system

Abstract

The system according to this embodiment aims to automate design based on the latest policies. [Solution] The system according to the embodiment comprises an acquisition unit, a design unit, and a verification unit. The acquisition unit acquires the latest policy. The design unit automatically performs a design based on the policy acquired by the acquisition unit. The verification unit verifies the design generated by the design unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that manual design based on the latest policy is inefficient and error-prone.

[0005] The system according to the embodiment aims to automate the design based on the latest policy.

Means for Solving the Problems

[0006] The system according to the embodiment includes an acquisition unit, a design unit, and a verification unit. The acquisition unit acquires the latest policy. The design unit automatically performs design based on the policy acquired by the acquisition unit. The verification unit verifies the design generated by the design unit.

Effects of the Invention

[0007] The system according to this embodiment can automate the design based on the latest policy. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An automated design system according to an embodiment of the present invention is a system that automates the design of a building that meets standards by incorporating the latest policies. This automated design system acquires the latest policies and automatically designs the building based on the acquired policies. This design process is performed using AI, and a design that meets the standards is generated. This improves the efficiency and accuracy of the design. For example, the automated design system acquires the latest policies. In this case, it is important to always acquire the latest information as policies are updated regularly. For example, the latest policies such as the Building Standards Act and disaster prevention standards are acquired. This information is input into the AI. Next, the automated design system automatically designs the building based on the acquired policies. The AI ​​analyzes the acquired policies and generates a design that meets the standards. For example, based on the Building Standards Act, it designs considering earthquake resistance and fire prevention. It also designs evacuation routes and the placement of emergency equipment based on disaster prevention standards. The generated design is verified again by the AI ​​to confirm whether it meets the standards. This improves the accuracy of the design and ensures that a design that meets the standards is generated. This improves the efficiency and accuracy of the design. Designers no longer need to manually check the standards, and the design process is significantly shortened. Furthermore, AI-driven automation improves design accuracy, ensuring that designs that meet standards are reliably generated. For example, designs that consider earthquake resistance and fire resistance are automatically generated, reducing design errors and improving safety. In this way, automated design systems can achieve both increased efficiency and improved accuracy in design.

[0029] The design automation system according to this embodiment comprises an acquisition unit, a design unit, and a verification unit. The acquisition unit acquires the latest policies. The acquisition unit periodically acquires the latest policies, such as the Building Standards Act and disaster prevention standards. The acquisition unit automatically acquires the latest policies, for example, via the internet. The acquisition unit can also monitor policy update information and acquire the latest policies as soon as they are published. Furthermore, the user can set the frequency of policy acquisition for the acquisition unit. For example, the acquisition unit can be set to acquire daily, weekly, or monthly. The design unit automatically performs design based on the policies acquired by the acquisition unit. The design unit performs design considering earthquake resistance and fire resistance, for example. The design unit performs design considering earthquake resistance based on the Building Standards Act. The design unit can also perform design considering fire resistance. The design unit designs evacuation routes and the placement of emergency equipment, for example, based on disaster prevention standards. The design unit designs the width of evacuation routes and the placement of emergency equipment, for example. The design unit performs design using AI. The design department, for example, uses AI to analyze policies and generate designs that meet the standards. The design department, for example, uses AI to design buildings that take into account seismic resistance and fire prevention based on the Building Standards Act. The design department, for example, uses AI to design evacuation routes and the placement of emergency equipment based on disaster prevention standards. The verification department verifies the designs generated by the design department. The verification department, for example, verifies whether the generated designs meet the standards. The verification department, for example, uses AI to verify the designs. The verification department, for example, uses AI to analyze the generated designs and confirm whether they meet the standards. The verification department, for example, uses AI to verify the designs based on the Building Standards Act and disaster prevention standards. As a result, the design automation system according to the embodiment can automate the design of buildings that meet the standards by incorporating the latest policies, thereby improving the efficiency and accuracy of the design.

