system

Abstract

The system according to this embodiment aims to efficiently generate spatial designs based on the floor plan of a property and to propose the selection and arrangement of furniture. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, an output unit, and a proposal unit. The reception unit receives a floor plan of the property as input. The generation unit generates a spatial design based on the floor plan and spatial image input by the reception unit. The output unit outputs the spatial design generated by the generation unit as a 3D map. The proposal unit proposes furniture selection and placement, as well as price information, based on the 3D map output by the output unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process of generating a space design based on a floor plan of a property and proposing the selection and arrangement of furniture requires labor and time.

[0005] The system according to the embodiment aims to efficiently generate a space design based on a floor plan of a property and propose the selection and arrangement of furniture.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, an output unit, and a proposal unit. The reception unit receives a floor plan of the property as input. The generation unit generates a spatial design based on the floor plan and spatial image input by the reception unit. The output unit outputs the spatial design generated by the generation unit as a 3D map. The proposal unit proposes furniture selection and placement, as well as price information, based on the 3D map output by the output unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently generate spatial designs based on the floor plan of a property and propose furniture selection and placement. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​system according to an embodiment of the present invention is a system that generates designs on a 3D map simply by inputting a room floor plan and an image of the space to be created. This system aims to reduce the man-hours required to realize the ideal interior during renovation and to fulfill the buyer's ideal. First, the generating AI learns in advance information such as space design, furniture size, and price. Next, the user inputs the floor plan of the property, an image of the space to be created, and a budget. For example, if the user inputs "I want to create a resort-style living room," the generating AI will generate an optimal space design based on that information. The generated space design is output as a 3D map, and the user can check the design. Furthermore, the generating AI also suggests furniture selection, placement, and price information. For example, it provides information on what kind of furniture is suitable for a resort-style living room, how to arrange that furniture, and price information. This system reduces the workload of architects and improves residents' satisfaction with the design. Users can easily realize their ideal space, and it also contributes to cost reduction during renovation. This allows the AI ​​system to generate designs and layouts on a 3D map, suggest furniture selections and placements, and provide price information, simply by the user inputting a floor plan of the room and an image of the space they want to create.

[0029] The AI ​​system according to this embodiment comprises a reception unit, a generation unit, an output unit, and a proposal unit. The reception unit receives a floor plan of a property as input. For example, the user can scan the floor plan of the property into the reception unit. The reception unit can also receive a floor plan in digital format directly as input. Furthermore, the reception unit can receive a handwritten floor plan taken with a camera as input. The generation unit generates a spatial design based on the floor plan and spatial image input by the reception unit. For example, the generation unit uses a generation AI to analyze the spatial image input by the user and generate an optimal spatial design. Because the generation AI has learned spatial design, furniture size, price information, etc., the generation unit can provide a design that meets the user's needs. The output unit outputs the spatial design generated by the generation unit as a 3D map. For example, the output unit can display the 3D map on a display. The output unit can also print the 3D map with a printer. Furthermore, the output unit can save the 3D map as digital data. The proposal unit suggests furniture selection, placement, and pricing information based on the 3D map output by the output unit. For example, the proposal unit uses generation AI to select furniture suitable for the 3D map and provides placement methods and pricing information. The proposal unit can also suggest the optimal furniture selection and placement based on the budget information entered by the user. As a result, the AI ​​system can generate designs and layouts on a 3D map and suggest furniture selection, placement, and pricing information simply by inputting the property's floor plan and spatial image.

[0030] The reception desk inputs floor plans of properties. For example, users can scan floor plans into the reception desk. Specifically, users place a paper floor plan in the scanner and press the scan button, and the floor plan is imported into the system as digital data. The reception desk can also directly input digital floor plans. For example, users can upload an already digitized floor plan file to the system, and the floor plan is imported. Furthermore, the reception desk can also input handwritten floor plans by users taking a picture of it with a camera. Users take a picture of a handwritten floor plan using the camera of their smartphone or tablet and upload the image to the system. The system uses image recognition technology to analyze the handwritten floor plan and import it as digital data. This allows the reception desk to provide flexibility in allowing users to input floor plans in various formats, improving user convenience. In addition, the reception desk has a function to check the quality of the entered floor plans and prompt the user to make corrections if there is missing information or unclear parts. For example, if part of the floor plan is unclear, the system will highlight that part and send a notification to the user requesting re-entry. This allows the reception desk to import accurate and complete floor plan data into the system.

[0031] The generation unit generates spatial designs based on floor plans and spatial images entered by the reception unit. For example, the generation unit uses a generation AI to analyze the user's input spatial image and generate the optimal spatial design. Specifically, the generation AI receives the user's floor plan and spatial image as input data and generates a spatial design based on this data. Because the generation AI learns about spatial design, furniture size, and price information, it can provide designs that meet the user's needs. For example, if a user desires a "modern living room," the generation AI selects modern furniture and decorations and arranges them according to the floor plan. Furthermore, the generation AI considers not only furniture placement but also the overall design of the space, including lighting placement, wall color, and flooring selection. In addition, the generation AI can generate and propose multiple design options according to the user's budget and preferences. For example, it can offer designs in different price ranges, such as designs using high-end furniture or cost-effective designs. This allows the generation unit to quickly generate and provide a variety of spatial designs that meet the user's needs.

[0032] The output unit outputs the spatial design generated by the generation unit as a 3D map. The output unit can, for example, display the 3D map on a screen. Specifically, it can display the generated 3D map on a high-resolution screen, allowing the user to visually confirm the spatial design. The output unit can also print the 3D map using a printer. For example, it can use a 3D printer to output the generated spatial design as a three-dimensional model, allowing the user to physically examine it. Furthermore, the output unit can save the 3D map as digital data. For example, it can save the generated 3D map to cloud storage, allowing the user to access it at any time. This allows the output unit to output the generated spatial design in various formats, improving user convenience. Additionally, the output unit provides interactive features that allow the user to manipulate the 3D map and view the spatial design from different perspectives. For example, the user can rotate the 3D map and zoom in and out using a mouse or touchscreen. This allows the output unit to support the user in thoroughly examining the spatial design and selecting a design they are satisfied with.

