system

Abstract

The system according to this embodiment aims to efficiently support the buying, selling, and development of virtual real estate within the metaverse. [Solution] The system according to the embodiment comprises a proposal unit, a support unit, an analysis unit, a promotion unit, and a management unit. The proposal unit proposes the most suitable virtual land and buildings according to the user's purpose and budget. The support unit supports the design and addition of functions to the virtual land and buildings proposed by the proposal unit. The analysis unit analyzes the value and future potential of the virtual space designed by the support unit using data. The promotion unit promotes interaction with nearby users and the holding of events based on the data analyzed by the analysis unit. The management unit manages ownership of virtual real estate using NFTs based on the interactions and events promoted by the promotion unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, a system that efficiently supports the sale and development of virtual real estate in the metaverse has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently support the sale and development of virtual real estate in the metaverse.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a proposal unit, a support unit, an analysis unit, a promotion unit, and a management unit. The proposal unit proposes the most suitable virtual land and buildings according to the user's purpose and budget. The support unit supports the design and addition of functions to the virtual land and buildings proposed by the proposal unit. The analysis unit analyzes the value and future potential of the virtual space designed by the support unit using data. The promotion unit promotes interaction with nearby users and the holding of events based on the data analyzed by the analysis unit. The management unit manages ownership of virtual real estate using NFTs based on the interactions and events promoted by the promotion unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently support the buying, selling, and development of virtual real estate within the metaverse. [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 controls 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) The MetaEstate system according to an embodiment of the present invention is an AI agent system that supports the buying, selling, and development of virtual real estate within the metaverse. The MetaEstate system allows users to find optimal virtual land and buildings with the help of AI. The MetaEstate system enables users to design and develop their own virtual spaces with AI support. The MetaEstate system analyzes the value and future potential of virtual real estate using data and performs investment analysis. The MetaEstate system also includes community building functions that promote interaction and event hosting among nearby users. The MetaEstate system manages real estate ownership using NFTs, ensuring secure transactions. For example, the MetaEstate system allows users to find optimal virtual land and buildings with the help of AI. In this process, the AI ​​proposes the most suitable properties according to the user's objectives and budget. For example, if an investor is looking for a property with high expected returns, the AI ​​analyzes past data and market trends to recommend the most suitable property. Next, the MetaEstate system allows users to design and develop their own virtual spaces with AI support. For example, if a creator wants to create a virtual building with their own unique design, the AI ​​automatically generates a design that meets the user's requirements. This allows users to easily create virtual spaces. Furthermore, the MetaEstate system analyzes the value and future potential of virtual real estate using data to perform investment analysis. For example, AI can analyze big data to predict the future value of a specific virtual plot of land. This allows investors to invest in virtual real estate with confidence. The MetaEstate system also has community-building features that promote interaction and event hosting among nearby users. For example, AI can analyze the interests and preferences of nearby users and suggest events that connect users with shared hobbies. This increases interaction among users and fosters community building. Finally, the MetaEstate system manages real estate ownership using NFTs, ensuring secure transactions.For example, AI can use blockchain technology to issue real estate ownership as NFTs, ensuring transparency and security in transactions. This allows users to trade virtual real estate with peace of mind. As a result, the MetaEstate system can propose, design, analyze, facilitate interaction with, and manage ownership of virtual land and buildings that are optimally suited to the user's purpose and budget.

[0029] The MetaEstate system according to this embodiment comprises a proposal unit, a support unit, an analysis unit, a promotion unit, and a management unit. The proposal unit proposes the most suitable virtual land and buildings according to the user's purpose and budget. For example, the proposal unit proposes the most suitable virtual land and buildings based on the user's purpose and budget. The proposal unit can use AI to propose the most suitable virtual land and buildings based on the user's purpose and budget. The support unit supports the design and addition of functions to the virtual space. For example, the support unit automatically generates a virtual space design that matches the user's requests. The support unit can use AI to automatically generate a virtual space design that matches the user's requests. The analysis unit analyzes the value and future potential of virtual real estate using data. For example, the analysis unit analyzes big data to predict the value and future potential of virtual real estate. The analysis unit can use AI to analyze big data to predict the value and future potential of virtual real estate. The promotion unit promotes interaction with nearby users and the holding of events. For example, the promotion unit analyzes the interests and concerns of nearby users and proposes events that connect users with common hobbies. The promotion department can use AI to analyze the interests and preferences of nearby users and propose events that connect users with shared hobbies. The management department manages ownership of virtual real estate using NFTs. The management department can, for example, use blockchain technology to issue ownership of real estate as NFTs, ensuring transparency and security of transactions. The management department can use AI and blockchain technology to issue ownership of real estate as NFTs, ensuring transparency and security of transactions. As a result, the MetaEstate system according to this embodiment can propose, design, analyze, facilitate interaction, and manage ownership of virtual land and buildings that are optimal for the user's purpose and budget.

