System

Abstract

An object of the system according to the embodiment is to virtually reproduce a conversation with a deceased person and provide emotional support.SOLUTION: A system includes a reception unit, a generation unit, and a response unit. The receiving unit receives information input by a user. The generation unit generates a virtual model on the basis of the information received by the reception unit. The response unit causes the virtual model generated by the generation unit to respond to the user's question or speech.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 had the problem of making it difficult to recreate conversations with deceased people, resulting in a lack of emotional support.

[0005] The system according to the embodiment aims to virtually recreate a conversation with a deceased person and provide emotional support. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, and a responding unit. The receiving unit receives information input by a user. The generating unit generates a virtual model based on the information received by the receiving unit. The responding unit causes the virtual model generated by the generating unit to respond to questions or comments from the user. [Effects of the Invention]

[0007] The system according to the embodiment can virtually recreate a conversation with a deceased person and provide emotional support. [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) A VR system according to an embodiment of the present invention recreates the experience of "if only there was someone like that" when a familiar person has already left and the user wants someone to listen to them or give them advice. In this VR system, a user inputs information about the person the user wants to listen to or receive advice from. A generation AI generates a virtual model based on that information and responds appropriately to the user's questions and conversations. For example, the user inputs detailed information about the person the user wants to listen to, such as their name, characteristics, and past conversations. The generation AI analyzes that information to generate a virtual model. The generated virtual model responds appropriately to the user's questions and conversations. This allows the user to feel as if the person is right in front of the user. This allows the VR system to provide a user with the experience of being directly in front of the user when the user wants someone to listen to them or give them advice. For example, a user can input "if only my mother were here" and generate a virtual model of their mother, allowing the user to have their mother listen to them or give them advice. Furthermore, the virtual model responds appropriately to the user's questions and conversations, allowing the user to feel as if they are actually having a conversation with that person.

[0029] The VR system according to the embodiment includes a reception unit, a generation unit, and a response unit. The reception unit receives information input by a user. The information input by the user includes, for example, the name and characteristics of the person the user wants to talk to, and past conversations, but is not limited to these examples. The reception unit can receive, for example, text information, audio information, image information, and the like. The generation unit generates a virtual model based on the information received by the reception unit using a generation AI. The generation AI reproduces the appearance, voice, speaking style, and the like of the person the user wants to talk to, for example, using a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit generates, for example, a virtual model of the person the user wants to talk to, based on the information input by the user. The generation unit can also analyze past conversations using the generation AI and generate responses from the virtual model. The response unit causes the virtual model generated by the generation unit to respond to the user's questions and comments. For example, when the user says, "I've been having trouble at work lately," the response unit responds by asking, "What are you worried about?" The response unit can also use a generation AI to generate appropriate responses to the user's questions or conversations, allowing the VR system according to the embodiment to generate a virtual model of someone the user wants to talk to or get advice from, and provide an appropriate response.

[0030] The VR system includes an analysis unit that analyzes past conversation content. The analysis unit analyzes the past conversation content received by the reception unit. The analysis unit analyzes the past conversation content, for example, using natural language processing technology, and uses the analysis to generate a virtual model. For example, the analysis unit extracts the characteristics and speaking style of the person who wants to be listened to based on the past conversation content. The analysis unit can also analyze the past conversation content and provide data for generating responses for the virtual model. In this way, a more appropriate virtual model can be generated by analyzing the past conversation content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the past conversation content to a generation AI and have the generation AI analyze the conversation content.

[0031] The generation unit can generate a virtual model based on input information. The generation unit generates the virtual model based on the input information using a generation AI. The generation AI, for example, uses a text generation AI (e.g., LLM) or a multimodal generation AI to reproduce the appearance, voice, speaking style, etc. of a person who wants to be listened to. The generation unit generates a virtual model of a person who wants to be listened to, for example, based on information input by a user. The generation unit can also use the generation AI to analyze past conversation content and generate a response of the virtual model. In this way, by generating a virtual model based on the input information, it is possible to provide a virtual model that meets the user's request. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input information input by a user to the generation AI and cause the generation AI to generate a virtual model.

[0032] The response unit can provide an appropriate response to the user's questions or comments. In the response unit, the virtual model generated by the generation unit responds to the user's questions or comments. For example, when the user says, "I've been having trouble at work lately," the response unit responds by asking, "What are you worried about?" The response unit can also use a generation AI to generate an appropriate response to the user's questions or comments. This makes it possible to provide a response that meets the user's request by providing an appropriate response to the user's questions or comments. Some or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can input the user's questions or comments into the generation AI and have the generation AI generate an appropriate response.

[0033] The reception unit can analyze the user's past input history and select the optimal reception method. The reception unit analyzes the user's past input history and selects the optimal reception method. The reception unit, for example, uses AI to analyze the user's past input history and proposes the optimal reception method. For example, it prioritizes the proposal of input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also propose similar input methods based on information previously input by the user. The reception unit can also predict and propose an input method to be used in a specific time period from the user's past input history. In this way, the optimal reception method can be provided by analyzing the user's past input history. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's past input history to a generation AI and have the generation AI select the optimal reception method.

[0034] The reception unit can select the optimal reception means according to the user's input method when receiving input information. The reception unit selects the optimal reception means according to the user's input method (voice, text, image, etc.) when receiving input information. The reception unit, for example, uses AI to analyze the user's input method and propose the optimal reception means. For example, if the user selects voice input, the reception unit can accept the input using voice recognition technology. Also, if the user selects text input, keyboard input can be preferentially accepted. Also, if the user selects image input, the reception unit can accept the input using image recognition technology. In this way, by selecting the optimal reception means according to the user's input method, information can be accepted in a more appropriate manner. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input data on the user's input method to a generation AI and have the generation AI select the optimal reception means.

[0035] When receiving input information, the reception unit can prioritize receiving highly relevant information taking into account the user's geographical location information. When receiving input information, the reception unit prioritizes receiving highly relevant information taking into account the user's geographical location information. The reception unit, for example, uses AI to analyze the user's geographical location information and suggest highly relevant information. For example, if the user is in a specific area, the reception unit can prioritize receiving information related to that area. Also, if the user is traveling, the reception unit can prioritize receiving information related to the travel destination. Also, if the user is at home, the reception unit can prioritize receiving information related to the home. In this way, by taking the user's geographical location information into account, highly relevant information can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to a generation AI and cause the generation AI to select highly relevant information.

[0036] The reception unit can analyze the user's social media activity and receive related information when receiving input information. The reception unit can analyze the user's social media activity and receive related information when receiving input information. The reception unit can, for example, use AI to analyze the user's social media activity and suggest related information. For example, the reception unit can analyze the user's social media posts and receive related information. The reception unit can also analyze the content of the user's social media posts and receive related information. The reception unit can also receive related information by referring to the activities of the user's friends on social media. In this way, the user's social media activity can be analyzed and related information can be efficiently received. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input data on the user's social media activity into a generation AI and cause the generation AI to select related information.

[0037] The reception unit can customize the reception method by reflecting the user's past feedback when receiving input information. The reception unit customizes the reception method by reflecting the user's past feedback when receiving input information. The reception unit, for example, uses AI to analyze the user's past feedback and propose an optimal reception method. For example, the reception unit proposes an optimal reception method based on feedback provided by the user in the past. It can also preferentially propose a specific reception method based on the user's past feedback. It can also analyze the user's past feedback and customize the optimal reception method. In this way, it is possible to provide an optimal reception method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input data of the user's past feedback to a generation AI and have the generation AI customize the reception method.