[0033] The proposal unit suggests furniture selection, placement, and pricing information based on the 3D map output by the output unit. For example, the proposal unit uses a generation AI to select furniture suitable for the 3D map and provides placement methods and pricing information. Specifically, the generation AI analyzes the spatial design of the 3D map and selects and places the optimal furniture. For instance, it selects furniture such as a sofa, table, and TV stand for a living room and places them in the optimal locations. The generation AI also considers detailed information such as furniture size, color, and material to ensure overall harmony in the space. Furthermore, the proposal unit can suggest optimal furniture selection and placement based on budget information entered by the user. For example, if the user sets a budget, the generation AI selects the most suitable furniture within that budget and provides pricing information. This allows the user to achieve their ideal spatial design within their budget. The proposal unit also provides information on furniture suppliers and delivery times to support the user in purchasing furniture smoothly. For example, it provides links to the suggested furniture, allowing the user to purchase directly from online shops. The proposal unit also provides furniture placement methods and assembly instructions to enable the user to easily install the furniture. This allows the proposal department to provide comprehensive support to users and assist them in realizing their ideal spatial design.

[0034] The generation unit can learn spatial design, furniture size, and price information. For example, the generation unit uses a generation AI to learn spatial design trends, furniture size, and price information. The generation unit can generate more accurate spatial designs by having the generation AI learn from a large amount of data. For example, the generation unit can have the generation AI learn from past design data and provide designs that meet the user's needs. The generation unit can also have the generation AI learn furniture size and price information and make suggestions that fit the user's budget. In this way, the generation unit can generate more accurate spatial designs through learning.

[0035] The reception desk can receive spatial images and budget information entered by the user. For example, the reception desk can receive spatial images entered by the user in text format. The reception desk can also receive spatial images entered by the user in image format. Furthermore, the reception desk can also receive budget information entered by the user in numerical format. As a result, the reception desk can generate more specific spatial designs by receiving spatial images and budget information entered by the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the spatial image entered by the user into the AI ​​and have the AI ​​perform analysis of the spatial image.

[0036] The generation unit can generate an optimal spatial design based on the spatial image input by the user. For example, the generation unit uses a generation AI to analyze the spatial image input by the user and generate an optimal spatial design. Because the generation AI learns about spatial design trends and user requests, the generation unit can provide a design that is close to the user's ideal. For example, the generation AI generates an optimal spatial design based on keywords and images input by the user. The generation unit can also provide an optimal design by having the generation AI consider the user's budget information. In this way, the generation unit can provide a design that is close to the user's ideal by generating an optimal spatial design based on the spatial image input by the user.

[0037] The output unit can display the generated spatial design as a 3D map. For example, the output unit can display the generated spatial design on a display. The output unit can also print the generated spatial design using a printer. Furthermore, the output unit can save the generated spatial design as digital data. This allows the user to visually confirm the design by displaying the generated spatial design as a 3D map. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the generated spatial design into AI and have the AI ​​generate the 3D map.

[0038] The proposal unit can suggest furniture selection, placement, and pricing information based on the generated 3D map. For example, the proposal unit can use a generating AI to select furniture suitable for the 3D map and provide placement methods and pricing information. The proposal unit can also suggest the optimal furniture selection and placement based on budget information entered by the user. For example, the proposal unit can use a generating AI to learn furniture size and pricing information and make suggestions that meet the user's needs. The proposal unit can also use a generating AI to suggest the optimal furniture placement based on the user's spatial image. In this way, by suggesting furniture selection, placement, and pricing information based on the generated 3D map, the proposal unit allows the user to understand the specific furniture placement and prices. Some or all of the above processes in the proposal unit may be performed using AI, or not. For example, the proposal unit can input the generated 3D map into the AI ​​and have the AI ​​perform the furniture selection, placement, and pricing information suggestion.

[0039] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. Furthermore, the reception desk can customize input methods by referring to floor plans and spatial images that the user has previously entered. In this way, the reception desk can provide the user with the optimal input method by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history into AI and have the AI ​​select the optimal input method.

[0040] The reception desk can filter input floor plans and spatial images based on the user's current projects and areas of interest. For example, the reception desk can prioritize displaying floor plans and spatial images related to the user's current ongoing projects. The reception desk can also filter input candidates based on the user's areas of interest (e.g., resort style, modern style). Furthermore, the reception desk can suggest relevant floor plans and spatial images based on projects the user has shown interest in in the past. In this way, the reception desk can provide highly relevant information by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's project information into AI and have the AI ​​perform the filtering.

[0041] The reception desk can prioritize retrieving highly relevant information by considering the user's geographical location when inputting floor plans or spatial images. For example, the reception desk can suggest relevant floor plans and spatial images by referencing the architectural styles and designs of the area where the user is currently located. The reception desk can also prioritize displaying designs suitable for the local climate and environment based on the user's geographical location. Furthermore, the reception desk can suggest relevant floor plans and spatial images by referencing the designs of places the user has visited in the past. In this way, the reception desk can provide designs and information suitable for the region by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location information into the AI ​​and have the AI ​​retrieve highly relevant information.

[0042] The reception desk can analyze the user's social media activity and obtain relevant information when inputting floor plans and spatial images. For example, the reception desk can suggest relevant floor plans and spatial images based on designs and inspirations shared by the user on social media. The reception desk can also obtain relevant information based on designs shared by the user's social media followers and friends. Furthermore, the reception desk can suggest relevant floor plans and spatial images based on designs that the user has "liked" or commented on on social media. In this way, the reception desk can provide designs and information that match the user's preferences by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity into AI and have the AI ​​perform the acquisition of relevant information.

[0043] The generation unit can adjust the level of detail in the spatial design based on the importance of the floor plan. For example, the generation unit can generate detailed designs for important rooms (such as the living room and kitchen) and simplified designs for other rooms. The generation unit can also generate detailed designs for rooms that the user particularly values. Furthermore, the generation unit can generate detailed designs for the most frequently used rooms in the floor plan. In this way, the generation unit can provide detailed designs for important rooms by adjusting the level of detail based on the importance of the floor plan. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the importance of the floor plan into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the design.

[0044] The generation unit can apply different design algorithms depending on the category of the space when generating spatial designs. For example, the generation unit can apply a relaxing design algorithm to a living room and a functional design algorithm to a kitchen. It can also apply a design algorithm that prioritizes comfort to a bedroom and a design algorithm that prioritizes cleanliness to a bathroom. Furthermore, it can apply a design algorithm that incorporates playfulness to a child's room and a design algorithm that enhances concentration to a study. In this way, the generation unit can provide the optimal design for each space by applying different design algorithms depending on the category of the space. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the category of the space into the generation AI and have the generation AI execute the application of the design algorithm.

