system

Abstract

The system according to the embodiment aims to make building drawings easier to understand visually. [Solution] A system according to an embodiment includes an input unit, a generation unit, and a display unit. The input unit inputs a drawing of a property. The generation unit analyzes the drawing input by the input unit and generates a 3D model. The display unit displays the 3D model generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, when designing based on building drawings, there was a problem that the drawings were difficult to understand and required a lot of work.

[0005] The system according to the embodiment aims to make building drawings easier to understand visually. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a generation unit, and a display unit. The input unit inputs a drawing of a property. The generation unit analyzes the drawing input by the input unit and generates a 3D model. The display unit displays the 3D model generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can make building drawings easier to understand visually. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A design support system according to an embodiment of the present invention converts building drawings into 3D models and visually confirms them, thereby reducing the difficulty of design. This design support system allows designers to intuitively understand the interior of a building by inputting the building drawings, analyzing the drawings using a generation AI, and displaying the generated 3D model. For example, when determining the optimal location for installing a base station, a designer can use the 3D model to view the building's interior while proceeding with the design. This improves design accuracy and reduces labor costs. Even for designers who are not accustomed to reading blueprints, using a 3D model makes visual understanding easier and reduces the hurdles to work with. Furthermore, the generated 3D model can also be used to check the progress of the design. For example, the 3D model can be updated at each design step, allowing the progress to be visually confirmed. This simplifies design progress management and allows for more efficient design. This system reduces in-house labor costs and operational hurdles, allowing anyone to efficiently design base stations. The design support system converts building drawings into 3D models and visually confirms them, thereby reducing the difficulty of design.

[0029] A design support system according to an embodiment includes an input unit, a generation unit, and a display unit. The input unit inputs drawings of a property. Examples of drawings of a property include, but are not limited to, architectural drawings, mechanical drawings, and electrical drawings. The input unit, for example, scans paper drawings and converts them into digital data. The input unit can also directly read drawings provided in digital format. The generation unit uses a generative AI to analyze the drawings input by the input unit and generate a 3D model. The generative AI analyzes the drawings using technologies such as deep learning and generative adversarial networks (GANs) to generate the 3D model. The generation unit, for example, analyzes the internal structure and layout of a building in detail and generates the 3D model. The display unit displays the 3D model generated by the generation unit. The display unit displays the 3D model on a device such as a PC or tablet. The display unit provides an interface for visually viewing the generated 3D model. This allows designers to intuitively understand the interior of a building, reducing the difficulty of designing. Furthermore, the display unit includes an update unit that updates the 3D model according to the design progress. The update unit updates the 3D model at each design step, allowing the progress to be visually confirmed. This makes it easier to manage the design progress and allows the design to proceed efficiently. As a result, the design support system according to the embodiment can convert the property drawings into a 3D model and visually confirm it, thereby reducing the difficulty of the design.

[0030] The generation unit can analyze the drawings using a generative AI and generate a 3D model. The generative AI can analyze the drawings using technologies such as deep learning or generative artificial network (GAN) to generate the 3D model. The generative AI can analyze the internal structure and layout of a building in detail and generate a 3D model. The generative AI can analyze the internal structure and layout of a building based on information such as a floor plan and elevation of the building to generate a 3D model. The generative AI can analyze the internal structure and layout of a building in detail and generate a 3D model. This allows the use of generative AI to efficiently analyze the drawings and generate a 3D model. Some or all of the above-described processing in the generation unit can be performed using, for example, the generative AI, or can be performed without using the generative AI. For example, the generation unit can analyze the drawings and generate a 3D model using an algorithm for analyzing drawings and generating a 3D model.

[0031] The display unit can display the generated 3D model on a PC or tablet device. The display unit displays the 3D model on a device such as a PC or tablet. The display unit provides an interface for visually checking the generated 3D model. The display unit can display the 3D model on a PC screen, for example, to make it easier for designers to intuitively understand the interior of a building. The display unit can also display the 3D model on a tablet screen to make it easier for designers to intuitively understand the interior of a building. This makes it easier to check the design by displaying the generated 3D model on various devices. Some or all of the above-mentioned processing in the display unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the display unit can display the 3D model using software for displaying the generated 3D model.

[0032] The generation unit may include an update unit that updates the 3D model according to the progress of the design. The update unit updates the 3D model at each step of the design, allowing the progress to be visually confirmed. For example, the update unit generates a simplified 3D model in the early stages of the design and generates a detailed 3D model as the design progresses. The update unit may also update the 3D model step by step according to the progress of the design. For example, a 3D model showing the general structure of a building may be generated in the early stages of the design, and a 3D model showing the detailed layout of the interior may be generated as the design progresses. This makes it easier to manage the progress of the design by updating the 3D model according to the progress of the design. Some or all of the above-described processing in the update unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the update unit may analyze the progress of the design and update the 3D model using an algorithm for updating the 3D model.

[0033] The update unit updates the 3D model at each design step, allowing the progress to be visually confirmed. For example, the update unit generates a simplified 3D model in the early stages of design and generates a detailed 3D model as the design progresses. The update unit can also update the 3D model step by step according to the progress of the design. For example, a 3D model showing the general structure of a building is generated in the early stages of design, and a 3D model showing the detailed layout of the interior is generated as the design progresses. In this way, by updating the 3D model at each design step, the progress can be visually confirmed. Some or all of the above-described processing in the update unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the update unit can analyze the progress of the design and update the 3D model using an algorithm for updating the 3D model.

