System

Abstract

An object of a system according to an embodiment is to quickly generate a design drawing or a drawing of a house in consideration of building conditions and to enable the design drawing or the drawing of the house to be checked by AR.SOLUTION: A system includes a reception unit, a calculation unit, a generation unit, and a display unit. A reception part inputs information on an area map of a land, a photograph of the land, an address, and the number of rooms. The calculation part analyzes the information inputted by the reception part and performs structural calculation on the basis of the construction conditions of the address. The generation unit generates a design drawing and a drawing of a house based on a result of the structural calculation performed by the calculation unit. The display unit generates a completed house that can be checked in AR on the basis of the design drawing generated by the generation unit and the drawing of the house.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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] Conventional technology had the problem of making it difficult to quickly generate blueprints and house drawings that took building conditions into account and then to check them using AR.

[0005] The system of the embodiment aims to quickly generate blueprints and house drawings that take into account building conditions and make them available for viewing using AR. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a calculation unit, a generation unit, and a display unit. The reception unit inputs information such as a land area map, a photo of the land, the address, and the number of rooms. The calculation unit analyzes the information input by the reception unit and performs structural calculations based on the building conditions of the address. The generation unit generates a design drawing and a house plan based on the results of the structural calculations performed by the calculation unit. The display unit generates a completed house that can be viewed in AR based on the design drawing and the house plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly generate blueprints and house drawings that take into account building conditions and make them available for viewing using AR. [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 system according to an embodiment of the present invention allows an AI to input information such as a land area map, a photo of the land, an address, and the number of rooms. The AI ​​then considers the building conditions of the address and generates a blueprint with structural calculations, house plans, and a completed house viewable in AR. For example, a user inputs information such as a land area map, a photo of the land, an address, and the number of rooms. The user simply uploads this information to the system. The AI ​​then analyzes the input information and performs structural calculations taking into account the building conditions of the address. For example, the AI ​​calculates the building's strength and durability, taking into account external conditions such as earthquakes and wind pressure. Based on the results of the structural calculations, the AI ​​generates a blueprint and house plans. The blueprints include detailed descriptions of the building's exterior, interior layout, and the location of each room. Furthermore, based on the generated blueprint and house plans, the AI ​​generates a completed house viewable in AR. The user can view the completed house in AR using a smartphone or tablet. This allows users to easily create blueprints and plans for a house and view the completed house in AR. The system allows users to easily create blueprints and plans for a house and view the completed house in AR. For example, a user who has just purchased land can immediately begin designing their home. Additionally, the ability to visually confirm the design allows for corrections and changes to be made before construction begins, streamlining the construction process and increasing user satisfaction.

[0029] A design support system according to an embodiment includes a reception unit, a calculation unit, a generation unit, and a display unit. The reception unit receives input information such as a land area map, a photo of the land, an address, and the number of rooms. For example, a user can scan and upload the land area map or photo, and input the address and number of rooms. The calculation unit analyzes the information received by the reception unit and performs structural calculations taking into account the building conditions of the address. For example, the calculation unit calculates the strength and durability of the building taking into account external conditions such as earthquakes and wind pressure. Some or all of the above-described processing by the calculation unit may be performed using AI or without AI. The generation unit generates a blueprint and a house plan based on the results of the structural calculations performed by the calculation unit. For example, the blueprint includes detailed descriptions of the building's exterior, interior layout, and the location of each room. Some or all of the above-described processing by the generation unit may be performed using AI or without AI. The display unit generates a completed house that can be viewed in AR based on the blueprint and house plan generated by the generation unit. For example, a user can view the completed house in AR using a smartphone or tablet. Some or all of the above-described processing on the display unit may be performed using AI, or may be performed without AI. As a result, the design support system according to the embodiment can perform structural calculations based on information such as a land area map, a photo of the land, the address, and the number of rooms, and generate blueprints, house drawings, and a completed house that can be viewed using AR.

[0030] The reception unit can input information such as a land area map, a photo of the land, an address, and the number of rooms. Examples of land area maps include, but are not limited to, paper drawings and digital data. Examples of land photos include, but are not limited to, drone photos and smartphone photos. Examples of addresses include, but are not limited to, postal codes, street addresses, and building names. Examples of the number of rooms include, but are not limited to, divisions such as living rooms, bedrooms, and bathrooms. As a result, the reception unit can input information such as a land area map, a photo of the land, an address, and the number of rooms, allowing the system to obtain the necessary information.

[0031] The calculation unit can acquire legal constraints and local conditions from the database and perform structural calculations based on them. Legal constraints include, but are not limited to, building codes and local ordinances. Local conditions include, but are not limited to, climatic conditions and ground conditions. The calculation unit can, for example, acquire local building codes from the database and calculate building strength based on them. The calculation unit can also acquire local wind pressure and earthquake risk from the database and calculate durability based on them. The calculation unit can also acquire local land use regulations from the database and calculate building layout based on them. This allows the calculation unit to perform structural calculations taking legal constraints and local conditions into consideration, enabling safe and appropriate designs. Some or all of the above-mentioned processing in the calculation unit may be performed using AI, or may be performed without AI.

[0032] The generation unit can automatically generate the layout of each room, the exterior of the building, and the interior layout. The layout of each room includes, for example, room size and layout rules, but is not limited to these examples. The exterior of the building includes, for example, exterior wall design and color selection, but is not limited to these examples. The interior layout includes, for example, furniture placement and traffic flow design, but is not limited to these examples. The generation unit, for example, automatically optimizes the layout of each room to generate an efficient layout. The generation unit can also automatically design the exterior of the building to generate a visually attractive appearance. The generation unit can also automatically design the interior layout to generate a usable space. As a result, the generation unit automatically generates the layout of each room, the exterior of the building, and the interior layout, enabling efficient design. Some or all of the above-mentioned processing in the generation unit may be performed using AI or without AI.

[0033] The display unit can display the completed house superimposed on the real landscape using a smartphone or tablet. The smartphone or tablet may include, for example, a compatible OS and required specifications, but is not limited to these examples. The method of displaying the house superimposed on the real landscape may include, for example, a method of using AR technology and display accuracy, but is not limited to these examples. The display unit can display the completed house superimposed on the real landscape, for example, through a smartphone camera. The display unit can also display the completed house superimposed on the real landscape through a tablet camera. The display unit can also display the completed house superimposed on the real landscape using the AR function of the smartphone or tablet. By displaying the completed house superimposed on the real landscape, the user can visually confirm the completed house at the location where the building will actually be built. Some or all of the above-described processing on the display unit may be performed using AI or without AI.

[0034] The display unit may include an operation unit for user operation. Examples of the operation unit include, but are not limited to, a touch screen, a voice command, and the like. The display unit allows the user to operate the display of the generated house using, for example, a touch screen. The display unit may also allow the user to operate the display of the generated house using a voice command. The display unit may also allow the user to operate the display of the generated house using gesture recognition technology. Thus, by including the operation unit in the display unit, the user can operate the display of the generated house. Some or all of the above-described processing in the display unit may be performed using AI or may be performed without using AI.

[0035] The reception unit can analyze the user's past input history and suggest the optimal input method. Examples of the user's past input history include, but are not limited to, log data collection and machine learning analysis. Examples of the optimal input method include, but are not limited to, input speed and accuracy. For example, the reception unit automatically displays land area maps and addresses frequently input by the user in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest information to be used during a specific time period based on the user's past input history. By analyzing the user's past input history, the reception unit can suggest the optimal input method and streamline input work. Some or all of the above-described processing by the reception unit may be performed using AI or without AI.