[0045] The generation unit can determine design priorities based on the submission timing of floor plans when generating spatial designs. For example, the generation unit will prioritize generating designs for floor plans with approaching submission deadlines. Furthermore, if the user is in a hurry, the generation unit can adjust the design priorities based on the submission timing. In addition, the generation unit can generate detailed designs for floor plans with later submission deadlines. This allows the generation unit to provide designs that meet submission deadlines by prioritizing designs based on the floor plan submission timing. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input submission timing information into the AI ​​and have the AI ​​determine the design priorities.

[0046] The generation unit can adjust the order of designs based on the relationships between the floor plans when generating spatial designs. For example, the generation unit can prioritize generating designs for the most relevant rooms in the floor plan. It can also postpone generating designs for less relevant rooms in the floor plan. Furthermore, the generation unit can alternate between designing highly relevant and less relevant rooms in the floor plan. In this way, the generation unit can prioritize providing designs to highly relevant rooms by adjusting the order of designs based on the relationships between the floor plans. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relationships between the floor plans into the AI ​​and have the AI ​​perform the adjustment of the design order.

[0047] The output unit can select the optimal display method when displaying a 3D map by referring to the user's past operation history. For example, the output unit may prioritize suggesting display methods that the user has previously preferred. The output unit can also predict and suggest display methods to be used during specific time periods based on the user's past operation history. Furthermore, the output unit can select the optimal display method by referring to the styles of 3D maps that the user has previously displayed. In this way, the output unit can provide the user with the optimal display method by referring to the user's past operation history. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's operation history into AI and have the AI ​​select the optimal display method.

[0048] The output unit can select the optimal display method when displaying a 3D map, taking into account the user's device information. For example, if the user is using a smartphone, the output unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the output unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the output unit can provide a concise and highly visible display method. Thus, by considering the user's device information, the output unit can provide a display method optimized for the device. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's device information into the AI ​​and have the AI ​​select the optimal display method.

[0049] The output unit can select the optimal display method when displaying a 3D map, taking into account the user's geographical location information. For example, the output unit can display a relevant 3D map by referencing the architectural style and design of the area where the user is currently located. The output unit can also display a 3D map that is suitable for the climate and environment of the region based on the user's geographical location information. Furthermore, the output unit can display a relevant 3D map by referencing the design of places the user has visited in the past. In this way, the output unit can provide a display method that is appropriate for the region by taking into account the user's geographical location information. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's geographical location information into AI and have the AI ​​select the optimal display method.

[0050] The output unit can analyze the user's social media activity and display relevant information when displaying a 3D map. For example, the output unit can display relevant 3D maps based on designs and inspirations shared by the user on social media. It can also display relevant information based on designs shared by the user's social media followers and friends. Furthermore, the output unit can display relevant 3D maps based on designs that the user has "liked" or commented on on social media. In this way, the output unit can provide information tailored to the user's preferences by analyzing the user's social media activity. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's social media activity into AI and have the AI ​​display relevant information.

[0051] The suggestion unit can analyze the user's past purchasing behavior to select the optimal suggestion method when proposing furniture selection, arrangement, and price information. For example, the suggestion unit can suggest the most suitable furniture by referring to the style and price range of furniture the user has purchased in the past. The suggestion unit can also prioritize suggesting specific brands or designs based on the user's past purchasing behavior. Furthermore, the suggestion unit can suggest the optimal arrangement by referring to how the user has arranged furniture in the past. In this way, the suggestion unit can provide the most suitable suggestions for the user by analyzing the user's past purchasing behavior. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's purchasing behavior data into AI and have the AI ​​select the optimal suggestion method.

[0052] The suggestion unit can customize its suggestions based on the user's current living situation when selecting, arranging, and providing price information for furniture. For example, if the user has children, the suggestion unit will suggest furniture that prioritizes safety. If the user has pets, the suggestion unit can also suggest pet-friendly furniture. Furthermore, if the user lives alone, the suggestion unit can suggest compact and functional furniture. In this way, the suggestion unit can provide the user with the most suitable suggestions by customizing its suggestions based on the user's current living situation. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's living situation data into AI and have the AI ​​customize the suggestion methods.

[0053] The suggestion unit can select the optimal suggestion method by considering the user's geographical location when suggesting furniture selection, placement, and price information. For example, the suggestion unit can suggest furniture suitable for the climate and environment of the area where the user is currently located. It can also suggest furniture that matches the local architectural style and design based on the user's geographical location. Furthermore, the suggestion unit can suggest related furniture by referencing the design of places the user has visited in the past. In this way, the suggestion unit can provide suggestions that are appropriate for the region by considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's geographical location information into AI and have the AI ​​select the optimal suggestion method.

[0054] The suggestion unit can analyze the user's social media activity to propose methods for suggesting furniture selection, placement, and pricing information. For example, the suggestion unit can suggest relevant furniture based on designs and inspirations shared by the user on social media. It can also suggest relevant furniture based on designs shared by the user's social media followers and friends. Furthermore, it can suggest relevant furniture based on designs that the user has "liked" or commented on on social media. In this way, the suggestion unit can provide suggestions that match the user's preferences by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's social media activity into AI and have the AI ​​select the means of making suggestions.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The generation unit can adjust the level of detail in the spatial design based on the importance of the floor plan. For example, it can generate detailed designs for important rooms (such as the living room and kitchen) and simplified designs for other rooms. It can also generate detailed designs for rooms that the user particularly values. Furthermore, it can generate detailed designs for the most frequently used rooms in the floor plan. In this way, the generation unit can provide detailed designs for important rooms by adjusting the level of detail based on the importance of the floor plan. Some or all of the above processing in the generation unit may be performed using AI or not.

[0057] The proposal department can analyze the user's past purchasing behavior to select the optimal proposal method when suggesting furniture selection, arrangement, and price information. For example, it can suggest the most suitable furniture by referring to the style and price range of furniture the user has purchased in the past. It can also prioritize suggesting specific brands or designs based on the user's past purchasing behavior. Furthermore, it can suggest the optimal arrangement by referring to how the user has arranged furniture in the past. In this way, the proposal department can provide the most suitable proposals for the user by analyzing the user's past purchasing behavior. Some or all of the above processing in the proposal department may be performed using AI, or it may be performed without using AI.

[0058] The output unit can select the optimal display method when displaying a 3D map by referring to the user's past operation history. For example, it can prioritize suggesting display methods that the user has previously preferred. It can also predict and suggest display methods to be used during specific time periods based on the user's past operation history. Furthermore, it can select the optimal display method by referring to the style of 3D maps that the user has displayed in the past. In this way, the output unit can provide the user with the optimal display method by referring to the user's past operation history. Some or all of the above processing in the output unit may be performed using AI, or it may be performed without using AI.