[0038] The generation unit can adjust the level of detail of the generated virtual model based on the importance of the input information when generating the virtual model. The generation unit adjusts the level of detail of the generated virtual model based on the importance of the input information when generating the virtual model. The generation unit, for example, uses AI to analyze the importance of the input information and adjust the level of detail of the generated virtual model. For example, when important information is input, a detailed virtual model is generated. Also, when general information is input, a virtual model with a standard level of detail can be generated. Also, when simple information is input, a simplified virtual model can be generated. In this way, by adjusting the level of detail of the generated virtual model based on the importance of the input information, a more appropriate virtual model can be provided. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the importance of the input information to the generation AI and cause the generation AI to adjust the level of detail of the generated virtual model.

[0039] The generation unit can apply different generation algorithms depending on the category of the target person when generating a virtual model. The generation unit applies different generation algorithms depending on the category of the target person when generating a virtual model. The generation unit, for example, uses AI to analyze the category of the target person and select an appropriate generation algorithm. For example, when generating a virtual model of a family, an algorithm with characteristics specific to the family can be applied. Also, when generating a virtual model of a friend, an algorithm with characteristics specific to the friend can be applied. Also, when generating a virtual model of a coworker at work, an algorithm with characteristics specific to the workplace can be applied. In this way, by applying different generation algorithms depending on the category of the target person, a more appropriate virtual model can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input category data of the target person to the generation AI and cause the generation AI to select a generation algorithm.

[0040] The generation unit can improve the accuracy of generation by referring to the user's past generation results when generating a virtual model. The generation unit can improve the accuracy of generation by referring to the user's past generation results when generating a virtual model. The generation unit, for example, uses AI to analyze the user's past generation results and improve the accuracy of generation. For example, a highly accurate virtual model is generated based on data of a virtual model previously generated by the user. The generation unit can also analyze the user's past generation results and apply an optimal generation algorithm. A virtual model with specific characteristics can also be generated from the user's past generation results. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0041] The generation unit can determine the generation priority based on the target person's past behavioral history when generating a virtual model. The generation unit determines the generation priority based on the target person's past behavioral history when generating a virtual model. The generation unit, for example, uses AI to analyze the target person's past behavioral history and determine the generation priority. For example, the generation unit prioritizes generation based on the target person's frequent past behavior. It is also possible to prioritize generation of important behaviors from the target person's past behavioral history. It is also possible to analyze the target person's past behavioral history and determine the optimal generation order. As a result, a more appropriate virtual model can be provided by determining the generation priority based on the target person's past behavioral history. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the target person's past behavioral history data into the generation AI and have the generation AI determine the generation priority.

[0042] The generation unit can improve the accuracy of generation by referring to literature related to the target person when generating a virtual model. The generation unit can improve the accuracy of generation by referring to literature related to the target person when generating a virtual model. The generation unit can, for example, use AI to analyze literature related to the target person and improve the accuracy of generation. For example, the generation unit can refer to literature related to the target person to generate a highly accurate virtual model. It can also analyze literature related to the target person and apply an optimal generation algorithm. It can also generate a virtual model with specific characteristics from literature related to the target person. In this way, the accuracy of generation can be improved by referring to literature related to the target person. Some or all of the above-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input literature data related to the target person into the generation AI and cause the generation AI to improve the accuracy of generation.

[0043] The generation unit can generate a virtual model taking into consideration the social background of the target person. The generation unit generates a virtual model taking into consideration the social background of the target person. The generation unit, for example, uses AI to analyze the social background of the target person and select an appropriate generation method. For example, the generation unit generates an appropriate virtual model taking into consideration the social background of the target person. The generation unit can also analyze the social background of the target person and apply an optimal generation algorithm. A virtual model with specific characteristics can also be generated from the social background of the target person. This makes it possible to provide a more appropriate virtual model by taking into consideration the social background of the target person. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input social background data of the target person into the generation AI and cause the generation AI to select a generation method.

[0044] The response unit can adjust the level of detail of the response based on the importance of the question or topic when responding. The response unit adjusts the level of detail of the response based on the importance of the question or topic when responding. The response unit, for example, uses AI to analyze the importance of the question or topic and adjust the level of detail of the response. For example, if an important question is asked, a detailed response is provided. Also, if a general question is asked, a response with a standard level of detail can be provided. Also, if a simple question is asked, a simplified response can be provided. In this way, by adjusting the level of detail of the response based on the importance of the question or topic, a more appropriate response can be provided. Some or all of the above-described processing in the response unit may be performed using AI, for example, or may be performed without using AI. For example, the response unit can input importance data of the question or topic to a generation AI and have the generation AI adjust the level of detail of the response.

[0045] The response unit can apply different response algorithms depending on the category of the question or conversation when responding. The response unit applies different response algorithms depending on the category of the question or conversation when responding. The response unit, for example, uses AI to analyze the category of the question or conversation and select an appropriate response algorithm. For example, if a question about family is asked, a family-specific response algorithm can be applied. Also, if a question about work is asked, a work-specific response algorithm can be applied. Also, if a question about friends is asked, a friend-specific response algorithm can be applied. In this way, by applying different response algorithms depending on the category of the question or conversation, a more appropriate response can be provided. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input question or conversation category data to a generation AI and have the generation AI select a response algorithm.

[0046] The response unit can improve the accuracy of the response by referring to the user's past response results when responding. The response unit can improve the accuracy of the response by referring to the user's past response results when responding. The response unit, for example, uses AI to analyze the user's past response results and improve the accuracy of the response. For example, the response unit provides a highly accurate response based on data on responses the user has received in the past. The response unit can also analyze the user's past response results and apply an optimal response algorithm. It can also provide a response with specific characteristics based on the user's past response results. In this way, the accuracy of the response can be improved by referring to the user's past response results. Some or all of the above-described processing in the response unit may be performed using AI, for example, or may be performed without using AI. For example, the response unit can input the user's past response result data into the generation AI and cause the generation AI to improve the accuracy of the response.

[0047] The response unit, when responding, can determine the priority of responses based on the time when the question or story was submitted. The response unit, when responding, can determine the priority of responses based on the time when the question or story was submitted. The response unit, for example, uses AI to analyze the time when the question or story was submitted and determine the priority of responses. For example, the response unit prioritizes responses to recent questions or stories. It can also provide appropriate responses to past questions or stories. It can also prioritize responses to important questions or stories based on the time when they were submitted. In this way, by determining the priority of responses based on the time when the question or story was submitted, it is possible to provide a more appropriate response. Some or all of the above-mentioned processing in the response unit may be performed using AI, for example, or may be performed without using AI. For example, the response unit can input data on the time when the question or story was submitted to a generation AI and have the generation AI determine the priority of responses.

[0048] The response unit can adjust the order of responses based on the relevance of the questions and topics when responding. The response unit adjusts the order of responses based on the relevance of the questions and topics when responding. The response unit, for example, uses AI to analyze the relevance of the questions and topics and adjust the order of responses. For example, responses can be given priority to highly relevant questions and topics. Appropriate responses can also be provided to less relevant questions and topics. The optimal order of responses can also be determined based on the relevance of the questions and topics. As a result, more appropriate responses can be provided by adjusting the order of responses based on the relevance of the questions and topics. Some or all of the above-described processing in the response unit may be performed using AI, for example, or may be performed without using AI. For example, the response unit can input relevance data of questions and topics to a generation AI and have the generation AI adjust the order of responses.