[0030] The proposal department suggests the most suitable virtual land and buildings based on the user's purpose and budget. Specifically, the proposal department uses AI to select the best candidates from a vast virtual real estate database based on the purpose and budget entered by the user. The AI ​​learns the user's past preferences and behavioral history to suggest the virtual land and buildings that are most suitable for each individual user. For example, for commercial purposes, it considers accessibility and the ability of surrounding virtual stores to attract customers, while for residential purposes, it emphasizes a quiet environment and good scenery. The proposal department also provides detailed information on the virtual land and buildings desired by the user, allowing them to visually confirm it through 3D models and virtual tours. This allows users to have an experience as if they were actually visiting the location, enabling them to make a more satisfying choice. Furthermore, the proposal department collects user feedback and continuously improves the accuracy of its suggestions. For example, when a user evaluates the suggested virtual land and buildings, the AI ​​learns from that evaluation and incorporates it into future suggestions. This allows the proposal department to respond more accurately to the user's needs.

[0031] The support department assists with the design and addition of functions to virtual spaces. Specifically, the support department uses AI to automatically generate virtual space designs based on user requests. The AI ​​learns the design style and functions desired by the user and generates the optimal design based on that. For example, if a user requests a modern design, the AI ​​selects the most suitable design elements from a database of modern architectural styles and reflects them in the virtual space. The support department also assists in incorporating functions and facilities that users want to add into the virtual space. For example, if a user wants to add a new room or garden to the virtual space, the AI ​​proposes the optimal layout and design according to the request and automatically reflects it in the virtual space. Furthermore, the support department provides a function for users to share their designed virtual spaces with other users. This allows users to show their designs to other users and receive feedback. The support department provides flexible design support tailored to user needs and assists in the creation of virtual spaces.

[0032] The analytics department analyzes the value and future potential of virtual real estate using data. Specifically, the analytics department analyzes big data to predict the value and future potential of virtual real estate. AI analyzes past transaction data, market trends, and user behavior data to predict the current and future value of virtual real estate. For example, it predicts how much a particular virtual plot of land will be worth in the future and what factors will influence that value. The analytics department also provides indicators for evaluating the value of virtual real estate, making it easier for users to understand its value. For example, it evaluates the location of a virtual plot of land, the surrounding development status, and accessibility, and calculates the value based on these factors. Furthermore, the analytics department conducts simulations to evaluate the future potential of virtual real estate. For example, it simulates how the value of the surrounding area will change if a new facility is built on a particular virtual plot of land, and provides this simulation to the user. This allows users to understand the value and future potential of virtual real estate and make more appropriate investment decisions. The analytics department provides accurate, data-driven valuations and future predictions to support user decision-making.

[0033] The Promotion Department facilitates interaction and event hosting among nearby users. Specifically, it analyzes the interests and preferences of nearby users and proposes events that connect users with shared hobbies. AI analyzes user profiles and behavioral history to identify users with common hobbies and interests. For example, it proposes events tailored to user interests, such as sports events or art exhibitions in virtual space, and encourages participation. The Promotion Department also provides communication tools to promote interaction among users. For example, it enables real-time communication between users through chat and video call functions. Furthermore, the Promotion Department supports users in planning and hosting their own events. For example, it provides necessary tools and resources when users plan parties or workshops in virtual space, enabling them to host events smoothly. This allows users to enjoy interaction in virtual space and feel connected as part of a community. The Promotion Department revitalizes interaction among users and supports community building within virtual space.

[0034] The management department will manage virtual property ownership using NFTs. Specifically, the management department will use blockchain technology to issue property ownership as NFTs, ensuring transparency and security of transactions. AI will automatically record ownership management and transaction history to prevent fraudulent transactions. For example, if ownership of virtual property changes, that information will be recorded on the blockchain, and anyone can verify the history. The management department will also issue ownership certificates, allowing users to prove their ownership. Furthermore, the management department will provide a platform to support virtual property transactions. For example, it will simplify the transaction process when users buy or sell virtual property. The management department will also implement security measures to ensure the safety of transactions. For example, it will introduce two-factor authentication during transactions to prevent unauthorized access. This will allow users to trade virtual property with peace of mind. The management department will ensure transparency in ownership management and transactions, thereby gaining the trust of users.

[0035] The proposal unit can analyze the user's past purchase history and select the optimal proposal method. For example, the proposal unit can analyze the characteristics of virtual land previously purchased by the user and propose similar properties. For example, the proposal unit can also make new proposals by referencing the designs of virtual buildings previously purchased by the user. For example, the proposal unit can identify specific trends and patterns from the user's past purchase history and make proposals based on them. This enables optimal proposals based on the user's past purchase history. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's past purchase history data into a generating AI and have the generating AI select the optimal proposal method.

[0036] The suggestion unit can filter suggestions based on the user's current projects and areas of interest. For example, the suggestion unit can prioritize suggesting virtual land and buildings related to the user's current projects. The suggestion unit can also suggest relevant virtual land and buildings based on the user's areas of interest (e.g., art, sports, education, etc.). If the user is interested in a particular theme, the suggestion unit can also suggest virtual land and buildings related to that theme. This enables optimal suggestions based on the user's current projects and areas of interest. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0037] The support unit can adjust the level of detail of the design based on the importance of the virtual space during design generation. For example, the support unit can apply detailed designs to important virtual spaces. For example, the support unit can apply simpler designs to less important virtual spaces. For example, the support unit can provide customizable detailed designs to virtual spaces that are particularly important to the user. This enables the optimal level of design detail according to the importance of the virtual space. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input virtual space importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the design.

