertainment industry, VR technology can simulate conversations with characters in a VR space. For example, a situation can be recreated in which a character in a game interacts with a user. This makes it possible to apply VR technology to a wide range of industries, enabling a wide range of simulations.

[0056] VR technology can be used to develop systems that can perform simulations using devices. For example, VR technology uses AR glasses to perform simulations that overlay customers on real space. For example, it recreates a situation in which a user interacts with a customer in a real store. Also, an app that performs customer simulations can be developed using smartphones. For example, a customer can be displayed on the smartphone screen and a conversation can take place. Also, a system that performs customer simulations can be developed using tablets. For example, a customer can be displayed on the tablet screen and a conversation can take place. This will broaden the range of use by developing systems that can perform simulations on devices other than VR headsets.

[0057] Generative AI can learn from a customer's past behavioral history and generate more consistent responses. For example, generative AI can learn from a customer's past behavioral history and generate consistent responses. For example, it can generate responses based on the products a customer has purchased in the past and the details of their inquiries. In addition, the customer's behavioral history can be stored in a database, and the generative AI can generate responses based on that data. For example, it can generate responses based on the stores a customer has visited in the past and the services they have used. In addition, generative AI can learn from a customer's past behavioral history and generate responses based on past behavioral patterns. For example, it can generate responses based on the questions and requests a customer has made in the past. In this way, the system can learn from a customer's past behavioral history and generate more consistent responses, achieving a realistic simulation.

[0058] The generative AI can collect user reactions to customer responses as feedback, continuously improving the accuracy of its responses. For example, the generative AI can collect user reactions to customer responses as feedback and use this as learning data for the generative AI. For example, it can record responses that satisfied or dissatisfied users. It can also develop a system that collects user reactions in real time and improves the response accuracy of the generative AI. For example, it can analyze reactions from the user's facial expressions and voice and collect this as feedback. The generative AI can also learn from user reactions and continuously improve its response accuracy. For example, it can adjust the content and tone of the response based on user feedback. In this way, it can collect user reactions to customer responses as feedback and continuously improve the response accuracy of the generative AI, achieving more effective training.

[0059] Generative AI can change a customer's emotional state in real time using an emotion estimation function. Generative AI, for example, uses the emotion estimation function to change a customer's emotional state in real time. For example, it can change a customer's emotions depending on the user's speech and behavior. It also uses AI technology to change a customer's emotional state in real time. For example, a customer can react with happiness, anger, sadness, etc. depending on the user's emotional state. It also uses the emotion estimation function to monitor a customer's emotional state in real time and change emotions based on that data. For example, if the user is smiling, the customer will respond with a smile. This allows for a more dynamic simulation by changing the customer's emotional state in real time.

[0060] The generation AI can automatically generate roles other than customers. For example, the generation AI automatically generates the roles of colleagues and superiors. For example, it recreates a situation in which a user works together with colleagues to progress a project. The generation AI also automatically generates the role of a customer. For example, it recreates a situation in which a user negotiates business with a customer. The generation AI can also automatically generate multiple roles simultaneously. For example, it recreates a situation in which a user holds a meeting with a colleague and superior. In this way, by automatically generating roles other than customers, a wider variety of situations can be recreated.

[0061] Generative AI can generate responses that correspond to different languages ​​and cultures. For example, generative AI can learn data from different languages ​​and generate responses in multiple languages. For example, it can support languages ​​such as English, French, and Chinese. Generative AI can also learn data from different cultures and generate responses that correspond to the culture. For example, it can generate responses based on the customs and manners of different cultures. Furthermore, generative AI can generate responses that correspond to different languages ​​and cultures in order to build an international training system. For example, it can recreate international business situations. This allows it to build an international training system by generating responses that correspond to different languages ​​and cultures.

[0062] The generative AI can use an emotion estimation function to generate responses based on the customer's emotions. For example, the generative AI uses the emotion estimation function to generate responses based on the customer's emotions. For example, if the customer is happy, it generates a positive response. It also utilizes AI technology to generate responses based on the customer's emotions. For example, if the customer is angry, it generates a calm response. It also uses the emotion estimation function to monitor the customer's emotional state in real time and generate responses based on that emotion based on that data. For example, if the customer is sad, it generates a comforting response. In this way, by generating responses based on the customer's emotions, it provides a simulation that users can easily empathize with emotionally.

[0063] Persona setting can generate a customer's backstory and detailed profile. Persona setting, for example, generates a customer's backstory. For example, it sets the customer's past experiences, hobbies, family composition, etc. Persona setting is also used to generate a detailed customer profile. For example, it sets the customer's occupation, age, personality, etc. in detail. Furthermore, based on the persona setting, a customer's backstory and detailed profile are generated to recreate more realistic situations. For example, it sets a situation in which a customer has a specific problem. In this way, a customer's backstory and detailed profile are generated to recreate more realistic situations.

[0064] An interface that can be dynamically changed can be provided for persona settings. For example, an interface that can be dynamically changed can be provided for persona settings. For example, the customer's age or personality can be changed during training. Furthermore, a system that can adjust persona settings in real time can be developed to flexibly change situations during training. For example, the customer's occupation or background can be changed. Furthermore, an interface that can dynamically change persona settings can be provided to flexibly change situations during training. For example, the customer's problems or requests can be changed. In this way, by providing an interface that can dynamically change persona settings, situations can be flexibly changed during training.

[0065] Persona setting can change a customer's emotional state in real time using an emotion estimation function. Persona setting, for example, uses the emotion estimation function to change a customer's emotional state in real time. For example, if a customer is happy, a positive response is generated. AI technology is also used to change the persona's emotional state in real time. For example, if a customer is angry, a calm response is generated. The emotion estimation function also monitors the persona's emotional state in real time and generates an emotion-based response based on that data. For example, if a customer is sad, a comforting response is generated. In this way, by changing the persona's emotional state in real time, users can hone their skills in dealing with customers in different emotional states.

