System

Abstract

To automatically measure the dimensions of a room and to propose and simulate an interior meeting the desire of a user.SOLUTION: The imaging unit images the room by the user using a smartphone or a tablet. The dimension determination unit automatically determines the dimensions of the room from the imaging data acquired by the imaging unit. The 3D model generation unit generates a detailed 3D model based on the dimensions determined by the dimensions determination unit. The interior proposal unit analyzes a preference, an image, and a budget related to the interior of the user, and proposes an avatar of furniture matching a desire. The simulation unit arranges the avatar of the furniture proposed by the interior proposal unit in the virtual space and performs simulation. The design proposal part proposes the design of the custom-made furniture when the desired furniture is not commercially available. The ordering unit orders the design proposed by the design proposal unit to the cooperating factory.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the process of accurately measuring room dimensions and simulating the interior was time-consuming and laborious, making it difficult to find furniture that met the user's needs.

[0005] The system according to the embodiment aims to automatically measure the dimensions of a room and propose and simulate an interior design that matches the user's wishes. [Means for solving the problem]

[0006] The system according to the embodiment includes a photography unit, a dimension determination unit, a 3D model generation unit, an interior proposal unit, a simulation unit, a design proposal unit, and an ordering unit. The photography unit allows a user to photograph a room using a smartphone or tablet. The dimension determination unit automatically determines the dimensions of the room from the photographed data acquired by the photography unit. The 3D model generation unit generates a detailed 3D model based on the dimensions determined by the dimension determination unit. The interior proposal unit analyzes the user's interior preferences, image, and budget, and proposes furniture avatars that match the user's needs. The simulation unit arranges and simulates the furniture avatars proposed by the interior proposal unit in a virtual space. The design proposal unit proposes custom-made furniture designs if furniture that matches the user's needs is not commercially available. The ordering unit places an order for the design proposed by the design proposal unit with an affiliated factory. [Effects of the Invention]

[0007] The system according to the embodiment can automatically measure the dimensions of a room and propose and simulate interior designs that match the user's preferences. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The 3D Interior Planner according to an embodiment of the present invention is an interior planning service that utilizes AI technology to generate a 3D model of a space simply by a user taking a photo of the room using a smartphone or tablet. This allows the user to easily plan the interior of the room and realize their ideal space.

[0029] A 3D interior planner according to an embodiment includes a photographing unit, a dimension determination unit, a 3D model generation unit, an interior proposal unit, a simulation unit, a design proposal unit, and an ordering unit. The photographing unit allows a user to photograph a room using a smartphone or tablet. For example, the user can simply photograph the four corners of the room, and the photographing unit acquires the data. The photographing unit can also capture a panoramic image of the entire room. The dimension determination unit automatically determines the dimensions of the room from the photographed data acquired by the photographing unit. For example, the dimension determination unit measures the dimensions of the walls and floors using image analysis technology. The dimension determination unit can also measure the dimensions of the room using a sensor. The 3D model generation unit generates a detailed 3D model based on the dimensions determined by the dimension determination unit. For example, the 3D model generation unit creates a 3D model of the room using dedicated software. The 3D model generation unit can also integrate multiple photographed data to improve the accuracy of the model. The interior proposal unit analyzes the user's interior preferences, image, and budget, and proposes furniture avatars that match their needs. For example, the interior proposal department analyzes user preferences based on survey data and past purchase history. The interior proposal department also uses generative AI to create an interior plan based on the user's requests. The simulation department arranges avatars of furniture proposed by the interior proposal department in a virtual space and simulates the layout. For example, the simulation department allows the user to find the optimal layout by trial and error when arranging sofas and tables. The simulation department can also display the furniture layout in the virtual space in real time. The design proposal department proposes designs for custom-made furniture when furniture that meets the user's needs is not commercially available. For example, the design proposal department proposes designs for storage shelves and tables based on the user's requests. The design proposal department can also create optimal designs using generative AI. The ordering department places an order for the design proposed by the design proposal department with a partner factory. For example, the ordering department sends design data to the factory and requests the production of custom-made furniture. The ordering department can also manage the progress of production.As a result, the 3D interior planner according to the embodiment allows users to easily plan the interior of a room and realize their ideal space. For example, users can accurately grasp the dimensions of a room and arrange the furniture they desire in a virtual space, allowing them to check the optimal layout before actually purchasing it. Furthermore, custom-made furniture can be proposed to meet the individual needs of users.

[0030] When the camera captures a room, the generation AI displays the dimensions in real time, allowing the user to check them as they proceed with the shoot. For example, when a user takes a picture of a room with a smartphone or tablet, the generation AI displays the dimensions of the walls and floors on the screen in real time. For example, when the camera is pointed at a corner of the room, the distance from corner to corner is instantly displayed. The camera also updates the displayed dimensions in real time so that the user can check them as they proceed with the shoot. For example, when the user moves the camera, the displayed dimensions change accordingly. This allows the user to check the dimensions in real time as they proceed with the shoot, preventing shooting errors.

