system

Abstract

The system according to this embodiment aims to generate an optimal room layout based on the user's image and real-world environment data. [Solution] The system according to the embodiment comprises a reception unit, a data input unit, a generation unit, and a display unit. The reception unit inputs the user's image. The data input unit inputs the actual environment data of the target room. The generation unit generates a layout based on the information input by the reception unit and the data input unit. The display unit displays the layout generated by the generation 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 the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to the description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been sufficiently carried out to generate an optimal room layout based on the user's image and real environment data, and there is room for improvement.

[0005] The system according to the embodiment aims to generate an optimal room layout based on the user's image and real environment data.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a data input unit, a generation unit, and a display unit. The reception unit receives an image of the user. The data input unit receives actual environmental data of the target room. The generation unit generates a layout based on the information entered by the reception unit and the data input unit. The display unit displays the layout generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can generate an optimal room layout based on the user's image and real-world environment data. [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 manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device ۱۲ 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 layout suggestion system according to an embodiment of the present invention is a system that suggests room layouts using a generative AI. This layout suggestion system allows the user to input their image via chat and input real-world environment data of the target room (data read by LiDAR, images, and videos). The generative AI then generates and suggests a layout suitable for the real environment. The generated layout includes furniture and home appliances sold by manufacturers, which the user can then view and purchase. For example, the user inputs their image via chat. For instance, they might input a specific request such as, "I want to place a modern sofa in the living room." This information is input to the generative AI. Next, the user inputs real-world environment data of the target room. This includes data read by LiDAR, images, and videos. This allows the generative AI to understand the room's dimensions, shape, and the placement of existing furniture. Based on the input image and real-world environment data, the generative AI generates an optimal layout. For example, it might suggest a layout where the user's desired modern sofa is placed in the center of the room, surrounded by appropriate furniture and home appliances. This layout can be viewed in detail on the screen and displayed as a 3D model. Furthermore, the generated layout includes furniture and home appliances sold by manufacturers. Users can review the products within the suggested layout, confirming their exact size, color, and overall feel. They can also purchase items directly through product links. The system includes features for creating layouts using existing furniture, designing layouts for individual rooms such as bathrooms and toilets, and even for non-room items like desks and shelves. It also incorporates behavioral economics-based suggestions for product and shelf placement. This system allows users to easily consider and review room layouts, helping them avoid mistakes when purchasing furniture and appliances. It also benefits manufacturers and retailers by boosting sales. Ultimately, the layout suggestion system generates and displays optimal layouts based on the user's vision and real-world environmental data.

[0029] The layout suggestion system according to the embodiment comprises a reception unit, a data input unit, a generation unit, and a display unit. The reception unit receives an image from the user. The user's image includes, but is not limited to, text, images, and audio. For example, the user can input their image via chat. The data input unit receives real-world environment data of the target room. The real-world environment data includes, but is not limited to, room dimensions, furniture arrangement, and lighting conditions. For example, the data input unit can receive data, images, and videos read by LiDAR. The generation unit generates a layout based on the information input by the reception unit and the data input unit. The generation unit generates a layout using, for example, a generation AI. The generation AI can use algorithms such as deep learning or reinforcement learning. For example, the generation unit generates a layout in which a modern sofa desired by the user is placed in the center of the room, with appropriate furniture and appliances placed around it. The display unit displays the layout generated by the generation unit. The display unit can display the generated layout as a 3D model. The 3D model is displayed using, for example, methods for manipulating the viewpoint or rendering techniques. The display unit can, for example, confirm the products within the proposed layout and display their accurate size, color, and overall appearance. The display unit can also, for example, display information for direct purchase through product links. As a result, the layout proposal system according to the embodiment can generate and display an optimal layout based on the user's image and real-world environment data.

[0030] The reception desk receives input from the user. This input includes, but is not limited to, text, images, and audio. For example, users can input their ideas via chat. Specifically, users can input text about their desired room atmosphere and style, and the types of furniture they want to place, through a chat window. They can also upload images to provide interior design samples or reference photos. Furthermore, voice input allows the system to recognize and convert user verbal descriptions into text. This allows users to communicate their ideas to the reception desk in various ways, and the system can collect basic data to create layout suggestions based on this information. The reception desk analyzes user input in real time and can ask additional questions as needed to gather more detailed information. For example, if a user inputs "modern living room," the reception desk will automatically generate and present questions such as "What specific furniture would you like to place?" or "Do you have any color preferences?" This allows the system to gain a more concrete understanding of the user's ideas and gather information to create more accurate layout suggestions.

[0031] The data input unit inputs real-world environmental data of the target room. This real-world data includes, but is not limited to, room dimensions, furniture placement, and lighting conditions. For example, the data input unit can input data, images, and videos read by LiDAR. Specifically, users can measure room dimensions using a smartphone or dedicated device and input that data into the system. By using a device equipped with a LiDAR sensor, a detailed 3D model of the room can be created, allowing for an accurate understanding of furniture placement and lighting conditions. Users can also take photos and videos of the room and upload them to the system to provide visual information. This allows the system to accurately understand the actual layout and lighting conditions of the room and make more realistic layout suggestions. The data input unit centrally manages this real-world data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the generation and display units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data input unit to efficiently and effectively collect real-world data and improve the overall performance of the system.

[0032] The generation unit generates a layout based on the information entered by the reception unit and the data input unit. The generation unit generates the layout using, for example, a generation AI. The generation AI can use algorithms such as deep learning and reinforcement learning. Specifically, the generation AI integrates the user's image with real-world environment data to build a model for proposing the optimal layout. For example, it generates a layout that places the modern sofa desired by the user in the center of the room, with appropriate furniture and appliances placed around it. The generation AI can learn from past data and trends to propose a layout that suits the user's preferences. Furthermore, the generation AI can modify the layout based on user feedback to make more satisfying suggestions. For example, if the user requests, "I want to move this sofa a little to the left," the generation AI will regenerate the layout to reflect that request. This allows the generation unit to make flexible layout suggestions that meet the user's needs. The generation unit can also generate multiple layout options and provide the user with choices. This allows the user to choose a layout that suits their preferences and achieve a more satisfying result.

[0033] The display unit shows the layout generated by the generation unit. For example, the display unit can display the generated layout as a 3D model. The 3D model is displayed using methods such as viewpoint manipulation and rendering techniques. Specifically, users can rotate the 3D model and zoom in and out to view the proposed layout from various angles. This allows users to intuitively understand the overall layout of the room and the placement of each piece of furniture. Furthermore, the display unit can show the products within the proposed layout, displaying their accurate size, color, and overall feel. For example, clicking on a specific piece of furniture displays detailed information and a purchase link for that furniture. This allows users to select furniture and appliances to actually purchase based on the proposed layout. The display unit can also overlay the proposed layout onto the user's actual room using AR (augmented reality) technology. This allows users to see in real time how the proposed layout will look in their own room. Additionally, the display unit can collect user feedback and continuously improve the accuracy and effectiveness of the displayed content. For example, if a user provides feedback such as "the color of this furniture is different from the actual color," the display unit will correct the displayed content based on that information to provide more accurate information. In this way, the display unit can provide users with an intuitive and accurate layout display and support optimal layout suggestions.