[0038] The support unit can apply different design algorithms depending on the category of the virtual space during design generation. For example, the support unit can apply a business-oriented design algorithm to commercial facilities. For example, the support unit can apply a resident-oriented design algorithm to residential areas. For example, the support unit can apply a design algorithm suitable for public use to public facilities. This makes it possible to apply the optimal design algorithm according to the category of the virtual space. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input virtual space category data into a generation AI and have the generation AI execute the application of the design algorithm.

[0039] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of virtual properties during the analysis process. For example, the analysis unit can analyze factors that influence each other by considering the spatial relationships of virtual properties. The analysis unit can also analyze factors that influence each other by considering, for example, the attribute information of the owners of virtual properties. The analysis unit can also analyze factors that influence each other by considering, for example, the market trends of virtual properties. This enables optimal analysis that takes into account the interrelationships of virtual properties. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input virtual property interrelationship data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0040] The analysis unit can perform analysis while considering the attribute information of the virtual property owner. For example, the analysis unit may consider the age and occupation of the virtual property owner. The analysis unit may also consider the past transaction history of the virtual property owner. The analysis unit may also consider the interests and preferences of the virtual property owner. This enables optimal analysis that takes into account the attribute information of the virtual property owner. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the attribute information data of the virtual property owner into a generating AI and have the generating AI perform the analysis.

[0041] The promotion unit can predict the current event by referring to past event data when proposing an event. For example, the promotion unit can predict the current event based on data from successful past events. The promotion unit can also predict the current event by referring to feedback from past event participants. The promotion unit can also predict the current event by analyzing trends in past events. This makes it possible to predict the best event based on past event data. Some or all of the above processing in the promotion unit may be performed using AI, for example, or without AI. For example, the promotion unit can input past event data into a generating AI and have the generating AI perform a prediction of the current event.

[0042] The promotion unit can apply different event proposal methods to each category of virtual space when proposing events. For example, the promotion unit can apply a business-oriented event proposal method to commercial facilities. For example, the promotion unit can apply a resident-oriented event proposal method to residential areas. For example, the promotion unit can apply an event proposal method suitable for public use to public facilities. This makes it possible to apply the optimal event proposal method according to the category of virtual space. Some or all of the above processing in the promotion unit may be performed using AI, for example, or without AI. For example, the promotion unit can input virtual space category data into a generating AI and have the generating AI execute the application of event proposal methods.

[0043] The management department can improve the accuracy of management by considering the interrelationships of virtual properties during management. For example, the management department can manage factors that influence each other by considering the spatial relationships of virtual properties. The management department can also manage factors that influence each other by considering the attribute information of the owners of virtual properties. The management department can also manage factors that influence each other by considering market trends of virtual properties. This enables optimal management that takes into account the interrelationships of virtual properties. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input interrelationship data of virtual properties into a generating AI and have the generating AI perform the task of improving the accuracy of management.

[0044] The management department can perform management while considering the attribute information of the virtual property owner. For example, the management department can perform management while considering the age and occupation of the virtual property owner. The management department can also perform management while considering the past transaction history of the virtual property owner. The management department can also perform management while considering the interests and concerns of the virtual property owner. This enables optimal management that takes into account the attribute information of the virtual property owner. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input the attribute information data of the virtual property owner into a generating AI and have the generating AI perform the management.

[0045] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0046] The proposal unit can analyze the user's past purchase history and select the optimal proposal method. For example, it can analyze the characteristics of virtual land previously purchased by the user and propose similar properties. It can also make new proposals based on the designs of virtual buildings previously purchased by the user. Furthermore, it can identify specific trends and patterns from the user's past purchase history and make proposals based on them. This enables optimal proposals based on the user's past purchase history. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input the user's past purchase history data into a generating AI and have the generating AI select the optimal proposal method.

[0047] The support unit can adjust the level of detail in the design based on the importance of the virtual space during design generation. For example, important virtual spaces can be given detailed designs, while less important virtual spaces can be given simpler designs. Furthermore, customizable detailed designs can be provided for virtual spaces that the user particularly values. This enables the optimal level of design detail according to the importance of the virtual space. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input virtual space importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the design.

[0048] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of virtual properties. For example, it can analyze factors that mutually influence each other by considering the spatial relationships of virtual properties. It can also analyze factors that mutually influence each other by considering the attribute information of the virtual property owners. Furthermore, it can analyze factors that mutually influence each other by considering the market trends of virtual properties. This enables optimal analysis that takes into account the interrelationships of virtual properties. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the interrelationship data of virtual properties into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0049] The promotion unit can predict current events by referring to past event data when proposing events. For example, it can predict current events based on data from past successful events. It can also predict current events by referring to feedback from past event participants. Furthermore, it can predict current events by analyzing trends in past events. This makes it possible to predict optimal events based on past event data. Some or all of the above processing in the promotion unit may be performed using AI, for example, or without AI. For example, the promotion unit can input past event data into a generating AI and have the generating AI perform a prediction of the current event.

[0050] The management department can perform management while considering the attribute information of the virtual property owner. For example, it can consider the age and occupation of the virtual property owner when performing management. It can also consider the virtual property owner's past transaction history when performing management. Furthermore, it can consider the interests and preferences of the virtual property owner when performing management. This enables optimal management that takes into account the attribute information of the virtual property owner. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the attribute information data of the virtual property owner into a generating AI and have the generating AI perform the management.

