System

Abstract

An object of a system according to an embodiment is to detect and notify a design error through analysis of an architectural drawing.SOLUTION: A system includes an architectural drawing analysis part, a design error detection part, and a notification part. The building drawing analysis unit analyzes a building drawing. The design error detection part detects a design error from the result analyzed by the construction drawing analysis part. The notification unit notifies the designer of the design error detected by the design error detection unit.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] With conventional technology, there is a high risk of human error occurring when checking architectural drawings, and design errors may go unnoticed.

[0005] The system according to the embodiment aims to detect and notify design errors through the analysis of architectural drawings. [Means for solving the problem]

[0006] The system according to the embodiment includes an architectural drawing analysis unit, a design error detection unit, and a notification unit. The architectural drawing analysis unit analyzes architectural drawings. The design error detection unit detects design errors from the analysis results obtained by the architectural drawing analysis unit. The notification unit notifies the designer of the design errors detected by the design error detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect and notify design errors through analysis of architectural drawings. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The architectural drawing checking system according to an embodiment of the present invention uses AI to analyze architectural drawings, automatically detect design errors and deficiencies, eliminate human error, reduce labor costs associated with design, and contribute to the creation of high-quality, inexpensive buildings. As a result, the architectural drawing checking system can prevent construction defects, streamline the design process, and mitigate the impact of rising construction material prices and labor shortages.

[0029] An architectural drawing check system according to an embodiment includes an architectural drawing analysis unit, a design error detection unit, and a notification unit. The architectural drawing analysis unit analyzes architectural drawings. For example, the architectural drawing analysis unit analyzes architectural drawings input as digital data to detect dimensional errors and structural inconsistencies. The architectural drawing analysis unit can also detect areas that violate legal regulations. Furthermore, the architectural drawing analysis unit can refer to past construction data and focus on checking areas where similar errors are likely to occur. The design error detection unit detects design errors from the results of the analysis by the architectural drawing analysis unit. For example, the design error detection unit detects dimensional errors, structural inconsistencies, and violations of legal regulations. The design error detection unit can also identify areas where similar errors are likely to occur based on the past construction data. The notification unit notifies the designer of the design errors detected by the design error detection unit. For example, the notification unit can send email notifications, display alerts, or provide audio notifications. The notification unit can also provide an interface compatible with the designer's device to notify the designer of design errors in real time. As a result, the architectural drawing checking system can eliminate human error and improve design quality. For example, the architectural drawing checking system can eliminate the need for manual checking work by designers, significantly reducing the time and cost required for design. The architectural drawing checking system can also prevent construction defects and reduce the number of construction defects discovered upon completion. Furthermore, the architectural drawing checking system can mitigate the impact of rising construction material prices and labor shortages, and support efficient design work.

[0030] The architectural drawing analysis unit can detect dimensional errors and structural inconsistencies. For example, the architectural drawing analysis unit analyzes the dimensions of architectural drawings to detect dimensional errors and identifies errors that exceed the allowable error range. In addition, the architectural drawing analysis unit performs structural calculations of the architectural drawings and compares them with design standards to detect structural inconsistencies. For example, the architectural drawing analysis unit analyzes the dimensions of architectural drawings and issues a warning if errors exceed the allowable range. In addition, the architectural drawing analysis unit performs structural calculations and displays an alert if the calculations do not conform to the design standards. This makes it possible to detect design errors early and prevent poor construction.

[0031] The architectural drawing analysis unit can detect areas that violate legal regulations. For example, the architectural drawing analysis unit analyzes architectural drawings based on legal regulations such as the Building Standards Act, the Fire Service Act, and environmental regulations in order to detect areas that violate legal regulations. For example, the architectural drawing analysis unit analyzes architectural drawings based on the Building Standards Act and identifies areas that violate legal regulations. The architectural drawing analysis unit can also analyze architectural drawings based on the Fire Service Act and detect deficiencies in the placement of firefighting equipment or evacuation routes. Furthermore, the architectural drawing analysis unit can analyze architectural drawings based on environmental regulations and identify areas that do not comply with environmental standards. This makes it possible to prevent violations of legal regulations.

[0032] The architectural drawing analysis unit can refer to past construction data and focus on checking areas where similar mistakes are likely to occur. The architectural drawing analysis unit, for example, refers to past construction data and analyzes past project data and construction history to identify areas where similar mistakes are likely to occur. For example, the architectural drawing analysis unit can refer to a database of past construction mistakes and identify areas where mistakes frequently occur. The architectural drawing analysis unit can also focus on checking areas where similar mistakes are likely to occur based on past project data. Furthermore, the architectural drawing analysis unit can analyze past construction history and identify patterns and frequency of mistakes. This makes it possible to prevent mistakes by utilizing past data.

