system

Abstract

The system according to this embodiment aims to allow the user to converse with a version of themselves who has grown up in a different world or environment. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a simulation unit, and a provision unit. The reception unit receives the user's selection. The generation unit generates a character based on the selection received by the reception unit. The simulation unit simulates a conversation with the character generated by the generation unit. The provision unit provides the conversation simulated by the simulation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that a user cannot talk to himself / herself who has grown up in a different world or environment, and the opportunities for self-understanding and self-growth are limited.

[0005] The system according to the embodiment aims to enable a user to talk to himself / herself who has grown up in a different world or environment.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a simulation unit, and a provision unit. The reception unit receives user selections. The generation unit generates a character based on the selection received by the reception unit. The simulation unit simulates a conversation with the character generated by the generation unit. The provision unit provides the conversation simulated by the simulation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows the user to converse with a version of themselves who has grown up in a different world or environment. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An embodiment of the present invention is a parallel self system that utilizes generative AI to enable users to converse with themselves who have grown up in different worlds and environments. The parallel self system allows users to choose from a variety of world settings, such as fantasy, science fiction, historical backgrounds, and different cultures. Next, the user customizes the appearance, personality, skills, and backstory of their parallel self. The generative AI generates a character based on the user's selection, enabling realistic conversations. Next, the generative AI simulates a conversation with the user's parallel self in real time. The conversation is provided in text-based, voice-based, or interactive formats utilizing VR / AR. This gives users an opportunity to deepen their self-understanding by conversing with themselves in different worlds and cultures. For example, if a user chooses a fantasy world of swords and magic and customizes their parallel self as a hero, the generative AI generates a character based on that setting and simulates a conversation between the user and themselves as a hero. Users can gain an opportunity to re-examine themselves from a different perspective, promoting self-growth. Furthermore, if a user selects a futuristic cyberpunk city and customizes their parallel self as a hacker, the generating AI will create a character based on that setting and simulate a conversation between the user and their hacker self. This allows the user to gain new perspectives and ways of thinking. In this way, the parallel self system aims to provide users with new perspectives and experiences, promoting self-understanding and growth. Through conversations with themselves in different worlds and cultures, users can embark on a journey of self-discovery and achieve a richer life. Thus, the parallel self system allows users to converse with themselves who have grown up in different worlds and environments.

[0029] The parallel self-system according to this embodiment comprises a reception unit, a generation unit, a simulation unit, and a provision unit. The reception unit receives user selections. User selections include, but are not limited to, world settings such as fantasy, science fiction, historical background, or different cultures. The reception unit can receive user selections by methods such as menu selection, voice input, or touch operation. The generation unit generates a character based on the selections received by the reception unit, using a generation AI. The generation unit generates the character's appearance, personality, skills, and backstory based on the user's selections, for example. The generation AI generates the character using technologies such as deep learning or a generation model. The simulation unit simulates a conversation with the character generated by the generation unit. The simulation unit simulates the conversation in real time using a generation AI. The simulation unit simulates the conversation using, for example, natural language processing technology or a dialogue model. The provision unit provides the conversation simulated by the simulation unit. The provision unit provides the conversation in an interactive format, for example, text-based, voice-based, or utilizing VR / AR. As a result, the parallel self system according to the embodiment allows the user to converse with a version of themselves that has grown up in a different world or environment. Some or all of the above-described processes in the generation unit are performed using a generative AI. For example, the generation unit can generate a character using a generative AI model that takes the user's selection as input and outputs a character. Some or all of the above-described processes in the simulation unit are performed using a generative AI. For example, the simulation unit can simulate a conversation using a generative AI model that takes a conversation with a generated character as input and outputs a simulated conversation. Some or all of the above-described processes in the provision unit are performed using a generative AI. For example, the provision unit can provide a conversation using a generative AI model that takes a simulated conversation as input and outputs it in text-based, voice-based, or VR / AR format.

[0030] The reception desk accepts user selections. These selections may include, but are not limited to, world settings such as fantasy, science fiction, historical background, or different cultures. The reception desk can accept user selections through methods such as menu selection, voice input, and touch operation. Specifically, when a user accesses the system, an interface is displayed, allowing the user to select their desired world setting using dropdown menus, radio buttons, checkboxes, etc. For voice input, the user speaks their desired setting into a microphone, and voice recognition technology analyzes the content to accept the selection. For touch operation, the user makes a selection by touching the screen of a tablet or smartphone. This allows the reception desk to support diverse input methods, enhancing user convenience. Furthermore, the reception desk can record the user's past selection history for reference during subsequent access. For example, it can save information on previously selected world settings and characters, enabling quicker response when the same setting is selected again. This allows users to enjoy a consistent experience and makes system usage smoother. The reception desk can also dynamically display relevant options and settings based on the user's selections. For example, if a user selects a fantasy world setting, the next options presented will be character races and professions such as elves, dwarves, and wizards. This allows users to make choices intuitively and improves the system's usability.

[0031] The generation unit uses a generation AI to generate characters based on selections received by the reception unit. For example, the generation unit generates the character's appearance, personality, skills, and backstory based on the user's selection. The generation AI uses technologies such as deep learning and generative models to generate characters. Specifically, the generation AI receives the world setting and character attributes selected by the user as input and generates a detailed character profile based on this. For example, for a user who selects a fantasy world setting, the generation AI generates an elf wizard character, creating details such as long ears and green eyes for its appearance, a strong desire for knowledge and a calm and collected personality, and skills such as fire magic and healing magic. The generation AI generates the optimal character for the user's selection based on a large dataset that it has previously trained on. Furthermore, the generation unit can also respond to additional information and customization requests provided by the user. For example, if the user specifies certain appearance or personality traits, the generation AI will generate a character that reflects those requests. This allows users to create their own original characters, increasing the value of the system. The generation unit can also save the generated character information in a database for later reuse. This allows users to recall and use characters they have created before, enabling them to enjoy a continuous experience.

[0032] The simulation unit simulates conversations with characters generated by the generation unit. The simulation unit simulates conversations in real time using a generative AI. The simulation unit simulates conversations using, for example, natural language processing technology and dialogue models. Specifically, the generative AI analyzes user input and generates appropriate responses. For example, if a user asks, "What is your specialty magic?", the generative AI, based on the character's profile, will generate a response such as, "I'm good at fire magic. I can create fireballs in particular." The generative AI can generate natural and fluent conversations based on pre-learned dialogue data. Furthermore, the simulation unit can understand the context of the conversation and maintain a continuous dialogue. For example, if a user follows up with, "Have you ever used that magic before?", the generative AI will refer to the previous response and generate a response such as, "Yes, I used it when I fought a dragon before." This allows the user to enjoy a realistic conversation with the character. The simulation unit can also analyze the user's emotions and intentions and generate appropriate responses. For example, if the user is excited, the generative AI will generate a response that matches that emotion, improving the naturalness of the conversation. This allows the simulation unit to make user interaction richer and more engaging.

[0033] The service provider delivers conversations simulated by the simulation unit. The service provider delivers conversations in various formats, such as text-based, voice-based, or interactive formats utilizing VR / AR. Specifically, in the text-based format, the simulated conversation is displayed on the user's screen, and the user can input responses using a keyboard or touchscreen. In the voice-based format, the system outputs the generated conversation as speech using speech synthesis technology, and the user can input responses using a microphone. In the VR / AR format, the user wears a headset or AR glasses and can interact with the character in a virtual space or augmented reality. This allows the user to enjoy a more immersive experience. Furthermore, the service provider can collect user feedback and use it to improve the system. For example, users can evaluate the content of the conversation and the character's responses, and the generation AI model can be updated based on that evaluation. This allows the system to continuously learn and provide more natural and engaging conversations. The service provider can also support multiple output formats simultaneously. For example, it can provide conversations in both text-based and voice-based formats, allowing the user to choose their preferred format. This allows the user to utilize the system in the most optimal way according to their environment and situation.

