System

Abstract

An object of a system according to an embodiment is to automate a design process of building equipment and improve efficiency.SOLUTION: A system includes a construction drawing analysis part, a capacity calculation part, an equipment drawing creation part, and an approximate construction amount calculation part. The building drawing analysis unit analyzes a building drawing. The capacity calculation part calculates the capacity based on the information analyzed by the construction drawing analysis part. The equipment drawing creation unit creates an equipment drawing based on the capacity calculated by the capacity calculation unit. The estimated construction amount calculation unit calculates an estimated construction amount based on the equipment drawing created by the equipment drawing creation 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] Conventional technology requires manual data entry and drawing when designing building equipment, which makes it inefficient.

[0005] The system according to the embodiment aims to automate the design process of building equipment and improve its efficiency. [Means for solving the problem]

[0006] The system according to the embodiment includes an architectural drawing analysis unit, a capacity calculation unit, an equipment drawing creation unit, and an estimated construction cost calculation unit. The architectural drawing analysis unit analyzes architectural drawings. The capacity calculation unit calculates capacity based on information analyzed by the architectural drawing analysis unit. The equipment drawing creation unit creates equipment drawings based on the capacity calculated by the capacity calculation unit. The estimated construction cost calculation unit calculates an estimated construction cost based on the equipment drawings created by the equipment drawing creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automate the building equipment design process and improve efficiency. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The automated design system according to an embodiment of the present invention automatically analyzes architectural drawings, calculates capacity, creates equipment drawings, and calculates the approximate cost of construction work. As a result, the automated design system automatically calculates capacity, creates equipment drawings, and calculates the approximate cost of construction work simply by inputting architectural drawings, and the user only needs to confirm the contents of the generated calculations and drawings.

[0029] An automated design system according to an embodiment includes an architectural drawing analysis unit, a capacity calculation unit, an equipment drawing creation unit, and a construction cost estimation calculation unit. The architectural drawing analysis unit analyzes architectural drawings. For example, the architectural drawing analysis unit automatically recognizes the size and use of a building, the location of columns and walls, and the like, and extracts necessary information. The architectural drawing analysis unit receives an architectural drawing data file as input and performs analysis based on the data. The capacity calculation unit calculates capacity based on the information analyzed by the architectural drawing analysis unit. For example, the capacity calculation unit calculates the capacity of air conditioning equipment and electrical equipment based on the building's area and use. The capacity calculation unit automatically calculates the required capacity based on the building's size and use. The equipment drawing creation unit creates equipment drawings based on the capacity calculated by the capacity calculation unit. For example, the equipment drawing creation unit draws the layout of air conditioning ducts and the route of electrical wiring on the architectural drawings. The equipment drawing creation unit automatically creates equipment drawings showing the installation locations of equipment and piping and wiring routes based on the results of the capacity calculation. The approximate construction cost calculation unit calculates the approximate construction cost based on the equipment drawings created by the equipment drawing creation unit. For example, the approximate construction cost calculation unit estimates the necessary costs based on the materials used and the scale of the construction. The approximate construction cost calculation unit also automatically calculates the approximate construction cost based on the information in the equipment drawings. As a result, the automated design system according to the embodiment automates processes from analyzing architectural drawings to calculating capacity, creating equipment drawings, and calculating the approximate construction cost, thereby significantly improving the work efficiency of design offices.

[0030] The architectural drawing analysis unit can take into account the historical background and cultural elements of architectural drawings and reflect them in the design. For example, when the generation AI analyzes architectural drawings, the architectural drawing analysis unit retrieves the building's historical background and cultural elements from a database and reflects them in the design. For example, it creates designs that take into account the restoration and reuse of historical buildings. The architectural drawing analysis unit also takes into account local culture and traditions when analyzing architectural drawings and reflects them in the design. For example, it creates designs that incorporate architectural styles and decorations unique to the region. The architectural drawing analysis unit also develops algorithms to take into account the building's historical background and cultural elements when the generation AI analyzes architectural drawings and reflect them in the design. For example, it automatically extracts features of historical buildings and incorporates them into new designs. This makes it possible to design buildings that take into account their historical background and cultural elements.

[0031] The architectural drawing analysis unit can evaluate the energy efficiency and environmental impact of architectural drawings and propose optimal designs. For example, when the generation AI analyzes architectural drawings, the architectural drawing analysis unit evaluates the energy efficiency of a building and proposes optimal designs. For example, it optimizes the placement of insulation materials and the location of windows. The architectural drawing analysis unit also evaluates the environmental impact when analyzing architectural drawings and proposes sustainable designs. For example, it creates designs that take into account the use of renewable energy and waste reduction. The architectural drawing analysis unit also builds a database for evaluating energy efficiency and environmental impact when the generation AI analyzes architectural drawings, and proposes optimal designs based on that. For example, it creates designs that minimize energy consumption and CO2 emissions. This enables sustainable designs that take energy efficiency and environmental impact into account.

[0032] The architectural drawing analysis unit can compare the analysis results with other architectural projects and extract best practices. For example, the architectural drawing analysis unit stores the analysis results of architectural drawings in a database and builds a system for comparing them with other architectural projects. For example, it extracts best practices based on past success stories. The architectural drawing analysis unit also compares the analysis results with other architectural projects to identify common success factors. For example, it analyzes whether the use of specific design methods or materials contributed to success. The architectural drawing analysis unit also develops algorithms for comparing the analysis results of architectural drawings with other projects and automatically extracts best practices. For example, it can propose optimal designs based on data from similar projects. This makes it possible to propose optimal designs by comparing with other architectural projects.

[0033] The architectural drawing analysis unit can automatically generate the analysis results as a 3D model, allowing them to be visually confirmed. For example, the architectural drawing analysis unit develops a system in which a generative AI analyzes architectural drawings and automatically generates a 3D model based on the results. For example, the internal structure and exterior of a building are displayed in 3D. The architectural drawing analysis unit also automatically generates the analysis results of architectural drawings as a 3D model, allowing users to visually confirm them. For example, VR technology can be used to virtually walk around the interior of a building. The architectural drawing analysis unit also builds a system in which a generative AI analyzes architectural drawings and generates a 3D model based on the results in real time. For example, design changes are instantly reflected in the 3D model. This allows the analysis results to be visually confirmed as a 3D model.

[0034] The capacity calculation unit can propose a flexible design that takes future expansion and changes into consideration when calculating capacity. For example, the capacity calculation unit develops a system that proposes a design that takes future expansion and changes into consideration when the generation AI performs capacity calculations. For example, it creates a design that allows for future expansion. The capacity calculation unit also proposes a flexible design that can accommodate future changes based on the results of the capacity calculation. For example, it creates a design that makes it easy to change the layout of equipment. The capacity calculation unit also builds a database to take future expansion and changes into consideration when the generation AI performs capacity calculations, and proposes a design based on that. For example, it creates a design that can accommodate future technological innovations. This makes it possible to have a flexible design that takes future expansion and changes into consideration.