[0049] The response unit can adjust the use of technical terminology in the response according to the user's level of expertise when responding. The response unit can adjust the use of technical terminology in the response according to the user's level of expertise when responding. The response unit can, for example, use AI to analyze the user's level of expertise and use appropriate technical terminology. For example, if the user has technical expertise, the response can be made using technical terminology. Alternatively, if the user does not have technical expertise, the response can be made in simple language. The optimal response method can also be selected based on the user's level of expertise. This allows for adjusting the use of technical terminology in the response according to the user's level of expertise, thereby providing a more appropriate response. Some or all of the above-described processing in the response unit can be performed using AI, for example, or without AI. For example, the response unit can input the user's level of expertise data into a generation AI and have the generation AI use technical terminology.

[0050] The analysis unit can adjust the level of detail of the analysis based on the importance of the content of the past conversation during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the content of the past conversation during analysis. The analysis unit, for example, uses AI to analyze the importance of the content of the past conversation and adjust the level of detail of the analysis. For example, if there is important content of the conversation, a detailed analysis is performed. Also, if there is general content of the conversation, an analysis with a standard level of detail can be performed. Also, if there is simple content of the conversation, a simplified analysis can be performed. In this way, by adjusting the level of detail of the analysis based on the importance of the content of the past conversation, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input importance data of the content of the past conversation to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0051] The analysis unit can apply different analysis algorithms depending on the category of the conversation content during analysis. The analysis unit applies different analysis algorithms depending on the category of the conversation content during analysis. The analysis unit, for example, uses AI to analyze the category of the conversation content and select an appropriate analysis algorithm. For example, if the conversation content is about family, an analysis algorithm specific to family can be applied. Also, if the conversation content is about work, an analysis algorithm specific to work can be applied. Also, if the conversation content is about friends, an analysis algorithm specific to friends can be applied. In this way, by applying different analysis algorithms depending on the category of the conversation content, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the conversation content to the generation AI and cause the generation AI to select an analysis algorithm.

[0052] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit, for example, uses AI to analyze the user's past analysis results and improve the accuracy of the analysis. For example, the analysis unit can perform a highly accurate analysis based on data from analyses the user performed in the past. The analysis unit can also analyze the user's past analysis results and apply an optimal analysis algorithm. It can also perform an analysis with specific characteristics from the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0053] The analysis unit can determine the analysis priority based on the submission time of the conversation content during analysis. The analysis unit determines the analysis priority based on the submission time of the conversation content during analysis. The analysis unit, for example, uses AI to analyze the submission time of the conversation content and determine the analysis priority. For example, the analysis unit prioritizes analysis of recent conversation content. It can also perform appropriate analysis of past conversation content. It can also prioritize analysis of important conversation content based on the submission time. In this way, by determining the analysis priority based on the submission time of the conversation content, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data on the submission time of the conversation content to the generation AI and have the generation AI determine the analysis priority.

[0054] The analysis unit can adjust the order of analysis based on the relevance of the conversation content during analysis. The analysis unit adjusts the order of analysis based on the relevance of the conversation content during analysis. The analysis unit, for example, uses AI to analyze the relevance of the conversation content and adjust the order of analysis. For example, the analysis unit prioritizes analysis of highly relevant conversation content. It can also perform appropriate analysis on less relevant conversation content. It can also determine an optimal analysis order based on the relevance of the conversation content. As a result, adjusting the analysis order based on the relevance of the conversation content enables more appropriate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input relevance data of the conversation content to a generation AI and cause the generation AI to adjust the order of analysis.

[0055] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit can, for example, use AI to analyze the user's level of expertise and use appropriate technical terms. For example, if the user has technical expertise, the analysis can be performed using technical terms. Alternatively, if the user does not have technical expertise, the analysis can be performed using simple language. The optimal analysis method can also be selected based on the user's level of expertise. This allows for more appropriate analysis by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's level of expertise data into a generation AI and have the generation AI use technical terms.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The VR system may also include an interest response unit that generates a customized response based on the user's hobbies and interests. The interest response unit may, for example, analyze information about the user's hobbies and interests and provide related topics and advice. For example, if the user is interested in music, the interest response unit may provide information about the latest music trends and recommended artists. Alternatively, if the user is interested in sports, the interest response unit may provide the latest game results and advice on training methods. Furthermore, the content of the response may be customized based on the user's hobbies and interests. This allows the user to provide an appropriate response according to the user's hobbies and interests.

[0058] The VR system may also include a behavior prediction unit that analyzes the user's past behavioral patterns and generates a response based on the behavior prediction. The behavior prediction unit, for example, analyzes the user's past behavioral data and predicts the next behavior to be taken. For example, if the user has previously exercised at a specific time of day, the behavior prediction unit generates a response encouraging exercise at that time of day. Also, if the user has previously engaged in a specific activity at a specific location, the behavior prediction unit can suggest an activity at that location. Furthermore, the content of the response can be customized based on the user's behavioral patterns. This makes it possible to provide an appropriate response according to the user's behavioral patterns.

[0059] The VR system may also include a network response unit that analyzes the user's social network and generates a response based on the relationships within the network. The network response unit may analyze, for example, the user's social media or contact information and generate a response based on the relationships. For example, if the user frequently communicates with a particular friend, the network response unit may provide topics related to that friend. Also, if the user belongs to a particular group, the network response unit may provide information related to that group. Furthermore, the content of the response may be customized based on the user's social network. This allows the user to provide an appropriate response according to the user's social network.

[0060] The VR system may also include a learning response unit that analyzes the user's learning history and generates a response based on the learning content. The learning response unit may analyze, for example, what the user has learned in the past or what the user is currently learning, and generate a relevant response. For example, if the user is studying a particular subject, the learning response unit may provide an appropriate response to a question related to that subject. Also, if the user is mastering a particular skill, the learning response unit may provide advice related to that skill. Furthermore, the content of the response may be customized based on the user's learning history. This makes it possible to provide an appropriate response according to the user's learning content.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit receives information input by the user. The information input by the user includes, but is not limited to, the name and characteristics of the person the user wants to talk to, and the contents of past conversations. The reception unit can receive text information, audio information, image information, and the like. Step 2: The generation unit uses a generation AI to generate a virtual model based on the information received by the reception unit. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to reproduce the appearance, voice, speaking style, etc. of the person the user wants to listen to. The generation unit generates a virtual model of the person the user wants to listen to based on the information entered by the user. The generation unit can also use the generation AI to analyze past conversation content and generate responses for the virtual model. Step 3: The response unit uses the virtual model generated by the generation unit to respond to the user's questions and comments. For example, if the user says, "I've been having trouble at work lately," the virtual model will respond with, "What are you worried about?" The response unit can also use generation AI to generate appropriate responses to the user's questions and comments.

[0063] (Example 2) A VR system according to an embodiment of the present invention recreates the experience of "if only there was someone like that" when a familiar person has already left and the user wants someone to listen to them or give them advice. In this VR system, a user inputs information about the person the user wants to listen to or receive advice from. A generation AI generates a virtual model based on that information and responds appropriately to the user's questions and conversations. For example, the user inputs detailed information about the person the user wants to listen to, such as their name, characteristics, and past conversations. The generation AI analyzes that information to generate a virtual model. The generated virtual model responds appropriately to the user's questions and conversations. This allows the user to feel as if the person is right in front of the user. This allows the VR system to provide a user with the experience of being directly in front of the user when the user wants someone to listen to them or give them advice. For example, a user can input "if only my mother were here" and generate a virtual model of their mother, allowing the user to have their mother listen to them or give them advice. Furthermore, the virtual model responds appropriately to the user's questions and conversations, allowing the user to feel as if they are actually having a conversation with that person.