[0051] The following briefly describes the processing flow for example form 1.

[0052] Step 1: The proposal department suggests the most suitable virtual land and buildings based on the user's objectives and budget. The proposal department can use AI to suggest the most suitable virtual land and buildings based on the user's objectives and budget. Step 2: The support department assists with the design and functional additions of virtual land and buildings proposed by the proposal department. The support department can use AI to automatically generate virtual space designs tailored to the user's requests. Step 3: The Analysis Department analyzes the value and future potential of the virtual space designed by the Support Department using data. The Analysis Department can use AI to analyze big data and predict the value and future potential of virtual real estate. Step 4: The Promotion Department promotes interaction and event hosting among nearby users based on the data analyzed by the Analysis Department. The Promotion Department can use AI to analyze the interests and preferences of nearby users and propose events that connect users with shared hobbies. Step 5: The Management Department manages virtual property ownership using NFTs based on interactions and events facilitated by the Promotion Department. The Management Department can use blockchain technology to issue property ownership as NFTs, ensuring transparency and security of transactions.

[0053] (Example of form 2) The MetaEstate system according to an embodiment of the present invention is an AI agent system that supports the buying, selling, and development of virtual real estate within the metaverse. The MetaEstate system allows users to find optimal virtual land and buildings with the help of AI. The MetaEstate system enables users to design and develop their own virtual spaces with AI support. The MetaEstate system analyzes the value and future potential of virtual real estate using data and performs investment analysis. The MetaEstate system also includes community building functions that promote interaction and event hosting among nearby users. The MetaEstate system manages real estate ownership using NFTs, ensuring secure transactions. For example, the MetaEstate system allows users to find optimal virtual land and buildings with the help of AI. In this process, the AI ​​proposes the most suitable properties according to the user's objectives and budget. For example, if an investor is looking for a property with high expected returns, the AI ​​analyzes past data and market trends to recommend the most suitable property. Next, the MetaEstate system allows users to design and develop their own virtual spaces with AI support. For example, if a creator wants to create a virtual building with their own unique design, the AI ​​automatically generates a design that meets the user's requirements. This allows users to easily create virtual spaces. Furthermore, the MetaEstate system analyzes the value and future potential of virtual real estate using data to perform investment analysis. For example, AI can analyze big data to predict the future value of a specific virtual plot of land. This allows investors to invest in virtual real estate with confidence. The MetaEstate system also has community-building features that promote interaction and event hosting among nearby users. For example, AI can analyze the interests and preferences of nearby users and suggest events that connect users with shared hobbies. This increases interaction among users and fosters community building. Finally, the MetaEstate system manages real estate ownership using NFTs, ensuring secure transactions.For example, AI can use blockchain technology to issue real estate ownership as NFTs, ensuring transparency and security in transactions. This allows users to trade virtual real estate with peace of mind. As a result, the MetaEstate system can propose, design, analyze, facilitate interaction with, and manage ownership of virtual land and buildings that are optimally suited to the user's purpose and budget.

[0054] The MetaEstate system according to this embodiment comprises a proposal unit, a support unit, an analysis unit, a promotion unit, and a management unit. The proposal unit proposes the most suitable virtual land and buildings according to the user's purpose and budget. For example, the proposal unit proposes the most suitable virtual land and buildings based on the user's purpose and budget. The proposal unit can use AI to propose the most suitable virtual land and buildings based on the user's purpose and budget. The support unit supports the design and addition of functions to the virtual space. For example, the support unit automatically generates a virtual space design that matches the user's requests. The support unit can use AI to automatically generate a virtual space design that matches the user's requests. The analysis unit analyzes the value and future potential of virtual real estate using data. For example, the analysis unit analyzes big data to predict the value and future potential of virtual real estate. The analysis unit can use AI to analyze big data to predict the value and future potential of virtual real estate. The promotion unit promotes interaction with nearby users and the holding of events. For example, the promotion unit analyzes the interests and concerns of nearby users and proposes events that connect users with common hobbies. The promotion department can use AI to analyze the interests and preferences of nearby users and propose events that connect users with shared hobbies. The management department manages ownership of virtual real estate using NFTs. The management department can, for example, use blockchain technology to issue ownership of real estate as NFTs, ensuring transparency and security of transactions. The management department can use AI and blockchain technology to issue ownership of real estate as NFTs, ensuring transparency and security of transactions. As a result, the MetaEstate system according to this embodiment can propose, design, analyze, facilitate interaction, and manage ownership of virtual land and buildings that are optimal for the user's purpose and budget.

[0055] The proposal department suggests the most suitable virtual land and buildings based on the user's purpose and budget. Specifically, the proposal department uses AI to select the best candidates from a vast virtual real estate database based on the purpose and budget entered by the user. The AI ​​learns the user's past preferences and behavioral history to suggest the virtual land and buildings that are most suitable for each individual user. For example, for commercial purposes, it considers accessibility and the ability of surrounding virtual stores to attract customers, while for residential purposes, it emphasizes a quiet environment and good scenery. The proposal department also provides detailed information on the virtual land and buildings desired by the user, allowing them to visually confirm it through 3D models and virtual tours. This allows users to have an experience as if they were actually visiting the location, enabling them to make a more satisfying choice. Furthermore, the proposal department collects user feedback and continuously improves the accuracy of its suggestions. For example, when a user evaluates the suggested virtual land and buildings, the AI ​​learns from that evaluation and incorporates it into future suggestions. This allows the proposal department to respond more accurately to the user's needs.

