system

Abstract

The system according to this embodiment aims to provide an optimal furniture layout that is appropriate for the space of a room. [Solution] The system according to the embodiment comprises a scanning unit, an analysis unit, a generation unit, a visualization unit, and an adjustment unit. The scanning unit 3D scans the space of the room. The analysis unit analyzes the data scanned by the scanning unit. The generation unit generates an optimal furniture layout based on the data analyzed by the analysis unit. The visualization unit visualizes the layout generated by the generation unit using augmented reality (AR). The adjustment unit interactively adjusts the layout visualized by the visualization unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it takes time and effort to consider furniture selection and room layout when moving or redecorating.

[0005] The system according to the embodiment aims to provide an optimal furniture layout according to the space of the room.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a scanning unit, an analysis unit, a generation unit, a visualization unit, and an adjustment unit. The scanning unit 3D scans the room space. The analysis unit analyzes the data scanned by the scanning unit. The generation unit generates an optimal furniture layout based on the data analyzed by the analysis unit. The visualization unit visualizes the layout generated by the generation unit using augmented reality (AR). The adjustment unit interactively adjusts the layout visualized by the visualization unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide an optimal furniture layout tailored to the space of the room. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The spatial design system according to an embodiment of the present invention is a system that provides optimal furniture selection and spatial design according to the space of a room. The spatial design system combines multimodal generation AI and AR to provide furniture selection and spatial design according to the space of a room. Specifically, it provides a smartphone application with the following functions. First, the user 3D scans the space of the room using a smartphone. Next, the generation AI analyzes the scan data and generates the optimal furniture layout. The generated layout is visualized in the room using AR, and the user can interactively adjust the placement image. Furthermore, it also provides support for purchasing the suggested furniture. For example, the user 3D scans the space of the room using a smartphone. In this case, the entire room is photographed using the smartphone's camera and 3D data is generated. For example, by scanning the four corners of the room and the positions of furniture in detail, an accurate 3D model can be created. Next, the generation AI analyzes the scan data and generates the optimal furniture layout. The generation AI proposes the optimal layout considering the size and shape of the room, the arrangement of existing furniture, etc. For example, in a living room, the space can be effectively utilized by optimizing the placement of sofas, tables, and televisions. The generated layout is visualized in the room using AR. Users can use their smartphones to look around their room and see the suggested furniture arrangement in real time. For example, they can change the position of the sofa or adjust the size of the table. Furthermore, users can interactively adjust the arrangement image. For example, they can change the position of furniture by tapping the smartphone screen or adjust its size by dragging it. This makes it easy for users to create a layout that suits their preferences. Finally, the app also provides support for purchasing the suggested furniture. Users can purchase the suggested furniture within the app, and the purchase process is also simple. For example, they can purchase the suggested sofa or table directly from the online shop.In this way, by using a smartphone app that combines multimodal generation AI and AR, it is possible to provide optimal furniture selection and spatial design tailored to the room's space. Users can realize their ideal room without spending time and effort. As a result, the spatial design system can provide optimal furniture selection and spatial design tailored to the room's space.

[0029] The spatial design system according to this embodiment comprises a scanning unit, an analysis unit, a generation unit, a visualization unit, and an adjustment unit. The scanning unit 3D scans the space of a room. The scanning unit generates 3D data by, for example, using a smartphone camera to photograph the entire room. The scanning unit can create an accurate 3D model by, for example, scanning the four corners of the room and the positions of furniture in detail. The scanning unit can also generate 3D data by, for example, using a smartphone camera to photograph from multiple angles when scanning the entire room. The analysis unit analyzes the data scanned by the scanning unit. The analysis unit analyzes the scanned data using, for example, a generation AI, and proposes an optimal layout considering the size and shape of the room, the arrangement of existing furniture, etc. The analysis unit, for example, uses a generation AI to take the scanned data as input and outputs the optimal furniture arrangement. The analysis unit can also propose an optimal furniture arrangement by, for example, using a generation AI to analyze the size and shape of the room. The generation unit generates an optimal furniture layout based on the data analyzed by the analysis unit. The generation unit generates the optimal layout, taking into account factors such as the size and shape of the room and the arrangement of existing furniture, for example, using a generation AI. The generation unit can, for example, take the size and shape of the room as input and output the optimal furniture arrangement. The generation unit can also, for example, have the generation AI analyze the size and shape of the room and generate the optimal furniture arrangement. The visualization unit visualizes the layout generated by the generation unit using augmented reality (AR). The visualization unit allows users to, for example, look around the room using a smartphone to check the proposed furniture arrangement in real time. The visualization unit allows users to, for example, check the proposed furniture arrangement in real time through the smartphone screen. The visualization unit can also, for example, check the proposed furniture arrangement in real time using the smartphone camera. The adjustment unit interactively adjusts the layout visualized by the visualization unit. The adjustment unit allows users to, for example, change the position of furniture by tapping the smartphone screen or adjust its size by dragging it.The adjustment unit can, for example, change the position or adjust the size of furniture via a smartphone screen. This allows the spatial design system according to the embodiment to provide optimal furniture selection and spatial design tailored to the room's space.

[0030] The scanning unit performs a 3D scan of the room space. For example, the scanning unit uses a smartphone camera to capture images of the entire room and generates 3D data. Specifically, it takes images from multiple angles, pointing the smartphone camera at the four corners and the center of the room, and integrates these images to create an accurate 3D model. The scanning unit can create an accurate 3D model by, for example, scanning the four corners of the room and the positions of furniture in detail. The scanning unit can also generate 3D data by taking images from multiple angles using a smartphone camera when scanning the entire room. Furthermore, the scanning unit can also scan the height of the room and the shape of the ceiling, thereby creating a more detailed 3D model. The scanning unit can also capture information such as the room's lighting conditions, wall color, and texture, and reflect this information in the 3D model. As a result, the scanning unit can scan the room space in detail and accurately, providing high-quality data necessary for subsequent analysis and generation processes.

[0031] The analysis unit analyzes the data scanned by the scanning unit. For example, using a generative AI, the analysis unit analyzes the scanned data and proposes an optimal layout, taking into account the room's size and shape, the placement of existing furniture, and other factors. Specifically, the generative AI receives the scanned data as input, analyzes the room's dimensions and shape, and outputs optimal suggestions regarding furniture placement. For example, the generative AI analyzes the room's shape and evaluates whether the furniture placement is efficient. The generative AI can also propose optimal furniture placement based on the room's purpose and the occupants' lifestyle. For example, in a living room, it might suggest optimizing the placement of the television and sofa to improve the viewing experience. Furthermore, the analysis unit can analyze the room's lighting conditions and natural light based on the scanned data and adjust the furniture placement accordingly. This allows the analysis unit to analyze the scanned data in detail and propose an optimal layout to maximize the use of the room's space.

[0032] The generation unit generates the optimal furniture layout based on the data analyzed by the analysis unit. For example, the generation unit uses generation AI to generate the optimal layout, taking into account the size and shape of the room, the arrangement of existing furniture, etc. Specifically, the generation AI takes the dimensions and shape of the room as input and outputs the optimal furniture arrangement. For example, the generation AI analyzes the shape of the room and evaluates whether the furniture arrangement is efficient. The generation AI can also suggest the optimal furniture arrangement based on the room's purpose and the occupants' lifestyle. For example, in the case of a living room, it can suggest optimizing the position of the TV and sofa to improve the viewing experience. Furthermore, the generation unit can analyze the room's lighting conditions and the amount of natural light entering the room based on the scan data and adjust the furniture arrangement accordingly. This allows the generation unit to analyze the scan data in detail and suggest the optimal layout to make the most of the room's space.

[0033] The visualization unit visualizes the layout generated by the generation unit using augmented reality (AR). The visualization unit allows users to, for example, view the room using a smartphone to check the proposed furniture arrangement in real time. Specifically, it scans the room using the smartphone's camera and overlays it with the generated 3D model to display the proposed furniture arrangement in real time. The visualization unit allows users to check the proposed furniture arrangement in real time through their smartphone screen. Furthermore, the visualization unit can display not only the furniture arrangement but also colors, textures, and lighting effects in real time. This allows users to intuitively understand how the proposed layout will look in their actual room. The visualization unit can also perform simulations, such as changing the furniture arrangement or adding new furniture. This allows users to try various layouts and find the optimal arrangement.

