System

Abstract

To provide a system that automatically generates an optimal layout based on property's floor plan information and furniture owned by users and checks the layout in 3D visual manner.SOLUTION: A system according to the embodiment comprises a reception unit, a generation unit, and a display unit. The reception unit receives property's floor plan information and furniture information owned by a user. The generation unit analyzes the information input by the reception unit, and generates a layout. The display unit performs display to check the layout generated by the generation unit in 3D visual manner.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately automate the generation of optimal layouts based on information about the property's floor plan and the furniture owned by the owner, and does not provide the ability to check these layouts in 3D visuals, so there is room for improvement.

[0005] The system of the embodiment aims to automatically generate an optimal layout based on information about the property's floor plan and the furniture owned by the owner, and to enable confirmation of the layout in 3D visual form. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a display unit. The reception unit inputs floor plan information of a property and information on furniture owned by a user. The generation unit analyzes the information input by the reception unit and generates a layout. The display unit displays the layout generated by the generation unit in 3D visual confirmation. Effect of the Invention

[0007] The system of the embodiment automatically generates the optimal layout based on the property's floor plan information and information about the furniture owned, and allows users to check the layout in 3D visuals. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

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

[0028] (Example 1) The layout generation system according to the embodiment of the present invention is a system that automatically generates an optimal layout and allows confirmation in 3D visuals by simply inputting the floor plan information of the property and the furniture information owned. In this layout generation system, by inputting the floor plan information and furniture information of the property, the generation AI generates an optimal layout and allows confirmation in 3D visuals using AR technology. Users can also customize within the app as necessary. For example, the user inputs the floor plan information of the property. For example, information such as the size and shape of the room, and the positions of the windows and doors is input. This information is input to the generation AI. Next, the user inputs the furniture information owned by the user. For example, information such as the type and size of the furniture and the place where it is desired to be placed is input. This information is also input to the generation AI. The generation AI analyzes the input floor plan information and furniture information of the property and generates an optimal layout. For example, a layout is generated in which the furniture is efficiently arranged and the space in the room is maximized. The generated layout can be confirmed in 3D visuals using AR technology. The user can check the generated layout while walking around the room using a smartphone or tablet. This allows visual confirmation of the actual state in which the furniture is arranged. Furthermore, users can customize the layouts generated within the app. For example, they can rearrange the furniture or add new furniture. This allows users to create layouts that suit their preferences. The service is expected to be available for a monthly or yearly fee. In addition, it is expected that the app will partner with real estate companies and furniture manufacturers to allow users to receive special benefits and discounts by transitioning from each service to the app. For example, users can receive special benefits such as automatically entering property information by transitioning from a real estate company's website to the app, or receiving a discount on furniture by transitioning from a furniture manufacturer's website. This allows the layout generation system to automatically generate the optimal layout and check it in 3D visuals simply by users inputting the property's floor plan information and furniture information.

[0029] The layout generation system according to the embodiment includes a reception unit, a generation unit, and a display unit. The reception unit inputs floor plan information of a property. The floor plan information of the property includes, for example, room size, arrangement, and door and window positions, but is not limited to such examples. The reception unit, for example, saves the floor plan information of the property input by the user in a database. The reception unit also inputs furniture information owned by the user. The furniture information includes, for example, furniture size, shape, color, and material, but is not limited to such examples. The reception unit, for example, saves the furniture information input by the user in a database. The generation unit uses a generation AI to analyze the floor plan information and furniture information of the property input by the reception unit and generate an optimal layout. The generation AI uses, for example, a text generation AI (for example, LLM) to generate an optimal layout based on the floor plan information and furniture information of the property. The generation unit can also use the generation AI to analyze the floor plan information and furniture information of the property and generate an efficient layout. For example, the generation AI generates a layout that can make the most of the space in the room. The display unit is a part for confirming the layout generated by the generation unit in 3D visual. The display unit uses, for example, AR technology to display the generated layout in 3D visuals. The user can use a smartphone or tablet to walk around the room and check the generated layout. This allows the user to visually check the state in which the furniture is actually arranged. The display unit uses, for example, a camera of the smartphone or tablet to display the inside of the room in real time and overlays the generated layout. This allows the user to check how the furniture is actually arranged in the room. As a result, the layout generation system according to the embodiment can automatically generate an optimal layout and check it in 3D visuals by simply inputting floor plan information and furniture information of the property by the user.

[0030] The reception unit inputs floor plan information of the property. The floor plan information of the property includes, for example, the size and arrangement of the rooms, the positions of the doors and windows, and the like, but is not limited to such examples. Specifically, the user can input detailed information such as the dimensions and shape of the rooms, the purpose of each room, the positions of the doors and windows, and the height of the ceiling through a dedicated interface. This information is stored in a database in the system and used for subsequent processing. The reception unit also inputs information about the furniture owned. The furniture information includes, for example, the size, shape, color, and material of the furniture, but is not limited to such examples. The user can select from a furniture catalog or manually input detailed information. For example, it is possible to input detailed specifications such as the width, depth, height, color, material, and even the presence or absence of a storage function of the sofa. In this way, the system grasps detailed data of the furniture owned by the user and utilizes it as basic data for generating an optimal layout. The reception unit is designed to efficiently collect this information and store it in a database, so that the user can easily input information. Furthermore, the reception unit also has a function of storing and reusing information previously input by the user. This saves the user the trouble of having to re-enter information that he or she has already entered.

[0031] The generation unit uses the generation AI to analyze the property layout information and furniture information input by the reception unit and generate an optimal layout. The generation AI uses, for example, a text generation AI (e.g., LLM) to generate an optimal layout based on the property layout information and furniture information. Specifically, the generation AI analyzes the input floor plan information and furniture information and proposes a layout that maximizes the use of the room space. For example, in a living room, the arrangement of the sofa, table, and TV is optimized, and a visually balanced layout is generated while ensuring traffic lines. The generation AI can also propose customized layouts based on the user's preferences and lifestyle. For example, the generation AI proposes optimal furniture arrangements taking into account family composition, hobbies, daily traffic lines, etc. Furthermore, in order to generate an efficient layout, the generation AI learns past data and trends and derives optimal layout patterns. For example, by referring to the latest interior design trends and patterns of layouts that have been successful in the past, a more sophisticated layout can be proposed. The generation unit makes full use of these functions to automatically generate and provide the optimal layout for the user.

[0032] The display unit is a part for confirming the layout generated by the generation unit in a 3D visual. The display unit displays the generated layout in a 3D visual, for example, using AR technology. Specifically, the user can use a smartphone or tablet to check the generated layout while walking around the room. This allows the user to visually check the state in which the furniture is actually arranged. The display unit, for example, uses a camera of the smartphone or tablet to display the inside of the room in real time and overlays the generated layout. This allows the user to check how the furniture is arranged in the actual room. Furthermore, the display unit has a function of freely changing the viewpoint so that the user can check the layout from different viewpoints. For example, the layout can be checked from various angles, such as a viewpoint from above or a viewpoint focusing on a specific piece of furniture, in addition to walking around the room. In addition, the display unit also has a function of the user providing feedback to the generated layout. For example, the position of the furniture can be fine-tuned or different furniture can be tried. This allows the user to customize the layout to his or her liking and determine the final arrangement. Through these functions, the display unit provides the user with an intuitive and visual layout confirmation experience, realizing optimal furniture arrangement.