[0056] The support department assists with the design and addition of functions to virtual spaces. Specifically, the support department uses AI to automatically generate virtual space designs based on user requests. The AI ​​learns the design style and functions desired by the user and generates the optimal design based on that. For example, if a user requests a modern design, the AI ​​selects the most suitable design elements from a database of modern architectural styles and reflects them in the virtual space. The support department also assists in incorporating functions and facilities that users want to add into the virtual space. For example, if a user wants to add a new room or garden to the virtual space, the AI ​​proposes the optimal layout and design according to the request and automatically reflects it in the virtual space. Furthermore, the support department provides a function for users to share their designed virtual spaces with other users. This allows users to show their designs to other users and receive feedback. The support department provides flexible design support tailored to user needs and assists in the creation of virtual spaces.

[0057] The analytics department analyzes the value and future potential of virtual real estate using data. Specifically, the analytics department analyzes big data to predict the value and future potential of virtual real estate. AI analyzes past transaction data, market trends, and user behavior data to predict the current and future value of virtual real estate. For example, it predicts how much a particular virtual plot of land will be worth in the future and what factors will influence that value. The analytics department also provides indicators for evaluating the value of virtual real estate, making it easier for users to understand its value. For example, it evaluates the location of a virtual plot of land, the surrounding development status, and accessibility, and calculates the value based on these factors. Furthermore, the analytics department conducts simulations to evaluate the future potential of virtual real estate. For example, it simulates how the value of the surrounding area will change if a new facility is built on a particular virtual plot of land, and provides this simulation to the user. This allows users to understand the value and future potential of virtual real estate and make more appropriate investment decisions. The analytics department provides accurate, data-driven valuations and future predictions to support user decision-making.

[0058] The Promotion Department facilitates interaction and event hosting among nearby users. Specifically, it analyzes the interests and preferences of nearby users and proposes events that connect users with shared hobbies. AI analyzes user profiles and behavioral history to identify users with common hobbies and interests. For example, it proposes events tailored to user interests, such as sports events or art exhibitions in virtual space, and encourages participation. The Promotion Department also provides communication tools to promote interaction among users. For example, it enables real-time communication between users through chat and video call functions. Furthermore, the Promotion Department supports users in planning and hosting their own events. For example, it provides necessary tools and resources when users plan parties or workshops in virtual space, enabling them to host events smoothly. This allows users to enjoy interaction in virtual space and feel connected as part of a community. The Promotion Department revitalizes interaction among users and supports community building within virtual space.

[0059] The management department will manage virtual property ownership using NFTs. Specifically, the management department will use blockchain technology to issue property ownership as NFTs, ensuring transparency and security of transactions. AI will automatically record ownership management and transaction history to prevent fraudulent transactions. For example, if ownership of virtual property changes, that information will be recorded on the blockchain, and anyone can verify the history. The management department will also issue ownership certificates, allowing users to prove their ownership. Furthermore, the management department will provide a platform to support virtual property transactions. For example, it will simplify the transaction process when users buy or sell virtual property. The management department will also implement security measures to ensure the safety of transactions. For example, it will introduce two-factor authentication during transactions to prevent unauthorized access. This will allow users to trade virtual property with peace of mind. The management department will ensure transparency in ownership management and transactions, thereby gaining the trust of users.

[0060] The suggestion unit can estimate the user's emotions and adjust the types of virtual land and buildings it suggests based on those emotions. For example, if the user is relaxed, the suggestion unit might suggest a resort or a virtual land with abundant nature. If the user is excited, the suggestion unit might suggest an entertainment facility or an amusement park. If the user is stressed, the suggestion unit might suggest a quiet residential area or a virtual land with a relaxation facility. This enables the suggestion of the most suitable virtual land and buildings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the suggestions based on the emotions.

[0061] The proposal unit can analyze the user's past purchase history and select the optimal proposal method. For example, the proposal unit can analyze the characteristics of virtual land previously purchased by the user and propose similar properties. For example, the proposal unit can also make new proposals by referencing the designs of virtual buildings previously purchased by the user. For example, the proposal unit can identify specific trends and patterns from the user's past purchase history and make proposals based on them. This enables optimal proposals based on the user's past purchase history. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's past purchase history data into a generating AI and have the generating AI select the optimal proposal method.

[0062] The suggestion unit can filter suggestions based on the user's current projects and areas of interest. For example, the suggestion unit can prioritize suggesting virtual land and buildings related to the user's current projects. The suggestion unit can also suggest relevant virtual land and buildings based on the user's areas of interest (e.g., art, sports, education, etc.). If the user is interested in a particular theme, the suggestion unit can also suggest virtual land and buildings related to that theme. This enables optimal suggestions based on the user's current projects and areas of interest. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0063] The support unit can estimate the user's emotions and adjust the design expression based on the estimated emotions. For example, if the user is relaxed, the support unit may suggest calm colors and designs. If the user is excited, the support unit may suggest vibrant colors and dynamic designs. If the user is stressed, the support unit may suggest simple and visually calming designs. This enables the optimal design expression 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 support unit may be performed using AI, for example, or without AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI adjust the design expression.