[0034] The adjustment unit interactively adjusts the layout visualized by the visualization unit. For example, the adjustment unit allows users to change the position of furniture by tapping the smartphone screen or adjust its size by dragging it. Specifically, users can select furniture by tapping the smartphone screen, change its position by dragging it, or adjust its size with pinch gestures. The adjustment unit can, for example, change the position and size of furniture through the smartphone screen. Furthermore, the adjustment unit reflects user changes in real time and, in conjunction with the visualization unit, displays the new layout. This allows users to experiment with various layouts to find the optimal arrangement. The adjustment unit also provides features such as changing the color and texture of furniture, allowing users to customize the room design down to the smallest detail. Thus, the adjustment unit provides a powerful tool for users to interactively adjust the room layout and achieve the optimal spatial design.

[0035] The system includes a purchase support unit that assists users in purchasing the suggested furniture. The purchase support unit helps users easily purchase the suggested furniture. For example, the purchase support unit provides links to purchase the suggested furniture from online shops. For example, the purchase support unit provides an interface to easily carry out the purchase procedure for the suggested furniture. For example, the purchase support unit provides support regarding the purchase of the suggested furniture. This makes it easy for users to purchase the suggested furniture. Some or all of the above processes in the purchase support unit may be performed using AI, for example, or not using AI. For example, the purchase support unit can automate the purchase procedure for the suggested furniture using an AI model.

[0036] The scanning unit can capture an entire room using a smartphone camera and generate 3D data. For example, the scanning unit can create an accurate 3D model by precisely scanning the corners of the room and the positions of furniture using a smartphone camera. The scanning unit can also capture a room from multiple angles using a smartphone camera and generate 3D data. Furthermore, the scanning unit can scan the entire room using a smartphone camera and generate 3D data. This allows for the creation of an accurate 3D model using a smartphone camera. Some or all of the above-described processes in the scanning unit may be performed using AI, or without AI. For example, the scanning unit can input image data acquired by a smartphone camera into a generation AI and have the generation AI perform the 3D data generation.

[0037] The analysis unit can propose an optimal layout considering the size and shape of the room, the arrangement of existing furniture, etc. For example, the analysis unit uses a generation AI to analyze the scan data and propose an optimal layout considering the size and shape of the room, the arrangement of existing furniture, etc. For example, the analysis unit can use a generation AI to take scan data as input and output the optimal furniture arrangement. For example, the analysis unit can use a generation AI to analyze the size and shape of the room and propose an optimal furniture arrangement. This allows the analysis unit to propose an optimal layout based on the characteristics of the room. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input scan data into a generation AI and have the generation AI propose an optimal layout.

[0038] The visualization unit allows users to view the proposed furniture arrangement in real time by looking around the room using a smartphone. The visualization unit can, for example, view the proposed furniture arrangement in real time through the smartphone screen. The visualization unit can also, for example, view the proposed furniture arrangement in real time using the smartphone camera. The visualization unit can also, for example, view the proposed furniture arrangement in real time using the smartphone screen. This allows users to view the proposed furniture arrangement in real time. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input image data acquired by the smartphone camera into a generating AI and have the generating AI perform real-time visualization.

[0039] The adjustment unit can change the position of furniture by tapping the smartphone screen or adjust its size by dragging it. The adjustment unit can change the position of furniture or adjust its size through the smartphone screen, for example. The adjustment unit can also change the position of furniture or adjust its size using the smartphone screen, for example. The adjustment unit can also change the position of furniture or adjust its size by tapping the smartphone screen or dragging it, for example. This allows the user to interactively adjust the furniture arrangement. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input operation data acquired from the smartphone screen into a generating AI and have the generating AI perform adjustments to the furniture's position and size.

[0040] The scanning unit can automatically adjust the room's lighting conditions to obtain optimal scan results. For example, before starting a scan, the scanning unit can automatically brighten the room's lighting if it is dim. During the scan, the scanning unit can adjust the color temperature of the lighting to improve the color reproduction of the scan data. After the scan is complete, the scanning unit can return the lighting conditions to their original state. This allows for optimal scan results by automatically adjusting the room's lighting conditions. Some or all of the above processes in the scanning unit may be performed using AI, for example, or without AI. For example, the scanning unit can input lighting condition data into a generating AI and have the generating AI perform the optimal adjustment of the lighting conditions.

[0041] The scanning unit can improve scanning accuracy by taking into account the room temperature and humidity during scanning. For example, before starting a scan, if the room temperature is too high, the scanning unit can automatically adjust the air conditioner to an appropriate temperature. For example, during a scan, if the humidity is high, the scanning unit can automatically activate a dehumidifier to lower the humidity. For example, after the scan is completed, the scanning unit can restore the temperature and humidity to their original levels. In this way, scanning accuracy can be improved by taking into account the room temperature and humidity. Some or all of the above processing in the scanning unit may be performed using AI, for example, or without AI. For example, the scanning unit can input temperature and humidity data into a generating AI and have the generating AI perform adjustments to the optimal scanning conditions.

[0042] The scanning unit can correct the scan data while considering the acoustic characteristics of the room. For example, the scanning unit measures the room's reverberation during scanning and removes noise from the scan data. For example, after scanning, the scanning unit corrects the data based on the room's acoustic characteristics to generate a more accurate 3D model. For example, before scanning, the scanning unit adjusts the room's layout to optimize the acoustic characteristics. This allows the scan data to be corrected by considering the room's acoustic characteristics. Some or all of the above processing in the scanning unit may be performed using AI, for example, or without AI. For example, the scanning unit can input acoustic characteristic data into a generating AI and have the generating AI perform the correction of the scan data.

[0043] The scanning unit can improve scanning accuracy by considering the characteristics of the room's wall and floor materials during scanning. For example, the scanning unit automatically detects the characteristics of the wall and floor materials before starting the scan and optimizes the scan settings. For example, the scanning unit corrects the scan data during scanning by considering the reflective characteristics of the wall and floor materials. For example, the scanning unit readjusts the data based on the characteristics of the wall and floor materials after the scan is completed to improve accuracy. In this way, scanning accuracy can be improved by considering the characteristics of the room's wall and floor materials. Some or all of the above processing in the scanning unit may be performed using AI, for example, or without AI. For example, the scanning unit can input characteristic data of the wall and floor materials into a generating AI and have the generating AI perform the scan accuracy improvement.

[0044] The analysis unit can change its analysis method depending on the intended use of the room during the analysis. For example, in the case of a living room, the analysis unit uses an analysis method that prioritizes comfort. For example, in the case of an office, the analysis unit uses an analysis method that prioritizes efficiency. For example, in the case of a kitchen, the analysis unit uses an analysis method that prioritizes functionality. By changing the analysis method according to the intended use of the room, the optimal layout can be proposed. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input room intended use data into a generative AI and have the generative AI select the optimal analysis method.

[0045] The analysis unit can improve its analysis accuracy by referring to the room's historical data during analysis. For example, the analysis unit can refer to past layout data and propose an optimal arrangement. For example, the analysis unit can propose an optimal furniture arrangement based on the room's usage history. For example, the analysis unit can refer to the room's renovation history and propose an optimal layout. In this way, the analysis accuracy can be improved by referring to the room's historical data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input historical data into a generative AI and have the generative AI perform the improvement of analysis accuracy.

[0046] The analysis unit can correct the analysis results by considering the acoustic characteristics of the room during the analysis. For example, the analysis unit measures the reverberation of the room and removes noise from the analysis results. For example, the analysis unit corrects the analysis results based on the acoustic characteristics of the room and proposes a more accurate layout. For example, the analysis unit adjusts the room layout to optimize the acoustic characteristics. In this way, the analysis results can be corrected by considering the acoustic characteristics of the room. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input acoustic characteristic data into a generation AI and have the generation AI perform the correction of the analysis results.

[0047] The analysis unit can correct the analysis results by taking into account the room's lighting conditions during the analysis. For example, if the room lighting is dim, the analysis unit will correct the analysis results to make them brighter. For example, the analysis unit will improve the color reproduction of the analysis results by taking into account the color temperature of the lighting. For example, the analysis unit will readjust the analysis results based on the lighting conditions to improve accuracy. In this way, the analysis results can be corrected by taking into account the room's lighting conditions. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input lighting condition data into a generating AI and have the generating AI perform the correction of the analysis results.

[0048] The generation unit can optimize the layout based on the frequency of room use during the generation process. For example, if the living room is frequently used, the generation unit will generate a layout that prioritizes comfort. For example, if the office is frequently used, the generation unit will generate a layout that prioritizes efficiency. For example, if the kitchen is frequently used, the generation unit will generate a layout that prioritizes functionality. This allows the layout to be optimized based on the frequency of room use. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input usage frequency data into a generation AI and have the generation AI generate the optimal layout.