[0030] The acquisition unit retrieves the latest policies. For example, it regularly retrieves the latest policies such as the Building Standards Act and disaster prevention standards. Specifically, the acquisition unit has a function to access the official websites of various laws and standards via the internet and automatically download the latest policies. This eliminates the need for manual policy verification and ensures that the latest information is always available. The acquisition unit also has a dedicated module for monitoring policy update information, and can receive immediate notifications when policies are updated, for example, using RSS feeds or APIs. Furthermore, the acquisition unit can retrieve policies based on the acquisition frequency set by the user. For example, the user can select an acquisition frequency such as daily, weekly, or monthly on the acquisition unit's settings screen. This allows for flexible policy acquisition according to the user's needs. The acquisition unit stores the acquired policies in a database, making them accessible to the design and verification units. This ensures that the entire system can use consistent and up-to-date policies.

[0031] The design department automatically performs design based on policies acquired by the acquisition department. For example, the design department performs designs that take into account earthquake resistance and fire resistance. Specifically, the design department uses AI to analyze policies and generate designs that meet the standards. The AI ​​uses natural language processing technology to understand the content of the policies and extract the requirements necessary for the design. For example, when designing with earthquake resistance in mind based on the Building Standards Act, the AI ​​automatically determines the building structure, material selection, and placement of seismic reinforcement. Similarly, when designing with fire resistance in mind, the AI ​​determines the placement of firewalls, evacuation routes, and emergency equipment. For example, the design department designs evacuation routes and emergency equipment placement based on disaster prevention standards. Specifically, the AI ​​designs the width of evacuation routes and the placement of emergency equipment, and performs simulations to determine the optimal placement. The design department visualizes the generated designs as 3D models for user review. This allows the design department to perform efficient and highly accurate designs automatically. Furthermore, the design department can receive feedback from users and modify the design content. For example, if a user wants to add a specific design requirement, the design department will generate a new design that reflects that requirement. This allows the design department to provide flexible designs that meet the user's needs.

[0032] The Verification Unit verifies the designs generated by the Design Unit. For example, the Verification Unit verifies whether the generated designs meet the standards. Specifically, the Verification Unit uses AI to verify the designs. The AI ​​analyzes the design data and confirms that the designs are properly carried out in accordance with building codes and disaster prevention standards. For example, in seismic resistance verification, the AI ​​performs structural analysis of the building and simulates the response during an earthquake to evaluate seismic performance. In fire resistance verification, the AI ​​performs fire simulations to confirm whether the placement of firewalls and evacuation routes is appropriate. Furthermore, the Verification Unit refers to the latest policy information provided by the Acquisition Unit to confirm whether the designs comply with the latest policies. This allows the Verification Unit to ensure that the designs always meet the latest standards. The Verification Unit generates verification results as a report and provides it to the user. The report details the suitability and areas for improvement of the design, which the user can use to modify or improve the design. Furthermore, the Verification Unit can save past verification results in a database and use them as a reference for future designs. This allows the Verification Unit to continuously improve the quality of the designs.

[0033] The acquisition unit can periodically acquire the latest policies, such as building codes and disaster prevention standards. The acquisition unit can, for example, automatically acquire the latest policies via the internet. The acquisition unit can also, for example, monitor policy update information and acquire the latest policies as soon as they are published. The acquisition unit also allows the user to set the frequency of policy acquisition. For example, the acquisition unit can be set to acquire policies daily, weekly, or monthly. This ensures that designs are always based on the latest standards by regularly acquiring the latest policies. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input policy data acquired from the internet into a generating AI and have the generating AI perform policy analysis.

[0034] The design department can perform designs that take into account seismic resistance and fire resistance based on acquired policies. For example, the design department can perform designs that take into account seismic resistance based on the Building Standards Act. The design department can also perform designs that take into account fire resistance, for example. The design department can perform designs using AI, for example. For example, the design department can have AI analyze policies and generate designs that meet the standards. For example, the design department can have AI perform designs that take into account seismic resistance and fire resistance based on the Building Standards Act. This makes it possible to create highly safe designs by performing designs that take into account seismic resistance and fire resistance. Some or all of the above processes in the design department may be performed using AI, for example, or without AI. For example, the design department can input acquired policy data into a generation AI and have the generation AI perform the design generation.