[0033] The generation unit can analyze the floor plan information and furniture information of the property by the generation AI and generate a layout. The generation unit can, for example, use the generation AI to analyze the floor plan information and furniture information of the property and generate an optimal layout. The generation AI can, for example, use a text generation AI (for example, LLM) to generate an optimal layout based on the floor plan information and furniture information of the property. The generation unit can also use the generation AI to analyze the floor plan information and furniture information of the property and generate an efficient layout. For example, the generation AI generates a layout that can maximize the use of the space in the room. As a result, the generation AI can be used to analyze the floor plan information and furniture information of the property and generate an optimal layout. A part or all of the above-mentioned processing in the generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the generation unit can generate a layout using an AI model that inputs the floor plan information and furniture information of the property and outputs an optimal layout.

[0034] The display unit can display the layout in 3D visuals using AR technology. The display unit, for example, uses AR technology to display the generated layout in 3D visuals. AR technology includes, for example, a technology for displaying the inside of a room in real time using a camera of a smartphone or tablet and displaying the generated layout in a superimposed manner. This allows the user to check how the furniture is arranged in the actual room. For example, the display unit can display the inside of a room in real time using a camera of a smartphone or tablet and display the generated layout in a superimposed manner. The display unit can also display the generated layout in 3D visuals using AR technology. For example, the display unit can display the inside of a room in real time using a camera of a smartphone or tablet and display the generated layout in a superimposed manner. This allows the layout to be displayed in 3D visuals by using AR technology. Part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can display the layout using an AI model that inputs the generated layout and outputs a 3D visual.

[0035] The display unit allows the user to check the generated layout while moving around the room using a smartphone or tablet. The display unit, for example, uses a camera of the smartphone or tablet to display the inside of the room in real time and displays the generated layout in an overlaid manner. This allows the user to check how the furniture is arranged in the actual room. For example, the display unit uses a camera of the smartphone or tablet to display the inside of the room in real time and displays the generated layout in an overlaid manner. The display unit can also display the generated layout in 3D visuals using AR technology. For example, the display unit uses a camera of the smartphone or tablet to display the inside of the room in real time and displays the generated layout in an overlaid manner. This allows the user to check the generated layout while walking around the room using a smartphone or tablet. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can display the layout using an AI model that inputs the generated layout and outputs a 3D visual.

[0036] The generation unit allows the user to customize the layout generated in the app. The generation unit allows the user to customize the layout generated in the app, for example, using a generation AI. The generation AI customizes the layout generated in the app, for example, using a text generation AI (for example, LLM). The generation unit can also customize the layout generated in the app, for example, using the generation AI. For example, the generation AI allows the user to change the arrangement of furniture or add new furniture. This allows the user to create a layout that suits his or her preferences. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can customize the layout using an AI model that inputs a layout customized by the user and outputs an optimal layout.

[0037] The reception unit can provide a privilege in which property information and furniture information are automatically input by transitioning from a real estate company or furniture manufacturer's site. The reception unit can provide a privilege in which property information is automatically input by transitioning from a real estate company's site to this application, for example. The reception unit can also provide a privilege in which furniture information is automatically input by transitioning from a furniture manufacturer's site. For example, the reception unit can provide a privilege in which property information is automatically input by transitioning from a real estate company's site to this application. The reception unit can also provide a privilege in which furniture information is automatically input by transitioning from a furniture manufacturer's site. This makes it possible to provide a privilege in which property information and furniture information are automatically input by transitioning from a real estate company or furniture manufacturer's site. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input property information acquired from a real estate company's site, and input the property information using an AI model that automatically inputs the property information.

[0038] The reception unit can analyze the user's past floor plan information input history and select an input method. The reception unit, for example, analyzes the user's past floor plan information input history and selects an optimal input method. For example, the optimal input method is proposed based on floor plan information previously input by the user. The reception unit can also automatically display frequently used input items from the user's past input history. Furthermore, the reception unit can also analyze the user's past input patterns and propose an efficient input method. In this way, the optimal input method can be selected by analyzing the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can select an input method using an AI model that inputs the user's past input history data and outputs the optimal input method.

[0039] The reception unit can perform filtering based on the user's current project or field of interest when inputting floor plan information. For example, the reception unit performs filtering based on the user's current project or field of interest when inputting floor plan information. For example, the reception unit preferentially displays floor plan information related to a project currently in progress by the user. The reception unit can also filter related floor plan information based on the user's field of interest. Furthermore, the reception unit can also suggest appropriate floor plan information according to the progress of the user's project. This makes it possible to preferentially display related floor plan information based on the user's current project or field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can perform filtering using an AI model that inputs the user's project data and field of interest data and outputs related floor plan information.

[0040] The reception unit can preferentially input information with high relevance based on the geographical location information of the user when inputting floor plan information. For example, the reception unit preferentially inputs information with high relevance in consideration of the geographical location information of the user when inputting floor plan information. For example, based on the user's current location, related floor plan information is preferentially displayed. The reception unit can also suggest optimal floor plan information in consideration of the geographical location information of the user. Furthermore, the reception unit can also preferentially input area-specific floor plan information based on the user's location information. This makes it possible to suggest optimal floor plan information in consideration of the user's geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input information using an AI model that inputs the user's geographical location information and outputs highly relevant floor plan information.

[0041] The reception unit can analyze the user's social media activity and input related information when inputting floor plan information. For example, the reception unit can analyze the user's social media activity and input related information when inputting floor plan information. For example, the reception unit can extract floor plan information of interest from the user's social media activity and support the input. The reception unit can also analyze the content of the user's social media posts and suggest related floor plan information. Furthermore, the reception unit can input optimal floor plan information based on information about the user's social media followers and friends. This makes it possible to extract floor plan information of interest from the user's social media activity and support the input. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input information using an AI model that inputs the user's social media data and outputs related floor plan information.

[0042] The reception unit can analyze the user's past furniture information input history and select an input method. The reception unit, for example, analyzes the user's past furniture information input history and selects an optimal input method. For example, the reception unit proposes an optimal input method based on furniture information input by the user in the past. The reception unit can also automatically display frequently used input items from the user's past input history. Furthermore, the reception unit can analyze the user's past input patterns and propose an efficient input method. In this way, the optimal input method can be selected by analyzing the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can select an input method using an AI model that inputs the user's past input history data and outputs the optimal input method.

[0043] The reception unit can perform filtering based on the user's current living situation and field of interest when inputting furniture information. For example, the reception unit performs filtering based on the user's current living situation and field of interest when inputting furniture information. For example, based on the user's current living situation, related furniture information is preferentially displayed. The reception unit can also filter related furniture information based on the user's field of interest. Furthermore, the reception unit can also suggest appropriate furniture information according to changes in the user's living situation. This makes it possible to preferentially display related furniture information based on the user's current living situation and field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can perform filtering using an AI model that inputs the user's living situation data and field of interest data and outputs related furniture information.

[0044] The reception unit can preferentially input highly relevant information based on the geographical location information of the user when inputting furniture information. For example, the reception unit preferentially inputs highly relevant information in consideration of the geographical location information of the user when inputting furniture information. For example, based on the user's current location, related furniture information is preferentially displayed. The reception unit can also suggest optimal furniture information in consideration of the geographical location information of the user. Furthermore, the reception unit can also preferentially input furniture information specific to the region based on the user's location information. This makes it possible to suggest optimal furniture information in consideration of the user's geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input information using an AI model that inputs the user's geographical location information and outputs highly relevant furniture information.