[0059] The reception desk can analyze the user's past input history and select the optimal input method. For example, it can prioritize suggesting input methods that the user has frequently used in the past (such as voice or text). It can also predict and suggest input methods that the user will use at specific times of day based on their past input history. Furthermore, it can customize input methods by referring to floor plans or spatial images that the user has entered in the past. In this way, the reception desk can provide the user with the optimal input method by analyzing their past input history. Some or all of the above processing in the reception desk may be performed using AI, or it may not be performed using AI.

[0060] The generation unit can apply different design algorithms depending on the category of the space when generating spatial designs. For example, a relaxing design algorithm can be applied to a living room, and a functional design algorithm to a kitchen. A comfort-focused design algorithm can be applied to a bedroom, and a cleanliness-focused design algorithm to a bathroom. Furthermore, a playful design algorithm can be applied to a child's room, and a concentration-enhancing design algorithm to a study. In this way, the generation unit can provide the optimal design for each space by applying different design algorithms according to the category of the space. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk inputs the property's floor plan. The reception desk can receive floor plans from users via scanner. It can also directly input digital floor plans. Furthermore, users can input handwritten floor plans by taking a picture of them with a camera. Step 2: The generation unit generates a spatial design based on the floor plan and spatial image entered by the reception unit. The generation unit uses a generation AI to analyze the spatial image entered by the user and generate the optimal spatial design. The generation AI has learned spatial design, furniture size, price information, etc., and provides a design that meets the user's requirements. Step 3: The output unit outputs the spatial design generated by the generation unit as a 3D map. The output unit can display the 3D map on a screen. It is also possible to print the 3D map using a printer or save it as digital data. Step 4: The proposal unit suggests furniture selection, placement, and pricing information based on the 3D map output by the output unit. The proposal unit uses generation AI to select furniture suitable for the 3D map and provides placement methods and pricing information. Furthermore, it can suggest the optimal furniture selection and placement based on the budget information entered by the user.

[0063] (Example of form 2) The AI ​​system according to an embodiment of the present invention is a system that generates designs on a 3D map simply by inputting a room floor plan and an image of the space to be created. This system aims to reduce the man-hours required to realize the ideal interior during renovation and to fulfill the buyer's ideal. First, the generating AI learns in advance information such as space design, furniture size, and price. Next, the user inputs the floor plan of the property, an image of the space to be created, and a budget. For example, if the user inputs "I want to create a resort-style living room," the generating AI will generate an optimal space design based on that information. The generated space design is output as a 3D map, and the user can check the design. Furthermore, the generating AI also suggests furniture selection, placement, and price information. For example, it provides information on what kind of furniture is suitable for a resort-style living room, how to arrange that furniture, and price information. This system reduces the workload of architects and improves residents' satisfaction with the design. Users can easily realize their ideal space, and it also contributes to cost reduction during renovation. This allows the AI ​​system to generate designs and layouts on a 3D map, suggest furniture selections and placements, and provide price information, simply by the user inputting a floor plan of the room and an image of the space they want to create.

[0064] The AI ​​system according to this embodiment comprises a reception unit, a generation unit, an output unit, and a proposal unit. The reception unit receives a floor plan of a property as input. For example, the user can scan the floor plan of the property into the reception unit. The reception unit can also receive a floor plan in digital format directly as input. Furthermore, the reception unit can receive a handwritten floor plan taken with a camera as input. The generation unit generates a spatial design based on the floor plan and spatial image input by the reception unit. For example, the generation unit uses a generation AI to analyze the spatial image input by the user and generate an optimal spatial design. Because the generation AI has learned spatial design, furniture size, price information, etc., the generation unit can provide a design that meets the user's needs. The output unit outputs the spatial design generated by the generation unit as a 3D map. For example, the output unit can display the 3D map on a display. The output unit can also print the 3D map with a printer. Furthermore, the output unit can save the 3D map as digital data. The proposal unit suggests furniture selection, placement, and pricing information based on the 3D map output by the output unit. For example, the proposal unit uses generation AI to select furniture suitable for the 3D map and provides placement methods and pricing information. The proposal unit can also suggest the optimal furniture selection and placement based on the budget information entered by the user. As a result, the AI ​​system can generate designs and layouts on a 3D map and suggest furniture selection, placement, and pricing information simply by inputting the property's floor plan and spatial image.

[0065] The reception desk inputs floor plans of properties. For example, users can scan floor plans into the reception desk. Specifically, users place a paper floor plan in the scanner and press the scan button, and the floor plan is imported into the system as digital data. The reception desk can also directly input digital floor plans. For example, users can upload an already digitized floor plan file to the system, and the floor plan is imported. Furthermore, the reception desk can also input handwritten floor plans by users taking a picture of it with a camera. Users take a picture of a handwritten floor plan using the camera of their smartphone or tablet and upload the image to the system. The system uses image recognition technology to analyze the handwritten floor plan and import it as digital data. This allows the reception desk to provide flexibility in allowing users to input floor plans in various formats, improving user convenience. In addition, the reception desk has a function to check the quality of the entered floor plans and prompt the user to make corrections if there is missing information or unclear parts. For example, if part of the floor plan is unclear, the system will highlight that part and send a notification to the user requesting re-entry. This allows the reception desk to import accurate and complete floor plan data into the system.

[0066] The generation unit generates spatial designs based on floor plans and spatial images entered by the reception unit. For example, the generation unit uses a generation AI to analyze the user's input spatial image and generate the optimal spatial design. Specifically, the generation AI receives the user's floor plan and spatial image as input data and generates a spatial design based on this data. Because the generation AI learns about spatial design, furniture size, and price information, it can provide designs that meet the user's needs. For example, if a user desires a "modern living room," the generation AI selects modern furniture and decorations and arranges them according to the floor plan. Furthermore, the generation AI considers not only furniture placement but also the overall design of the space, including lighting placement, wall color, and flooring selection. In addition, the generation AI can generate and propose multiple design options according to the user's budget and preferences. For example, it can offer designs in different price ranges, such as designs using high-end furniture or cost-effective designs. This allows the generation unit to quickly generate and provide a variety of spatial designs that meet the user's needs.

[0067] The output unit outputs the spatial design generated by the generation unit as a 3D map. The output unit can, for example, display the 3D map on a screen. Specifically, it can display the generated 3D map on a high-resolution screen, allowing the user to visually confirm the spatial design. The output unit can also print the 3D map using a printer. For example, it can use a 3D printer to output the generated spatial design as a three-dimensional model, allowing the user to physically examine it. Furthermore, the output unit can save the 3D map as digital data. For example, it can save the generated 3D map to cloud storage, allowing the user to access it at any time. This allows the output unit to output the generated spatial design in various formats, improving user convenience. Additionally, the output unit provides interactive features that allow the user to manipulate the 3D map and view the spatial design from different perspectives. For example, the user can rotate the 3D map and zoom in and out using a mouse or touchscreen. This allows the output unit to support the user in thoroughly examining the spatial design and selecting a design they are satisfied with.