[0034] The generation unit can generate layouts using a generative AI. For example, the generation unit generates layouts using a generative AI. The generative AI can utilize algorithms such as deep learning or reinforcement learning. For example, the generation unit generates a layout that places a modern sofa desired by the user in the center of the room, surrounded by appropriate furniture and appliances. Some or all of the above processing in the generation unit is performed using a generative AI. This improves the accuracy of layout generation by using a generative AI.

[0035] The data input unit can input data read by LiDAR, images, and videos. For example, the data input unit can input data read by LiDAR. LiDAR can acquire data using, for example, scan range, resolution, and data format. For example, the data input unit can input images. Images are used to understand, for example, the dimensions and shape of a room, and the arrangement of existing furniture. For example, the data input unit can input videos. Videos are used to understand, for example, the dimensions and shape of a room, and the arrangement of existing furniture. As a result, by inputting LiDAR data, images, and videos, a layout based on the real environment can be generated. Some or all of the above processing in the data input unit is performed using generation AI.

[0036] The display unit can display the generated layout as a 3D model. The display unit can, for example, display the generated layout as a 3D model. The 3D model is displayed using, for example, methods for manipulating the viewpoint or rendering techniques. The display unit can, for example, allow the generated layout to be examined in detail. The display unit can, for example, display the generated layout on the screen so that the user can visually examine it. This allows the user to visually examine the layout by displaying it as a 3D model. Some or all of the above processing in the display unit is performed using generation AI.

[0037] The generation unit can generate layouts that include furniture and home appliances sold by specific manufacturers. For example, the generation unit generates layouts that include furniture and home appliances sold by specific manufacturers. Specific manufacturers include, but are not limited to, furniture manufacturer A and home appliance manufacturer B. The generation unit can generate, for example, layouts that include furniture and home appliances desired by the user. For example, the generation unit generates layouts that place the furniture and home appliances desired by the user in the center of the room, with appropriate furniture and home appliances placed around them. This allows users to view and purchase products by generating layouts that include furniture and home appliances sold by manufacturers. Some or all of the above processing in the generation unit is performed using generation AI.

[0038] The display unit can verify the products within the proposed layout and display their specific size, color, and overall appearance. For example, the display unit can verify the products within the proposed layout and display their accurate size, color, and overall appearance. Specific size, color, and overall appearance include, but are not limited to, actual-size displays and color simulations. The display unit can, for example, provide detailed information about the products within the proposed layout. For example, the display unit can display the products within the proposed layout on the screen, allowing the user to visually verify them. This allows the user to verify the products within the proposed layout in detail. Some or all of the above processing in the display unit is performed using a generation AI.

[0039] The display unit can display information for direct purchase through product links. For example, the display unit displays information for direct purchase through product links. Product links include, but are not limited to, URL links or 2D codes (e.g., QR Code®). The display unit allows users to, for example, view products within a proposed layout and purchase them directly through product links. The display unit, for example, displays product links on the screen so that users can visually confirm them. This allows users to directly purchase the proposed products. Some or all of the above processing in the display unit is performed using generation AI.

[0040] The generation unit can create layouts by combining furniture that the user already owns. For example, the generation unit can create layouts by combining furniture that the user already owns. Furniture that the user already owns may include, but is not limited to, user input or database lookups. For example, the generation unit can generate a layout in which the user's existing furniture is placed in the center of the room, and appropriate furniture and appliances are placed around it. This makes it possible to make suggestions that meet the user's needs by combining furniture that the user already owns. Some or all of the above processing in the generation unit is performed using generation AI.

[0041] The generation unit can create room-specific layouts for toilets and bathrooms. For example, the generation unit creates room-specific layouts for toilets and bathrooms. Toilets and bathrooms include, but are not limited to, hygiene standards and frequency of use. The generation unit generates, for example, layouts for toilets and bathrooms. For example, the generation unit generates layouts that place the toilet or bathroom in the center of the room and arrange appropriate furniture and appliances around it. By creating room-specific layouts, it is possible to propose the optimal layout for each room. Some or all of the above processing in the generation unit is performed using generation AI.

[0042] The generation unit can create layouts for desks and shelves other than rooms. For example, the generation unit creates layouts for desks and shelves other than rooms. Desks and shelves include, but are not limited to, storage efficiency and intended use. For example, the generation unit generates layouts for desks and shelves. For example, the generation unit generates a layout in which desks and shelves are placed in the center of a room, with appropriate furniture and appliances placed around them. This allows the generation unit to meet the diverse needs of users by creating layouts other than rooms. Some or all of the above processing in the generation unit is performed using generation AI.

[0043] The generation unit can propose product and shelf arrangement methods based on behavioral economics. For example, the generation unit proposes product and shelf arrangement methods based on behavioral economics. Behavioral economics includes, but is not limited to, consumer behavior models and purchasing psychology. For example, the generation unit proposes product and shelf arrangement methods based on behavioral economics. For example, the generation unit generates a layout that places products and shelves in the center of a room, with appropriate furniture and appliances placed around them. This, by proposing arrangement methods based on behavioral economics, leads to increased product sales. Some or all of the above processing in the generation unit is performed using a generation AI.

[0044] The reception unit can analyze the user's past image input history and select the optimal input method. For example, the reception unit analyzes the user's past image input history and selects the optimal input method. Past image input history includes, but is not limited to, data mining and pattern recognition. For example, the reception unit automatically displays images that the user has frequently entered in the past as candidates. For example, the reception unit prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit predicts and suggests images to be used during a specific time period based on the user's past input history. In this way, the optimal input method can be selected by analyzing past image input history. Some or all of the above processing in the reception unit is performed using generative AI.

[0045] The reception unit can filter images based on the user's current lifestyle and areas of interest when they are input. For example, the reception unit filters images based on the user's current lifestyle and areas of interest when they are input. Current lifestyle and areas of interest include, but are not limited to, surveys and behavioral history analysis. For example, the reception unit prioritizes input of images relevant to the user's current lifestyle. For example, the reception unit suggests relevant images based on the user's areas of interest. For example, the reception unit filters the input images based on the user's lifestyle and areas of interest. This allows for the input of more relevant images by filtering images based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception unit is performed using generative AI.

[0046] The reception unit can prioritize inputting images that are highly relevant when the user inputs images, taking into account the user's geographical location information. For example, the reception unit prioritizes inputting images that are highly relevant when the user inputs images, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. For example, the reception unit prioritizes inputting relevant images based on the user's current location. For example, the reception unit suggests relevant images based on the user's geographical location information. For example, the reception unit filters the input images, taking into account the user's geographical location information. This allows for the priority input of highly relevant images by considering the user's geographical location information. Some or all of the above processing in the reception unit is performed using generative AI.

[0047] The reception unit can analyze the user's social media activity and input relevant images when an image is input. For example, the reception unit analyzes the user's social media activity and inputs relevant images when an image is input. Social media activity includes, but is not limited to, analysis of posts and follower analysis. For example, the reception unit prioritizes inputting relevant images based on the user's social media activity. For example, the reception unit analyzes the user's social media activity and suggests relevant images. For example, the reception unit filters the input images considering the user's social media activity. This allows for the input of relevant images by analyzing the user's social media activity. Some or all of the above processing in the reception unit is performed using generative AI.