[0049] The generation unit can propose a layout that takes into account future changes to the room during the generation process. For example, the generation unit can propose a layout with ample space, taking into account the possibility of adding furniture in the future. For example, the generation unit can propose a flexible layout, taking into account the possibility of changes in the room's use in the future. For example, the generation unit can propose a layout that is easy to modify, taking into account future renovations. This allows the generation unit to propose a layout that takes future changes into account. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input future change data into the generation AI and have the generation AI propose the optimal layout.

[0050] The generation unit can propose a layout that takes into account the acoustic characteristics of the room during generation. For example, the generation unit can measure the reverberation of the room and propose a layout that optimizes the acoustic characteristics. For example, the generation unit can propose a layout that adjusts the placement of furniture based on the acoustic characteristics of the room. For example, the generation unit can propose a layout that adjusts the arrangement of the room in order to optimize the acoustic characteristics. In this way, a layout can be proposed that takes into account the acoustic characteristics of the room. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input acoustic characteristic data into a generation AI and have the generation AI propose an optimal layout.

[0051] The generation unit can propose a layout considering the room's lighting conditions during generation. For example, if the room lighting is dim, the generation unit will propose a layout to brighten it. For example, the generation unit will propose a layout with optimal color reproduction considering the color temperature of the lighting. For example, the generation unit will propose a layout that adjusts the furniture placement based on the lighting conditions. In this way, the generation unit can propose a layout considering the room's lighting conditions. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input lighting condition data into a generation AI and have the generation AI propose an optimal layout.

[0052] The visualization unit can simulate the effect of natural light in a room during visualization. For example, the visualization unit can simulate how natural light enters through windows during visualization and adjust the furniture arrangement. For example, the visualization unit can simulate changes in natural light throughout the day and suggest the optimal furniture arrangement. For example, the visualization unit can simulate changes in natural light throughout the seasons and optimize the brightness of the room. By simulating the effect of natural light in a room, a more realistic visualization can be provided. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input natural light data into a generating AI and have the generating AI perform the simulation.

[0053] The visualization unit can realistically reproduce the materials of the furniture in a room during visualization. For example, the visualization unit can accurately reproduce the materials of the furniture during visualization, providing a realistic appearance. For example, the visualization unit can realistically reproduce light reflections and shadows based on the materials of the furniture. For example, the visualization unit can visually represent tactile sensations and textures based on the materials of the furniture. By realistically reproducing the materials of the furniture in a room, it is possible to provide a more realistic visualization. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input furniture material data into a generating AI and have the generating AI perform a realistic reproduction.

[0054] The visualization unit can simulate the acoustic characteristics of a room during visualization. For example, the visualization unit can simulate the reverberation of a room and optimize its acoustic characteristics during visualization. For example, the visualization unit can provide a visualization that adjusts the placement of furniture based on the acoustic characteristics of the room. For example, the visualization unit can provide a visualization that adjusts the room layout to optimize the acoustic characteristics. By simulating the acoustic characteristics of a room, a more realistic visualization can be provided. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input acoustic characteristic data into a generating AI and have the generating AI perform the simulation.

[0055] The visualization unit can simulate the effects of room temperature and humidity during visualization. For example, the visualization unit can simulate changes in room temperature and humidity during visualization and propose a comfortable environment. For example, the visualization unit can provide a visualization that adjusts furniture placement based on changes in temperature and humidity. For example, the visualization unit can simulate seasonal changes in temperature and humidity and propose the optimal furniture placement. By simulating the effects of room temperature and humidity, it is possible to provide a more realistic visualization. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input temperature and humidity data into a generating AI and have the generating AI execute the simulation.

[0056] The adjustment unit can provide adjustment options according to the intended use of the room during adjustment. For example, in the case of a living room, the adjustment unit provides adjustment options that prioritize comfort. For example, in the case of an office, the adjustment unit provides adjustment options that prioritize efficiency. For example, in the case of a kitchen, the adjustment unit provides adjustment options that prioritize functionality. This allows the adjustment unit to provide adjustment options according to the intended use of the room. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input usage data into a generating AI and have the generating AI perform the task of providing the optimal adjustment options.

[0057] The adjustment unit can improve the accuracy of adjustments by referring to the room's historical data during the adjustment process. For example, the adjustment unit can refer to past layout data to provide the optimal adjustment option. For example, the adjustment unit can provide the optimal adjustment option based on the room's usage history. For example, the adjustment unit can refer to the room's renovation history to provide the optimal adjustment option. This allows for improved adjustment accuracy by referring to the room's historical data. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input historical data into a generating AI and have the generating AI perform the improvement of adjustment accuracy.

[0058] The adjustment unit can provide adjustment options while considering the acoustic characteristics of the room. For example, the adjustment unit can measure the reverberation of the room and provide adjustment options to optimize the acoustic characteristics. For example, the adjustment unit can provide options to adjust the placement of furniture based on the acoustic characteristics of the room. For example, the adjustment unit can provide options to adjust the room layout to optimize the acoustic characteristics. In this way, adjustment options can be provided by considering the acoustic characteristics of the room. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input acoustic characteristic data into a generating AI and have the generating AI perform the task of providing the optimal adjustment options.

[0059] The adjustment unit can provide adjustment options while considering the room's lighting conditions. For example, if the room lighting is dim, the adjustment unit can provide an adjustment option to brighten it. For example, the adjustment unit can provide an adjustment option with optimal color reproduction by considering the color temperature of the lighting. For example, the adjustment unit can provide an option to adjust the furniture arrangement based on the lighting conditions. In this way, adjustment options can be provided by considering the room's lighting conditions. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input lighting condition data into a generating AI and have the generating AI perform the task of providing the optimal adjustment options.

[0060] The purchase support unit can make optimal suggestions by referring to the user's past purchase history during the purchase support process. For example, the purchase support unit can suggest items that are compatible with furniture the user has purchased in the past. For example, the purchase support unit can suggest items that suit the user's preferences based on their past purchase history. For example, the purchase support unit can analyze the user's past purchase history and suggest the optimal purchase options. In this way, the unit can make optimal suggestions by referring to the user's past purchase history. Some or all of the above processes in the purchase support unit may be performed using AI, for example, or without AI. For example, the purchase support unit can input past purchase history data into a generating AI and have the generating AI execute optimal suggestions.

[0061] The purchase support unit can make optimal suggestions by considering the user's geographical location information during the purchase support process. For example, the purchase support unit can suggest items that can be purchased at nearby stores based on the user's current location. For example, the purchase support unit can suggest options with shorter delivery times based on the user's geographical location information. For example, the purchase support unit can suggest the optimal purchase option by considering the user's geographical location information. In this way, optimal suggestions can be made by considering the user's geographical location information. Some or all of the above processes in the purchase support unit may be performed using AI, for example, or without AI. For example, the purchase support unit can input geographical location data into a generating AI and have the generating AI execute optimal suggestions.

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

[0063] The spatial design system can also include a history reference unit that suggests the most suitable furniture by referring to the user's past furniture purchase history. For example, the history reference unit might suggest new furniture that complements the furniture the user has previously purchased. Alternatively, it might suggest furniture that suits the user's preferences based on their past purchase history. Finally, it might analyze the user's past purchase history and suggest the most suitable furniture combination. In this way, the system can suggest the most suitable furniture by referring to the user's past purchase history.

[0064] The spatial design system can further include a purpose-of-use analysis unit that proposes the optimal furniture arrangement according to the room's intended use. For example, in the case of a living room, the purpose-of-use analysis unit proposes an arrangement that prioritizes comfort. For example, in the case of an office, the purpose-of-use analysis unit proposes an arrangement that prioritizes efficiency. For example, in the case of a kitchen, the purpose-of-use analysis unit proposes an arrangement that prioritizes functionality. This allows the system to propose the optimal furniture arrangement according to the room's intended use.

[0065] The spatial design system may also include a lighting adjustment unit that automatically adjusts the room's lighting conditions to obtain optimal scan results. For example, the lighting adjustment unit automatically brightens the room's lighting if it is dim before the scan begins. For example, during the scan, the lighting adjustment unit adjusts the color temperature of the lighting to improve the color reproduction of the scan data. For example, after the scan is completed, the lighting adjustment unit returns the lighting conditions to their original state. This allows for optimal scan results by automatically adjusting the room's lighting conditions.

[0066] The spatial design system can also be equipped with a temperature and humidity control unit that takes into account the room's temperature and humidity to improve scanning accuracy. For example, before the scan begins, if the room temperature is too high, the temperature and humidity control unit will automatically adjust the air conditioner to an appropriate temperature. For example, during the scan, if the humidity is high, the temperature and humidity control unit will automatically activate a dehumidifier to lower the humidity. For example, after the scan is completed, the temperature and humidity control unit will return the temperature and humidity to their original levels. In this way, scanning accuracy can be improved by taking the room's temperature and humidity into consideration.