[0064] The support unit can adjust the level of detail of the design based on the importance of the virtual space during design generation. For example, the support unit can apply detailed designs to important virtual spaces. For example, the support unit can apply simpler designs to less important virtual spaces. For example, the support unit can provide customizable detailed designs to virtual spaces that are particularly important to the user. This enables the optimal level of design detail according to the importance of the virtual space. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input virtual space importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the design.

[0065] The support unit can apply different design algorithms depending on the category of the virtual space during design generation. For example, the support unit can apply a business-oriented design algorithm to commercial facilities. For example, the support unit can apply a resident-oriented design algorithm to residential areas. For example, the support unit can apply a design algorithm suitable for public use to public facilities. This makes it possible to apply the optimal design algorithm according to the category of the virtual space. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input virtual space category data into a generation AI and have the generation AI execute the application of the design algorithm.

[0066] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. For example, if the user is excited, the analysis unit can provide visually stimulating analysis results. This allows for the adjustment of the analysis criteria to the optimal level 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-described processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the analysis criteria.

[0067] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of virtual properties during the analysis process. For example, the analysis unit can analyze factors that influence each other by considering the spatial relationships of virtual properties. The analysis unit can also analyze factors that influence each other by considering, for example, the attribute information of the owners of virtual properties. The analysis unit can also analyze factors that influence each other by considering, for example, the market trends of virtual properties. This enables optimal analysis that takes into account the interrelationships of virtual properties. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input virtual property interrelationship data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0068] The analysis unit can perform analysis while considering the attribute information of the virtual property owner. For example, the analysis unit may consider the age and occupation of the virtual property owner. The analysis unit may also consider the past transaction history of the virtual property owner. The analysis unit may also consider the interests and preferences of the virtual property owner. This enables optimal analysis that takes into account the attribute information of the virtual property owner. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the attribute information data of the virtual property owner into a generating AI and have the generating AI perform the analysis.

[0069] The facilitator can estimate the user's emotions and adjust how events are displayed based on the estimated emotions. For example, if the user is relaxed, the facilitator can display events with calming colors and designs. If the user is excited, the facilitator can also display events with vibrant colors and dynamic designs. If the user is stressed, the facilitator can also display events with a simple and visually calming design. This enables the optimal display method of events 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 facilitator may be performed using AI, for example, or without AI. For example, the facilitator can input user emotion data into the generative AI and have the generative AI adjust how events are displayed.

[0070] The promotion unit can predict the current event by referring to past event data when proposing an event. For example, the promotion unit can predict the current event based on data from successful past events. The promotion unit can also predict the current event by referring to feedback from past event participants. The promotion unit can also predict the current event by analyzing trends in past events. This makes it possible to predict the best event based on past event data. Some or all of the above processing in the promotion unit may be performed using AI, for example, or without AI. For example, the promotion unit can input past event data into a generating AI and have the generating AI perform a prediction of the current event.

[0071] The promotion unit can apply different event proposal methods to each category of virtual space when proposing events. For example, the promotion unit can apply a business-oriented event proposal method to commercial facilities. For example, the promotion unit can apply a resident-oriented event proposal method to residential areas. For example, the promotion unit can apply an event proposal method suitable for public use to public facilities. This makes it possible to apply the optimal event proposal method according to the category of virtual space. Some or all of the above processing in the promotion unit may be performed using AI, for example, or without AI. For example, the promotion unit can input virtual space category data into a generating AI and have the generating AI execute the application of event proposal methods.

[0072] The management unit can estimate the user's emotions and determine the priority of NFTs to manage based on the estimated emotions. For example, if the user is relaxed, the management unit can prioritize relaxing NFTs. If the user is excited, the management unit can also prioritize entertaining NFTs. If the user is stressed, the management unit can also prioritize relaxation NFTs. This enables optimal NFT management according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, or not using AI. For example, the management unit can input user emotion data into a generative AI and have the generative AI determine the priority of NFTs.

[0073] The management department can improve the accuracy of management by considering the interrelationships of virtual properties during management. For example, the management department can manage factors that influence each other by considering the spatial relationships of virtual properties. The management department can also manage factors that influence each other by considering the attribute information of the owners of virtual properties. The management department can also manage factors that influence each other by considering market trends of virtual properties. This enables optimal management that takes into account the interrelationships of virtual properties. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input interrelationship data of virtual properties into a generating AI and have the generating AI perform the task of improving the accuracy of management.

[0074] The management department can perform management while considering the attribute information of the virtual property owner. For example, the management department can perform management while considering the age and occupation of the virtual property owner. The management department can also perform management while considering the past transaction history of the virtual property owner. The management department can also perform management while considering the interests and concerns of the virtual property owner. This enables optimal management that takes into account the attribute information of the virtual property owner. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input the attribute information data of the virtual property owner into a generating AI and have the generating AI perform the management.