[0034] The customization section can customize the character's appearance and personality based on user selections. For example, the customization section can customize the character based on the user's selected hairstyle, clothing, personality traits, etc. The customization section can use a generative AI to customize the character's appearance and personality based on user selections. For example, the customization section inputs image data of the hairstyle and clothing selected by the user into the generative AI, and the generative AI generates the character's appearance based on that data. The customization section also inputs personality traits selected by the user into the generative AI, and the generative AI generates the character's personality based on that data. This allows the user to customize the character to their liking. Some or all of the above processes in the customization section are performed using a generative AI. For example, the customization section can customize the character using a generative AI model that takes user selections as input and outputs a customized character.

[0035] The format selection unit allows the user to choose the format of the conversation. The format selection unit provides conversation formats such as dialogue format, question format, and storytelling format. Based on the conversation format selected by the user, the format selection unit uses a generative AI to simulate the conversation. For example, if the user selects the dialogue format, the generative AI simulates a dialogue format conversation. Also, if the user selects the question format, the generative AI can simulate a question format conversation. This allows the user to choose the conversation format according to their preference. Some or all of the above processing in the format selection unit is performed using a generative AI. For example, the format selection unit can select the conversation format using a generative AI model that takes the user's selection as input and outputs the selected conversation format.

[0036] The generation unit can generate characters based on user selections using a generative AI. For example, the generation unit generates characters based on the user's selected appearance, personality, skills, and backstory. The generative AI generates characters using technologies such as deep learning and generative models. For example, the generation unit inputs image data of the user's selected appearance into the generative AI, and the generative AI generates the character's appearance based on that data. The generation unit can also input personality traits selected by the user into the generative AI, and the generative AI generates the character's personality based on that data. In this way, by using the generative AI, it is possible to generate characters based on user selections. Some or all of the above-described processes in the generation unit are performed using the generative AI. For example, the generation unit can generate characters using a generative AI model that takes user selections as input and outputs characters.

[0037] The simulation unit can simulate conversations in real time using generative AI. For example, the simulation unit can simulate a conversation between a user and a generated character in real time using generative AI. The generative AI simulates conversations using natural language processing techniques and dialogue models. For example, the simulation unit inputs user input into the generative AI, and the generative AI generates a conversation in real time based on that input. The simulation unit can also generate responses to user statements in real time using the generative AI. In this way, conversations can be simulated in real time by using generative AI. Some or all of the above processing in the simulation unit is performed using generative AI. For example, the simulation unit can simulate a conversation using a generative AI model that takes user input as input and outputs a conversation generated in real time.

[0038] The service provider can deliver conversations in text-based, voice-based, or interactive formats utilizing VR / AR. For example, the service provider can deliver simulated conversations in text-based format using generative AI. It can also deliver simulated conversations in voice-based format using generative AI. Furthermore, it can deliver simulated conversations in interactive formats utilizing VR / AR. For example, the service provider can deliver conversations in text chat format using text data generated by generative AI. It can also deliver conversations using speech synthesis technology with voice data generated by generative AI. Furthermore, it can deliver interactive conversations through a VR / AR interface using data generated by generative AI. This allows for the delivery of conversations in a variety of formats. Some or all of the above-described processes in the service provider are performed using generative AI. For example, the service provider can deliver conversations using a generative AI model that takes simulated conversations as input and outputs them in text-based, voice-based, or VR / AR format.

[0039] The reception desk can analyze the user's past selection history and suggest the optimal world settings. For example, the reception desk can suggest similar settings based on the user's past selections. It can also prioritize displaying settings frequently selected by the user. Furthermore, the reception desk can predict and suggest preferred settings for specific time periods based on the user's selection history. This allows the reception desk to suggest the optimal world settings based on the user's past selection history. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk can suggest world settings using an AI model that takes the user's past selection history as input and outputs the optimal world settings.

[0040] The reception desk can filter world settings based on the user's current interests and preferences. For example, it can suggest world settings based on the genres of books the user has recently read or movies they have watched. It can also analyze the user's social media activity and suggest relevant world settings. Furthermore, it can filter world settings based on keywords the user has recently searched for. This allows for filtering world settings based on the user's current interests and preferences. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk can filter world settings using an AI model that takes the user's current interests and preferences as input and outputs filtered world settings.

[0041] The reception desk can prioritize presenting highly relevant world settings by considering the user's geographical location. For example, if the user is in an urban area, the reception desk may suggest a futuristic cyberpunk city. If the user is in a natural environment, the reception desk may suggest a fantasy world. Furthermore, if the user is in a historical location, the reception desk may suggest a world with a historical background. This allows the reception desk to present highly relevant world settings based on the user's geographical location. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk can present world settings using an AI model that takes the user's geographical location as input and outputs highly relevant world settings.

[0042] The reception desk can analyze a user's social media activity and suggest relevant world settings. For example, it can suggest world settings based on articles and posts the user has recently shared. It can also suggest world settings that the user's followers and friends are interested in. Furthermore, it can suggest world settings based on topics in online communities the user participates in. This allows the reception desk to suggest relevant world settings based on the user's social media activity. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk can suggest world settings using an AI model that takes the user's social media activity as input and outputs relevant world settings.

[0043] The generation unit can generate the optimal character by referring to the user's past selection history during character generation. For example, the generation unit can generate a new character based on the characteristics of characters the user has previously selected. Furthermore, the generation unit can prioritize the personality traits of characters frequently selected by the user. In addition, the generation unit can predict and generate characters preferred at specific times based on the user's selection history. This allows for the generation of the optimal character based on the user's past selection history. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can generate characters using a generation AI model that takes the user's past selection history as input and outputs the optimal character.

[0044] The generation unit can customize character attributes based on the user's current interests and preferences when generating characters. For example, it can customize attributes based on characters from books the user has recently read or movies they have recently watched. It can also analyze the user's social media activity and customize relevant attributes. Furthermore, it can customize character attributes based on keywords the user has recently searched for. This allows for the customization of character attributes based on the user's current interests and preferences. Some or all of the above processing in the generation unit is performed using generative AI. For example, the generation unit can customize character attributes using a generative AI model that takes the user's current interests and preferences as input and outputs a customized character.

[0045] The generation unit can generate highly relevant characters by considering the user's geographical location information during character generation. For example, if the user is in an urban area, the generation unit can generate a character with a futuristic appearance. If the user is in a place rich in nature, the generation unit can generate a character with a nature theme. Furthermore, if the user is in a historical place, the generation unit can generate a character with a historical background. In this way, highly relevant characters can be generated based on the user's geographical location information. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can generate characters using a generation AI model that takes the user's geographical location information as input and outputs highly relevant characters.

[0046] The generation unit can analyze the user's social media activity and generate relevant characters during character generation. For example, it can generate characters based on articles and posts recently shared by the user. It can also generate characters that the user's followers and friends are interested in. Furthermore, it can generate characters based on topics in online communities the user participates in. This allows for the generation of relevant characters based on the user's social media activity. Some or all of the above processing in the generation unit is performed using a generative AI. For example, the generation unit can generate characters using a generative AI model that takes the user's social media activity as input and outputs relevant characters.