[0067] The spatial design system may also include an acoustic correction unit that corrects scan data by considering the acoustic characteristics of the room. For example, the acoustic correction unit measures the room's reverberation during scanning and removes noise from the scan data. Alternatively, it may correct the data after scanning based on the room's acoustic characteristics to generate a more accurate 3D model. Or, for example, it may adjust the room's layout before scanning to optimize the acoustic characteristics. This allows the scan data to be corrected by considering the room's acoustic characteristics.

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

[0069] Step 1: The scanning unit 3D scans the room space. For example, it can use a smartphone camera to capture the entire room and generate 3D data. The scanning unit can create an accurate 3D model by scanning the four corners of the room and the positions of furniture in detail. It is also possible to generate more accurate 3D data by taking photos from multiple angles. Step 2: The analysis unit analyzes the data scanned by the scanning unit. For example, it uses a generation AI to propose the optimal layout, taking into account the size and shape of the room and the arrangement of existing furniture. The analysis unit takes the scanned data as input and outputs the optimal furniture arrangement. Step 3: The generation unit generates the optimal furniture layout based on the data analyzed by the analysis unit. For example, using generation AI, it generates the optimal layout considering the size and shape of the room, the arrangement of existing furniture, etc. The generation unit takes the size and shape of the room as input and outputs the optimal furniture arrangement. Step 4: The visualization unit visualizes the layout generated by the generation unit using AR. For example, by looking around the room with a smartphone, the proposed furniture arrangement can be checked in real time. The proposed furniture arrangement can be checked in real time through the smartphone screen. Step 5: The adjustment unit interactively adjusts the layout visualized by the visualization unit. For example, you can change the position of furniture by tapping the smartphone screen or adjust its size by dragging it. You can change the position and adjust the size of furniture through the smartphone screen.

[0070] (Example of form 2) The spatial design system according to an embodiment of the present invention is a system that provides optimal furniture selection and spatial design according to the space of a room. The spatial design system combines multimodal generation AI and AR to provide furniture selection and spatial design according to the space of a room. Specifically, it provides a smartphone application with the following functions. First, the user 3D scans the space of the room using a smartphone. Next, the generation AI analyzes the scan data and generates the optimal furniture layout. The generated layout is visualized in the room using AR, and the user can interactively adjust the placement image. Furthermore, it also provides support for purchasing the suggested furniture. For example, the user 3D scans the space of the room using a smartphone. In this case, the entire room is photographed using the smartphone's camera and 3D data is generated. For example, by scanning the four corners of the room and the positions of furniture in detail, an accurate 3D model can be created. Next, the generation AI analyzes the scan data and generates the optimal furniture layout. The generation AI proposes the optimal layout considering the size and shape of the room, the arrangement of existing furniture, etc. For example, in a living room, the space can be effectively utilized by optimizing the placement of sofas, tables, and televisions. The generated layout is visualized in the room using AR. Users can use their smartphones to look around their room and see the suggested furniture arrangement in real time. For example, they can change the position of the sofa or adjust the size of the table. Furthermore, users can interactively adjust the arrangement image. For example, they can change the position of furniture by tapping the smartphone screen or adjust its size by dragging it. This makes it easy for users to create a layout that suits their preferences. Finally, the app also provides support for purchasing the suggested furniture. Users can purchase the suggested furniture within the app, and the purchase process is also simple. For example, they can purchase the suggested sofa or table directly from the online shop.In this way, by using a smartphone app that combines multimodal generation AI and AR, it is possible to provide optimal furniture selection and spatial design tailored to the room's space. Users can realize their ideal room without spending time and effort. As a result, the spatial design system can provide optimal furniture selection and spatial design tailored to the room's space.

[0071] The spatial design system according to this embodiment comprises a scanning unit, an analysis unit, a generation unit, a visualization unit, and an adjustment unit. The scanning unit 3D scans the space of a room. The scanning unit generates 3D data by, for example, using a smartphone camera to photograph the entire room. The scanning unit can create an accurate 3D model by, for example, scanning the four corners of the room and the positions of furniture in detail. The scanning unit can also generate 3D data by, for example, using a smartphone camera to photograph from multiple angles when scanning the entire room. The analysis unit analyzes the data scanned by the scanning unit. The analysis unit analyzes the scanned data using, for example, a generation AI, and proposes an optimal layout considering the size and shape of the room, the arrangement of existing furniture, etc. The analysis unit, for example, uses a generation AI to take the scanned data as input and outputs the optimal furniture arrangement. The analysis unit can also propose an optimal furniture arrangement by, for example, using a generation AI to analyze the size and shape of the room. The generation unit generates an optimal furniture layout based on the data analyzed by the analysis unit. The generation unit generates the optimal layout, taking into account factors such as the size and shape of the room and the arrangement of existing furniture, for example, using a generation AI. The generation unit can, for example, take the size and shape of the room as input and output the optimal furniture arrangement. The generation unit can also, for example, have the generation AI analyze the size and shape of the room and generate the optimal furniture arrangement. The visualization unit visualizes the layout generated by the generation unit using augmented reality (AR). The visualization unit allows users to, for example, look around the room using a smartphone to check the proposed furniture arrangement in real time. The visualization unit allows users to, for example, check the proposed furniture arrangement in real time through the smartphone screen. The visualization unit can also, for example, check the proposed furniture arrangement in real time using the smartphone camera. The adjustment unit interactively adjusts the layout visualized by the visualization unit. The adjustment unit allows users to, for example, change the position of furniture by tapping the smartphone screen or adjust its size by dragging it.The adjustment unit can, for example, change the position or adjust the size of furniture via a smartphone screen. This allows the spatial design system according to the embodiment to provide optimal furniture selection and spatial design tailored to the room's space.

[0072] The scanning unit performs a 3D scan of the room space. For example, the scanning unit uses a smartphone camera to capture images of the entire room and generates 3D data. Specifically, it takes images from multiple angles, pointing the smartphone camera at the four corners and the center of the room, and integrates these images to create an accurate 3D model. The scanning unit can create an accurate 3D model by, for example, scanning the four corners of the room and the positions of furniture in detail. The scanning unit can also generate 3D data by taking images from multiple angles using a smartphone camera when scanning the entire room. Furthermore, the scanning unit can also scan the height of the room and the shape of the ceiling, thereby creating a more detailed 3D model. The scanning unit can also capture information such as the room's lighting conditions, wall color, and texture, and reflect this information in the 3D model. As a result, the scanning unit can scan the room space in detail and accurately, providing high-quality data necessary for subsequent analysis and generation processes.

[0073] The analysis unit analyzes the data scanned by the scanning unit. For example, using a generative AI, the analysis unit analyzes the scanned data and proposes an optimal layout, taking into account the room's size and shape, the placement of existing furniture, and other factors. Specifically, the generative AI receives the scanned data as input, analyzes the room's dimensions and shape, and outputs optimal suggestions regarding furniture placement. For example, the generative AI analyzes the room's shape and evaluates whether the furniture placement is efficient. The generative AI can also propose optimal furniture placement based on the room's purpose and the occupants' lifestyle. For example, in a living room, it might suggest optimizing the placement of the television and sofa to improve the viewing experience. Furthermore, the analysis unit can analyze the room's lighting conditions and natural light based on the scanned data and adjust the furniture placement accordingly. This allows the analysis unit to analyze the scanned data in detail and propose an optimal layout to maximize the use of the room's space.

[0074] The generation unit generates the optimal furniture layout based on the data analyzed by the analysis unit. For example, the generation unit uses generation AI to generate the optimal layout, taking into account the size and shape of the room, the arrangement of existing furniture, etc. Specifically, the generation AI takes the dimensions and shape of the room as input and outputs the optimal furniture arrangement. For example, the generation AI analyzes the shape of the room and evaluates whether the furniture arrangement is efficient. The generation AI can also suggest the optimal furniture arrangement based on the room's purpose and the occupants' lifestyle. For example, in the case of a living room, it can suggest optimizing the position of the TV and sofa to improve the viewing experience. Furthermore, the generation unit can analyze the room's lighting conditions and the amount of natural light entering the room based on the scan data and adjust the furniture arrangement accordingly. This allows the generation unit to analyze the scan data in detail and suggest the optimal layout to make the most of the room's space.