[0036] The reception unit can automatically complete the input content based on the user's current project and areas of interest during input. Examples of current projects include, but are not limited to, data from a project management tool and the user's input. Examples of areas of interest include, but are not limited to, past search history and social media activity. For example, the reception unit can automatically complete the area map and address of related land based on information about projects the user has previously completed. The reception unit can also suggest an appropriate number of rooms and layout based on the architectural style the user is interested in. The reception unit can also analyze the user's past project history and automatically complete the optimal input content. This auto-completion of input content based on the user's current project and areas of interest streamlines the input process. Some or all of the above-described processing by the reception unit may be performed using AI or without AI.

[0037] The reception unit can select the optimal input means depending on the user's input method when inputting data. Examples of input methods include, but are not limited to, voice input, text input, and image input. Examples of optimal input means include, but are not limited to, input accuracy and user convenience. For example, if the user selects voice input, the reception unit inputs a land area map or address using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also provide an interface that supports keyboard input. Furthermore, if the user selects image input, the reception unit can analyze land photos and maps using image recognition technology. This allows the optimal input means to be selected depending on the user's input method, thereby streamlining input work. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI.

[0038] The reception unit can prioritize inputting highly relevant information by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. Examples of highly relevant information include, but are not limited to, geographically nearby information and related topics. For example, the reception unit can prioritize inputting information about land close to the user's current location. Furthermore, if the user is interested in a specific area, the reception unit can also prioritize inputting the building conditions of that area. Furthermore, the reception unit can prioritize inputting area maps and addresses of related land based on the user's geographical location information. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI or without AI.

[0039] The reception unit can analyze the user's social media activity at the time of input and automatically input related information. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. Related information includes, for example, but is not limited to, the content of past posts and reactions from followers. For example, the reception unit automatically inputs photos of land shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and automatically input related addresses and number of rooms. The reception unit can also automatically input related land information by referring to the activity of the user's friends on social media. In this way, related information can be automatically input by analyzing the user's social media activity. Some or all of the above-mentioned processing by the reception unit may be performed using AI or without AI.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting data. Examples of past feedback include, but are not limited to, survey results and user comments. Examples of customizing the input method include, but are not limited to, changing the input interface and adding an input means. The reception unit provides an optimal input interface based on, for example, feedback provided by the user in the past. The reception unit can also improve the input procedure by reflecting the user's past feedback. The reception unit can also enhance the auto-completion function for input content based on the user's past feedback. By reflecting the user's past feedback, the input method can be customized, improving the efficiency of input work. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without using AI.

[0041] During calculation, the calculation unit can acquire legal constraints and local conditions from a database and perform calculations based on them. Legal constraints include, but are not limited to, building standards laws and local ordinances. Local conditions include, but are not limited to, climatic conditions and ground conditions. The calculation unit, for example, acquires local building standards laws from a database and calculates building strength based on them. The calculation unit can also acquire local wind pressure and earthquake risk from a database and calculate durability based on them. The calculation unit can also acquire local land use regulations from a database and calculate building layout based on them. This allows appropriate calculations to be performed by taking legal constraints and local conditions into consideration. Some or all of the above-mentioned processing in the calculation unit may be performed using AI or without AI.

[0042] During calculation, the calculation unit can calculate the strength and durability of a building by taking into account external conditions such as earthquakes and wind pressure. External conditions such as earthquakes and wind pressure include, but are not limited to, weather data and earthquake risk assessments. Building strength and durability include, but are not limited to, structural analysis software and material properties. For example, the calculation unit calculates the earthquake resistance of a building by taking into account earthquake risk. The calculation unit can also calculate the wind resistance of a building by taking into account wind pressure risk. The calculation unit can also calculate the overall strength and durability of a building by taking into account both earthquakes and wind pressure. This makes it possible to design safe and durable buildings by taking into account external conditions such as earthquakes and wind pressure. Some or all of the above-mentioned processing in the calculation unit may be performed using AI, or may be performed without using AI.

[0043] The calculation unit can improve the accuracy of calculations by referring to the user's past design data during calculations. Past design data includes, but is not limited to, design drawings, calculation results, etc. Calculation accuracy includes, but is not limited to, data accuracy, algorithm improvements, etc. The calculation unit improves the accuracy of calculations, for example, based on design data created by the user in the past. The calculation unit can also analyze the user's past design data and propose an optimal calculation method. The calculation unit can also minimize calculation errors by referring to the user's past design data. As a result, the calculation accuracy is improved by referring to the user's past design data. Some or all of the above-described processing in the calculation unit may be performed using AI or without AI.

[0044] During calculation, the calculation unit can determine the priority of calculations based on the submission time of the ideas. The submission time of the ideas includes, but is not limited to, for example, the submission date and time, deadline, etc. The calculation priority includes, but is not limited to, for example, the submission deadline, importance, etc. For example, if a user is in a hurry, the calculation unit can prioritize ideas with upcoming submission times. Furthermore, if a user has time, the calculation unit can also prioritize ideas with more distant submission times. Furthermore, the calculation unit can automatically adjust the priority of calculations based on the submission time. Thus, by determining the priority of calculations based on the submission time of ideas, calculations can be performed to meet the submission deadline. Some or all of the above-described processing in the calculation unit may be performed using AI, or may be performed without AI.

[0045] During calculation, the calculation unit can adjust the order of calculation based on the relevance of ideas. Examples of the relevance of ideas include, but are not limited to, commonality of themes and technical relevance. Examples of the order of calculation include, but are not limited to, descending order of relevance or descending order of importance. For example, the calculation unit prioritizes calculation of highly relevant ideas. The calculation unit can also postpone calculation of less relevant ideas. The calculation unit can also automatically adjust the order of calculation based on the relevance of ideas. In this way, by adjusting the order of calculation based on the relevance of ideas, highly relevant ideas can be prioritized in calculation. Some or all of the above-described processing in the calculation unit may be performed using AI or without AI.

[0046] The calculation unit can adjust the level of detail of the calculation according to the user's level of expertise during calculation. Examples of the level of expertise include, but are not limited to, whether the user has a qualification or years of experience. Examples of the level of detail of the calculation include, but are not limited to, a detailed analysis or a simple analysis. For example, the calculation unit provides detailed calculation results when the user has expertise. Alternatively, the calculation unit can provide simple calculation results when the user does not have expertise. The calculation unit can also automatically adjust the level of detail of the calculation based on the user's level of expertise. By adjusting the level of detail of the calculation according to the user's level of expertise, calculation results that are easy for the user to understand can be provided. Some or all of the above-described processing in the calculation unit may be performed using AI or without AI.

[0047] During generation, the generation unit can automatically generate the layout of each room, the exterior of the building, and the interior layout. The layout of each room includes, for example, room size and layout rules, but is not limited to these examples. The exterior of the building includes, for example, exterior wall design and color selection, but is not limited to these examples. The interior layout includes, for example, furniture placement and traffic flow design, but is not limited to these examples. The generation unit, for example, automatically optimizes the layout of each room to generate an efficient layout. The generation unit can also automatically design the exterior of the building to generate a visually attractive appearance. The generation unit can also automatically design the interior layout to generate a usable space. This enables efficient design by automatically generating the layout of each room, the exterior of the building, and the interior layout. Some or all of the above-mentioned processing in the generation unit may be performed using AI or without AI.