[0034] The generation unit can use a generation AI to analyze the internal structure and layout of a building and generate a 3D model. The generation AI can use technologies such as deep learning and generative artificial network (GAN) to analyze the internal structure and layout of a building and generate a 3D model. The generation AI can analyze the internal structure and layout of a building in detail based on information such as a floor plan and elevation of the building and generate a 3D model. The generation AI can analyze the internal structure and layout of a building in detail and generate a 3D model. In this way, the generation AI can analyze the internal structure and layout of a building in detail and generate a 3D model. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can analyze the internal structure and layout of a building and generate a 3D model using an algorithm for analyzing the internal structure and layout of a building and generating a 3D model.

[0035] When inputting a drawing, the input unit can analyze the user's past input history and select an appropriate input method. For example, the input unit can automatically display as candidates the drawing formats that the user has frequently input in the past. The input unit can also preferentially suggest input methods (handwriting, scanning, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest the drawing format to be used during a specific time period based on the user's past input history. In this way, the optimal input method can be provided by analyzing the user's past input history. Some or all of the above-mentioned processing in the input unit may be performed using, or without, a generation AI. For example, the input unit can input the user's past input history into the generation AI and have the generation AI select the optimal input method.

[0036] The input unit can filter drawings based on the user's current project or area of ​​interest when inputting the drawings. For example, the input unit can display only drawings related to the user's current project. The input unit can also prioritize displaying highly relevant drawings based on the user's area of ​​interest. Furthermore, the input unit can automatically filter necessary drawings according to the progress of the user's project. This makes it possible to provide highly relevant drawings by filtering based on the user's current project or area of ​​interest. Some or all of the above-described processing in the input unit can be performed using, or without, a generation AI. For example, the input unit can input the user's current project or area of ​​interest to the generation AI and have the generation AI filter out highly relevant drawings.

[0037] When inputting drawings, the input unit can prioritize inputting highly relevant drawings by taking into account the user's geographical location information. The input unit, for example, can prioritize displaying drawings related to the user's current location. The input unit can also prioritize inputting drawings of nearby buildings based on the user's geographical location information. Furthermore, when the user is in a specific area, the input unit can also prioritize displaying drawings related to that area. In this way, highly relevant drawings can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the input unit may be performed using, or without, a generation AI. For example, the input unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant drawings.

[0038] The input unit can analyze the user's social media activity when inputting drawings and input related drawings. For example, the input unit can prioritize displaying drawings related to buildings shared by the user on social media. The input unit can also suggest drawings of buildings of interest to the user based on the user's social media activity. Furthermore, the input unit can prioritize displaying drawings by architects and designers the user follows on social media. In this way, related drawings can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the input unit can be performed using, or without, a generation AI. For example, the input unit can input the user's social media activity to the generation AI and cause the generation AI to select related drawings.

[0039] When generating a 3D model, the generation unit can adjust the level of detail of the generated 3D model based on the importance of the drawing. For example, the generation unit generates a detailed 3D model based on an important drawing. The generation unit can also generate a simplified 3D model based on a drawing with a lower importance. Furthermore, the generation unit can gradually adjust the level of detail of the generated 3D model according to the importance of the drawing. This enables efficient 3D model generation by adjusting the level of detail of the generated 3D model based on the importance of the drawing. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the importance of the drawing to the generation AI and cause the generation AI to adjust the level of detail of the 3D model to be generated.

[0040] When generating a 3D model, the generation unit can apply different generation algorithms depending on the category of the drawing. For example, the generation unit can apply an algorithm that reproduces detailed internal structures to drawings related to the internal structure of a building. The generation unit can also apply an algorithm that emphasizes appearance to drawings related to the exterior of a building. Furthermore, the generation unit can apply an algorithm that accurately reproduces the layout of facilities to drawings related to the layout of facilities. In this way, by applying different generation algorithms depending on the category of the drawing, an appropriate 3D model can be generated. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the category of the drawing to the generation AI and cause the generation AI to select an appropriate generation algorithm.

[0041] When generating a 3D model, the generation unit can determine the generation priority based on the submission date of the drawing. For example, the generation unit prioritizes the generation of drawings with an upcoming submission deadline as a 3D model. The generation unit can also postpone the generation of drawings with a more distant submission deadline. Furthermore, the generation unit can gradually adjust the priority of the 3D models to be generated depending on the submission date. This enables efficient 3D model generation by determining the generation priority based on the submission date of the drawing. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the submission date of the drawing into the generation AI and have the generation AI determine the priority of the 3D models to be generated.

[0042] When generating a 3D model, the generation unit can adjust the order of generation based on the relevance of the drawings. For example, the generation unit prioritizes generating highly relevant drawings as 3D models. The generation unit can also postpone generating less relevant drawings. Furthermore, the generation unit can gradually adjust the order of 3D models to be generated according to the relevance of the drawings. This enables efficient 3D model generation by adjusting the order of generation based on the relevance of the drawings. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the relevance of the drawings to the generation AI and cause the generation AI to adjust the order of the 3D models to be generated.

[0043] When displaying a 3D model, the display unit can select an appropriate display method by referring to the user's past operation history. For example, the display unit can preferentially provide a display method that the user has used in the past. The display unit can also suggest an optimal display method based on the user's past operation history. Furthermore, the display unit can automatically set a display method that the user has previously preferred. This makes it possible to provide an optimal display method by referring to the user's past operation history. Some or all of the above-described processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input the user's past operation history into the generation AI and have the generation AI select an optimal display method.