[0047] The simulation unit can simulate optimal conversations by referring to the user's past conversation history during conversation simulations. For example, the simulation unit can simulate new conversations based on the content of conversations the user has had in the past. The simulation unit can also prioritize simulating topics that the user frequently brings up. Furthermore, the simulation unit can predict and simulate topics preferred by the user at specific times of day based on the user's conversation history. This allows for the simulation of optimal conversations based on the user's past conversation history. Some or all of the above processing in the simulation unit is performed using AI. For example, the simulation unit can simulate conversations using an AI model that takes the user's past conversation history as input and outputs the optimal conversation.

[0048] The simulation unit can customize the content of a conversation based on the user's current interests and concerns during a conversation simulation. For example, the simulation unit can simulate topics such as books the user has recently read or movies they have watched. It can also analyze the user's social media activity and simulate relevant topics. Furthermore, the simulation unit can customize the content of a conversation based on keywords the user has recently searched for. This allows for the customization of conversation content based on the user's current interests and concerns. Some or all of the above processing in the simulation unit is performed using AI. For example, the simulation unit can customize the content of a conversation using an AI model that takes the user's current interests and concerns as input and outputs a customized conversation.

[0049] The simulation unit can simulate highly relevant conversations by taking into account the user's geographical location during conversation simulations. For example, if the user is in an urban area, the simulation unit can simulate topics related to urban life. Similarly, if the user is in a natural environment, the simulation unit can simulate topics related to nature. Furthermore, if the user is in a historical location, the simulation unit can simulate historical topics related to that location. This allows for the simulation of highly relevant conversations based on the user's geographical location. Some or all of the above processing in the simulation unit is performed using AI. For example, the simulation unit can simulate conversations using an AI model that takes the user's geographical location as input and outputs highly relevant conversations.

[0050] The simulation unit can analyze a user's social media activity and simulate relevant conversations during conversation simulations. For example, the simulation unit can simulate conversations based on articles and posts recently shared by the user. It can also simulate topics that the user's followers and friends are interested in. Furthermore, the simulation unit can simulate conversations based on topics in online communities the user participates in. This allows for the simulation of relevant conversations based on the user's social media activity. Some or all of the above processing in the simulation unit is performed using AI. For example, the simulation unit can simulate conversations using an AI model that takes the user's social media activity as input and outputs relevant conversations.

[0051] The service provider can select the optimal service delivery method by referring to the user's past usage history when providing conversations. For example, the service provider can prioritize delivery methods (text, voice, VR / AR) that the user has preferred in the past. Furthermore, the service provider can predict and select a preferred delivery method at a specific time based on the user's past usage history. In addition, the service provider can provide new conversations based on delivery methods frequently used by the user. This allows for the selection of the optimal delivery method based on the user's past usage history. Some or all of the above processing in the service provider is performed using AI. For example, the service provider can select a delivery method using an AI model that takes the user's past usage history as input and outputs the optimal delivery method.

[0052] The service provider can customize the content offered during conversation based on the user's current interests and preferences. For example, it can offer topics related to books the user has recently read or movies they have watched. It can also analyze the user's social media activity and offer relevant topics. Furthermore, it can customize the content based on keywords the user has recently searched for. This allows for the content to be customized based on the user's current interests and preferences. Some or all of the above processing in the service provider is performed using AI. For example, the service provider can customize the content using an AI model that takes the user's current interests and preferences as input and outputs customized content.

[0053] The service provider can select the optimal service delivery method when providing conversation, taking into account the user's geographical location. For example, if the user is in an urban area, the service provider can provide topics related to urban life. If the user is in a place rich in nature, the service provider can provide topics related to nature. Furthermore, if the user is in a historical place, the service provider can provide historical topics related to that place. This allows the service provider to select the optimal service delivery method based on the user's geographical location. Some or all of the above processing in the service provider is performed using AI. For example, the service provider can select a service delivery method using an AI model that takes the user's geographical location as input and outputs the optimal service delivery method.

[0054] The service provider can analyze the user's social media activity and select relevant content when providing conversations. For example, it can provide conversations based on articles and posts the user has recently shared. It can also provide topics that the user's followers and friends are interested in. Furthermore, it can provide conversations based on topics in online communities the user participates in. This allows for the selection of relevant content based on the user's social media activity. Some or all of the above processing in the service provider is performed using AI. For example, the service provider can select content using an AI model that takes the user's social media activity as input and outputs relevant content.

[0055] The customization section can suggest the optimal customization when customizing a character by referring to the user's past customization history. For example, the customization section can suggest a new customization based on the characteristics of characters the user has previously selected. Furthermore, the customization section can prioritize suggesting customization options that the user has frequently selected. In addition, the customization section can predict and suggest customizations preferred at specific times of day based on the user's customization history. This allows the system to suggest the optimal customization based on the user's past customization history. Some or all of the above processes in the customization section are performed using AI. For example, the customization section can suggest customizations using an AI model that takes the user's past customization history as input and outputs the optimal customization.

[0056] The customization section can suggest the optimal customization when customizing a character, taking into account the user's geographical location. For example, if the user is in an urban area, the customization section can suggest a futuristic-looking customization. If the user is in a natural area, the customization section can suggest a nature-themed customization. Furthermore, if the user is in a historical location, the customization section can suggest a customization with a historical background. In this way, the system can suggest the optimal customization based on the user's geographical location. Some or all of the above processing in the customization section is performed using AI. For example, the customization section can suggest a customization using an AI model that takes the user's geographical location as input and outputs the optimal customization.

[0057] The format selection unit can suggest the most suitable format when selecting a conversation format, by referring to the user's past selection history. For example, the format selection unit can prioritize suggesting conversation formats (text, voice, VR / AR) that the user has previously preferred. Furthermore, the format selection unit can predict and suggest conversation formats preferred at specific times based on the user's past selection history. In addition, the format selection unit can suggest new conversation formats based on the conversation formats the user has frequently used. This allows the system to suggest the most suitable format based on the user's past selection history. Some or all of the above processing in the format selection unit is performed using AI. For example, the format selection unit can suggest conversation formats using an AI model that takes the user's past selection history as input and outputs the most suitable conversation format.

[0058] The format selection unit can suggest the most suitable format when selecting a conversation format, taking into account the user's geographical location. For example, if the user is in an urban area, the format selection unit can suggest a format that provides topics related to urban life. Furthermore, if the user is in a place rich in nature, the format selection unit can suggest a format that provides topics related to nature. In addition, if the user is in a historical place, the format selection unit can suggest a format that provides historical topics related to that place. This allows the system to suggest the most suitable format based on the user's geographical location. Some or all of the above processing in the format selection unit is performed using AI. For example, the format selection unit can suggest a conversation format using an AI model that takes the user's geographical location as input and outputs the most suitable conversation format.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The Parallel Self system can analyze a user's past conversation history and suggest optimal conversation topics. For example, it can prioritize suggesting topics the user has shown interest in in the past. It can also generate new conversation topics based on topics the user frequently discusses. Furthermore, it can predict and suggest topics the user prefers at specific times of day based on their conversation history. This allows the system to suggest optimal conversation topics based on the user's past conversation history.

[0061] The Parallel Self system can customize conversation content based on the user's current interests. For example, it can offer topics related to books the user has recently read or movies they have watched. It can also analyze the user's social media activity and offer relevant topics. Furthermore, it can customize conversation content based on keywords the user has recently searched for. This allows the conversation to be tailored to the user's current interests.

[0062] The Parallel Self System can provide highly relevant conversation topics by considering the user's geographical location. For example, if the user is in an urban area, it can provide topics related to urban life. If the user is in a natural environment, it can provide topics related to nature. Furthermore, if the user is in a historical site, it can provide historical topics related to that site. This allows the system to provide highly relevant conversation topics based on the user's geographical location.