[0031] The 3D model generation unit can automatically recognize the materials and textures of a room based on the captured data and reflect them in the 3D model. For example, the 3D model generation unit analyzes the captured data, and the generation AI automatically recognizes the materials and textures of walls and floors. For example, it identifies materials such as wood, tile, and carpet and reflects them in the 3D model. The 3D model generation unit also analyzes the details of the texture and reproduces realistic textures. For example, it accurately reproduces the patterns on walls and the texture of floors. Furthermore, the 3D model generation unit integrates multiple captured data to generate a more detailed 3D model. For example, it combines data captured from different angles to accurately reproduce the materials and textures of the entire room. This automatically recognizes the materials and textures of a room and provides a more realistic 3D model.

[0032] During a furniture layout simulation in a virtual space, the generation AI evaluates the flow lines and usability of the furniture layout in real time, and can propose the optimal layout. For example, when arranging furniture in a virtual space, the simulation unit has the generation AI evaluate the flow lines and usability in real time and propose the optimal layout. For example, the furniture layout is adjusted so that it does not obstruct the flow lines. The simulation unit also evaluates the usability of the furniture layout and proposes the optimal layout for the user. For example, it evaluates whether the furniture layout is easy to use and finds the optimal layout. Furthermore, the simulation unit displays the evaluation results in real time, making it easier for the user to adjust the layout. For example, it displays the evaluation results on a screen and updates them each time the user changes the layout. This makes it possible to evaluate the flow lines and usability of the furniture layout in real time and propose the optimal layout.

[0033] When proposing designs for custom-made furniture, the design proposal unit allows the generation AI to learn the user's past purchase history and preferences, enabling it to propose more accurate designs. For example, the design proposal unit stores the user's past purchase history and preferences in a database, and the generation AI learns from that data. For example, it analyzes trends in the design and materials of furniture purchased in the past. The design proposal unit also proposes designs that suit the user's preferences based on the learned data. For example, it proposes custom-made furniture that reflects the user's preferred designs and colors. Furthermore, the design proposal unit can customize the design according to the user's requests. For example, it adjusts the size and shape to fit a specific space. This allows it to learn the user's past purchase history and preferences and propose more accurate designs.

[0034] The interior proposal unit learns the user's past interior selection history and can make more accurate suggestions. For example, the interior proposal unit stores the user's past interior selection history in a database, and the generation AI learns from that data. For example, it analyzes trends in furniture and interior items selected in the past. The interior proposal unit also uses the learned data to propose interior plans that match the user's preferences. For example, it creates interior plans that reflect the user's preferred styles and colors. Furthermore, the interior proposal unit can customize proposals according to the user's requests. For example, it can propose the optimal interior items within a specific budget. This allows the unit to learn the user's past interior selection history and make more accurate suggestions.

[0035] The interior proposal unit allows the generation AI to automatically visualize the preferences and images verbally communicated by the user and allow the user to confirm them. For example, the generation AI automatically visualizes the preferences and images verbally communicated by the user. For example, it visualizes a request such as "a modern design in black and white" and allows the user to confirm it. The interior proposal unit also makes specific suggestions to the user based on the visualized image. For example, it suggests optimal furniture and interior items based on the visualized image. Furthermore, the interior proposal unit can adjust the suggestions while the user is checking the visualized image. For example, the user can change the color or design while looking at the image. In this way, the preferences and images verbally communicated by the user can be visualized and confirmed, allowing for a more specific interior plan to be proposed.

[0036] The simulation unit can have the generation AI simulate the acoustic and lighting effects resulting from the furniture arrangement when simulating furniture arrangement in the virtual space and provide the results to the user. For example, when arranging furniture in the virtual space, the simulation unit has the generation AI simulate the acoustic and lighting effects and provide the results to the user. For example, the simulation unit adjusts the furniture arrangement to take into account the effects of sound reverberation and lighting. The simulation unit also suggests an optimal arrangement to the user based on the acoustic and lighting effects. For example, the simulation unit changes the furniture arrangement to reduce sound reverberation. Furthermore, the simulation unit displays the simulation results in real time, making it easier for the user to adjust the arrangement. For example, the simulation results are displayed on a screen and updated each time the user changes the arrangement. This allows the acoustic and lighting effects resulting from the furniture arrangement to be simulated and provided to the user.

[0037] The photography unit stores photographed data of a room in the cloud and makes it accessible from different devices, allowing multiple users to collaborate on interior planning. The photography unit, for example, stores photographed data of a room in the cloud and makes it accessible from different devices. For example, data photographed with a smartphone can be uploaded to the cloud and made accessible from a tablet or PC. The photography unit also provides a data sharing function so that multiple users can collaborate on interior planning. For example, planning can be carried out together with family and friends. Furthermore, the photography unit manages data on the cloud and ensures security. For example, data encryption and access control are performed. This allows multiple users to collaborate on interior planning.

[0038] The interior proposal unit can add a function that allows a user to share their preferences and images with other users and receive feedback within the community. The interior proposal unit adds, for example, a function that allows a user to share their preferences and images with other users and receive feedback within the community. For example, an interior plan can be shared and comments and ratings can be received from other users. The interior proposal unit also improves the proposal based on the shared feedback. For example, it creates an interior plan that reflects the opinions of other users. Furthermore, the interior proposal unit provides a function to promote interaction within the community. For example, a chat function that allows users to exchange opinions with each other can be added. This allows a user to share their preferences and images with other users and receive feedback within the community.