[0064] The VR system according to the embodiment includes a reception unit, a generation unit, and a response unit. The reception unit receives information input by a user. The information input by the user includes, for example, the name and characteristics of the person the user wants to talk to, and past conversations, but is not limited to these examples. The reception unit can receive, for example, text information, audio information, image information, and the like. The generation unit generates a virtual model based on the information received by the reception unit using a generation AI. The generation AI reproduces the appearance, voice, speaking style, and the like of the person the user wants to talk to, for example, using a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit generates, for example, a virtual model of the person the user wants to talk to, based on the information input by the user. The generation unit can also analyze past conversations using the generation AI and generate responses from the virtual model. The response unit causes the virtual model generated by the generation unit to respond to the user's questions and comments. For example, when the user says, "I've been having trouble at work lately," the response unit responds by asking, "What are you worried about?" The response unit can also use a generation AI to generate appropriate responses to the user's questions or conversations, allowing the VR system according to the embodiment to generate a virtual model of someone the user wants to talk to or get advice from, and provide an appropriate response.

[0065] The VR system includes an emotional response unit that generates a response according to the user's emotions. The emotional response unit generates a response according to the user's emotions based on the response generated by the response unit. The emotional response unit analyzes the user's emotions using, for example, an emotion analysis algorithm and generates an appropriate response. For example, if the user is sad, the emotional response unit responds with kind words. If the user is excited, the emotional response unit can also respond with lively words. If the user is relaxed, the emotional response unit can also respond with calm words. This allows an appropriate response to be provided according to the user's emotions. Some or all of the above-described processing in the emotional response unit may be performed using, for example, AI, or may be performed without using AI. For example, the emotional response unit may input the user's emotional data into a generation AI and cause the generation AI to generate a response according to the emotion.

[0066] The VR system includes an analysis unit that analyzes past conversation content. The analysis unit analyzes the past conversation content received by the reception unit. The analysis unit analyzes the past conversation content, for example, using natural language processing technology, and uses the analysis to generate a virtual model. For example, the analysis unit extracts the characteristics and speaking style of the person who wants to be listened to based on the past conversation content. The analysis unit can also analyze the past conversation content and provide data for generating responses for the virtual model. In this way, a more appropriate virtual model can be generated by analyzing the past conversation content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the past conversation content to a generation AI and have the generation AI analyze the conversation content.

[0067] The generation unit can generate a virtual model based on input information. The generation unit generates the virtual model based on the input information using a generation AI. The generation AI, for example, uses a text generation AI (e.g., LLM) or a multimodal generation AI to reproduce the appearance, voice, speaking style, etc. of a person who wants to be listened to. The generation unit generates a virtual model of a person who wants to be listened to, for example, based on information input by a user. The generation unit can also use the generation AI to analyze past conversation content and generate a response of the virtual model. In this way, by generating a virtual model based on the input information, it is possible to provide a virtual model that meets the user's request. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input information input by a user to the generation AI and cause the generation AI to generate a virtual model.

[0068] The response unit can provide an appropriate response to the user's questions or comments. In the response unit, the virtual model generated by the generation unit responds to the user's questions or comments. For example, when the user says, "I've been having trouble at work lately," the response unit responds by asking, "What are you worried about?" The response unit can also use a generation AI to generate an appropriate response to the user's questions or comments. This makes it possible to provide a response that meets the user's request by providing an appropriate response to the user's questions or comments. Some or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can input the user's questions or comments into the generation AI and have the generation AI generate an appropriate response.

[0069] The reception unit can estimate the user's emotion and adjust the timing of receiving input information based on the estimated user's emotion. The reception unit estimates the user's emotion and adjusts the timing of receiving input information based on the estimated user's emotion. The reception unit, for example, uses an emotion engine to analyze the user's emotion and accept information at an appropriate timing. For example, if the user is sad, the emotion engine can detect this and accept input slowly. Alternatively, if the user is excited, the emotion engine can detect this and accept input quickly. Alternatively, if the user is relaxed, the emotion engine can detect this and accept input at a normal speed. This allows the timing of receiving input information to be adjusted according to the user's emotion, thereby accepting information at a more appropriate timing. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the reception timing according to the emotion.

[0070] The reception unit can analyze the user's past input history and select the optimal reception method. The reception unit analyzes the user's past input history and selects the optimal reception method. The reception unit, for example, uses AI to analyze the user's past input history and proposes the optimal reception method. For example, it prioritizes the proposal of input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also propose similar input methods based on information previously input by the user. The reception unit can also predict and propose an input method to be used in a specific time period from the user's past input history. In this way, the optimal reception method can be provided by analyzing the user's past input history. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's past input history to a generation AI and have the generation AI select the optimal reception method.

[0071] The reception unit may perform filtering based on the user's current psychological state and areas of interest when receiving input information. The reception unit may perform filtering based on the user's current psychological state and areas of interest when receiving input information. The reception unit may use, for example, AI to analyze the user's psychological state and areas of interest and accept appropriate information. For example, if the user is stressed, simple input options may be provided. Alternatively, if the user is relaxed, detailed input options may be provided. Furthermore, if the user has a specific area of ​​interest, input options related to that area may be preferentially displayed. By filtering based on the user's current psychological state and areas of interest, more appropriate information can be accepted. The psychological state may be estimated using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, for example, 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-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit may input data on the user's psychological state and areas of interest into the generation AI and have the generation AI perform filtering.

[0072] The reception unit can select the optimal reception means according to the user's input method when receiving input information. The reception unit selects the optimal reception means according to the user's input method (voice, text, image, etc.) when receiving input information. The reception unit, for example, uses AI to analyze the user's input method and propose the optimal reception means. For example, if the user selects voice input, the reception unit can accept the input using voice recognition technology. Also, if the user selects text input, keyboard input can be preferentially accepted. Also, if the user selects image input, the reception unit can accept the input using image recognition technology. In this way, by selecting the optimal reception means according to the user's input method, information can be accepted in a more appropriate manner. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input data on the user's input method to a generation AI and have the generation AI select the optimal reception means.

[0073] The reception unit can estimate the user's emotion and determine the priority of information to be received based on the estimated user's emotion. The reception unit can estimate the user's emotion and determine the priority of information to be received based on the estimated user's emotion. The reception unit, for example, uses an emotion engine to analyze the user's emotion and prioritize receiving important information. For example, if the user is sad, the emotion engine can detect this and prioritize receiving important information. Also, if the user is excited, the emotion engine can detect this and quickly receive information. Also, if the user is relaxed, the emotion engine can detect this and receive information with normal priority. In this way, by determining the priority of information according to the user's emotion, important information can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotional data into the generation AI and have the generation AI determine the priority of information according to the emotion.

[0074] When receiving input information, the reception unit can prioritize receiving highly relevant information taking into account the user's geographical location information. When receiving input information, the reception unit prioritizes receiving highly relevant information taking into account the user's geographical location information. The reception unit, for example, uses AI to analyze the user's geographical location information and suggest highly relevant information. For example, if the user is in a specific area, the reception unit can prioritize receiving information related to that area. Also, if the user is traveling, the reception unit can prioritize receiving information related to the travel destination. Also, if the user is at home, the reception unit can prioritize receiving information related to the home. In this way, by taking the user's geographical location information into account, highly relevant information can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to a generation AI and cause the generation AI to select highly relevant information.