[0048] The data input unit can analyze the user's past data input history and select the optimal input method when inputting real-world data. For example, the data input unit analyzes the user's past data input history and selects the optimal input method when inputting real-world data. Past data input history includes, but is not limited to, data mining and pattern recognition. For example, the data input unit automatically displays data that the user has frequently entered in the past as candidates. For example, the data input unit prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the data input unit predicts and suggests data to be used during a specific time period based on the user's past input history. In this way, the optimal input method can be selected by analyzing past data input history. Some or all of the above processing in the data input unit is performed using generative AI.

[0049] The data input unit can filter real-world data based on the user's current living situation and areas of interest. For example, the data input unit filters real-world data based on the user's current living situation and areas of interest. Current living situation and areas of interest include, but are not limited to, surveys and behavioral history analysis. For example, the data input unit prioritizes inputting relevant data based on the user's current living situation. For example, the data input unit suggests relevant data based on the user's areas of interest. For example, the data input unit filters the data to be input based on the user's living situation and areas of interest. This allows for the input of more relevant data by filtering data based on the user's living situation and areas of interest. Some or all of the above processing in the data input unit is performed using generative AI.

[0050] The data input unit can prioritize inputting highly relevant data when inputting real-world data, taking into account the user's geographical location information. For example, the data input unit prioritizes inputting highly relevant data when inputting real-world data, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location-based services. For example, the data input unit prioritizes inputting relevant data based on the user's current location. For example, the data input unit suggests relevant data based on the user's geographical location information. For example, the data input unit filters the data to be input, taking into account the user's geographical location information. This allows for the priority input of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data input unit is performed using generative AI.

[0051] The data entry unit can analyze the user's social media activity and input relevant data when inputting real-world data. For example, the data entry unit analyzes the user's social media activity and inputs relevant data when inputting real-world data. Social media activity includes, but is not limited to, analyzing post content and follower analysis. For example, the data entry unit prioritizes inputting relevant data based on the user's social media activity. For example, the data entry unit analyzes the user's social media activity and suggests relevant data. For example, the data entry unit filters the data to be input, taking the user's social media activity into consideration. This allows for the input of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data entry unit is performed using generative AI.

[0052] The generation unit can analyze the user's past layout history and select the optimal generation method when generating a layout. For example, the generation unit analyzes the user's past layout history and selects the optimal generation method when generating a layout. Past layout history includes, but is not limited to, data mining and pattern recognition. For example, the generation unit proposes the optimal layout based on layouts the user has used in the past. For example, the generation unit proposes a layout that avoids congestion based on the user's past layout history. For example, the generation unit analyzes the user's past layout history and proposes the most efficient layout. In this way, the optimal generation method can be selected by analyzing past layout history. Some or all of the above processing in the generation unit is performed using generation AI.

[0053] The generation unit can filter layouts based on the user's current lifestyle and areas of interest during layout generation. For example, the generation unit filters layouts based on the user's current lifestyle and areas of interest during layout generation. Current lifestyle and areas of interest include, but are not limited to, surveys and behavioral history analysis. For example, the generation unit prioritizes generating layouts relevant to the user based on their current lifestyle. For example, the generation unit suggests relevant layouts based on the user's areas of interest. For example, the generation unit filters the layouts to be generated based on the user's lifestyle and areas of interest. By filtering layouts based on the user's lifestyle and areas of interest, more relevant layouts can be generated. Some or all of the above processing in the generation unit is performed using generation AI.

[0054] The generation unit can prioritize the generation of layouts that are highly relevant, taking into account the user's geographical location information during layout generation. For example, the generation unit prioritizes the generation of layouts that are highly relevant, taking into account the user's geographical location information during layout generation. Geographical location information includes, but is not limited to, GPS data and location services. For example, the generation unit prioritizes the generation of relevant layouts based on the user's current location. For example, the generation unit suggests relevant layouts based on the user's geographical location information. For example, the generation unit filters the layouts to be generated, taking into account the user's geographical location information. This allows for the priority generation of layouts that are highly relevant, by considering the user's geographical location information. Some or all of the above processing in the generation unit is performed using a generation AI.

[0055] The generation unit can analyze the user's social media activity and generate relevant layouts when generating layouts. For example, the generation unit analyzes the user's social media activity and generates relevant layouts when generating layouts. Social media activity includes, but is not limited to, analyzing post content and follower analysis. For example, the generation unit prioritizes generating relevant layouts based on the user's social media activity. For example, the generation unit analyzes the user's social media activity and suggests relevant layouts. For example, the generation unit filters the layouts to be generated, taking the user's social media activity into consideration. This allows the generation of relevant layouts by analyzing the user's social media activity. Some or all of the above processing in the generation unit is performed using generation AI.

[0056] The display unit can analyze the user's past viewing history and select the optimal display method when displaying a layout. For example, the display unit analyzes the user's past viewing history and selects the optimal display method when displaying a layout. Past viewing history includes, but is not limited to, data mining and pattern recognition. For example, the display unit proposes the optimal display method based on the display methods the user has used in the past. For example, the display unit proposes a display method that avoids congestion based on the user's past viewing history. For example, the display unit analyzes the user's past viewing history and proposes the most efficient display method. In this way, the optimal display method can be selected by analyzing past viewing history. Some or all of the above processing in the display unit is performed using generative AI.

[0057] The display unit can filter layouts based on the user's current lifestyle and areas of interest. For example, the display unit filters layouts based on the user's current lifestyle and areas of interest. Current lifestyle and areas of interest include, but are not limited to, surveys and behavioral history analysis. For example, the display unit prioritizes displaying layouts relevant to the user based on their current lifestyle. For example, the display unit suggests relevant layouts based on the user's areas of interest. For example, the display unit filters the layouts to display based on the user's lifestyle and areas of interest. By filtering layouts based on the user's lifestyle and areas of interest, more relevant layouts can be displayed. Some or all of the above processing in the display unit is performed using a generation AI.

[0058] The display unit can prioritize displaying layouts that are highly relevant to the user, taking into account the user's geographical location information. For example, the display unit prioritizes displaying layouts that are highly relevant to the user, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. For example, the display unit prioritizes displaying relevant layouts based on the user's current location. For example, the display unit suggests relevant layouts based on the user's geographical location information. For example, the display unit filters the layouts to display, taking into account the user's geographical location information. This allows for the priority display of highly relevant layouts by considering the user's geographical location information. Some or all of the above processing in the display unit is performed using a generation AI.

[0059] The display unit can analyze the user's social media activity and display relevant layouts when displaying layouts. For example, the display unit analyzes the user's social media activity and displays relevant layouts when displaying layouts. Social media activity includes, but is not limited to, analyzing post content and analyzing followers. For example, the display unit prioritizes displaying relevant layouts based on the user's social media activity. For example, the display unit analyzes the user's social media activity and suggests relevant layouts. For example, the display unit filters the layouts to display, taking the user's social media activity into consideration. This allows the display of relevant layouts by analyzing the user's social media activity. Some or all of the above processing in the display unit is performed using generative AI.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] A layout suggestion system can analyze a user's past layout history and propose the optimal layout. For example, it can suggest similar styles based on layout styles the user has chosen in the past. It can also consider layouts the user has avoided in the past and offer different suggestions. By learning the user's past choices and the placement patterns of specific furniture and appliances, it can propose the optimal arrangement. This makes it possible to suggest layouts that match the user's preferences.