[0044] When displaying a 3D model, the display unit can select an appropriate display method by taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a PC, the display unit can also provide a high-resolution display method. This makes it possible to provide an optimal display method by taking into account the user's device information. Some or all of the above-described processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input the user's device information into the generation AI and cause the generation AI to select the optimal display method.

[0045] When displaying a 3D model, the display unit can select an appropriate display method by taking into account the user's geographical location information. For example, the display unit can prioritize displaying parts related to the user's current location. The display unit can also prioritize displaying 3D models of nearby buildings based on the user's geographical location information. Furthermore, if the user is in a specific area, the display unit can also prioritize displaying parts related to that area. This makes it possible to provide an optimal display method by taking the user's geographical location information into consideration. Some or all of the above-described processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal display method.

[0046] The display unit can analyze the user's social media activity and display related information when displaying the 3D model. For example, the display unit can prioritize displaying parts related to buildings shared by the user on social media. The display unit can also suggest 3D models of buildings of interest to the user based on the user's social media activity. Furthermore, the display unit can prioritize displaying 3D models of architects and designers the user follows on social media. This makes it possible to provide related information by analyzing the user's social media activity. Some or all of the above-described processing in the display unit can be performed using, or without, a generation AI. For example, the display unit can input the user's social media activity into the generation AI and cause the generation AI to display related information.

[0047] When updating a 3D model, the update unit can analyze the user's past update history and select an appropriate update method. For example, the update unit can prioritize and provide update methods that the user has used in the past. The update unit can also suggest an optimal update method based on the user's past update history. Furthermore, the update unit can automatically set an update method that the user has previously preferred. In this way, the optimal update method can be provided by analyzing the user's past update history. Some or all of the above-described processing in the update unit may be performed using, or without, a generation AI. For example, the update unit can input the user's past update history into the generation AI and have the generation AI select an optimal update method.

[0048] When updating the 3D model, the update unit can perform filtering based on the user's current project or areas of interest. For example, the update unit updates only parts related to the user's current project. The update unit can also prioritize updating highly relevant parts based on the user's areas of interest. Furthermore, the update unit can automatically filter necessary parts according to the progress of the user's project. This makes it possible to update highly relevant parts by filtering based on the user's current project or areas of interest. Some or all of the above-described processing in the update unit may be performed using, or without, a generation AI. For example, the update unit can input the user's current project or areas of interest into the generation AI and cause the generation AI to filter out highly relevant parts.

[0049] When updating a 3D model, the update unit can prioritize updating highly relevant models by taking into account the user's geographical location information. For example, the update unit prioritizes updating parts related to the user's current location. The update unit can also prioritize updating 3D models of nearby buildings based on the user's geographical location information. Furthermore, if the user is in a specific area, the update unit can also prioritize updating parts related to that area. This allows highly relevant models to be updated preferentially by taking the user's geographical location information into account. Some or all of the above-described processing in the update unit may be performed using, or without, the generation AI. For example, the update unit can input the user's geographical location information to the generation AI and cause the generation AI to update highly relevant models.

[0050] When updating a 3D model, the update unit can analyze the user's social media activity and update the relevant model. For example, the update unit can prioritize updating parts related to buildings shared by the user on social media. The update unit can also suggest 3D models of buildings of interest to the user based on the user's social media activity. Furthermore, the update unit can prioritize updating 3D models of architects and designers the user follows on social media. This makes it possible to provide relevant models by analyzing the user's social media activity. Some or all of the above-described processing in the update unit can be performed using, or without, a generation AI. For example, the update unit can input the user's social media activity into the generation AI and cause the generation AI to update the relevant model.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The input unit can analyze data from the user's past design projects and automatically suggest drawings for similar projects. For example, the input unit can prioritize and display drawings related to the current project based on data from buildings the user has designed in the past. The input unit can also analyze the format and style of drawings used in past projects and suggest drawings suitable for the current project. Furthermore, the input unit can analyze the design progress and problems in past projects and provide drawings to support the design progress of the current project. This enables efficient drawing input by utilizing data from the user's past design projects.

[0053] The generation unit can learn the user's design style and generate a 3D model that matches the user's preferences. For example, the generation unit can analyze data on 3D models created by the user in the past to identify the user's design style. The generation unit can also adjust the details and decorations of the 3D model based on the user's design style. Furthermore, the generation unit can select the color and material of the 3D model to match the user's design style. This can increase user satisfaction by generating a 3D model that matches the user's design style.

[0054] The display unit can customize the display method of the 3D model based on the user's visual preferences. For example, the display unit can set the user's preferred color tone and brightness. The display unit can also automatically select the user's preferred viewpoint and camera angle. Furthermore, the display unit can apply the user's preferred display effects (e.g., shadows, reflections, etc.). This can improve the user's visual satisfaction by providing a display method of the 3D model that matches the user's visual preferences.

[0055] The generation unit may have a feedback function for reflecting the user's design intent when generating a 3D model. For example, the generation unit may provide an interface for the user to provide feedback on a specific portion of the 3D model. The generation unit may also modify a specific portion of the 3D model based on the user's feedback. Furthermore, the generation unit may learn the user's feedback and reflect it when generating future 3D models. This allows the generation of a 3D model that reflects the user's design intent, thereby improving user satisfaction.