[0048] During generation, the generation unit can improve the accuracy of generation by referring to the user's past design data. Past design data includes, for example, design drawings, calculation results, etc., but is not limited to these examples. Generation accuracy includes, for example, data accuracy, algorithm improvements, etc., but is not limited to these examples. The generation unit improves the accuracy of generation, for example, based on design data created by the user in the past. The generation unit can also analyze the user's past design data and propose an optimal generation method. The generation unit can also minimize generation errors by referring to the user's past design data. As a result, the accuracy of generation is improved by referring to the user's past design data. Some or all of the above-mentioned processing in the generation unit may be performed using AI or without using AI.

[0049] The generation unit can improve the content of the blueprint by reflecting user feedback during generation. User feedback includes, but is not limited to, survey results and user comments. The content of the blueprint includes, but is not limited to, design modifications and the addition of functions. The generation unit can improve the layout of the blueprint based on, for example, user feedback. The generation unit can also adjust the exterior of the building by reflecting user feedback. The generation unit can also optimize the interior layout based on user feedback. In this way, the content of the blueprint can be improved by reflecting user feedback, and a blueprint that meets the user's needs can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using AI or without AI.

[0050] The generation unit can generate an optimal blueprint by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. Examples of optimal blueprints include, but are not limited to, local climatic conditions and topographical conditions. The generation unit generates an optimal blueprint based on, for example, information about land near the user's current location. If the user is interested in a specific area, the generation unit can also generate a blueprint by taking into account the building conditions of that area. The generation unit can also generate a blueprint by taking into account the area map and address of related land based on the user's geographical location information. This allows the generation of an optimal blueprint by taking into account the user's geographical location information. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without using AI.

[0051] During generation, the generation unit can analyze the user's social media activity to suggest content for the blueprint. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The content for the blueprint includes, for example, but is not limited to, the user's interests and trends. The generation unit can suggest content for the blueprint based on, for example, the architectural style shared by the user on social media. The generation unit can also analyze the content of the user's social media posts to suggest related designs. The generation unit can also suggest related content for the blueprint based on the activity of the user's friends on social media. In this way, related content for the blueprint can be suggested by analyzing the user's social media activity. Some or all of the above-described processing in the generation unit may be performed using AI or without AI.

[0052] The generation unit can customize the content of the blueprint by reflecting the user's past feedback during generation. Past feedback includes, but is not limited to, survey results and user comments. The content of the blueprint includes, but is not limited to, design modifications and the addition of functions. The generation unit customizes the layout of the blueprint, for example, based on feedback provided by the user in the past. The generation unit can also customize the exterior of the building by reflecting the user's feedback. The generation unit can also customize the interior layout based on the user's feedback. In this way, the content of the blueprint can be customized by reflecting the user's past feedback, and a blueprint that meets the user's needs can be provided. Some or all of the above-described processing in the generation unit may be performed using AI or without AI.

[0053] The display unit can display the completed house superimposed on real scenery using a smartphone or tablet. The smartphone or tablet may include, for example, a compatible OS and required specifications, but is not limited to these examples. The method of displaying the completed house superimposed on real scenery may include, for example, a method of using AR technology and display accuracy, but is not limited to these examples. The display unit can display the completed house superimposed on real scenery, for example, through a smartphone camera. The display unit can also display the completed house superimposed on real scenery through a tablet camera. The display unit can also display the completed house superimposed on real scenery using the AR function of the smartphone or tablet. By displaying the completed house superimposed on real scenery using a smartphone or tablet, the user can visually confirm the completed house at the location where the building will actually be built. Some or all of the above-described processing on the display unit may be performed using AI or without AI.

[0054] When displaying, the display unit can select an optimal display method by referring to the user's past operation history. Examples of the past operation history include, but are not limited to, log data collection and analysis using machine learning. Examples of the optimal display method include, but are not limited to, the user's operation pattern and display accuracy. For example, the display unit provides an optimal AR display method based on a display method used by the user in the past. The display unit can also analyze the user's past operation history and suggest an optimal display method. The display unit can also customize the display method by referring to the user's past operation history. This allows the optimal display method to be selected by referring to the user's past operation history, enabling a display that is easy for the user to understand. Some or all of the above-mentioned processing in the display unit may be performed using AI or without AI.

[0055] The display unit can customize the display content according to the user's current task when displaying the content. Examples of the current task include, but are not limited to, data from a project management tool and user input. Customizing the display content includes, but is not limited to, selecting information to display and changing the display format. For example, when the user is reviewing a design, the display unit displays details of the blueprint. Furthermore, when the user is reviewing the progress of construction, the display unit can also display the progress status. Furthermore, when the user provides feedback, the display unit can provide display content based on the feedback. By customizing the display content according to the user's current task, necessary information can be provided to the user. Some or all of the above-described processing in the display unit may be performed using AI or without AI.

[0056] The display unit can select the optimal display method by taking into account the user's device information when displaying. Device information includes, but is not limited to, for example, the device type and OS version. The optimal display method includes, but is not limited to, for example, device performance and display accuracy. For example, if the user is using a smartphone, the display unit can provide a display method tailored to 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 smartwatch, the display unit can also provide a simple, highly visible display method. This allows the optimal display method to be selected by taking into account the user's device information, enabling a display that is easy for the user to understand. Some or all of the above-described processing in the display unit may be performed using AI or without AI.

[0057] The display unit can make the display content multilingual according to the user's language setting when displaying. Examples of language settings include, but are not limited to, the device's language setting and the user's selection. Examples of multilingual support include, but are not limited to, the accuracy of translation and the types of supported languages. The display unit automatically sets the display content based on, for example, the user's device's language setting. The display unit can also provide a language switching function when the user uses multiple languages. The display unit can also provide the display content in a specific language when the user selects that language. By making the display content multilingual according to the user's language setting, a display that is easy for the user to understand can be provided. Some or all of the above-described processing in the display unit may be performed using AI or without AI.

[0058] The display unit can improve the display method by reflecting user feedback during display. User feedback includes, but is not limited to, survey results, user comments, and the like. Improvements to the display method include, but are not limited to, display accuracy, user convenience, and the like. The display unit improves the display method based on, for example, feedback provided by the user. The display unit can also adjust the display content by reflecting user feedback. The display unit can also customize the display method based on user feedback. This improves the display method by reflecting user feedback, enabling a display that is easy for the user to understand. Some or all of the above-described processing in the display unit may be performed using AI or without using AI.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can analyze the user's input in real time and evaluate the accuracy of the input. For example, when a user uploads a land area map, the reception unit checks whether the map is accurate and immediately notifies the user if there is an error. When a user enters an address, the reception unit can check whether the address exists and, if not, prompt the user to correct it. Furthermore, when a user enters the number of rooms, the reception unit can evaluate whether the number of rooms is realistic and, if it is unrealistic, can suggest an appropriate number of rooms. In this way, the reception unit can evaluate the accuracy of the input in real time, reducing user input errors and enabling more efficient input work.

[0061] The calculation unit can analyze design trends based on the user's past design data and propose designs based on the latest trends. For example, it can extract popular architectural styles from past design data and propose them to the user. It can also analyze past data for energy-efficient designs and recommend them to the user. It can also extract designs with excellent earthquake and wind resistance from past data and provide them to the user. In this way, the calculation unit can utilize past design data to propose designs based on the latest trends, allowing the user to obtain more attractive and functional designs.

