system

Abstract

The system according to this embodiment aims to generate a 3D model of a building in real time based on the user's design drawings and requests, and to enable users to experience it in a VR environment. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a navigation unit. The reception unit receives input from the user, such as design drawings and requests. The generation unit generates a 3D model of the building in real time based on the information received by the reception unit. The navigation unit enables the user to experience the 3D model generated by the generation unit in a VR environment.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the process of generating a 3D model of a building based on a user's design drawing or requirements and experiencing it in a VR environment is not performed in real time, and there is room for improvement.

[0005] The system according to the embodiment aims to generate a 3D model of a building in real time based on a user's design drawing or requirements and enable it to be experienced in a VR environment.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, and a navigation unit. The reception unit receives input from the user, such as design drawings and requests. The generation unit generates a 3D model of the building in real time based on the information received by the reception unit. The navigation unit enables users to experience the 3D model generated by the generation unit in a VR environment. [Effects of the Invention]

[0007] The system according to this embodiment can generate a 3D model of a building in real time based on the user's design drawings and requests, and make it available for experience in a VR environment. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that uses an AI agent to generate a 3D model of a building in real time based on design drawings and requests provided by the user, and makes it available for experience in a VR environment. This system allows users to adjust the interior and layout of their home, and changes are reflected immediately, allowing them to experience the actual space before design. For example, the user inputs design drawings and requests. Next, the AI ​​agent analyzes the input information and generates a 3D model of the building in real time. The generated 3D model can then be experienced in a VR environment. Furthermore, the AI ​​agent updates the architectural model in real time based on user feedback and generates optimal design proposals. This mechanism reduces the time and cost associated with design changes, improving client satisfaction. It also speeds up project approval, leading to increased efficiency in architectural projects. This AI agent is extremely useful for families considering new construction or renovation, real estate developers collaborating with architects and designers, and individuals interested in interior design. The innovation of combining AI and VR enables dynamic environmental adjustments based on user input, and also provides cloud-based data management and easy access. This allows AI-powered systems to generate 3D models in real time based on user-provided blueprints and requests, making them available for experience in a VR environment.

[0029] The system according to this embodiment comprises a reception unit, a generation unit, and a navigation unit. The reception unit receives input of the user's design plans and requests. The reception unit provides, for example, an interface for the user to input design plans and requests. The reception unit allows the user to input design plans and requests in text format. The reception unit also allows the user to input design plans and requests using voice input. For example, the reception unit uses speech recognition technology to convert the user's voice input into text data. The generation unit generates a 3D model of the building in real time based on the information received by the reception unit. The generation unit uses, for example, an AI agent to analyze the user's design plans and requests and generate a 3D model. The generation unit can generate a detailed 3D model using the latest CG technology. For example, the generation unit generates a high-resolution 3D model so that the user can examine the details. The navigation unit allows the 3D model generated by the generation unit to be experienced in a VR environment. The navigation unit allows, for example, the user to wear a VR headset and check the interior and layout of their home in a virtual space. The navigation unit allows the user to move freely in the virtual space and change the interior and layout. For example, the navigation unit allows the user to move around in the virtual space using a VR controller and change the interior and layout. This enables the system according to the embodiment to generate a 3D model in real time based on the user's design drawings and requests, and make it available for experience in the VR environment.

[0030] The reception desk accepts user input of design plans and requests. For example, the reception desk provides an interface for users to input design plans and requests. Specifically, the reception desk features a user-friendly graphical user interface (GUI) designed for intuitive operation. Users can utilize file selection buttons for uploading design plans and text boxes for entering requests. The reception desk also allows users to input design plans and requests using voice input. For example, the reception desk uses speech recognition technology to convert user voice input into text data. This speech recognition technology incorporates natural language processing (NLP) algorithms to accurately analyze and transcribe user speech. Furthermore, the reception desk automatically saves user-inputted data, allowing for re-editing and modification as needed. This makes it easy for users to make changes or additions during the design process. The reception desk transmits user input data to a secure server and uses encryption technology to ensure data protection and privacy. This minimizes the risk of sensitive user information being leaked to third parties.

[0031] The generation unit generates a 3D model of the building in real time based on information received by the reception unit. For example, the generation unit uses an AI agent to analyze user design drawings and requirements and generate a 3D model. Specifically, the generation unit utilizes deep learning technology to analyze user input data and automatically generate the building's structure and design. The AI ​​agent learns from past design data and regulatory information such as building codes, enabling it to propose the optimal design according to the user's requirements. The generation unit can generate detailed 3D models using the latest CG technology. For example, it can generate high-resolution 3D models, allowing users to examine even the smallest details. The generated 3D model is updated in real time, and any changes or modifications entered by the user are immediately reflected. Furthermore, the generation unit incorporates a physics engine, enabling it to simulate the structural stability and durability of the building. This allows users to verify the building's safety during the design phase. The generation unit saves the generated 3D model to a cloud server, allowing users to access it at any time. The generation unit also includes collaboration features, enabling multiple users to access and design collaboratively simultaneously. This enables the generation unit to efficiently and accurately generate 3D models, supporting the user's design process.

[0032] The navigation unit enables users to experience 3D models generated by the generation unit in a VR environment. For example, the navigation unit allows users to wear a VR headset and view the interior and layout of their home in a virtual space. Specifically, the navigation unit works in conjunction with the VR headset, allowing users to freely move around the virtual space and change the interior and layout. The navigation unit allows users to move around the virtual space and change the interior and layout using VR controllers. For example, users can use VR controllers to rearrange furniture or change wall colors. The navigation unit reflects user actions in real time and instantly displays changes in the virtual space. Furthermore, the navigation unit has a function that allows users to record their virtual space experience and play it back later. This allows users to review changes and modifications made during the design process and use them as a reference when deciding on the final design. The navigation unit also has multi-user support, allowing multiple users to collaborate in the virtual space simultaneously. This enables users to communicate in real time with other users in remote locations and collaborate on the design. The navigation system provides high-resolution graphics and realistic sound effects to enhance the user experience. This allows users to feel as if they are actually in the space they are in.

[0033] The system includes an update unit that updates the architectural model in real time based on user feedback. For example, when a user changes the interior or layout within the VR environment, the update unit reflects those changes in real time. The update unit uses an AI agent to analyze user feedback and update the architectural model. For instance, when a user changes the layout of the interior, the update unit instantly reflects those changes and updates the 3D model. The update unit can also optimize the design and layout of the architectural model based on user feedback. For example, it generates optimal design suggestions based on user feedback. This allows the architectural model to be updated in real time based on user feedback.

[0034] The system includes a proposal unit that generates optimal design proposals. This unit, for example, uses an AI agent to analyze the user's design drawings and requirements and generate optimal design proposals. The proposal unit provides optimal design proposals considering the user's requirements and constraints. For example, it proposes the optimal interior design considering the user's budget and space constraints. The proposal unit can also provide customized design proposals based on the user's preferences. For example, it generates optimal design proposals based on the user's past preferences and feedback. This allows the system to provide the user with the most suitable design proposal.

[0035] The system includes a data management unit that enables cloud-based data management and easy access. The data management unit manages data such as user blueprints, requirements, and feedback in the cloud. It uses cloud-based storage to facilitate data storage and access. For example, it allows users to access data via the internet. The data management unit can also provide data backup and restore functions. For example, it regularly backs up data to ensure data security. This facilitates cloud-based data management and access.