[0063] The Parallel Self System can analyze a user's social media activity and suggest relevant conversation topics. For example, it can suggest conversations based on articles and posts the user has recently shared. It can also suggest topics that the user's followers and friends are interested in. Furthermore, it can suggest conversations based on topics in online communities the user participates in. This allows the system to suggest relevant conversation topics based on the user's social media activity.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The reception desk accepts the user's selection. User selections include world settings such as fantasy, science fiction, historical background, and different cultures. The reception desk can accept user selections through methods such as menu selection, voice input, and touch operation. Step 2: The generation unit uses a generation AI to generate a character based on the selections received by the reception unit. The generation unit generates the character's appearance, personality, skills, and backstory based on the user's selections. The generation AI generates the character using technologies such as deep learning and generative models. Step 3: The simulation unit simulates a conversation with the character generated by the generation unit. The simulation unit simulates the conversation in real time using a generation AI. The simulation unit simulates the conversation using natural language processing technology and dialogue models. Step 4: The service provider delivers the conversation simulated by the simulation service provider. The service provider delivers the conversation in a text-based, voice-based, or interactive format utilizing VR / AR.

[0066] (Example of form 2) An embodiment of the present invention is a parallel self system that utilizes generative AI to enable users to converse with themselves who have grown up in different worlds and environments. The parallel self system allows users to choose from a variety of world settings, such as fantasy, science fiction, historical backgrounds, and different cultures. Next, the user customizes the appearance, personality, skills, and backstory of their parallel self. The generative AI generates a character based on the user's selection, enabling realistic conversations. Next, the generative AI simulates a conversation with the user's parallel self in real time. The conversation is provided in text-based, voice-based, or interactive formats utilizing VR / AR. This gives users an opportunity to deepen their self-understanding by conversing with themselves in different worlds and cultures. For example, if a user chooses a fantasy world of swords and magic and customizes their parallel self as a hero, the generative AI generates a character based on that setting and simulates a conversation between the user and themselves as a hero. Users can gain an opportunity to re-examine themselves from a different perspective, promoting self-growth. Furthermore, if a user selects a futuristic cyberpunk city and customizes their parallel self as a hacker, the generating AI will create a character based on that setting and simulate a conversation between the user and their hacker self. This allows the user to gain new perspectives and ways of thinking. In this way, the parallel self system aims to provide users with new perspectives and experiences, promoting self-understanding and growth. Through conversations with themselves in different worlds and cultures, users can embark on a journey of self-discovery and achieve a richer life. Thus, the parallel self system allows users to converse with themselves who have grown up in different worlds and environments.

[0067] The parallel self-system according to this embodiment comprises a reception unit, a generation unit, a simulation unit, and a provision unit. The reception unit receives user selections. User selections include, but are not limited to, world settings such as fantasy, science fiction, historical background, or different cultures. The reception unit can receive user selections by methods such as menu selection, voice input, or touch operation. The generation unit generates a character based on the selections received by the reception unit, using a generation AI. The generation unit generates the character's appearance, personality, skills, and backstory based on the user's selections, for example. The generation AI generates the character using technologies such as deep learning or a generation model. The simulation unit simulates a conversation with the character generated by the generation unit. The simulation unit simulates the conversation in real time using a generation AI. The simulation unit simulates the conversation using, for example, natural language processing technology or a dialogue model. The provision unit provides the conversation simulated by the simulation unit. The provision unit provides the conversation in an interactive format, for example, text-based, voice-based, or utilizing VR / AR. As a result, the parallel self system according to the embodiment allows the user to converse with a version of themselves that has grown up in a different world or environment. Some or all of the above-described processes in the generation unit are performed using a generative AI. For example, the generation unit can generate a character using a generative AI model that takes the user's selection as input and outputs a character. Some or all of the above-described processes in the simulation unit are performed using a generative AI. For example, the simulation unit can simulate a conversation using a generative AI model that takes a conversation with a generated character as input and outputs a simulated conversation. Some or all of the above-described processes in the provision unit are performed using a generative AI. For example, the provision unit can provide a conversation using a generative AI model that takes a simulated conversation as input and outputs it in text-based, voice-based, or VR / AR format.

[0068] The reception desk accepts user selections. These selections may include, but are not limited to, world settings such as fantasy, science fiction, historical background, or different cultures. The reception desk can accept user selections through methods such as menu selection, voice input, and touch operation. Specifically, when a user accesses the system, an interface is displayed, allowing the user to select their desired world setting using dropdown menus, radio buttons, checkboxes, etc. For voice input, the user speaks their desired setting into a microphone, and voice recognition technology analyzes the content to accept the selection. For touch operation, the user makes a selection by touching the screen of a tablet or smartphone. This allows the reception desk to support diverse input methods, enhancing user convenience. Furthermore, the reception desk can record the user's past selection history for reference during subsequent access. For example, it can save information on previously selected world settings and characters, enabling quicker response when the same setting is selected again. This allows users to enjoy a consistent experience and makes system usage smoother. The reception desk can also dynamically display relevant options and settings based on the user's selections. For example, if a user selects a fantasy world setting, the next options presented will be character races and professions such as elves, dwarves, and wizards. This allows users to make choices intuitively and improves the system's usability.

[0069] The generation unit uses a generation AI to generate characters based on selections received by the reception unit. For example, the generation unit generates the character's appearance, personality, skills, and backstory based on the user's selection. The generation AI uses technologies such as deep learning and generative models to generate characters. Specifically, the generation AI receives the world setting and character attributes selected by the user as input and generates a detailed character profile based on this. For example, for a user who selects a fantasy world setting, the generation AI generates an elf wizard character, creating details such as long ears and green eyes for its appearance, a strong desire for knowledge and a calm and collected personality, and skills such as fire magic and healing magic. The generation AI generates the optimal character for the user's selection based on a large dataset that it has previously trained on. Furthermore, the generation unit can also respond to additional information and customization requests provided by the user. For example, if the user specifies certain appearance or personality traits, the generation AI will generate a character that reflects those requests. This allows users to create their own original characters, increasing the value of the system. The generation unit can also save the generated character information in a database for later reuse. This allows users to recall and use characters they have created before, enabling them to enjoy a continuous experience.

[0070] The simulation unit simulates conversations with characters generated by the generation unit. The simulation unit simulates conversations in real time using a generative AI. The simulation unit simulates conversations using, for example, natural language processing technology and dialogue models. Specifically, the generative AI analyzes user input and generates appropriate responses. For example, if a user asks, "What is your specialty magic?", the generative AI, based on the character's profile, will generate a response such as, "I'm good at fire magic. I can create fireballs in particular." The generative AI can generate natural and fluent conversations based on pre-learned dialogue data. Furthermore, the simulation unit can understand the context of the conversation and maintain a continuous dialogue. For example, if a user follows up with, "Have you ever used that magic before?", the generative AI will refer to the previous response and generate a response such as, "Yes, I used it when I fought a dragon before." This allows the user to enjoy a realistic conversation with the character. The simulation unit can also analyze the user's emotions and intentions and generate appropriate responses. For example, if the user is excited, the generative AI will generate a response that matches that emotion, improving the naturalness of the conversation. This allows the simulation unit to make user interaction richer and more engaging.