[0062] The layout suggestion system can propose layouts based on the user's current living situation and areas of interest. For example, if a user has pets, it will suggest a layout that takes pets into consideration. If a user enjoys gardening as a hobby, it will suggest spaces for plants. If a user works remotely, it will suggest a layout that enhances work efficiency. This makes it possible to propose layouts that suit the user's lifestyle.

[0063] The layout suggestion system can propose layouts that take into account the user's geographical location. For example, it can suggest a layout that prioritizes warmth to users living in cold climates, a layout that prioritizes space efficiency to users living in urban areas, and a layout that allows users to enjoy the ocean view to users living by the sea. This makes it possible to propose layouts that are suitable for the user's living environment.

[0064] The layout suggestion system can analyze a user's social media activity and suggest relevant layouts. For example, if a user frequently posts about interior design, it will suggest a layout that matches that style. If a user prefers a particular brand of furniture, it will suggest a layout that includes products from that brand. It will also suggest layouts that reflect trends that the user's followers are interested in. This makes it possible to suggest layouts based on the user's interests and preferences.

[0065] The layout suggestion system can analyze a user's past data entry history and select the optimal input method. For example, it can automatically display data that the user has frequently entered in the past as a suggestion. It prioritizes suggesting input methods that the user has used in the past (voice, text, etc.). Based on the user's past input history, it predicts and suggests data that will be used during specific time periods. In this way, by analyzing past data entry history, the system can select the optimal input method.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk inputs the user's image. The user's image may include, but is not limited to, text, images, or audio. The reception desk can, for example, allow the user to input their image via chat. Step 2: The data input unit inputs real-world environmental data of the target room. This real-world data includes, but is not limited to, room dimensions, furniture arrangement, and lighting conditions. The data input unit can also input data, images, and videos read by, for example, LiDAR. Step 3: The generation unit generates a layout based on the information entered by the reception unit and the data input unit. The generation unit generates the layout using, for example, a generation AI. The generation AI can use algorithms such as deep learning or reinforcement learning. For example, the generation unit generates a layout in which a modern sofa desired by the user is placed in the center of the room, with appropriate furniture and appliances placed around it. Step 4: The display unit displays the layout generated by the generation unit. The display unit can, for example, display the generated layout as a 3D model. The 3D model is displayed using, for example, viewpoint manipulation methods and rendering techniques. The display unit can, for example, allow users to check the products within the proposed layout and display their exact size, color, and overall appearance. The display unit can, for example, display information for direct purchase through product links.

[0068] (Example of form 2) The layout suggestion system according to an embodiment of the present invention is a system that suggests room layouts using a generative AI. This layout suggestion system allows the user to input their image via chat and input real-world environment data of the target room (data read by LiDAR, images, and videos). The generative AI then generates and suggests a layout suitable for the real environment. The generated layout includes furniture and home appliances sold by manufacturers, which the user can then view and purchase. For example, the user inputs their image via chat. For instance, they might input a specific request such as, "I want to place a modern sofa in the living room." This information is input to the generative AI. Next, the user inputs real-world environment data of the target room. This includes data read by LiDAR, images, and videos. This allows the generative AI to understand the room's dimensions, shape, and the placement of existing furniture. Based on the input image and real-world environment data, the generative AI generates an optimal layout. For example, it might suggest a layout where the user's desired modern sofa is placed in the center of the room, surrounded by appropriate furniture and home appliances. This layout can be viewed in detail on the screen and displayed as a 3D model. Furthermore, the generated layout includes furniture and home appliances sold by manufacturers. Users can review the products within the suggested layout, confirming their exact size, color, and overall feel. They can also purchase items directly through product links. The system includes features for creating layouts using existing furniture, designing layouts for individual rooms such as bathrooms and toilets, and even for non-room items like desks and shelves. It also incorporates behavioral economics-based suggestions for product and shelf placement. This system allows users to easily consider and review room layouts, helping them avoid mistakes when purchasing furniture and appliances. It also benefits manufacturers and retailers by boosting sales. Ultimately, the layout suggestion system generates and displays optimal layouts based on the user's vision and real-world environmental data.

[0069] The layout suggestion system according to the embodiment comprises a reception unit, a data input unit, a generation unit, and a display unit. The reception unit receives an image from the user. The user's image includes, but is not limited to, text, images, and audio. For example, the user can input their image via chat. The data input unit receives real-world environment data of the target room. The real-world environment data includes, but is not limited to, room dimensions, furniture arrangement, and lighting conditions. For example, the data input unit can receive data, images, and videos read by LiDAR. The generation unit generates a layout based on the information input by the reception unit and the data input unit. The generation unit generates a layout using, for example, a generation AI. The generation AI can use algorithms such as deep learning or reinforcement learning. For example, the generation unit generates a layout in which a modern sofa desired by the user is placed in the center of the room, with appropriate furniture and appliances placed around it. The display unit displays the layout generated by the generation unit. The display unit can display the generated layout as a 3D model. The 3D model is displayed using, for example, methods for manipulating the viewpoint or rendering techniques. The display unit can, for example, confirm the products within the proposed layout and display their accurate size, color, and overall appearance. The display unit can also, for example, display information for direct purchase through product links. As a result, the layout proposal system according to the embodiment can generate and display an optimal layout based on the user's image and real-world environment data.

[0070] The reception desk receives input from the user. This input includes, but is not limited to, text, images, and audio. For example, users can input their ideas via chat. Specifically, users can input text about their desired room atmosphere and style, and the types of furniture they want to place, through a chat window. They can also upload images to provide interior design samples or reference photos. Furthermore, voice input allows the system to recognize and convert user verbal descriptions into text. This allows users to communicate their ideas to the reception desk in various ways, and the system can collect basic data to create layout suggestions based on this information. The reception desk analyzes user input in real time and can ask additional questions as needed to gather more detailed information. For example, if a user inputs "modern living room," the reception desk will automatically generate and present questions such as "What specific furniture would you like to place?" or "Do you have any color preferences?" This allows the system to gain a more concrete understanding of the user's ideas and gather information to create more accurate layout suggestions.

[0071] The data input unit inputs real-world environmental data of the target room. This real-world data includes, but is not limited to, room dimensions, furniture placement, and lighting conditions. For example, the data input unit can input data, images, and videos read by LiDAR. Specifically, users can measure room dimensions using a smartphone or dedicated device and input that data into the system. By using a device equipped with a LiDAR sensor, a detailed 3D model of the room can be created, allowing for an accurate understanding of furniture placement and lighting conditions. Users can also take photos and videos of the room and upload them to the system to provide visual information. This allows the system to accurately understand the actual layout and lighting conditions of the room and make more realistic layout suggestions. The data input unit centrally manages this real-world data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the generation and display units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data input unit to efficiently and effectively collect real-world data and improve the overall performance of the system.

[0072] The generation unit generates a layout based on the information entered by the reception unit and the data input unit. The generation unit generates the layout using, for example, a generation AI. The generation AI can use algorithms such as deep learning and reinforcement learning. Specifically, the generation AI integrates the user's image with real-world environment data to build a model for proposing the optimal layout. For example, it generates a layout that places the modern sofa desired by the user in the center of the room, with appropriate furniture and appliances placed around it. The generation AI can learn from past data and trends to propose a layout that suits the user's preferences. Furthermore, the generation AI can modify the layout based on user feedback to make more satisfying suggestions. For example, if the user requests, "I want to move this sofa a little to the left," the generation AI will regenerate the layout to reflect that request. This allows the generation unit to make flexible layout suggestions that meet the user's needs. The generation unit can also generate multiple layout options and provide the user with choices. This allows the user to choose a layout that suits their preferences and achieve a more satisfying result.