[0036] The generation unit can generate detailed 3D models using the latest CG technology. For example, it can generate high-resolution 3D models, allowing users to examine details. The generation unit can reproduce realistic textures and light reflections using the latest rendering technology. For example, it can reproduce realistic textures using physically based rendering technology. The generation unit can reproduce complex shapes and details using the latest modeling technology. For example, it can reproduce complex shapes using sculpting technology. This allows for the generation of detailed 3D models using the latest CG technology.

[0037] The navigation unit allows users to wear a VR headset and view the interior and layout of their home in a virtual space. For example, the navigation unit allows users to freely move around the virtual space while wearing a VR headset and view the interior and layout. The navigation unit also allows users to move around the virtual space using VR controllers and change the interior and layout. For example, the navigation unit allows users to change the placement of furniture using VR controllers and reflects those changes in real time. The navigation unit can also provide visual guides for users to view the interior and layout in the virtual space. For example, the navigation unit displays visual guidelines for users to view the placement of furniture in the virtual space. This allows users to wear a VR headset and view the interior and layout of their home in a virtual space.

[0038] The reception desk can analyze the user's past design drawings and requests and suggest the optimal input method. For example, the reception desk can automatically display design drawings and requests that the user has frequently entered in the past as suggestions. The reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk will prioritize suggesting input methods that the user has used in the past. The reception desk can also predict and suggest design drawings and requests that will be used during specific time periods based on the user's past input history. For example, the reception desk will predict and suggest design drawings and requests that will be used during specific time periods based on the user's past input history. This allows the reception desk to suggest the optimal input method based on the user's past history.

[0039] The reception system can filter design drawings and requests based on the user's current projects and areas of interest. For example, the reception system can prioritize displaying design drawings and requests related to the user's current projects. The reception system can also filter and display relevant design drawings and requests based on the user's areas of interest. For example, the reception system can filter and display relevant design drawings and requests based on the user's areas of interest. The reception system can also suggest relevant design drawings and requests based on projects the user has shown interest in in the past. For example, the reception system can suggest relevant design drawings and requests based on projects the user has shown interest in in the past. This allows the system to provide highly relevant information by filtering based on the user's current projects and areas of interest.

[0040] The reception desk can prioritize retrieving highly relevant information based on the user's geographical location when inputting design drawings and requests. For example, the reception desk can prioritize displaying relevant design drawings and requests based on the user's current location. The reception desk can also suggest design drawings and requests related to nearby projects based on the user's geographical location. For example, the reception desk can suggest design drawings and requests related to nearby projects based on the user's geographical location. If the user is interested in a particular region, the reception desk can also prioritize displaying design drawings and requests related to that region. For example, if the user is interested in a particular region, the reception desk will prioritize displaying design drawings and requests related to that region. This allows the system to prioritize retrieving highly relevant information based on the user's geographical location.

[0041] The reception desk can analyze the user's social media activity and obtain relevant information when they input design drawings and requests. For example, the reception desk can suggest relevant design drawings and requests based on projects the user has shared on social media. The reception desk can also analyze the user's social media activity to identify their design interests and trends and display relevant design drawings and requests. For example, the reception desk can analyze the user's social media activity to identify their design interests and trends and display relevant design drawings and requests. The reception desk can also suggest relevant design drawings and requests based on projects by designers and architects the user follows. For example, the reception desk can suggest relevant design drawings and requests based on projects by designers and architects the user follows. This allows the system to obtain relevant information based on the user's social media activity.

[0042] The generation unit can adjust the level of detail of the generated 3D model based on the importance of the design drawing. For example, if the design drawing is important, the generation unit will generate a detailed 3D model. If the design drawing is of low importance, the generation unit can generate a simplified 3D model. For example, if the design drawing is of low importance, the generation unit will generate a simplified 3D model. The generation unit can also adjust the level of detail of the generated 3D model in stages according to the importance of the design drawing. For example, the generation unit adjusts the level of detail of the generated 3D model in stages according to the importance of the design drawing. This allows the level of detail of the 3D model to be adjusted according to the importance of the design drawing.

[0043] The generation unit can apply different generation algorithms depending on the category of the design drawing when generating 3D models. For example, in the case of residential design, the generation unit applies a generation algorithm specifically for residential buildings. For example, in the case of commercial building design, the generation unit applies a generation algorithm specifically for commercial buildings. For example, in the case of public building design, the generation unit applies a generation algorithm specifically for public buildings. This allows for the application of different generation algorithms depending on the category of the design drawing.

[0044] The generation unit can determine the generation priority based on the submission dates of the design drawings when generating 3D models. For example, the generation unit can prioritize the generation of design drawings that are submitted earlier. The generation unit can also postpone the generation of design drawings that are submitted later. For example, the generation unit can postpone the generation of design drawings that are submitted later. The generation unit can also adjust the priority of the 3D models to be generated in stages according to the submission dates. For example, the generation unit can adjust the priority of the 3D models to be generated in stages according to the submission dates. This allows the generation priority to be determined based on the submission dates of the design drawings.

[0045] The generation unit can adjust the generation order based on the relationships between design drawings when generating 3D models. For example, the generation unit can prioritize the generation of design drawings that are highly relevant. The generation unit can also postpone the generation of design drawings that are less relevant. For example, the generation unit can postpone the generation of design drawings that are less relevant. The generation unit can also adjust the order of the 3D models to be generated in stages according to the relationships between the design drawings. For example, the generation unit can adjust the order of the 3D models to be generated in stages according to the relationships between the design drawings. This allows the generation order to be adjusted based on the relationships between the design drawings.

[0046] The navigation unit can select the optimal display method when displaying a VR environment by referring to the user's past operation history. For example, the navigation unit can suggest the optimal display method based on the display methods the user has used in the past. The navigation unit can also suggest a highly visible display method based on the user's past operation history. For example, the navigation unit can suggest a highly visible display method based on the user's past operation history. The navigation unit can also analyze the user's past operation history and suggest the most efficient display method. For example, the navigation unit analyzes the user's past operation history and suggests the most efficient display method. This allows the system to select the optimal display method based on the user's past operation history.

[0047] The navigation unit can customize the displayed content based on the user's current project when displaying the VR environment. For example, the navigation unit prioritizes displaying information related to the project the user is currently working on. The navigation unit can customize the displayed content based on the user's current project. For example, the navigation unit customizes the displayed content based on the user's current project. The navigation unit can also suggest displayed content based on projects the user has shown interest in in the past. For example, the navigation unit suggests displayed content based on projects the user has shown interest in in the past. This allows the displayed content to be customized based on the user's current project.

[0048] The navigation unit can select the optimal display method when displaying a VR environment, taking into account the user's device information. For example, if the user is using a smartphone, the navigation unit can provide a display method that matches the screen size. If the user is using a tablet, the navigation unit can provide a display method optimized for a larger screen. For example, if the user is using a tablet, the navigation unit can provide a display method optimized for a larger screen. If the user is using a smartwatch, the navigation unit can also provide a concise and highly visible display method. For example, if the user is using a smartwatch, the navigation unit can provide a concise and highly visible display method. This allows the system to select the optimal display method based on the user's device information.

[0049] The navigation unit can analyze the user's social media activity when displaying a VR environment and display relevant information. For example, the navigation unit can display relevant information based on projects the user has shared on social media. The navigation unit can also analyze the user's interests in design and trends from their social media activity and display relevant information. For example, the navigation unit can analyze the user's interests in design and trends from their social media activity and display relevant information. The navigation unit can also display relevant information based on projects by designers and architects the user follows. For example, the navigation unit can display relevant information based on projects by designers and architects the user follows. This allows the system to display relevant information based on the user's social media activity.