[0035] The design department can design evacuation routes and emergency equipment layouts based on disaster prevention standards. For example, the design department designs the width of evacuation routes and the placement of emergency equipment based on disaster prevention standards. The design department uses AI to perform the design, for example. The design department uses AI to analyze policies and generate designs that meet the standards. The design department uses AI to design evacuation routes and emergency equipment layouts based on disaster prevention standards, for example. By designing based on disaster prevention standards, safety during disasters is improved. Some or all of the above processes in the design department may be performed using AI, for example, or without AI. For example, the design department can input acquired policy data into a generating AI and have the generating AI perform the design generation.

[0036] The verification unit can verify whether the generated design meets the standards. For example, the verification unit verifies whether the generated design meets the standards. For example, the verification unit verifies the design using AI. For example, the verification unit analyzes the generated design using AI and confirms whether it meets the standards. For example, the verification unit verifies the design using AI based on building codes and disaster prevention standards. This improves the accuracy of the design by verifying whether the design meets the standards. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the generated design data into a generating AI and have the generating AI perform the design verification.

[0037] The acquisition unit can analyze past policy change history and select the optimal acquisition method when acquiring policies. For example, the acquisition unit can analyze past policy change history and prioritize the acquisition of policies that are frequently changed. For example, the acquisition unit can analyze policy change patterns and acquire policies at times when changes are predicted. For example, the acquisition unit can identify high-priority policies from past policy change history and prioritize their acquisition. This allows for efficient policy acquisition by selecting the optimal policy acquisition method through analysis of past policy change history. Some or all of the above processes in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input past policy change history data into a generating AI and have the generating AI select the optimal acquisition method.

[0038] The acquisition unit can filter policies by considering regional building codes and disaster prevention standards when acquiring them. For example, the acquisition unit can consider regional building codes and acquire only the policies for the relevant region. For example, the acquisition unit can consider regional disaster prevention standards and acquire only the policies for the relevant region. For example, the acquisition unit can filter regional standards and acquire only the necessary policies. This improves the accuracy of the design by acquiring policies suitable for the relevant region by considering regional standards. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input regional standard data into a generating AI and have the generating AI perform the filtering.

[0039] The acquisition unit can prioritize the acquisition of highly relevant policies by considering the user's geographical location information when acquiring policies. For example, the acquisition unit can prioritize the acquisition of policies for a given region based on the user's current location. For example, the acquisition unit can acquire highly relevant regional policies based on the user's past travel history. For example, the acquisition unit can acquire highly relevant regional policies by considering the user's future travel plans. This improves the accuracy of the design by prioritizing the acquisition of highly relevant policies by considering the user's geographical location information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's geographical location information data into a generating AI and have the generating AI select highly relevant policies.

[0040] The acquisition unit can analyze the user's social media activity and acquire relevant policies when acquiring policies. For example, the acquisition unit can analyze the content of the user's social media posts and acquire relevant policies. For example, the acquisition unit can analyze the user's followers and followed accounts on social media and acquire relevant policies. For example, the acquisition unit can analyze the user's social media activity time and acquire policies at the optimal time. This allows for efficient acquisition of relevant policies by analyzing the user's social media activity, improving the accuracy of the design. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's social media data into a generating AI and have the generating AI select relevant policies.

[0041] The design department can adjust the level of detail in the design based on the importance of the policies during the design process. For example, the design department can perform a detailed design based on high-importance policies. For example, the design department can perform a simplified design based on low-importance policies. For example, the design department can adjust the level of detail in the design stepwise according to the importance of the policies. This allows for efficient design by adjusting the level of detail in the design based on the importance of the policies. Some or all of the above processes in the design department may be performed using AI, for example, or not using AI. For example, the design department can input policy data into a generating AI and have the generating AI perform the adjustment of the level of detail in the design.