[0039] The interior proposal unit allows the AI ​​that generates interior preferences and images to automatically post them on social media, thereby widely soliciting opinions. For example, the interior proposal unit allows the AI ​​that generates the user's preferences and images to automatically post them on social media, thereby widely soliciting opinions. For example, the AI ​​shares an interior plan on social media and collects comments and ratings from followers. The interior proposal unit also improves its proposal based on the collected opinions. For example, it creates an interior plan that reflects the opinions of followers. Furthermore, the interior proposal unit provides functions to promote interaction on social media. For example, it adds a comment function that allows users to exchange opinions with each other. This allows users to post interior preferences and images on social media and widely solicit opinions.

[0040] The design proposal unit can add a function to share design proposals for custom-made furniture with other users and obtain feedback. The design proposal unit adds a function to share design proposals for custom-made furniture with other users and obtain feedback, for example, by sharing the design proposal on a social networking site and collecting comments and ratings from other users. The design proposal unit also improves the proposal based on the shared feedback, for example, by creating a design that reflects the opinions of other users. Furthermore, the design proposal unit provides a function to promote interaction within the community, for example, by adding a chat function that allows users to exchange opinions with each other. This allows design proposals for custom-made furniture to be shared with other users and obtain feedback.

[0041] The design proposal unit allows the AI ​​that generates the design proposal to automatically post it on social media, thereby widely soliciting opinions. For example, the design proposal unit allows the AI ​​that generates the design proposal for custom-made furniture to automatically post it on social media, thereby widely soliciting opinions. For example, the design proposal is shared on social media, and comments and ratings are collected from followers. The design proposal unit also improves the proposal based on the collected opinions. For example, it creates a design that reflects the opinions of followers. Furthermore, the design proposal unit provides functions to promote interaction on social media. For example, it adds a comment function that allows users to exchange opinions with each other. This allows the design proposal to be posted on social media, thereby widely soliciting opinions.

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

[0043] The interior suggestion unit can make suggestions that take into account the user's health condition. For example, if the user has allergies, it can suggest furniture made from allergen-free materials. The interior suggestion unit can also suggest interior items that promote health based on the user's lifestyle. For example, it can suggest a chair that supports posture for a user who often sits for long periods of time. Furthermore, the interior suggestion unit can create an optimal interior plan based on the user's health data. For example, it can suggest beds and lighting that will improve the quality of sleep.

[0044] When simulating furniture layout in a virtual space, the generation AI evaluates the energy efficiency of the furniture layout and can propose the optimal layout. For example, the furniture layout is adjusted to make the most of natural light. The simulation unit also proposes the optimal layout for the user based on energy efficiency. For example, the furniture layout is changed to improve heating and cooling efficiency. Furthermore, the simulation unit displays the evaluation results in real time, making it easier for the user to adjust the layout. For example, the evaluation results are displayed on the screen and updated each time the user changes the layout. This makes it possible to evaluate the energy efficiency of furniture layout and propose the optimal layout.

[0045] When the camera captures a room, the generation AI displays the color balance in real time, allowing the user to check it as they proceed with the shoot. For example, when a user takes a picture of a room with a smartphone or tablet, the generation AI displays the color balance of the walls and floor on the screen in real time. For example, if the user points the camera at a corner of the room, the color balance from corner to corner is instantly displayed. The camera also updates the color balance display in real time so that the user can check it as they proceed with the shoot. For example, if the user moves the camera, the displayed color balance changes accordingly. This allows the user to check the color balance in real time as they proceed with the shoot, preventing shooting errors.

[0046] The photography unit stores photographed data of a room in the cloud and makes it accessible from different devices, allowing multiple users to collaborate on interior planning. For example, room photographed data can be stored in the cloud and made accessible from different devices. For example, data taken with a smartphone can be uploaded to the cloud and made accessible from a tablet or PC. The photography unit also provides a data sharing function so that multiple users can collaborate on interior planning. For example, planning can be carried out together with family and friends. Furthermore, the photography unit manages data on the cloud and ensures security. For example, data encryption and access control are performed. This allows multiple users to collaborate on interior planning.

[0047] When simulating furniture placement in a virtual space, the generation AI can also simulate the acoustic and lighting effects resulting from the furniture placement and provide them to the user. For example, when placing furniture in a virtual space, the generation AI simulates the acoustic and lighting effects and provides them to the user. For example, the furniture placement is adjusted to take into account the effects of sound reverberation and lighting. The simulation unit also suggests an optimal placement to the user based on the acoustic and lighting effects. For example, the furniture placement may be changed to reduce sound reverberation. Furthermore, the simulation unit displays the simulation results in real time, making it easier for the user to adjust the placement. For example, the simulation results may be displayed on a screen and updated each time the user changes the placement. This allows the acoustic and lighting effects resulting from the furniture placement to be simulated and provided to the user.