[0033] The architectural drawing analysis unit can generate a 3D model to enable visual confirmation. For example, the architectural drawing analysis unit generates a 3D model based on digital data of architectural drawings to enable visual confirmation of design errors. For example, the architectural drawing analysis unit generates a 3D model based on CAD data to intuitively detect design errors. The architectural drawing analysis unit can also generate a 3D model based on a BIM model to visually confirm design errors. Furthermore, the architectural drawing analysis unit can visually confirm design errors using a 3D viewer or VR technology. This makes it possible to intuitively detect design errors.

[0034] The architectural drawing analysis unit can enable the designer to give verbal instructions using voice input. The architectural drawing analysis unit, for example, is equipped with a voice input function to build a system that allows the designer to give verbal instructions. For example, the designer gives instructions by voice, and the architectural drawing analysis unit analyzes the architectural drawings according to those instructions. The architectural drawing analysis unit can also use voice recognition technology to analyze the designer's voice commands and check the architectural drawings. Furthermore, the architectural drawing analysis unit can accurately recognize the designer's instructions by taking into account the type of microphone and the accuracy of voice recognition. This allows the designer to give instructions by voice.

[0035] The architectural drawing analysis unit can automatically check compatibility between different design software and detect compatibility issues. For example, the architectural drawing analysis unit analyzes architectural drawings created with different design software and identifies compatibility issues. For example, the architectural drawing analysis unit checks the compatibility of drawings created with AutoCAD and Revit and checks data consistency. The architectural drawing analysis unit can also check the compatibility of drawings created with ArchiCAD and drawings created with other design software. Furthermore, the architectural drawing analysis unit can detect compatibility issues based on differences in file formats and data consistency. This makes it possible to check compatibility between different design software.

[0036] The architectural drawing analysis unit can analyze the designer's past work history and propose an optimal work sequence. The architectural drawing analysis unit, for example, analyzes the designer's past work history and proposes an optimal work sequence. For example, the architectural drawing analysis unit automatically generates an efficient work sequence based on past work logs and project history. The architectural drawing analysis unit can also propose an optimal work sequence from the perspective of work efficiency and time management based on past work data. Furthermore, the architectural drawing analysis unit can also optimize the work sequence by taking into account resource usage. This makes it possible to propose an efficient work sequence by utilizing past work history.

[0037] The architectural drawing analysis unit monitors the usage status of tools and software used by designers in real time and can support the selection of optimal tools. The architectural drawing analysis unit, for example, monitors the usage status of tools and software used by designers in real time. For example, the architectural drawing analysis unit analyzes the performance of tools used by designers and suggests optimal tools. The architectural drawing analysis unit can also support the selection of optimal tools based on the frequency and duration of software use. Furthermore, the architectural drawing analysis unit can also suggest optimal tools taking into account the cost-performance of the tools. This makes it possible to support designers in selecting optimal tools.

[0038] The architectural drawing analysis unit can automatically share resources between different projects and optimize them. The architectural drawing analysis unit, for example, automatically shares resources between different projects. For example, the architectural drawing analysis unit analyzes the resource usage status for each project and proposes optimal resource allocation. The architectural drawing analysis unit can also use cloud storage to efficiently share resources. Furthermore, the architectural drawing analysis unit can also optimize resources to efficiently use resources and reduce waste. This can support efficient project management by optimizing resources.

[0039] The architectural drawing analysis unit can provide an interface compatible with the device used by the designer. The architectural drawing analysis unit provides an interface compatible with the device used by the designer. For example, the architectural drawing analysis unit provides a UI optimized for tablets and smartphones. The architectural drawing analysis unit can also provide an interface compatible with desktops and laptops. Furthermore, the architectural drawing analysis unit can adopt a responsive design to improve usability. This makes it possible to streamline the design process by providing an interface compatible with the device used by the designer.

[0040] The architectural drawing analysis unit can refer to past construction data and focus on checking areas where similar mistakes are likely to occur. The architectural drawing analysis unit, for example, refers to past construction data and analyzes past project data and construction history to identify areas where similar mistakes are likely to occur. For example, the architectural drawing analysis unit can refer to a database of past construction mistakes and identify areas where mistakes frequently occur. The architectural drawing analysis unit can also focus on checking areas where similar mistakes are likely to occur based on past project data. Furthermore, the architectural drawing analysis unit can analyze past construction history and identify patterns and frequency of mistakes. This makes it possible to prevent mistakes by utilizing past data.