[0042] The design department can apply different design algorithms to buildings depending on their intended use during the design phase. For example, the design department might apply a design algorithm that prioritizes livability to residential buildings, a design algorithm that prioritizes profitability to commercial buildings, or a design algorithm that prioritizes safety to public facilities. By applying a design algorithm appropriate to the building's intended use, it becomes possible to create designs suitable for that purpose. Some or all of the above-described processes in the design department may be performed using AI, for example, or without AI. For example, the design department can input building use data into a generating AI and have the generating AI select the design algorithm to apply.

[0043] The design department can determine design priorities based on policy update timings during the design phase. For example, the design department can determine design priorities based on the latest policies. For example, the design department can prioritize the incorporation of high-priority policies into the design, taking policy update timings into consideration. For example, the design department can adjust design priorities based on policy update frequency. This allows for design based on the latest standards by determining design priorities based on policy update timings. Some or all of the above processes in the design department may be performed using AI, for example, or not. For example, the design department can input policy update timing data into a generating AI and have the generating AI perform the determination of design priorities.

[0044] The design department can adjust the design sequence based on the relationships between buildings during the design process. For example, the design department can adjust the design sequence based on the building's use. For example, the design department can adjust the design sequence based on the building's size. For example, the design department can adjust the design sequence based on the building's location. This allows for efficient design by adjusting the design sequence based on the relationships between buildings. Some or all of the above processes in the design department may be performed using AI, for example, or without AI. For example, the design department can input building relationship data into a generating AI and have the generating AI perform the adjustment of the design sequence.

[0045] The verification unit can improve the accuracy of verification by considering the interrelationships of the design during verification. For example, the verification unit analyzes the interrelationships of the design and confirms consistency. For example, the verification unit detects inconsistencies by considering the interrelationships of the design. For example, the verification unit selects the optimal verification method based on the interrelationships of the design. As a result, the accuracy of verification is improved by considering the interrelationships of the design. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the interrelationship data of the design into a generating AI and have the generating AI perform the verification accuracy improvement.

[0046] The verification unit can perform verification while considering the designer's attribute information. For example, the verification unit can adjust the rigor of the verification by considering the designer's years of experience. For example, the verification unit can apply appropriate verification criteria by considering the designer's field of expertise. For example, the verification unit can adjust the focus of the verification based on the designer's past performance. In this way, appropriate verification is performed by considering the designer's attribute information. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the designer's attribute information data into a generating AI and have the generating AI perform the verification adjustments.

[0047] The verification unit can perform verification while considering the geographical distribution of the design. For example, the verification unit can perform verification based on regional criteria, taking into account the geographical distribution of the design. For example, the verification unit can analyze the geographical distribution of the design and perform verification while considering the characteristics of each region. For example, the verification unit can select the optimal verification method based on the geographical distribution of the design. As a result, verification that conforms to regional criteria is performed by considering the geographical distribution of the design. Some or all of the above processes in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input geographical distribution data of the design into a generating AI and have the generating AI perform the verification adjustments.

[0048] The verification unit can improve the accuracy of the verification by referring to relevant design literature during the verification process. For example, the verification unit refers to relevant design literature to confirm compliance with the standards. For example, the verification unit improves the accuracy of the verification based on the relevant design literature. For example, the verification unit analyzes the relevant design literature and selects the optimal verification method. As a result, the accuracy of the verification is improved by referring to the relevant design literature. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the relevant design literature data into a generating AI and have the generating AI perform the verification accuracy improvement.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The automated design system may also include a history analysis unit that optimizes the design by considering the user's past design history. The history analysis unit, for example, analyzes data from past design projects to identify successful and unsuccessful design patterns. It also collects user feedback from past design projects to identify areas for design improvement. Furthermore, it proposes the optimal design methodology based on data from past design projects. This improves the accuracy and efficiency of the design by considering past design history, providing a design that meets the user's needs.