[0050] The update unit can select the optimal update method when updating the architectural model by referring to the user's past feedback. For example, the update unit can propose the optimal update method based on the feedback the user has provided in the past. The update unit can provide an update method that reflects improvements based on the user's past feedback. For example, the update unit can provide an update method that reflects improvements based on the user's past feedback. The update unit can also analyze the user's past feedback and select the most efficient update method. For example, the update unit analyzes the user's past feedback and selects the most efficient update method. This allows the system to select the optimal update method based on the user's past feedback.

[0051] The update unit can customize the update content based on the user's current project when updating the architectural model. For example, the update unit prioritizes providing updates related to the user's current project. The update unit can customize relevant updates based on the user's current project. For example, the update unit customizes relevant updates based on the user's current project. The update unit can also suggest relevant updates based on projects the user has shown interest in in the past. For example, the update unit suggests relevant updates based on projects the user has shown interest in in the past. This allows for customization of updates based on the user's current project.

[0052] The update unit can select the optimal update method when updating the building model, taking into account the user's geographical location information. For example, the update unit can prioritize providing relevant updates based on the user's current location. The update unit can also suggest updates related to nearby projects based on the user's geographical location information. For example, the update unit can suggest updates related to nearby projects based on the user's geographical location information. If the user is interested in a particular region, the update unit can also prioritize providing updates related to that region. For example, if the user is interested in a particular region, the update unit will prioritize providing updates related to that region. This allows the system to select the optimal update method based on the user's geographical location information.

[0053] The update unit can analyze users' social media activity and reflect relevant information when updating architectural models. For example, the update unit can provide relevant updates based on projects shared by users on social media. The update unit can analyze users' interests in design and trends from their social media activity and reflect relevant updates. For example, the update unit can analyze users' interests in design and trends from their social media activity and reflect relevant updates. The update unit can also provide relevant updates based on projects by designers and architects that users follow. For example, the update unit can provide relevant updates based on projects by designers and architects that users follow. This allows the system to reflect relevant information based on users' social media activity.

[0054] The proposal department can provide optimal proposals by referring to the user's past design history when making design proposals. For example, the proposal department can provide optimal proposals based on the design history previously provided by the user. The proposal department can provide proposals that reflect improvements based on the user's past design history. For example, the proposal department can provide proposals that reflect improvements based on the user's past design history. The proposal department can also analyze the user's past design history and provide the most efficient proposal. For example, the proposal department can analyze the user's past design history and provide the most efficient proposal. This allows the proposal department to provide optimal proposals based on the user's past design history.

[0055] The proposal department can customize the proposal content based on the user's current project when making design proposals. For example, the proposal department will prioritize providing proposals related to the user's current project. The proposal department can customize relevant proposals based on the user's current project. For example, the proposal department will customize relevant proposals based on the user's current project. The proposal department can also provide relevant proposals based on projects the user has shown interest in in the past. For example, the proposal department will provide relevant proposals based on projects the user has shown interest in in the past. This allows the proposal content to be customized based on the user's current project.

[0056] The proposal department can provide optimal proposals by considering the user's geographical location when making design proposals. For example, the proposal department can prioritize providing relevant proposals based on the user's current location. The proposal department can also provide proposals related to nearby projects based on the user's geographical location. For example, the proposal department can provide proposals related to nearby projects based on the user's geographical location. If the user is interested in a particular region, the proposal department can also prioritize providing proposals related to that region. For example, if the user is interested in a particular region, the proposal department can prioritize providing proposals related to that region. This allows the department to provide optimal proposals based on the user's geographical location.

[0057] The proposal department can analyze a user's social media activity when making design proposals and provide relevant suggestions. For example, the proposal department can provide relevant suggestions based on projects the user has shared on social media. The proposal department can analyze the user's interests in design and trends from their social media activity and provide relevant suggestions. For example, the proposal department can analyze the user's interests in design and trends from their social media activity and provide relevant suggestions. The proposal department can also provide relevant suggestions based on projects by designers and architects the user follows. For example, the proposal department can provide relevant suggestions based on projects by designers and architects the user follows. This allows the proposal department to provide relevant suggestions based on the user's social media activity.

[0058] The data management department can select the optimal data management method by referring to the user's past data usage history during data management. For example, the data management department can propose the optimal management method based on the data management methods the user has used in the past. The data management department can provide efficient data management methods based on the user's past data usage history. For example, the data management department can provide efficient data management methods based on the user's past data usage history. The data management department can also analyze the user's past data usage history and select the most efficient data management method. For example, the data management department analyzes the user's past data usage history and selects the most efficient data management method. This allows the department to select the optimal management method based on the user's past data usage history.

[0059] The data management department can customize data management based on the user's current project. For example, the data management department prioritizes managing data related to the user's current project. The data management department can also provide relevant data management based on projects the user has shown interest in in the past. This allows for customization of data management based on the user's current project.

[0060] The data management department can select the optimal management method when managing data, taking into account the user's geographical location. For example, the data management department can prioritize managing relevant data based on the user's current location. The data management department can also manage data related to nearby projects based on the user's geographical location. For example, the data management department can manage data related to nearby projects based on the user's geographical location. If the user is interested in a particular region, the data management department can also prioritize managing data related to that region. For example, if the user is interested in a particular region, the data management department will prioritize managing data related to that region. This allows the department to select the optimal management method based on the user's geographical location.

[0061] The data management department can analyze users' social media activity and manage relevant data during data management. For example, the data management department can manage relevant data based on projects that users have shared on social media. The data management department can analyze the designs and trends that users are interested in from their social media activity and manage relevant data. For example, the data management department can analyze the designs and trends that users are interested in from their social media activity and manage relevant data. The data management department can also manage relevant data based on the projects of designers and architects that users follow. For example, the data management department can manage relevant data based on the projects of designers and architects that users follow. This allows for the management of relevant data based on users' social media activity.

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

[0063] The reception desk can analyze the user's past design drawings and requests and suggest the optimal input method. For example, it can automatically display design drawings and requests that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest design drawings and requests that the user will use at specific times based on their past input history. This allows the system to suggest the optimal input method based on the user's past history.

[0064] The generation unit can adjust the level of detail generated during 3D model generation based on the importance of the design drawings. For example, for important design drawings, a detailed 3D model can be generated. For less important design drawings, a simplified 3D model can be generated. Furthermore, the level of detail of the generated 3D model can be adjusted in stages according to the importance of the design drawings. This allows the level of detail of the 3D model to be adjusted according to the importance of the design drawings.

[0065] The navigation unit can select the optimal display method when displaying a VR environment by referring to the user's past operation history. For example, it can suggest the optimal display method based on the display methods the user has used in the past. It can also suggest a highly visible display method based on the user's past operation history. Furthermore, it can analyze the user's past operation history and suggest the most efficient display method. This allows the system to select the optimal display method based on the user's past operation history.

[0066] The update unit can select the optimal update method when updating the architectural model by referring to past user feedback. For example, it can propose the optimal update method based on feedback previously provided by the user. It can also provide an update method that reflects improvements based on past user feedback. Furthermore, it can analyze past user feedback and select the most efficient update method. This allows for the selection of the optimal update method based on past user feedback.