[0066] Persona settings can be applied to different work situations. For example, persona settings can be applied to emergency response situations, such as a situation where a customer has an urgent problem. Persona settings can also be applied to team meeting situations, such as a situation where a customer participates as a team member. Persona settings can also be applied to presentation situations, such as a situation where a customer receives a presentation. By applying persona settings to different work situations, a wide range of training is possible.

[0067] Persona settings can be automatically optimized based on the user's past training history. For example, a system is developed that analyzes the user's past training history and automatically optimizes persona settings. For example, training is focused on situations that the user is not good at. Furthermore, persona settings are dynamically adjusted based on the user's training history. For example, a persona is set based on situations that the user has experienced in the past. Furthermore, a system is developed that optimizes persona settings based on the user's training history. For example, the difficulty of the persona is adjusted according to the user's skill level. In this way, more effective training is achieved by automatically optimizing persona settings based on the user's past training history.

[0068] Persona setting can use the emotion estimation function to generate situations based on the customer's emotions. Persona setting, for example, uses the emotion estimation function to generate situations based on the customer's emotions. For example, if the customer is happy, a positive situation is generated. AI technology is also used to generate situations based on the persona's emotions. For example, if the customer is angry, a calm situation is generated. The emotion estimation function also monitors the persona's emotional state in real time and generates emotion-based situations based on that data. For example, if the customer is sad, a comforting situation is generated. In this way, by generating situations based on the persona's emotions, training is provided that makes it easier for users to empathize emotionally.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The customer simulation system can also monitor the user's stress level and suggest breaks at appropriate times. For example, it can use sensors to detect the user's heart rate and breathing patterns and suggest breaks if stress levels rise. It can also adjust the difficulty of the simulation according to the user's stress level. For example, it can provide easier situations if the user is highly stressed, and more difficult situations if the user is relaxed. This reduces the user's stress and enables more effective training.

[0071] The customer simulation system can also analyze the user's tone and speed of voice and provide feedback on appropriate communication skills. For example, if the user is speaking too quickly, it can suggest that they speak more slowly. If the user's tone of voice is too high, it can also instruct them to speak in a calmer tone. This can improve the user's communication skills and provide more effective training.

[0072] The customer simulation system can also analyze the user's gestures and body language and provide appropriate feedback. For example, if the user has their arms crossed, it can suggest that they adopt an open posture. If the user does not make eye contact, it can instruct them to make eye contact. This can improve the user's non-verbal communication skills and provide more effective training.

[0073] The customer simulation system can also analyze a user's past training data and provide an individually customized training plan. For example, it can focus on training situations that the user found difficult in the past. It can also dynamically adjust the training content based on the user's progress. This allows it to provide optimal training according to the user's skill level.

[0074] The customer simulation system can also provide simulation scenarios for different industries. For example, in the medical industry, it can simulate interactions with patients, in the education industry, it can simulate interactions between teachers and students, and in the entertainment industry, it can simulate interactions with characters. This allows for training that is tailored to a wide range of industries.

[0075] The customer simulation system can also collect user feedback in real time and use it to improve the system. For example, users can fill out a questionnaire after completing a simulation, and the system's accuracy can be improved based on that feedback. It can also collect user reactions in real time and adjust the system's response. This makes it possible to utilize user feedback to provide more effective training.

[0076] The customer simulation system can also dynamically change the simulation scenario depending on the user's emotional state. For example, if the user is nervous, it can provide a relaxing situation. Alternatively, if the user is confident, it can provide a more difficult situation. This allows the system to provide optimal training according to the user's emotional state.

[0077] The customer simulation system can also analyze the content of the user's speech and provide appropriate feedback. For example, if the user does not use appropriate language, it can suggest appropriate language to use. Also, if the user does not provide an appropriate answer to a question, it can provide guidance on how to respond appropriately. This can improve the user's speaking skills and provide more effective training.

[0078] The customer simulation system can also learn the user's behavioral patterns and provide individually customized feedback. For example, it can provide feedback based on the user's past behavioral patterns. It can also dynamically adjust the training content according to the user's behavioral patterns. This allows it to provide optimal training based on the user's behavioral patterns.

[0079] The customer simulation system can also monitor the user's emotional state and provide positive feedback at appropriate times. For example, it can provide praising feedback when the user succeeds, and encouraging feedback when the user fails. This can help maintain the user's motivation and provide more effective training.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: VR technology simulates a customer. For example, a user can wear a VR headset and interact with a customer in a virtual space. Step 2: The generative AI automatically generates a customer and generates a response. For example, the generative AI generates a customer based on a pre-defined persona and generates an appropriate answer to the user's question. Step 3: Set up a persona for your customers. For example, you can simulate various types of customers by setting attributes such as age, gender, occupation, and personality.

[0082] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0088] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0089] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0093] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0096] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0110] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

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

[0118] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0123] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0140] 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.

[0141] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0142] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. VR technology that uses VR technology to conduct customer simulations, A generation AI that automatically generates customers and generates responses using a generation AI. Persona setting for setting a customer persona; A system characterized by:

2. The VR technology is Change the customer's movements and facial expressions in real time 2. The system of claim 1.

3. The generated AI is Analyzes user behavior and speech and provides appropriate feedback in real time 2. The system of claim 1.

4. The generated AI is Generating a response of the customer according to the emotional state of the user 2. The system of claim 1.

5. The VR technology is Applied to different industries 2. The system of claim 1.

Images