[0045] The reception unit can analyze the user's social media activity and input related information when furniture information is input. For example, the reception unit can analyze the user's social media activity and input related information when furniture information is input. For example, furniture information of interest is extracted from the user's social media activity and support the input. The reception unit can also analyze the content of the user's social media posts and suggest related furniture information. Furthermore, the reception unit can input optimal furniture information based on information on the user's social media followers and friends. This makes it possible to extract furniture information of interest from the user's social media activity and support the input. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input information using an AI model that inputs the user's social media data and outputs related furniture information.

[0046] The generation unit can adjust the level of detail of the layout in consideration of the importance of the furniture when generating the layout. For example, the generation unit adjusts the level of detail of the layout based on the importance of the furniture when generating the layout. For example, important furniture is arranged in the center to generate a detailed layout. The generation unit can also simplify and arrange furniture with low importance to generate an efficient layout. Furthermore, the generation unit can adjust the level of detail of the layout according to the importance of the furniture. As a result, an efficient layout can be generated by adjusting the level of detail of the layout according to the importance of the furniture. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can generate a layout using an AI model that inputs furniture importance data and adjusts the level of detail of the layout.

[0047] The generation unit can apply different layout algorithms based on the room category when generating a layout. The generation unit can apply different layout algorithms based on the room category when generating a layout. For example, a layout algorithm that allows relaxation can be applied to the living room. The generation unit can also apply a layout algorithm that allows efficient work to the kitchen. Furthermore, the generation unit can also apply a layout algorithm that allows comfortable sleep to the bedroom. In this way, an efficient layout can be generated by applying an optimal layout algorithm according to the room category. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can generate a layout using an AI model that uses room category data as input and applies different layout algorithms.

[0048] The generation unit can determine the priority of the layout in consideration of the time when the furniture was arranged when generating the layout. For example, the generation unit determines the priority of the layout based on the time when the furniture was arranged when generating the layout. For example, the generation unit generates a layout by preferentially arranging newly purchased furniture. The generation unit can also generate a layout by arranging furniture that has been used for a long time later. Furthermore, the generation unit can adjust the priority of the layout according to the time when the furniture was arranged. In this way, an efficient layout can be generated by determining the priority of the layout according to the time when the furniture was arranged. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can generate a layout using an AI model that inputs furniture arrangement time data and determines the priority of the layout.

[0049] The generation unit can adjust the order of the layout in consideration of the relevance of the furniture when generating the layout. For example, the generation unit adjusts the order of the layout based on the relevance of the furniture when generating the layout. For example, the generation unit generates a layout by arranging furniture with high relevance close to each other. The generation unit can also generate a layout by arranging furniture with low relevance far away from each other. Furthermore, the generation unit can adjust the order of the layout according to the relevance of the furniture. As a result, an efficient layout can be generated by adjusting the order of the layout according to the relevance of the furniture. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can generate a layout using an AI model that uses furniture relevance data as input and adjusts the order of the layout.

[0050] The display unit can select a display method based on the user's past operation history when displaying 3D visuals. For example, the display unit selects an optimal display method by referring to the user's past operation history when displaying 3D visuals. For example, the display unit proposes an optimal 3D visual display method based on a display method used by the user in the past. The display unit can also automatically apply frequently used display settings from the user's past operation history. Furthermore, the display unit can analyze the user's past operation patterns and propose an efficient 3D visual display method. In this way, the optimal 3D visual display method can be selected by referring to the user's past operation history. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can select a display method using an AI model that inputs the user's operation history data and outputs the optimal display method.

[0051] The display unit can select a display method based on the device information of the user when displaying 3D visuals. For example, the display unit selects an optimal display method by considering the device information of the user when displaying 3D visuals. For example, when the user is using a smartphone, the display unit provides a 3D visual display method that matches the screen size. In addition, when the user is using a tablet, the display unit can also provide a 3D visual display method optimized for a large screen. Furthermore, when the user is using a VR device, the display unit can also provide a 3D visual display method that provides an immersive feeling. In this way, by considering the device information of the user, an optimal 3D visual display method can be provided. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can select a display method using an AI model that inputs the device information of the user and outputs the optimal display method.

[0052] The display unit can select a display method based on the geographical location information of the user when displaying 3D visuals. For example, the display unit selects an optimal display method by considering the geographical location information of the user when displaying 3D visuals. For example, based on the user's current location, a related 3D visual display method is provided. The display unit can also propose an optimal 3D visual display method by considering the geographical location information of the user. Furthermore, the display unit can also provide a region-specific 3D visual display method based on the user's location information. This makes it possible to provide an optimal 3D visual display method by considering the geographical location information of the user. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can select a display method using an AI model that inputs the geographical location information of the user and outputs the optimal display method.

[0053] The display unit can analyze the user's social media activity and display related information when displaying the 3D visual. For example, the display unit can analyze the user's social media activity and display related information when displaying the 3D visual. For example, the display unit can provide a 3D visual display method of interest based on the user's social media activity. The display unit can also analyze the content of the user's social media posts and suggest a related 3D visual display method. Furthermore, the display unit can provide an optimal 3D visual display method based on information about the user's social media followers and friends. This makes it possible to provide a 3D visual display method of interest based on the user's social media activity. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can display information using an AI model that uses the user's social media data as input and displays related information.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The reception unit can analyze the user's past furniture purchase history and propose an optimal furniture layout. For example, the reception unit can propose an optimal layout for the current floor plan based on the size and design of furniture purchased by the user in the past. The reception unit can also prioritize frequently used furniture based on the user's past purchase history. Furthermore, the reception unit can analyze the user's past purchase patterns and propose an efficient furniture layout. In this way, the optimal furniture layout can be proposed by analyzing the user's past purchase history.

[0056] The generation unit can customize the layout based on the lifestyle of the user. For example, if the user has a pet, the generation unit can generate a layout that takes into account space for the pet. In addition, if the user needs a home office, the generation unit can generate a layout that provides an efficient work space. Furthermore, if the user frequently invites guests, the generation unit can generate a layout that takes into account space for the guests. This makes it possible to provide an optimal layout according to the lifestyle of the user.

[0057] The reception unit can analyze the health data of the user and suggest furniture layouts based on the analyzed health data. For example, if the user has back pain, a furniture layout that is gentle on the back can be suggested. Also, if the user has eyesight problems, a layout that is visually easy to see can be suggested. Furthermore, if the user has allergies, a layout that avoids allergens can be suggested. In this way, it is possible to provide an optimal furniture layout according to the user's health condition.

[0058] The reception unit can suggest furniture layouts based on the user's hobbies and interests. For example, if the user enjoys reading, the reception unit can suggest a layout that takes into consideration a reading space. If the user enjoys music, the reception unit can also suggest a layout that takes into consideration acoustic effects. Furthermore, if the user enjoys cooking, the reception unit can also suggest an efficient kitchen layout. This makes it possible to provide an optimal furniture layout that matches the user's hobbies and interests.

[0059] The generation unit can customize the layout based on the user's family structure. For example, in the case of a family with children, a layout that takes safety into consideration can be generated. In addition, in the case of a family with pets, a layout that takes space for pets into consideration can be generated. Furthermore, in the case of a family with elderly people, a layout that takes barrier-free access into consideration can be generated. In this way, it is possible to provide an optimal layout according to the user's family structure.