[0071] The service provider delivers conversations simulated by the simulation unit. The service provider delivers conversations in various formats, such as text-based, voice-based, or interactive formats utilizing VR / AR. Specifically, in the text-based format, the simulated conversation is displayed on the user's screen, and the user can input responses using a keyboard or touchscreen. In the voice-based format, the system outputs the generated conversation as speech using speech synthesis technology, and the user can input responses using a microphone. In the VR / AR format, the user wears a headset or AR glasses and can interact with the character in a virtual space or augmented reality. This allows the user to enjoy a more immersive experience. Furthermore, the service provider can collect user feedback and use it to improve the system. For example, users can evaluate the content of the conversation and the character's responses, and the generation AI model can be updated based on that evaluation. This allows the system to continuously learn and provide more natural and engaging conversations. The service provider can also support multiple output formats simultaneously. For example, it can provide conversations in both text-based and voice-based formats, allowing the user to choose their preferred format. This allows the user to utilize the system in the most optimal way according to their environment and situation.

[0072] The customization section can customize the character's appearance and personality based on user selections. For example, the customization section can customize the character based on the user's selected hairstyle, clothing, personality traits, etc. The customization section can use a generative AI to customize the character's appearance and personality based on user selections. For example, the customization section inputs image data of the hairstyle and clothing selected by the user into the generative AI, and the generative AI generates the character's appearance based on that data. The customization section also inputs personality traits selected by the user into the generative AI, and the generative AI generates the character's personality based on that data. This allows the user to customize the character to their liking. Some or all of the above processes in the customization section are performed using a generative AI. For example, the customization section can customize the character using a generative AI model that takes user selections as input and outputs a customized character.

[0073] The format selection unit allows the user to choose the format of the conversation. The format selection unit provides conversation formats such as dialogue format, question format, and storytelling format. Based on the conversation format selected by the user, the format selection unit uses a generative AI to simulate the conversation. For example, if the user selects the dialogue format, the generative AI simulates a dialogue format conversation. Also, if the user selects the question format, the generative AI can simulate a question format conversation. This allows the user to choose the conversation format according to their preference. Some or all of the above processing in the format selection unit is performed using a generative AI. For example, the format selection unit can select the conversation format using a generative AI model that takes the user's selection as input and outputs the selected conversation format.

[0074] The generation unit can generate characters based on user selections using a generative AI. For example, the generation unit generates characters based on the user's selected appearance, personality, skills, and backstory. The generative AI generates characters using technologies such as deep learning and generative models. For example, the generation unit inputs image data of the user's selected appearance into the generative AI, and the generative AI generates the character's appearance based on that data. The generation unit can also input personality traits selected by the user into the generative AI, and the generative AI generates the character's personality based on that data. In this way, by using the generative AI, it is possible to generate characters based on user selections. Some or all of the above-described processes in the generation unit are performed using the generative AI. For example, the generation unit can generate characters using a generative AI model that takes user selections as input and outputs characters.

[0075] The simulation unit can simulate conversations in real time using generative AI. For example, the simulation unit can simulate a conversation between a user and a generated character in real time using generative AI. The generative AI simulates conversations using natural language processing techniques and dialogue models. For example, the simulation unit inputs user input into the generative AI, and the generative AI generates a conversation in real time based on that input. The simulation unit can also generate responses to user statements in real time using the generative AI. In this way, conversations can be simulated in real time by using generative AI. Some or all of the above processing in the simulation unit is performed using generative AI. For example, the simulation unit can simulate a conversation using a generative AI model that takes user input as input and outputs a conversation generated in real time.

[0076] The service provider can deliver conversations in text-based, voice-based, or interactive formats utilizing VR / AR. For example, the service provider can deliver simulated conversations in text-based format using generative AI. It can also deliver simulated conversations in voice-based format using generative AI. Furthermore, it can deliver simulated conversations in interactive formats utilizing VR / AR. For example, the service provider can deliver conversations in text chat format using text data generated by generative AI. It can also deliver conversations using speech synthesis technology with voice data generated by generative AI. Furthermore, it can deliver interactive conversations through a VR / AR interface using data generated by generative AI. This allows for the delivery of conversations in a variety of formats. Some or all of the above-described processes in the service provider are performed using generative AI. For example, the service provider can deliver conversations using a generative AI model that takes simulated conversations as input and outputs them in text-based, voice-based, or VR / AR format.

[0077] The reception desk can estimate the user's emotions and adjust the world setting selection method based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize suggesting a relaxing fantasy world. If the user is excited, the reception desk can suggest an action-packed cyberpunk city. Furthermore, if the user is calm, the reception desk can suggest a world with a historical background. This allows for the selection of a world setting that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk is performed using generative AI. For example, the reception desk can adjust the world setting selection method using a generative AI model that takes user emotion data as input and outputs a world setting selection method based on emotions.

[0078] The reception desk can analyze the user's past selection history and suggest the optimal world settings. For example, the reception desk can suggest similar settings based on the user's past selections. It can also prioritize displaying settings frequently selected by the user. Furthermore, the reception desk can predict and suggest preferred settings for specific time periods based on the user's selection history. This allows the reception desk to suggest the optimal world settings based on the user's past selection history. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk can suggest world settings using an AI model that takes the user's past selection history as input and outputs the optimal world settings.

[0079] The reception desk can filter world settings based on the user's current interests and preferences. For example, it can suggest world settings based on the genres of books the user has recently read or movies they have watched. It can also analyze the user's social media activity and suggest relevant world settings. Furthermore, it can filter world settings based on keywords the user has recently searched for. This allows for filtering world settings based on the user's current interests and preferences. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk can filter world settings using an AI model that takes the user's current interests and preferences as input and outputs filtered world settings.

[0080] The reception desk can estimate the user's emotions and determine the priority of choices based on those emotions. For example, if the user is tired, the reception desk may prioritize displaying relaxing world settings. If the user is excited, the reception desk may prioritize displaying action-packed world settings. Furthermore, if the user is calm, the reception desk may prioritize displaying intellectual world settings. This allows the system to determine the priority of choices according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk is performed using generative AI. For example, the reception desk can determine the priority of choices using a generative AI model that takes user emotion data as input and outputs an emotion-based priority of choices.

[0081] The reception desk can prioritize presenting highly relevant world settings by considering the user's geographical location. For example, if the user is in an urban area, the reception desk may suggest a futuristic cyberpunk city. If the user is in a natural environment, the reception desk may suggest a fantasy world. Furthermore, if the user is in a historical location, the reception desk may suggest a world with a historical background. This allows the reception desk to present highly relevant world settings based on the user's geographical location. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk can present world settings using an AI model that takes the user's geographical location as input and outputs highly relevant world settings.

[0082] The reception desk can analyze a user's social media activity and suggest relevant world settings. For example, it can suggest world settings based on articles and posts the user has recently shared. It can also suggest world settings that the user's followers and friends are interested in. Furthermore, it can suggest world settings based on topics in online communities the user participates in. This allows the reception desk to suggest relevant world settings based on the user's social media activity. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk can suggest world settings using an AI model that takes the user's social media activity as input and outputs relevant world settings.

[0083] The generation unit can estimate the user's emotions and adjust the character generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a character with a calm personality. If the user is excited, the generation unit can generate a character with an active personality. Furthermore, if the user is calm, the generation unit can generate a character with an intelligent personality. This makes it possible to generate characters that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using a generative AI. For example, the generation unit can adjust the character generation method using a generative AI model that takes user emotion data as input and outputs characters based on emotions.

[0084] The generation unit can generate the optimal character by referring to the user's past selection history during character generation. For example, the generation unit can generate a new character based on the characteristics of characters the user has previously selected. Furthermore, the generation unit can prioritize the personality traits of characters frequently selected by the user. In addition, the generation unit can predict and generate characters preferred at specific times based on the user's selection history. This allows for the generation of the optimal character based on the user's past selection history. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can generate characters using a generation AI model that takes the user's past selection history as input and outputs the optimal character.