[0035] The capacity calculation unit can calculate the optimal capacity by taking into account the frequency of building use and seasonal fluctuations when calculating capacity. For example, the capacity calculation unit develops a system that takes into account the frequency of building use and seasonal fluctuations when the generation AI calculates capacity. For example, it calculates different air conditioning capacities for summer and winter. The capacity calculation unit also proposes the optimal capacity that can accommodate the frequency of building use and seasonal fluctuations based on the results of the capacity calculation. For example, it creates a design that accommodates time periods with high usage. The capacity calculation unit also builds a database that takes into account the frequency of use and seasonal fluctuations when the generation AI calculates capacity, and calculates the optimal capacity based on that. For example, it adjusts the capacity based on past usage data. This makes it possible to calculate the optimal capacity that takes into account the frequency of use and seasonal fluctuations.

[0036] The capacity calculation unit can compare the results of the capacity calculation with other building projects and propose the optimal design. For example, the capacity calculation unit stores the results of the capacity calculation in a database and builds a system for comparing with other building projects. For example, it proposes the optimal design based on past successful cases. The capacity calculation unit also compares the results of the capacity calculation with other building projects to identify common success factors. For example, it analyzes whether the use of specific design methods or materials contributed to success. The capacity calculation unit also develops an algorithm for comparing the results of the capacity calculation with other projects and automatically proposes the optimal design. For example, it proposes the optimal design based on data from similar projects. This makes it possible to propose the optimal design by comparing with other building projects.

[0037] The capacity calculation unit can perform calculations that take into account different scenarios when calculating capacity. For example, the capacity calculation unit develops a system that takes disasters and emergencies into account when the generation AI performs capacity calculations. For example, it calculates the air conditioning and power supply capacity that can be used in an emergency. The capacity calculation unit also proposes a design that can be used in a disaster or emergency based on the results of the capacity calculation. For example, it creates a design that takes into account emergency power sources and evacuation routes. The capacity calculation unit also builds a database that takes into account different scenarios when the generation AI performs capacity calculations, and calculates the optimal capacity based on that. For example, it adjusts the capacity based on simulation data from a disaster. This makes it possible to calculate capacity that takes into account different scenarios.

[0038] The equipment drawing creation department can create designs that reflect the latest architectural trends and technologies when creating equipment drawings. For example, the equipment drawing creation department develops a system that reflects the latest architectural trends and technologies when the generating AI creates equipment drawings. For example, it creates designs that incorporate the latest energy efficiency technologies and smart home technologies. In addition, when creating equipment drawings, the equipment drawing creation department retrieves the latest architectural trends and technologies from a database and creates designs based on that. For example, it creates designs that incorporate the latest energy-saving equipment and automated systems. In addition, the equipment drawing creation department develops algorithms that reflect the latest architectural trends and technologies when the generating AI creates equipment drawings. For example, it automatically analyzes the latest technological trends and reflects them in the design. This makes it possible to create designs that reflect the latest architectural trends and technologies.

[0039] The equipment drawing creation unit can create designs that take into consideration the aesthetics and design of the building when creating equipment drawings. For example, the equipment drawing creation unit develops a system that takes into consideration the aesthetics and design of the building when the generating AI creates equipment drawings. For example, it designs the placement of piping and wiring so as not to spoil the aesthetics. Furthermore, when creating equipment drawings, the equipment drawing creation unit retrieves the design of the building from a database and creates the design based on that. For example, it creates equipment placement that harmonizes with the interior design. Furthermore, the equipment drawing creation unit develops algorithms that take into consideration the aesthetics and design when the generating AI creates equipment drawings. For example, it designs based on an index that evaluates the beauty of the design. This makes it possible to create designs that take into consideration the aesthetics and design of the building.

[0040] The equipment drawing creation department can compare the results of equipment drawings with other architectural projects and propose the optimal design. For example, the equipment drawing creation department stores the results of equipment drawings in a database and builds a system for comparing them with other architectural projects. For example, it proposes the optimal design based on past success stories. The equipment drawing creation department also compares the results of equipment drawings with other architectural projects and identifies common success factors. For example, it analyzes whether the use of specific design methods or materials contributed to success. The equipment drawing creation department also develops an algorithm for comparing the results of equipment drawings with other projects and automatically proposes the optimal design. For example, it proposes the optimal design based on data from similar projects. This makes it possible to propose the optimal design by comparing with other architectural projects.

[0041] The equipment drawing creation unit can present different design options when creating equipment drawings, allowing the user to select from them. The equipment drawing creation unit, for example, develops a system that presents different design options when the generation AI creates equipment drawings. For example, it proposes multiple piping routes and equipment layouts. The equipment drawing creation unit also retrieves different design options from a database when creating equipment drawings, allowing the user to select from them. For example, it proposes equipment with different designs and functions. The equipment drawing creation unit also develops an algorithm for presenting different design options when the generation AI creates equipment drawings. For example, it proposes the optimal option based on the user's requests. This allows different design options to be presented and the user to select from.

[0042] The approximate construction cost calculation unit can reflect the latest market prices and material costs in real time when calculating the approximate construction cost. For example, the approximate construction cost calculation unit develops a system in which, when the generation AI calculates the approximate construction cost, it obtains the latest market prices and material costs in real time and makes an estimate based on that. For example, it obtains price information from an online database. Furthermore, when calculating the approximate construction cost, the approximate construction cost calculation unit obtains the latest market prices and material costs from the database and makes an estimate based on that. For example, it makes an estimate that takes material price fluctuations into account. Furthermore, the approximate construction cost calculation unit develops an algorithm to reflect the latest market prices and material costs in real time when the generation AI calculates the approximate construction cost. For example, it automatically updates price information and reflects it in the estimate. This makes it possible to calculate an approximate construction cost that reflects the latest market prices and material costs in real time.

[0043] The approximate construction cost calculation unit can improve accuracy by referring to past project data when calculating an approximate construction cost. For example, the approximate construction cost calculation unit develops a system that refers to past project data when the generation AI calculates an approximate construction cost. For example, accuracy is improved based on past estimate data. Furthermore, when calculating an approximate construction cost, the approximate construction cost calculation unit retrieves past project data from a database and makes an estimate based on that. For example, an estimate is made by referring to data on similar projects. Furthermore, the approximate construction cost calculation unit develops an algorithm that refers to past project data when the generation AI calculates an approximate construction cost. For example, past data is analyzed and reflected in the estimate. This makes it possible to calculate an approximate construction cost with improved accuracy by referring to past project data.

[0044] The approximate construction cost calculation unit can compare the calculation results of the approximate construction cost with other construction projects and propose the optimal cost plan. The approximate construction cost calculation unit, for example, develops a system that stores the calculation results of the approximate construction cost in a database and compares it with other construction projects. For example, it proposes an optimal cost plan based on past successful cases. The approximate construction cost calculation unit also compares the calculation results of the approximate construction cost with other construction projects and identifies common success factors. For example, it analyzes whether the use of specific design methods or materials contributed to success. The approximate construction cost calculation unit also develops an algorithm for comparing the calculation results of the approximate construction cost with other projects and automatically proposes the optimal cost plan. For example, it proposes the optimal cost plan based on data from similar projects. In this way, it is possible to propose the optimal cost plan by comparing it with other construction projects.