[0075] The reception unit can analyze the user's social media activity and receive related information when receiving input information. The reception unit can analyze the user's social media activity and receive related information when receiving input information. The reception unit can, for example, use AI to analyze the user's social media activity and suggest related information. For example, the reception unit can analyze the user's social media posts and receive related information. The reception unit can also analyze the content of the user's social media posts and receive related information. The reception unit can also receive related information by referring to the activities of the user's friends on social media. In this way, the user's social media activity can be analyzed and related information can be efficiently received. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input data on the user's social media activity into a generation AI and cause the generation AI to select related information.

[0076] The reception unit can customize the reception method by reflecting the user's past feedback when receiving input information. The reception unit customizes the reception method by reflecting the user's past feedback when receiving input information. The reception unit, for example, uses AI to analyze the user's past feedback and propose an optimal reception method. For example, the reception unit proposes an optimal reception method based on feedback provided by the user in the past. It can also preferentially propose a specific reception method based on the user's past feedback. It can also analyze the user's past feedback and customize the optimal reception method. In this way, it is possible to provide an optimal reception method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input data of the user's past feedback to a generation AI and have the generation AI customize the reception method.

[0077] The generation unit can estimate the user's emotion and adjust the virtual model generation method based on the estimated user's emotion. The generation unit estimates the user's emotion and adjusts the virtual model generation method based on the estimated user's emotion. The generation unit analyzes the user's emotion using, for example, an emotion engine and adjusts the virtual model generation method. For example, if the user is sad, the emotion engine can detect this and generate a virtual model with a gentle expression. Alternatively, if the user is excited, the emotion engine can detect this and generate a virtual model with a lively expression. Alternatively, if the user is relaxed, the emotion engine can detect this and generate a virtual model with a calm expression. This allows for adjusting the virtual model generation method according to the user's emotion to provide a more appropriate virtual model. Emotion estimation is achieved using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the method of generating a virtual model according to the emotion.

[0078] The generation unit can adjust the level of detail of the generated virtual model based on the importance of the input information when generating the virtual model. The generation unit adjusts the level of detail of the generated virtual model based on the importance of the input information when generating the virtual model. The generation unit, for example, uses AI to analyze the importance of the input information and adjust the level of detail of the generated virtual model. For example, when important information is input, a detailed virtual model is generated. Also, when general information is input, a virtual model with a standard level of detail can be generated. Also, when simple information is input, a simplified virtual model can be generated. In this way, by adjusting the level of detail of the generated virtual model based on the importance of the input information, a more appropriate virtual model can be provided. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the importance of the input information to the generation AI and cause the generation AI to adjust the level of detail of the generated virtual model.

[0079] The generation unit can apply different generation algorithms depending on the category of the target person when generating a virtual model. The generation unit applies different generation algorithms depending on the category of the target person when generating a virtual model. The generation unit, for example, uses AI to analyze the category of the target person and select an appropriate generation algorithm. For example, when generating a virtual model of a family, an algorithm with characteristics specific to the family can be applied. Also, when generating a virtual model of a friend, an algorithm with characteristics specific to the friend can be applied. Also, when generating a virtual model of a coworker at work, an algorithm with characteristics specific to the workplace can be applied. In this way, by applying different generation algorithms depending on the category of the target person, a more appropriate virtual model can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input category data of the target person to the generation AI and cause the generation AI to select a generation algorithm.

[0080] The generation unit can improve the accuracy of generation by referring to the user's past generation results when generating a virtual model. The generation unit can improve the accuracy of generation by referring to the user's past generation results when generating a virtual model. The generation unit, for example, uses AI to analyze the user's past generation results and improve the accuracy of generation. For example, a highly accurate virtual model is generated based on data of a virtual model previously generated by the user. The generation unit can also analyze the user's past generation results and apply an optimal generation algorithm. A virtual model with specific characteristics can also be generated from the user's past generation results. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0081] The generation unit can estimate the user's emotion and adjust the appearance and voice characteristics of the virtual model based on the estimated user's emotion. The generation unit can estimate the user's emotion and adjust the appearance and voice characteristics of the virtual model based on the estimated user's emotion. The generation unit can analyze the user's emotion using, for example, an emotion engine and adjust the appearance and voice characteristics of the virtual model. For example, if the user is sad, the emotion engine can detect this and generate a virtual model with a gentle voice. Alternatively, if the user is excited, the emotion engine can detect this and generate a virtual model with a lively voice. Alternatively, if the user is relaxed, the emotion engine can detect this and generate a virtual model with a calm voice. This allows for adjusting the appearance and voice characteristics of the virtual model according to the user's emotion, thereby providing a more appropriate virtual model. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotional data into the generation AI and have the generation AI adjust the appearance and voice characteristics of the virtual model according to the emotion.

[0082] The generation unit can determine the generation priority based on the target person's past behavioral history when generating a virtual model. The generation unit determines the generation priority based on the target person's past behavioral history when generating a virtual model. The generation unit, for example, uses AI to analyze the target person's past behavioral history and determine the generation priority. For example, the generation unit prioritizes generation based on the target person's frequent past behavior. It is also possible to prioritize generation of important behaviors from the target person's past behavioral history. It is also possible to analyze the target person's past behavioral history and determine the optimal generation order. As a result, a more appropriate virtual model can be provided by determining the generation priority based on the target person's past behavioral history. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the target person's past behavioral history data into the generation AI and have the generation AI determine the generation priority.

[0083] The generation unit can improve the accuracy of generation by referring to literature related to the target person when generating a virtual model. The generation unit can improve the accuracy of generation by referring to literature related to the target person when generating a virtual model. The generation unit can, for example, use AI to analyze literature related to the target person and improve the accuracy of generation. For example, the generation unit can refer to literature related to the target person to generate a highly accurate virtual model. It can also analyze literature related to the target person and apply an optimal generation algorithm. It can also generate a virtual model with specific characteristics from literature related to the target person. In this way, the accuracy of generation can be improved by referring to literature related to the target person. Some or all of the above-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input literature data related to the target person into the generation AI and cause the generation AI to improve the accuracy of generation.

[0084] The generation unit can generate a virtual model taking into consideration the social background of the target person. The generation unit generates a virtual model taking into consideration the social background of the target person. The generation unit, for example, uses AI to analyze the social background of the target person and select an appropriate generation method. For example, the generation unit generates an appropriate virtual model taking into consideration the social background of the target person. The generation unit can also analyze the social background of the target person and apply an optimal generation algorithm. A virtual model with specific characteristics can also be generated from the social background of the target person. This makes it possible to provide a more appropriate virtual model by taking into consideration the social background of the target person. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input social background data of the target person into the generation AI and cause the generation AI to select a generation method.

[0085] The response unit can estimate the user's emotion and adjust the way a response is expressed based on the estimated user's emotion. The response unit can estimate the user's emotion and adjust the way a response is expressed based on the estimated user's emotion. The response unit can, for example, use an emotion engine to analyze the user's emotion and respond in an appropriate way. For example, if the user is sad, the emotion engine can detect this and respond with kind words. Alternatively, if the user is excited, the emotion engine can detect this and respond with lively words. Alternatively, if the user is relaxed, the emotion engine can detect this and respond with calm words. This allows for a more appropriate response to be provided by adjusting the way a response is expressed based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 processing in the response unit can be performed, for example, using AI or without AI. For example, the response unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the response is expressed according to the emotion.