[0048] The design proposal unit can add a function to share design proposals for custom-made furniture with other users and obtain feedback. For example, a function to share design proposals for custom-made furniture with other users and obtain feedback is added. For example, the design proposals can be shared on social media and comments and ratings from other users can be collected. The design proposal unit can also improve the proposals based on the shared feedback. For example, it can create designs that reflect the opinions of other users. Furthermore, the design proposal unit can provide a function to promote interaction within the community. For example, a chat function can be added that allows users to exchange opinions with each other. This allows design proposals for custom-made furniture to be shared with other users and obtain feedback.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The user takes a picture of the room using a smartphone or tablet. For example, the user can simply take a picture of the four corners of the room, and the camera acquires that data. The camera can also take a panoramic picture of the entire room. Step 2: The dimension determination unit automatically determines the dimensions of the room from the photographed data acquired by the photographing unit. For example, the dimension determination unit measures the dimensions of the walls and floors using image analysis technology. The dimension determination unit can also measure the dimensions of the room using a sensor. Step 3: The 3D model generator generates a detailed 3D model based on the dimensions determined by the dimension determiner. For example, the 3D model generator uses dedicated software to create a 3D model of a room. The 3D model generator can also integrate multiple photographic data to improve the accuracy of the model. Step 4: The interior design proposal unit analyzes the user's interior preferences, image, and budget, and proposes furniture avatars that match their needs. For example, the interior design proposal unit analyzes the user's preferences based on survey data and past purchase history. The interior design proposal unit also uses generative AI to create an interior design plan based on the user's requests. Step 5: The simulation unit arranges the furniture avatars proposed by the interior proposal unit in the virtual space and simulates it. For example, the simulation unit allows the user to find the optimal layout by trial and error when arranging sofas and tables. The simulation unit can also display the furniture arrangement in the virtual space in real time. Step 6: The Design Proposal Department proposes custom-made furniture designs if the desired furniture is not commercially available. For example, the Design Proposal Department proposes designs for storage shelves and tables based on the user's requests. The Design Proposal Department can also use generative AI to create optimal designs. Step 7: The ordering department places an order with a partner factory for the design proposed by the design proposal department. For example, the ordering department sends the design data to the factory and requests the production of custom-made furniture. The ordering department can also manage the progress of production.

[0051] (Example 2) The 3D Interior Planner according to an embodiment of the present invention is an interior planning service that utilizes AI technology to generate a 3D model of a space simply by a user taking a photo of the room using a smartphone or tablet. This allows the user to easily plan the interior of the room and realize their ideal space.

[0052] A 3D interior planner according to an embodiment includes a photographing unit, a dimension determination unit, a 3D model generation unit, an interior proposal unit, a simulation unit, a design proposal unit, and an ordering unit. The photographing unit allows a user to photograph a room using a smartphone or tablet. For example, the user can simply photograph the four corners of the room, and the photographing unit acquires the data. The photographing unit can also capture a panoramic image of the entire room. The dimension determination unit automatically determines the dimensions of the room from the photographed data acquired by the photographing unit. For example, the dimension determination unit measures the dimensions of the walls and floors using image analysis technology. The dimension determination unit can also measure the dimensions of the room using a sensor. The 3D model generation unit generates a detailed 3D model based on the dimensions determined by the dimension determination unit. For example, the 3D model generation unit creates a 3D model of the room using dedicated software. The 3D model generation unit can also integrate multiple photographed data to improve the accuracy of the model. The interior proposal unit analyzes the user's interior preferences, image, and budget, and proposes furniture avatars that match their needs. For example, the interior proposal department analyzes user preferences based on survey data and past purchase history. The interior proposal department also uses generative AI to create an interior plan based on the user's requests. The simulation department arranges avatars of furniture proposed by the interior proposal department in a virtual space and simulates the layout. For example, the simulation department allows the user to find the optimal layout by trial and error when arranging sofas and tables. The simulation department can also display the furniture layout in the virtual space in real time. The design proposal department proposes designs for custom-made furniture when furniture that meets the user's needs is not commercially available. For example, the design proposal department proposes designs for storage shelves and tables based on the user's requests. The design proposal department can also create optimal designs using generative AI. The ordering department places an order for the design proposed by the design proposal department with a partner factory. For example, the ordering department sends design data to the factory and requests the production of custom-made furniture. The ordering department can also manage the progress of production.As a result, the 3D interior planner according to the embodiment allows users to easily plan the interior of a room and realize their ideal space. For example, users can accurately grasp the dimensions of a room and arrange the furniture they desire in a virtual space, allowing them to check the optimal layout before actually purchasing it. Furthermore, custom-made furniture can be proposed to meet the individual needs of users.

[0053] When the camera captures a room, the generation AI displays the dimensions in real time, allowing the user to check them as they proceed with the shoot. For example, when a user takes a picture of a room with a smartphone or tablet, the generation AI displays the dimensions of the walls and floors on the screen in real time. For example, when the camera is pointed at a corner of the room, the distance from corner to corner is instantly displayed. The camera also updates the displayed dimensions in real time so that the user can check them as they proceed with the shoot. For example, when the user moves the camera, the displayed dimensions change accordingly. This allows the user to check the dimensions in real time as they proceed with the shoot, preventing shooting errors.