[0045] The approximate construction cost calculation unit can perform calculations that take into account different scenarios when calculating the approximate construction cost. For example, the approximate construction cost calculation unit develops a system that allows the generation AI to take different scenarios into account when calculating the approximate construction cost. For example, it generates an estimate that takes into account changes in materials or a shortened construction period. Furthermore, the approximate construction cost calculation unit retrieves different scenarios from a database when calculating the approximate construction cost and generates an estimate based on those scenarios. For example, it generates an estimate for when different materials or construction methods are used. Furthermore, the approximate construction cost calculation unit develops an algorithm that allows the generation AI to take into account different scenarios when calculating the approximate construction cost. For example, it automatically calculates the cost for each scenario and reflects it in the estimate. This makes it possible to calculate the approximate construction cost by taking different scenarios into account.

[0046] The contents of calculations and drawings automatically generated by generative AI will be automatically requested to be reviewed by third-party experts, thereby improving their accuracy. A system will be developed that automatically sends the contents of calculations and drawings automatically generated by generative AI to third-party experts for review. For example, the contents will be revised based on expert feedback. The accuracy of automatically generated calculations and drawings will also be improved by having them reviewed by third-party experts. For example, revisions will be made to reflect the opinions of experts. An algorithm will also be developed to automatically request a review by third-party experts of the contents of calculations and drawings automatically generated by generative AI. For example, the contents will be improved based on expert evaluations. This will improve the accuracy of calculations and drawings through review by third-party experts.

[0047] The contents of calculations and drawings automatically generated by the generation AI can be compared with past project data to detect outliers. A system will be developed that compares the contents of calculations and drawings automatically generated by the generation AI with past project data. For example, it will automatically detect outliers and suggest corrections. Furthermore, automatically generated calculations and drawings will be compared with past project data to identify outliers. For example, it will detect outliers based on past data and make corrections. Furthermore, an algorithm will be developed to compare the contents of calculations and drawings automatically generated by the generation AI with past project data. For example, it will automatically detect outliers and suggest corrections. This will make it possible to detect outliers and suggest corrections by comparing with past project data.

[0048] The contents of calculations and drawings automatically generated by generative AI can be compared with other architectural projects to propose the optimal design. We will develop a system that compares the contents of calculations and drawings automatically generated by generative AI with other architectural projects. For example, we will propose the optimal design based on past successful cases. We will also compare the contents of automatically generated calculations and drawings with other architectural projects to identify common success factors. For example, we will analyze whether the use of specific design methods or materials contributed to success. We will also develop an algorithm to compare the contents of calculations and drawings automatically generated by generative AI with other projects to automatically propose the optimal design. For example, we will propose the optimal design based on data from similar projects. This will make it possible to propose the optimal design by comparing with other architectural projects.

[0049] The contents of calculations and drawings automatically generated by generative AI can be output in different formats, improving user convenience. We will develop a system that outputs the contents of calculations and drawings automatically generated by generative AI in different formats. For example, outputting as PDF or CAD data. We will also output the contents of automatically generated calculations and drawings in different formats, providing them in a format that is easy for users to use. For example, we will allow users to select the format according to their needs. We will also develop an algorithm to output the contents of calculations and drawings automatically generated by generative AI in different formats. For example, we will automatically perform format conversion and provide them to the user. This will improve user convenience by outputting them in different formats.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The automated design system can further include a seismic evaluation unit that evaluates the seismic performance of a building. The seismic evaluation unit evaluates the seismic performance of a building based on the information analyzed by the architectural drawing analysis unit. For example, the seismic evaluation unit evaluates the building's resistance to shaking during an earthquake, taking into account the structure and material properties of the building. The seismic evaluation unit also checks whether the building design complies with the latest seismic standards and proposes design modifications as necessary. Furthermore, the seismic evaluation unit can simulate the seismic performance of a building based on past earthquake data and propose optimal seismic designs. This makes it possible to evaluate the seismic performance of a building and achieve safer designs.

[0052] The automated design system can further include a fire evaluation unit that evaluates the fire resistance of a building. The fire evaluation unit evaluates the fire resistance of a building based on the information analyzed by the architectural drawing analysis unit. For example, the fire evaluation unit evaluates the fire resistance of the building's structure and the materials used to confirm safety in the event of a fire. The fire evaluation unit also checks whether the building design complies with the latest fire safety standards and proposes design modifications as necessary. Furthermore, the fire evaluation unit can simulate the fire resistance of a building based on past fire data and propose an optimal fire resistance design. This makes it possible to evaluate the fire resistance of a building and achieve a safer design.

[0053] The automated design system can further include an acoustic evaluation unit that evaluates the acoustic performance of a building. The acoustic evaluation unit evaluates the acoustic performance of a building based on the information analyzed by the architectural drawing analysis unit. For example, the acoustic evaluation unit simulates how sound propagates, taking into account the structure of the building's walls and floors. The acoustic evaluation unit also checks whether the building design complies with the latest acoustic standards and proposes design modifications as necessary. Furthermore, the acoustic evaluation unit can simulate the acoustic performance of a building based on past acoustic data and propose an optimal acoustic design. This makes it possible to evaluate the acoustic performance of a building and create a more comfortable environment.

[0054] The automated design system can further include an energy optimization unit that optimizes the energy consumption of a building. The energy optimization unit optimizes the energy consumption of a building based on the information analyzed by the architectural drawing analysis unit. For example, the energy optimization unit evaluates the building's insulation performance and the efficiency of the air conditioning system, and proposes a design that minimizes energy consumption. The energy optimization unit can also consider the use of renewable energy and propose the introduction of solar power generation or wind power generation. Furthermore, the energy optimization unit can simulate the energy consumption of a building based on past energy consumption data and propose optimal energy management. This makes it possible to optimize the energy consumption of a building and achieve a sustainable design.

[0055] The automated design system can further include a life cycle cost evaluation unit that evaluates the life cycle cost of a building. The life cycle cost evaluation unit evaluates the life cycle cost of a building based on the information analyzed by the architectural drawing analysis unit. For example, the life cycle cost evaluation unit evaluates the overall cost by taking into account not only the construction cost of the building but also the operating and maintenance costs. The life cycle cost evaluation unit also checks whether the building design is economical in the long term and proposes design modifications as necessary. Furthermore, the life cycle cost evaluation unit can simulate the life cycle cost of a building based on past project data and propose optimal cost management. This makes it possible to evaluate the life cycle cost of a building and achieve an economical design.

[0056] The automated design system can further include a comfort evaluation unit that evaluates the comfort of the building. The comfort evaluation unit evaluates the comfort of the building based on the information analyzed by the architectural drawing analysis unit. For example, the comfort evaluation unit takes into account the building's temperature, humidity, and lighting arrangement to evaluate the environment in which residents can live comfortably. The comfort evaluation unit also checks whether the building design complies with the latest comfort standards and proposes design modifications as necessary. Furthermore, the comfort evaluation unit can simulate the comfort of the building based on past comfort data and propose an optimal comfort design. This makes it possible to evaluate the comfort of the building and create a more comfortable environment.