[0056] When inputting a drawing, the input unit can prioritize inputting highly relevant drawings by taking into account the user's geographical location information. For example, the input unit can prioritize displaying drawings related to the user's current location. The input unit can also prioritize inputting drawings of nearby buildings based on the user's geographical location information. Furthermore, when the user is in a specific area, the input unit can also prioritize displaying drawings related to that area. In this way, highly relevant drawings can be prioritized by taking into account the user's geographical location information.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The input section inputs the property drawings. Property drawings include architectural drawings, mechanical drawings, electrical drawings, etc. The input section either scans the paper drawings and converts them into digital data, or directly reads drawings provided in digital format. Step 2: The generation unit uses the generation AI to analyze the drawing input by the input unit and generate a 3D model. The generation AI analyzes the drawing using technologies such as deep learning and generative artificial network (GAN), and generates a 3D model by analyzing the internal structure and layout of the building in detail. Step 3: The display unit displays the 3D model generated by the generation unit. The display unit displays the 3D model on devices such as PCs and tablets, providing an interface for visually checking it. This makes it easier for designers to intuitively understand the interior of the building, reducing the difficulty of the design. The display unit also has an update unit that updates the 3D model according to the progress of the design, updating the 3D model at each step of the design and allowing the progress to be visually checked.

[0059] (Example 2) A design support system according to an embodiment of the present invention converts building drawings into 3D models and visually confirms them, thereby reducing the difficulty of design. This design support system allows designers to intuitively understand the interior of a building by inputting the building drawings, analyzing the drawings using a generation AI, and displaying the generated 3D model. For example, when determining the optimal location for installing a base station, a designer can use the 3D model to view the building's interior while proceeding with the design. This improves design accuracy and reduces labor costs. Even for designers who are not accustomed to reading blueprints, using a 3D model makes visual understanding easier and reduces the hurdles to work with. Furthermore, the generated 3D model can also be used to check the progress of the design. For example, the 3D model can be updated at each design step, allowing the progress to be visually confirmed. This simplifies design progress management and allows for more efficient design. This system reduces in-house labor costs and operational hurdles, allowing anyone to efficiently design base stations. The design support system converts building drawings into 3D models and visually confirms them, thereby reducing the difficulty of design.

[0060] A design support system according to an embodiment includes an input unit, a generation unit, and a display unit. The input unit inputs drawings of a property. Examples of drawings of a property include, but are not limited to, architectural drawings, mechanical drawings, and electrical drawings. The input unit, for example, scans paper drawings and converts them into digital data. The input unit can also directly read drawings provided in digital format. The generation unit uses a generative AI to analyze the drawings input by the input unit and generate a 3D model. The generative AI analyzes the drawings using technologies such as deep learning and generative adversarial networks (GANs) to generate the 3D model. The generation unit, for example, analyzes the internal structure and layout of a building in detail and generates the 3D model. The display unit displays the 3D model generated by the generation unit. The display unit displays the 3D model on a device such as a PC or tablet. The display unit provides an interface for visually viewing the generated 3D model. This allows designers to intuitively understand the interior of a building, reducing the difficulty of designing. Furthermore, the display unit includes an update unit that updates the 3D model according to the design progress. The update unit updates the 3D model at each design step, allowing the progress to be visually confirmed. This makes it easier to manage the design progress and allows the design to proceed efficiently. As a result, the design support system according to the embodiment can convert the property drawings into a 3D model and visually confirm it, thereby reducing the difficulty of the design.

[0061] The generation unit can analyze the drawings using a generative AI and generate a 3D model. The generative AI can analyze the drawings using technologies such as deep learning or generative artificial network (GAN) to generate the 3D model. The generative AI can analyze the internal structure and layout of a building in detail and generate a 3D model. The generative AI can analyze the internal structure and layout of a building based on information such as a floor plan and elevation of the building to generate a 3D model. The generative AI can analyze the internal structure and layout of a building in detail and generate a 3D model. This allows the use of generative AI to efficiently analyze the drawings and generate a 3D model. Some or all of the above-described processing in the generation unit can be performed using, for example, the generative AI, or can be performed without using the generative AI. For example, the generation unit can analyze the drawings and generate a 3D model using an algorithm for analyzing drawings and generating a 3D model.

[0062] The display unit can display the generated 3D model on a PC or tablet device. The display unit displays the 3D model on a device such as a PC or tablet. The display unit provides an interface for visually checking the generated 3D model. The display unit can display the 3D model on a PC screen, for example, to make it easier for designers to intuitively understand the interior of a building. The display unit can also display the 3D model on a tablet screen to make it easier for designers to intuitively understand the interior of a building. This makes it easier to check the design by displaying the generated 3D model on various devices. Some or all of the above-mentioned processing in the display unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the display unit can display the 3D model using software for displaying the generated 3D model.

[0063] The generation unit may include an update unit that updates the 3D model according to the progress of the design. The update unit updates the 3D model at each step of the design, allowing the progress to be visually confirmed. For example, the update unit generates a simplified 3D model in the early stages of the design and generates a detailed 3D model as the design progresses. The update unit may also update the 3D model step by step according to the progress of the design. For example, a 3D model showing the general structure of a building may be generated in the early stages of the design, and a 3D model showing the detailed layout of the interior may be generated as the design progresses. This makes it easier to manage the progress of the design by updating the 3D model according to the progress of the design. Some or all of the above-described processing in the update unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the update unit may analyze the progress of the design and update the 3D model using an algorithm for updating the 3D model.