[0073] The display unit shows the layout generated by the generation unit. For example, the display unit can display the generated layout as a 3D model. The 3D model is displayed using methods such as viewpoint manipulation and rendering techniques. Specifically, users can rotate the 3D model and zoom in and out to view the proposed layout from various angles. This allows users to intuitively understand the overall layout of the room and the placement of each piece of furniture. Furthermore, the display unit can show the products within the proposed layout, displaying their accurate size, color, and overall feel. For example, clicking on a specific piece of furniture displays detailed information and a purchase link for that furniture. This allows users to select furniture and appliances to actually purchase based on the proposed layout. The display unit can also overlay the proposed layout onto the user's actual room using AR (augmented reality) technology. This allows users to see in real time how the proposed layout will look in their own room. Additionally, the display unit can collect user feedback and continuously improve the accuracy and effectiveness of the displayed content. For example, if a user provides feedback such as "the color of this furniture is different from the actual color," the display unit will correct the displayed content based on that information to provide more accurate information. In this way, the display unit can provide users with an intuitive and accurate layout display and support optimal layout suggestions.

[0074] The generation unit can generate layouts using a generative AI. For example, the generation unit generates layouts using a generative AI. The generative AI can utilize algorithms such as deep learning or reinforcement learning. For example, the generation unit generates a layout that places a modern sofa desired by the user in the center of the room, surrounded by appropriate furniture and appliances. Some or all of the above processing in the generation unit is performed using a generative AI. This improves the accuracy of layout generation by using a generative AI.

[0075] The data input unit can input data read by LiDAR, images, and videos. For example, the data input unit can input data read by LiDAR. LiDAR can acquire data using, for example, scan range, resolution, and data format. For example, the data input unit can input images. Images are used to understand, for example, the dimensions and shape of a room, and the arrangement of existing furniture. For example, the data input unit can input videos. Videos are used to understand, for example, the dimensions and shape of a room, and the arrangement of existing furniture. As a result, by inputting LiDAR data, images, and videos, a layout based on the real environment can be generated. Some or all of the above processing in the data input unit is performed using generation AI.

[0076] The display unit can display the generated layout as a 3D model. The display unit can, for example, display the generated layout as a 3D model. The 3D model is displayed using, for example, methods for manipulating the viewpoint or rendering techniques. The display unit can, for example, allow the generated layout to be examined in detail. The display unit can, for example, display the generated layout on the screen so that the user can visually examine it. This allows the user to visually examine the layout by displaying it as a 3D model. Some or all of the above processing in the display unit is performed using generation AI.

[0077] The generation unit can generate layouts that include furniture and home appliances sold by specific manufacturers. For example, the generation unit generates layouts that include furniture and home appliances sold by specific manufacturers. Specific manufacturers include, but are not limited to, furniture manufacturer A and home appliance manufacturer B. The generation unit can generate, for example, layouts that include furniture and home appliances desired by the user. For example, the generation unit generates layouts that place the furniture and home appliances desired by the user in the center of the room, with appropriate furniture and home appliances placed around them. This allows users to view and purchase products by generating layouts that include furniture and home appliances sold by manufacturers. Some or all of the above processing in the generation unit is performed using generation AI.

[0078] The display unit can verify the products within the proposed layout and display their specific size, color, and overall appearance. For example, the display unit can verify the products within the proposed layout and display their accurate size, color, and overall appearance. Specific size, color, and overall appearance include, but are not limited to, actual-size displays and color simulations. The display unit can, for example, provide detailed information about the products within the proposed layout. For example, the display unit can display the products within the proposed layout on the screen, allowing the user to visually verify them. This allows the user to verify the products within the proposed layout in detail. Some or all of the above processing in the display unit is performed using a generation AI.

[0079] The display unit can display information for direct purchase through product links. For example, the display unit displays information for direct purchase through product links. Product links include, but are not limited to, URL links or 2D codes (e.g., QR codes). The display unit allows users to, for example, view products within a proposed layout and purchase them directly through product links. The display unit, for example, displays product links on the screen so that users can visually confirm them. This allows users to directly purchase the proposed products. Some or all of the above processing in the display unit is performed using generation AI.

[0080] The generation unit can create layouts by combining furniture that the user already owns. For example, the generation unit can create layouts by combining furniture that the user already owns. Furniture that the user already owns may include, but is not limited to, user input or database lookups. For example, the generation unit can generate a layout in which the user's existing furniture is placed in the center of the room, and appropriate furniture and appliances are placed around it. This makes it possible to make suggestions that meet the user's needs by combining furniture that the user already owns. Some or all of the above processing in the generation unit is performed using generation AI.

[0081] The generation unit can create room-specific layouts for toilets and bathrooms. For example, the generation unit creates room-specific layouts for toilets and bathrooms. Toilets and bathrooms include, but are not limited to, hygiene standards and frequency of use. The generation unit generates, for example, layouts for toilets and bathrooms. For example, the generation unit generates layouts that place the toilet or bathroom in the center of the room and arrange appropriate furniture and appliances around it. By creating room-specific layouts, it is possible to propose the optimal layout for each room. Some or all of the above processing in the generation unit is performed using generation AI.

[0082] The generation unit can create layouts for desks and shelves other than rooms. For example, the generation unit creates layouts for desks and shelves other than rooms. Desks and shelves include, but are not limited to, storage efficiency and intended use. For example, the generation unit generates layouts for desks and shelves. For example, the generation unit generates a layout in which desks and shelves are placed in the center of a room, with appropriate furniture and appliances placed around them. This allows the generation unit to meet the diverse needs of users by creating layouts other than rooms. Some or all of the above processing in the generation unit is performed using generation AI.

[0083] The generation unit can propose product and shelf arrangement methods based on behavioral economics. For example, the generation unit proposes product and shelf arrangement methods based on behavioral economics. Behavioral economics includes, but is not limited to, consumer behavior models and purchasing psychology. For example, the generation unit proposes product and shelf arrangement methods based on behavioral economics. For example, the generation unit generates a layout that places products and shelves in the center of a room, with appropriate furniture and appliances placed around them. This, by proposing arrangement methods based on behavioral economics, leads to increased product sales. Some or all of the above processing in the generation unit is performed using a generation AI.

[0084] The reception unit can estimate the user's emotions and adjust the image input method based on the estimated emotions. For example, the reception unit can estimate the user's emotions and adjust the image input method based on the estimated emotions. User emotions include, but are not limited to, facial recognition and voice analysis. For example, if the user is stressed, the reception unit can provide a simple interface and minimize input steps. For example, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception unit can prioritize voice input to allow for quick image input. This allows for the input of more appropriate images by adjusting the image input method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the reception unit is performed using generative AI.