[0062] The reception unit can analyze the user's input and automatically suggest related information based on the input. For example, when a user uploads a land area map, the building conditions and legal restrictions related to that land can be automatically displayed. Also, when a user enters an address, the local climatic and ground conditions can be automatically displayed. Furthermore, when a user enters the number of rooms, the system can suggest the optimal room arrangement and layout based on past data. In this way, the reception unit can automatically suggest related information based on the input, allowing users to proceed with their design work more efficiently.

[0063] The generation unit can improve the accuracy of the design based on the user's past design data. For example, it can extract errors and defects from the past design data and improve the design to prevent similar errors from occurring. It can also analyze the user's preferences and trends from the past data and customize the design based on that. It can also extract the optimal design method from the past data and use that to improve the accuracy of the design. In this way, the generation unit can improve the accuracy of the design by utilizing the past design data, allowing the user to obtain a higher quality design.

[0064] The reception unit can analyze the user's input content and suggest the optimal input method based on the input content. For example, if the user selects voice input, a land area map or address can be input using voice recognition technology. If the user selects text input, an interface that supports keyboard input can be provided. Furthermore, if the user selects image input, photos and maps of the land can be analyzed using image recognition technology. This allows the reception unit to suggest the optimal input method based on the user's input content, making input work more efficient.

[0065] The display unit can propose the optimal display method based on the user's past operation history. For example, it can provide the optimal AR display method based on the display method the user has used in the past. It can also analyze the user's past operation history and propose the optimal display method. It can also customize the display method by referring to the user's past operation history. In this way, the display unit can propose the optimal display method by utilizing the user's past operation history, enabling a display that is easy for the user to understand.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit inputs information such as a land area map, a photo of the land, the address, and the number of rooms. For example, the user can scan and upload a land area map or photo, and input the address and number of rooms. Step 2: The calculation unit analyzes the information input by the reception unit and performs structural calculations taking into account the architectural conditions of the address. For example, it calculates the strength and durability of the building taking into account external conditions such as earthquakes and wind pressure. Some or all of the above-mentioned processing in the calculation unit may be performed using AI, or may be performed without using AI. Step 3: The generation unit generates a blueprint and a house plan based on the results of the structural calculations performed by the calculation unit. For example, the blueprint details the building's exterior, interior layout, and the location of each room. Some or all of the above-mentioned processing in the generation unit may be performed using AI, or may be performed without AI. Step 4: The display unit generates a completed house that can be viewed in AR based on the blueprint and house plan generated by the generation unit. For example, a user can view the completed house in AR using a smartphone or tablet. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI.

[0068] (Example 2) A system according to an embodiment of the present invention allows an AI to input information such as a land area map, a photo of the land, an address, and the number of rooms. The AI ​​then considers the building conditions of the address and generates a blueprint with structural calculations, house plans, and a completed house viewable in AR. For example, a user inputs information such as a land area map, a photo of the land, an address, and the number of rooms. The user simply uploads this information to the system. The AI ​​then analyzes the input information and performs structural calculations taking into account the building conditions of the address. For example, the AI ​​calculates the building's strength and durability, taking into account external conditions such as earthquakes and wind pressure. Based on the results of the structural calculations, the AI ​​generates a blueprint and house plans. The blueprints include detailed descriptions of the building's exterior, interior layout, and the location of each room. Furthermore, based on the generated blueprint and house plans, the AI ​​generates a completed house viewable in AR. The user can view the completed house in AR using a smartphone or tablet. This allows users to easily create blueprints and plans for a house and view the completed house in AR. The system allows users to easily create blueprints and plans for a house and view the completed house in AR. For example, a user who has just purchased land can immediately begin designing their home. Additionally, the ability to visually confirm the design allows for corrections and changes to be made before construction begins, streamlining the construction process and increasing user satisfaction.

[0069] A design support system according to an embodiment includes a reception unit, a calculation unit, a generation unit, and a display unit. The reception unit receives input information such as a land area map, a photo of the land, an address, and the number of rooms. For example, a user can scan and upload the land area map or photo, and input the address and number of rooms. The calculation unit analyzes the information received by the reception unit and performs structural calculations taking into account the building conditions of the address. For example, the calculation unit calculates the strength and durability of the building taking into account external conditions such as earthquakes and wind pressure. Some or all of the above-described processing by the calculation unit may be performed using AI or without AI. The generation unit generates a blueprint and a house plan based on the results of the structural calculations performed by the calculation unit. For example, the blueprint includes detailed descriptions of the building's exterior, interior layout, and the location of each room. Some or all of the above-described processing by the generation unit may be performed using AI or without AI. The display unit generates a completed house that can be viewed in AR based on the blueprint and house plan generated by the generation unit. For example, a user can view the completed house in AR using a smartphone or tablet. Some or all of the above-described processing on the display unit may be performed using AI, or may be performed without AI. As a result, the design support system according to the embodiment can perform structural calculations based on information such as a land area map, a photo of the land, the address, and the number of rooms, and generate blueprints, house drawings, and a completed house that can be viewed using AR.

[0070] The reception unit can input information such as a land area map, a photo of the land, an address, and the number of rooms. Examples of land area maps include, but are not limited to, paper drawings and digital data. Examples of land photos include, but are not limited to, drone photos and smartphone photos. Examples of addresses include, but are not limited to, postal codes, street addresses, and building names. Examples of the number of rooms include, but are not limited to, divisions such as living rooms, bedrooms, and bathrooms. As a result, the reception unit can input information such as a land area map, a photo of the land, an address, and the number of rooms, allowing the system to obtain the necessary information.

[0071] The calculation unit can acquire legal constraints and local conditions from the database and perform structural calculations based on them. Legal constraints include, but are not limited to, building codes and local ordinances. Local conditions include, but are not limited to, climatic conditions and ground conditions. The calculation unit can, for example, acquire local building codes from the database and calculate building strength based on them. The calculation unit can also acquire local wind pressure and earthquake risk from the database and calculate durability based on them. The calculation unit can also acquire local land use regulations from the database and calculate building layout based on them. This allows the calculation unit to perform structural calculations taking legal constraints and local conditions into consideration, enabling safe and appropriate designs. Some or all of the above-mentioned processing in the calculation unit may be performed using AI, or may be performed without AI.

[0072] The generation unit can automatically generate the layout of each room, the exterior of the building, and the interior layout. The layout of each room includes, for example, room size and layout rules, but is not limited to these examples. The exterior of the building includes, for example, exterior wall design and color selection, but is not limited to these examples. The interior layout includes, for example, furniture placement and traffic flow design, but is not limited to these examples. The generation unit, for example, automatically optimizes the layout of each room to generate an efficient layout. The generation unit can also automatically design the exterior of the building to generate a visually attractive appearance. The generation unit can also automatically design the interior layout to generate a usable space. As a result, the generation unit automatically generates the layout of each room, the exterior of the building, and the interior layout, enabling efficient design. Some or all of the above-mentioned processing in the generation unit may be performed using AI or without AI.