[0064] The update unit updates the 3D model at each design step, allowing the progress to be visually confirmed. For example, the update unit generates a simplified 3D model in the early stages of design and generates a detailed 3D model as the design progresses. The update unit can also update the 3D model step by step according to the progress of the design. For example, a 3D model showing the general structure of a building is generated in the early stages of design, and a 3D model showing the detailed layout of the interior is generated as the design progresses. In this way, by updating the 3D model at each design step, the progress can be visually confirmed. Some or all of the above-described processing in the update unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the update unit can analyze the progress of the design and update the 3D model using an algorithm for updating the 3D model.

[0065] The generation unit can use a generation AI to analyze the internal structure and layout of a building and generate a 3D model. The generation AI can use technologies such as deep learning and generative artificial network (GAN) to analyze the internal structure and layout of a building and generate a 3D model. The generation AI can analyze the internal structure and layout of a building in detail based on information such as a floor plan and elevation of the building and generate a 3D model. The generation AI can analyze the internal structure and layout of a building in detail and generate a 3D model. In this way, the generation AI can analyze the internal structure and layout of a building in detail and generate a 3D model. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can analyze the internal structure and layout of a building and generate a 3D model using an algorithm for analyzing the internal structure and layout of a building and generating a 3D model.

[0066] The input unit can detect the user's emotion and adjust the timing of drawing input based on the detected emotion. For example, the input unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the input unit calculates an emotion score based on changes in facial expression and adjusts the timing of drawing input. The input unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the input unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the timing of drawing input. The input unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the input unit calculates an emotion score based on heart rate fluctuations and adjusts the timing of drawing input. This adjusts the timing of drawing input according to the user's emotion, thereby reducing the burden on the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the input unit may input image data of a user taken by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0067] When inputting a drawing, the input unit can analyze the user's past input history and select an appropriate input method. For example, the input unit can automatically display as candidates the drawing formats that the user has frequently input in the past. The input unit can also preferentially suggest input methods (handwriting, scanning, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest the drawing format to be used during a specific time period based on the user's past input history. In this way, the optimal input method can be provided by analyzing the user's past input history. Some or all of the above-mentioned processing in the input unit may be performed using, or without, a generation AI. For example, the input unit can input the user's past input history into the generation AI and have the generation AI select the optimal input method.

[0068] The input unit can filter drawings based on the user's current project or area of ​​interest when inputting the drawings. For example, the input unit can display only drawings related to the user's current project. The input unit can also prioritize displaying highly relevant drawings based on the user's area of ​​interest. Furthermore, the input unit can automatically filter necessary drawings according to the progress of the user's project. This makes it possible to provide highly relevant drawings by filtering based on the user's current project or area of ​​interest. Some or all of the above-described processing in the input unit can be performed using, or without, a generation AI. For example, the input unit can input the user's current project or area of ​​interest to the generation AI and have the generation AI filter out highly relevant drawings.

[0069] The input unit can estimate the user's emotions and determine the priority of drawings to be input based on the estimated user emotions. For example, if the user is feeling stressed, the input unit can cause the user to start inputting simple drawings. Furthermore, if the user is relaxed, the input unit can also cause the user to input complex drawings first. Furthermore, if the user is in a hurry, the input unit can also cause the user to input important drawings first. This enables efficient input by determining the priority of drawings according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the input unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of drawings.

[0070] When inputting drawings, the input unit can prioritize inputting highly relevant drawings by taking into account the user's geographical location information. The input unit, for example, can prioritize displaying drawings related to the user's current location. The input unit can also prioritize inputting drawings of nearby buildings based on the user's geographical location information. Furthermore, when the user is in a specific area, the input unit can also prioritize displaying drawings related to that area. In this way, highly relevant drawings can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the input unit may be performed using, or without, a generation AI. For example, the input unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant drawings.

[0071] The input unit can analyze the user's social media activity when inputting drawings and input related drawings. For example, the input unit can prioritize displaying drawings related to buildings shared by the user on social media. The input unit can also suggest drawings of buildings of interest to the user based on the user's social media activity. Furthermore, the input unit can prioritize displaying drawings by architects and designers the user follows on social media. In this way, related drawings can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the input unit can be performed using, or without, a generation AI. For example, the input unit can input the user's social media activity to the generation AI and cause the generation AI to select related drawings.

[0072] The generation unit can estimate the user's emotions and adjust the generation method of the 3D model based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a detailed 3D model. If the user is in a hurry, the generation unit can also generate a simplified 3D model. Furthermore, if the user is excited, the generation unit can generate a 3D model with visually appealing effects. By adjusting the generation method of the 3D model according to the user's emotions, a 3D model that meets the user's needs can be generated. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the generation method of the 3D model.

[0073] When generating a 3D model, the generation unit can adjust the level of detail of the generated 3D model based on the importance of the drawing. For example, the generation unit generates a detailed 3D model based on an important drawing. The generation unit can also generate a simplified 3D model based on a drawing with a lower importance. Furthermore, the generation unit can gradually adjust the level of detail of the generated 3D model according to the importance of the drawing. This enables efficient 3D model generation by adjusting the level of detail of the generated 3D model based on the importance of the drawing. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the importance of the drawing to the generation AI and cause the generation AI to adjust the level of detail of the 3D model to be generated.