[0054] The 3D model generation unit can automatically recognize the materials and textures of a room based on the captured data and reflect them in the 3D model. For example, the 3D model generation unit analyzes the captured data, and the generation AI automatically recognizes the materials and textures of walls and floors. For example, it identifies materials such as wood, tile, and carpet and reflects them in the 3D model. The 3D model generation unit also analyzes the details of the texture and reproduces realistic textures. For example, it accurately reproduces the patterns on walls and the texture of floors. Furthermore, the 3D model generation unit integrates multiple captured data to generate a more detailed 3D model. For example, it combines data captured from different angles to accurately reproduce the materials and textures of the entire room. This automatically recognizes the materials and textures of a room and provides a more realistic 3D model.

[0055] During a furniture layout simulation in a virtual space, the generation AI evaluates the flow lines and usability of the furniture layout in real time, and can propose the optimal layout. For example, when arranging furniture in a virtual space, the simulation unit has the generation AI evaluate the flow lines and usability in real time and propose the optimal layout. For example, the furniture layout is adjusted so that it does not obstruct the flow lines. The simulation unit also evaluates the usability of the furniture layout and proposes the optimal layout for the user. For example, it evaluates whether the furniture layout is easy to use and finds the optimal layout. Furthermore, the simulation unit displays the evaluation results in real time, making it easier for the user to adjust the layout. For example, it displays the evaluation results on a screen and updates them each time the user changes the layout. This makes it possible to evaluate the flow lines and usability of the furniture layout in real time and propose the optimal layout.

[0056] When proposing designs for custom-made furniture, the design proposal unit allows the generation AI to learn the user's past purchase history and preferences, enabling it to propose more accurate designs. For example, the design proposal unit stores the user's past purchase history and preferences in a database, and the generation AI learns from that data. For example, it analyzes trends in the design and materials of furniture purchased in the past. The design proposal unit also proposes designs that suit the user's preferences based on the learned data. For example, it proposes custom-made furniture that reflects the user's preferred designs and colors. Furthermore, the design proposal unit can customize the design according to the user's requests. For example, it adjusts the size and shape to fit a specific space. This allows it to learn the user's past purchase history and preferences and propose more accurate designs.

[0057] The interior proposal unit learns the user's past interior selection history and can make more accurate suggestions. For example, the interior proposal unit stores the user's past interior selection history in a database, and the generation AI learns from that data. For example, it analyzes trends in furniture and interior items selected in the past. The interior proposal unit also uses the learned data to propose interior plans that match the user's preferences. For example, it creates interior plans that reflect the user's preferred styles and colors. Furthermore, the interior proposal unit can customize proposals according to the user's requests. For example, it can propose the optimal interior items within a specific budget. This allows the unit to learn the user's past interior selection history and make more accurate suggestions.

[0058] The interior proposal unit allows the generation AI to automatically visualize the preferences and images verbally communicated by the user and allow the user to confirm them. For example, the generation AI automatically visualizes the preferences and images verbally communicated by the user. For example, it visualizes a request such as "a modern design in black and white" and allows the user to confirm it. The interior proposal unit also makes specific suggestions to the user based on the visualized image. For example, it suggests optimal furniture and interior items based on the visualized image. Furthermore, the interior proposal unit can adjust the suggestions while the user is checking the visualized image. For example, the user can change the color or design while looking at the image. In this way, the preferences and images verbally communicated by the user can be visualized and confirmed, allowing for a more specific interior plan to be proposed.

[0059] The simulation unit can have the generation AI simulate the acoustic and lighting effects resulting from the furniture arrangement when simulating furniture arrangement in the virtual space and provide the results to the user. For example, when arranging furniture in the virtual space, the simulation unit has the generation AI simulate the acoustic and lighting effects and provide the results to the user. For example, the simulation unit adjusts the furniture arrangement to take into account the effects of sound reverberation and lighting. The simulation unit also suggests an optimal arrangement to the user based on the acoustic and lighting effects. For example, the simulation unit changes the furniture arrangement to reduce sound reverberation. Furthermore, the simulation unit displays the simulation results in real time, making it easier for the user to adjust the arrangement. For example, the simulation results are displayed on a screen and updated each time the user changes the arrangement. This allows the acoustic and lighting effects resulting from the furniture arrangement to be simulated and provided to the user.

[0060] The simulation unit can use the emotion estimation function to measure the level of satisfaction felt by the user during the simulation in real time and propose the layout that provides the highest level of satisfaction. For example, the simulation unit can analyze the user's emotions in real time during the simulation and measure the level of satisfaction. For example, if the user is satisfied, the simulation unit can preferentially propose that layout. The simulation unit can also use the emotion estimation function to analyze the user's emotions. For example, the simulation unit can use facial expression recognition technology to analyze the user's facial expressions and determine the emotions. The simulation unit can also use an algorithm to measure satisfaction in real time. For example, the simulation unit can preferentially propose the layout or layout that the user prefers. This allows the simulation unit to measure the user's satisfaction in real time and propose the layout that provides the highest level of satisfaction.

[0061] The photography unit stores photographed data of a room in the cloud and makes it accessible from different devices, allowing multiple users to collaborate on interior planning. The photography unit, for example, stores photographed data of a room in the cloud and makes it accessible from different devices. For example, data photographed with a smartphone can be uploaded to the cloud and made accessible from a tablet or PC. The photography unit also provides a data sharing function so that multiple users can collaborate on interior planning. For example, planning can be carried out together with family and friends. Furthermore, the photography unit manages data on the cloud and ensures security. For example, data encryption and access control are performed. This allows multiple users to collaborate on interior planning.