[0073] The display unit can display the completed house superimposed on the real landscape using a smartphone or tablet. The smartphone or tablet may include, for example, a compatible OS and required specifications, but is not limited to these examples. The method of displaying the house superimposed on the real landscape may include, for example, a method of using AR technology and display accuracy, but is not limited to these examples. The display unit can display the completed house superimposed on the real landscape, for example, through a smartphone camera. The display unit can also display the completed house superimposed on the real landscape through a tablet camera. The display unit can also display the completed house superimposed on the real landscape using the AR function of the smartphone or tablet. By displaying the completed house superimposed on the real landscape, the user can visually confirm the completed house at the location where the building will actually be built. Some or all of the above-described processing on the display unit may be performed using AI or without AI.

[0074] The display unit may include an operation unit for user operation. Examples of the operation unit include, but are not limited to, a touch screen, a voice command, and the like. The display unit allows the user to operate the display of the generated house using, for example, a touch screen. The display unit may also allow the user to operate the display of the generated house using a voice command. The display unit may also allow the user to operate the display of the generated house using gesture recognition technology. Thus, by including the operation unit in the display unit, the user can operate the display of the generated house. Some or all of the above-described processing in the display unit may be performed using AI or may be performed without using AI.

[0075] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user emotions. Examples of user emotions include, but are not limited to, tension, enjoyment, and fatigue. Examples of input interface design include, but are not limited to, changing the color and adjusting the layout. For example, if the user is nervous, the reception unit can provide an interface with subdued colors to reduce visual stress. For example, if the user is enjoying themselves, the reception unit can provide an interface with bright colors to make input work more enjoyable. For example, if the user is tired, the reception unit can provide a simple, highly visible interface to make input work easier. This adjusts the design of the input interface according to the user's emotions, making input work more comfortable for the user. Emotion estimation is achieved 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 reception unit may be performed using AI or without AI.

[0076] The reception unit can analyze the user's past input history and suggest the optimal input method. Examples of the user's past input history include, but are not limited to, log data collection and machine learning analysis. Examples of the optimal input method include, but are not limited to, input speed and accuracy. For example, the reception unit automatically displays land area maps and addresses frequently input by the user in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest information to be used during a specific time period based on the user's past input history. By analyzing the user's past input history, the reception unit can suggest the optimal input method and streamline input work. Some or all of the above-described processing by the reception unit may be performed using AI or without AI.

[0077] The reception unit can automatically complete the input content based on the user's current project and areas of interest during input. Examples of current projects include, but are not limited to, data from a project management tool and the user's input. Examples of areas of interest include, but are not limited to, past search history and social media activity. For example, the reception unit can automatically complete the area map and address of related land based on information about projects the user has previously completed. The reception unit can also suggest an appropriate number of rooms and layout based on the architectural style the user is interested in. The reception unit can also analyze the user's past project history and automatically complete the optimal input content. This auto-completion of input content based on the user's current project and areas of interest streamlines the input process. Some or all of the above-described processing by the reception unit may be performed using AI or without AI.

[0078] The reception unit can select the optimal input means depending on the user's input method when inputting data. Examples of input methods include, but are not limited to, voice input, text input, and image input. Examples of optimal input means include, but are not limited to, input accuracy and user convenience. For example, if the user selects voice input, the reception unit inputs a land area map or address using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also provide an interface that supports keyboard input. Furthermore, if the user selects image input, the reception unit can analyze land photos and maps using image recognition technology. This allows the optimal input means to be selected depending on the user's input method, thereby streamlining input work. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI.

[0079] The reception unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. Examples of user emotions include, but are not limited to, whether the user is in a hurry, relaxed, or stressed. Examples of input content priorities include, but are not limited to, importance and urgency. For example, if the user is in a hurry, the reception unit can prioritize input of important information (such as an address or number of rooms). Furthermore, if the user is relaxed, the reception unit can prioritize input of detailed information (such as a land area map or photos). Furthermore, if the user is stressed, the reception unit can simplify the input procedure and prioritize input of minimal information. Thus, by prioritizing input content according to the user's emotions, important information can be prioritized. The 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 (such as an LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI or without AI.

[0080] The reception unit can prioritize inputting highly relevant information by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. Examples of highly relevant information include, but are not limited to, geographically nearby information and related topics. For example, the reception unit can prioritize inputting information about land close to the user's current location. Furthermore, if the user is interested in a specific area, the reception unit can also prioritize inputting the building conditions of that area. Furthermore, the reception unit can prioritize inputting area maps and addresses of related land based on the user's geographical location information. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI or without AI.

[0081] The reception unit can analyze the user's social media activity at the time of input and automatically input related information. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. Related information includes, for example, but is not limited to, the content of past posts and reactions from followers. For example, the reception unit automatically inputs photos of land shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and automatically input related addresses and number of rooms. The reception unit can also automatically input related land information by referring to the activity of the user's friends on social media. In this way, related information can be automatically input by analyzing the user's social media activity. Some or all of the above-mentioned processing by the reception unit may be performed using AI or without AI.

[0082] The reception unit can customize the input method by reflecting the user's past feedback when inputting data. Examples of past feedback include, but are not limited to, survey results and user comments. Examples of customizing the input method include, but are not limited to, changing the input interface and adding an input means. The reception unit provides an optimal input interface based on, for example, feedback provided by the user in the past. The reception unit can also improve the input procedure by reflecting the user's past feedback. The reception unit can also enhance the auto-completion function for input content based on the user's past feedback. By reflecting the user's past feedback, the input method can be customized, improving the efficiency of input work. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without using AI.

[0083] The calculation unit can estimate the user's emotions and adjust the priority of calculations based on the estimated user's emotions. Examples of user emotions include, but are not limited to, being in a hurry, being relaxed, or feeling stressed. Examples of calculation priorities include, but are not limited to, importance and urgency. For example, if the user is in a hurry, the calculation unit can prioritize important calculations (such as the strength and durability of the building). Furthermore, if the user is relaxed, the calculation unit can prioritize detailed calculations (such as the interior layout and room arrangement). Furthermore, if the user is feeling stressed, the calculation unit can simplify the calculation procedure and prioritize minimal calculations. In this way, by adjusting the priority of calculations according to the user's emotions, important calculations can be prioritized. 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 (such as an LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the calculation unit may be performed using AI or without AI.

[0084] During calculation, the calculation unit can acquire legal constraints and local conditions from a database and perform calculations based on them. Legal constraints include, but are not limited to, building standards laws and local ordinances. Local conditions include, but are not limited to, climatic conditions and ground conditions. The calculation unit, for example, acquires local building standards laws from a database and calculates building strength based on them. The calculation unit can also acquire local wind pressure and earthquake risk from a database and calculate durability based on them. The calculation unit can also acquire local land use regulations from a database and calculate building layout based on them. This allows appropriate calculations to be performed by taking legal constraints and local conditions into consideration. Some or all of the above-mentioned processing in the calculation unit may be performed using AI or without AI.

[0085] During calculation, the calculation unit can calculate the strength and durability of a building by taking into account external conditions such as earthquakes and wind pressure. External conditions such as earthquakes and wind pressure include, but are not limited to, weather data and earthquake risk assessments. Building strength and durability include, but are not limited to, structural analysis software and material properties. For example, the calculation unit calculates the earthquake resistance of a building by taking into account earthquake risk. The calculation unit can also calculate the wind resistance of a building by taking into account wind pressure risk. The calculation unit can also calculate the overall strength and durability of a building by taking into account both earthquakes and wind pressure. This makes it possible to design safe and durable buildings by taking into account external conditions such as earthquakes and wind pressure. Some or all of the above-mentioned processing in the calculation unit may be performed using AI, or may be performed without using AI.