[0085] The generation unit can customize character attributes based on the user's current interests and preferences when generating characters. For example, it can customize attributes based on characters from books the user has recently read or movies they have recently watched. It can also analyze the user's social media activity and customize relevant attributes. Furthermore, it can customize character attributes based on keywords the user has recently searched for. This allows for the customization of character attributes based on the user's current interests and preferences. Some or all of the above processing in the generation unit is performed using generative AI. For example, the generation unit can customize character attributes using a generative AI model that takes the user's current interests and preferences as input and outputs a customized character.

[0086] The generation unit can estimate the user's emotions and adjust the character's appearance and personality based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a character with a calm appearance and personality. If the user is excited, the generation unit can generate a character with an active appearance and personality. Furthermore, if the user is calm, the generation unit can generate a character with an intelligent appearance and personality. This makes it possible to adjust the character's appearance and personality according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using a generative AI. For example, the generation unit can take user emotion data as input and adjust the character's appearance and personality using a generative AI model that outputs a character's appearance and personality based on emotions.

[0087] The generation unit can generate highly relevant characters by considering the user's geographical location information during character generation. For example, if the user is in an urban area, the generation unit can generate a character with a futuristic appearance. If the user is in a place rich in nature, the generation unit can generate a character with a nature theme. Furthermore, if the user is in a historical place, the generation unit can generate a character with a historical background. In this way, highly relevant characters can be generated based on the user's geographical location information. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can generate characters using a generation AI model that takes the user's geographical location information as input and outputs highly relevant characters.

[0088] The generation unit can analyze the user's social media activity and generate relevant characters during character generation. For example, it can generate characters based on articles and posts recently shared by the user. It can also generate characters that the user's followers and friends are interested in. Furthermore, it can generate characters based on topics in online communities the user participates in. This allows for the generation of relevant characters based on the user's social media activity. Some or all of the above processing in the generation unit is performed using a generative AI. For example, the generation unit can generate characters using a generative AI model that takes the user's social media activity as input and outputs relevant characters.

[0089] The simulation unit can estimate the user's emotions and adjust the conversation simulation method based on the estimated user emotions. For example, if the user is relaxed, the simulation unit can simulate the conversation in a calm tone. If the user is excited, the simulation unit can simulate the conversation in an active tone. Furthermore, if the user is calm, the simulation unit can simulate the conversation in an intelligent tone. This makes it possible to simulate conversations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit is performed using generative AI. For example, the simulation unit can adjust the conversation simulation method using a generative AI model that takes user emotion data as input and outputs an emotion-based conversation simulation method.

[0090] The simulation unit can simulate optimal conversations by referring to the user's past conversation history during conversation simulations. For example, the simulation unit can simulate new conversations based on the content of conversations the user has had in the past. The simulation unit can also prioritize simulating topics that the user frequently brings up. Furthermore, the simulation unit can predict and simulate topics preferred by the user at specific times of day based on the user's conversation history. This allows for the simulation of optimal conversations based on the user's past conversation history. Some or all of the above processing in the simulation unit is performed using AI. For example, the simulation unit can simulate conversations using an AI model that takes the user's past conversation history as input and outputs the optimal conversation.

[0091] The simulation unit can customize the content of a conversation based on the user's current interests and concerns during a conversation simulation. For example, the simulation unit can simulate topics such as books the user has recently read or movies they have watched. It can also analyze the user's social media activity and simulate relevant topics. Furthermore, the simulation unit can customize the content of a conversation based on keywords the user has recently searched for. This allows for the customization of conversation content based on the user's current interests and concerns. Some or all of the above processing in the simulation unit is performed using AI. For example, the simulation unit can customize the content of a conversation using an AI model that takes the user's current interests and concerns as input and outputs a customized conversation.

[0092] The simulation unit can estimate the user's emotions and adjust the tone and tempo of the conversation based on the estimated emotions. For example, if the user is relaxed, the simulation unit can simulate the conversation with a relaxed tone and tempo. If the user is excited, the simulation unit can simulate the conversation with a fast tempo. Furthermore, if the user is calm, the simulation unit can simulate the conversation with an intelligent tone and tempo. This makes it possible to adjust the tone and tempo of the conversation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit is performed using generative AI. For example, the simulation unit can take user emotion data as input and adjust the tone and tempo of the conversation using a generative AI model that outputs an emotion-based tone and tempo of conversation.

[0093] The simulation unit can simulate highly relevant conversations by taking into account the user's geographical location during conversation simulations. For example, if the user is in an urban area, the simulation unit can simulate topics related to urban life. Similarly, if the user is in a natural environment, the simulation unit can simulate topics related to nature. Furthermore, if the user is in a historical location, the simulation unit can simulate historical topics related to that location. This allows for the simulation of highly relevant conversations based on the user's geographical location. Some or all of the above processing in the simulation unit is performed using AI. For example, the simulation unit can simulate conversations using an AI model that takes the user's geographical location as input and outputs highly relevant conversations.

[0094] The simulation unit can analyze a user's social media activity and simulate relevant conversations during conversation simulations. For example, the simulation unit can simulate conversations based on articles and posts recently shared by the user. It can also simulate topics that the user's followers and friends are interested in. Furthermore, the simulation unit can simulate conversations based on topics in online communities the user participates in. This allows for the simulation of relevant conversations based on the user's social media activity. Some or all of the above processing in the simulation unit is performed using AI. For example, the simulation unit can simulate conversations using an AI model that takes the user's social media activity as input and outputs relevant conversations.

[0095] The service provider can estimate the user's emotions and adjust the way the conversation is delivered based on the estimated emotions. For example, if the user is relaxed, the service provider can deliver the conversation in a calm tone. If the user is excited, the service provider can deliver the conversation in an active tone. Furthermore, if the user is calm, the service provider can deliver the conversation in an intelligent tone. This makes it possible to adjust the way the conversation is delivered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider is performed using generative AI. For example, the service provider can adjust the way the conversation is delivered using a generative AI model that takes user emotion data as input and outputs an emotion-based way of delivering conversation.

[0096] The service provider can select the optimal service delivery method by referring to the user's past usage history when providing conversations. For example, the service provider can prioritize delivery methods (text, voice, VR / AR) that the user has preferred in the past. Furthermore, the service provider can predict and select a preferred delivery method at a specific time based on the user's past usage history. In addition, the service provider can provide new conversations based on delivery methods frequently used by the user. This allows for the selection of the optimal delivery method based on the user's past usage history. Some or all of the above processing in the service provider is performed using AI. For example, the service provider can select a delivery method using an AI model that takes the user's past usage history as input and outputs the optimal delivery method.

[0097] The service provider can customize the content offered during conversation based on the user's current interests and preferences. For example, it can offer topics related to books the user has recently read or movies they have watched. It can also analyze the user's social media activity and offer relevant topics. Furthermore, it can customize the content based on keywords the user has recently searched for. This allows for the content to be customized based on the user's current interests and preferences. Some or all of the above processing in the service provider is performed using AI. For example, the service provider can customize the content using an AI model that takes the user's current interests and preferences as input and outputs customized content.

[0098] The service provider can estimate the user's emotions and adjust the conversation delivery format based on the estimated emotions. For example, if the user is relaxed, the service provider can deliver a voice conversation in a calm tone. If the user is excited, the service provider can deliver a voice conversation in an active tone. Furthermore, if the user is calm, the service provider can deliver a voice conversation in an intelligent tone. This makes it possible to adjust the conversation delivery format according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider is performed using generative AI. For example, the service provider can adjust the conversation delivery format using a generative AI model that takes user emotion data as input and outputs an emotion-based conversation delivery format.