[0057] The automated design system can further include a security evaluation unit that evaluates the security performance of a building. The security evaluation unit evaluates the security performance of a building based on the information analyzed by the architectural drawing analysis unit. For example, the security evaluation unit evaluates the risk of unauthorized intrusion by taking into account the layout of the building's entrances and windows. The security evaluation unit also checks whether the building design complies with the latest security standards and proposes design modifications as necessary. Furthermore, the security evaluation unit can simulate the security performance of a building based on past security data and propose an optimal security design. This makes it possible to evaluate the security performance of a building and achieve a safer design.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The architectural drawing analysis unit analyzes the architectural drawings. For example, the architectural drawing analysis unit automatically recognizes the size and use of the building, the position of pillars and walls, and extracts the necessary information. The architectural drawing analysis unit also receives architectural drawing data files as input and performs analysis based on that data. Step 2: The capacity calculation unit calculates the capacity based on the information analyzed by the architectural drawing analysis unit. For example, the capacity calculation unit calculates the capacity of air conditioning equipment and electrical equipment based on the building's area and intended use. The capacity calculation unit also automatically calculates the required capacity based on the building's size and intended use. Step 3: The equipment drawing creation unit creates an equipment drawing based on the capacity calculated by the capacity calculation unit. For example, the equipment drawing creation unit draws the layout of air conditioning ducts and the route of electrical wiring on an architectural drawing. Furthermore, the equipment drawing creation unit automatically creates an equipment drawing showing the installation location of the equipment and the piping and wiring route based on the results of the capacity calculation. Step 4: The approximate construction cost calculation unit calculates the approximate construction cost based on the equipment drawings created by the equipment drawing creation unit. For example, the approximate construction cost calculation unit estimates the necessary costs based on the materials to be used and the scale of the construction. The approximate construction cost calculation unit also automatically calculates the approximate construction cost based on the information in the equipment drawings.

[0060] (Example 2) The automated design system according to an embodiment of the present invention automatically analyzes architectural drawings, calculates capacity, creates equipment drawings, and calculates the approximate cost of construction work. As a result, the automated design system automatically calculates capacity, creates equipment drawings, and calculates the approximate cost of construction work simply by inputting architectural drawings, and the user only needs to confirm the contents of the generated calculations and drawings.

[0061] An automated design system according to an embodiment includes an architectural drawing analysis unit, a capacity calculation unit, an equipment drawing creation unit, and a construction cost estimation calculation unit. The architectural drawing analysis unit analyzes architectural drawings. For example, the architectural drawing analysis unit automatically recognizes the size and use of a building, the location of columns and walls, and the like, and extracts necessary information. The architectural drawing analysis unit receives an architectural drawing data file as input and performs analysis based on the data. The capacity calculation unit calculates capacity based on the information analyzed by the architectural drawing analysis unit. For example, the capacity calculation unit calculates the capacity of air conditioning equipment and electrical equipment based on the building's area and use. The capacity calculation unit automatically calculates the required capacity based on the building's size and use. The equipment drawing creation unit creates equipment drawings based on the capacity calculated by the capacity calculation unit. For example, the equipment drawing creation unit draws the layout of air conditioning ducts and the route of electrical wiring on the architectural drawings. The equipment drawing creation unit automatically creates equipment drawings showing the installation locations of equipment and piping and wiring routes based on the results of the capacity calculation. The approximate construction cost calculation unit calculates the approximate construction cost based on the equipment drawings created by the equipment drawing creation unit. For example, the approximate construction cost calculation unit estimates the necessary costs based on the materials used and the scale of the construction. The approximate construction cost calculation unit also automatically calculates the approximate construction cost based on the information in the equipment drawings. As a result, the automated design system according to the embodiment automates processes from analyzing architectural drawings to calculating capacity, creating equipment drawings, and calculating the approximate construction cost, thereby significantly improving the work efficiency of design offices.

[0062] The architectural drawing analysis unit can take into account the historical background and cultural elements of architectural drawings and reflect them in the design. For example, when the generation AI analyzes architectural drawings, the architectural drawing analysis unit retrieves the building's historical background and cultural elements from a database and reflects them in the design. For example, it creates designs that take into account the restoration and reuse of historical buildings. The architectural drawing analysis unit also takes into account local culture and traditions when analyzing architectural drawings and reflects them in the design. For example, it creates designs that incorporate architectural styles and decorations unique to the region. The architectural drawing analysis unit also develops algorithms to take into account the building's historical background and cultural elements when the generation AI analyzes architectural drawings and reflect them in the design. For example, it automatically extracts features of historical buildings and incorporates them into new designs. This makes it possible to design buildings that take into account their historical background and cultural elements.

[0063] The architectural drawing analysis unit can evaluate the energy efficiency and environmental impact of architectural drawings and propose optimal designs. For example, when the generation AI analyzes architectural drawings, the architectural drawing analysis unit evaluates the energy efficiency of a building and proposes optimal designs. For example, it optimizes the placement of insulation materials and the location of windows. The architectural drawing analysis unit also evaluates the environmental impact when analyzing architectural drawings and proposes sustainable designs. For example, it creates designs that take into account the use of renewable energy and waste reduction. The architectural drawing analysis unit also builds a database for evaluating energy efficiency and environmental impact when the generation AI analyzes architectural drawings, and proposes optimal designs based on that. For example, it creates designs that minimize energy consumption and CO2 emissions. This enables sustainable designs that take energy efficiency and environmental impact into account.

[0064] The architectural drawing analysis unit can use the emotion estimation function to reflect the designer's intentions and emotions in the analysis results of architectural drawings. For example, the architectural drawing analysis unit uses the emotion estimation function to provide analysis results that reflect the designer's intentions and emotions when analyzing architectural drawings. For example, it proposes design options based on the designer's emotion data. The architectural drawing analysis unit also analyzes the designer's emotions in real time when analyzing architectural drawings and reflects the results in the design based on that analysis result. For example, it creates a design that emphasizes elements that the designer particularly values. The architectural drawing analysis unit also uses the emotion estimation function to develop an algorithm for reflecting the designer's emotions in the analysis results of architectural drawings. For example, it adjusts the design based on the designer's emotion score. This makes it possible to create a design that reflects the designer's intentions and emotions.

[0065] The architectural drawing analysis unit can compare the analysis results with other architectural projects and extract best practices. For example, the architectural drawing analysis unit stores the analysis results of architectural drawings in a database and builds a system for comparing them with other architectural projects. For example, it extracts best practices based on past success stories. The architectural drawing analysis unit also compares the analysis results with other architectural projects to identify common success factors. For example, it analyzes whether the use of specific design methods or materials contributed to success. The architectural drawing analysis unit also develops algorithms for comparing the analysis results of architectural drawings with other projects and automatically extracts best practices. For example, it can propose optimal designs based on data from similar projects. This makes it possible to propose optimal designs by comparing with other architectural projects.