[0060] The generation unit can customize the layout based on the user's life rhythm. For example, if the user is a nocturnal person, a layout suitable for nighttime activities can be generated. Also, if the user is a morning person, a layout suitable for morning activities can be generated. Furthermore, if the user has an irregular life rhythm, a layout that can flexibly accommodate the user's life rhythm can be generated. This makes it possible to provide an optimal layout according to the user's life rhythm.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The reception unit inputs the property's floor plan information. The property's floor plan information includes room size, layout, and door and window positions. The property's floor plan information input by the user is saved in a database. Next, the reception unit inputs information about the furniture owned. The furniture information includes the size, shape, color, and material of the furniture. The furniture information input by the user is also saved in the database. Step 2: The generation unit uses a generation AI to analyze the property layout information and furniture information input by the reception unit and generate an optimal layout. The generation AI uses a text generation AI (e.g., LLM) to generate an optimal layout based on the property layout information and furniture information. The generation unit can also generate a layout that makes the most of the space in the room. Step 3: The display unit is a part for confirming the layout generated by the generation unit in 3D visuals. The display unit uses AR technology to display the generated layout in 3D visuals. The user can use a smartphone or tablet to walk around the room and check the generated layout. This allows the user to visually confirm how the furniture is actually arranged. The display unit uses the smartphone or tablet camera to display the inside of the room in real time, and overlays the generated layout.

[0063] (Example 2) The layout generation system according to the embodiment of the present invention is a system that automatically generates an optimal layout and allows confirmation in 3D visuals by simply inputting the floor plan information of the property and the furniture information owned. In this layout generation system, by inputting the floor plan information and furniture information of the property, the generation AI generates an optimal layout and allows confirmation in 3D visuals using AR technology. Users can also customize within the app as necessary. For example, the user inputs the floor plan information of the property. For example, information such as the size and shape of the room, and the positions of the windows and doors is input. This information is input to the generation AI. Next, the user inputs the furniture information owned by the user. For example, information such as the type and size of the furniture and the place where it is desired to be placed is input. This information is also input to the generation AI. The generation AI analyzes the input floor plan information and furniture information of the property and generates an optimal layout. For example, a layout is generated in which the furniture is efficiently arranged and the space in the room is maximized. The generated layout can be confirmed in 3D visuals using AR technology. The user can check the generated layout while walking around the room using a smartphone or tablet. This allows visual confirmation of the actual state in which the furniture is arranged. Furthermore, users can customize the layouts generated within the app. For example, they can rearrange the furniture or add new furniture. This allows users to create layouts that suit their preferences. The service is expected to be available for a monthly or yearly fee. In addition, it is expected that the app will partner with real estate companies and furniture manufacturers to allow users to receive special benefits and discounts by transitioning from each service to the app. For example, users can receive special benefits such as automatically entering property information by transitioning from a real estate company's website to the app, or receiving a discount on furniture by transitioning from a furniture manufacturer's website. This allows the layout generation system to automatically generate the optimal layout and check it in 3D visuals simply by users inputting the property's floor plan information and furniture information.

[0064] The layout generation system according to the embodiment includes a reception unit, a generation unit, and a display unit. The reception unit inputs floor plan information of a property. The floor plan information of the property includes, for example, room size, arrangement, and door and window positions, but is not limited to such examples. The reception unit, for example, saves the floor plan information of the property input by the user in a database. The reception unit also inputs furniture information owned by the user. The furniture information includes, for example, furniture size, shape, color, and material, but is not limited to such examples. The reception unit, for example, saves the furniture information input by the user in a database. The generation unit uses a generation AI to analyze the floor plan information and furniture information of the property input by the reception unit and generate an optimal layout. The generation AI uses, for example, a text generation AI (for example, LLM) to generate an optimal layout based on the floor plan information and furniture information of the property. The generation unit can also use the generation AI to analyze the floor plan information and furniture information of the property and generate an efficient layout. For example, the generation AI generates a layout that can make the most of the space in the room. The display unit is a part for confirming the layout generated by the generation unit in 3D visual. The display unit uses, for example, AR technology to display the generated layout in 3D visuals. The user can use a smartphone or tablet to walk around the room and check the generated layout. This allows the user to visually check the state in which the furniture is actually arranged. The display unit uses, for example, a camera of the smartphone or tablet to display the inside of the room in real time and overlays the generated layout. This allows the user to check how the furniture is actually arranged in the room. As a result, the layout generation system according to the embodiment can automatically generate an optimal layout and check it in 3D visuals by simply inputting floor plan information and furniture information of the property by the user.

[0065] The reception unit inputs floor plan information of the property. The floor plan information of the property includes, for example, the size and arrangement of the rooms, the positions of the doors and windows, and the like, but is not limited to such examples. Specifically, the user can input detailed information such as the dimensions and shape of the rooms, the purpose of each room, the positions of the doors and windows, and the height of the ceiling through a dedicated interface. This information is stored in a database in the system and used for subsequent processing. The reception unit also inputs information about the furniture owned. The furniture information includes, for example, the size, shape, color, and material of the furniture, but is not limited to such examples. The user can select from a furniture catalog or manually input detailed information. For example, it is possible to input detailed specifications such as the width, depth, height, color, material, and even the presence or absence of a storage function of the sofa. In this way, the system grasps detailed data of the furniture owned by the user and utilizes it as basic data for generating an optimal layout. The reception unit is designed to efficiently collect this information and store it in a database, so that the user can easily input information. Furthermore, the reception unit also has a function of storing and reusing information previously input by the user. This saves the user the trouble of having to re-enter information that he or she has already entered.

[0066] The generation unit uses the generation AI to analyze the property layout information and furniture information input by the reception unit and generate an optimal layout. The generation AI uses, for example, a text generation AI (e.g., LLM) to generate an optimal layout based on the property layout information and furniture information. Specifically, the generation AI analyzes the input floor plan information and furniture information and proposes a layout that maximizes the use of the room space. For example, in a living room, the arrangement of the sofa, table, and TV is optimized, and a visually balanced layout is generated while ensuring traffic lines. The generation AI can also propose customized layouts based on the user's preferences and lifestyle. For example, the generation AI proposes optimal furniture arrangements taking into account family composition, hobbies, daily traffic lines, etc. Furthermore, in order to generate an efficient layout, the generation AI learns past data and trends and derives optimal layout patterns. For example, by referring to the latest interior design trends and patterns of layouts that have been successful in the past, a more sophisticated layout can be proposed. The generation unit makes full use of these functions to automatically generate and provide the optimal layout for the user.

[0067] The display unit is a part for confirming the layout generated by the generation unit in a 3D visual. The display unit displays the generated layout in a 3D visual, for example, using AR technology. Specifically, the user can use a smartphone or tablet to check the generated layout while walking around the room. This allows the user to visually check the state in which the furniture is actually arranged. The display unit, for example, uses a camera of the smartphone or tablet to display the inside of the room in real time and overlays the generated layout. This allows the user to check how the furniture is arranged in the actual room. Furthermore, the display unit has a function of freely changing the viewpoint so that the user can check the layout from different viewpoints. For example, the layout can be checked from various angles, such as a viewpoint from above or a viewpoint focusing on a specific piece of furniture, in addition to walking around the room. In addition, the display unit also has a function of the user providing feedback to the generated layout. For example, the position of the furniture can be fine-tuned or different furniture can be tried. This allows the user to customize the layout to his or her liking and determine the final arrangement. Through these functions, the display unit provides the user with an intuitive and visual layout confirmation experience, realizing optimal furniture arrangement.