[0074] When generating a 3D model, the generation unit can apply different generation algorithms depending on the category of the drawing. For example, the generation unit can apply an algorithm that reproduces detailed internal structures to drawings related to the internal structure of a building. The generation unit can also apply an algorithm that emphasizes appearance to drawings related to the exterior of a building. Furthermore, the generation unit can apply an algorithm that accurately reproduces the layout of facilities to drawings related to the layout of facilities. In this way, by applying different generation algorithms depending on the category of the drawing, an appropriate 3D model can be generated. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the category of the drawing to the generation AI and cause the generation AI to select an appropriate generation algorithm.

[0075] The generation unit can estimate the user's emotions and adjust the generation speed of the 3D model based on the estimated user's emotions. For example, if the user is in a hurry, the generation unit can increase the generation speed to quickly generate a 3D model. Furthermore, if the user is relaxed, the generation unit can also slow down the generation speed to generate a detailed 3D model. Furthermore, if the user is excited, the generation unit can adjust the generation speed to generate a visually appealing 3D model. This allows the generation speed of the 3D model to be adjusted according to the user's emotions, thereby providing a generation speed that meets the user's needs. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the generation speed of the 3D model.

[0076] When generating a 3D model, the generation unit can determine the generation priority based on the submission date of the drawing. For example, the generation unit prioritizes the generation of drawings with an upcoming submission deadline as a 3D model. The generation unit can also postpone the generation of drawings with a more distant submission deadline. Furthermore, the generation unit can gradually adjust the priority of the 3D models to be generated depending on the submission date. This enables efficient 3D model generation by determining the generation priority based on the submission date of the drawing. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the submission date of the drawing into the generation AI and have the generation AI determine the priority of the 3D models to be generated.

[0077] When generating a 3D model, the generation unit can adjust the order of generation based on the relevance of the drawings. For example, the generation unit prioritizes generating highly relevant drawings as 3D models. The generation unit can also postpone generating less relevant drawings. Furthermore, the generation unit can gradually adjust the order of 3D models to be generated according to the relevance of the drawings. This enables efficient 3D model generation by adjusting the order of generation based on the relevance of the drawings. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the relevance of the drawings to the generation AI and cause the generation AI to adjust the order of the 3D models to be generated.

[0078] The display unit can detect the user's emotions and adjust the display method of the 3D model based on the detected user's emotions. For example, if the user is nervous, the display unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the display unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the display unit can provide a display method that focuses on the main points. This allows the display method of the 3D model to be adjusted according to the user's emotions, thereby providing a display method that meets the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit can be performed using, for example, the generation AI. For example, the display unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the 3D model.

[0079] When displaying a 3D model, the display unit can select an appropriate display method by referring to the user's past operation history. For example, the display unit can preferentially provide a display method that the user has used in the past. The display unit can also suggest an optimal display method based on the user's past operation history. Furthermore, the display unit can automatically set a display method that the user has previously preferred. This makes it possible to provide an optimal display method by referring to the user's past operation history. Some or all of the above-described processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input the user's past operation history into the generation AI and have the generation AI select an optimal display method.

[0080] When displaying a 3D model, the display unit can select an appropriate display method by taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a PC, the display unit can also provide a high-resolution display method. This makes it possible to provide an optimal display method by taking into account the user's device information. Some or all of the above-described processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input the user's device information into the generation AI and cause the generation AI to select the optimal display method.

[0081] The display unit can detect the user's emotions and adjust the display order of the 3D models based on the detected user emotions. For example, if the user is nervous, the display unit can display important parts first. Furthermore, if the user is relaxed, the display unit can display the entire content in an orderly manner. Furthermore, if the user is in a hurry, the display unit can display the most important parts first. This allows the display order of the 3D models to be adjusted according to the user's emotions, thereby providing a display order that meets the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit can be performed using, for example, the generation AI. For example, the display unit can input the user's emotion data into the generation AI and have the generation AI adjust the display order of the 3D models.

[0082] When displaying a 3D model, the display unit can select an appropriate display method by taking into account the user's geographical location information. For example, the display unit can prioritize displaying parts related to the user's current location. The display unit can also prioritize displaying 3D models of nearby buildings based on the user's geographical location information. Furthermore, if the user is in a specific area, the display unit can also prioritize displaying parts related to that area. This makes it possible to provide an optimal display method by taking the user's geographical location information into consideration. Some or all of the above-described processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal display method.

[0083] The display unit can analyze the user's social media activity and display related information when displaying the 3D model. For example, the display unit can prioritize displaying parts related to buildings shared by the user on social media. The display unit can also suggest 3D models of buildings of interest to the user based on the user's social media activity. Furthermore, the display unit can prioritize displaying 3D models of architects and designers the user follows on social media. This makes it possible to provide related information by analyzing the user's social media activity. Some or all of the above-described processing in the display unit can be performed using, or without, a generation AI. For example, the display unit can input the user's social media activity into the generation AI and cause the generation AI to display related information.

[0084] The update unit can detect the user's emotions and adjust the update timing of the 3D model based on the detected user's emotions. For example, if the user is relaxed, the update unit can perform a detailed update. Furthermore, if the user is in a hurry, the update unit can also perform a simplified update. Furthermore, if the user is excited, the update unit can also perform an update that adds a visually appealing effect. This allows the update timing of the 3D model to be adjusted according to the user's emotions, thereby providing update timing that meets the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the update unit can be performed using, for example, the generation AI, or without the generation AI. For example, the update unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the update timing of the 3D model.