[0086] The response unit can adjust the level of detail of the response based on the importance of the question or topic when responding. The response unit adjusts the level of detail of the response based on the importance of the question or topic when responding. The response unit, for example, uses AI to analyze the importance of the question or topic and adjust the level of detail of the response. For example, if an important question is asked, a detailed response is provided. Also, if a general question is asked, a response with a standard level of detail can be provided. Also, if a simple question is asked, a simplified response can be provided. In this way, by adjusting the level of detail of the response based on the importance of the question or topic, a more appropriate response can be provided. Some or all of the above-described processing in the response unit may be performed using AI, for example, or may be performed without using AI. For example, the response unit can input importance data of the question or topic to a generation AI and have the generation AI adjust the level of detail of the response.

[0087] The response unit can apply different response algorithms depending on the category of the question or conversation when responding. The response unit applies different response algorithms depending on the category of the question or conversation when responding. The response unit, for example, uses AI to analyze the category of the question or conversation and select an appropriate response algorithm. For example, if a question about family is asked, a family-specific response algorithm can be applied. Also, if a question about work is asked, a work-specific response algorithm can be applied. Also, if a question about friends is asked, a friend-specific response algorithm can be applied. In this way, by applying different response algorithms depending on the category of the question or conversation, a more appropriate response can be provided. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input question or conversation category data to a generation AI and have the generation AI select a response algorithm.

[0088] The response unit can improve the accuracy of the response by referring to the user's past response results when responding. The response unit can improve the accuracy of the response by referring to the user's past response results when responding. The response unit, for example, uses AI to analyze the user's past response results and improve the accuracy of the response. For example, the response unit provides a highly accurate response based on data on responses the user has received in the past. The response unit can also analyze the user's past response results and apply an optimal response algorithm. It can also provide a response with specific characteristics based on the user's past response results. In this way, the accuracy of the response can be improved by referring to the user's past response results. Some or all of the above-described processing in the response unit may be performed using AI, for example, or may be performed without using AI. For example, the response unit can input the user's past response result data into the generation AI and cause the generation AI to improve the accuracy of the response.

[0089] The response unit can estimate the user's emotion and adjust the length of the response based on the estimated user's emotion. The response unit can estimate the user's emotion and adjust the length of the response based on the estimated user's emotion. The response unit can, for example, use an emotion engine to analyze the user's emotion and provide a response of an appropriate length. For example, if the user is sad, the emotion engine can detect this and provide a longer response. Alternatively, if the user is excited, the emotion engine can detect this and provide a shorter response. Alternatively, if the user is relaxed, the emotion engine can detect this and provide a normal length response. This allows for adjusting the length of the response according to the user's emotion, thereby providing a more appropriate response. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the response unit can be performed, for example, using AI, or without AI. For example, the response unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the length of the response according to the emotion.

[0090] The response unit, when responding, can determine the priority of responses based on the time when the question or story was submitted. The response unit, when responding, can determine the priority of responses based on the time when the question or story was submitted. The response unit, for example, uses AI to analyze the time when the question or story was submitted and determine the priority of responses. For example, the response unit prioritizes responses to recent questions or stories. It can also provide appropriate responses to past questions or stories. It can also prioritize responses to important questions or stories based on the time when they were submitted. In this way, by determining the priority of responses based on the time when the question or story was submitted, it is possible to provide a more appropriate response. Some or all of the above-mentioned processing in the response unit may be performed using AI, for example, or may be performed without using AI. For example, the response unit can input data on the time when the question or story was submitted to a generation AI and have the generation AI determine the priority of responses.

[0091] The response unit can adjust the order of responses based on the relevance of the questions and topics when responding. The response unit adjusts the order of responses based on the relevance of the questions and topics when responding. The response unit, for example, uses AI to analyze the relevance of the questions and topics and adjust the order of responses. For example, responses can be given priority to highly relevant questions and topics. Appropriate responses can also be provided to less relevant questions and topics. The optimal order of responses can also be determined based on the relevance of the questions and topics. As a result, more appropriate responses can be provided by adjusting the order of responses based on the relevance of the questions and topics. Some or all of the above-described processing in the response unit may be performed using AI, for example, or may be performed without using AI. For example, the response unit can input relevance data of questions and topics to a generation AI and have the generation AI adjust the order of responses.

[0092] The response unit can adjust the use of technical terminology in the response according to the user's level of expertise when responding. The response unit can adjust the use of technical terminology in the response according to the user's level of expertise when responding. The response unit can, for example, use AI to analyze the user's level of expertise and use appropriate technical terminology. For example, if the user has technical expertise, the response can be made using technical terminology. Alternatively, if the user does not have technical expertise, the response can be made in simple language. The optimal response method can also be selected based on the user's level of expertise. This allows for adjusting the use of technical terminology in the response according to the user's level of expertise, thereby providing a more appropriate response. Some or all of the above-described processing in the response unit can be performed using AI, for example, or without AI. For example, the response unit can input the user's level of expertise data into a generation AI and have the generation AI use technical terminology.

[0093] The emotion response unit can estimate the user's emotion and adjust the emotional expression of the response based on the estimated user's emotion. The emotion response unit can estimate the user's emotion and adjust the emotional expression of the response based on the estimated user's emotion. The emotion response unit can, for example, use an emotion engine to analyze the user's emotion and respond with an appropriate emotional expression. For example, if the user is sad, the emotion engine can detect this and respond with a gentle emotional expression. Alternatively, if the user is excited, the emotion engine can detect this and respond with a lively emotional expression. Alternatively, if the user is relaxed, the emotion engine can detect this and respond with a gentle emotional expression. This allows for adjusting the emotional expression of the response according to the user's emotion, thereby providing a more appropriate emotional response. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 processing in the emotion response unit can be performed, for example, using AI or without AI. For example, the emotional response unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the emotional expression of the response according to the emotion.

[0094] The emotional response unit can predict a current emotion by referring to past emotional data when generating an emotional response. The emotional response unit predicts a current emotion by referring to past emotional data when generating an emotional response. The emotional response unit, for example, uses AI to analyze past emotional data and predict a current emotion. For example, the emotional response unit predicts a current emotion based on the user's past emotional data. The emotional response unit can also analyze past emotional data to generate an optimal emotional response. It can also generate a response with a specific emotion from past emotional data. This makes it possible to predict a current emotion by referring to past emotional data and provide a more appropriate emotional response. Some or all of the above-described processing in the emotional response unit may be performed using AI, for example, or may be performed without using AI. For example, the emotional response unit can input past emotional data to the generation AI and have the generation AI predict a current emotion.

[0095] The emotional response unit can customize the emotion of the response based on the user's emotional history when generating an emotional response. The emotional response unit customizes the emotion of the response based on the user's emotional history when generating an emotional response. The emotional response unit, for example, uses AI to analyze the user's emotional history and generate an optimal emotional response. For example, the emotional response unit generates an optimal emotional response based on the user's emotional history. The emotional response unit can also analyze the user's emotional history and apply an optimal emotional expression. A response having a specific emotion can also be generated from the user's emotional history. In this way, by customizing the emotion of the response based on the user's emotional history, a more appropriate emotional response can be provided. Some or all of the above-described processing in the emotional response unit may be performed using AI, for example, or may be performed without using AI. For example, the emotional response unit can input the user's emotional history data into the generation AI and cause the generation AI to customize the response according to the emotion.