[0051] The design automation system may also include a progress monitoring unit that monitors the design progress in real time and notifies the user. The progress monitoring unit, for example, monitors each step of the design process and notifies the user of the progress. For example, the progress monitoring unit sends an alert to the user if a design delay occurs. For example, the progress monitoring unit visually displays the design progress using graphs or charts, making it easy for the user to understand. This improves the efficiency of the design process and reduces user stress by allowing real-time monitoring of the design progress.

[0052] The automated design system may further include a quality evaluation unit that evaluates the quality of the design. The quality evaluation unit, for example, evaluates each element of the design and calculates a quality score. It may also evaluate the durability and safety of the design and provide feedback to the user. Furthermore, it may evaluate the aesthetics and functionality of the design and suggest areas for improvement. By evaluating the quality of the design, the accuracy and reliability of the design are improved, resulting in a valuable design for the user.

[0053] The automated design system can further incorporate a cost optimization unit to optimize design costs. This unit, for example, analyzes the cost of each design element and proposes cost reductions. It also suggests cost-effective options in areas such as material selection and construction method selection. Furthermore, it monitors the overall design cost in real time to support budget-based design. By optimizing design costs, this enables more economical design and reduces the user's cost burden.

[0054] The automated design system may also include an environmental assessment unit that evaluates the environmental impact of the design. For example, the environmental assessment unit evaluates the environmental impact of each design element and proposes ways to minimize the environmental burden. For example, the environmental assessment unit evaluates energy efficiency and resource usage and recommends environmentally friendly designs. For example, the environmental assessment unit evaluates the environmental impact throughout the entire design lifecycle and supports sustainable design. This allows for sustainable design through the evaluation of the environmental impact of the design, contributing to environmental protection.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The acquisition unit acquires the latest policies. The acquisition unit regularly acquires the latest policies, such as the Building Standards Act and disaster prevention standards. The acquisition unit automatically acquires the latest policies via the internet and monitors policy update information, so it can acquire the latest policies as soon as they are published. In addition, the acquisition unit allows users to set the frequency of policy acquisition, such as daily, weekly, or monthly. Step 2: The design department automatically performs the design based on the policies acquired by the acquisition department. The design department performs the design considering seismic resistance and fire resistance, and designs the seismic resistance in accordance with the Building Standards Act, and the placement of evacuation routes and emergency equipment in accordance with disaster prevention standards. The design department uses AI to analyze the policies and generate a design that meets the standards. Step 3: The Verification Unit verifies the design generated by the Design Unit. The Verification Unit verifies whether the generated design meets the standards, analyzes the design using AI, and verifies the design based on the Building Standards Act and disaster prevention standards.

[0057] (Example of form 2) An automated design system according to an embodiment of the present invention is a system that automates the design of a building that meets standards by incorporating the latest policies. This automated design system acquires the latest policies and automatically designs the building based on the acquired policies. This design process is performed using AI, and a design that meets the standards is generated. This improves the efficiency and accuracy of the design. For example, the automated design system acquires the latest policies. In this case, it is important to always acquire the latest information as policies are updated regularly. For example, the latest policies such as the Building Standards Act and disaster prevention standards are acquired. This information is input into the AI. Next, the automated design system automatically designs the building based on the acquired policies. The AI ​​analyzes the acquired policies and generates a design that meets the standards. For example, based on the Building Standards Act, it designs considering earthquake resistance and fire prevention. It also designs evacuation routes and the placement of emergency equipment based on disaster prevention standards. The generated design is verified again by the AI ​​to confirm whether it meets the standards. This improves the accuracy of the design and ensures that a design that meets the standards is generated. This improves the efficiency and accuracy of the design. Designers no longer need to manually check the standards, and the design process is significantly shortened. Furthermore, AI-driven automation improves design accuracy, ensuring that designs that meet standards are reliably generated. For example, designs that consider earthquake resistance and fire resistance are automatically generated, reducing design errors and improving safety. In this way, automated design systems can achieve both increased efficiency and improved accuracy in design.