[0086] The calculation unit can improve the accuracy of calculations by referring to the user's past design data during calculations. Past design data includes, but is not limited to, design drawings, calculation results, etc. Calculation accuracy includes, but is not limited to, data accuracy, algorithm improvements, etc. The calculation unit improves the accuracy of calculations, for example, based on design data created by the user in the past. The calculation unit can also analyze the user's past design data and propose an optimal calculation method. The calculation unit can also minimize calculation errors by referring to the user's past design data. As a result, the calculation accuracy is improved by referring to the user's past design data. Some or all of the above-described processing in the calculation unit may be performed using AI or without AI.

[0087] The calculation unit can estimate the user's emotion and adjust the display method of the calculation result based on the estimated user's emotion. Examples of user emotions include, but are not limited to, tension, relaxation, and hurry. Examples of display methods of the calculation result include, but are not limited to, graph display and text display. For example, if the user is tensioned, the calculation unit can provide a simple, highly visible display method. For example, if the user is relaxed, the calculation unit can provide a display method that includes detailed information. For example, if the user is hurrying, the calculation unit can provide a display method that focuses on the main points. This allows the display method of the calculation result to be adjusted according to the user's emotion, making it easy for the user to understand. Emotion estimation is achieved 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 calculation unit may be performed using AI or without AI.

[0088] During calculation, the calculation unit can determine the priority of calculations based on the submission time of the ideas. The submission time of the ideas includes, but is not limited to, for example, the submission date and time, deadline, etc. The calculation priority includes, but is not limited to, for example, the submission deadline, importance, etc. For example, if a user is in a hurry, the calculation unit can prioritize ideas with upcoming submission times. Furthermore, if a user has time, the calculation unit can also prioritize ideas with more distant submission times. Furthermore, the calculation unit can automatically adjust the priority of calculations based on the submission time. Thus, by determining the priority of calculations based on the submission time of ideas, calculations can be performed to meet the submission deadline. Some or all of the above-described processing in the calculation unit may be performed using AI, or may be performed without AI.

[0089] During calculation, the calculation unit can adjust the order of calculation based on the relevance of ideas. Examples of the relevance of ideas include, but are not limited to, commonality of themes and technical relevance. Examples of the order of calculation include, but are not limited to, descending order of relevance or descending order of importance. For example, the calculation unit prioritizes calculation of highly relevant ideas. The calculation unit can also postpone calculation of less relevant ideas. The calculation unit can also automatically adjust the order of calculation based on the relevance of ideas. In this way, by adjusting the order of calculation based on the relevance of ideas, highly relevant ideas can be prioritized in calculation. Some or all of the above-described processing in the calculation unit may be performed using AI or without AI.

[0090] The calculation unit can adjust the level of detail of the calculation according to the user's level of expertise during calculation. Examples of the level of expertise include, but are not limited to, whether the user has a qualification or years of experience. Examples of the level of detail of the calculation include, but are not limited to, a detailed analysis or a simple analysis. For example, the calculation unit provides detailed calculation results when the user has expertise. Alternatively, the calculation unit can provide simple calculation results when the user does not have expertise. The calculation unit can also automatically adjust the level of detail of the calculation based on the user's level of expertise. By adjusting the level of detail of the calculation according to the user's level of expertise, calculation results that are easy for the user to understand can be provided. Some or all of the above-described processing in the calculation unit may be performed using AI or without AI.

[0091] The generation unit can estimate the user's emotions and adjust the style of the generated blueprint based on the estimated user's emotions. Examples of user emotions include, but are not limited to, relaxed, rushed, and excited. Examples of blueprint styles include, but are not limited to, design themes and color selections. For example, if the user is relaxed, the generation unit can generate a blueprint with a relaxed design. If the user is rushed, the generation unit can also generate a blueprint with a simple and to-the-point design. If the user is excited, the generation unit can also generate a blueprint with a visually stimulating design. This allows the blueprint to be tailored to the user's needs by adjusting the style of the blueprint according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. Examples of generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using AI or without AI.

[0092] During generation, the generation unit can automatically generate the layout of each room, the exterior of the building, and the interior layout. The layout of each room includes, for example, room size and layout rules, but is not limited to these examples. The exterior of the building includes, for example, exterior wall design and color selection, but is not limited to these examples. The interior layout includes, for example, furniture placement and traffic flow design, but is not limited to these examples. The generation unit, for example, automatically optimizes the layout of each room to generate an efficient layout. The generation unit can also automatically design the exterior of the building to generate a visually attractive appearance. The generation unit can also automatically design the interior layout to generate a usable space. This enables efficient design by automatically generating the layout of each room, the exterior of the building, and the interior layout. Some or all of the above-mentioned processing in the generation unit may be performed using AI or without AI.

[0093] During generation, the generation unit can improve the accuracy of generation by referring to the user's past design data. Past design data includes, for example, design drawings, calculation results, etc., but is not limited to these examples. Generation accuracy includes, for example, data accuracy, algorithm improvements, etc., but is not limited to these examples. The generation unit improves the accuracy of generation, for example, based on design data created by the user in the past. The generation unit can also analyze the user's past design data and propose an optimal generation method. The generation unit can also minimize generation errors by referring to the user's past design data. As a result, the accuracy of generation is improved by referring to the user's past design data. Some or all of the above-mentioned processing in the generation unit may be performed using AI or without using AI.

[0094] The generation unit can improve the content of the blueprint by reflecting user feedback during generation. User feedback includes, but is not limited to, survey results and user comments. The content of the blueprint includes, but is not limited to, design modifications and the addition of functions. The generation unit can improve the layout of the blueprint based on, for example, user feedback. The generation unit can also adjust the exterior of the building by reflecting user feedback. The generation unit can also optimize the interior layout based on user feedback. In this way, the content of the blueprint can be improved by reflecting user feedback, and a blueprint that meets the user's needs can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using AI or without AI.

[0095] The generation unit can estimate the user's emotions and determine the priority of the blueprints to be generated based on the estimated user emotions. Examples of user emotions include, but are not limited to, being in a hurry, being relaxed, or feeling stressed. Examples of blueprint priorities include, but are not limited to, importance and urgency. For example, if the user is in a hurry, the generation unit can prioritize generating important blueprints. Furthermore, if the user is relaxed, the generation unit can prioritize generating detailed blueprints. Furthermore, if the user is stressed, the generation unit can prioritize generating simplified blueprints. Thus, by determining the priority of blueprints according to the user's emotions, important blueprints can be prioritized. The 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 generation unit may be performed using AI or without AI.

[0096] The generation unit can generate an optimal blueprint by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. Examples of optimal blueprints include, but are not limited to, local climatic conditions and topographical conditions. The generation unit generates an optimal blueprint based on, for example, information about land near the user's current location. If the user is interested in a specific area, the generation unit can also generate a blueprint by taking into account the building conditions of that area. The generation unit can also generate a blueprint by taking into account the area map and address of related land based on the user's geographical location information. This allows the generation of an optimal blueprint by taking into account the user's geographical location information. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without using AI.

[0097] During generation, the generation unit can analyze the user's social media activity to suggest content for the blueprint. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The content for the blueprint includes, for example, but is not limited to, the user's interests and trends. The generation unit can suggest content for the blueprint based on, for example, the architectural style shared by the user on social media. The generation unit can also analyze the content of the user's social media posts to suggest related designs. The generation unit can also suggest related content for the blueprint based on the activity of the user's friends on social media. In this way, related content for the blueprint can be suggested by analyzing the user's social media activity. Some or all of the above-described processing in the generation unit may be performed using AI or without AI.