[0062] The interior suggestion unit can use the emotion estimation function to analyze the user's emotions during verbal input and prioritize suggestions based on positive emotions. The interior suggestion unit, for example, analyzes the user's emotions during verbal input in real time and prioritizes suggestions based on positive emotions. For example, if the user is speaking happily, it proposes an interior plan based on that emotion. The interior suggestion unit also uses the emotion estimation function to analyze the user's emotions. For example, it uses voice recognition technology to analyze the user's voice and determine the user's emotions. Furthermore, the interior suggestion unit uses an algorithm for making suggestions based on positive emotions. For example, it prioritizes suggestions based on the user's preferred styles and colors. This makes it possible to analyze the user's emotions and prioritize suggestions based on positive emotions.

[0063] The interior proposal unit can add a function that allows a user to share their preferences and images with other users and receive feedback within the community. The interior proposal unit adds, for example, a function that allows a user to share their preferences and images with other users and receive feedback within the community. For example, an interior plan can be shared and comments and ratings can be received from other users. The interior proposal unit also improves the proposal based on the shared feedback. For example, it creates an interior plan that reflects the opinions of other users. Furthermore, the interior proposal unit provides a function to promote interaction within the community. For example, a chat function that allows users to exchange opinions with each other can be added. This allows a user to share their preferences and images with other users and receive feedback within the community.

[0064] The interior proposal unit allows the AI ​​that generates interior preferences and images to automatically post them on social media, thereby widely soliciting opinions. For example, the interior proposal unit allows the AI ​​that generates the user's preferences and images to automatically post them on social media, thereby widely soliciting opinions. For example, the AI ​​shares an interior plan on social media and collects comments and ratings from followers. The interior proposal unit also improves its proposal based on the collected opinions. For example, it creates an interior plan that reflects the opinions of followers. Furthermore, the interior proposal unit provides functions to promote interaction on social media. For example, it adds a comment function that allows users to exchange opinions with each other. This allows users to post interior preferences and images on social media and widely solicit opinions.

[0065] The interior suggestion unit can use the emotion estimation function to collect other users' emotional reactions to the user's preferences and images and reflect them in the proposal. The interior suggestion unit, for example, uses the emotion estimation function to collect other users' emotional reactions to the user's preferences and images. For example, it analyzes comments and reactions on social media and reflects them in the proposal. The interior suggestion unit also improves the proposal based on the collected emotional reactions. For example, it creates an interior plan that reflects the emotions of other users. Furthermore, the interior suggestion unit uses an algorithm to analyze the emotional reactions in real time and reflect them in the proposal. For example, it prioritizes reflecting positive emotions. This allows the emotional reactions of other users to the user's preferences and images to be collected and reflected in the proposal.

[0066] The design proposal unit can add a function to share design proposals for custom-made furniture with other users and obtain feedback. The design proposal unit adds a function to share design proposals for custom-made furniture with other users and obtain feedback, for example, by sharing the design proposal on a social networking site and collecting comments and ratings from other users. The design proposal unit also improves the proposal based on the shared feedback, for example, by creating a design that reflects the opinions of other users. Furthermore, the design proposal unit provides a function to promote interaction within the community, for example, by adding a chat function that allows users to exchange opinions with each other. This allows design proposals for custom-made furniture to be shared with other users and obtain feedback.

[0067] The design proposal unit allows the AI ​​that generates the design proposal to automatically post it on social media, thereby widely soliciting opinions. For example, the design proposal unit allows the AI ​​that generates the design proposal for custom-made furniture to automatically post it on social media, thereby widely soliciting opinions. For example, the design proposal is shared on social media, and comments and ratings are collected from followers. The design proposal unit also improves the proposal based on the collected opinions. For example, it creates a design that reflects the opinions of followers. Furthermore, the design proposal unit provides functions to promote interaction on social media. For example, it adds a comment function that allows users to exchange opinions with each other. This allows the design proposal to be posted on social media, thereby widely soliciting opinions.

[0068] The design proposal unit can use the emotion estimation function to collect other users' emotional reactions to the design proposal and reflect them in the proposal. The design proposal unit, for example, uses the emotion estimation function to collect other users' emotional reactions to the design proposal. For example, it analyzes comments and reactions on social media and reflects them in the proposal. The design proposal unit also improves the proposal based on the collected emotional reactions. For example, it creates a design that reflects the emotions of other users. Furthermore, the design proposal unit uses an algorithm to analyze the emotional reactions in real time and reflect them in the proposal. For example, it prioritizes reflecting positive emotions. This allows the emotional reactions of other users to be collected and reflected in the proposal.

[0069] The image capture unit can use the emotion estimation function to provide real-time guidance and advice to reduce stress and anxiety felt by the user when capturing images. For example, the image capture unit analyzes the user's facial expressions and voice while capturing images and uses the emotion estimation function to detect stress and anxiety. For example, if the user looks anxious, the image capture unit displays advice to help the user relax. Furthermore, the image capture unit can provide real-time guidance and advice when the user feels stressed or anxious. For example, the image capture unit displays a guide that clearly explains the capture procedure. Furthermore, the image capture unit can also suggest music and lighting to help the user relax. For example, the image capture unit can play relaxing music and adjust the lighting. This allows real-time guidance and advice to be provided to reduce stress and anxiety felt by the user when capturing images.