[0041] The architectural drawing analysis unit can analyze real-time video of the construction site and immediately detect and notify of construction errors. The architectural drawing analysis unit, for example, analyzes real-time video of the construction site and immediately detects construction errors. For example, the architectural drawing analysis unit analyzes camera video in real time and notifies if a construction error is detected. The architectural drawing analysis unit can also use video analysis algorithms to detect violations of construction procedures or failure to meet quality standards. Furthermore, the architectural drawing analysis unit can display an alert or issue an audio notification if a construction error is detected. This makes it possible to prevent construction defects by detecting construction errors in real time and notifying immediately.

[0042] The architectural drawing analysis unit can use voice input to enable the builder to give verbal instructions. The architectural drawing analysis unit is, for example, equipped with a voice input function to build a system that allows the builder to give verbal instructions. For example, the builder gives instructions by voice, and the architectural drawing analysis unit checks the construction site according to those instructions. The architectural drawing analysis unit can also use voice recognition technology to analyze the builder's voice commands and detect construction errors. Furthermore, the architectural drawing analysis unit can accurately recognize the builder's instructions by taking into account the type of microphone and the accuracy of voice recognition. This allows the builder to give instructions by voice.

[0043] The architectural drawing analysis unit can automatically check compatibility between different construction management software and detect compatibility issues. For example, the architectural drawing analysis unit analyzes construction data created with different construction management software and identifies compatibility issues. For example, the architectural drawing analysis unit checks the compatibility of data created with Procore and data created with Buildertrend to check data consistency. The architectural drawing analysis unit can also check the compatibility of data created with CoConstruct and data created with other construction management software. Furthermore, the architectural drawing analysis unit can detect compatibility issues based on differences in file formats and data consistency. This makes it possible to check compatibility between different construction management software.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The architectural drawing analysis unit can analyze the designer's past work history and propose the optimal work sequence. For example, the architectural drawing analysis unit automatically generates an efficient work sequence based on past work logs and project history. The architectural drawing analysis unit can also propose the optimal work sequence from the perspective of work efficiency and time management based on past work data. Furthermore, the architectural drawing analysis unit can also optimize the work sequence by taking into account resource usage. This makes it possible to propose an efficient work sequence by utilizing past work history.

[0046] The architectural drawing analysis unit can analyze real-time video footage of the construction site and instantly detect and notify construction errors. For example, the architectural drawing analysis unit can analyze camera footage in real time and notify if a construction error is detected. The architectural drawing analysis unit can also use video analysis algorithms to detect violations of construction procedures or failure to meet quality standards. Furthermore, the architectural drawing analysis unit can display an alert or issue an audio notification if a construction error is detected. This makes it possible to detect construction errors in real time and immediately notify, thereby preventing poor construction work.

[0047] The architectural drawing analysis unit can automatically check compatibility between different design software and detect compatibility issues. For example, the architectural drawing analysis unit analyzes architectural drawings created with different design software and identifies compatibility issues. For example, the architectural drawing analysis unit checks the compatibility of drawings created with AutoCAD and drawings created with Revit and checks data consistency. The architectural drawing analysis unit can also check the compatibility of drawings created with ArchiCAD and drawings created with other design software. Furthermore, the architectural drawing analysis unit can detect compatibility issues based on differences in file formats and data consistency. This makes it possible to check compatibility between different design software.

[0048] The architectural drawing analysis unit can use voice input to enable the designer to give verbal instructions. For example, the architectural drawing analysis unit is equipped with a voice input function to build a system that allows the designer to give verbal instructions. For example, the designer gives instructions by voice, and the architectural drawing analysis unit analyzes the architectural drawings in accordance with the instructions. The architectural drawing analysis unit can also use voice recognition technology to analyze the designer's voice commands and check the architectural drawings. Furthermore, the architectural drawing analysis unit can accurately recognize the designer's instructions by taking into account the type of microphone and the accuracy of voice recognition. This allows the designer to give instructions by voice.

[0049] The architectural drawing analysis unit monitors the usage status of tools and software used by designers in real time and can support the selection of the most appropriate tools. For example, the architectural drawing analysis unit analyzes the performance of the tools used by designers and suggests the most appropriate tools. The architectural drawing analysis unit can also support the selection of the most appropriate tools based on the frequency and duration of software use. Furthermore, the architectural drawing analysis unit can also suggest the most appropriate tools taking into account the cost-effectiveness of the tools. This makes it possible to support designers in selecting the most appropriate tools.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The architectural drawing analysis unit analyzes architectural drawings. For example, it analyzes architectural drawings entered as digital data to detect dimensional errors and structural inconsistencies. It can also detect areas that violate legal regulations. Furthermore, it can refer to past construction data and focus on areas where similar mistakes are likely to occur. Step 2: The design error detection unit detects design errors from the results of analysis by the architectural drawing analysis unit. For example, it detects dimensional errors, structural inconsistencies, and violations of legal regulations. It can also identify areas where similar errors are likely to occur based on past construction data. Step 3: The notification unit notifies the designer of the design errors detected by the design error detection unit. For example, it can notify the designer by email, alert display, or voice notification. It can also provide an interface compatible with the designer's device and notify the designer of design errors in real time.