[0068] The generation unit can analyze the floor plan information and furniture information of the property by the generation AI and generate a layout. The generation unit can, for example, use the generation AI to analyze the floor plan information and furniture information of the property and generate an optimal layout. The generation AI can, for example, use a text generation AI (for example, LLM) to generate an optimal layout based on the floor plan information and furniture information of the property. The generation unit can also use the generation AI to analyze the floor plan information and furniture information of the property and generate an efficient layout. For example, the generation AI generates a layout that can maximize the use of the space in the room. As a result, the generation AI can be used to analyze the floor plan information and furniture information of the property and generate an optimal layout. A part or all of the above-mentioned processing in the generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the generation unit can generate a layout using an AI model that inputs the floor plan information and furniture information of the property and outputs an optimal layout.

[0069] The display unit can display the layout in 3D visuals using AR technology. The display unit, for example, uses AR technology to display the generated layout in 3D visuals. AR technology includes, for example, a technology for displaying the inside of a room in real time using a camera of a smartphone or tablet and displaying the generated layout in a superimposed manner. This allows the user to check how the furniture is arranged in the actual room. For example, the display unit can display the inside of a room in real time using a camera of a smartphone or tablet and display the generated layout in a superimposed manner. The display unit can also display the generated layout in 3D visuals using AR technology. For example, the display unit can display the inside of a room in real time using a camera of a smartphone or tablet and display the generated layout in a superimposed manner. This allows the layout to be displayed in 3D visuals by using AR technology. Part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can display the layout using an AI model that inputs the generated layout and outputs a 3D visual.

[0070] The display unit allows the user to check the generated layout while moving around the room using a smartphone or tablet. The display unit, for example, uses a camera of the smartphone or tablet to display the inside of the room in real time and displays the generated layout in an overlaid manner. This allows the user to check how the furniture is arranged in the actual room. For example, the display unit uses a camera of the smartphone or tablet to display the inside of the room in real time and displays the generated layout in an overlaid manner. The display unit can also display the generated layout in 3D visuals using AR technology. For example, the display unit uses a camera of the smartphone or tablet to display the inside of the room in real time and displays the generated layout in an overlaid manner. This allows the user to check the generated layout while walking around the room using a smartphone or tablet. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can display the layout using an AI model that inputs the generated layout and outputs a 3D visual.

[0071] The generation unit allows the user to customize the layout generated in the app. The generation unit allows the user to customize the layout generated in the app, for example, using a generation AI. The generation AI customizes the layout generated in the app, for example, using a text generation AI (for example, LLM). The generation unit can also customize the layout generated in the app, for example, using the generation AI. For example, the generation AI allows the user to change the arrangement of furniture or add new furniture. This allows the user to create a layout that suits his or her preferences. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can customize the layout using an AI model that inputs a layout customized by the user and outputs an optimal layout.

[0072] The reception unit can provide a privilege in which property information and furniture information are automatically input by transitioning from a real estate company or furniture manufacturer's site. The reception unit can provide a privilege in which property information is automatically input by transitioning from a real estate company's site to this application, for example. The reception unit can also provide a privilege in which furniture information is automatically input by transitioning from a furniture manufacturer's site. For example, the reception unit can provide a privilege in which property information is automatically input by transitioning from a real estate company's site to this application. The reception unit can also provide a privilege in which furniture information is automatically input by transitioning from a furniture manufacturer's site. This makes it possible to provide a privilege in which property information and furniture information are automatically input by transitioning from a real estate company or furniture manufacturer's site. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input property information acquired from a real estate company's site, and input the property information using an AI model that automatically inputs the property information.

[0073] The reception unit can analyze the user's emotions and adjust the input timing of the floor plan information based on the analyzed user's emotions. The reception unit, for example, estimates the user's emotions and adjusts the input timing of the floor plan information based on the estimated user's emotions. The estimation of emotions is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. For example, when the user feels stressed, the input timing can be delayed so that the user can input in a relaxed state. In addition, when the user is in a hurry, the input timing can be advanced so that the user can input information quickly. Furthermore, when the user is concentrating, the input timing can be adjusted so that the user can input information at an optimal timing. In this way, by adjusting the input timing of the floor plan information according to the user's emotions, the information can be input at a more appropriate timing. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the reception unit can adjust the input timing of the floor plan information using an AI model that receives the user's emotion data as an input and adjusts the input timing.

[0074] The reception unit can analyze the user's past floor plan information input history and select an input method. The reception unit, for example, analyzes the user's past floor plan information input history and selects an optimal input method. For example, the optimal input method is proposed based on floor plan information previously input by the user. The reception unit can also automatically display frequently used input items from the user's past input history. Furthermore, the reception unit can also analyze the user's past input patterns and propose an efficient input method. In this way, the optimal input method can be selected by analyzing the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can select an input method using an AI model that inputs the user's past input history data and outputs the optimal input method.

[0075] The reception unit can perform filtering based on the user's current project or field of interest when inputting floor plan information. For example, the reception unit performs filtering based on the user's current project or field of interest when inputting floor plan information. For example, the reception unit preferentially displays floor plan information related to a project currently in progress by the user. The reception unit can also filter related floor plan information based on the user's field of interest. Furthermore, the reception unit can also suggest appropriate floor plan information according to the progress of the user's project. This makes it possible to preferentially display related floor plan information based on the user's current project or field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can perform filtering using an AI model that inputs the user's project data and field of interest data and outputs related floor plan information.

[0076] The reception unit can analyze the user's emotions and determine the priority of the floor plan information to be input based on the analyzed user's emotions. The reception unit, for example, estimates the user's emotions and determines the priority of the floor plan information to be input based on the estimated user's emotions. The estimation of emotions is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is relaxed, detailed floor plan information is input preferentially. In addition, when the user is in a hurry, basic floor plan information can also be input preferentially. Furthermore, when the user is stressed, simple floor plan information can also be input preferentially. In this way, by determining the priority of the floor plan information to be input according to the user's emotions, more appropriate information can be input preferentially. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can determine the priority using an AI model that inputs the user's emotion data and determines the priority of the floor plan information to be input.

[0077] The reception unit can preferentially input information with high relevance based on the geographical location information of the user when inputting floor plan information. For example, the reception unit preferentially inputs information with high relevance in consideration of the geographical location information of the user when inputting floor plan information. For example, based on the user's current location, related floor plan information is preferentially displayed. The reception unit can also suggest optimal floor plan information in consideration of the geographical location information of the user. Furthermore, the reception unit can also preferentially input area-specific floor plan information based on the user's location information. This makes it possible to suggest optimal floor plan information in consideration of the user's geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input information using an AI model that inputs the user's geographical location information and outputs highly relevant floor plan information.

[0078] The reception unit can analyze the user's social media activity and input related information when inputting floor plan information. For example, the reception unit can analyze the user's social media activity and input related information when inputting floor plan information. For example, the reception unit can extract floor plan information of interest from the user's social media activity and support the input. The reception unit can also analyze the content of the user's social media posts and suggest related floor plan information. Furthermore, the reception unit can input optimal floor plan information based on information about the user's social media followers and friends. This makes it possible to extract floor plan information of interest from the user's social media activity and support the input. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input information using an AI model that inputs the user's social media data and outputs related floor plan information.