[0068] The proposal unit suggests furniture selection, placement, and pricing information based on the 3D map output by the output unit. For example, the proposal unit uses a generation AI to select furniture suitable for the 3D map and provides placement methods and pricing information. Specifically, the generation AI analyzes the spatial design of the 3D map and selects and places the optimal furniture. For instance, it selects furniture such as a sofa, table, and TV stand for a living room and places them in the optimal locations. The generation AI also considers detailed information such as furniture size, color, and material to ensure overall harmony in the space. Furthermore, the proposal unit can suggest optimal furniture selection and placement based on budget information entered by the user. For example, if the user sets a budget, the generation AI selects the most suitable furniture within that budget and provides pricing information. This allows the user to achieve their ideal spatial design within their budget. The proposal unit also provides information on furniture suppliers and delivery times to support the user in purchasing furniture smoothly. For example, it provides links to the suggested furniture, allowing the user to purchase directly from online shops. The proposal unit also provides furniture placement methods and assembly instructions to enable the user to easily install the furniture. This allows the proposal department to provide comprehensive support to users and assist them in realizing their ideal spatial design.

[0069] The generation unit can learn spatial design, furniture size, and price information. For example, the generation unit uses a generation AI to learn spatial design trends, furniture size, and price information. The generation unit can generate more accurate spatial designs by having the generation AI learn from a large amount of data. For example, the generation unit can have the generation AI learn from past design data and provide designs that meet the user's needs. The generation unit can also have the generation AI learn furniture size and price information and make suggestions that fit the user's budget. In this way, the generation unit can generate more accurate spatial designs through learning.

[0070] The reception desk can receive spatial images and budget information entered by the user. For example, the reception desk can receive spatial images entered by the user in text format. The reception desk can also receive spatial images entered by the user in image format. Furthermore, the reception desk can also receive budget information entered by the user in numerical format. As a result, the reception desk can generate more specific spatial designs by receiving spatial images and budget information entered by the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the spatial image entered by the user into the AI ​​and have the AI ​​perform analysis of the spatial image.

[0071] The generation unit can generate an optimal spatial design based on the spatial image input by the user. For example, the generation unit uses a generation AI to analyze the spatial image input by the user and generate an optimal spatial design. Because the generation AI learns about spatial design trends and user requests, the generation unit can provide a design that is close to the user's ideal. For example, the generation AI generates an optimal spatial design based on keywords and images input by the user. The generation unit can also provide an optimal design by having the generation AI consider the user's budget information. In this way, the generation unit can provide a design that is close to the user's ideal by generating an optimal spatial design based on the spatial image input by the user.

[0072] The output unit can display the generated spatial design as a 3D map. For example, the output unit can display the generated spatial design on a display. The output unit can also print the generated spatial design using a printer. Furthermore, the output unit can save the generated spatial design as digital data. This allows the user to visually confirm the design by displaying the generated spatial design as a 3D map. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the generated spatial design into AI and have the AI ​​generate the 3D map.

[0073] The proposal unit can suggest furniture selection, placement, and pricing information based on the generated 3D map. For example, the proposal unit can use a generating AI to select furniture suitable for the 3D map and provide placement methods and pricing information. The proposal unit can also suggest the optimal furniture selection and placement based on budget information entered by the user. For example, the proposal unit can use a generating AI to learn furniture size and pricing information and make suggestions that meet the user's needs. The proposal unit can also use a generating AI to suggest the optimal furniture placement based on the user's spatial image. In this way, by suggesting furniture selection, placement, and pricing information based on the generated 3D map, the proposal unit allows the user to understand the specific furniture placement and prices. Some or all of the above processes in the proposal unit may be performed using AI, or not. For example, the proposal unit can input the generated 3D map into the AI ​​and have the AI ​​perform the furniture selection, placement, and pricing information suggestion.

[0074] The reception unit can estimate the user's emotions and adjust the input timing of floor plans and spatial images based on the estimated emotions. For example, if the user is stressed, the reception unit can delay the input timing to allow them to relax. Conversely, if the user is excited, the reception unit can speed up the input timing to expedite the process. Furthermore, if the user is tired, the reception unit can adjust the input timing to include breaks. In this way, the reception unit can reduce user stress and promote efficient input by adjusting the input timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0075] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. Furthermore, the reception desk can customize input methods by referring to floor plans and spatial images that the user has previously entered. In this way, the reception desk can provide the user with the optimal input method by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history into AI and have the AI ​​select the optimal input method.

[0076] The reception desk can filter input floor plans and spatial images based on the user's current projects and areas of interest. For example, the reception desk can prioritize displaying floor plans and spatial images related to the user's current ongoing projects. The reception desk can also filter input candidates based on the user's areas of interest (e.g., resort style, modern style). Furthermore, the reception desk can suggest relevant floor plans and spatial images based on projects the user has shown interest in in the past. In this way, the reception desk can provide highly relevant information by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's project information into AI and have the AI ​​perform the filtering.

[0077] The reception unit can estimate the user's emotions and, based on the estimated emotions, determine the priority of floor plans and spatial images to be input. For example, if the user is relaxed, the reception unit may prioritize detailed floor plans and spatial images. If the user is in a hurry, the reception unit may prioritize simplified floor plans and spatial images. Furthermore, if the user is excited, the reception unit may prioritize visually appealing floor plans and spatial images. In this way, the reception unit can facilitate input that meets the user's needs by determining the input priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0078] The reception desk can prioritize retrieving highly relevant information by considering the user's geographical location when inputting floor plans or spatial images. For example, the reception desk can suggest relevant floor plans and spatial images by referencing the architectural styles and designs of the area where the user is currently located. The reception desk can also prioritize displaying designs suitable for the local climate and environment based on the user's geographical location. Furthermore, the reception desk can suggest relevant floor plans and spatial images by referencing the designs of places the user has visited in the past. In this way, the reception desk can provide designs and information suitable for the region by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location information into the AI ​​and have the AI ​​retrieve highly relevant information.