[0067] The proposal department can provide optimal proposals by referring to the user's past design history when submitting design proposals. For example, it can provide optimal proposals based on the design history previously submitted by the user. It can also provide proposals that reflect improvements based on the user's past design history. Furthermore, it can analyze the user's past design history and provide the most efficient proposals. In this way, it can provide optimal proposals based on the user's past design history.

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

[0069] Step 1: The reception desk receives user design drawings and requests. The reception desk provides an interface for users to input design drawings and requests, allowing them to input them using text or voice input. For example, speech recognition technology is used to convert the user's voice input into text data. Step 2: The generation unit generates a 3D model of the building in real time based on the information received by the reception unit. The generation unit uses an AI agent to analyze the user's design drawings and requests, and generates a detailed 3D model using the latest CG technology. For example, it generates a high-resolution 3D model so that the user can examine the details. Step 3: The navigation unit enables users to experience the 3D models generated by the generation unit in a VR environment. The navigation unit allows users to wear a VR headset and view the interior and layout of their home in a virtual space, move freely within the virtual space, and change the interior and layout. For example, users can use VR controllers to move around in the virtual space and change the interior and layout.

[0070] (Example of form 2) The system according to an embodiment of the present invention is a system that uses an AI agent to generate a 3D model of a building in real time based on design drawings and requests provided by the user, and makes it available for experience in a VR environment. This system allows users to adjust the interior and layout of their home, and changes are reflected immediately, allowing them to experience the actual space before design. For example, the user inputs design drawings and requests. Next, the AI ​​agent analyzes the input information and generates a 3D model of the building in real time. The generated 3D model can then be experienced in a VR environment. Furthermore, the AI ​​agent updates the architectural model in real time based on user feedback and generates optimal design proposals. This mechanism reduces the time and cost associated with design changes, improving client satisfaction. It also speeds up project approval, leading to increased efficiency in architectural projects. This AI agent is extremely useful for families considering new construction or renovation, real estate developers collaborating with architects and designers, and individuals interested in interior design. The innovation of combining AI and VR enables dynamic environmental adjustments based on user input, and also provides cloud-based data management and easy access. This allows AI-powered systems to generate 3D models in real time based on user-provided blueprints and requests, making them available for experience in a VR environment.

[0071] The system according to this embodiment comprises a reception unit, a generation unit, and a navigation unit. The reception unit receives input of the user's design plans and requests. The reception unit provides, for example, an interface for the user to input design plans and requests. The reception unit allows the user to input design plans and requests in text format. The reception unit also allows the user to input design plans and requests using voice input. For example, the reception unit uses speech recognition technology to convert the user's voice input into text data. The generation unit generates a 3D model of the building in real time based on the information received by the reception unit. The generation unit uses, for example, an AI agent to analyze the user's design plans and requests and generate a 3D model. The generation unit can generate a detailed 3D model using the latest CG technology. For example, the generation unit generates a high-resolution 3D model so that the user can examine the details. The navigation unit allows the 3D model generated by the generation unit to be experienced in a VR environment. The navigation unit allows, for example, the user to wear a VR headset and check the interior and layout of their home in a virtual space. The navigation unit allows the user to move freely in the virtual space and change the interior and layout. For example, the navigation unit allows the user to move around in the virtual space using a VR controller and change the interior and layout. This enables the system according to the embodiment to generate a 3D model in real time based on the user's design drawings and requests, and make it available for experience in the VR environment.

[0072] The reception desk accepts user input of design plans and requests. For example, the reception desk provides an interface for users to input design plans and requests. Specifically, the reception desk features a user-friendly graphical user interface (GUI) designed for intuitive operation. Users can utilize file selection buttons for uploading design plans and text boxes for entering requests. The reception desk also allows users to input design plans and requests using voice input. For example, the reception desk uses speech recognition technology to convert user voice input into text data. This speech recognition technology incorporates natural language processing (NLP) algorithms to accurately analyze and transcribe user speech. Furthermore, the reception desk automatically saves user-inputted data, allowing for re-editing and modification as needed. This makes it easy for users to make changes or additions during the design process. The reception desk transmits user input data to a secure server and uses encryption technology to ensure data protection and privacy. This minimizes the risk of sensitive user information being leaked to third parties.

[0073] The generation unit generates a 3D model of the building in real time based on information received by the reception unit. For example, the generation unit uses an AI agent to analyze user design drawings and requirements and generate a 3D model. Specifically, the generation unit utilizes deep learning technology to analyze user input data and automatically generate the building's structure and design. The AI ​​agent learns from past design data and regulatory information such as building codes, enabling it to propose the optimal design according to the user's requirements. The generation unit can generate detailed 3D models using the latest CG technology. For example, it can generate high-resolution 3D models, allowing users to examine even the smallest details. The generated 3D model is updated in real time, and any changes or modifications entered by the user are immediately reflected. Furthermore, the generation unit incorporates a physics engine, enabling it to simulate the structural stability and durability of the building. This allows users to verify the building's safety during the design phase. The generation unit saves the generated 3D model to a cloud server, allowing users to access it at any time. The generation unit also includes collaboration features, enabling multiple users to access and design collaboratively simultaneously. This enables the generation unit to efficiently and accurately generate 3D models, supporting the user's design process.

[0074] The navigation unit enables users to experience 3D models generated by the generation unit in a VR environment. For example, the navigation unit allows users to wear a VR headset and view the interior and layout of their home in a virtual space. Specifically, the navigation unit works in conjunction with the VR headset, allowing users to freely move around the virtual space and change the interior and layout. The navigation unit allows users to move around the virtual space and change the interior and layout using VR controllers. For example, users can use VR controllers to rearrange furniture or change wall colors. The navigation unit reflects user actions in real time and instantly displays changes in the virtual space. Furthermore, the navigation unit has a function that allows users to record their virtual space experience and play it back later. This allows users to review changes and modifications made during the design process and use them as a reference when deciding on the final design. The navigation unit also has multi-user support, allowing multiple users to collaborate in the virtual space simultaneously. This enables users to communicate in real time with other users in remote locations and collaborate on the design. The navigation system provides high-resolution graphics and realistic sound effects to enhance the user experience. This allows users to feel as if they are actually in the space they are in.

[0075] The system includes an update unit that updates the architectural model in real time based on user feedback. For example, when a user changes the interior or layout within the VR environment, the update unit reflects those changes in real time. The update unit uses an AI agent to analyze user feedback and update the architectural model. For instance, when a user changes the layout of the interior, the update unit instantly reflects those changes and updates the 3D model. The update unit can also optimize the design and layout of the architectural model based on user feedback. For example, it generates optimal design suggestions based on user feedback. This allows the architectural model to be updated in real time based on user feedback.

[0076] The system includes a proposal unit that generates optimal design proposals. This unit, for example, uses an AI agent to analyze the user's design drawings and requirements and generate optimal design proposals. The proposal unit provides optimal design proposals considering the user's requirements and constraints. For example, it proposes the optimal interior design considering the user's budget and space constraints. The proposal unit can also provide customized design proposals based on the user's preferences. For example, it generates optimal design proposals based on the user's past preferences and feedback. This allows the system to provide the user with the most suitable design proposal.

[0077] The system includes a data management unit that enables cloud-based data management and easy access. The data management unit manages data such as user blueprints, requirements, and feedback in the cloud. It uses cloud-based storage to facilitate data storage and access. For example, it allows users to access data via the internet. The data management unit can also provide data backup and restore functions. For example, it regularly backs up data to ensure data security. This facilitates cloud-based data management and access.