[0079] The reception unit can analyze the user's emotions and adjust the input timing of the furniture information based on the analyzed user's emotions. The reception unit, for example, estimates the user's emotions and adjusts the input timing of the furniture information based on the estimated user's emotions. The estimation of emotions is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. For example, when the user is relaxed, the input timing can be delayed so that detailed furniture information can be input. In addition, when the user is in a hurry, the input timing can be advanced so that the furniture information can be input quickly. Furthermore, when the user is stressed, the input timing can be adjusted so that the furniture information can be input at an optimal timing. As a result, by adjusting the input timing of the furniture information according to the user's emotions, the information can be input at a more appropriate timing. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can adjust the input timing of the furniture information using an AI model that receives the user's emotion data as an input and adjusts the input timing.

[0080] The reception unit can analyze the user's past furniture information input history and select an input method. The reception unit, for example, analyzes the user's past furniture information input history and selects an optimal input method. For example, the reception unit proposes an optimal input method based on furniture information input by the user in the past. The reception unit can also automatically display frequently used input items from the user's past input history. Furthermore, the reception unit can analyze the user's past input patterns and propose an efficient input method. In this way, the optimal input method can be selected by analyzing the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can select an input method using an AI model that inputs the user's past input history data and outputs the optimal input method.

[0081] The reception unit can perform filtering based on the user's current living situation and field of interest when inputting furniture information. For example, the reception unit performs filtering based on the user's current living situation and field of interest when inputting furniture information. For example, based on the user's current living situation, related furniture information is preferentially displayed. The reception unit can also filter related furniture information based on the user's field of interest. Furthermore, the reception unit can also suggest appropriate furniture information according to changes in the user's living situation. This makes it possible to preferentially display related furniture information based on the user's current living situation and field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can perform filtering using an AI model that inputs the user's living situation data and field of interest data and outputs related furniture information.

[0082] The reception unit can analyze the user's emotions and determine the priority of the furniture information to be input based on the analyzed user's emotions. The reception unit, for example, estimates the user's emotions and determines the priority of the furniture information to be input based on the estimated user's emotions. The estimation of emotions is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is relaxed, detailed furniture information is input preferentially. In addition, when the user is in a hurry, basic furniture information can also be input preferentially. Furthermore, when the user is stressed, simple furniture information can also be input preferentially. In this way, by determining the priority of the furniture information to be input according to the user's emotions, more appropriate information can be input preferentially. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can determine the priority using an AI model that inputs the user's emotion data and determines the priority of the furniture information to be input.

[0083] The reception unit can preferentially input highly relevant information based on the geographical location information of the user when inputting furniture information. For example, the reception unit preferentially inputs highly relevant information in consideration of the geographical location information of the user when inputting furniture information. For example, based on the user's current location, related furniture information is preferentially displayed. The reception unit can also suggest optimal furniture information in consideration of the geographical location information of the user. Furthermore, the reception unit can also preferentially input furniture information specific to the region based on the user's location information. This makes it possible to suggest optimal furniture information in consideration of the user's geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input information using an AI model that inputs the user's geographical location information and outputs highly relevant furniture information.

[0084] The reception unit can analyze the user's social media activity and input related information when furniture information is input. For example, the reception unit can analyze the user's social media activity and input related information when furniture information is input. For example, furniture information of interest is extracted from the user's social media activity and support the input. The reception unit can also analyze the content of the user's social media posts and suggest related furniture information. Furthermore, the reception unit can input optimal furniture information based on information on the user's social media followers and friends. This makes it possible to extract furniture information of interest from the user's social media activity and support the input. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input information using an AI model that inputs the user's social media data and outputs related furniture information.

[0085] The generation unit can analyze the user's emotions and adjust the layout generation method based on the analyzed user's emotions. The generation unit, for example, estimates the user's emotions and adjusts the layout generation method based on the estimated user's emotions. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is relaxed, a spacious layout is generated. Also, when the user is in a hurry, an efficient layout can be generated. Furthermore, when the user is stressed, a simple layout can be generated. As a result, a more appropriate layout can be generated by adjusting the layout generation method according to the user's emotions. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can generate a layout using an AI model that inputs user's emotion data and adjusts the layout generation method.

[0086] The generation unit can adjust the level of detail of the layout in consideration of the importance of the furniture when generating the layout. For example, the generation unit adjusts the level of detail of the layout based on the importance of the furniture when generating the layout. For example, important furniture is arranged in the center to generate a detailed layout. The generation unit can also simplify and arrange furniture with low importance to generate an efficient layout. Furthermore, the generation unit can adjust the level of detail of the layout according to the importance of the furniture. As a result, an efficient layout can be generated by adjusting the level of detail of the layout according to the importance of the furniture. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can generate a layout using an AI model that inputs furniture importance data and adjusts the level of detail of the layout.

[0087] The generation unit can apply different layout algorithms based on the room category when generating a layout. The generation unit can apply different layout algorithms based on the room category when generating a layout. For example, a layout algorithm that allows relaxation can be applied to the living room. The generation unit can also apply a layout algorithm that allows efficient work to the kitchen. Furthermore, the generation unit can also apply a layout algorithm that allows comfortable sleep to the bedroom. In this way, an efficient layout can be generated by applying an optimal layout algorithm according to the room category. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can generate a layout using an AI model that uses room category data as input and applies different layout algorithms.

[0088] The generation unit can analyze the user's emotion and adjust the length of the layout based on the analyzed user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the layout based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is relaxed, a detailed layout is generated. Also, when the user is in a hurry, a concise layout can be generated. Furthermore, when the user is stressed, a simple layout can be generated. As a result, a more appropriate layout can be generated by adjusting the length of the layout according to the user's emotion. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can generate a layout using an AI model that inputs user's emotion data and adjusts the length of the layout.

[0089] The generation unit can determine the priority of the layout in consideration of the time when the furniture was arranged when generating the layout. For example, the generation unit determines the priority of the layout based on the time when the furniture was arranged when generating the layout. For example, the generation unit generates a layout by preferentially arranging newly purchased furniture. The generation unit can also generate a layout by arranging furniture that has been used for a long time later. Furthermore, the generation unit can adjust the priority of the layout according to the time when the furniture was arranged. In this way, an efficient layout can be generated by determining the priority of the layout according to the time when the furniture was arranged. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can generate a layout using an AI model that inputs furniture arrangement time data and determines the priority of the layout.

[0090] The generation unit can adjust the order of the layout in consideration of the relevance of the furniture when generating the layout. For example, the generation unit adjusts the order of the layout based on the relevance of the furniture when generating the layout. For example, the generation unit generates a layout by arranging furniture with high relevance close to each other. The generation unit can also generate a layout by arranging furniture with low relevance far away from each other. Furthermore, the generation unit can adjust the order of the layout according to the relevance of the furniture. As a result, an efficient layout can be generated by adjusting the order of the layout according to the relevance of the furniture. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can generate a layout using an AI model that uses furniture relevance data as input and adjusts the order of the layout.