[0096] The emotion response unit can estimate the user's emotion and adjust the emotion intensity of the response based on the estimated user's emotion. The emotion response unit can estimate the user's emotion and adjust the emotion intensity of the response based on the estimated user's emotion. The emotion response unit can, for example, use an emotion engine to analyze the user's emotion and respond with an appropriate emotion intensity. For example, if the user is sad, the emotion engine can detect this and respond with a strong emotion expression. Alternatively, if the user is excited, the emotion engine can detect this and respond with a strong emotion expression. Alternatively, if the user is relaxed, the emotion engine can detect this and respond with a gentle emotion expression. This allows for adjusting the emotion intensity of the response according to the user's emotion, thereby providing a more appropriate emotional response. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 processing in the emotion response unit can be performed, for example, using AI or without AI. For example, the emotion response unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the emotion intensity of the response according to the emotion.

[0097] The emotional response unit can adjust the emotion of the response by taking into account the user's current psychological state when generating an emotional response. The emotional response unit adjusts the emotion of the response by taking into account the user's current psychological state when generating an emotional response. The emotional response unit, for example, uses an emotion engine to analyze the user's psychological state and respond with an appropriate emotional expression. For example, if the user is sad, the emotion engine can detect this and respond with a gentle emotional expression. Alternatively, if the user is excited, the emotion engine can detect this and respond with a lively emotional expression. Alternatively, if the user is relaxed, the emotion engine can detect this and respond with a gentle emotional expression. This allows for a more appropriate emotional response to be provided by taking the user's current psychological state into account. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the emotional response unit may be performed, for example, using AI or without AI. For example, the emotional response unit can input the user's psychological state data into the generation AI and cause the generation AI to adjust the emotional expression of the response according to the psychological state.

[0098] The emotional response unit can customize the emotion of the response by reflecting the user's past emotional feedback when generating an emotional response. The emotional response unit customizes the emotion of the response by reflecting the user's past emotional feedback when generating an emotional response. The emotional response unit, for example, uses AI to analyze the user's past emotional feedback and generate an optimal emotional response. For example, the emotional response unit can generate an optimal emotional response based on the user's past emotional feedback. The emotional response unit can also analyze the user's past emotional feedback and apply an optimal emotional expression. The emotional response unit can also generate a response with a specific emotion from the user's past emotional feedback. This makes it possible to provide a more appropriate emotional response by reflecting the user's past emotional feedback. Some or all of the above-described processing in the emotional response unit may be performed using AI, for example, or may be performed without using AI. For example, the emotional response unit can input the user's past emotional feedback data into the generation AI and cause the generation AI to customize the response according to the emotion.

[0099] The analysis unit can estimate the user's emotions and adjust the analysis method of past conversation content based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the analysis method of past conversation content based on the estimated user emotions. The analysis unit can analyze the user's emotions using, for example, an emotion engine and analyze the past conversation content in an appropriate manner. For example, if the user is sad, the emotion engine can detect this and analyze the past conversation content using gentle words. Alternatively, if the user is excited, the emotion engine can detect this and analyze the past conversation content using lively words. Alternatively, if the user is relaxed, the emotion engine can detect this and analyze the past conversation content using gentle words. This allows for more appropriate analysis by adjusting the analysis method of past conversation content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the analysis method according to the emotion.

[0100] The analysis unit can adjust the level of detail of the analysis based on the importance of the content of the past conversation during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the content of the past conversation during analysis. The analysis unit, for example, uses AI to analyze the importance of the content of the past conversation and adjust the level of detail of the analysis. For example, if there is important content of the conversation, a detailed analysis is performed. Also, if there is general content of the conversation, an analysis with a standard level of detail can be performed. Also, if there is simple content of the conversation, a simplified analysis can be performed. In this way, by adjusting the level of detail of the analysis based on the importance of the content of the past conversation, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input importance data of the content of the past conversation to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0101] The analysis unit can apply different analysis algorithms depending on the category of the conversation content during analysis. The analysis unit applies different analysis algorithms depending on the category of the conversation content during analysis. The analysis unit, for example, uses AI to analyze the category of the conversation content and select an appropriate analysis algorithm. For example, if the conversation content is about family, an analysis algorithm specific to family can be applied. Also, if the conversation content is about work, an analysis algorithm specific to work can be applied. Also, if the conversation content is about friends, an analysis algorithm specific to friends can be applied. In this way, by applying different analysis algorithms depending on the category of the conversation content, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the conversation content to the generation AI and cause the generation AI to select an analysis algorithm.

[0102] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit, for example, uses AI to analyze the user's past analysis results and improve the accuracy of the analysis. For example, the analysis unit can perform a highly accurate analysis based on data from analyses the user performed in the past. The analysis unit can also analyze the user's past analysis results and apply an optimal analysis algorithm. It can also perform an analysis with specific characteristics from the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0103] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. The analysis unit, for example, uses an emotion engine to analyze the user's emotions and prioritize analysis of important conversation content. For example, if the user is sad, the emotion engine can detect this and prioritize analysis of important conversation content. Also, if the user is excited, the emotion engine can detect this and quickly analyze the conversation content. Also, if the user is relaxed, the emotion engine can detect this and analyze the conversation content with normal priority. In this way, by determining the analysis priority according to the user's emotions, important conversation content can be prioritized for analysis. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI determine the priority of analysis according to the emotion.

[0104] The analysis unit can determine the analysis priority based on the submission time of the conversation content during analysis. The analysis unit determines the analysis priority based on the submission time of the conversation content during analysis. The analysis unit, for example, uses AI to analyze the submission time of the conversation content and determine the analysis priority. For example, the analysis unit prioritizes analysis of recent conversation content. It can also perform appropriate analysis of past conversation content. It can also prioritize analysis of important conversation content based on the submission time. In this way, by determining the analysis priority based on the submission time of the conversation content, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data on the submission time of the conversation content to the generation AI and have the generation AI determine the analysis priority.

[0105] The analysis unit can adjust the order of analysis based on the relevance of the conversation content during analysis. The analysis unit adjusts the order of analysis based on the relevance of the conversation content during analysis. The analysis unit, for example, uses AI to analyze the relevance of the conversation content and adjust the order of analysis. For example, the analysis unit prioritizes analysis of highly relevant conversation content. It can also perform appropriate analysis on less relevant conversation content. It can also determine an optimal analysis order based on the relevance of the conversation content. As a result, adjusting the analysis order based on the relevance of the conversation content enables more appropriate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input relevance data of the conversation content to a generation AI and cause the generation AI to adjust the order of analysis.