[0079] The reception desk can analyze the user's social media activity and obtain relevant information when inputting floor plans and spatial images. For example, the reception desk can suggest relevant floor plans and spatial images based on designs and inspirations shared by the user on social media. The reception desk can also obtain relevant information based on designs shared by the user's social media followers and friends. Furthermore, the reception desk can suggest relevant floor plans and spatial images based on designs that the user has "liked" or commented on on social media. In this way, the reception desk can provide designs and information that match the user's preferences by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity into AI and have the AI ​​perform the acquisition of relevant information.

[0080] The generation unit can estimate the user's emotions and adjust the expression of the spatial design based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a design that uses soft colors and many curves. If the user is excited, the generation unit can also generate a design with vivid colors and bold designs. Furthermore, if the user is stressed, the generation unit can generate a design with calm colors and simple designs. In this way, the generation unit can provide a design that matches the user's emotions by adjusting the expression of the spatial design according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform design adjustments based on emotions.

[0081] The generation unit can adjust the level of detail in the spatial design based on the importance of the floor plan. For example, the generation unit can generate detailed designs for important rooms (such as the living room and kitchen) and simplified designs for other rooms. The generation unit can also generate detailed designs for rooms that the user particularly values. Furthermore, the generation unit can generate detailed designs for the most frequently used rooms in the floor plan. In this way, the generation unit can provide detailed designs for important rooms by adjusting the level of detail based on the importance of the floor plan. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the importance of the floor plan into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the design.

[0082] The generation unit can apply different design algorithms depending on the category of the space when generating spatial designs. For example, the generation unit can apply a relaxing design algorithm to a living room and a functional design algorithm to a kitchen. It can also apply a design algorithm that prioritizes comfort to a bedroom and a design algorithm that prioritizes cleanliness to a bathroom. Furthermore, it can apply a design algorithm that incorporates playfulness to a child's room and a design algorithm that enhances concentration to a study. In this way, the generation unit can provide the optimal design for each space by applying different design algorithms depending on the category of the space. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the category of the space into the generation AI and have the generation AI execute the application of the design algorithm.

[0083] The generation unit can estimate the user's emotions and adjust the length of the spatial design based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a longer design with detailed explanations. If the user is in a hurry, the generation unit can also generate a short, concise design. Furthermore, if the user is excited, the generation unit can generate a design with visually stimulating effects. In this way, the generation unit can provide a design that meets the user's needs by adjusting the length of the spatial design according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion-based design adjustments.

[0084] The generation unit can determine design priorities based on the submission timing of floor plans when generating spatial designs. For example, the generation unit will prioritize generating designs for floor plans with approaching submission deadlines. Furthermore, if the user is in a hurry, the generation unit can adjust the design priorities based on the submission timing. In addition, the generation unit can generate detailed designs for floor plans with later submission deadlines. This allows the generation unit to provide designs that meet submission deadlines by prioritizing designs based on the floor plan submission timing. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input submission timing information into the AI ​​and have the AI ​​determine the design priorities.

[0085] The generation unit can adjust the order of designs based on the relationships between the floor plans when generating spatial designs. For example, the generation unit can prioritize generating designs for the most relevant rooms in the floor plan. It can also postpone generating designs for less relevant rooms in the floor plan. Furthermore, the generation unit can alternate between designing highly relevant and less relevant rooms in the floor plan. In this way, the generation unit can prioritize providing designs to highly relevant rooms by adjusting the order of designs based on the relationships between the floor plans. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relationships between the floor plans into the AI ​​and have the AI ​​perform the adjustment of the design order.

[0086] The output unit can estimate the user's emotions and adjust the display method of the 3D map based on the estimated user emotions. For example, if the user is relaxed, the output unit can display a 3D map with soft colors and many curves. If the user is excited, the output unit can also display a 3D map with vivid colors and a bold design. Furthermore, if the user is stressed, the output unit can display a 3D map with calm colors and a simple design. In this way, the output unit can provide a display that matches the user's emotions by adjusting the display method of the 3D map according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the output unit may be performed using AI, for example, or not using AI. For example, the output unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method based on the emotions.

[0087] The output unit can select the optimal display method when displaying a 3D map by referring to the user's past operation history. For example, the output unit may prioritize suggesting display methods that the user has previously preferred. The output unit can also predict and suggest display methods to be used during specific time periods based on the user's past operation history. Furthermore, the output unit can select the optimal display method by referring to the styles of 3D maps that the user has previously displayed. In this way, the output unit can provide the user with the optimal display method by referring to the user's past operation history. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's operation history into AI and have the AI ​​select the optimal display method.

[0088] The output unit can select the optimal display method when displaying a 3D map, taking into account the user's device information. For example, if the user is using a smartphone, the output unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the output unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the output unit can provide a concise and highly visible display method. Thus, by considering the user's device information, the output unit can provide a display method optimized for the device. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's device information into the AI ​​and have the AI ​​select the optimal display method.

[0089] The output unit can estimate the user's emotions and adjust the 3D map's operation procedures based on the estimated emotions. For example, if the user is tense, the output unit can provide simple and highly visible operation procedures. If the user is relaxed, the output unit can also provide operation procedures that include detailed information. Furthermore, if the user is in a hurry, the output unit can provide concise operation procedures. In this way, the output unit can provide user-friendly operation procedures by adjusting them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the output unit may be performed using AI, for example, or not using AI. For example, the output unit can input user emotion data into a generative AI and have the generative AI perform emotion-based adjustments to the operation procedures.

[0090] The output unit can select the optimal display method when displaying a 3D map, taking into account the user's geographical location information. For example, the output unit can display a relevant 3D map by referencing the architectural style and design of the area where the user is currently located. The output unit can also display a 3D map that is suitable for the climate and environment of the region based on the user's geographical location information. Furthermore, the output unit can display a relevant 3D map by referencing the design of places the user has visited in the past. In this way, the output unit can provide a display method that is appropriate for the region by taking into account the user's geographical location information. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's geographical location information into AI and have the AI ​​select the optimal display method.

[0091] The output unit can analyze the user's social media activity and display relevant information when displaying a 3D map. For example, the output unit can display relevant 3D maps based on designs and inspirations shared by the user on social media. It can also display relevant information based on designs shared by the user's social media followers and friends. Furthermore, the output unit can display relevant 3D maps based on designs that the user has "liked" or commented on on social media. In this way, the output unit can provide information tailored to the user's preferences by analyzing the user's social media activity. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's social media activity into AI and have the AI ​​display relevant information.