[0078] The generation unit can generate detailed 3D models using the latest CG technology. For example, it can generate high-resolution 3D models, allowing users to examine details. The generation unit can reproduce realistic textures and light reflections using the latest rendering technology. For example, it can reproduce realistic textures using physically based rendering technology. The generation unit can reproduce complex shapes and details using the latest modeling technology. For example, it can reproduce complex shapes using sculpting technology. This allows for the generation of detailed 3D models using the latest CG technology.

[0079] The navigation unit allows users to wear a VR headset and view the interior and layout of their home in a virtual space. For example, the navigation unit allows users to freely move around the virtual space while wearing a VR headset and view the interior and layout. The navigation unit also allows users to move around the virtual space using VR controllers and change the interior and layout. For example, the navigation unit allows users to change the placement of furniture using VR controllers and reflects those changes in real time. The navigation unit can also provide visual guides for users to view the interior and layout in the virtual space. For example, the navigation unit displays visual guidelines for users to view the placement of furniture in the virtual space. This allows users to wear a VR headset and view the interior and layout of their home in a virtual space.

[0080] The reception desk can estimate the user's emotions and adjust the input method for blueprints and requests based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can provide detailed input options and suggest customizable input methods. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of blueprints and requests. For example, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of blueprints and requests. This allows the input method for blueprints and requests to be adjusted according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The reception desk can analyze the user's past design drawings and requests and suggest the optimal input method. For example, the reception desk can automatically display design drawings and requests that the user has frequently entered in the past as suggestions. The reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk will prioritize suggesting input methods that the user has used in the past. The reception desk can also predict and suggest design drawings and requests that will be used during specific time periods based on the user's past input history. For example, the reception desk will predict and suggest design drawings and requests that will be used during specific time periods based on the user's past input history. This allows the reception desk to suggest the optimal input method based on the user's past history.

[0082] The reception system can filter design drawings and requests based on the user's current projects and areas of interest. For example, the reception system can prioritize displaying design drawings and requests related to the user's current projects. The reception system can also filter and display relevant design drawings and requests based on the user's areas of interest. For example, the reception system can filter and display relevant design drawings and requests based on the user's areas of interest. The reception system can also suggest relevant design drawings and requests based on projects the user has shown interest in in the past. For example, the reception system can suggest relevant design drawings and requests based on projects the user has shown interest in in the past. This allows the system to provide highly relevant information by filtering based on the user's current projects and areas of interest.

[0083] The reception desk can estimate the user's emotions and prioritize input content based on those emotions. For example, if the user is stressed, the reception desk can prioritize displaying important input items and postpone other items. If the user is relaxed, the reception desk can prioritize displaying detailed input items and suggest customizable input methods. If the user is in a hurry, the reception desk can also prioritize displaying the most important input items to allow for quick input. This allows the system to prioritize input content according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The reception desk can prioritize retrieving highly relevant information based on the user's geographical location when inputting design drawings and requests. For example, the reception desk can prioritize displaying relevant design drawings and requests based on the user's current location. The reception desk can also suggest design drawings and requests related to nearby projects based on the user's geographical location. For example, the reception desk can suggest design drawings and requests related to nearby projects based on the user's geographical location. If the user is interested in a particular region, the reception desk can also prioritize displaying design drawings and requests related to that region. For example, if the user is interested in a particular region, the reception desk will prioritize displaying design drawings and requests related to that region. This allows the system to prioritize retrieving highly relevant information based on the user's geographical location.

[0085] The reception desk can analyze the user's social media activity and obtain relevant information when they input design drawings and requests. For example, the reception desk can suggest relevant design drawings and requests based on projects the user has shared on social media. The reception desk can also analyze the user's social media activity to identify their design interests and trends and display relevant design drawings and requests. For example, the reception desk can analyze the user's social media activity to identify their design interests and trends and display relevant design drawings and requests. The reception desk can also suggest relevant design drawings and requests based on projects by designers and architects the user follows. For example, the reception desk can suggest relevant design drawings and requests based on projects by designers and architects the user follows. This allows the system to obtain relevant information based on the user's social media activity.

[0086] The generation unit can estimate the user's emotions and adjust the method of generating the 3D model based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a 3D model that progresses at a leisurely pace. If the user is in a hurry, the generation unit can generate a 3D model that emphasizes the shortest route. For example, if the user is in a hurry, the generation unit can generate a 3D model that emphasizes the shortest route. If the user is excited, the generation unit can also generate a 3D model with visually stimulating effects. For example, if the user is excited, the generation unit can generate a 3D model with visually stimulating effects. This allows the method of generating the 3D model to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0087] The generation unit can adjust the level of detail of the generated 3D model based on the importance of the design drawing. For example, if the design drawing is important, the generation unit will generate a detailed 3D model. If the design drawing is of low importance, the generation unit can generate a simplified 3D model. For example, if the design drawing is of low importance, the generation unit will generate a simplified 3D model. The generation unit can also adjust the level of detail of the generated 3D model in stages according to the importance of the design drawing. For example, the generation unit adjusts the level of detail of the generated 3D model in stages according to the importance of the design drawing. This allows the level of detail of the 3D model to be adjusted according to the importance of the design drawing.

[0088] The generation unit can apply different generation algorithms depending on the category of the design drawing when generating 3D models. For example, in the case of residential design, the generation unit applies a generation algorithm specifically for residential buildings. For example, in the case of commercial building design, the generation unit applies a generation algorithm specifically for commercial buildings. For example, in the case of public building design, the generation unit applies a generation algorithm specifically for public buildings. This allows for the application of different generation algorithms depending on the category of the design drawing.

[0089] The generation unit can estimate the user's emotions and adjust the generation speed of the 3D model based on the estimated emotions. For example, if the user is in a hurry, the generation unit can speed up the generation to quickly generate the 3D model. If the user is relaxed, the generation unit can slow down the generation speed to generate a more detailed 3D model. For example, if the user is relaxed, the generation unit can slow down the generation speed to generate a more detailed 3D model. If the user is excited, the generation unit can also adjust the generation speed to generate a visually stimulating 3D model. For example, if the user is excited, the generation unit can adjust the generation speed to generate a visually stimulating 3D model. This allows the generation speed of the 3D model to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The generation unit can determine the generation priority based on the submission dates of the design drawings when generating 3D models. For example, the generation unit can prioritize the generation of design drawings that are submitted earlier. The generation unit can also postpone the generation of design drawings that are submitted later. For example, the generation unit can postpone the generation of design drawings that are submitted later. The generation unit can also adjust the priority of the 3D models to be generated in stages according to the submission dates. For example, the generation unit can adjust the priority of the 3D models to be generated in stages according to the submission dates. This allows the generation priority to be determined based on the submission dates of the design drawings.

[0091] The generation unit can adjust the generation order based on the relationships between design drawings when generating 3D models. For example, the generation unit can prioritize the generation of design drawings that are highly relevant. The generation unit can also postpone the generation of design drawings that are less relevant. For example, the generation unit can postpone the generation of design drawings that are less relevant. The generation unit can also adjust the order of the 3D models to be generated in stages according to the relationships between the design drawings. For example, the generation unit can adjust the order of the 3D models to be generated in stages according to the relationships between the design drawings. This allows the generation order to be adjusted based on the relationships between the design drawings.