[0091] The display unit can analyze the user's emotions and adjust the display method of the 3D visuals based on the analyzed user's emotions. The display unit, for example, estimates the user's emotions and adjusts the display method of the 3D visuals based on the estimated user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is relaxed, the 3D visuals are displayed at a leisurely pace. Also, when the user is in a hurry, the 3D visuals can be displayed quickly. Furthermore, when the user is stressed, a simple 3D visual can be displayed. This allows for more appropriate display by adjusting the display method of the 3D visuals according to the user's emotions. Part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can adjust the display method using an AI model that inputs the user's emotion data and adjusts the display method of the 3D visuals.

[0092] The display unit can select a display method based on the user's past operation history when displaying 3D visuals. For example, the display unit selects an optimal display method by referring to the user's past operation history when displaying 3D visuals. For example, the display unit proposes an optimal 3D visual display method based on a display method used by the user in the past. The display unit can also automatically apply frequently used display settings from the user's past operation history. Furthermore, the display unit can analyze the user's past operation patterns and propose an efficient 3D visual display method. In this way, the optimal 3D visual display method can be selected by referring to the user's past operation history. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can select a display method using an AI model that inputs the user's operation history data and outputs the optimal display method.

[0093] The display unit can select a display method based on the device information of the user when displaying 3D visuals. For example, the display unit selects an optimal display method by considering the device information of the user when displaying 3D visuals. For example, when the user is using a smartphone, the display unit provides a 3D visual display method that matches the screen size. In addition, when the user is using a tablet, the display unit can also provide a 3D visual display method optimized for a large screen. Furthermore, when the user is using a VR device, the display unit can also provide a 3D visual display method that provides an immersive feeling. In this way, by considering the device information of the user, an optimal 3D visual display method can be provided. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can select a display method using an AI model that inputs the device information of the user and outputs the optimal display method.

[0094] The display unit can analyze the user's emotions and adjust the operation procedure of the 3D visual based on the analyzed user's emotions. The display unit, for example, estimates the user's emotions and adjusts the operation procedure of the 3D visual based on the estimated user's emotions. The estimation of emotions is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. For example, when the user is relaxed, a relaxed operation procedure is provided. In addition, when the user is in a hurry, a quick operation procedure can be provided. Furthermore, when the user is stressed, a simple operation procedure can be provided. This allows for more appropriate operation by adjusting the operation procedure of the 3D visual according to the user's emotions. Part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can adjust the operation procedure using an AI model that inputs the user's emotion data and adjusts the operation procedure.

[0095] The display unit can select a display method based on the geographical location information of the user when displaying 3D visuals. For example, the display unit selects an optimal display method by considering the geographical location information of the user when displaying 3D visuals. For example, based on the user's current location, a related 3D visual display method is provided. The display unit can also propose an optimal 3D visual display method by considering the geographical location information of the user. Furthermore, the display unit can also provide a region-specific 3D visual display method based on the user's location information. This makes it possible to provide an optimal 3D visual display method by considering the geographical location information of the user. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can select a display method using an AI model that inputs the geographical location information of the user and outputs the optimal display method.

[0096] The display unit can analyze the user's social media activity and display related information when displaying the 3D visual. For example, the display unit can analyze the user's social media activity and display related information when displaying the 3D visual. For example, the display unit can provide a 3D visual display method of interest based on the user's social media activity. The display unit can also analyze the content of the user's social media posts and suggest a related 3D visual display method. Furthermore, the display unit can provide an optimal 3D visual display method based on information about the user's social media followers and friends. This makes it possible to provide a 3D visual display method of interest based on the user's social media activity. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can display information using an AI model that uses the user's social media data as input and displays related information.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The reception unit can analyze the user's past furniture purchase history and propose an optimal furniture layout. For example, the reception unit can propose an optimal layout for the current floor plan based on the size and design of furniture purchased by the user in the past. The reception unit can also prioritize frequently used furniture based on the user's past purchase history. Furthermore, the reception unit can analyze the user's past purchase patterns and propose an efficient furniture layout. In this way, the optimal furniture layout can be proposed by analyzing the user's past purchase history.

[0099] The generation unit can customize the layout based on the lifestyle of the user. For example, if the user has a pet, the generation unit can generate a layout that takes into account space for the pet. In addition, if the user needs a home office, the generation unit can generate a layout that provides an efficient work space. Furthermore, if the user frequently invites guests, the generation unit can generate a layout that takes into account space for the guests. This makes it possible to provide an optimal layout according to the lifestyle of the user.

[0100] The display unit can analyze the user's emotions and adjust the color tone of the 3D visuals based on the analyzed user's emotions. For example, if the user is relaxed, the display unit can display 3D visuals in calm colors. If the user is excited, the display unit can display 3D visuals in vivid colors. If the user is stressed, the display unit can display 3D visuals in simple, calm colors. This allows for more appropriate display by adjusting the color tone of the 3D visuals according to the user's emotions.

[0101] The reception unit can analyze the health data of the user and suggest furniture layouts based on the analyzed health data. For example, if the user has back pain, a furniture layout that is gentle on the back can be suggested. Also, if the user has eyesight problems, a layout that is visually easy to see can be suggested. Furthermore, if the user has allergies, a layout that avoids allergens can be suggested. In this way, it is possible to provide an optimal furniture layout according to the user's health condition.

[0102] The generation unit can analyze the user's emotions and adjust the complexity of the layout based on the analyzed user's emotions. For example, when the user is relaxed, a complex layout can be generated. When the user is in a hurry, a simple layout can be generated. Furthermore, when the user is stressed, a simple layout can be generated. In this way, a more appropriate layout can be generated by adjusting the complexity of the layout according to the user's emotions.

[0103] The reception unit can suggest furniture layouts based on the user's hobbies and interests. For example, if the user enjoys reading, the reception unit can suggest a layout that takes into consideration a reading space. If the user enjoys music, the reception unit can also suggest a layout that takes into consideration acoustic effects. Furthermore, if the user enjoys cooking, the reception unit can also suggest an efficient kitchen layout. This makes it possible to provide an optimal furniture layout that matches the user's hobbies and interests.

[0104] The display unit can analyze the user's emotions and adjust the viewpoint of the 3D visuals based on the analyzed emotions of the user. For example, if the user is relaxed, the 3D visuals can be displayed with a wide viewpoint. If the user is in a hurry, the 3D visuals can be displayed with a narrow viewpoint. Furthermore, if the user is feeling stressed, the 3D visuals can be displayed with a fixed viewpoint. This allows for more appropriate display by adjusting the viewpoint of the 3D visuals according to the user's emotions.

[0105] The generation unit can customize the layout based on the user's family structure. For example, in the case of a family with children, a layout that takes safety into consideration can be generated. In addition, in the case of a family with pets, a layout that takes space for pets into consideration can be generated. Furthermore, in the case of a family with elderly people, a layout that takes barrier-free access into consideration can be generated. In this way, it is possible to provide an optimal layout according to the user's family structure.

[0106] The reception unit can analyze the user's emotions and suggest furniture options based on the analyzed user's emotions. For example, when the user is relaxed, comfortable furniture can be suggested. When the user is excited, furniture with a modern design can be suggested. When the user is stressed, furniture with a simple and calm design can be suggested. In this way, optimal furniture options can be suggested according to the user's emotions.

[0107] The generation unit can customize the layout based on the user's life rhythm. For example, if the user is a nocturnal person, a layout suitable for nighttime activities can be generated. Also, if the user is a morning person, a layout suitable for morning activities can be generated. Furthermore, if the user has an irregular life rhythm, a layout that can flexibly accommodate the user's life rhythm can be generated. This makes it possible to provide an optimal layout according to the user's life rhythm.