[0085] The reception unit can analyze the user's past image input history and select the optimal input method. For example, the reception unit analyzes the user's past image input history and selects the optimal input method. Past image input history includes, but is not limited to, data mining and pattern recognition. For example, the reception unit automatically displays images that the user has frequently entered in the past as candidates. For example, the reception unit prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit predicts and suggests images to be used during a specific time period based on the user's past input history. In this way, the optimal input method can be selected by analyzing past image input history. Some or all of the above processing in the reception unit is performed using generative AI.

[0086] The reception unit can filter images based on the user's current lifestyle and areas of interest when they are input. For example, the reception unit filters images based on the user's current lifestyle and areas of interest when they are input. Current lifestyle and areas of interest include, but are not limited to, surveys and behavioral history analysis. For example, the reception unit prioritizes input of images relevant to the user's current lifestyle. For example, the reception unit suggests relevant images based on the user's areas of interest. For example, the reception unit filters the input images based on the user's lifestyle and areas of interest. This allows for the input of more relevant images by filtering images based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception unit is performed using generative AI.

[0087] The reception unit can estimate the user's emotions and determine the priority of input images based on the estimated emotions. For example, the reception unit estimates the user's emotions and determines the priority of input images based on the estimated emotions. User emotions include, but are not limited to, facial recognition and voice analysis. For example, if the user is stressed, the reception unit will prioritize inputting relaxing images. For example, if the user is relaxed, the reception unit will prioritize inputting detailed images. For example, if the user is in a hurry, the reception unit will prioritize inputting important images. This allows for the input of more appropriate images by determining the priority of images 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 includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the reception unit is performed using generative AI.

[0088] The reception unit can prioritize inputting images that are highly relevant when the user inputs images, taking into account the user's geographical location information. For example, the reception unit prioritizes inputting images that are highly relevant when the user inputs images, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. For example, the reception unit prioritizes inputting relevant images based on the user's current location. For example, the reception unit suggests relevant images based on the user's geographical location information. For example, the reception unit filters the input images, taking into account the user's geographical location information. This allows for the priority input of highly relevant images by considering the user's geographical location information. Some or all of the above processing in the reception unit is performed using generative AI.

[0089] The reception unit can analyze the user's social media activity and input relevant images when an image is input. For example, the reception unit analyzes the user's social media activity and inputs relevant images when an image is input. Social media activity includes, but is not limited to, analysis of posts and follower analysis. For example, the reception unit prioritizes inputting relevant images based on the user's social media activity. For example, the reception unit analyzes the user's social media activity and suggests relevant images. For example, the reception unit filters the input images considering the user's social media activity. This allows for the input of relevant images by analyzing the user's social media activity. Some or all of the above processing in the reception unit is performed using generative AI.

[0090] The data input unit can estimate the user's emotions and adjust the input method for real-world data based on the estimated user emotions. For example, the data input unit can estimate the user's emotions and adjust the input method for real-world data based on the estimated user emotions. User emotions include, but are not limited to, facial recognition and voice analysis. For example, if the user is stressed, the data input unit provides a simple interface and minimizes the input steps. For example, if the user is relaxed, the data input unit provides detailed input options and suggests a customizable input method. For example, if the user is in a hurry, the data input unit prioritizes voice input to allow for rapid input of real-world data. This allows for the input of more appropriate data by adjusting the input method for real-world data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes in the data input unit are performed using generative AI.

[0091] The data input unit can analyze the user's past data input history and select the optimal input method when inputting real-world data. For example, the data input unit analyzes the user's past data input history and selects the optimal input method when inputting real-world data. Past data input history includes, but is not limited to, data mining and pattern recognition. For example, the data input unit automatically displays data that the user has frequently entered in the past as candidates. For example, the data input unit prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the data input unit predicts and suggests data to be used during a specific time period based on the user's past input history. In this way, the optimal input method can be selected by analyzing past data input history. Some or all of the above processing in the data input unit is performed using generative AI.

[0092] The data input unit can filter real-world data based on the user's current living situation and areas of interest. For example, the data input unit filters real-world data based on the user's current living situation and areas of interest. Current living situation and areas of interest include, but are not limited to, surveys and behavioral history analysis. For example, the data input unit prioritizes inputting relevant data based on the user's current living situation. For example, the data input unit suggests relevant data based on the user's areas of interest. For example, the data input unit filters the data to be input based on the user's living situation and areas of interest. This allows for the input of more relevant data by filtering data based on the user's living situation and areas of interest. Some or all of the above processing in the data input unit is performed using generative AI.

[0093] The data input unit can prioritize inputting highly relevant data when inputting real-world data, taking into account the user's geographical location information. For example, the data input unit prioritizes inputting highly relevant data when inputting real-world data, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location-based services. For example, the data input unit prioritizes inputting relevant data based on the user's current location. For example, the data input unit suggests relevant data based on the user's geographical location information. For example, the data input unit filters the data to be input, taking into account the user's geographical location information. This allows for the priority input of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data input unit is performed using generative AI.

[0094] The data entry unit can analyze the user's social media activity and input relevant data when inputting real-world data. For example, the data entry unit analyzes the user's social media activity and inputs relevant data when inputting real-world data. Social media activity includes, but is not limited to, analyzing post content and follower analysis. For example, the data entry unit prioritizes inputting relevant data based on the user's social media activity. For example, the data entry unit analyzes the user's social media activity and suggests relevant data. For example, the data entry unit filters the data to be input, taking the user's social media activity into consideration. This allows for the input of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data entry unit is performed using generative AI.

[0095] The generation unit can estimate the user's emotions and adjust the layout generation method based on the estimated user emotions. For example, the generation unit estimates the user's emotions and adjusts the layout generation method based on the estimated user emotions. User emotions include, but are not limited to, facial recognition and voice analysis. For example, the generation unit generates a relaxed layout when the user is relaxed. For example, the generation unit generates an efficient layout when the user is in a hurry. For example, the generation unit generates a visually stimulating layout when the user is excited. In this way, a more appropriate layout can be generated by adjusting the layout generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit is performed using a generation AI.

[0096] The generation unit can analyze the user's past layout history and select the optimal generation method when generating a layout. For example, the generation unit analyzes the user's past layout history and selects the optimal generation method when generating a layout. Past layout history includes, but is not limited to, data mining and pattern recognition. For example, the generation unit proposes the optimal layout based on layouts the user has used in the past. For example, the generation unit proposes a layout that avoids congestion based on the user's past layout history. For example, the generation unit analyzes the user's past layout history and proposes the most efficient layout. In this way, the optimal generation method can be selected by analyzing past layout history. Some or all of the above processing in the generation unit is performed using generation AI.

[0097] The generation unit can filter layouts based on the user's current lifestyle and areas of interest during layout generation. For example, the generation unit filters layouts based on the user's current lifestyle and areas of interest during layout generation. Current lifestyle and areas of interest include, but are not limited to, surveys and behavioral history analysis. For example, the generation unit prioritizes generating layouts relevant to the user based on their current lifestyle. For example, the generation unit suggests relevant layouts based on the user's areas of interest. For example, the generation unit filters the layouts to be generated based on the user's lifestyle and areas of interest. By filtering layouts based on the user's lifestyle and areas of interest, more relevant layouts can be generated. Some or all of the above processing in the generation unit is performed using generation AI.