[0092] The navigation unit can estimate the user's emotions and adjust the display method of the VR environment based on the estimated emotions. For example, if the user is tense, the navigation unit can provide a simple and highly visible display method. If the user is relaxed, the navigation unit can provide a display method that includes detailed information. For example, if the user is relaxed, the navigation unit can provide a display method that includes detailed information. If the user is in a hurry, the navigation unit can also provide a display method that gets to the point. For example, if the user is in a hurry, the navigation unit can provide a display method that gets to the point. This allows the display method of the VR environment to be adjusted 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.

[0093] The navigation unit can select the optimal display method when displaying a VR environment by referring to the user's past operation history. For example, the navigation unit can suggest the optimal display method based on the display methods the user has used in the past. The navigation unit can also suggest a highly visible display method based on the user's past operation history. For example, the navigation unit can suggest a highly visible display method based on the user's past operation history. The navigation unit can also analyze the user's past operation history and suggest the most efficient display method. For example, the navigation unit analyzes the user's past operation history and suggests the most efficient display method. This allows the system to select the optimal display method based on the user's past operation history.

[0094] The navigation unit can customize the displayed content based on the user's current project when displaying the VR environment. For example, the navigation unit prioritizes displaying information related to the project the user is currently working on. The navigation unit can customize the displayed content based on the user's current project. For example, the navigation unit customizes the displayed content based on the user's current project. The navigation unit can also suggest displayed content based on projects the user has shown interest in in the past. For example, the navigation unit suggests displayed content based on projects the user has shown interest in in the past. This allows the displayed content to be customized based on the user's current project.

[0095] The navigation unit can estimate the user's emotions and adjust the VR environment's operation procedures based on the estimated emotions. For example, if the user is tense, the navigation unit can provide simple and easily visible operation procedures. If the user is relaxed, the navigation unit can provide detailed operation procedures. For example, if the user is relaxed, the navigation unit can provide detailed operation procedures. If the user is in a hurry, the navigation unit can also provide concise operation procedures. For example, if the user is in a hurry, the navigation unit can provide concise operation procedures. This allows the VR environment's operation procedures to be adjusted 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.

[0096] The navigation unit can select the optimal display method when displaying a VR environment, taking into account the user's device information. For example, if the user is using a smartphone, the navigation unit can provide a display method that matches the screen size. If the user is using a tablet, the navigation unit can provide a display method optimized for a larger screen. For example, if the user is using a tablet, the navigation unit can provide a display method optimized for a larger screen. If the user is using a smartwatch, the navigation unit can also provide a concise and highly visible display method. For example, if the user is using a smartwatch, the navigation unit can provide a concise and highly visible display method. This allows the system to select the optimal display method based on the user's device information.

[0097] The navigation unit can analyze the user's social media activity when displaying a VR environment and display relevant information. For example, the navigation unit can display relevant information based on projects the user has shared on social media. The navigation unit can also analyze the user's interests in design and trends from their social media activity and display relevant information. For example, the navigation unit can analyze the user's interests in design and trends from their social media activity and display relevant information. The navigation unit can also display relevant information based on projects by designers and architects the user follows. For example, the navigation unit can display relevant information based on projects by designers and architects the user follows. This allows the system to display relevant information based on the user's social media activity.

[0098] The update unit can estimate the user's emotions and adjust the method of updating the architectural model based on the estimated user emotions. For example, if the user is relaxed, the update unit can provide an update method that proceeds at a leisurely pace. If the user is in a hurry, the update unit can provide a method that updates quickly. For example, if the user is in a hurry, the update unit can provide a method that updates quickly. If the user is excited, the update unit can also provide an update method that adds visually stimulating effects. For example, if the user is excited, the update unit can provide an update method that adds visually stimulating effects. This allows the method of updating the architectural model to be adjusted 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.

[0099] The update unit can select the optimal update method when updating the architectural model by referring to the user's past feedback. For example, the update unit can propose the optimal update method based on the feedback the user has provided in the past. The update unit can provide an update method that reflects improvements based on the user's past feedback. For example, the update unit can provide an update method that reflects improvements based on the user's past feedback. The update unit can also analyze the user's past feedback and select the most efficient update method. For example, the update unit analyzes the user's past feedback and selects the most efficient update method. This allows the system to select the optimal update method based on the user's past feedback.

[0100] The update unit can customize the update content based on the user's current project when updating the architectural model. For example, the update unit prioritizes providing updates related to the user's current project. The update unit can customize relevant updates based on the user's current project. For example, the update unit customizes relevant updates based on the user's current project. The update unit can also suggest relevant updates based on projects the user has shown interest in in the past. For example, the update unit suggests relevant updates based on projects the user has shown interest in in the past. This allows for customization of updates based on the user's current project.

[0101] The update unit can estimate the user's emotions and adjust the update frequency of the architectural model based on the estimated emotions. For example, if the user is in a hurry, the update unit can increase the update frequency for faster updates. If the user is relaxed, the update unit can decrease the update frequency for more detailed updates. For example, if the user is relaxed, the update unit can decrease the update frequency for more detailed updates. If the user is excited, the update unit can also adjust the update frequency for more visually stimulating updates. For example, if the user is excited, the update unit can adjust the update frequency for more visually stimulating updates. This allows the update frequency of the architectural model to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The update unit can select the optimal update method when updating the building model, taking into account the user's geographical location information. For example, the update unit can prioritize providing relevant updates based on the user's current location. The update unit can also suggest updates related to nearby projects based on the user's geographical location information. For example, the update unit can suggest updates related to nearby projects based on the user's geographical location information. If the user is interested in a particular region, the update unit can also prioritize providing updates related to that region. For example, if the user is interested in a particular region, the update unit will prioritize providing updates related to that region. This allows the system to select the optimal update method based on the user's geographical location information.

[0103] The update unit can analyze users' social media activity and reflect relevant information when updating architectural models. For example, the update unit can provide relevant updates based on projects shared by users on social media. The update unit can analyze users' interests in design and trends from their social media activity and reflect relevant updates. For example, the update unit can analyze users' interests in design and trends from their social media activity and reflect relevant updates. The update unit can also provide relevant updates based on projects by designers and architects that users follow. For example, the update unit can provide relevant updates based on projects by designers and architects that users follow. This allows the system to reflect relevant information based on users' social media activity.

[0104] The proposal unit can estimate the user's emotions and adjust the presentation of design proposals based on the estimated emotions. For example, if the user is relaxed, the proposal unit can provide detailed design proposals. If the user is in a hurry, the proposal unit can provide concise design proposals. For example, if the user is in a hurry, the proposal unit can provide concise design proposals. If the user is excited, the proposal unit can also provide visually stimulating design proposals. For example, if the user is excited, the proposal unit can provide visually stimulating design proposals. This allows the presentation of design proposals to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The proposal department can provide optimal proposals by referring to the user's past design history when making design proposals. For example, the proposal department can provide optimal proposals based on the design history previously provided by the user. The proposal department can provide proposals that reflect improvements based on the user's past design history. For example, the proposal department can provide proposals that reflect improvements based on the user's past design history. The proposal department can also analyze the user's past design history and provide the most efficient proposal. For example, the proposal department can analyze the user's past design history and provide the most efficient proposal. This allows the proposal department to provide optimal proposals based on the user's past design history.