[0085] When updating a 3D model, the update unit can analyze the user's past update history and select an appropriate update method. For example, the update unit can prioritize and provide update methods that the user has used in the past. The update unit can also suggest an optimal update method based on the user's past update history. Furthermore, the update unit can automatically set an update method that the user has previously preferred. In this way, the optimal update method can be provided by analyzing the user's past update history. Some or all of the above-described processing in the update unit may be performed using, or without, a generation AI. For example, the update unit can input the user's past update history into the generation AI and have the generation AI select an optimal update method.

[0086] When updating the 3D model, the update unit can perform filtering based on the user's current project or areas of interest. For example, the update unit updates only parts related to the user's current project. The update unit can also prioritize updating highly relevant parts based on the user's areas of interest. Furthermore, the update unit can automatically filter necessary parts according to the progress of the user's project. This makes it possible to update highly relevant parts by filtering based on the user's current project or areas of interest. Some or all of the above-described processing in the update unit may be performed using, or without, a generation AI. For example, the update unit can input the user's current project or areas of interest into the generation AI and cause the generation AI to filter out highly relevant parts.

[0087] The update unit can detect the user's emotions and determine the priority of the 3D models to be updated based on the detected user's emotions. For example, if the user is feeling stressed, the update unit can start updating from simple parts. Furthermore, if the user is relaxed, the update unit can prioritize updating complex parts. Furthermore, if the user is in a hurry, the update unit can prioritize updating important parts. This enables efficient updating by determining the priority of the 3D models according to the user's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the update unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the update unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of the 3D models to be updated.

[0088] When updating a 3D model, the update unit can prioritize updating highly relevant models by taking into account the user's geographical location information. For example, the update unit prioritizes updating parts related to the user's current location. The update unit can also prioritize updating 3D models of nearby buildings based on the user's geographical location information. Furthermore, if the user is in a specific area, the update unit can also prioritize updating parts related to that area. This allows highly relevant models to be updated preferentially by taking the user's geographical location information into account. Some or all of the above-described processing in the update unit may be performed using, or without, the generation AI. For example, the update unit can input the user's geographical location information to the generation AI and cause the generation AI to update highly relevant models.

[0089] When updating a 3D model, the update unit can analyze the user's social media activity and update the relevant model. For example, the update unit can prioritize updating parts related to buildings shared by the user on social media. The update unit can also suggest 3D models of buildings of interest to the user based on the user's social media activity. Furthermore, the update unit can prioritize updating 3D models of architects and designers the user follows on social media. This makes it possible to provide relevant models by analyzing the user's social media activity. Some or all of the above-described processing in the update unit can be performed using, or without, a generation AI. For example, the update unit can input the user's social media activity into the generation AI and cause the generation AI to update the relevant model. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, generation unit, display unit, and update unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by the reception device 38 of the smart device 14, which scans paper drawings and converts them into digital data. The generation unit is realized by the specific processing unit 290 of the data processing device 12, which analyzes the drawings using a generation AI and generates a 3D model. The display unit is realized by the output device 40 of the smart device 14, which provides an interface for visually checking the generated 3D model. The update unit is realized by the specific processing unit 290 of the data processing device 12, which updates the 3D model at each design step, allowing the progress to be visually confirmed. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, generation unit, display unit, and update unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the smart glasses 214 and can read a drawing using voice input. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the drawing using a generation AI to generate a 3D model. The display unit is realized by the speaker 240 of the smart glasses 214 and provides an interface for visually confirming the generated 3D model. The update unit is realized by the specific processing unit 290 of the data processing device 12 and updates the 3D model at each step of the design, allowing the progress to be visually confirmed. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, generation unit, display unit, and update unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the headset-type terminal 314 and can read a drawing using voice input. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the drawing using a generation AI to generate a 3D model. The display unit is realized by the display 343 of the headset-type terminal 314 and provides an interface for visually checking the generated 3D model. The update unit is realized by the specific processing unit 290 of the data processing device 12 and updates the 3D model at each step of the design, allowing the progress to be visually checked. === Hard Collateral 1-4 === Each of the multiple elements, including the input unit, generation unit, display unit, and update unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the robot 414 and can read a drawing using voice input. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the drawing using a generation AI to generate a 3D model. The display unit is realized by the speaker 240 of the robot 414 and provides an interface for visually checking the generated 3D model. The update unit is realized by the specific processing unit 290 of the data processing device 12 and updates the 3D model at each step of the design, allowing the progress to be visually checked.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The input unit can analyze data from the user's past design projects and automatically suggest drawings for similar projects. For example, the input unit can prioritize and display drawings related to the current project based on data from buildings the user has designed in the past. The input unit can also analyze the format and style of drawings used in past projects and suggest drawings suitable for the current project. Furthermore, the input unit can analyze the design progress and problems in past projects and provide drawings to support the design progress of the current project. This enables efficient drawing input by utilizing data from the user's past design projects.

[0092] The generation unit can learn the user's design style and generate a 3D model that matches the user's preferences. For example, the generation unit can analyze data on 3D models created by the user in the past to identify the user's design style. The generation unit can also adjust the details and decorations of the 3D model based on the user's design style. Furthermore, the generation unit can select the color and material of the 3D model to match the user's design style. This can increase user satisfaction by generating a 3D model that matches the user's design style.