[0066] The architectural drawing analysis unit can automatically generate the analysis results as a 3D model, allowing them to be visually confirmed. For example, the architectural drawing analysis unit develops a system in which a generative AI analyzes architectural drawings and automatically generates a 3D model based on the results. For example, the internal structure and exterior of a building are displayed in 3D. The architectural drawing analysis unit also automatically generates the analysis results of architectural drawings as a 3D model, allowing users to visually confirm them. For example, VR technology can be used to virtually walk around the interior of a building. The architectural drawing analysis unit also builds a system in which a generative AI analyzes architectural drawings and generates a 3D model based on the results in real time. For example, design changes are instantly reflected in the 3D model. This allows the analysis results to be visually confirmed as a 3D model.

[0067] The architectural drawing analysis unit can use the emotion estimation function to collect the user's emotional reactions to the analysis results of the architectural drawings, thereby improving the accuracy of the analysis. The architectural drawing analysis unit, for example, develops a system that uses the emotion estimation function to collect the user's emotional reactions to the analysis results of the architectural drawings. For example, the architectural drawing analysis unit analyzes the user's facial expressions and voice and calculates an emotion score. The architectural drawing analysis unit also develops an algorithm that improves the analysis accuracy of the architectural drawings based on the user's emotional reaction data. For example, the architectural drawing analysis unit prioritizes the adoption of analysis results with a high number of positive emotional reactions. The architectural drawing analysis unit also builds a system that collects emotion estimation data in real time and reflects it in the analysis results of the architectural drawings. For example, the analysis results are dynamically adjusted according to changes in the user's emotions. This allows the user's emotional reactions to be collected and the analysis accuracy to be improved.

[0068] The capacity calculation unit can propose a flexible design that takes future expansion and changes into consideration when calculating capacity. For example, the capacity calculation unit develops a system that proposes a design that takes future expansion and changes into consideration when the generation AI performs capacity calculations. For example, it creates a design that allows for future expansion. The capacity calculation unit also proposes a flexible design that can accommodate future changes based on the results of the capacity calculation. For example, it creates a design that makes it easy to change the layout of equipment. The capacity calculation unit also builds a database to take future expansion and changes into consideration when the generation AI performs capacity calculations, and proposes a design based on that. For example, it creates a design that can accommodate future technological innovations. This makes it possible to have a flexible design that takes future expansion and changes into consideration.

[0069] The capacity calculation unit can calculate the optimal capacity by taking into account the frequency of building use and seasonal fluctuations when calculating capacity. For example, the capacity calculation unit develops a system that takes into account the frequency of building use and seasonal fluctuations when the generation AI calculates capacity. For example, it calculates different air conditioning capacities for summer and winter. The capacity calculation unit also proposes the optimal capacity that can accommodate the frequency of building use and seasonal fluctuations based on the results of the capacity calculation. For example, it creates a design that accommodates time periods with high usage. The capacity calculation unit also builds a database that takes into account the frequency of use and seasonal fluctuations when the generation AI calculates capacity, and calculates the optimal capacity based on that. For example, it adjusts the capacity based on past usage data. This makes it possible to calculate the optimal capacity that takes into account the frequency of use and seasonal fluctuations.

[0070] The capacity calculation unit can use the emotion estimation function to perform capacity calculations that reflect the user's expectations and desires. The capacity calculation unit, for example, uses the emotion estimation function to develop a system that performs capacity calculations that reflect the user's expectations and desires. For example, the capacity calculation unit proposes an optimal capacity based on the user's emotion data. The capacity calculation unit also analyzes the user's emotional response in real time and calculates capacity based on the results. For example, it calculates a capacity that satisfies the user. The capacity calculation unit also develops an algorithm that performs capacity calculations that reflect the user's expectations and desires based on the emotion estimation data. For example, it adjusts the capacity based on the user's emotion score. This makes it possible to calculate capacity that reflects the user's expectations and desires.

[0071] The capacity calculation unit can compare the results of the capacity calculation with other building projects and propose the optimal design. For example, the capacity calculation unit stores the results of the capacity calculation in a database and builds a system for comparing with other building projects. For example, it proposes the optimal design based on past successful cases. The capacity calculation unit also compares the results of the capacity calculation with other building projects to identify common success factors. For example, it analyzes whether the use of specific design methods or materials contributed to success. The capacity calculation unit also develops an algorithm for comparing the results of the capacity calculation with other projects and automatically proposes the optimal design. For example, it proposes the optimal design based on data from similar projects. This makes it possible to propose the optimal design by comparing with other building projects.

[0072] The capacity calculation unit can perform calculations that take into account different scenarios when calculating capacity. For example, the capacity calculation unit develops a system that takes disasters and emergencies into account when the generation AI performs capacity calculations. For example, it calculates the air conditioning and power supply capacity that can be used in an emergency. The capacity calculation unit also proposes a design that can be used in a disaster or emergency based on the results of the capacity calculation. For example, it creates a design that takes into account emergency power sources and evacuation routes. The capacity calculation unit also builds a database that takes into account different scenarios when the generation AI performs capacity calculations, and calculates the optimal capacity based on that. For example, it adjusts the capacity based on simulation data from a disaster. This makes it possible to calculate capacity that takes into account different scenarios.

[0073] The capacity calculation unit can use the emotion estimation function to collect the user's emotional response to the capacity calculation results and improve the calculation accuracy. The capacity calculation unit, for example, uses the emotion estimation function to develop a system that collects the user's emotional response to the capacity calculation results. For example, the capacity calculation unit analyzes the user's facial expressions and voice and calculates an emotion score. The capacity calculation unit also develops an algorithm that improves the accuracy of the capacity calculation based on the user's emotional response data. For example, it prioritizes the adoption of calculation results with a high number of positive emotional responses. The capacity calculation unit also builds a system that collects emotion estimation data in real time and reflects it in the capacity calculation results. For example, it dynamically adjusts the calculation results according to changes in the user's emotions. This allows the user's emotional responses to be collected and the calculation accuracy to be improved.

[0074] The equipment drawing creation department can create designs that reflect the latest architectural trends and technologies when creating equipment drawings. For example, the equipment drawing creation department develops a system that reflects the latest architectural trends and technologies when the generating AI creates equipment drawings. For example, it creates designs that incorporate the latest energy efficiency technologies and smart home technologies. In addition, when creating equipment drawings, the equipment drawing creation department retrieves the latest architectural trends and technologies from a database and creates designs based on that. For example, it creates designs that incorporate the latest energy-saving equipment and automated systems. In addition, the equipment drawing creation department develops algorithms that reflect the latest architectural trends and technologies when the generating AI creates equipment drawings. For example, it automatically analyzes the latest technological trends and reflects them in the design. This makes it possible to create designs that reflect the latest architectural trends and technologies.