[0092] The suggestion unit can estimate the user's emotions and adjust the method of suggesting furniture selection, arrangement, and price information based on the estimated emotions. For example, if the user is relaxed, the suggestion unit may suggest furniture with soft colors and many curves. If the user is excited, the suggestion unit may also suggest furniture with bright colors and bold designs. Furthermore, if the user is stressed, the suggestion unit may also suggest furniture with calm colors and simple designs. In this way, the suggestion unit can provide suggestions that match the user's emotions by adjusting the suggestion method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the suggestion method based on emotions.

[0093] The suggestion unit can analyze the user's past purchasing behavior to select the optimal suggestion method when proposing furniture selection, arrangement, and price information. For example, the suggestion unit can suggest the most suitable furniture by referring to the style and price range of furniture the user has purchased in the past. The suggestion unit can also prioritize suggesting specific brands or designs based on the user's past purchasing behavior. Furthermore, the suggestion unit can suggest the optimal arrangement by referring to how the user has arranged furniture in the past. In this way, the suggestion unit can provide the most suitable suggestions for the user by analyzing the user's past purchasing behavior. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's purchasing behavior data into AI and have the AI ​​select the optimal suggestion method.

[0094] The suggestion unit can customize its suggestions based on the user's current living situation when selecting, arranging, and providing price information for furniture. For example, if the user has children, the suggestion unit will suggest furniture that prioritizes safety. If the user has pets, the suggestion unit can also suggest pet-friendly furniture. Furthermore, if the user lives alone, the suggestion unit can suggest compact and functional furniture. In this way, the suggestion unit can provide the user with the most suitable suggestions by customizing its suggestions based on the user's current living situation. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's living situation data into AI and have the AI ​​customize the suggestion methods.

[0095] The suggestion unit can estimate the user's emotions and, based on the estimated emotions, prioritize furniture selection, placement, and price information. For example, if the user is relaxed, the suggestion unit will prioritize suggesting detailed furniture selection and placement. If the user is in a hurry, the suggestion unit can also prioritize suggesting simplified furniture selection and placement. Furthermore, if the user is excited, the suggestion unit can prioritize suggesting visually appealing furniture selection and placement. In this way, the suggestion unit can provide suggestions that meet the user's needs by prioritizing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0096] The suggestion unit can select the optimal suggestion method by considering the user's geographical location when suggesting furniture selection, placement, and price information. For example, the suggestion unit can suggest furniture suitable for the climate and environment of the area where the user is currently located. It can also suggest furniture that matches the local architectural style and design based on the user's geographical location. Furthermore, the suggestion unit can suggest related furniture by referencing the design of places the user has visited in the past. In this way, the suggestion unit can provide suggestions that are appropriate for the region by considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's geographical location information into AI and have the AI ​​select the optimal suggestion method.

[0097] The suggestion unit can analyze the user's social media activity to propose methods for suggesting furniture selection, placement, and pricing information. For example, the suggestion unit can suggest relevant furniture based on designs and inspirations shared by the user on social media. It can also suggest relevant furniture based on designs shared by the user's social media followers and friends. Furthermore, it can suggest relevant furniture based on designs that the user has "liked" or commented on on social media. In this way, the suggestion unit can provide suggestions that match the user's preferences by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's social media activity into AI and have the AI ​​select the means of making suggestions.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The reception desk can estimate the user's emotions and customize the input method for floor plans and spatial images based on the estimated emotions. For example, if the user is stressed, the reception desk can facilitate input through simple questions, while if the user is relaxed, it can request more detailed input. It can also provide a visually appealing interface if the user is excited. This allows the reception desk to reduce user stress and promote efficient input by providing input methods tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the processing described above in the reception desk may be performed using AI or not.

[0100] The generation unit can estimate the user's emotions and adjust the style of the spatial design based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a design that uses soft colors and many curves, and if the user is excited, it can generate a design with vibrant colors and bold designs. Also, if the user is stressed, it can generate a design with calm colors and simple designs. In this way, the generation unit can provide a design that matches the user's emotions, thereby creating a space that suits the user's emotions. Emotion estimation is achieved using an emotion engine or a generation AI. Some or all of the above processing in the generation unit may be performed using AI or not.

[0101] The suggestion unit can estimate the user's emotions and adjust the method of suggesting furniture selection, arrangement, and price information based on the estimated emotions. For example, if the user is relaxed, it can suggest furniture with soft colors and many curves; if the user is excited, it can suggest furniture with bright colors and bold designs. If the user is stressed, it can suggest furniture with calm colors and simple designs. In this way, the suggestion unit can select furniture that matches the user's emotions by providing suggestions that are appropriate for their state of mind. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the suggestion unit may be performed using AI or not.

[0102] The output unit can estimate the user's emotions and adjust the display method of the 3D map based on the estimated emotions. For example, if the user is relaxed, it can display a 3D map with soft colors and many curves, and if the user is excited, it can display a 3D map with vivid colors and bold designs. Also, if the user is stressed, it can display a 3D map with calm colors and simple designs. In this way, the output unit can display a 3D map that matches the user's emotions by providing a display method that suits the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the output unit may be performed using AI or not.

[0103] The reception desk can estimate the user's emotions and adjust the timing of inputting floor plans and spatial images based on the estimated emotions. For example, if the user is stressed, the input timing can be delayed to allow them to relax, and if the user is excited, the input timing can be sped up to allow for faster progress. Also, if the user is tired, the input timing can be adjusted to allow for breaks. In this way, the reception desk can reduce user stress and promote efficient input by providing input timing that matches the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the reception desk may be performed using AI or not.

[0104] The generation unit can adjust the level of detail in the spatial design based on the importance of the floor plan. For example, it can generate detailed designs for important rooms (such as the living room and kitchen) and simplified designs for other rooms. It can also generate detailed designs for rooms that the user particularly values. Furthermore, it can generate detailed designs for the most frequently used rooms in the floor plan. In this way, the generation unit can provide detailed designs for important rooms by adjusting the level of detail based on the importance of the floor plan. Some or all of the above processing in the generation unit may be performed using AI or not.

[0105] The proposal department can analyze the user's past purchasing behavior to select the optimal proposal method when suggesting furniture selection, arrangement, and price information. For example, it can suggest the most suitable furniture by referring to the style and price range of furniture the user has purchased in the past. It can also prioritize suggesting specific brands or designs based on the user's past purchasing behavior. Furthermore, it can suggest the optimal arrangement by referring to how the user has arranged furniture in the past. In this way, the proposal department can provide the most suitable proposals for the user by analyzing the user's past purchasing behavior. Some or all of the above processing in the proposal department may be performed using AI, or it may be performed without using AI.