[0075] The visualization unit visualizes the layout generated by the generation unit using augmented reality (AR). The visualization unit allows users to, for example, view the room using a smartphone to check the proposed furniture arrangement in real time. Specifically, it scans the room using the smartphone's camera and overlays it with the generated 3D model to display the proposed furniture arrangement in real time. The visualization unit allows users to check the proposed furniture arrangement in real time through their smartphone screen. Furthermore, the visualization unit can display not only the furniture arrangement but also colors, textures, and lighting effects in real time. This allows users to intuitively understand how the proposed layout will look in their actual room. The visualization unit can also perform simulations, such as changing the furniture arrangement or adding new furniture. This allows users to try various layouts and find the optimal arrangement.

[0076] The adjustment unit interactively adjusts the layout visualized by the visualization unit. For example, the adjustment unit allows users to change the position of furniture by tapping the smartphone screen or adjust its size by dragging it. Specifically, users can select furniture by tapping the smartphone screen, change its position by dragging it, or adjust its size with pinch gestures. The adjustment unit can, for example, change the position and size of furniture through the smartphone screen. Furthermore, the adjustment unit reflects user changes in real time and, in conjunction with the visualization unit, displays the new layout. This allows users to experiment with various layouts to find the optimal arrangement. The adjustment unit also provides features such as changing the color and texture of furniture, allowing users to customize the room design down to the smallest detail. Thus, the adjustment unit provides a powerful tool for users to interactively adjust the room layout and achieve the optimal spatial design.

[0077] The system includes a purchase support unit that assists users in purchasing the suggested furniture. The purchase support unit helps users easily purchase the suggested furniture. For example, the purchase support unit provides links to purchase the suggested furniture from online shops. For example, the purchase support unit provides an interface to easily carry out the purchase procedure for the suggested furniture. For example, the purchase support unit provides support regarding the purchase of the suggested furniture. This makes it easy for users to purchase the suggested furniture. Some or all of the above processes in the purchase support unit may be performed using AI, for example, or not using AI. For example, the purchase support unit can automate the purchase procedure for the suggested furniture using an AI model.

[0078] The scanning unit can capture an entire room using a smartphone camera and generate 3D data. For example, the scanning unit can create an accurate 3D model by precisely scanning the corners of the room and the positions of furniture using a smartphone camera. The scanning unit can also capture a room from multiple angles using a smartphone camera and generate 3D data. Furthermore, the scanning unit can scan the entire room using a smartphone camera and generate 3D data. This allows for the creation of an accurate 3D model using a smartphone camera. Some or all of the above-described processes in the scanning unit may be performed using AI, or without AI. For example, the scanning unit can input image data acquired by a smartphone camera into a generation AI and have the generation AI perform the 3D data generation.

[0079] The analysis unit can propose an optimal layout considering the size and shape of the room, the arrangement of existing furniture, etc. For example, the analysis unit uses a generation AI to analyze the scan data and propose an optimal layout considering the size and shape of the room, the arrangement of existing furniture, etc. For example, the analysis unit can use a generation AI to take scan data as input and output the optimal furniture arrangement. For example, the analysis unit can use a generation AI to analyze the size and shape of the room and propose an optimal furniture arrangement. This allows the analysis unit to propose an optimal layout based on the characteristics of the room. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input scan data into a generation AI and have the generation AI propose an optimal layout.

[0080] The visualization unit allows users to view the proposed furniture arrangement in real time by looking around the room using a smartphone. The visualization unit can, for example, view the proposed furniture arrangement in real time through the smartphone screen. The visualization unit can also, for example, view the proposed furniture arrangement in real time using the smartphone camera. The visualization unit can also, for example, view the proposed furniture arrangement in real time using the smartphone screen. This allows users to view the proposed furniture arrangement in real time. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input image data acquired by the smartphone camera into a generating AI and have the generating AI perform real-time visualization.

[0081] The adjustment unit can change the position of furniture by tapping the smartphone screen or adjust its size by dragging it. The adjustment unit can change the position of furniture or adjust its size through the smartphone screen, for example. The adjustment unit can also change the position of furniture or adjust its size using the smartphone screen, for example. The adjustment unit can also change the position of furniture or adjust its size by tapping the smartphone screen or dragging it, for example. This allows the user to interactively adjust the furniture arrangement. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input operation data acquired from the smartphone screen into a generating AI and have the generating AI perform adjustments to the furniture's position and size.

[0082] The scanning unit can estimate the user's emotions and adjust the timing of the scan based on the estimated emotions. For example, if the user is relaxed, the scanning unit can display a guide to maintain a relaxed state before starting the scan. For example, if the user is tense, the scanning unit can suggest breathing techniques to help them relax before starting the scan. For example, if the user is in a hurry, the scanning unit can provide a simplified procedure to expedite the scanning process. This allows the timing of the scan to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scanning unit may be performed using AI or not using AI. For example, the scanning unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0083] The scanning unit can automatically adjust the room's lighting conditions to obtain optimal scan results. For example, before starting a scan, the scanning unit can automatically brighten the room's lighting if it is dim. During the scan, the scanning unit can adjust the color temperature of the lighting to improve the color reproduction of the scan data. After the scan is complete, the scanning unit can return the lighting conditions to their original state. This allows for optimal scan results by automatically adjusting the room's lighting conditions. Some or all of the above processes in the scanning unit may be performed using AI, for example, or without AI. For example, the scanning unit can input lighting condition data into a generating AI and have the generating AI perform the optimal adjustment of the lighting conditions.

[0084] The scanning unit can improve scanning accuracy by taking into account the room temperature and humidity during scanning. For example, before starting a scan, if the room temperature is too high, the scanning unit can automatically adjust the air conditioner to an appropriate temperature. For example, during a scan, if the humidity is high, the scanning unit can automatically activate a dehumidifier to lower the humidity. For example, after the scan is completed, the scanning unit can restore the temperature and humidity to their original levels. In this way, scanning accuracy can be improved by taking into account the room temperature and humidity. Some or all of the above processing in the scanning unit may be performed using AI, for example, or without AI. For example, the scanning unit can input temperature and humidity data into a generating AI and have the generating AI perform adjustments to the optimal scanning conditions.

[0085] The scanning unit can estimate the user's emotions and determine the scanning priority based on the estimated emotions. For example, if the user is stressed, the scanning unit can set a lower scanning priority to allow time to relax. For example, if the user is relaxed, the scanning unit can set a higher scanning priority to allow the scan to proceed smoothly. For example, if the user is in a hurry, the scanning unit can set the scanning priority to the highest priority to complete the scan quickly. This allows the scanning priority to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scanning unit may be performed using AI, for example, or without AI. For example, the scanning unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0086] The scanning unit can correct the scan data while considering the acoustic characteristics of the room. For example, the scanning unit measures the room's reverberation during scanning and removes noise from the scan data. For example, after scanning, the scanning unit corrects the data based on the room's acoustic characteristics to generate a more accurate 3D model. For example, before scanning, the scanning unit adjusts the room's layout to optimize the acoustic characteristics. This allows the scan data to be corrected by considering the room's acoustic characteristics. Some or all of the above processing in the scanning unit may be performed using AI, for example, or without AI. For example, the scanning unit can input acoustic characteristic data into a generating AI and have the generating AI perform the correction of the scan data.

[0087] The scanning unit can improve scanning accuracy by considering the characteristics of the room's wall and floor materials during scanning. For example, the scanning unit automatically detects the characteristics of the wall and floor materials before starting the scan and optimizes the scan settings. For example, the scanning unit corrects the scan data during scanning by considering the reflective characteristics of the wall and floor materials. For example, the scanning unit readjusts the data based on the characteristics of the wall and floor materials after the scan is completed to improve accuracy. In this way, scanning accuracy can be improved by considering the characteristics of the room's wall and floor materials. Some or all of the above processing in the scanning unit may be performed using AI, for example, or without AI. For example, the scanning unit can input characteristic data of the wall and floor materials into a generating AI and have the generating AI perform the scan accuracy improvement.

[0088] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit performs a detailed analysis and proposes a highly accurate layout. If the user is in a hurry, the analysis unit performs a rapid analysis and proposes a simplified layout. If the user is excited, the analysis unit proposes a visually appealing layout. This allows the analysis algorithm to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0089] The analysis unit can change its analysis method depending on the intended use of the room during the analysis. For example, in the case of a living room, the analysis unit uses an analysis method that prioritizes comfort. For example, in the case of an office, the analysis unit uses an analysis method that prioritizes efficiency. For example, in the case of a kitchen, the analysis unit uses an analysis method that prioritizes functionality. By changing the analysis method according to the intended use of the room, the optimal layout can be proposed. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input room intended use data into a generative AI and have the generative AI select the optimal analysis method.