[0075] The equipment drawing creation unit can create designs that take into consideration the aesthetics and design of the building when creating equipment drawings. For example, the equipment drawing creation unit develops a system that takes into consideration the aesthetics and design of the building when the generating AI creates equipment drawings. For example, it designs the placement of piping and wiring so as not to spoil the aesthetics. Furthermore, when creating equipment drawings, the equipment drawing creation unit retrieves the design of the building from a database and creates the design based on that. For example, it creates equipment placement that harmonizes with the interior design. Furthermore, the equipment drawing creation unit develops algorithms that take into consideration the aesthetics and design when the generating AI creates equipment drawings. For example, it designs based on an index that evaluates the beauty of the design. This makes it possible to create designs that take into consideration the aesthetics and design of the building.

[0076] The equipment drawing creation unit can use the emotion estimation function to create equipment drawings that reflect the user's design preferences and emotions. The equipment drawing creation unit, for example, uses the emotion estimation function to develop a system that creates equipment drawings that reflect the user's design preferences and emotions. For example, it proposes an optimal design based on the user's emotion data. The equipment drawing creation unit also analyzes the user's emotional response in real time and creates equipment drawings based on the results. For example, it reflects a design that satisfies the user. The equipment drawing creation unit also develops an algorithm that creates equipment drawings that reflect the user's design preferences and emotions based on the emotion estimation data. For example, it adjusts the design based on the user's emotion score. This makes it possible to create equipment drawings that reflect the user's design preferences and emotions.

[0077] The equipment drawing creation department can compare the results of equipment drawings with other architectural projects and propose the optimal design. For example, the equipment drawing creation department stores the results of equipment drawings in a database and builds a system for comparing them with other architectural projects. For example, it proposes the optimal design based on past success stories. The equipment drawing creation department also compares the results of equipment drawings with other architectural projects and identifies common success factors. For example, it analyzes whether the use of specific design methods or materials contributed to success. The equipment drawing creation department also develops an algorithm for comparing the results of equipment drawings with other projects and automatically proposes the optimal design. For example, it proposes the optimal design based on data from similar projects. This makes it possible to propose the optimal design by comparing with other architectural projects.

[0078] The equipment drawing creation unit can present different design options when creating equipment drawings, allowing the user to select from them. The equipment drawing creation unit, for example, develops a system that presents different design options when the generation AI creates equipment drawings. For example, it proposes multiple piping routes and equipment layouts. The equipment drawing creation unit also retrieves different design options from a database when creating equipment drawings, allowing the user to select from them. For example, it proposes equipment with different designs and functions. The equipment drawing creation unit also develops an algorithm for presenting different design options when the generation AI creates equipment drawings. For example, it proposes the optimal option based on the user's requests. This allows different design options to be presented and the user to select from.

[0079] The equipment drawing creation unit can use the emotion estimation function to collect users' emotional reactions to the results of equipment drawing creation and improve design accuracy. The equipment drawing creation unit, for example, uses the emotion estimation function to develop a system that collects users' emotional reactions to the results of equipment drawing creation. For example, the system analyzes the user's facial expressions and voice and calculates an emotion score. The equipment drawing creation unit also develops an algorithm that improves the design accuracy of equipment drawings based on the user's emotional reaction data. For example, it prioritizes the adoption of designs with a high number of positive emotional reactions. The equipment drawing creation unit also builds a system that collects emotion estimation data in real time and reflects it in the results of equipment drawing creation. For example, it dynamically adjusts the design according to changes in the user's emotions. In this way, the user's emotional reactions are collected and design accuracy is improved.

[0080] The approximate construction cost calculation unit can reflect the latest market prices and material costs in real time when calculating the approximate construction cost. For example, the approximate construction cost calculation unit develops a system in which, when the generation AI calculates the approximate construction cost, it obtains the latest market prices and material costs in real time and makes an estimate based on that. For example, it obtains price information from an online database. Furthermore, when calculating the approximate construction cost, the approximate construction cost calculation unit obtains the latest market prices and material costs from the database and makes an estimate based on that. For example, it makes an estimate that takes material price fluctuations into account. Furthermore, the approximate construction cost calculation unit develops an algorithm to reflect the latest market prices and material costs in real time when the generation AI calculates the approximate construction cost. For example, it automatically updates price information and reflects it in the estimate. This makes it possible to calculate an approximate construction cost that reflects the latest market prices and material costs in real time.

[0081] The approximate construction cost calculation unit can improve accuracy by referring to past project data when calculating an approximate construction cost. For example, the approximate construction cost calculation unit develops a system that refers to past project data when the generation AI calculates an approximate construction cost. For example, accuracy is improved based on past estimate data. Furthermore, when calculating an approximate construction cost, the approximate construction cost calculation unit retrieves past project data from a database and makes an estimate based on that. For example, an estimate is made by referring to data on similar projects. Furthermore, the approximate construction cost calculation unit develops an algorithm that refers to past project data when the generation AI calculates an approximate construction cost. For example, past data is analyzed and reflected in the estimate. This makes it possible to calculate an approximate construction cost with improved accuracy by referring to past project data.

[0082] The estimated construction cost calculation unit can use the emotion estimation function to calculate an estimated construction cost that reflects the user's sense of budget and expectations. The estimated construction cost calculation unit, for example, uses the emotion estimation function to develop a system that calculates an estimated construction cost that reflects the user's sense of budget and expectations. For example, the estimated construction cost calculation unit proposes an optimal estimate based on the user's emotion data. The estimated construction cost calculation unit also analyzes the user's emotional response in real time and calculates an estimated construction cost based on the results. For example, it provides an estimate that satisfies the user. The estimated construction cost calculation unit also develops an algorithm that calculates an estimated construction cost that reflects the user's sense of budget and expectations based on the emotion estimation data. For example, it adjusts the estimate based on the user's emotion score. This makes it possible to calculate an estimated construction cost that reflects the user's sense of budget and expectations.

[0083] The approximate construction cost calculation unit can compare the calculation results of the approximate construction cost with other construction projects and propose the optimal cost plan. The approximate construction cost calculation unit, for example, develops a system that stores the calculation results of the approximate construction cost in a database and compares it with other construction projects. For example, it proposes an optimal cost plan based on past successful cases. The approximate construction cost calculation unit also compares the calculation results of the approximate construction cost with other construction projects and identifies common success factors. For example, it analyzes whether the use of specific design methods or materials contributed to success. The approximate construction cost calculation unit also develops an algorithm for comparing the calculation results of the approximate construction cost with other projects and automatically proposes the optimal cost plan. For example, it proposes the optimal cost plan based on data from similar projects. In this way, it is possible to propose the optimal cost plan by comparing it with other construction projects.

[0084] The approximate construction cost calculation unit can perform calculations that take into account different scenarios when calculating the approximate construction cost. For example, the approximate construction cost calculation unit develops a system that allows the generation AI to take different scenarios into account when calculating the approximate construction cost. For example, it generates an estimate that takes into account changes in materials or a shortened construction period. Furthermore, the approximate construction cost calculation unit retrieves different scenarios from a database when calculating the approximate construction cost and generates an estimate based on those scenarios. For example, it generates an estimate for when different materials or construction methods are used. Furthermore, the approximate construction cost calculation unit develops an algorithm that allows the generation AI to take into account different scenarios when calculating the approximate construction cost. For example, it automatically calculates the cost for each scenario and reflects it in the estimate. This makes it possible to calculate the approximate construction cost by taking different scenarios into account.