[0093] The display unit can customize the display method of the 3D model based on the user's visual preferences. For example, the display unit can set the user's preferred color tone and brightness. The display unit can also automatically select the user's preferred viewpoint and camera angle. Furthermore, the display unit can apply the user's preferred display effects (e.g., shadows, reflections, etc.). This can improve the user's visual satisfaction by providing a display method of the 3D model that matches the user's visual preferences.

[0094] The generation unit may have a feedback function for reflecting the user's design intent when generating a 3D model. For example, the generation unit may provide an interface for the user to provide feedback on a specific portion of the 3D model. The generation unit may also modify a specific portion of the 3D model based on the user's feedback. Furthermore, the generation unit may learn the user's feedback and reflect it when generating future 3D models. This allows the generation of a 3D model that reflects the user's design intent, thereby improving user satisfaction.

[0095] The display unit can estimate the user's emotions and adjust the display method of the 3D model based on the estimated user's emotions. For example, if the user is nervous, the display unit can provide a simple, highly visible display method. If the user is relaxed, the display unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the display unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method of the 3D model according to the user's emotions, it is possible to provide a display method that meets the user's needs.

[0096] The generation unit can estimate the user's emotions and adjust the generation speed of the 3D model based on the estimated user's emotions. For example, if the user is in a hurry, the generation unit can increase the generation speed to quickly generate a 3D model. Alternatively, if the user is relaxed, the generation unit can decrease the generation speed to generate a detailed 3D model. Furthermore, if the user is excited, the generation unit can adjust the generation speed to generate a visually appealing 3D model. In this way, by adjusting the generation speed of the 3D model according to the user's emotions, it is possible to provide a generation speed that meets the user's needs.

[0097] The input unit can detect the user's emotion and adjust the timing of drawing input based on the detected user emotion. For example, the input unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the input unit calculates an emotion score based on changes in facial expression and adjusts the timing of drawing input. The input unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the input unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the timing of drawing input. The input unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the input unit calculates an emotion score based on fluctuations in heart rate and adjusts the timing of drawing input. This can reduce the burden on the user by adjusting the timing of drawing input according to the user's emotion.

[0098] The generation unit can estimate the user's emotions and adjust the method for generating the 3D model based on the estimated user's emotions. For example, the generation unit can generate a detailed 3D model when the user is relaxed. The generation unit can also generate a simplified 3D model when the user is in a hurry. Furthermore, the generation unit can generate a 3D model with visually appealing effects when the user is excited. In this way, by adjusting the method for generating the 3D model according to the user's emotions, it is possible to generate a 3D model that meets the user's needs.

[0099] The update unit can detect the user's emotions and adjust the update timing of the 3D model based on the detected user's emotions. For example, the update unit can perform a detailed update when the user is relaxed. Alternatively, the update unit can perform a simplified update when the user is in a hurry. Furthermore, the update unit can perform an update that adds a visually appealing effect when the user is excited. In this way, by adjusting the update timing of the 3D model according to the user's emotions, it is possible to provide update timing that meets the user's needs.

[0100] When inputting a drawing, the input unit can prioritize inputting highly relevant drawings by taking into account the user's geographical location information. For example, the input unit can prioritize displaying drawings related to the user's current location. The input unit can also prioritize inputting drawings of nearby buildings based on the user's geographical location information. Furthermore, when the user is in a specific area, the input unit can also prioritize displaying drawings related to that area. In this way, highly relevant drawings can be prioritized by taking into account the user's geographical location information.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The input section inputs the property drawings. Property drawings include architectural drawings, mechanical drawings, electrical drawings, etc. The input section either scans the paper drawings and converts them into digital data, or directly reads drawings provided in digital format. Step 2: The generation unit uses the generation AI to analyze the drawing input by the input unit and generate a 3D model. The generation AI analyzes the drawing using technologies such as deep learning and generative artificial network (GAN), and generates a 3D model by analyzing the internal structure and layout of the building in detail. Step 3: The display unit displays the 3D model generated by the generation unit. The display unit displays the 3D model on devices such as PCs and tablets, providing an interface for visually checking it. This makes it easier for designers to intuitively understand the interior of the building, reducing the difficulty of the design. The display unit also has an update unit that updates the 3D model according to the progress of the design, updating the 3D model at each step of the design and allowing the progress to be visually checked.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] 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.

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input section for inputting drawings of the property; a generation unit that analyzes the drawing input by the input unit and generates a 3D model; a display unit that displays the 3D model generated by the generation unit; and Equipped with A system characterized by:

2. The generation unit Analyze drawings using generative AI and generate 3D models The system of claim 1 .

3. The display unit View the generated 3D model on your PC or tablet device The system of claim 1 .

4. The generation unit Equipped with an update section that updates the 3D model according to the design progress The system of claim 1 .

5. The update unit Update the 3D model at each step of the design to visually see progress 5. The system of claim 4.

6. The generation unit Generative AI is used to analyze the internal structure and layout of a building and generate a 3D model. The system of claim 1 .

7. The input unit Detects the user's emotions and adjusts the timing of drawing input based on the detected emotions. The system of claim 1 .

8. The input unit When inputting drawings, analyze the user's past input history and select the appropriate input method. The system of claim 1 .

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