[0098] The generation unit can estimate the user's emotions and determine the priority of the layouts to be generated based on the estimated user emotions. For example, the generation unit estimates the user's emotions and determines the priority of the layouts to be generated based on the estimated user emotions. User emotions include, for example, facial recognition and voice analysis, but are not limited to such examples. For example, if the user is stressed, the generation unit will prioritize generating relaxing layouts. For example, if the user is relaxed, the generation unit will prioritize generating detailed layouts. For example, if the user is in a hurry, the generation unit will prioritize generating important layouts. In this way, by determining the priority of layouts according to the user's emotions, a more appropriate layout can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit is performed using a generation AI.

[0099] The generation unit can prioritize the generation of layouts that are highly relevant, taking into account the user's geographical location information during layout generation. For example, the generation unit prioritizes the generation of layouts that are highly relevant, taking into account the user's geographical location information during layout generation. Geographical location information includes, but is not limited to, GPS data and location services. For example, the generation unit prioritizes the generation of relevant layouts based on the user's current location. For example, the generation unit suggests relevant layouts based on the user's geographical location information. For example, the generation unit filters the layouts to be generated, taking into account the user's geographical location information. This allows for the priority generation of layouts that are highly relevant, by considering the user's geographical location information. Some or all of the above processing in the generation unit is performed using a generation AI.

[0100] The generation unit can analyze the user's social media activity and generate relevant layouts when generating layouts. For example, the generation unit analyzes the user's social media activity and generates relevant layouts when generating layouts. Social media activity includes, but is not limited to, analyzing post content and follower analysis. For example, the generation unit prioritizes generating relevant layouts based on the user's social media activity. For example, the generation unit analyzes the user's social media activity and suggests relevant layouts. For example, the generation unit filters the layouts to be generated, taking the user's social media activity into consideration. This allows the generation of relevant layouts by analyzing the user's social media activity. Some or all of the above processing in the generation unit is performed using generation AI.

[0101] The display unit can estimate the user's emotions and adjust the layout display method based on the estimated user emotions. For example, the display unit can estimate the user's emotions and adjust the layout display method based on the estimated user emotions. User emotions include, but are not limited to, facial recognition and voice analysis. For example, if the user is tense, the display unit provides a simple and highly visible display method. For example, if the user is relaxed, the display unit provides a display method that includes detailed information. For example, if the user is in a hurry, the display unit provides a display method that gets straight to the point. This allows for a more appropriate display by adjusting the layout display method 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 includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the display unit is performed using generative AI.

[0102] The display unit can analyze the user's past viewing history and select the optimal display method when displaying a layout. For example, the display unit analyzes the user's past viewing history and selects the optimal display method when displaying a layout. Past viewing history includes, but is not limited to, data mining and pattern recognition. For example, the display unit proposes the optimal display method based on the display methods the user has used in the past. For example, the display unit proposes a display method that avoids congestion based on the user's past viewing history. For example, the display unit analyzes the user's past viewing history and proposes the most efficient display method. In this way, the optimal display method can be selected by analyzing past viewing history. Some or all of the above processing in the display unit is performed using generative AI.

[0103] The display unit can filter layouts based on the user's current lifestyle and areas of interest. For example, the display unit filters layouts based on the user's current lifestyle and areas of interest. Current lifestyle and areas of interest include, but are not limited to, surveys and behavioral history analysis. For example, the display unit prioritizes displaying layouts relevant to the user based on their current lifestyle. For example, the display unit suggests relevant layouts based on the user's areas of interest. For example, the display unit filters the layouts to display based on the user's lifestyle and areas of interest. By filtering layouts based on the user's lifestyle and areas of interest, more relevant layouts can be displayed. Some or all of the above processing in the display unit is performed using a generation AI.

[0104] The display unit can estimate the user's emotions and determine the priority of the layout to display based on the estimated user emotions. For example, the display unit estimates the user's emotions and determines the priority of the layout to display based on the estimated user emotions. User emotions include, for example, facial recognition and voice analysis, but are not limited to such examples. For example, if the user is stressed, the display unit will prioritize displaying a relaxing layout. For example, if the user is relaxed, the display unit will prioritize displaying a detailed layout. For example, if the user is in a hurry, the display unit will prioritize displaying an important layout. In this way, a more appropriate layout can be displayed by determining the priority of the layout 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 includes, for example, text generation AI (e.g., LLM) and multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the display unit is performed using generative AI.

[0105] The display unit can prioritize displaying layouts that are highly relevant to the user, taking into account the user's geographical location information. For example, the display unit prioritizes displaying layouts that are highly relevant to the user, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. For example, the display unit prioritizes displaying relevant layouts based on the user's current location. For example, the display unit suggests relevant layouts based on the user's geographical location information. For example, the display unit filters the layouts to display, taking into account the user's geographical location information. This allows for the priority display of highly relevant layouts by considering the user's geographical location information. Some or all of the above processing in the display unit is performed using a generation AI.

[0106] The display unit can analyze the user's social media activity and display relevant layouts when displaying layouts. For example, the display unit analyzes the user's social media activity and displays relevant layouts when displaying layouts. Social media activity includes, but is not limited to, analyzing post content and analyzing followers. For example, the display unit prioritizes displaying relevant layouts based on the user's social media activity. For example, the display unit analyzes the user's social media activity and suggests relevant layouts. For example, the display unit filters the layouts to display, taking the user's social media activity into consideration. This allows the display of relevant layouts by analyzing the user's social media activity. Some or all of the above processing in the display unit is performed using generative AI.

[0107] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0108] The layout suggestion system can estimate the user's emotions and adjust the layout suggestions based on those emotions. For example, if the user is stressed, it suggests a relaxing space. If the user is relaxed, it provides more detailed customization options. If the user is in a hurry, it suggests a layout that can be quickly implemented. This enables the system to suggest the optimal layout according to the user's emotions. Emotion estimation is performed using methods such as facial recognition and voice analysis.

[0109] A layout suggestion system can analyze a user's past layout history and propose the optimal layout. For example, it can suggest similar styles based on layout styles the user has chosen in the past. It can also consider layouts the user has avoided in the past and offer different suggestions. By learning the user's past choices and the placement patterns of specific furniture and appliances, it can propose the optimal arrangement. This makes it possible to suggest layouts that match the user's preferences.

[0110] The layout suggestion system can propose layouts based on the user's current living situation and areas of interest. For example, if a user has pets, it will suggest a layout that takes pets into consideration. If a user enjoys gardening as a hobby, it will suggest spaces for plants. If a user works remotely, it will suggest a layout that enhances work efficiency. This makes it possible to propose layouts that suit the user's lifestyle.

[0111] The layout suggestion system can propose layouts that take into account the user's geographical location. For example, it can suggest a layout that prioritizes warmth to users living in cold climates, a layout that prioritizes space efficiency to users living in urban areas, and a layout that allows users to enjoy the ocean view to users living by the sea. This makes it possible to propose layouts that are suitable for the user's living environment.

[0112] The layout suggestion system can analyze a user's social media activity and suggest relevant layouts. For example, if a user frequently posts about interior design, it will suggest a layout that matches that style. If a user prefers a particular brand of furniture, it will suggest a layout that includes products from that brand. It will also suggest layouts that reflect trends that the user's followers are interested in. This makes it possible to suggest layouts based on the user's interests and preferences.