[0085] The estimated construction cost calculation unit can use the emotion estimation function to collect the user's emotional response to the calculation result of the estimated construction cost, thereby improving the calculation accuracy. The estimated construction cost calculation unit, for example, develops a system that uses the emotion estimation function to collect the user's emotional response to the calculation result of the estimated construction cost. For example, the system analyzes the user's facial expressions and voice to calculate an emotion score. The estimated construction cost calculation unit also develops an algorithm that improves the calculation accuracy of the estimated construction cost based on the user's emotional response data. For example, it prioritizes the adoption of estimates with a high number of positive emotional responses. The estimated construction cost calculation unit also builds a system that collects emotion estimation data in real time and reflects it in the calculation result of the estimated construction cost. For example, it dynamically adjusts the estimate according to changes in the user's emotions. This allows the user's emotional response to be collected and the calculation accuracy to be improved.

[0086] The contents of calculations and drawings automatically generated by generative AI will be automatically requested to be reviewed by third-party experts, thereby improving their accuracy. A system will be developed that automatically sends the contents of calculations and drawings automatically generated by generative AI to third-party experts for review. For example, the contents will be revised based on expert feedback. The accuracy of automatically generated calculations and drawings will also be improved by having them reviewed by third-party experts. For example, revisions will be made to reflect the opinions of experts. An algorithm will also be developed to automatically request a review by third-party experts of the contents of calculations and drawings automatically generated by generative AI. For example, the contents will be improved based on expert evaluations. This will improve the accuracy of calculations and drawings through review by third-party experts.

[0087] The contents of calculations and drawings automatically generated by the generation AI can be compared with past project data to detect outliers. A system will be developed that compares the contents of calculations and drawings automatically generated by the generation AI with past project data. For example, it will automatically detect outliers and suggest corrections. Furthermore, automatically generated calculations and drawings will be compared with past project data to identify outliers. For example, it will detect outliers based on past data and make corrections. Furthermore, an algorithm will be developed to compare the contents of calculations and drawings automatically generated by the generation AI with past project data. For example, it will automatically detect outliers and suggest corrections. This will make it possible to detect outliers and suggest corrections by comparing with past project data.

[0088] The content of calculations and drawings automatically generated by the generation AI can be analyzed using an emotion estimation function to analyze the user's emotions while reviewing the content, and suggestions can be made to reduce stress. We will develop a system that uses an emotion estimation function to analyze the user's emotions while reviewing the content of calculations and drawings automatically generated by the generation AI in real time, and makes suggestions to reduce stress. For example, it will suggest relaxation methods based on the user's emotion data. We will also analyze the user's emotional reactions in real time, and make suggestions to reduce stress during the review process based on the results. For example, it will provide an environment where the user can relax. We will also develop an algorithm that analyzes the user's emotions while reviewing based on the emotion estimation data, and makes suggestions to reduce stress. For example, it will suggest relaxation methods based on the user's emotion score. This will make it possible to analyze the user's emotions while reviewing and make suggestions to reduce stress.

[0089] The contents of calculations and drawings automatically generated by generative AI can be compared with other architectural projects to propose the optimal design. We will develop a system that compares the contents of calculations and drawings automatically generated by generative AI with other architectural projects. For example, we will propose the optimal design based on past successful cases. We will also compare the contents of automatically generated calculations and drawings with other architectural projects to identify common success factors. For example, we will analyze whether the use of specific design methods or materials contributed to success. We will also develop an algorithm to compare the contents of calculations and drawings automatically generated by generative AI with other projects to automatically propose the optimal design. For example, we will propose the optimal design based on data from similar projects. This will make it possible to propose the optimal design by comparing with other architectural projects.

[0090] The contents of calculations and drawings automatically generated by generative AI can be output in different formats, improving user convenience. We will develop a system that outputs the contents of calculations and drawings automatically generated by generative AI in different formats. For example, outputting as PDF or CAD data. We will also output the contents of automatically generated calculations and drawings in different formats, providing them in a format that is easy for users to use. For example, we will allow users to select the format according to their needs. We will also develop an algorithm to output the contents of calculations and drawings automatically generated by generative AI in different formats. For example, we will automatically perform format conversion and provide them to the user. This will improve user convenience by outputting them in different formats.

[0091] The efficiency of the verification work can be improved by using an emotion estimation function to collect users' emotional reactions to the contents of calculations and drawings automatically generated by a generation AI. We will develop a system that uses an emotion estimation function to collect users' emotional reactions to the contents of calculations and drawings automatically generated by a generation AI. For example, we will analyze the user's facial expressions and voice to calculate an emotion score. We will also develop an algorithm that improves the efficiency of the verification work of calculations and drawings based on the user's emotional response data. For example, we will prioritize the use of verification methods that generate a high number of positive emotional responses. We will also build a system that collects emotion estimation data in real time and reflects it in the verification work of calculations and drawings. For example, we will dynamically adjust the verification method according to changes in the user's emotions. This will allow us to collect users' emotional responses and improve the efficiency of the verification work.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The automated design system can further include a seismic evaluation unit that evaluates the seismic performance of a building. The seismic evaluation unit evaluates the seismic performance of a building based on the information analyzed by the architectural drawing analysis unit. For example, the seismic evaluation unit evaluates the building's resistance to shaking during an earthquake, taking into account the structure and material properties of the building. The seismic evaluation unit also checks whether the building design complies with the latest seismic standards and proposes design modifications as necessary. Furthermore, the seismic evaluation unit can simulate the seismic performance of a building based on past earthquake data and propose optimal seismic designs. This makes it possible to evaluate the seismic performance of a building and achieve safer designs.

[0094] The automated design system can further include a fire evaluation unit that evaluates the fire resistance of a building. The fire evaluation unit evaluates the fire resistance of a building based on the information analyzed by the architectural drawing analysis unit. For example, the fire evaluation unit evaluates the fire resistance of the building's structure and the materials used to confirm safety in the event of a fire. The fire evaluation unit also checks whether the building design complies with the latest fire safety standards and proposes design modifications as necessary. Furthermore, the fire evaluation unit can simulate the fire resistance of a building based on past fire data and propose an optimal fire resistance design. This makes it possible to evaluate the fire resistance of a building and achieve a safer design.

[0095] The automated design system can further include an acoustic evaluation unit that evaluates the acoustic performance of a building. The acoustic evaluation unit evaluates the acoustic performance of a building based on the information analyzed by the architectural drawing analysis unit. For example, the acoustic evaluation unit simulates how sound propagates, taking into account the structure of the building's walls and floors. The acoustic evaluation unit also checks whether the building design complies with the latest acoustic standards and proposes design modifications as necessary. Furthermore, the acoustic evaluation unit can simulate the acoustic performance of a building based on past acoustic data and propose an optimal acoustic design. This makes it possible to evaluate the acoustic performance of a building and create a more comfortable environment.