[0106] The proposal department can customize the proposal content based on the user's current project when making design proposals. For example, the proposal department will prioritize providing proposals related to the user's current project. The proposal department can customize relevant proposals based on the user's current project. For example, the proposal department will customize relevant proposals based on the user's current project. The proposal department can also provide relevant proposals based on projects the user has shown interest in in the past. For example, the proposal department will provide relevant proposals based on projects the user has shown interest in in the past. This allows the proposal content to be customized based on the user's current project.

[0107] The suggestion function can estimate the user's emotions and prioritize design suggestions based on those emotions. For example, if the user is in a hurry, the suggestion function will prioritize important suggestions. If the user is relaxed, the suggestion function can prioritize detailed suggestions. For example, if the user is relaxed, the suggestion function can prioritize detailed suggestions. If the user is excited, the suggestion function can also prioritize visually stimulating suggestions. For example, if the user is excited, the suggestion function can prioritize visually stimulating suggestions. This allows the system to prioritize design suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The proposal department can provide optimal proposals by considering the user's geographical location when making design proposals. For example, the proposal department can prioritize providing relevant proposals based on the user's current location. The proposal department can also provide proposals related to nearby projects based on the user's geographical location. For example, the proposal department can provide proposals related to nearby projects based on the user's geographical location. If the user is interested in a particular region, the proposal department can also prioritize providing proposals related to that region. For example, if the user is interested in a particular region, the proposal department can prioritize providing proposals related to that region. This allows the department to provide optimal proposals based on the user's geographical location.

[0109] The proposal department can analyze a user's social media activity when making design proposals and provide relevant suggestions. For example, the proposal department can provide relevant suggestions based on projects the user has shared on social media. The proposal department can analyze the user's interests in design and trends from their social media activity and provide relevant suggestions. For example, the proposal department can analyze the user's interests in design and trends from their social media activity and provide relevant suggestions. The proposal department can also provide relevant suggestions based on projects by designers and architects the user follows. For example, the proposal department can provide relevant suggestions based on projects by designers and architects the user follows. This allows the proposal department to provide relevant suggestions based on the user's social media activity.

[0110] The data management unit can estimate the user's emotions and adjust the data management method based on the estimated emotions. For example, if the user is relaxed, the data management unit can provide a detailed data management method. If the user is in a hurry, the data management unit can provide a concise data management method. For example, if the user is in a hurry, the data management unit can provide a concise data management method. If the user is excited, the data management unit can also provide a visually stimulating data management method. For example, if the user is excited, the data management unit can provide a visually stimulating data management method. This allows the data management method to be adjusted 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.

[0111] The data management department can select the optimal data management method by referring to the user's past data usage history during data management. For example, the data management department can propose the optimal management method based on the data management methods the user has used in the past. The data management department can provide efficient data management methods based on the user's past data usage history. For example, the data management department can provide efficient data management methods based on the user's past data usage history. The data management department can also analyze the user's past data usage history and select the most efficient data management method. For example, the data management department analyzes the user's past data usage history and selects the most efficient data management method. This allows the department to select the optimal management method based on the user's past data usage history.

[0112] The data management department can customize data management based on the user's current project. For example, the data management department prioritizes managing data related to the user's current project. The data management department can also provide relevant data management based on projects the user has shown interest in in the past. This allows for customization of data management based on the user's current project.

[0113] The data management unit can estimate the user's emotions and determine data management priorities based on those estimated emotions. For example, if the user is in a hurry, the data management unit will prioritize managing important data. If the user is relaxed, the data management unit can prioritize managing detailed data. For example, if the user is relaxed, the data management unit can prioritize managing detailed data. If the user is excited, the data management unit can also prioritize managing visually stimulating data. For example, if the user is excited, the data management unit can prioritize visually stimulating data. This allows data management priorities to be determined according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0114] The data management department can select the optimal management method when managing data, taking into account the user's geographical location. For example, the data management department can prioritize managing relevant data based on the user's current location. The data management department can also manage data related to nearby projects based on the user's geographical location. For example, the data management department can manage data related to nearby projects based on the user's geographical location. If the user is interested in a particular region, the data management department can also prioritize managing data related to that region. For example, if the user is interested in a particular region, the data management department will prioritize managing data related to that region. This allows the department to select the optimal management method based on the user's geographical location.

[0115] The data management department can analyze users' social media activity and manage relevant data during data management. For example, the data management department can manage relevant data based on projects that users have shared on social media. The data management department can analyze the designs and trends that users are interested in from their social media activity and manage relevant data. For example, the data management department can analyze the designs and trends that users are interested in from their social media activity and manage relevant data. The data management department can also manage relevant data based on the projects of designers and architects that users follow. For example, the data management department can manage relevant data based on the projects of designers and architects that users follow. This allows for the management of relevant data based on users' social media activity.

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

[0117] The reception desk can estimate the user's emotions and adjust the input method for blueprints and requests based on those estimates. For example, if the user is stressed, a simple interface can be provided, minimizing the input steps. If the user is relaxed, detailed input options can be provided, and customizable input methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow for quick input of blueprints and requests. This allows the input method for blueprints and requests to be adjusted according to the user's emotions.

[0118] The generation unit can estimate the user's emotions and adjust the 3D model generation method based on the estimated emotions. For example, if the user is relaxed, it can generate a 3D model that progresses at a leisurely pace. If the user is in a hurry, it can generate a 3D model that emphasizes the shortest route. Furthermore, if the user is excited, it can generate a 3D model with visually stimulating effects. This allows the 3D model generation method to be adjusted according to the user's emotions.

[0119] The navigation unit can estimate the user's emotions and adjust the display method of the VR environment based on those emotions. For example, if the user is nervous, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that focuses on the essentials. In this way, the display method of the VR environment can be adjusted according to the user's emotions.

[0120] The update unit can estimate the user's emotions and adjust the method of updating the architectural model based on those emotions. For example, if the user is relaxed, it can provide an update method that proceeds at a leisurely pace. If the user is in a hurry, it can provide a method that updates quickly. Furthermore, if the user is excited, it can provide an update method that adds visually stimulating effects. This allows the method of updating the architectural model to be adjusted according to the user's emotions.

[0121] The proposal function can estimate the user's emotions and adjust the presentation of the design proposal based on those emotions. For example, if the user is relaxed, a detailed design proposal can be provided. If the user is in a hurry, a concise design proposal can be provided. Furthermore, if the user is excited, a visually stimulating design proposal can be provided. This allows the presentation of the design proposal to be adjusted according to the user's emotions.

[0122] The reception desk can analyze the user's past design drawings and requests and suggest the optimal input method. For example, it can automatically display design drawings and requests that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest design drawings and requests that the user will use at specific times based on their past input history. This allows the system to suggest the optimal input method based on the user's past history.

[0123] The generation unit can adjust the level of detail generated during 3D model generation based on the importance of the design drawings. For example, for important design drawings, a detailed 3D model can be generated. For less important design drawings, a simplified 3D model can be generated. Furthermore, the level of detail of the generated 3D model can be adjusted in stages according to the importance of the design drawings. This allows the level of detail of the 3D model to be adjusted according to the importance of the design drawings.

[0124] The navigation unit can select the optimal display method when displaying a VR environment by referring to the user's past operation history. For example, it can suggest the optimal display method based on the display methods the user has used in the past. It can also suggest a highly visible display method based on the user's past operation history. Furthermore, it can analyze the user's past operation history and suggest the most efficient display method. This allows the system to select the optimal display method based on the user's past operation history.