[0070] The imaging unit can use the emotion estimation function to analyze the user's emotions at the time of shooting and suggest music and lighting to elicit positive emotions. The imaging unit, for example, analyzes the user's emotions in real time at the time of shooting and suggests music to elicit positive emotions. For example, automatically plays music that helps the user relax. The imaging unit also uses the emotion estimation function to analyze the user's emotions. For example, it uses facial expression recognition technology to analyze the user's facial expressions and determine the emotions. The imaging unit also suggests lighting to elicit positive emotions. For example, it adjusts the lighting so that the user can relax. This makes it possible to analyze the user's emotions at the time of shooting and suggest music and lighting to elicit positive emotions.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The interior suggestion unit can make suggestions that take into account the user's health condition. For example, if the user has allergies, it can suggest furniture made from allergen-free materials. The interior suggestion unit can also suggest interior items that promote health based on the user's lifestyle. For example, it can suggest a chair that supports posture for a user who often sits for long periods of time. Furthermore, the interior suggestion unit can create an optimal interior plan based on the user's health data. For example, it can suggest beds and lighting that will improve the quality of sleep.

[0073] When simulating furniture layout in a virtual space, the generation AI evaluates the energy efficiency of the furniture layout and can propose the optimal layout. For example, the furniture layout is adjusted to make the most of natural light. The simulation unit also proposes the optimal layout for the user based on energy efficiency. For example, the furniture layout is changed to improve heating and cooling efficiency. Furthermore, the simulation unit displays the evaluation results in real time, making it easier for the user to adjust the layout. For example, the evaluation results are displayed on the screen and updated each time the user changes the layout. This makes it possible to evaluate the energy efficiency of furniture layout and propose the optimal layout.

[0074] When the camera captures a room, the generation AI displays the color balance in real time, allowing the user to check it as they proceed with the shoot. For example, when a user takes a picture of a room with a smartphone or tablet, the generation AI displays the color balance of the walls and floor on the screen in real time. For example, if the user points the camera at a corner of the room, the color balance from corner to corner is instantly displayed. The camera also updates the color balance display in real time so that the user can check it as they proceed with the shoot. For example, if the user moves the camera, the displayed color balance changes accordingly. This allows the user to check the color balance in real time as they proceed with the shoot, preventing shooting errors.

[0075] The interior suggestion unit can use the emotion estimation function to suggest interior items with a relaxing effect based on the user's emotions. For example, if the user is feeling stressed, it can suggest lighting or plants with a relaxing effect. The interior suggestion unit also uses the emotion estimation function to analyze the user's emotions. For example, it can use facial expression recognition technology to analyze the user's facial expressions and determine the emotion. The interior suggestion unit also uses an algorithm to suggest interior items with a relaxing effect. For example, it can prioritize suggesting items with colors and designs that help the user relax. This makes it possible to suggest interior items with a relaxing effect based on the user's emotions.

[0076] The design proposal unit uses the emotion estimation function to analyze the emotions of the user when receiving a design proposal and can propose designs that elicit positive emotions. For example, if the user feels joy or excitement when receiving a design proposal, the design proposal unit will preferentially propose that design. The design proposal unit also uses the emotion estimation function to analyze the user's emotions. For example, it may use facial expression recognition technology to analyze the user's facial expressions and determine their emotions. Furthermore, the design proposal unit uses an algorithm to propose designs that elicit positive emotions. For example, it may preferentially propose colors and shapes that the user prefers. This makes it possible to analyze the user's emotions and propose designs that elicit positive emotions.

[0077] The photography unit stores photographed data of a room in the cloud and makes it accessible from different devices, allowing multiple users to collaborate on interior planning. For example, room photographed data can be stored in the cloud and made accessible from different devices. For example, data taken with a smartphone can be uploaded to the cloud and made accessible from a tablet or PC. The photography unit also provides a data sharing function so that multiple users can collaborate on interior planning. For example, planning can be carried out together with family and friends. Furthermore, the photography unit manages data on the cloud and ensures security. For example, data encryption and access control are performed. This allows multiple users to collaborate on interior planning.

[0078] The interior suggestion unit can use the emotion estimation function to analyze the user's emotions during verbal input and prioritize suggestions based on positive emotions. For example, the emotion estimation function can analyze the user's emotions during verbal input in real time and prioritize suggestions based on positive emotions. For example, if the user is speaking happily, the interior suggestion unit can propose an interior plan based on that emotion. The interior suggestion unit also uses the emotion estimation function to analyze the user's emotions. For example, the interior suggestion unit can analyze the user's voice using voice recognition technology to determine the user's emotions. The interior suggestion unit also uses an algorithm for making suggestions based on positive emotions. For example, the interior suggestion unit can prioritize suggestions based on the user's preferred styles and colors. This makes it possible to analyze the user's emotions and prioritize suggestions based on positive emotions.