[0058] The design automation system according to this embodiment comprises an acquisition unit, a design unit, and a verification unit. The acquisition unit acquires the latest policies. The acquisition unit periodically acquires the latest policies, such as the Building Standards Act and disaster prevention standards. The acquisition unit automatically acquires the latest policies, for example, via the internet. The acquisition unit can also monitor policy update information and acquire the latest policies as soon as they are published. Furthermore, the user can set the frequency of policy acquisition for the acquisition unit. For example, the acquisition unit can be set to acquire daily, weekly, or monthly. The design unit automatically performs design based on the policies acquired by the acquisition unit. The design unit performs design considering earthquake resistance and fire resistance, for example. The design unit performs design considering earthquake resistance based on the Building Standards Act. The design unit can also perform design considering fire resistance. The design unit designs evacuation routes and the placement of emergency equipment, for example, based on disaster prevention standards. The design unit designs the width of evacuation routes and the placement of emergency equipment, for example. The design unit performs design using AI. The design department, for example, uses AI to analyze policies and generate designs that meet the standards. The design department, for example, uses AI to design buildings that take into account seismic resistance and fire prevention based on the Building Standards Act. The design department, for example, uses AI to design evacuation routes and the placement of emergency equipment based on disaster prevention standards. The verification department verifies the designs generated by the design department. The verification department, for example, verifies whether the generated designs meet the standards. The verification department, for example, uses AI to verify the designs. The verification department, for example, uses AI to analyze the generated designs and confirm whether they meet the standards. The verification department, for example, uses AI to verify the designs based on the Building Standards Act and disaster prevention standards. As a result, the design automation system according to the embodiment can automate the design of buildings that meet the standards by incorporating the latest policies, thereby improving the efficiency and accuracy of the design.

[0059] The acquisition unit retrieves the latest policies. For example, it regularly retrieves the latest policies such as the Building Standards Act and disaster prevention standards. Specifically, the acquisition unit has a function to access the official websites of various laws and standards via the internet and automatically download the latest policies. This eliminates the need for manual policy verification and ensures that the latest information is always available. The acquisition unit also has a dedicated module for monitoring policy update information, and can receive immediate notifications when policies are updated, for example, using RSS feeds or APIs. Furthermore, the acquisition unit can retrieve policies based on the acquisition frequency set by the user. For example, the user can select an acquisition frequency such as daily, weekly, or monthly on the acquisition unit's settings screen. This allows for flexible policy acquisition according to the user's needs. The acquisition unit stores the acquired policies in a database, making them accessible to the design and verification units. This ensures that the entire system can use consistent and up-to-date policies.

[0060] The design department automatically performs design based on policies acquired by the acquisition department. For example, the design department performs designs that take into account earthquake resistance and fire resistance. Specifically, the design department uses AI to analyze policies and generate designs that meet the standards. The AI ​​uses natural language processing technology to understand the content of the policies and extract the requirements necessary for the design. For example, when designing with earthquake resistance in mind based on the Building Standards Act, the AI ​​automatically determines the building structure, material selection, and placement of seismic reinforcement. Similarly, when designing with fire resistance in mind, the AI ​​determines the placement of firewalls, evacuation routes, and emergency equipment. For example, the design department designs evacuation routes and emergency equipment placement based on disaster prevention standards. Specifically, the AI ​​designs the width of evacuation routes and the placement of emergency equipment, and performs simulations to determine the optimal placement. The design department visualizes the generated designs as 3D models for user review. This allows the design department to perform efficient and highly accurate designs automatically. Furthermore, the design department can receive feedback from users and modify the design content. For example, if a user wants to add a specific design requirement, the design department will generate a new design that reflects that requirement. This allows the design department to provide flexible designs that meet the user's needs.