[0099] The service provider can select the optimal service delivery method when providing conversation, taking into account the user's geographical location. For example, if the user is in an urban area, the service provider can provide topics related to urban life. If the user is in a place rich in nature, the service provider can provide topics related to nature. Furthermore, if the user is in a historical place, the service provider can provide historical topics related to that place. This allows the service provider to select the optimal service delivery method based on the user's geographical location. Some or all of the above processing in the service provider is performed using AI. For example, the service provider can select a service delivery method using an AI model that takes the user's geographical location as input and outputs the optimal service delivery method.

[0100] The service provider can analyze the user's social media activity and select relevant content when providing conversations. For example, it can provide conversations based on articles and posts the user has recently shared. It can also provide topics that the user's followers and friends are interested in. Furthermore, it can provide conversations based on topics in online communities the user participates in. This allows for the selection of relevant content based on the user's social media activity. Some or all of the above processing in the service provider is performed using AI. For example, the service provider can select content using an AI model that takes the user's social media activity as input and outputs relevant content.

[0101] The customization unit can estimate the user's emotions and adjust the character customization method based on the estimated emotions. For example, if the user is relaxed, the customization unit can suggest a character with a calm appearance and personality. If the user is excited, the customization unit can suggest a character with an active appearance and personality. Furthermore, if the user is calm, the customization unit can suggest a character with an intelligent appearance and personality. This makes it possible to customize the character according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit is performed using generative AI. For example, the customization unit can adjust the character customization method using a generative AI model that takes user emotion data as input and outputs a character customization method based on emotions.

[0102] The customization section can suggest the optimal customization when customizing a character by referring to the user's past customization history. For example, the customization section can suggest a new customization based on the characteristics of characters the user has previously selected. Furthermore, the customization section can prioritize suggesting customization options that the user has frequently selected. In addition, the customization section can predict and suggest customizations preferred at specific times of day based on the user's customization history. This allows the system to suggest the optimal customization based on the user's past customization history. Some or all of the above processes in the customization section are performed using AI. For example, the customization section can suggest customizations using an AI model that takes the user's past customization history as input and outputs the optimal customization.

[0103] The customization unit can estimate the user's emotions and determine customization priorities based on those emotions. For example, if the user is relaxed, the customization unit will prioritize suggesting calm appearance and personality customizations. If the user is excited, the customization unit can prioritize suggesting active appearance and personality customizations. Furthermore, if the user is calm, the customization unit can prioritize suggesting intelligent appearance and personality customizations. This allows the customization unit to determine customization priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit is performed using generative AI. For example, the customization unit can determine customization priorities using a generative AI model that takes user emotion data as input and outputs emotion-based customization priorities.

[0104] The customization section can suggest the optimal customization when customizing a character, taking into account the user's geographical location. For example, if the user is in an urban area, the customization section can suggest a futuristic-looking customization. If the user is in a natural area, the customization section can suggest a nature-themed customization. Furthermore, if the user is in a historical location, the customization section can suggest a customization with a historical background. In this way, the system can suggest the optimal customization based on the user's geographical location. Some or all of the above processing in the customization section is performed using AI. For example, the customization section can suggest a customization using an AI model that takes the user's geographical location as input and outputs the optimal customization.

[0105] The format selection unit can estimate the user's emotions and adjust the conversation format selection method based on the estimated user emotions. For example, if the user is relaxed, the format selection unit may suggest an audio-based conversation format. If the user is excited, the format selection unit may suggest an interactive VR / AR format. Furthermore, if the user is calm, the format selection unit may suggest a text-based conversation format. This enables the selection of a conversation format according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the format selection unit is performed using generative AI. For example, the format selection unit can adjust the conversation format selection method using a generative AI model that takes user emotion data as input and outputs a conversation format selection method based on emotions.

[0106] The format selection unit can suggest the most suitable format when selecting a conversation format, by referring to the user's past selection history. For example, the format selection unit can prioritize suggesting conversation formats (text, voice, VR / AR) that the user has previously preferred. Furthermore, the format selection unit can predict and suggest conversation formats preferred at specific times based on the user's past selection history. In addition, the format selection unit can suggest new conversation formats based on the conversation formats the user has frequently used. This allows the system to suggest the most suitable format based on the user's past selection history. Some or all of the above processing in the format selection unit is performed using AI. For example, the format selection unit can suggest conversation formats using an AI model that takes the user's past selection history as input and outputs the most suitable conversation format.

[0107] The format selection unit can estimate the user's emotions and determine the priority of conversation formats based on the estimated emotions. For example, if the user is relaxed, the format selection unit may preferentially suggest an audio-based conversation format. If the user is excited, the format selection unit may preferentially suggest an interactive VR / AR format. Furthermore, if the user is calm, the format selection unit may preferentially suggest a text-based conversation format. This allows for the determination of conversation format priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the format selection unit is performed using generative AI. For example, the format selection unit can determine the priority of conversation formats using a generative AI model that takes user emotion data as input and outputs a priority of conversation formats based on emotions.

[0108] The format selection unit can suggest the most suitable format when selecting a conversation format, taking into account the user's geographical location. For example, if the user is in an urban area, the format selection unit can suggest a format that provides topics related to urban life. Furthermore, if the user is in a place rich in nature, the format selection unit can suggest a format that provides topics related to nature. In addition, if the user is in a historical place, the format selection unit can suggest a format that provides historical topics related to that place. This allows the system to suggest the most suitable format based on the user's geographical location. Some or all of the above processing in the format selection unit is performed using AI. For example, the format selection unit can suggest a conversation format using an AI model that takes the user's geographical location as input and outputs the most suitable conversation format.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The Parallel Self System can estimate a user's emotions and dynamically change the conversation topic based on those estimates. For example, if a user is stressed, the system can offer relaxing topics. If a user is excited, the system can offer topics related to action or adventure. Furthermore, if a user is calm, the system can offer intellectual topics or deep conversations. This allows for an optimal conversational experience tailored to the user's emotions.

[0111] The Parallel Self system can analyze a user's past conversation history and suggest optimal conversation topics. For example, it can prioritize suggesting topics the user has shown interest in in the past. It can also generate new conversation topics based on topics the user frequently discusses. Furthermore, it can predict and suggest topics the user prefers at specific times of day based on their conversation history. This allows the system to suggest optimal conversation topics based on the user's past conversation history.

[0112] The Parallel Self system can customize conversation content based on the user's current interests. For example, it can offer topics related to books the user has recently read or movies they have watched. It can also analyze the user's social media activity and offer relevant topics. Furthermore, it can customize conversation content based on keywords the user has recently searched for. This allows the conversation to be tailored to the user's current interests.

[0113] The Parallel Self System can provide highly relevant conversation topics by considering the user's geographical location. For example, if the user is in an urban area, it can provide topics related to urban life. If the user is in a natural environment, it can provide topics related to nature. Furthermore, if the user is in a historical site, it can provide historical topics related to that site. This allows the system to provide highly relevant conversation topics based on the user's geographical location.

[0114] The Parallel Self System can analyze a user's social media activity and suggest relevant conversation topics. For example, it can suggest conversations based on articles and posts the user has recently shared. It can also suggest topics that the user's followers and friends are interested in. Furthermore, it can suggest conversations based on topics in online communities the user participates in. This allows the system to suggest relevant conversation topics based on the user's social media activity.

[0115] The Parallel Self System can estimate the user's emotions and adjust the character's response based on those estimates. For example, if the user is sad, the character can respond in a comforting manner. If the user is happy, the character can respond empathetically and share in their joy. Furthermore, if the user is angry, the character can respond calmly and soothe the user. This allows the system to provide the most appropriate character response for the user's emotions.