[0052] (Example 2) The architectural drawing checking system according to an embodiment of the present invention uses AI to analyze architectural drawings, automatically detect design errors and deficiencies, eliminate human error, reduce labor costs associated with design, and contribute to the creation of high-quality, inexpensive buildings. As a result, the architectural drawing checking system can prevent construction defects, streamline the design process, and mitigate the impact of rising construction material prices and labor shortages.

[0053] An architectural drawing check system according to an embodiment includes an architectural drawing analysis unit, a design error detection unit, and a notification unit. The architectural drawing analysis unit analyzes architectural drawings. For example, the architectural drawing analysis unit analyzes architectural drawings input as digital data to detect dimensional errors and structural inconsistencies. The architectural drawing analysis unit can also detect areas that violate legal regulations. Furthermore, the architectural drawing analysis unit can refer to past construction data and focus on checking areas where similar errors are likely to occur. The design error detection unit detects design errors from the results of the analysis by the architectural drawing analysis unit. For example, the design error detection unit detects dimensional errors, structural inconsistencies, and violations of legal regulations. The design error detection unit can also identify areas where similar errors are likely to occur based on the past construction data. The notification unit notifies the designer of the design errors detected by the design error detection unit. For example, the notification unit can send email notifications, display alerts, or provide audio notifications. The notification unit can also provide an interface compatible with the designer's device to notify the designer of design errors in real time. As a result, the architectural drawing checking system can eliminate human error and improve design quality. For example, the architectural drawing checking system can eliminate the need for manual checking work by designers, significantly reducing the time and cost required for design. The architectural drawing checking system can also prevent construction defects and reduce the number of construction defects discovered upon completion. Furthermore, the architectural drawing checking system can mitigate the impact of rising construction material prices and labor shortages, and support efficient design work.

[0054] The architectural drawing analysis unit can detect dimensional errors and structural inconsistencies. For example, the architectural drawing analysis unit analyzes the dimensions of architectural drawings to detect dimensional errors and identifies errors that exceed the allowable error range. In addition, the architectural drawing analysis unit performs structural calculations of the architectural drawings and compares them with design standards to detect structural inconsistencies. For example, the architectural drawing analysis unit analyzes the dimensions of architectural drawings and issues a warning if errors exceed the allowable range. In addition, the architectural drawing analysis unit performs structural calculations and displays an alert if the calculations do not conform to the design standards. This makes it possible to detect design errors early and prevent poor construction.

[0055] The architectural drawing analysis unit can detect areas that violate legal regulations. For example, the architectural drawing analysis unit analyzes architectural drawings based on legal regulations such as the Building Standards Act, the Fire Service Act, and environmental regulations in order to detect areas that violate legal regulations. For example, the architectural drawing analysis unit analyzes architectural drawings based on the Building Standards Act and identifies areas that violate legal regulations. The architectural drawing analysis unit can also analyze architectural drawings based on the Fire Service Act and detect deficiencies in the placement of firefighting equipment or evacuation routes. Furthermore, the architectural drawing analysis unit can analyze architectural drawings based on environmental regulations and identify areas that do not comply with environmental standards. This makes it possible to prevent violations of legal regulations.

[0056] The architectural drawing analysis unit can refer to past construction data and focus on checking areas where similar mistakes are likely to occur. The architectural drawing analysis unit, for example, refers to past construction data and analyzes past project data and construction history to identify areas where similar mistakes are likely to occur. For example, the architectural drawing analysis unit can refer to a database of past construction mistakes and identify areas where mistakes frequently occur. The architectural drawing analysis unit can also focus on checking areas where similar mistakes are likely to occur based on past project data. Furthermore, the architectural drawing analysis unit can analyze past construction history and identify patterns and frequency of mistakes. This makes it possible to prevent mistakes by utilizing past data.

[0057] The architectural drawing analysis unit can generate a 3D model to enable visual confirmation. For example, the architectural drawing analysis unit generates a 3D model based on digital data of architectural drawings to enable visual confirmation of design errors. For example, the architectural drawing analysis unit generates a 3D model based on CAD data to intuitively detect design errors. The architectural drawing analysis unit can also generate a 3D model based on a BIM model to visually confirm design errors. Furthermore, the architectural drawing analysis unit can visually confirm design errors using a 3D viewer or VR technology. This makes it possible to intuitively detect design errors.