[0098] The generation unit can customize the content of the blueprint by reflecting the user's past feedback during generation. Past feedback includes, but is not limited to, survey results and user comments. The content of the blueprint includes, but is not limited to, design modifications and the addition of functions. The generation unit customizes the layout of the blueprint, for example, based on feedback provided by the user in the past. The generation unit can also customize the exterior of the building by reflecting the user's feedback. The generation unit can also customize the interior layout based on the user's feedback. In this way, the content of the blueprint can be customized by reflecting the user's past feedback, and a blueprint that meets the user's needs can be provided. Some or all of the above-described processing in the generation unit may be performed using AI or without AI.

[0099] The display unit can estimate the user's emotions and adjust the AR display method based on the estimated user's emotions. Examples of user emotions include, but are not limited to, tension, relaxation, and hurry. Examples of AR display methods include, but are not limited to, display timing and selection of display content. For example, when the user is tensioned, the display unit can provide a simple, highly visible AR display method. For example, when the user is relaxed, the display unit can provide an AR display method that includes detailed information. For example, when the user is hurrying, the display unit can provide an AR display method that focuses on the main points. This allows the AR display method to be adjusted according to the user's emotions, enabling a display that is easy for the user to understand. Emotion estimation is achieved 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 display unit may be performed using AI or without AI.

[0100] The display unit can display the completed house superimposed on real scenery using a smartphone or tablet. The smartphone or tablet may include, for example, a compatible OS and required specifications, but is not limited to these examples. The method of displaying the completed house superimposed on real scenery may include, for example, a method of using AR technology and display accuracy, but is not limited to these examples. The display unit can display the completed house superimposed on real scenery, for example, through a smartphone camera. The display unit can also display the completed house superimposed on real scenery through a tablet camera. The display unit can also display the completed house superimposed on real scenery using the AR function of the smartphone or tablet. By displaying the completed house superimposed on real scenery using a smartphone or tablet, the user can visually confirm the completed house at the location where the building will actually be built. Some or all of the above-described processing on the display unit may be performed using AI or without AI.

[0101] When displaying, the display unit can select an optimal display method by referring to the user's past operation history. Examples of the past operation history include, but are not limited to, log data collection and analysis using machine learning. Examples of the optimal display method include, but are not limited to, the user's operation pattern and display accuracy. For example, the display unit provides an optimal AR display method based on a display method used by the user in the past. The display unit can also analyze the user's past operation history and suggest an optimal display method. The display unit can also customize the display method by referring to the user's past operation history. This allows the optimal display method to be selected by referring to the user's past operation history, enabling a display that is easy for the user to understand. Some or all of the above-mentioned processing in the display unit may be performed using AI or without AI.

[0102] The display unit can customize the display content according to the user's current task when displaying the content. Examples of the current task include, but are not limited to, data from a project management tool and user input. Customizing the display content includes, but is not limited to, selecting information to display and changing the display format. For example, when the user is reviewing a design, the display unit displays details of the blueprint. Furthermore, when the user is reviewing the progress of construction, the display unit can also display the progress status. Furthermore, when the user provides feedback, the display unit can provide display content based on the feedback. By customizing the display content according to the user's current task, necessary information can be provided to the user. Some or all of the above-described processing in the display unit may be performed using AI or without AI.

[0103] The display unit can estimate the user's emotions and determine the priority of AR display based on the estimated user's emotions. Examples of user emotions include, but are not limited to, being in a hurry, being relaxed, feeling stressed, etc. Examples of AR display priorities include, but are not limited to, importance and urgency. For example, when the user is in a hurry, the display unit can prioritize AR display of important information. Furthermore, when the user is relaxed, the display unit can prioritize AR display of detailed information. Furthermore, when the user is stressed, the display unit can prioritize AR display of simplified information. In this way, by determining the priority of AR display according to the user's emotions, important information can be prioritized and displayed. 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 display unit may be performed using AI or without AI.

[0104] The display unit can select the optimal display method by taking into account the user's device information when displaying. Device information includes, but is not limited to, for example, the device type and OS version. The optimal display method includes, but is not limited to, for example, device performance and display accuracy. For example, if the user is using a smartphone, the display unit can provide a display method tailored to 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 smartwatch, the display unit can also provide a simple, highly visible display method. This allows the optimal display method to be selected by taking into account the user's device information, enabling a display that is easy for the user to understand. Some or all of the above-described processing in the display unit may be performed using AI or without AI.

[0105] The display unit can make the display content multilingual according to the user's language setting when displaying. Examples of language settings include, but are not limited to, the device's language setting and the user's selection. Examples of multilingual support include, but are not limited to, the accuracy of translation and the types of supported languages. The display unit automatically sets the display content based on, for example, the user's device's language setting. The display unit can also provide a language switching function when the user uses multiple languages. The display unit can also provide the display content in a specific language when the user selects that language. By making the display content multilingual according to the user's language setting, a display that is easy for the user to understand can be provided. Some or all of the above-described processing in the display unit may be performed using AI or without AI.

[0106] The display unit can improve the display method by reflecting user feedback during display. User feedback includes, but is not limited to, survey results, user comments, and the like. Improvements to the display method include, but are not limited to, display accuracy, user convenience, and the like. The display unit improves the display method based on, for example, feedback provided by the user. The display unit can also adjust the display content by reflecting user feedback. The display unit can also customize the display method based on user feedback. This improves the display method by reflecting user feedback, enabling a display that is easy for the user to understand. Some or all of the above-described processing in the display unit may be performed using AI or without using AI. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, calculation unit, generation unit, and display unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can input information such as a land area map, a photo of the land, the address, and the number of rooms using the reception device 38 of the smart device 14. For example, the calculation unit can be implemented by the specific processing unit 290 of the data processing device 12, which analyzes the input information and performs structural calculations taking into account the building conditions. For example, the generation unit can be implemented by the specific processing unit 290 of the data processing device 12, which generates blueprints and house plans based on the results of the structural calculations. For example, the display unit can generate a completed house that can be viewed in AR using the output device 40 of the smart device 14, allowing the user to view it using a smartphone or tablet. === Hard Collateral 1-2 === Each of the above-described elements, including the reception unit, calculation unit, generation unit, and display unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can input information such as a land area map, a photo of the land, an address, and the number of rooms using the microphone 238 of the smart glasses 214. For example, the calculation unit can be implemented by the specific processing unit 290 of the data processing device 12, which analyzes the input information and performs structural calculations taking into account the building conditions. For example, the generation unit can be implemented by the specific processing unit 290 of the data processing device 12, which generates blueprints and house plans based on the results of the structural calculations. For example, the display unit can generate a completed house that can be viewed in AR using the speaker 240 of the smart glasses 214, allowing the user to view it using the smart glasses 214. === Hard Collateral 1-3 === Each of the above-described elements, including the reception unit, calculation unit, generation unit, and display unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit can input information such as a land area map, a photo of the land, the address, and the number of rooms using the microphone 238 of the headset terminal 314. For example, the calculation unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes the input information and performs structural calculations taking into account the building conditions. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12, which generates blueprints and house plans based on the results of the structural calculations. For example, the display unit generates a completed house that can be viewed in AR using the display 343 of the headset terminal 314, allowing the user to view it using the headset terminal 314. === Hard Collateral 1-4 === Each of the above-described elements, including the reception unit, calculation unit, generation unit, and display unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input information such as a land area map, a photo of the land, the address, and the number of rooms using the microphone 238 of the robot 414. For example, the calculation unit can be implemented by the specific processing unit 290 of the data processing device 12, which analyzes the input information and performs structural calculations taking into account the building conditions. For example, the generation unit can be implemented by the specific processing unit 290 of the data processing device 12, which generates blueprints and house plans based on the results of the structural calculations. For example, the display unit can generate a completed house that can be viewed in AR using the speaker 240 of the robot 414, allowing the user to view it using the robot 414.