[0096] The automated design system can further include an energy optimization unit that optimizes the energy consumption of a building. The energy optimization unit optimizes the energy consumption of a building based on the information analyzed by the architectural drawing analysis unit. For example, the energy optimization unit evaluates the building's insulation performance and the efficiency of the air conditioning system, and proposes a design that minimizes energy consumption. The energy optimization unit can also consider the use of renewable energy and propose the introduction of solar power generation or wind power generation. Furthermore, the energy optimization unit can simulate the energy consumption of a building based on past energy consumption data and propose optimal energy management. This makes it possible to optimize the energy consumption of a building and achieve a sustainable design.

[0097] The automated design system can further include a life cycle cost evaluation unit that evaluates the life cycle cost of a building. The life cycle cost evaluation unit evaluates the life cycle cost of a building based on the information analyzed by the architectural drawing analysis unit. For example, the life cycle cost evaluation unit evaluates the overall cost by taking into account not only the construction cost of the building but also the operating and maintenance costs. The life cycle cost evaluation unit also checks whether the building design is economical in the long term and proposes design modifications as necessary. Furthermore, the life cycle cost evaluation unit can simulate the life cycle cost of a building based on past project data and propose optimal cost management. This makes it possible to evaluate the life cycle cost of a building and achieve an economical design.

[0098] The automated design system can further include a comfort evaluation unit that evaluates the comfort of the building. The comfort evaluation unit evaluates the comfort of the building based on the information analyzed by the architectural drawing analysis unit. For example, the comfort evaluation unit takes into account the building's temperature, humidity, and lighting arrangement to evaluate the environment in which residents can live comfortably. The comfort evaluation unit also checks whether the building design complies with the latest comfort standards and proposes design modifications as necessary. Furthermore, the comfort evaluation unit can simulate the comfort of the building based on past comfort data and propose an optimal comfort design. This makes it possible to evaluate the comfort of the building and create a more comfortable environment.

[0099] The automated design system can further include a security evaluation unit that evaluates the security performance of a building. The security evaluation unit evaluates the security performance of a building based on the information analyzed by the architectural drawing analysis unit. For example, the security evaluation unit evaluates the risk of unauthorized intrusion by taking into account the layout of the building's entrances and windows. The security evaluation unit also checks whether the building design complies with the latest security standards and proposes design modifications as necessary. Furthermore, the security evaluation unit can simulate the security performance of a building based on past security data and propose an optimal security design. This makes it possible to evaluate the security performance of a building and achieve a safer design.

[0100] The automated design system may further include an emotional comfort evaluation unit that evaluates the emotional comfort of a building. The emotional comfort evaluation unit evaluates the emotional comfort of a building based on the information analyzed by the architectural drawing analysis unit. For example, the emotional comfort evaluation unit evaluates the emotional comfort of occupants by taking into account the building's design, color, and lighting arrangement. The emotional comfort evaluation unit also evaluates the impact of the building's design on occupant emotions and proposes design modifications as necessary. Furthermore, the emotional comfort evaluation unit can simulate the emotional comfort of a building based on past emotional data and propose an optimal emotional comfort design. This makes it possible to evaluate the emotional comfort of a building and create an environment in which occupants can live more comfortably.

[0101] The automated design system may further include an emotional safety evaluation unit that evaluates the emotional safety of a building. The emotional safety evaluation unit evaluates the emotional safety of a building based on the information analyzed by the architectural drawing analysis unit. For example, the emotional safety evaluation unit takes into account the design and layout of the building to evaluate an environment in which residents can feel safe. The emotional safety evaluation unit also evaluates the impact of the building design on the emotions of residents and suggests design modifications as necessary. Furthermore, the emotional safety evaluation unit can simulate the emotional safety of a building based on past emotional data and propose an optimal emotional safety design. This makes it possible to evaluate the emotional safety of a building and create an environment in which residents can feel more safe.

[0102] The automated design system can further include an emotional satisfaction evaluation unit that evaluates the emotional satisfaction of a building. The emotional satisfaction evaluation unit evaluates the emotional satisfaction of a building based on information analyzed by the architectural drawing analysis unit. For example, the emotional satisfaction evaluation unit evaluates the emotional satisfaction of residents by taking into account the design, functionality, and usability of the building. The emotional satisfaction evaluation unit also evaluates the impact of the building design on the emotions of residents and suggests design modifications as necessary. Furthermore, the emotional satisfaction evaluation unit can simulate the emotional satisfaction of a building based on past emotional data and propose an optimal emotional satisfaction design. This makes it possible to evaluate the emotional satisfaction of a building and create an environment in which residents can live more satisfied.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The architectural drawing analysis unit analyzes the architectural drawings. For example, the architectural drawing analysis unit automatically recognizes the size and use of the building, the position of pillars and walls, and extracts the necessary information. The architectural drawing analysis unit also receives architectural drawing data files as input and performs analysis based on that data. Step 2: The capacity calculation unit calculates the capacity based on the information analyzed by the architectural drawing analysis unit. For example, the capacity calculation unit calculates the capacity of air conditioning equipment and electrical equipment based on the building's area and intended use. The capacity calculation unit also automatically calculates the required capacity based on the building's size and intended use. Step 3: The equipment drawing creation unit creates an equipment drawing based on the capacity calculated by the capacity calculation unit. For example, the equipment drawing creation unit draws the layout of air conditioning ducts and the route of electrical wiring on an architectural drawing. Furthermore, the equipment drawing creation unit automatically creates an equipment drawing showing the installation location of the equipment and the piping and wiring route based on the results of the capacity calculation. Step 4: The approximate construction cost calculation unit calculates the approximate construction cost based on the equipment drawings created by the equipment drawing creation unit. For example, the approximate construction cost calculation unit estimates the necessary costs based on the materials to be used and the scale of the construction. The approximate construction cost calculation unit also automatically calculates the approximate construction cost based on the information in the equipment drawings.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

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

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

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

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

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

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

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

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

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

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

[0133] In the headset type terminal 314, the 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

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

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

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

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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]

[0172] 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 capacity calculation unit that calculates a capacity based on the information analyzed by the architectural drawing analysis unit; an equipment drawing creation unit that creates an equipment drawing based on the capacity calculated by the capacity calculation unit; an approximate construction cost calculation unit that calculates an approximate construction cost based on the equipment drawing created by the equipment drawing creation unit; A system characterized by:

2. The architectural drawing analysis unit Consider the historical background and cultural elements of the architectural drawings and reflect them in the design.

2. The system of claim 1.

3. The architectural drawing analysis unit Evaluate the energy efficiency and environmental impact of the architectural drawings and propose the optimal design 2. The system of claim 1.

4. The architectural drawing analysis unit Reflect the designer's intentions and feelings in the analysis results of the architectural drawings 2. The system of claim 1.

5. The architectural drawing analysis unit Compare analysis results with other building projects to extract best practices 2. The system of claim 1.

6. The architectural drawing analysis unit Analysis results are automatically generated as a 3D model, allowing for visual confirmation.

2. The system of claim 1.

Images