[0058] The architectural drawing analysis unit can enable the designer to give verbal instructions using voice input. The architectural drawing analysis unit, for example, is equipped with a voice input function to build a system that allows the designer to give verbal instructions. For example, the designer gives instructions by voice, and the architectural drawing analysis unit analyzes the architectural drawings according to those instructions. The architectural drawing analysis unit can also use voice recognition technology to analyze the designer's voice commands and check the architectural drawings. Furthermore, the architectural drawing analysis unit can accurately recognize the designer's instructions by taking into account the type of microphone and the accuracy of voice recognition. This allows the designer to give instructions by voice.

[0059] The architectural drawing analysis unit can automatically check compatibility between different design software and detect compatibility issues. For example, the architectural drawing analysis unit analyzes architectural drawings created with different design software and identifies compatibility issues. For example, the architectural drawing analysis unit checks the compatibility of drawings created with AutoCAD and Revit and checks data consistency. The architectural drawing analysis unit can also check the compatibility of drawings created with ArchiCAD and drawings created with other design software. Furthermore, the architectural drawing analysis unit can detect compatibility issues based on differences in file formats and data consistency. This makes it possible to check compatibility between different design software.

[0060] The architectural drawing analysis unit can estimate the designer's stress level using an emotion estimation function and automatically suggest taking a break if the stress level is high. The architectural drawing analysis unit, for example, analyzes the designer's facial expressions and voice to estimate the stress level in real time. For example, the architectural drawing analysis unit can use a camera or microphone to analyze the designer's facial expressions and tone of voice and suggest taking a break if the stress level is high. The architectural drawing analysis unit can also use biometrics technology to analyze the designer's heart rate and electrodermal activity to estimate the stress level. Furthermore, the architectural drawing analysis unit can suggest the length and timing of breaks if the stress level is high. This can reduce the designer's stress and support efficient work.

[0061] The architectural drawing analysis unit can monitor the designer's emotions in real time and make suggestions to elicit positive emotions. The architectural drawing analysis unit, for example, analyzes the designer's facial expressions and voice and calculates an emotion score in real time. For example, the architectural drawing analysis unit uses a camera and microphone to analyze the designer's facial expressions and tone of voice and makes suggestions to elicit positive emotions. The architectural drawing analysis unit can also use biometric technology to analyze the designer's heart rate and electrodermal activity and calculate an emotion score. Furthermore, the architectural drawing analysis unit can suggest relaxation methods and improvements to the work environment in order to elicit positive emotions. In this way, work efficiency can be improved by monitoring the designer's emotions and eliciting positive emotions.

[0062] To streamline the design process, the architectural drawing analysis unit can estimate the designer's level of concentration and automatically reallocate tasks if their concentration drops. The architectural drawing analysis unit, for example, analyzes the designer's facial expressions and voice to estimate their level of concentration in real time. For example, the architectural drawing analysis unit uses a camera or microphone to analyze the designer's facial expressions and tone of voice to detect a drop in concentration. The architectural drawing analysis unit can also use biometrics technology to analyze the designer's heart rate and electrodermal activity to estimate their level of concentration. Furthermore, if their concentration drops, the architectural drawing analysis unit can reevaluate task priorities and suggest ways to distribute the workload. This helps maintain the designer's concentration and support efficient work.

[0063] The architectural drawing analysis unit can analyze the designer's past work history and propose an optimal work sequence. The architectural drawing analysis unit, for example, analyzes the designer's past work history and proposes an optimal work sequence. For example, the architectural drawing analysis unit automatically generates an efficient work sequence based on past work logs and project history. The architectural drawing analysis unit can also propose an optimal work sequence from the perspective of work efficiency and time management based on past work data. Furthermore, the architectural drawing analysis unit can also optimize the work sequence by taking into account resource usage. This makes it possible to propose an efficient work sequence by utilizing past work history.

[0064] The architectural drawing analysis unit monitors the usage status of tools and software used by designers in real time and can support the selection of optimal tools. The architectural drawing analysis unit, for example, monitors the usage status of tools and software used by designers in real time. For example, the architectural drawing analysis unit analyzes the performance of tools used by designers and suggests optimal tools. The architectural drawing analysis unit can also support the selection of optimal tools based on the frequency and duration of software use. Furthermore, the architectural drawing analysis unit can also suggest optimal tools taking into account the cost-performance of the tools. This makes it possible to support designers in selecting optimal tools.