[0108] The process flow of the second embodiment will be briefly described below.

[0109] Step 1: The reception unit inputs the property's floor plan information. The property's floor plan information includes room size, layout, and door and window positions. The property's floor plan information input by the user is saved in a database. Next, the reception unit inputs information about the furniture owned. The furniture information includes the size, shape, color, and material of the furniture. The furniture information input by the user is also saved in the database. Step 2: The generation unit uses a generation AI to analyze the property layout information and furniture information input by the reception unit and generate an optimal layout. The generation AI uses a text generation AI (e.g., LLM) to generate an optimal layout based on the property layout information and furniture information. The generation unit can also generate a layout that makes the most of the space in the room. Step 3: The display unit is a part for confirming the layout generated by the generation unit in 3D visuals. The display unit uses AR technology to display the generated layout in 3D visuals. The user can use a smartphone or tablet to walk around the room and check the generated layout. This allows the user to visually confirm how the furniture is actually arranged. The display unit uses the smartphone or tablet camera to display the inside of the room in real time, and overlays the generated layout.

[0110] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0112] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements including the above-mentioned reception unit, generation unit, and display unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and stores the property layout information and furniture information input by the user in the database 24. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an optimal layout using a generation AI. The display unit is realized, for example, by the output device 40 of the smart device 14, and displays the generated layout in 3D visuals using AR technology. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0121] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0124] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0126] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements including the above-mentioned reception unit, generation unit, and display unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and stores the property layout information and furniture information input by the user in the database 24. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an optimal layout using a generation AI. The display unit is realized, for example, by the speaker 240 of the smart glasses 214, and displays the generated layout in 3D visuals using AR technology. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0132] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0137] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0140] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements including the above-mentioned reception unit, generation unit, and display unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and stores the property layout information and furniture information input by the user in the database 24. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an optimal layout using a generation AI. The display unit is realized, for example, by the display 343 of the headset type terminal 314, and displays the generated layout in 3D visual form using AR technology. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0154] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0157] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] Each of the multiple elements including the above-mentioned reception unit, generation unit, and display unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and stores the property layout information and furniture information input by the user in the database 24. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an optimal layout using a generation AI. The display unit is realized, for example, by LEDs in the eyes of the robot 414, and displays the generated layout in 3D visual form using AR technology. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0163] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0164] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0166] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0167] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0168] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0171] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0172] In addition, 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 upon request from the data processing device 12.

[0173] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0174] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0175] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0176] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0177] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0179] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0181] (Appendix 1) A reception unit for inputting information on the layout of the property and information on the furniture owned by the user; a generation unit that analyzes the information input by the reception unit and generates a layout; A display unit for visually confirming the layout generated by the generation unit in 3D. A system characterized by: (Appendix 2) The generation unit is Generative AI analyzes the property's floor plan and furniture information to generate a layout 2. The system of claim 1, (Appendix 3) The display unit is Use AR technology to display layouts in 3D visuals 2. The system of claim 1, (Appendix 4) The display unit is Users can use their smartphone or tablet to navigate the room and check the generated layout. 2. The system of claim 1, (Appendix 5) The generation unit is Users can customize the generated layout within the app 2. The system of claim 1, (Appendix 6) The reception unit is By switching from a real estate company or furniture manufacturer's website, property and furniture information will be automatically entered. 2. The system of claim 1, (Appendix 7) The reception unit is Analyze user emotions and adjust the timing of inputting floor plan information based on the analyzed user emotions 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past floor plan information input history and select the input method 2. The system of claim 1, (Appendix 9) The reception unit is Filter floor plan information based on the user's current project or area of ​​interest 2. The system of claim 1, (Appendix 10) The reception unit is Analyze user emotions and determine the priority of floor plan information to be input based on the analyzed user emotions 2. The system of claim 1, (Appendix 11) The reception unit is When entering floor plan information, prioritize the most relevant information based on the user's geographic location 2. The system of claim 1, (Appendix 12) The reception unit is When entering floor plan information, analyze the user's social media activity and enter related information 2. The system of claim 1, (Appendix 13) The reception unit is Analyze user emotions and adjust the timing of furniture information input based on the analyzed user emotions 2. The system of claim 1, (Appendix 14) The reception unit is Analyze the user's past furniture information input history and select the input method 2. The system of claim 1, (Appendix 15) The reception unit is As you enter your furniture information, filter it based on your current living situation or interests 2. The system of claim 1, (Appendix 16) The reception unit is Analyze user's emotions and determine the priority of furniture information to be input based on the analyzed user's emotions. 2. The system of claim 1, (Appendix 17) The reception unit is When entering furniture information, the system prioritizes relevant information based on the user's geographic location. 2. The system of claim 1, (Appendix 18) The reception unit is When entering furniture information, analyze the user's social media activity and enter related information 2. The system of claim 1, (Appendix 19) The generation unit is Analyze user emotions and adjust layout generation methods based on the analyzed user emotions 2. The system of claim 1, (Appendix 20) The generation unit is When generating a layout, adjust the level of detail of the layout by considering the importance of furniture. 2. The system of claim 1, (Appendix 21) The generation unit is Apply different layout algorithms based on room categories during layout generation 2. The system of claim 1, (Appendix 22) The generation unit is Analyze user sentiment and adjust layout length based on the analyzed user sentiment 2. The system of claim 1, (Appendix 23) The generation unit is When generating a layout, determine layout priorities by considering the time of furniture placement. 2. The system of claim 1, (Appendix 24) The generation unit is When generating a layout, adjust the order of the layout by considering the relevance of furniture. 2. The system of claim 1, (Appendix 25) The display unit is Analyze user emotions and adjust the way 3D visuals are displayed based on the analyzed user emotions 2. The system of claim 1, (Appendix 26) The display unit is When displaying 3D visualization, select the display method based on the user's past operation history. 2. The system of claim 1, (Appendix 27) The display unit is When displaying 3D visualization, select the display method based on the user's device information 2. The system of claim 1, (Appendix 28) The display unit is Analyze user emotions and adjust the operation procedure of the 3D visual based on the analyzed user emotions. 2. The system of claim 1, (Appendix 29) The display unit is When displaying 3D visualizations, choose the display method based on the user's geographic location 2. The system of claim 1, (Appendix 30) The display unit is Analyze users' social media activity and display relevant information when viewed in 3D visualization 2. The system of claim 1, [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit for inputting information on the layout of the property and information on the furniture owned by the user; a generation unit that analyzes the information input by the reception unit and generates a layout; A display unit for visually confirming the layout generated by the generation unit in 3D. A system characterized by:

2. The generation unit is Generative AI analyzes the property's floor plan and furniture information to generate a layout 2. The system of claim 1 .

3. The display unit is Use AR technology to display layouts in 3D visuals 2. The system of claim 1 .

4. The display unit is Users can use their smartphone or tablet to navigate the room and check the generated layout.

2. The system of claim 1 .

5. The generation unit is Users can customize the generated layout within the app 2. The system of claim 1 .

6. The reception unit is By switching from a real estate company or furniture manufacturer's website, property and furniture information will be automatically entered.

2. The system of claim 1 .

7. The reception unit is Analyze user emotions and adjust the timing of inputting floor plan information based on the analyzed user emotions 2. The system of claim 1 .

8. The reception unit is Analyze the user's past floor plan information input history and select the input method 2. The system of claim 1 .

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