[0090] The analysis unit can improve its analysis accuracy by referring to the room's historical data during analysis. For example, the analysis unit can refer to past layout data and propose an optimal arrangement. For example, the analysis unit can propose an optimal furniture arrangement based on the room's usage history. For example, the analysis unit can refer to the room's renovation history and propose an optimal layout. In this way, the analysis accuracy can be improved by referring to the room's historical data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input historical data into a generative AI and have the generative AI perform the improvement of analysis accuracy.

[0091] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. This allows the display method of the analysis results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0092] The analysis unit can correct the analysis results by considering the acoustic characteristics of the room during the analysis. For example, the analysis unit measures the reverberation of the room and removes noise from the analysis results. For example, the analysis unit corrects the analysis results based on the acoustic characteristics of the room and proposes a more accurate layout. For example, the analysis unit adjusts the room layout to optimize the acoustic characteristics. In this way, the analysis results can be corrected by considering the acoustic characteristics of the room. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input acoustic characteristic data into a generation AI and have the generation AI perform the correction of the analysis results.

[0093] The analysis unit can correct the analysis results by taking into account the room's lighting conditions during the analysis. For example, if the room lighting is dim, the analysis unit will correct the analysis results to make them brighter. For example, the analysis unit will improve the color reproduction of the analysis results by taking into account the color temperature of the lighting. For example, the analysis unit will readjust the analysis results based on the lighting conditions to improve accuracy. In this way, the analysis results can be corrected by taking into account the room's lighting conditions. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input lighting condition data into a generating AI and have the generating AI perform the correction of the analysis results.

[0094] The generation unit can estimate the user's emotions and adjust the style of the generated layout based on the estimated user emotions. For example, if the user is relaxed, the generation unit will generate a calm style layout. If the user is excited, the generation unit will generate a visually stimulating style layout. If the user is in a hurry, the generation unit will generate a simple and efficient style layout. This allows the layout style to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not using a generation AI. For example, the generation unit can input user facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0095] The generation unit can optimize the layout based on the frequency of room use during the generation process. For example, if the living room is frequently used, the generation unit will generate a layout that prioritizes comfort. For example, if the office is frequently used, the generation unit will generate a layout that prioritizes efficiency. For example, if the kitchen is frequently used, the generation unit will generate a layout that prioritizes functionality. This allows the layout to be optimized based on the frequency of room use. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input usage frequency data into a generation AI and have the generation AI generate the optimal layout.

[0096] The generation unit can propose a layout that takes into account future changes to the room during the generation process. For example, the generation unit can propose a layout with ample space, taking into account the possibility of adding furniture in the future. For example, the generation unit can propose a flexible layout, taking into account the possibility of changes in the room's use in the future. For example, the generation unit can propose a layout that is easy to modify, taking into account future renovations. This allows the generation unit to propose a layout that takes future changes into account. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input future change data into the generation AI and have the generation AI propose the optimal layout.

[0097] The generation unit can estimate the user's emotions and adjust the level of detail in the generated layout based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a detailed layout. If the user is in a hurry, the generation unit generates a simplified layout. If the user is excited, the generation unit generates a layout with visually stimulating effects. This allows the level of detail in the layout to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0098] The generation unit can propose a layout that takes into account the acoustic characteristics of the room during generation. For example, the generation unit can measure the reverberation of the room and propose a layout that optimizes the acoustic characteristics. For example, the generation unit can propose a layout that adjusts the placement of furniture based on the acoustic characteristics of the room. For example, the generation unit can propose a layout that adjusts the arrangement of the room in order to optimize the acoustic characteristics. In this way, a layout can be proposed that takes into account the acoustic characteristics of the room. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input acoustic characteristic data into a generation AI and have the generation AI propose an optimal layout.

[0099] The generation unit can propose a layout considering the room's lighting conditions during generation. For example, if the room lighting is dim, the generation unit will propose a layout to brighten it. For example, the generation unit will propose a layout with optimal color reproduction considering the color temperature of the lighting. For example, the generation unit will propose a layout that adjusts the furniture placement based on the lighting conditions. In this way, the generation unit can propose a layout considering the room's lighting conditions. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input lighting condition data into a generation AI and have the generation AI propose an optimal layout.

[0100] The visualization unit can estimate the user's emotions and adjust the color tone of the visualization based on the estimated user emotions. For example, if the user is relaxed, the visualization unit will provide a visualization with calming colors. For example, if the user is excited, the visualization unit will provide a visualization with visually stimulating colors. For example, if the user is tense, the visualization unit will provide a visualization with relaxing colors. This allows the color tone of the visualization to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0101] The visualization unit can simulate the effect of natural light in a room during visualization. For example, the visualization unit can simulate how natural light enters through windows during visualization and adjust the furniture arrangement. For example, the visualization unit can simulate changes in natural light throughout the day and suggest the optimal furniture arrangement. For example, the visualization unit can simulate changes in natural light throughout the seasons and optimize the brightness of the room. By simulating the effect of natural light in a room, a more realistic visualization can be provided. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input natural light data into a generating AI and have the generating AI perform the simulation.

[0102] The visualization unit can realistically reproduce the materials of the furniture in a room during visualization. For example, the visualization unit can accurately reproduce the materials of the furniture during visualization, providing a realistic appearance. For example, the visualization unit can realistically reproduce light reflections and shadows based on the materials of the furniture. For example, the visualization unit can visually represent tactile sensations and textures based on the materials of the furniture. By realistically reproducing the materials of the furniture in a room, it is possible to provide a more realistic visualization. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input furniture material data into a generating AI and have the generating AI perform a realistic reproduction.

[0103] The visualization unit can estimate the user's emotions and adjust the visualization's perspective based on the estimated emotions. For example, if the user is relaxed, the visualization unit provides a wide-angle view of the visualization. If the user is excited, the visualization unit provides a dynamic view of the visualization. If the user is tense, the visualization unit provides a stable view of the visualization. This allows the visualization's perspective to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0104] The visualization unit can simulate the acoustic characteristics of a room during visualization. For example, the visualization unit can simulate the reverberation of a room and optimize its acoustic characteristics during visualization. For example, the visualization unit can provide a visualization that adjusts the placement of furniture based on the acoustic characteristics of the room. For example, the visualization unit can provide a visualization that adjusts the room layout to optimize the acoustic characteristics. By simulating the acoustic characteristics of a room, a more realistic visualization can be provided. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input acoustic characteristic data into a generating AI and have the generating AI perform the simulation.

[0105] The visualization unit can simulate the effects of room temperature and humidity during visualization. For example, the visualization unit can simulate changes in room temperature and humidity during visualization and propose a comfortable environment. For example, the visualization unit can provide a visualization that adjusts furniture placement based on changes in temperature and humidity. For example, the visualization unit can simulate seasonal changes in temperature and humidity and propose the optimal furniture placement. By simulating the effects of room temperature and humidity, it is possible to provide a more realistic visualization. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input temperature and humidity data into a generating AI and have the generating AI execute the simulation.

[0106] The adjustment unit can estimate the user's emotions and customize the adjustment interface based on the estimated user emotions. For example, if the user is relaxed, the adjustment unit provides an interface that offers detailed adjustment options. For example, if the user is tense, the adjustment unit provides a simple and highly visible interface. For example, if the user is in a hurry, the adjustment unit provides an interface that allows for quick adjustments. This allows the adjustment interface to be customized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not using AI. For example, the adjustment unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0107] The adjustment unit can provide adjustment options according to the intended use of the room during adjustment. For example, in the case of a living room, the adjustment unit provides adjustment options that prioritize comfort. For example, in the case of an office, the adjustment unit provides adjustment options that prioritize efficiency. For example, in the case of a kitchen, the adjustment unit provides adjustment options that prioritize functionality. This allows the adjustment unit to provide adjustment options according to the intended use of the room. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input usage data into a generating AI and have the generating AI perform the task of providing the optimal adjustment options.

[0108] The adjustment unit can improve the accuracy of adjustments by referring to the room's historical data during the adjustment process. For example, the adjustment unit can refer to past layout data to provide the optimal adjustment option. For example, the adjustment unit can provide the optimal adjustment option based on the room's usage history. For example, the adjustment unit can refer to the room's renovation history to provide the optimal adjustment option. This allows for improved adjustment accuracy by referring to the room's historical data. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input historical data into a generating AI and have the generating AI perform the improvement of adjustment accuracy.