[0065] The architectural drawing analysis unit can automatically share resources between different projects and optimize them. The architectural drawing analysis unit, for example, automatically shares resources between different projects. For example, the architectural drawing analysis unit analyzes the resource usage status for each project and proposes optimal resource allocation. The architectural drawing analysis unit can also use cloud storage to efficiently share resources. Furthermore, the architectural drawing analysis unit can also optimize resources to efficiently use resources and reduce waste. This can support efficient project management by optimizing resources.

[0066] The architectural drawing analysis unit can provide an interface compatible with the device used by the designer. The architectural drawing analysis unit provides an interface compatible with the device used by the designer. For example, the architectural drawing analysis unit provides a UI optimized for tablets and smartphones. The architectural drawing analysis unit can also provide an interface compatible with desktops and laptops. Furthermore, the architectural drawing analysis unit can adopt a responsive design to improve usability. This makes it possible to streamline the design process by providing an interface compatible with the device used by the designer.

[0067] The architectural drawing analysis unit can use an emotion estimation function to monitor the designer's emotions in real time and make suggestions to elicit positive emotions. The architectural drawing analysis unit, for example, analyzes the designer's facial expressions and voice and calculates an emotion score in real time. For example, the architectural drawing analysis unit can use a camera or microphone to analyze the designer's facial expressions and tone of voice and make suggestions to elicit positive emotions. The architectural drawing analysis unit can also use biometric technology to analyze the designer's heart rate and electrodermal activity and calculate an emotion score. Furthermore, the architectural drawing analysis unit can suggest relaxation methods and improvements to the work environment to elicit positive emotions. In this way, work efficiency can be improved by monitoring the designer's emotions and eliciting positive emotions.

[0068] The architectural drawing analysis unit can estimate the stress level of a builder and automatically suggest taking a break if the stress level is high. The architectural drawing analysis unit, for example, analyzes the builder's facial expressions and voice to estimate the stress level in real time. For example, the architectural drawing analysis unit uses a camera or microphone to analyze the builder's facial expressions and tone of voice and suggests taking a break if the stress level is high. The architectural drawing analysis unit can also use biometric technology to analyze the builder's heart rate and electrodermal activity to estimate the stress level. Furthermore, the architectural drawing analysis unit can also suggest the length and timing of breaks if the stress level is high. This can reduce the builder's stress and support efficient work.

[0069] The architectural drawing analysis unit can refer to past construction data and focus on checking areas where similar mistakes are likely to occur. The architectural drawing analysis unit, for example, refers to past construction data and analyzes past project data and construction history to identify areas where similar mistakes are likely to occur. For example, the architectural drawing analysis unit can refer to a database of past construction mistakes and identify areas where mistakes frequently occur. The architectural drawing analysis unit can also focus on checking areas where similar mistakes are likely to occur based on past project data. Furthermore, the architectural drawing analysis unit can analyze past construction history and identify patterns and frequency of mistakes. This makes it possible to prevent mistakes by utilizing past data.

[0070] The architectural drawing analysis unit can analyze real-time video of the construction site and immediately detect and notify of construction errors. The architectural drawing analysis unit, for example, analyzes real-time video of the construction site and immediately detects construction errors. For example, the architectural drawing analysis unit analyzes camera video in real time and notifies if a construction error is detected. The architectural drawing analysis unit can also use video analysis algorithms to detect violations of construction procedures or failure to meet quality standards. Furthermore, the architectural drawing analysis unit can display an alert or issue an audio notification if a construction error is detected. This makes it possible to prevent construction defects by detecting construction errors in real time and notifying immediately.

[0071] The architectural drawing analysis unit can use voice input to enable the builder to give verbal instructions. The architectural drawing analysis unit is, for example, equipped with a voice input function to build a system that allows the builder to give verbal instructions. For example, the builder gives instructions by voice, and the architectural drawing analysis unit checks the construction site according to those instructions. The architectural drawing analysis unit can also use voice recognition technology to analyze the builder's voice commands and detect construction errors. Furthermore, the architectural drawing analysis unit can accurately recognize the builder's instructions by taking into account the type of microphone and the accuracy of voice recognition. This allows the builder to give instructions by voice.

[0072] The architectural drawing analysis unit can automatically check compatibility between different construction management software and detect compatibility issues. For example, the architectural drawing analysis unit analyzes construction data created with different construction management software and identifies compatibility issues. For example, the architectural drawing analysis unit checks the compatibility of data created with Procore and data created with Buildertrend to check data consistency. The architectural drawing analysis unit can also check the compatibility of data created with CoConstruct and data created with other construction management software. Furthermore, the architectural drawing analysis unit can detect compatibility issues based on differences in file formats and data consistency. This makes it possible to check compatibility between different construction management software.