[0106] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit can, for example, use AI to analyze the user's level of expertise and use appropriate technical terms. For example, if the user has technical expertise, the analysis can be performed using technical terms. Alternatively, if the user does not have technical expertise, the analysis can be performed using simple language. The optimal analysis method can also be selected based on the user's level of expertise. This allows for more appropriate analysis by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's level of expertise data into a generation AI and have the generation AI use technical terms. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described reception unit, generation unit, response unit, emotion response unit, and analysis unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive user input information via the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a virtual model using a generation AI. For example, the response unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and provides an appropriate response to a user's question or conversation. For example, the emotion response unit is realized by the specific processing unit 290 of the data processing device 12 and generates a response according to the user's emotion. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the content of past conversations. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, generation unit, response unit, emotion response unit, and analysis unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive user input information via the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a virtual model using a generation AI. For example, the response unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and provides an appropriate response to a user's question or conversation. For example, the emotion response unit is realized by the specific processing unit 290 of the data processing device 12 and generates a response according to the user's emotion. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the content of past conversations. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, generation unit, response unit, emotion response unit, and analysis unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can receive user input information via the microphone 238 of the headset-type terminal 314 or the communication I / F 26 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a virtual model using a generation AI. For example, the response unit is realized by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12 and provides an appropriate response to a user's question or speech. For example, the emotion response unit is realized by the specific processing unit 290 of the data processing device 12 and generates a response according to the user's emotion. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the content of past conversations. === Hard Collateral 1-4 === Each of the multiple elements including the above-described reception unit, generation unit, response unit, emotion response unit, and analysis unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive user input information via the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a virtual model using a generative AI. For example, the response unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and provides an appropriate response to a user's question or conversation. For example, the emotion response unit is realized by the specific processing unit 290 of the data processing device 12 and generates a response according to the user's emotion. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the content of past conversations.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The VR system may also include a health response unit that monitors the user's health state and generates a response according to the health state. The health response unit may, for example, measure the user's heart rate or stress level and generate an appropriate response. For example, if the user's heart rate is high, the health response unit may generate a response encouraging relaxation. Alternatively, if the user's stress level is high, the health response unit may provide advice on stress reduction. Furthermore, the tone and content of the response may be adjusted based on the user's health state. This allows for providing an appropriate response according to the user's health state.

[0109] The VR system may also include an interest response unit that generates a customized response based on the user's hobbies and interests. The interest response unit may, for example, analyze information about the user's hobbies and interests and provide related topics and advice. For example, if the user is interested in music, the interest response unit may provide information about the latest music trends and recommended artists. Alternatively, if the user is interested in sports, the interest response unit may provide the latest game results and advice on training methods. Furthermore, the content of the response may be customized based on the user's hobbies and interests. This allows the user to provide an appropriate response according to the user's hobbies and interests.

[0110] The VR system may also include a behavior prediction unit that analyzes the user's past behavioral patterns and generates a response based on the behavior prediction. The behavior prediction unit, for example, analyzes the user's past behavioral data and predicts the next behavior to be taken. For example, if the user has previously exercised at a specific time of day, the behavior prediction unit generates a response encouraging exercise at that time of day. Also, if the user has previously engaged in a specific activity at a specific location, the behavior prediction unit can suggest an activity at that location. Furthermore, the content of the response can be customized based on the user's behavioral patterns. This makes it possible to provide an appropriate response according to the user's behavioral patterns.

[0111] The VR system may also include a network response unit that analyzes the user's social network and generates a response based on the relationships within the network. The network response unit may analyze, for example, the user's social media or contact information and generate a response based on the relationships. For example, if the user frequently communicates with a particular friend, the network response unit may provide topics related to that friend. Also, if the user belongs to a particular group, the network response unit may provide information related to that group. Furthermore, the content of the response may be customized based on the user's social network. This allows the user to provide an appropriate response according to the user's social network.

[0112] The VR system may also include a learning response unit that analyzes the user's learning history and generates a response based on the learning content. The learning response unit may analyze, for example, what the user has learned in the past or what the user is currently learning, and generate a relevant response. For example, if the user is studying a particular subject, the learning response unit may provide an appropriate response to a question related to that subject. Also, if the user is mastering a particular skill, the learning response unit may provide advice related to that skill. Furthermore, the content of the response may be customized based on the user's learning history. This makes it possible to provide an appropriate response according to the user's learning content.

[0113] The VR system may also include an emotional tone adjustment unit that estimates the user's emotion and adjusts the tone of a response based on the estimated emotion. For example, the emotional tone adjustment unit may respond in a gentle tone if the user is sad. Alternatively, the emotional tone adjustment unit may respond in a lively tone if the user is excited. Furthermore, the emotional tone adjustment unit may respond in a calm tone if the user is relaxed. This allows a response to be provided in an appropriate tone according to the user's emotion.

[0114] The VR system may also include an emotional content adjustment unit that estimates the user's emotions and adjusts the content of the response based on the estimated emotions. For example, the emotional content adjustment unit may provide words of encouragement if the user is sad. Alternatively, the emotional content adjustment unit may provide advice to calm down if the user is excited. Furthermore, the emotional content adjustment unit may provide advice to maintain relaxation if the user is relaxed. This allows the system to provide a response with appropriate content according to the user's emotions.

[0115] The VR system may also include an emotion speed adjustment unit that estimates the user's emotion and adjusts the response speed based on the estimated emotion. For example, the emotion speed adjustment unit may respond at a slower speed if the user is sad. Alternatively, the emotion speed adjustment unit may respond at a faster speed if the user is excited. Furthermore, the emotion speed adjustment unit may respond at a normal speed if the user is relaxed. This allows the system to provide responses at an appropriate speed according to the user's emotion.

[0116] The VR system may also include an emotion length adjustment unit that estimates the user's emotion and adjusts the length of a response based on the estimated emotion. For example, the emotion length adjustment unit may provide a longer response if the user is sad. Alternatively, the emotion length adjustment unit may provide a shorter response if the user is excited. Furthermore, the emotion length adjustment unit may provide a response of normal length if the user is relaxed. This allows responses to be provided with an appropriate length according to the user's emotion.

[0117] The VR system may also include an emotion intensity adjustment unit that estimates the user's emotion and adjusts the emotion intensity of the response based on the estimated emotion. For example, the emotion intensity adjustment unit may respond with a strong emotion expression if the user is sad. Alternatively, the emotion intensity adjustment unit may respond with a strong emotion expression if the user is excited. Furthermore, the emotion intensity adjustment unit may respond with a gentle emotion expression if the user is relaxed. This allows the system to provide a response with an appropriate emotion intensity according to the user's emotion.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception unit receives information input by the user. The information input by the user includes, but is not limited to, the name and characteristics of the person the user wants to talk to, and the contents of past conversations. The reception unit can receive text information, audio information, image information, and the like. Step 2: The generation unit uses a generation AI to generate a virtual model based on the information received by the reception unit. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to reproduce the appearance, voice, speaking style, etc. of the person the user wants to listen to. The generation unit generates a virtual model of the person the user wants to listen to based on the information entered by the user. The generation unit can also use the generation AI to analyze past conversation content and generate responses for the virtual model. Step 3: The response unit uses the virtual model generated by the generation unit to respond to the user's questions and comments. For example, if the user says, "I've been having trouble at work lately," the virtual model will respond with, "What are you worried about?" The response unit can also use generation AI to generate appropriate responses to the user's questions and comments.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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 AI 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0148] 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.

[0149] 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.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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 AI 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, a 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0168] 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.

[0169] 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.

[0170] 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 AI 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.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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, in order to avoid confusion and to 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 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. a reception unit that receives information input by a user; a generating unit that generates a virtual model based on the information received by the receiving unit; The virtual model generated by the generating unit includes a response unit that responds to questions and comments from the user. A system characterized by:

2. Emotion response unit that generates a response according to the user's emotion A system characterized by:

3. Equipped with an analysis unit that analyzes past conversation content A system characterized by:

4. The generation unit Generate a virtual model based on input information 2. The system of claim 1.

5. The response unit Respond appropriately to user questions and comments 2. The system of claim 1.

6. The reception unit Estimates user emotions and adjusts the timing of accepting input information based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past input history and select the optimal reception method 2. The system of claim 1.

8. The reception unit When accepting input, filter it based on the user's current state of mind and areas of interest.

2. The system of claim 1.

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