[0079] When simulating furniture placement in a virtual space, the generation AI can also simulate the acoustic and lighting effects resulting from the furniture placement and provide them to the user. For example, when placing furniture in a virtual space, the generation AI simulates the acoustic and lighting effects and provides them to the user. For example, the furniture placement is adjusted to take into account the effects of sound reverberation and lighting. The simulation unit also suggests an optimal placement to the user based on the acoustic and lighting effects. For example, the furniture placement may be changed to reduce sound reverberation. Furthermore, the simulation unit displays the simulation results in real time, making it easier for the user to adjust the placement. For example, the simulation results may be displayed on a screen and updated each time the user changes the placement. This allows the acoustic and lighting effects resulting from the furniture placement to be simulated and provided to the user.

[0080] The interior suggestion unit can use the emotion estimation function to collect other users' emotional reactions to the user's preferences and images and reflect them in the proposals. For example, the emotion estimation function can be used to collect other users' emotional reactions to the user's preferences and images. For example, comments and reactions on social media can be analyzed and reflected in the proposals. The interior suggestion unit can also improve the proposals based on the collected emotional reactions. For example, it can create an interior plan that reflects the emotions of other users. Furthermore, the interior suggestion unit uses an algorithm to analyze the emotional reactions in real time and reflect them in the proposals. For example, it can prioritize positive emotions. This allows other users' emotional reactions to the user's preferences and images to be collected and reflected in the proposals.

[0081] The design proposal unit can add a function to share design proposals for custom-made furniture with other users and obtain feedback. For example, a function to share design proposals for custom-made furniture with other users and obtain feedback is added. For example, the design proposals can be shared on social media and comments and ratings from other users can be collected. The design proposal unit can also improve the proposals based on the shared feedback. For example, it can create designs that reflect the opinions of other users. Furthermore, the design proposal unit can provide a function to promote interaction within the community. For example, a chat function can be added that allows users to exchange opinions with each other. This allows design proposals for custom-made furniture to be shared with other users and obtain feedback.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The user takes a picture of the room using a smartphone or tablet. For example, the user can simply take a picture of the four corners of the room, and the camera acquires that data. The camera can also take a panoramic picture of the entire room. Step 2: The dimension determination unit automatically determines the dimensions of the room from the photographed data acquired by the photographing unit. For example, the dimension determination unit measures the dimensions of the walls and floors using image analysis technology. The dimension determination unit can also measure the dimensions of the room using a sensor. Step 3: The 3D model generator generates a detailed 3D model based on the dimensions determined by the dimension determiner. For example, the 3D model generator uses dedicated software to create a 3D model of a room. The 3D model generator can also integrate multiple photographic data to improve the accuracy of the model. Step 4: The interior design proposal unit analyzes the user's interior preferences, image, and budget, and proposes furniture avatars that match their needs. For example, the interior design proposal unit analyzes the user's preferences based on survey data and past purchase history. The interior design proposal unit also uses generative AI to create an interior design plan based on the user's requests. Step 5: The simulation unit arranges the furniture avatars proposed by the interior proposal unit in the virtual space and simulates it. For example, the simulation unit allows the user to find the optimal layout by trial and error when arranging sofas and tables. The simulation unit can also display the furniture arrangement in the virtual space in real time. Step 6: The Design Proposal Department proposes custom-made furniture designs if the desired furniture is not commercially available. For example, the Design Proposal Department proposes designs for storage shelves and tables based on the user's requests. The Design Proposal Department can also use generative AI to create optimal designs. Step 7: The ordering department places an order with a partner factory for the design proposed by the design proposal department. For example, the ordering department sends the design data to the factory and requests the production of custom-made furniture. The ordering department can also manage the progress of production.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] 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.

[0143] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A photographing unit that allows a user to photograph a room using a smartphone or tablet; a dimension determination unit that automatically determines the dimensions of a room from the photographed data acquired by the photographing unit; a 3D model generation unit that generates a detailed 3D model based on the dimensions determined by the dimension determination unit; The interior design department analyzes the user's interior preferences, image, and budget, and proposes furniture avatars that match their needs. a simulation unit that arranges avatars of the furniture proposed by the interior design proposal unit in a virtual space and performs a simulation; The Design Proposal Department offers custom-made furniture design proposals when desired furniture is not available on the market. an ordering unit that places an order for the design proposed by the design proposal unit to a partner factory. A system characterized by:

2. The 3D model generation unit The materials and textures of the room are automatically recognized based on the photographic data and reflected in the 3D model.

2. The system of claim 1.

3. The interior proposal unit The system learns the user's past interior selection history and makes more accurate suggestions.

2. The system of claim 1.

4. The simulation unit When simulating the arrangement of the furniture in the virtual space, the generation AI also simulates the acoustic and lighting effects resulting from the arrangement of the furniture and provides the simulation results to the user.

2. The system of claim 1.

5. The design proposal section Analyzing the emotions of the users when they received the design proposals, and prioritizing the designs based on positive emotions.

2. The system of claim 1.

6. The imaging unit is Providing real-time guidance and advice to reduce stress and anxiety experienced by the user while taking photos 2. The system of claim 1.

7. The interior proposal unit Analyzing the user's sentiment during verbal input and prioritizing the suggestions based on positive sentiment.

2. The system of claim 1.

8. The simulation unit The degree of satisfaction felt by the user during the simulation is measured in real time, and the arrangement that provides the highest degree of satisfaction is proposed.

2. The system of claim 1.

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