[0106] The output unit can select the optimal display method when displaying a 3D map by referring to the user's past operation history. For example, it can prioritize suggesting display methods that the user has previously preferred. It can also predict and suggest display methods to be used during specific time periods based on the user's past operation history. Furthermore, it can select the optimal display method by referring to the style of 3D maps that the user has displayed in the past. In this way, the output unit can provide the user with the optimal display method by referring to the user's past operation history. Some or all of the above processing in the output unit may be performed using AI, or it may be performed without using AI.

[0107] The reception desk can analyze the user's past input history and select the optimal input method. For example, it can prioritize suggesting input methods that the user has frequently used in the past (such as voice or text). It can also predict and suggest input methods that the user will use at specific times of day based on their past input history. Furthermore, it can customize input methods by referring to floor plans or spatial images that the user has entered in the past. In this way, the reception desk can provide the user with the optimal input method by analyzing their past input history. Some or all of the above processing in the reception desk may be performed using AI, or it may not be performed using AI.

[0108] The generation unit can apply different design algorithms depending on the category of the space when generating spatial designs. For example, a relaxing design algorithm can be applied to a living room, and a functional design algorithm to a kitchen. A comfort-focused design algorithm can be applied to a bedroom, and a cleanliness-focused design algorithm to a bathroom. Furthermore, a playful design algorithm can be applied to a child's room, and a concentration-enhancing design algorithm to a study. In this way, the generation unit can provide the optimal design for each space by applying different design algorithms according to the category of the space. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception desk inputs the property's floor plan. The reception desk can receive floor plans from users via scanner. It can also directly input digital floor plans. Furthermore, users can input handwritten floor plans by taking a picture of them with a camera. Step 2: The generation unit generates a spatial design based on the floor plan and spatial image entered by the reception unit. The generation unit uses a generation AI to analyze the spatial image entered by the user and generate the optimal spatial design. The generation AI has learned spatial design, furniture size, price information, etc., and provides a design that meets the user's requirements. Step 3: The output unit outputs the spatial design generated by the generation unit as a 3D map. The output unit can display the 3D map on a screen. It is also possible to print the 3D map using a printer or save it as digital data. Step 4: The proposal unit suggests furniture selection, placement, and pricing information based on the 3D map output by the output unit. The proposal unit uses generation AI to select furniture suitable for the 3D map and provides placement methods and pricing information. Furthermore, it can suggest the optimal furniture selection and placement based on the budget information entered by the user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the reception unit, generation unit, output unit, and proposal unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit can input a floor plan of a property using the camera 42 or reception device 38 of the smart device 14. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a spatial design using generation AI. The output unit can display a 3D map on the display 40A using, for example, the output device 40 of the smart device 14. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes furniture selection, placement, and price information. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the reception unit, generation unit, output unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit can input a floor plan of a property using the camera 42 and microphone 238 of the smart glasses 214. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and generates a spatial design using generation AI. The output unit can, for example, display a 3D map on the display of the smart glasses 214. The proposal unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and proposes furniture selection, placement, and price information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the reception unit, generation unit, output unit, and proposal unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit can input a floor plan of a property using the camera 42 and microphone 238 of the headset terminal 314. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a spatial design using generation AI. The output unit can display a 3D map on, for example, the display 343 of the headset terminal 314. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes furniture selection, arrangement, and price information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the reception unit, generation unit, output unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit can input a floor plan of a property using the camera 42 and microphone 238 of the robot 414. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a spatial design using generation AI. The output unit can, for example, display a 3D map on the display of the robot 414. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes furniture selection, placement, and price information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) The reception desk where you input the floor plan of the property, A generation unit generates a spatial design based on the floor plan and spatial image input by the reception unit, An output unit that outputs the spatial design generated by the generation unit as a 3D map, The system includes a proposal unit that suggests furniture selection, placement, and price information based on the 3D map output by the output unit. A system characterized by the following features. (Note 2) The generating unit is Learn about spatial design, furniture sizes, pricing information, and more. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is It accepts spatial image and budget information entered by the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is The system generates the optimal spatial design based on the spatial image entered by the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The output unit is, The generated spatial design is displayed as a 3D map. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Based on the generated 3D map, it suggests furniture selection, placement, and pricing information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of inputting floor plans and spatial images based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When inputting floor plans or spatial images, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of floor plans and spatial images to be input based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When inputting floor plans or spatial images, the system prioritizes retrieving highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users input floor plans or spatial images, the system analyzes their social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the spatial design's expression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating spatial designs, the level of detail in the design is adjusted based on the importance of the floor plan. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating spatial designs, different design algorithms are applied depending on the category of the space. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the spatial design based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating spatial designs, design priorities are determined based on when the floor plans were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating spatial designs, the order of the designs is adjusted based on the relationships between the floor plans. The system described in Appendix 1, characterized by the features described herein. (Note 19) The output unit is, It estimates the user's emotions and adjusts how the 3D map is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The output unit is, When displaying a 3D map, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The output unit is, When displaying a 3D map, the system selects the optimal display method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The output unit is, It estimates the user's emotions and adjusts the operation procedure of the 3D map based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The output unit is, When displaying a 3D map, the system selects the optimal display method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The output unit is, When displaying a 3D map, the system analyzes the user's social media activity and displays relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the furniture selection, placement, and pricing information suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When selecting furniture, arranging it, and suggesting pricing information, we analyze the user's past purchasing behavior to select the most optimal suggestion method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When suggesting furniture selection, placement, and pricing information, the system customizes the suggestion methods based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, The system estimates the user's emotions and uses those emotions to prioritize furniture selection, placement, and pricing information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When selecting furniture, arranging it, and suggesting pricing information, the system selects the most suitable suggestion method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When suggesting furniture selection, arrangement, and pricing information, we analyze the user's social media activity to propose methods for making suggestions. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk where you input the floor plan of the property, A generation unit generates a spatial design based on the floor plan and spatial image input by the reception unit, An output unit that outputs the spatial design generated by the generation unit as a 3D map, The system includes a proposal unit that suggests furniture selection, placement, and price information based on the 3D map output by the output unit. A system characterized by the following features.

2. The generating unit is Learn about spatial design, furniture sizes, pricing information, and more. The system according to feature 1.

3. The aforementioned reception unit is It accepts spatial image and budget information entered by the user. The system according to feature 1.

4. The generating unit is The system generates the optimal spatial design based on the spatial image entered by the user. The system according to feature 1.

5. The output unit is, The generated spatial design is displayed as a 3D map. The system according to feature 1.

6. The aforementioned proposal section is, Based on the generated 3D map, it suggests furniture selection, placement, and pricing information. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of inputting floor plans and spatial images based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system according to feature 1.

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