[0109] The adjustment unit can estimate the user's emotions and determine the priority of adjustments based on the estimated emotions. For example, if the user is stressed, the adjustment unit may set a low priority for adjustments and provide time for relaxation. For example, if the user is relaxed, the adjustment unit may set a high priority for adjustments and allow the adjustments to proceed smoothly. For example, if the user is in a hurry, the adjustment unit may set the adjustment to the highest priority and complete the adjustments quickly. This allows the adjustment priority to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0110] The adjustment unit can provide adjustment options while considering the acoustic characteristics of the room. For example, the adjustment unit can measure the reverberation of the room and provide adjustment options to optimize the acoustic characteristics. For example, the adjustment unit can provide options to adjust the placement of furniture based on the acoustic characteristics of the room. For example, the adjustment unit can provide options to adjust the room layout to optimize the acoustic characteristics. In this way, adjustment options can be provided by considering the acoustic characteristics of the room. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input acoustic characteristic data into a generating AI and have the generating AI perform the task of providing the optimal adjustment options.

[0111] The adjustment unit can provide adjustment options while considering the room's lighting conditions. For example, if the room lighting is dim, the adjustment unit can provide an adjustment option to brighten it. For example, the adjustment unit can provide an adjustment option with optimal color reproduction by considering the color temperature of the lighting. For example, the adjustment unit can provide an option to adjust the furniture arrangement based on the lighting conditions. In this way, adjustment options can be provided by considering the room's lighting conditions. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input lighting condition data into a generating AI and have the generating AI perform the task of providing the optimal adjustment options.

[0112] The purchase support unit can estimate the user's emotions and adjust its purchase support suggestions based on those emotions. For example, if the user is relaxed, the purchase support unit can provide detailed purchase options. If the user is tense, the purchase support unit can provide simple and easily visible purchase options. If the user is in a hurry, the purchase support unit can provide options for quick purchase. This allows the purchase support suggestions to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the purchase support unit may be performed using AI or not. For example, the purchase support unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0113] The purchase support unit can make optimal suggestions by referring to the user's past purchase history during the purchase support process. For example, the purchase support unit can suggest items that are compatible with furniture the user has purchased in the past. For example, the purchase support unit can suggest items that suit the user's preferences based on their past purchase history. For example, the purchase support unit can analyze the user's past purchase history and suggest the optimal purchase options. In this way, the unit can make optimal suggestions by referring to the user's past purchase history. Some or all of the above processes in the purchase support unit may be performed using AI, for example, or without AI. For example, the purchase support unit can input past purchase history data into a generating AI and have the generating AI execute optimal suggestions.

[0114] The purchase support unit can estimate the user's emotions and determine the priority of purchase support based on the estimated emotions. For example, if the user is stressed, the purchase support unit may set a low priority for purchase support and provide time to relax. For example, if the user is relaxed, the purchase support unit may set a high priority for purchase support and allow the purchase to proceed smoothly. For example, if the user is in a hurry, the purchase support unit may set the purchase support unit to the highest priority and allow the purchase to be completed quickly. In this way, the priority of purchase support can be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the purchase support unit may be performed using AI, for example, or not using AI. For example, the purchase support unit may input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0115] The purchase support unit can make optimal suggestions by considering the user's geographical location information during the purchase support process. For example, the purchase support unit can suggest items that can be purchased at nearby stores based on the user's current location. For example, the purchase support unit can suggest options with shorter delivery times based on the user's geographical location information. For example, the purchase support unit can suggest the optimal purchase option by considering the user's geographical location information. In this way, optimal suggestions can be made by considering the user's geographical location information. Some or all of the above processes in the purchase support unit may be performed using AI, for example, or without AI. For example, the purchase support unit can input geographical location data into a generating AI and have the generating AI execute optimal suggestions.

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

[0117] The spatial design system may further include an emotion estimation unit that estimates the user's emotions and proposes furniture arrangements based on those emotions. For example, if the user is relaxed, the emotion estimation unit will propose furniture in calming colors. If the user is excited, the emotion estimation unit will propose furniture with a visually stimulating design. If the user is stressed, the emotion estimation unit will propose a relaxing arrangement. This makes it possible to provide an optimal furniture arrangement that corresponds to the user's emotions.

[0118] The spatial design system can also include a history reference unit that suggests the most suitable furniture by referring to the user's past furniture purchase history. For example, the history reference unit might suggest new furniture that complements the furniture the user has previously purchased. Alternatively, it might suggest furniture that suits the user's preferences based on their past purchase history. Finally, it might analyze the user's past purchase history and suggest the most suitable furniture combination. In this way, the system can suggest the most suitable furniture by referring to the user's past purchase history.

[0119] The spatial design system may further include an adjustment unit that estimates the user's emotions and adjusts the furniture arrangement based on those emotions. For example, if the user is relaxed, the adjustment unit may suggest a spacious arrangement. If the user is excited, the adjustment unit may suggest a visually stimulating arrangement. If the user is stressed, the adjustment unit may suggest a relaxing arrangement. This allows for the provision of an optimal furniture arrangement tailored to the user's emotions.

[0120] The spatial design system can further include a purpose-of-use analysis unit that proposes the optimal furniture arrangement according to the room's intended use. For example, in the case of a living room, the purpose-of-use analysis unit proposes an arrangement that prioritizes comfort. For example, in the case of an office, the purpose-of-use analysis unit proposes an arrangement that prioritizes efficiency. For example, in the case of a kitchen, the purpose-of-use analysis unit proposes an arrangement that prioritizes functionality. This allows the system to propose the optimal furniture arrangement according to the room's intended use.

[0121] The spatial design system may further include a color adjustment unit that estimates the user's emotions and adjusts the furniture's color tones based on those emotions. For example, if the user is relaxed, the color adjustment unit suggests furniture with calming colors. If the user is excited, the color adjustment unit suggests furniture with visually stimulating colors. If the user is stressed, the color adjustment unit suggests furniture with relaxing colors. This allows the system to provide the optimal furniture color tones according to the user's emotions.

[0122] The spatial design system may also include a lighting adjustment unit that automatically adjusts the room's lighting conditions to obtain optimal scan results. For example, the lighting adjustment unit automatically brightens the room's lighting if it is dim before the scan begins. For example, during the scan, the lighting adjustment unit adjusts the color temperature of the lighting to improve the color reproduction of the scan data. For example, after the scan is completed, the lighting adjustment unit returns the lighting conditions to their original state. This allows for optimal scan results by automatically adjusting the room's lighting conditions.

[0123] The spatial design system may further include a scan timing adjustment unit that estimates the user's emotions and adjusts the scan timing based on the estimated emotions. For example, if the user is relaxed, the scan timing adjustment unit displays a guide to maintain a relaxed state before starting the scan. If the user is tense, the scan timing adjustment unit suggests breathing techniques to relax before starting the scan. If the user is in a hurry, the scan timing adjustment unit provides a simplified procedure to expedite the scanning process. This allows the scan timing to be adjusted according to the user's emotions.

[0124] The spatial design system can also be equipped with a temperature and humidity control unit that takes into account the room's temperature and humidity to improve scanning accuracy. For example, before the scan begins, if the room temperature is too high, the temperature and humidity control unit will automatically adjust the air conditioner to an appropriate temperature. For example, during the scan, if the humidity is high, the temperature and humidity control unit will automatically activate a dehumidifier to lower the humidity. For example, after the scan is completed, the temperature and humidity control unit will return the temperature and humidity to their original levels. In this way, scanning accuracy can be improved by taking the room's temperature and humidity into consideration.

[0125] The spatial design system may further include a scan priority determination unit that estimates the user's emotions and determines the priority of scans based on the estimated emotions. For example, if the user is feeling stressed, the scan priority determination unit will set a low priority to allow time to relax. For example, if the user is relaxed, the scan priority determination unit will set a high priority to allow the scan to proceed smoothly. For example, if the user is in a hurry, the scan priority determination unit will set the scan to the highest priority to complete the scan quickly. In this way, the scan priority can be determined according to the user's emotions.

[0126] The spatial design system may also include an acoustic correction unit that corrects scan data by considering the acoustic characteristics of the room. For example, the acoustic correction unit measures the room's reverberation during scanning and removes noise from the scan data. Alternatively, it may correct the data after scanning based on the room's acoustic characteristics to generate a more accurate 3D model. Or, for example, it may adjust the room's layout before scanning to optimize the acoustic characteristics. This allows the scan data to be corrected by considering the room's acoustic characteristics.