[0075] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0076] The suggestion unit can estimate the user's emotions and adjust the types of virtual land and buildings it suggests based on those emotions. For example, if the user is relaxed, it can suggest a resort or a virtual land with abundant nature. If the user is excited, it can suggest a virtual land with entertainment facilities or an amusement park. Furthermore, if the user is stressed, it can suggest a virtual land with a quiet residential area or a relaxation facility. This makes it possible to suggest the most suitable virtual land and buildings 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the suggestions based on the emotions.

[0077] The support unit can estimate the user's emotions and adjust the design expression based on the estimated emotions. For example, if the user is relaxed, it can suggest calm colors and designs. If the user is excited, it can suggest vibrant colors and dynamic designs. Furthermore, if the user is stressed, it can suggest simple and visually calming designs. This enables the expression of the design to be optimally tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI adjust the design expression.

[0078] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results. Furthermore, if the user is excited, it can provide visually stimulating analysis results. This allows for the adjustment of the analysis criteria to the optimal level 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-described processes in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the analysis criteria.

[0079] The facilitator can estimate the user's emotions and adjust how events are displayed based on those emotions. For example, if the user is relaxed, events can be displayed with calming colors and designs. If the user is excited, events can be displayed with vibrant colors and dynamic designs. Furthermore, if the user is stressed, events can be displayed with simple and visually calming designs. This enables the optimal display of events according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the facilitator may be performed using AI, or not. For example, the facilitator can input user emotion data into the generative AI and have the generative AI adjust how events are displayed.

[0080] The management unit can estimate the user's emotions and determine the priority of NFTs to manage based on the estimated emotions. For example, if the user is relaxed, relaxing NFTs can be prioritized. If the user is excited, entertaining NFTs can be prioritized. Furthermore, if the user is stressed, relaxation NFTs can be prioritized. This enables optimal NFT management 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 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 management unit may be performed using AI, or not using AI. For example, the management unit can input user emotion data into a generative AI and have the generative AI determine the priority of NFTs.

[0081] The proposal unit can analyze the user's past purchase history and select the optimal proposal method. For example, it can analyze the characteristics of virtual land previously purchased by the user and propose similar properties. It can also make new proposals based on the designs of virtual buildings previously purchased by the user. Furthermore, it can identify specific trends and patterns from the user's past purchase history and make proposals based on them. This enables optimal proposals based on the user's past purchase history. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input the user's past purchase history data into a generating AI and have the generating AI select the optimal proposal method.

[0082] The support unit can adjust the level of detail in the design based on the importance of the virtual space during design generation. For example, important virtual spaces can be given detailed designs, while less important virtual spaces can be given simpler designs. Furthermore, customizable detailed designs can be provided for virtual spaces that the user particularly values. This enables the optimal level of design detail according to the importance of the virtual space. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input virtual space importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the design.

[0083] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of virtual properties. For example, it can analyze factors that mutually influence each other by considering the spatial relationships of virtual properties. It can also analyze factors that mutually influence each other by considering the attribute information of the virtual property owners. Furthermore, it can analyze factors that mutually influence each other by considering the market trends of virtual properties. This enables optimal analysis that takes into account the interrelationships of virtual properties. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the interrelationship data of virtual properties into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0084] The promotion unit can predict current events by referring to past event data when proposing events. For example, it can predict current events based on data from past successful events. It can also predict current events by referring to feedback from past event participants. Furthermore, it can predict current events by analyzing trends in past events. This makes it possible to predict optimal events based on past event data. Some or all of the above processing in the promotion unit may be performed using AI, for example, or without AI. For example, the promotion unit can input past event data into a generating AI and have the generating AI perform a prediction of the current event.

[0085] The management department can perform management while considering the attribute information of the virtual property owner. For example, it can consider the age and occupation of the virtual property owner when performing management. It can also consider the virtual property owner's past transaction history when performing management. Furthermore, it can consider the interests and preferences of the virtual property owner when performing management. This enables optimal management that takes into account the attribute information of the virtual property owner. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the attribute information data of the virtual property owner into a generating AI and have the generating AI perform the management.

[0086] The following briefly describes the processing flow for example form 2.