[0073] The architectural drawing analysis unit can use an emotion estimation function to monitor the emotions of the builder in real time and make suggestions to elicit positive emotions. The architectural drawing analysis unit, for example, analyzes the builder's facial expressions and voice and calculates an emotion score in real time. For example, the architectural drawing analysis unit can use a camera or microphone to analyze the builder's facial expressions and tone of voice and make suggestions to elicit positive emotions. The architectural drawing analysis unit can also use biometric technology to analyze the builder's heart rate and electrodermal activity and calculate an emotion score. Furthermore, the architectural drawing analysis unit can suggest relaxation methods and improvements to the work environment to elicit positive emotions. In this way, work efficiency can be improved by monitoring the builder's emotions and eliciting positive emotions.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] The architectural drawing analysis unit can analyze the designer's past work history and propose the optimal work sequence. For example, the architectural drawing analysis unit automatically generates an efficient work sequence based on past work logs and project history. The architectural drawing analysis unit can also propose the optimal work sequence from the perspective of work efficiency and time management based on past work data. Furthermore, the architectural drawing analysis unit can also optimize the work sequence by taking into account resource usage. This makes it possible to propose an efficient work sequence by utilizing past work history.

[0076] The architectural drawing analysis unit can analyze real-time video footage of the construction site and instantly detect and notify construction errors. For example, the architectural drawing analysis unit can analyze camera footage in real time and notify if a construction error is detected. The architectural drawing analysis unit can also use video analysis algorithms to detect violations of construction procedures or failure to meet quality standards. Furthermore, the architectural drawing analysis unit can display an alert or issue an audio notification if a construction error is detected. This makes it possible to detect construction errors in real time and immediately notify, thereby preventing poor construction work.

[0077] The architectural drawing analysis unit can automatically check compatibility between different design software and detect compatibility issues. For example, the architectural drawing analysis unit analyzes architectural drawings created with different design software and identifies compatibility issues. For example, the architectural drawing analysis unit checks the compatibility of drawings created with AutoCAD and drawings created with Revit and checks data consistency. The architectural drawing analysis unit can also check the compatibility of drawings created with ArchiCAD and drawings created with other design software. Furthermore, the architectural drawing analysis unit can detect compatibility issues based on differences in file formats and data consistency. This makes it possible to check compatibility between different design software.

[0078] The architectural drawing analysis unit can use voice input to enable the designer to give verbal instructions. For example, the architectural drawing analysis unit is equipped with a voice input function to build a system that allows the designer to give verbal instructions. For example, the designer gives instructions by voice, and the architectural drawing analysis unit analyzes the architectural drawings in accordance with the instructions. The architectural drawing analysis unit can also use voice recognition technology to analyze the designer's voice commands and check the architectural drawings. Furthermore, the architectural drawing analysis unit can accurately recognize the designer's instructions by taking into account the type of microphone and the accuracy of voice recognition. This allows the designer to give instructions by voice.

[0079] The architectural drawing analysis unit monitors the usage status of tools and software used by designers in real time and can support the selection of the most appropriate tools. For example, the architectural drawing analysis unit analyzes the performance of the tools used by designers and suggests the most appropriate tools. The architectural drawing analysis unit can also support the selection of the most appropriate tools based on the frequency and duration of software use. Furthermore, the architectural drawing analysis unit can also suggest the most appropriate tools taking into account the cost-effectiveness of the tools. This makes it possible to support designers in selecting the most appropriate tools.

[0080] The architectural drawing analysis unit can estimate the designer's stress level using an emotion estimation function and automatically suggest taking a break if the stress level is high. For example, the architectural drawing analysis unit analyzes the designer's facial expressions and voice to estimate the stress level in real time. For example, the architectural drawing analysis unit uses a camera and microphone to analyze the designer's facial expressions and tone of voice and suggests taking a break if the stress level is high. The architectural drawing analysis unit can also use biometrics technology to analyze the designer's heart rate and electrodermal activity to estimate the stress level. Furthermore, the architectural drawing analysis unit can suggest the length and timing of breaks if the stress level is high. This can reduce the designer's stress and support efficient work.

[0081] The architectural drawing analysis unit can monitor the designer's emotions in real time and make suggestions to elicit positive emotions. For example, the architectural drawing analysis unit analyzes the designer's facial expressions and voice and calculates an emotion score in real time. For example, the architectural drawing analysis unit uses a camera and microphone to analyze the designer's facial expressions and tone of voice and makes suggestions to elicit positive emotions. The architectural drawing analysis unit can also use biometric technology to analyze the designer's heart rate and electrodermal activity and calculate an emotion score. Furthermore, the architectural drawing analysis unit can make suggestions for relaxation methods and improvements to the work environment in order to elicit positive emotions. In this way, work efficiency can be improved by monitoring the designer's emotions and eliciting positive emotions.