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

[0128] Step 1: The scanning unit 3D scans the room space. For example, it can use a smartphone camera to capture the entire room and generate 3D data. The scanning unit can create an accurate 3D model by scanning the four corners of the room and the positions of furniture in detail. It is also possible to generate more accurate 3D data by taking photos from multiple angles. Step 2: The analysis unit analyzes the data scanned by the scanning unit. For example, it uses a generation AI to propose the optimal layout, taking into account the size and shape of the room and the arrangement of existing furniture. The analysis unit takes the scanned data as input and outputs the optimal furniture arrangement. Step 3: The generation unit generates the optimal furniture layout based on the data analyzed by the analysis unit. For example, using generation AI, it generates the optimal layout considering the size and shape of the room, the arrangement of existing furniture, etc. The generation unit takes the size and shape of the room as input and outputs the optimal furniture arrangement. Step 4: The visualization unit visualizes the layout generated by the generation unit using AR. For example, by looking around the room with a smartphone, the proposed furniture arrangement can be checked in real time. The proposed furniture arrangement can be checked in real time through the smartphone screen. Step 5: The adjustment unit interactively adjusts the layout visualized by the visualization unit. For example, you can change the position of furniture by tapping the smartphone screen or adjust its size by dragging it. You can change the position and adjust the size of furniture through the smartphone screen.

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

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

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

[0132] Each of the multiple elements described above, including the scanning unit, analysis unit, generation unit, visualization unit, adjustment unit, and purchase support unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the scanning unit uses the camera of the smart device 14 to capture images of the entire room and generates 3D data. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the scan data using generation AI and proposes an optimal furniture layout. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates an optimal furniture layout based on the analyzed data. The visualization unit is implemented, for example, by the control unit 46A of the smart device 14, which visualizes the generated layout using AR. The adjustment unit is implemented, for example, by the control unit 46A of the smart device 14, which interactively adjusts the visualized layout. The purchase support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which supports the purchase procedure of the proposed furniture. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0148] Each of the multiple elements described above, including the scanning unit, analysis unit, generation unit, visualization unit, adjustment unit, and purchase support unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the scanning unit uses the camera of the smart glasses 214 to capture the entire room and generate 3D data. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the scan data using generation AI and proposes the optimal furniture layout. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates the optimal furniture layout based on the analyzed data. The visualization unit is implemented, for example, by the control unit 46A of the smart glasses 214, which visualizes the generated layout using AR. The adjustment unit is implemented, for example, by the control unit 46A of the smart glasses 214, which interactively adjusts the visualized layout. The purchase support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which supports the purchase procedure of the proposed furniture. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0164] Each of the multiple elements described above, including the scanning unit, analysis unit, generation unit, visualization unit, adjustment unit, and purchase support unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the scanning unit uses the camera of the headset terminal 314 to capture images of the entire room and generates 3D data. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the scan data using generation AI and proposes an optimal furniture layout. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates an optimal furniture layout based on the analyzed data. The visualization unit is implemented, for example, by the control unit 46A of the headset terminal 314, which visualizes the generated layout using AR. The adjustment unit is implemented, for example, by the control unit 46A of the headset terminal 314, which interactively adjusts the visualized layout. The purchase support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which supports the purchase procedure of the proposed furniture. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0181] Each of the multiple elements described above, including the scanning unit, analysis unit, generation unit, visualization unit, adjustment unit, and purchase support unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the scanning unit uses the camera of the robot 414 to photograph the entire room and generate 3D data. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the scan data using generation AI and proposes the optimal furniture layout. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates the optimal furniture layout based on the analyzed data. The visualization unit is implemented, for example, by the control unit 46A of the robot 414, which visualizes the generated layout using AR. The adjustment unit is implemented, for example, by the control unit 46A of the robot 414, which interactively adjusts the visualized layout. The purchase support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which supports the purchase procedure of the proposed furniture. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0200] (Note 1) A scanning unit that 3D scans the room space, An analysis unit that analyzes the data scanned by the aforementioned scanning unit, A generation unit generates an optimal furniture layout based on the data analyzed by the analysis unit, A visualization unit visualizes the layout generated by the generation unit using AR, The system includes an adjustment unit for interactively adjusting the layout visualized by the visualization unit. A system characterized by the following features. (Note 2) The company has a purchasing support department that provides assistance with purchasing the proposed furniture. The system described in Appendix 1, characterized by the features described herein. (Note 3) The scanning unit is Use your smartphone's camera to photograph the entire room and generate 3D data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, We propose the optimal layout considering the size and shape of the room, the arrangement of existing furniture, and other factors. The system described in Appendix 1, characterized by the features described herein. (Note 5) The visualization unit is, By using a smartphone to look around the room, you can check the suggested furniture arrangement in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The adjustment unit is, You can change the position of furniture by tapping the smartphone screen, or adjust its size by dragging it. The system described in Appendix 1, characterized by the features described herein. (Note 7) The scanning unit is It estimates the user's emotions and adjusts the timing of scans based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The scanning unit is Automatically adjusts room lighting conditions to obtain optimal scan results. The system described in Appendix 1, characterized by the features described herein. (Note 9) The scanning unit is During scanning, the system takes room temperature and humidity into account to improve scanning accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 10) The scanning unit is It estimates the user's emotions and determines the priority of scans based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The scanning unit is During scanning, the scan data is corrected to take into account the acoustic characteristics of the room. The system described in Appendix 1, characterized by the features described herein. (Note 12) The scanning unit is During scanning, the characteristics of the room's wall and floor materials are taken into consideration to improve scanning accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, the analysis method is changed according to the intended use of the room. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, historical data of the room is referenced to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the results are corrected to take into account the acoustic characteristics of the room. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, the analysis results are corrected to take into account the room's lighting conditions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the generated layout style based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the layout is optimized based on the frequency of room use. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, the layout is proposed taking into account future changes to the room. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the level of detail in the generated layout based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the layout is proposed taking into account the acoustic characteristics of the room. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the layout is suggested taking into account the room's lighting conditions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The visualization unit is, It estimates the user's emotions and adjusts the visualization's color tone based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The visualization unit is, The visualization simulates the effect of natural light in the room. The system described in Appendix 1, characterized by the features described herein. (Note 27) The visualization unit is, The materials of the furniture in the room are realistically reproduced during visualization. The system described in Appendix 1, characterized by the features described herein. (Note 28) The visualization unit is, It estimates the user's emotions and adjusts the visualization perspective based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The visualization unit is, Simulate the acoustic characteristics of the room during visualization. The system described in Appendix 1, characterized by the features described herein. (Note 30) The visualization unit is, The visualization simulates the effects of room temperature and humidity. The system described in Appendix 1, characterized by the features described herein. (Note 31) The adjustment unit is, It estimates the user's emotions and customizes the adjustment interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The adjustment unit is, During adjustment, we offer adjustment options according to the intended use of the room. The system described in Appendix 1, characterized by the features described herein. (Note 33) The adjustment unit is, During adjustments, historical data of the room is referenced to improve the accuracy of the adjustments. The system described in Appendix 1, characterized by the features described herein. (Note 34) The adjustment unit is, It estimates the user's emotions and determines the priority of adjustments based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The adjustment unit is, During adjustment, adjustment options are provided that take into account the acoustic characteristics of the room. The system described in Appendix 1, characterized by the features described herein. (Note 36) The adjustment unit is, During adjustment, we provide adjustment options that take into account the room's lighting conditions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned purchase support department, The system estimates the user's emotions and adjusts the purchase support suggestions based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned purchase support department, When providing purchase support, we refer to the user's past purchase history to make the most suitable suggestions. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned purchase support department, The system estimates the user's emotions and prioritizes purchase support based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 40) The aforementioned purchase support department, When providing purchase support, we take the user's geographical location into consideration to make the best possible suggestions. The system described in Appendix 2, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. A scanning unit that 3D scans the room space, An analysis unit that analyzes the data scanned by the aforementioned scanning unit, A generation unit generates an optimal furniture layout based on the data analyzed by the analysis unit, A visualization unit visualizes the layout generated by the generation unit using AR, The system includes an adjustment unit for interactively adjusting the layout visualized by the visualization unit. A system characterized by the following features.

2. The company has a purchasing support department that provides assistance with purchasing the proposed furniture. The system according to feature 1.

3. The scanning unit is Use your smartphone's camera to photograph the entire room and generate 3D data. The system according to feature 1.

4. The aforementioned analysis unit, We propose the optimal layout considering the size and shape of the room, the arrangement of existing furniture, and other factors. The system according to feature 1.

5. The visualization unit is, By using a smartphone to look around the room, you can check the suggested furniture arrangement in real time. The system according to feature 1.

6. The adjustment unit is, You can change the position of furniture by tapping the smartphone screen, or adjust its size by dragging it. The system according to feature 1.

7. The scanning unit is It estimates the user's emotions and adjusts the timing of scans based on the estimated emotions. The system according to feature 1.

8. The scanning unit is Automatically adjusts room lighting conditions to obtain optimal scan results. The system according to feature 1.

9. The scanning unit is During scanning, the system takes room temperature and humidity into account to improve scanning accuracy. The system according to feature 1.

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

Images