[0116] The Parallel Self System can analyze a user's past emotional data and suggest the most appropriate character responses. For example, it can generate new responses based on how the user reacted to specific emotions in the past. It can also adjust character responses based on emotions the user frequently displays. Furthermore, it can predict and suggest preferred responses at specific times of day based on the user's emotional data. This allows for the suggestion of the most appropriate character responses based on the user's past emotional data.

[0117] The Parallel Self System can dynamically change the character's appearance based on the user's current emotions. For example, if the user is relaxed, the character can have a calm appearance. If the user is excited, the character can have an energetic appearance. Furthermore, if the user is calm, the character can have an intellectual appearance. This allows the system to provide the optimal character appearance according to the user's emotions.

[0118] The Parallel Self System can estimate the user's emotions and adjust the conversation pace based on those estimates. For example, if the user is relaxed, the conversation pace can be slowed down. If the user is excited, the conversation pace can be sped up. Furthermore, if the user is calm, the conversation pace can be made more intelligent. This allows the system to provide an optimal conversation pace tailored to the user's emotions.

[0119] The Parallel Self System can estimate the user's emotions and adjust the tone of conversation based on those estimates. For example, if the user is relaxed, the tone of conversation can be made gentle. If the user is excited, the tone of conversation can be made lively. Furthermore, if the user is calm, the tone of conversation can be made intellectual. This allows the system to provide the optimal conversation tone according to the user's emotions.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The reception desk accepts the user's selection. User selections include world settings such as fantasy, science fiction, historical background, and different cultures. The reception desk can accept user selections through methods such as menu selection, voice input, and touch operation. Step 2: The generation unit uses a generation AI to generate a character based on the selections received by the reception unit. The generation unit generates the character's appearance, personality, skills, and backstory based on the user's selections. The generation AI generates the character using technologies such as deep learning and generative models. Step 3: The simulation unit simulates a conversation with the character generated by the generation unit. The simulation unit simulates the conversation in real time using a generation AI. The simulation unit simulates the conversation using natural language processing technology and dialogue models. Step 4: The service provider delivers the conversation simulated by the simulation service provider. The service provider delivers the conversation in a text-based, voice-based, or interactive format utilizing VR / AR.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0124] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements described above, including the reception unit, generation unit, simulation unit, provision unit, customization unit, and format selection unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and accepts the user's selection. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a character using a generation AI. The simulation unit is implemented by the specific processing unit 290 of the data processing device 12 and simulates a conversation with the generated character. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the simulated conversation. The customization unit is implemented by the control unit 46A of the smart device 14 and customizes the character based on the user's selection. The format selection unit is implemented by the control unit 46A of the smart device 14 and allows the user to select the format of the conversation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the reception unit, generation unit, simulation unit, provision unit, customization unit, and format selection unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and accepts the user's selection. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a character using a generation AI. The simulation unit is implemented by the specific processing unit 290 of the data processing device 12 and simulates a conversation with the generated character. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the simulated conversation. The customization unit is implemented by the control unit 46A of the smart glasses 214 and customizes the character based on the user's selection. The format selection unit is implemented by the control unit 46A of the smart glasses 214 and allows the user to select the format of the conversation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the reception unit, generation unit, simulation unit, provision unit, customization unit, and format selection unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and accepts the user's selection. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a character using a generation AI. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simulates a conversation with the generated character. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the simulated conversation. The customization unit is implemented by the control unit 46A of the headset terminal 314 and customizes the character based on the user's selection. The format selection unit is implemented by the control unit 46A of the headset terminal 314 and allows the user to select the format of the conversation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0166] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0167] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] Each of the multiple elements described above, including the reception unit, generation unit, simulation unit, provision unit, customization unit, and format selection unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and accepts the user's selection. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a character using a generation AI. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simulates a conversation with the generated character. The provision unit is implemented by the control unit 46A of the robot 414 and provides the simulated conversation. The customization unit is implemented by the control unit 46A of the robot 414 and customizes the character based on the user's selection. The format selection unit is implemented by the control unit 46A of the robot 414 and allows the user to select the format of the conversation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0175] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0177] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0178] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0179] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0183] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0184] 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.

[0185] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0186] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0187] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0188] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0190] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0191] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0192] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0193] (Note 1) A reception desk that accepts user selections, A generation unit that generates a character based on the selection received by the reception unit, A simulation unit that simulates a conversation with the character generated by the generation unit, The system comprises a providing unit that provides a conversation simulated by the aforementioned simulation unit. A system characterized by the following features. (Note 2) It features a customization section that allows users to customize the character's appearance and personality based on their choices. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a format selection section for selecting the format of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is The AI ​​generates characters based on user selections. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned simulation unit, Generative AI simulates conversations in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide conversations in text-based, voice-based, or interactive formats utilizing VR / AR. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts how world settings are selected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past selection history and suggests the optimal world setting. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Filter the world settings based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of choices based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is The system prioritizes presenting highly relevant world settings, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is Analyze users' social media activity and present relevant world settings. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the character generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a character, the system references the user's past selection history to generate the most suitable character. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When creating a character, customize the character's attributes based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the character's appearance and personality based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating characters, the system takes the user's geographical location into consideration to generate characters that are highly relevant to their location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating characters, the system analyzes the user's social media activity and generates characters that are relevant to that activity. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned simulation unit, It estimates the user's emotions and adjusts the conversation simulation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned simulation unit, During conversation simulations, the system references the user's past conversation history to simulate the most optimal conversation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned simulation unit, During conversation simulations, the content of the conversation is customized based on the user's current interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned simulation unit, It estimates the user's emotions and adjusts the tone and tempo of the conversation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned simulation unit, During conversation simulations, the system considers the user's geographical location to simulate more relevant conversations. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned simulation unit, During conversation simulations, the system analyzes the user's social media activity and simulates relevant conversations. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the conversation is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing conversations, the system selects the optimal delivery method by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing conversations, the content is customized based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and adjusts the conversation delivery format based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing conversations, the optimal delivery method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing conversations, the system analyzes the user's social media activity and selects relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned customization unit is It estimates the user's emotions and adjusts how the character is customized based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned customization unit is When customizing a character, the system will refer to the user's past customization history to suggest the optimal customization. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned customization unit is It estimates the user's emotions and determines the priority of customization based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned customization unit is When customizing a character, the system will suggest the optimal customization based on the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned format selection unit, It estimates the user's emotions and adjusts the conversation format selection based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned format selection unit, When selecting a conversation format, the system will refer to the user's past selection history to suggest the most suitable format. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned format selection unit, It estimates the user's emotions and determines the priority of conversational formats based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned format selection unit, When selecting a conversation format, the system will suggest the most suitable format considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts user selections, A generation unit that generates a character based on the selection received by the reception unit, A simulation unit that simulates a conversation with the character generated by the generation unit, The system comprises a providing unit that provides a conversation simulated by the aforementioned simulation unit. A system characterized by the following features.

2. It features a customization section that allows users to customize the character's appearance and personality based on their choices. The system according to feature 1.

3. It includes a format selection section for selecting the format of the conversation. The system according to feature 1.

4. The generating unit is The AI ​​generates characters based on user selections. The system according to feature 1.

5. The aforementioned simulation unit, Generative AI simulates conversations in real time. The system according to feature 1.

6. The aforementioned supply unit is, Provide conversations in text-based, voice-based, or interactive formats utilizing VR / AR. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts how world settings are selected based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past selection history and suggests the optimal world setting. The system according to feature 1.

9. The aforementioned reception unit is Filter the world settings based on the user's current interests and preferences. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of choices based on the estimated user emotions. The system according to feature 1.

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