[0087] Step 1: The proposal department suggests the most suitable virtual land and buildings based on the user's objectives and budget. The proposal department can use AI to suggest the most suitable virtual land and buildings based on the user's objectives and budget. Step 2: The support department assists with the design and functional additions of virtual land and buildings proposed by the proposal department. The support department can use AI to automatically generate virtual space designs tailored to the user's requests. Step 3: The Analysis Department analyzes the value and future potential of the virtual space designed by the Support Department using data. The Analysis Department can use AI to analyze big data and predict the value and future potential of virtual real estate. Step 4: The Promotion Department promotes interaction and event hosting among nearby users based on the data analyzed by the Analysis Department. The Promotion Department can use AI to analyze the interests and preferences of nearby users and propose events that connect users with shared hobbies. Step 5: The Management Department manages virtual property ownership using NFTs based on interactions and events facilitated by the Promotion Department. The Management Department can use blockchain technology to issue property ownership as NFTs, ensuring transparency and security of transactions.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] Each of the multiple elements described above, including the proposal unit, support unit, analysis unit, promotion unit, and management unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the proposal unit is implemented by the control unit 46A of the smart device 14 and proposes the optimal virtual land or building based on the user's purpose and budget. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically generates a virtual space design tailored to the user's requests. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes big data to predict the value and future potential of virtual real estate. The promotion unit is implemented by, for example, the control unit 46A of the smart device 14 and analyzes the interests and concerns of nearby users and proposes events that connect users with common hobbies. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses blockchain technology to issue ownership of real estate as NFTs, ensuring transparency and security of transactions. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0092] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] Each of the multiple elements described above, including the proposal unit, support unit, analysis unit, promotion unit, and management unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the proposal unit is implemented by the control unit 46A of the smart glasses 214 and proposes the optimal virtual land or building based on the user's purpose and budget. The support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and automatically generates a virtual space design tailored to the user's requests. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes big data to predict the value and future potential of virtual real estate. The promotion unit is implemented, for example, by the control unit 46A of the smart glasses 214 and analyzes the interests and concerns of nearby users and proposes events that connect users with common hobbies. The management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and uses blockchain technology to issue ownership of real estate as NFTs, ensuring transparency and security of transactions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0108] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] Each of the multiple elements described above, including the proposal unit, support unit, analysis unit, promotion unit, and management unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the proposal unit is implemented by the control unit 46A of the headset terminal 314 and proposes the optimal virtual land or building based on the user's purpose and budget. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically generates a virtual space design tailored to the user's requests. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes big data to predict the value and future potential of virtual real estate. The promotion unit is implemented by, for example, the control unit 46A of the headset terminal 314 and analyzes the interests and concerns of nearby users and proposes events that connect users with common hobbies. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses blockchain technology to issue ownership of real estate as NFTs, ensuring transparency and security of transactions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0124] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the proposal unit, support unit, analysis unit, promotion unit, and management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the proposal unit is implemented by the control unit 46A of the robot 414 and proposes the optimal virtual land or building based on the user's purpose and budget. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically generates a virtual space design tailored to the user's requests. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes big data to predict the value and future potential of virtual real estate. The promotion unit is implemented by, for example, the control unit 46A of the robot 414 and analyzes the interests and concerns of nearby users and proposes events that connect users with common hobbies. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses blockchain technology to issue ownership of real estate as NFTs, ensuring transparency and security of transactions. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] (Note 1) The proposal department proposes the most suitable virtual land and buildings according to the user's purpose and budget, The aforementioned proposal department provides support for the design and functional additions of virtual land and buildings proposed by the proposal department, The analysis department analyzes the value and future potential of the virtual space designed by the aforementioned support department using data, Based on the data analyzed by the aforementioned analysis unit, a promotion unit promotes interaction with nearby users and the holding of events. The system includes a management unit that manages ownership of virtual real estate using NFTs based on the interactions and events facilitated by the aforementioned promotion unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, It estimates the user's emotions and adjusts the types of virtual land and buildings suggested based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Analyze the user's past purchase history and select the most suitable suggestion method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, When making suggestions, filter them based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit, It estimates the user's emotions and adjusts the design's presentation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit, During design generation, adjust the level of detail in the design based on its importance in the virtual space. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned support unit, When generating designs, different design algorithms are applied depending on the category of the virtual space. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is During analysis, consider the interrelationships of virtual properties to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is During the analysis, the attribute information of the virtual property owners will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned promotion unit is It estimates the user's emotions and adjusts how events are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned promotion unit is When proposing an event, we predict the current event by referring to past event data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned promotion unit is When proposing events, different event proposal methods will be applied to each category of virtual space. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned management department, It estimates the user's emotions and determines the priority of NFTs to manage based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned management department, During management, consider the interrelationships of virtual properties to improve the accuracy of management. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned management department, During management, the attribute information of the virtual property owner is taken into consideration. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0160] 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. The proposal department proposes the most suitable virtual land and buildings according to the user's purpose and budget, The aforementioned proposal department provides support for the design and functional additions of virtual land and buildings proposed by the proposal department, The analysis department analyzes the value and future potential of the virtual space designed by the aforementioned support department using data, Based on the data analyzed by the aforementioned analysis unit, a promotion unit promotes interaction with nearby users and the holding of events. The system includes a management unit that manages ownership of virtual real estate using NFTs based on the interactions and events facilitated by the aforementioned promotion unit. A system characterized by the following features.

2. The aforementioned proposal section is, It estimates the user's emotions and adjusts the types of virtual land and buildings suggested based on those estimated emotions. The system according to feature 1.

3. The aforementioned proposal section is, Analyze the user's past purchase history and select the most suitable suggestion method. The system according to feature 1.

4. The aforementioned proposal section is, When making suggestions, filter them based on the user's current projects and areas of interest. The system according to feature 1.

5. The aforementioned support unit, It estimates the user's emotions and adjusts the design's presentation based on those estimated emotions. The system according to feature 1.

6. The aforementioned support unit, During design generation, adjust the level of detail in the design based on its importance in the virtual space. The system according to feature 1.

7. The aforementioned support unit, When generating designs, different design algorithms are applied depending on the category of the virtual space. The system according to feature 1.

8. The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system according to feature 1.

9. The aforementioned analysis unit is During analysis, consider the interrelationships of virtual properties to improve the accuracy of the analysis. The system according to feature 1.

10. The aforementioned analysis unit is During the analysis, the attribute information of the virtual property owners will be taken into consideration. The system according to feature 1.

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