[0125] The update unit can select the optimal update method when updating the architectural model by referring to past user feedback. For example, it can propose the optimal update method based on feedback previously provided by the user. It can also provide an update method that reflects improvements based on past user feedback. Furthermore, it can analyze past user feedback and select the most efficient update method. This allows for the selection of the optimal update method based on past user feedback.

[0126] The proposal department can provide optimal proposals by referring to the user's past design history when submitting design proposals. For example, it can provide optimal proposals based on the design history previously submitted by the user. It can also provide proposals that reflect improvements based on the user's past design history. Furthermore, it can analyze the user's past design history and provide the most efficient proposals. In this way, it can provide optimal proposals based on the user's past design history.

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

[0128] Step 1: The reception desk receives user design drawings and requests. The reception desk provides an interface for users to input design drawings and requests, allowing them to input them using text or voice input. For example, speech recognition technology is used to convert the user's voice input into text data. Step 2: The generation unit generates a 3D model of the building in real time based on the information received by the reception unit. The generation unit uses an AI agent to analyze the user's design drawings and requests, and generates a detailed 3D model using the latest CG technology. For example, it generates a high-resolution 3D model so that the user can examine the details. Step 3: The navigation unit enables users to experience the 3D models generated by the generation unit in a VR environment. The navigation unit allows users to wear a VR headset and view the interior and layout of their home in a virtual space, move freely within the virtual space, and change the interior and layout. For example, users can use VR controllers to move around in the virtual space and change the interior and layout.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

[0132] Each of the multiple elements described above, including the reception unit, generation unit, navigation unit, update unit, proposal unit, and data management unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives the user's design drawings and requests. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a 3D model of the building in real time. The navigation unit is implemented by, for example, the control unit 46A of the smart device 14 and allows the user to experience the 3D model in a VR environment. The update unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and updates the building model in real time based on user feedback. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates an optimal design proposal. The data management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and facilitates cloud-based data management and access. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0134] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0136] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

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

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

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

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

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

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

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

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

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

[0148] Each of the multiple elements described above, including the reception unit, generation unit, navigation unit, update unit, proposal unit, and data management unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives the user's design drawings and requests. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a 3D model of the building in real time. The navigation unit is implemented, for example, by the control unit 46A of the smart glasses 214 and allows the user to experience the 3D model in a VR environment. The update unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and updates the building model in real time based on user feedback. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates an optimal design proposal. The data management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and facilitates cloud-based data management and access. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0150] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0152] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

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

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

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

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

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

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

[0164] Each of the multiple elements described above, including the reception unit, generation unit, navigation unit, update unit, proposal unit, and data management unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives the user's design drawings and requests. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a 3D model of the building in real time. The navigation unit is implemented by, for example, the control unit 46A of the headset terminal 314 and allows the user to experience the 3D model in a VR environment. The update unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and updates the building model in real time based on user feedback. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates optimal design proposals. The data management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and facilitates cloud-based data management and access. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0166] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

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

[0171] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

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

[0173] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

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

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

[0178] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

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

[0181] Each of the multiple elements described above, including the reception unit, generation unit, navigation unit, update unit, proposal unit, and data management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives the user's design drawings and requests. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a 3D model of the building in real time. The navigation unit is implemented by, for example, the control unit 46A of the robot 414 and allows the user to experience the 3D model in a VR environment. The update unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and updates the building model in real time based on user feedback. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates an optimal design proposal. The data management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and facilitates cloud-based data management and access. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0182] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

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

[0184] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0185] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

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

[0187] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

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

[0190] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0191] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0192] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

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

[0194] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0195] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

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

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

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

[0200] (Note 1) A reception area that accepts user design drawings and requests, A generation unit generates a 3D model of the building in real time based on the information received by the reception unit, The system includes a navigation unit that enables users to experience the 3D model generated by the generation unit in a VR environment. A system characterized by the following features. (Note 2) It features an update unit that updates the architectural model in real time based on user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a proposal unit that generates optimal design proposals. The system described in Appendix 1, characterized by the features described herein. (Note 4) It features a data management unit that enables cloud-based data management and easy access. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is We use the latest CG technology to generate detailed 3D models. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned navigation unit is This allows users to wear a VR headset and view the interior and layout of their home in a virtual space. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the input methods for design specifications and requests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze the user's past design drawings and request history and propose the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When inputting design drawings and requirements, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When inputting design drawings and requirements, the system prioritizes retrieving highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users input design plans and requirements, the system analyzes their social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the 3D model generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating 3D models, adjust the level of detail based on the importance of the design specifications. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating 3D models, different generation algorithms are applied depending on the category of the design drawing. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the 3D model generation speed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating 3D models, the generation priority is determined based on the submission timing of the design drawings. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating 3D models, the generation order is adjusted based on the relationships between the design drawings. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned navigation unit is It estimates the user's emotions and adjusts the display method of the VR environment based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned navigation unit is When displaying a VR environment, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned navigation unit is When displaying a VR environment, the displayed content is customized based on the user's current project. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned navigation unit is It estimates the user's emotions and adjusts the VR environment's operation procedures based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned navigation unit is When displaying a VR environment, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned navigation unit is When displaying in a VR environment, the system analyzes the user's social media activity and displays relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned update unit is It estimates the user's emotions and adjusts how the architectural model is updated based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned update unit is When updating architectural models, the optimal update method is selected by referring to past user feedback. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned update unit is When updating architectural models, customize the updates based on the user's current project. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned update unit is It estimates the user's emotions and adjusts the update frequency of the architectural model based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned update unit is When updating the architectural model, the optimal update method is selected considering the user's geographical location information. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned update unit is When updating architectural models, analyze users' social media activity and reflect relevant information. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way design proposals are presented based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When proposing a design, we refer to the user's past design history to provide the most suitable proposal. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned proposal section is, When proposing a design, customize the proposal based on the user's current project. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned proposal section is, It estimates user emotions and prioritizes design proposals based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned proposal section is, When proposing a design, we provide the optimal proposal by taking into account the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned proposal section is, When proposing a design, we analyze the user's social media activity and make relevant suggestions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned data management unit, We estimate user sentiment and adjust data management methods based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned data management unit, During data management, the optimal management method is selected by referring to the user's past data usage history. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned data management unit, When managing data, customize the data management process based on the user's current project. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned data management unit, It estimates user sentiment and prioritizes data management based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned data management unit, When managing data, the optimal management method is selected by considering the user's geographical location information. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned data management unit, When managing data, analyze users' social media activity and manage relevant data. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. A reception area that accepts user design drawings and requests, A generation unit generates a 3D model of the building in real time based on the information received by the reception unit, The system includes a navigation unit that enables users to experience the 3D model generated by the generation unit in a VR environment. A system characterized by the following features.

2. It features an update unit that updates the architectural model in real time based on user feedback. The system according to feature 1.

3. It includes a proposal unit that generates optimal design proposals. The system according to feature 1.

4. It features a data management unit that enables cloud-based data management and easy access. The system according to feature 1.

5. The generating unit is We use the latest CG technology to generate detailed 3D models. The system according to feature 1.

6. The aforementioned navigation unit is This allows users to wear a VR headset and view the interior and layout of their home in a virtual space. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the input methods for design specifications and requests based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is We analyze the user's past design drawings and request history and propose the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When inputting design drawings and requirements, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

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