[0082] To streamline the design process, the architectural drawing analysis unit can use its emotion estimation function to estimate a designer's level of concentration and automatically reallocate tasks if their concentration wanes. For example, the architectural drawing analysis unit analyzes the designer's facial expressions and voice to estimate their concentration in real time. For example, the architectural drawing analysis unit uses a camera and microphone to analyze the designer's facial expressions and tone of voice to detect a lapse in concentration. The architectural drawing analysis unit can also use biometrics technology to analyze the designer's heart rate and electrodermal activity to estimate their concentration. Furthermore, if their concentration wanes, the architectural drawing analysis unit can reevaluate task priorities and suggest ways to distribute the workload. This helps maintain the designer's concentration and support efficient work.

[0083] The architectural drawing analysis unit can use an emotion estimation function to monitor the emotions of the builder in real time and make suggestions to elicit positive emotions. For example, the architectural drawing analysis unit analyzes the builder's facial expressions and voice and calculates an emotion score in real time. For example, the architectural drawing analysis unit uses a camera and microphone to analyze the builder's facial expressions and tone of voice and makes suggestions to elicit positive emotions. The architectural drawing analysis unit can also use biometric technology to analyze the builder's heart rate and electrodermal activity and calculate an emotion score. Furthermore, the architectural drawing analysis unit can suggest relaxation methods and improvements to the work environment to elicit positive emotions. In this way, work efficiency can be improved by monitoring the builder's emotions and eliciting positive emotions.

[0084] The architectural drawing analysis unit can estimate a builder's stress level using an emotion estimation function and automatically suggest taking a break if stress is high. For example, the architectural drawing analysis unit analyzes the builder's facial expressions and voice to estimate the stress level in real time. For example, the architectural drawing analysis unit uses a camera and microphone to analyze the builder's facial expressions and tone of voice and suggests taking a break if stress is high. The architectural drawing analysis unit can also use biometrics technology to analyze the builder's heart rate and electrodermal activity to estimate the builder's stress level. Furthermore, the architectural drawing analysis unit can suggest the length and timing of breaks if the stress level is high. This can reduce builder stress and support efficient work.

[0085] The processing flow of the second embodiment will be briefly explained below.

[0086] Step 1: The architectural drawing analysis unit analyzes architectural drawings. For example, it analyzes architectural drawings entered as digital data to detect dimensional errors and structural inconsistencies. It can also detect areas that violate legal regulations. Furthermore, it can refer to past construction data and focus on areas where similar mistakes are likely to occur. Step 2: The design error detection unit detects design errors from the results of analysis by the architectural drawing analysis unit. For example, it detects dimensional errors, structural inconsistencies, and violations of legal regulations. It can also identify areas where similar errors are likely to occur based on past construction data. Step 3: The notification unit notifies the designer of the design errors detected by the design error detection unit. For example, it can notify the designer by email, alert display, or voice notification. It can also provide an interface compatible with the designer's device and notify the designer of design errors in real time.

[0087] 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.

[0088] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0089] 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.

[0090] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0100] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0101] 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.

[0102] 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.

[0103] 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 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.

[0104] 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.

[0105] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] 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.

[0117] 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.

[0118] 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 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.

[0119] 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.

[0120] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0121] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] 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 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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."

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0153] 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. [Explanation of symbols]

[0154] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an architectural drawing analysis unit that analyzes architectural drawings; a design error detection unit that detects design errors from the results of the analysis by the architectural drawing analysis unit; a notification unit that notifies a designer of a design error detected by the design error detection unit. A system characterized by:

2. The architectural drawing analysis unit Generate the 3D model and make it visible 2. The system of claim 1.

3. The architectural drawing analysis unit Voice input allows the designer to give verbal instructions 2. The system of claim 1.

4. The architectural drawing analysis unit To streamline the design process, the system estimates the designer's concentration and automatically reallocates tasks when the designer's concentration drops.

2. The system of claim 1.

5. The architectural drawing analysis unit Automatically share resources between different projects and optimize them 2. The system of claim 1.

6. The architectural drawing analysis unit Estimates the worker's stress level and automatically suggests breaks if stress levels are high 2. The system of claim 1.

7. The architectural drawing analysis unit Analyzes real-time video footage of construction sites, instantly detects and notifies construction errors 2. The system of claim 1.

8. The architectural drawing analysis unit The system uses an emotion estimation function to estimate the designer's stress level and automatically suggests taking a break if the stress level is high.

2. The system of claim 1.

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