[0113] The layout suggestion system can estimate the user's emotions and adjust the layout display method based on those emotions. For example, if the user is tense, it provides a simple and highly visible display method. If the user is relaxed, it provides a display method that includes detailed information. If the user is in a hurry, it provides a display method that gets straight to the point. This enables optimal display according to the user's emotions. Emotion estimation is performed using methods such as facial recognition and voice analysis.

[0114] The layout suggestion system can estimate the user's emotions and adjust the layout generation method based on those emotions. For example, if the user is relaxed, it will generate a relaxed layout. If the user is in a hurry, it will generate an efficient layout. If the user is excited, it will generate a visually stimulating layout. This makes it possible to generate the optimal layout according to the user's emotions. Emotion estimation is performed using methods such as facial recognition and voice analysis.

[0115] The layout suggestion system can estimate the user's emotions and determine the priority of images to input based on those emotions. For example, if the user is stressed, relaxing images will be prioritized. If the user is relaxed, detailed images will be prioritized. If the user is in a hurry, important images will be prioritized. This allows for optimal image input tailored to the user's emotions. Emotion estimation is performed using methods such as facial recognition and voice analysis.

[0116] The layout suggestion system can estimate the user's emotions and adjust the input method for real-world data based on those emotions. For example, if the user is stressed, it provides a simple interface and minimizes the input steps. If the user is relaxed, it provides detailed input options and suggests a customizable input method. If the user is in a hurry, it prioritizes voice input to allow for quick input of real-world data. This enables optimal data input tailored to the user's emotions. Emotion estimation is performed using methods such as facial recognition and voice analysis.

[0117] The layout suggestion system can analyze a user's past data entry history and select the optimal input method. For example, it can automatically display data that the user has frequently entered in the past as a suggestion. It prioritizes suggesting input methods that the user has used in the past (voice, text, etc.). Based on the user's past input history, it predicts and suggests data that will be used during specific time periods. In this way, by analyzing past data entry history, the system can select the optimal input method.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The reception desk inputs the user's image. The user's image may include, but is not limited to, text, images, or audio. The reception desk can, for example, allow the user to input their image via chat. Step 2: The data input unit inputs real-world environmental data of the target room. This real-world data includes, but is not limited to, room dimensions, furniture arrangement, and lighting conditions. The data input unit can also input data, images, and videos read by, for example, LiDAR. Step 3: The generation unit generates a layout based on the information entered by the reception unit and the data input unit. The generation unit generates the layout using, for example, a generation AI. The generation AI can use algorithms such as deep learning or reinforcement learning. For example, the generation unit generates a layout in which a modern sofa desired by the user is placed in the center of the room, with appropriate furniture and appliances placed around it. Step 4: The display unit displays the layout generated by the generation unit. The display unit can, for example, display the generated layout as a 3D model. The 3D model is displayed using, for example, viewpoint manipulation methods and rendering techniques. The display unit can, for example, allow users to check the products within the proposed layout and display their exact size, color, and overall appearance. The display unit can, for example, display information for direct purchase through product links.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] Each of the multiple elements described above, including the reception unit, data input unit, generation unit, and display unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, allowing the user to input their image in a chat-based manner. The data input unit can input real-world data using the camera 42 and communication I / F 44 of the smart device 14. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, generating a layout using a generation AI. The display unit can display the generated layout as a 3D model using the display 40A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0124] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0125] 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.

[0126] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0127] The 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.

[0128] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

[0130] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the reception unit, data input unit, generation unit, and display unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, allowing the user to input their image in a chat-based manner. The data input unit can input real-world data using the camera 42 and communication I / F 44 of the smart glasses 214. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, generating a layout using a generation AI. The display unit can display the generated layout as a 3D model using the display of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0140] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the reception unit, data input unit, generation unit, and display unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, allowing the user to input their image in a chat-based manner. The data input unit can input real-world data using, for example, the camera 42 and communication I / F 44 of the headset terminal 314. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates a layout using a generation AI. The display unit can display the generated layout as a 3D model using, for example, the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0156] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the reception unit, data input unit, generation unit, and display unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, allowing the user to input their image in a chat-based manner. The data input unit can input real-world data using the camera 42 and communication I / F 44 of the robot 414. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, generating a layout using a generation AI. The display unit can display the generated layout as a 3D model using the display of the robot 414. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] (Note 1) A reception area where the user's image is entered, A data input unit for inputting actual environmental data of the target room, A generation unit that generates a layout based on the information input by the reception unit and the data input unit, The system includes a display unit that displays the layout generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Layouts are generated using AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned data input unit is Input data, images, and videos read by LiDAR. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is Display the generated layout as a 3D model. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generate a layout that includes furniture and home appliances sold by a specific manufacturer. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned display unit is Review the products in the proposed layout and display their specific size, color, and overall feel. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned display unit is Displaying information for direct purchase via product links. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is Arrange the furniture you already own by combining it with other furniture. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is Design the layout of the toilet and bathroom rooms separately. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is Layout the room excluding the desk and shelves. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is We propose product and shelf arrangement methods based on behavioral economics. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is It estimates the user's emotions and adjusts the image input method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is The system analyzes the user's past image input history and selects the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When inputting images, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input images based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reception unit is When inputting images, the system prioritizes inputting images that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reception unit is When inputting images, the system analyzes the user's social media activity and inputs relevant images. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned data input unit is It estimates the user's emotions and adjusts the input method for real-world data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned data input unit is When inputting real-world data, the system analyzes the user's past data input history and selects the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned data input unit is When inputting real-world data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned data input unit is When inputting real-world data, the system prioritizes inputting highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned data input unit is When inputting real-world data, analyze the user's social media activity and input relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is It estimates the user's emotions and adjusts the layout generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating a layout, the system analyzes the user's past layout history and selects the optimal generation method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating the layout, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is It estimates the user's emotions and determines the priority of the layout to generate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is When generating layouts, the system prioritizes generating layouts that are highly relevant to the user's geographical location, taking this information into account. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is During layout generation, the system analyzes the user's social media activity and generates relevant layouts. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is It estimates the user's emotions and adjusts the layout display based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned display unit is When displaying a layout, the system analyzes the user's past viewing history and selects the optimal display method. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned display unit is When displaying the layout, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned display unit is It estimates the user's emotions and determines the priority of the layout to display based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned display unit is When displaying layouts, the system prioritizes displaying layouts that are more relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned display unit is When displaying layouts, the system analyzes the user's social media activity and displays relevant layouts. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where the user's image is entered, A data input unit for inputting actual environmental data of the target room, A generation unit that generates a layout based on the information input by the reception unit and the data input unit, The system includes a display unit that displays the layout generated by the generation unit. A system characterized by the following features.

2. The generating unit is Layouts are generated using AI. The system according to feature 1.

3. The aforementioned data input unit is Input data, images, and videos read by LiDAR. The system according to feature 1.

4. The aforementioned display unit is Display the generated layout as a 3D model. The system according to feature 1.

5. The generating unit is Generate a layout that includes furniture and home appliances sold by a specific manufacturer. The system according to feature 1.

6. The aforementioned display unit is Review the products in the proposed layout and display their specific size, color, and overall feel. The system according to feature 1.

7. The aforementioned display unit is Displaying information for direct purchase via product links. The system according to feature 1.

8. The generating unit is Arrange the furniture you already own by combining it with other furniture. The system according to feature 1.

Images