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

[0108] The reception unit can analyze the user's input in real time and evaluate the accuracy of the input. For example, when a user uploads a land area map, the reception unit checks whether the map is accurate and immediately notifies the user if there is an error. When a user enters an address, the reception unit can check whether the address exists and, if not, prompt the user to correct it. Furthermore, when a user enters the number of rooms, the reception unit can evaluate whether the number of rooms is realistic and, if it is unrealistic, can suggest an appropriate number of rooms. In this way, the reception unit can evaluate the accuracy of the input in real time, reducing user input errors and enabling more efficient input work.

[0109] The calculation unit can analyze design trends based on the user's past design data and propose designs based on the latest trends. For example, it can extract popular architectural styles from past design data and propose them to the user. It can also analyze past data for energy-efficient designs and recommend them to the user. It can also extract designs with excellent earthquake and wind resistance from past data and provide them to the user. In this way, the calculation unit can utilize past design data to propose designs based on the latest trends, allowing the user to obtain more attractive and functional designs.

[0110] The generation unit can estimate the user's emotions and adjust the color and design of the blueprint based on the estimated user's emotions. For example, if the user is relaxed, a blueprint with calm colors can be generated. If the user is excited, a blueprint with vivid colors can be generated. Furthermore, if the user is stressed, a blueprint with a simple, highly visible design can be generated. In this way, the generation unit can adjust the color and design of the blueprint according to the user's emotions, thereby providing a blueprint that matches the user's emotions.

[0111] The display unit can estimate the user's emotions and adjust the AR display effects based on the estimated user's emotions. For example, if the user is relaxed, a calm effect can be used to display the AR display. If the user is excited, a dynamic effect can be used to display the AR display. Furthermore, if the user is feeling stressed, a simple, highly visible effect can be used to display the AR display. In this way, the display unit can adjust the AR display effects according to the user's emotions, thereby providing the user with a comfortable AR experience.

[0112] The reception unit can analyze the user's input and automatically suggest related information based on the input. For example, when a user uploads a land area map, the building conditions and legal restrictions related to that land can be automatically displayed. Also, when a user enters an address, the local climatic and ground conditions can be automatically displayed. Furthermore, when a user enters the number of rooms, the system can suggest the optimal room arrangement and layout based on past data. In this way, the reception unit can automatically suggest related information based on the input, allowing users to proceed with their design work more efficiently.

[0113] The calculation unit can estimate the user's emotions and adjust the level of detail of the calculation based on the estimated user's emotions. For example, if the user is relaxed, a detailed calculation result can be provided. If the user is in a hurry, a concise calculation result can be provided. Furthermore, if the user is feeling stressed, the calculation procedure can be simplified and a minimal calculation result can be provided. In this way, the calculation unit can adjust the level of detail of the calculation according to the user's emotions, thereby providing calculation results that are easy for the user to understand.

[0114] The generation unit can improve the accuracy of the design based on the user's past design data. For example, it can extract errors and defects from the past design data and improve the design to prevent similar errors from occurring. It can also analyze the user's preferences and trends from the past data and customize the design based on that. It can also extract the optimal design method from the past data and use that to improve the accuracy of the design. In this way, the generation unit can improve the accuracy of the design by utilizing the past design data, allowing the user to obtain a higher quality design.

[0115] The display unit can estimate the user's emotions and determine the priority of display content based on the estimated user's emotions. For example, if the user is in a hurry, important information can be displayed with priority. Also, if the user is relaxed, detailed information can be displayed with priority. Furthermore, if the user is feeling stressed, simplified information can be displayed with priority. In this way, the display unit can determine the priority of display content according to the user's emotions, thereby quickly providing the user with the information they need.

[0116] The reception unit can analyze the user's input content and suggest the optimal input method based on the input content. For example, if the user selects voice input, a land area map or address can be input using voice recognition technology. If the user selects text input, an interface that supports keyboard input can be provided. Furthermore, if the user selects image input, photos and maps of the land can be analyzed using image recognition technology. This allows the reception unit to suggest the optimal input method based on the user's input content, making input work more efficient.

[0117] The display unit can propose the optimal display method based on the user's past operation history. For example, it can provide the optimal AR display method based on the display method the user has used in the past. It can also analyze the user's past operation history and propose the optimal display method. It can also customize the display method by referring to the user's past operation history. In this way, the display unit can propose the optimal display method by utilizing the user's past operation history, enabling a display that is easy for the user to understand.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception unit inputs information such as a land area map, a photo of the land, the address, and the number of rooms. For example, the user can scan and upload a land area map or photo, and input the address and number of rooms. Step 2: The calculation unit analyzes the information input by the reception unit and performs structural calculations taking into account the architectural conditions of the address. For example, it calculates the strength and durability of the building taking into account external conditions such as earthquakes and wind pressure. Some or all of the above-mentioned processing in the calculation unit may be performed using AI, or may be performed without using AI. Step 3: The generation unit generates a blueprint and a house plan based on the results of the structural calculations performed by the calculation unit. For example, the blueprint details the building's exterior, interior layout, and the location of each room. Some or all of the above-mentioned processing in the generation unit may be performed using AI, or may be performed without AI. Step 4: The display unit generates a completed house that can be viewed in AR based on the blueprint and house plan generated by the generation unit. For example, a user can view the completed house in AR using a smartphone or tablet. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0121] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

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

[0125] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0137] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0139] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

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

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[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 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.

[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 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.

[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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

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

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

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0170] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0172] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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, in order to avoid confusion and to 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.

[0190] 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.

[0191] [Explanation of symbols]

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

Claims

1. A reception desk where you can input information such as the land area map, photos of the land, address, and number of rooms. a calculation unit that analyzes the information input by the reception unit and performs structural calculations based on the building conditions of the address; a generation unit that generates a design drawing and a house plan based on the results of the structural calculation performed by the calculation unit; a display unit that generates a completed house that can be viewed in AR based on the blueprint and house plan generated by the generation unit; Equipped with A system characterized by:

2. The reception unit Enter the land area map, land photo, address, and number of rooms.

2. The system of claim 1.

3. The calculation unit Legal restrictions and local conditions are retrieved from a database and structural calculations are performed based on them 2. The system of claim 1.

4. The generation unit Automatically generate the layout of each room, the exterior of the building, and the interior layout 2. The system of claim 1.

5. The display unit Use your smartphone or tablet to view your finished home overlaid on the real world 2. The system of claim 1.

6. The display unit Equipped with an operation unit for users to operate 2. The system of claim 1.

7. The reception unit Estimate user emotions and adjust the design of the input interface based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and suggests the optimal input method 2. The system of claim 1.

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