[0061] The Verification Unit verifies the designs generated by the Design Unit. For example, the Verification Unit verifies whether the generated designs meet the standards. Specifically, the Verification Unit uses AI to verify the designs. The AI ​​analyzes the design data and confirms that the designs are properly carried out in accordance with building codes and disaster prevention standards. For example, in seismic resistance verification, the AI ​​performs structural analysis of the building and simulates the response during an earthquake to evaluate seismic performance. In fire resistance verification, the AI ​​performs fire simulations to confirm whether the placement of firewalls and evacuation routes is appropriate. Furthermore, the Verification Unit refers to the latest policy information provided by the Acquisition Unit to confirm whether the designs comply with the latest policies. This allows the Verification Unit to ensure that the designs always meet the latest standards. The Verification Unit generates verification results as a report and provides it to the user. The report details the suitability and areas for improvement of the design, which the user can use to modify or improve the design. Furthermore, the Verification Unit can save past verification results in a database and use them as a reference for future designs. This allows the Verification Unit to continuously improve the quality of the designs.

[0062] The acquisition unit can periodically acquire the latest policies, such as building codes and disaster prevention standards. The acquisition unit can, for example, automatically acquire the latest policies via the internet. The acquisition unit can also, for example, monitor policy update information and acquire the latest policies as soon as they are published. The acquisition unit also allows the user to set the frequency of policy acquisition. For example, the acquisition unit can be set to acquire policies daily, weekly, or monthly. This ensures that designs are always based on the latest standards by regularly acquiring the latest policies. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input policy data acquired from the internet into a generating AI and have the generating AI perform policy analysis.

[0063] The design department can perform designs that take into account seismic resistance and fire resistance based on acquired policies. For example, the design department can perform designs that take into account seismic resistance based on the Building Standards Act. The design department can also perform designs that take into account fire resistance, for example. The design department can perform designs using AI, for example. For example, the design department can have AI analyze policies and generate designs that meet the standards. For example, the design department can have AI perform designs that take into account seismic resistance and fire resistance based on the Building Standards Act. This makes it possible to create highly safe designs by performing designs that take into account seismic resistance and fire resistance. Some or all of the above processes in the design department may be performed using AI, for example, or without AI. For example, the design department can input acquired policy data into a generation AI and have the generation AI perform the design generation.

[0064] The design department can design evacuation routes and emergency equipment layouts based on disaster prevention standards. For example, the design department designs the width of evacuation routes and the placement of emergency equipment based on disaster prevention standards. The design department uses AI to perform the design, for example. The design department uses AI to analyze policies and generate designs that meet the standards. The design department uses AI to design evacuation routes and emergency equipment layouts based on disaster prevention standards, for example. By designing based on disaster prevention standards, safety during disasters is improved. Some or all of the above processes in the design department may be performed using AI, for example, or without AI. For example, the design department can input acquired policy data into a generating AI and have the generating AI perform the design generation.

[0065] The verification unit can verify whether the generated design meets the standards. For example, the verification unit verifies whether the generated design meets the standards. For example, the verification unit verifies the design using AI. For example, the verification unit analyzes the generated design using AI and confirms whether it meets the standards. For example, the verification unit verifies the design using AI based on building codes and disaster prevention standards. This improves the accuracy of the design by verifying whether the design meets the standards. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the generated design data into a generating AI and have the generating AI perform the design verification.

[0066] The acquisition unit can estimate the user's emotions and adjust the timing of policy acquisition based on the estimated user emotions. For example, if the user is stressed, the acquisition unit may delay policy acquisition and acquire it when the user is relaxed. For example, if the user is in a hurry, the acquisition unit may quickly acquire the policy and immediately start the design process. For example, if the user is relaxed, the acquisition unit may acquire the policy periodically to always maintain the latest information. By adjusting the timing of policy acquisition according to the user's emotions, it is possible to reduce user stress and enable efficient policy acquisition. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or not using AI. For example, the acquisition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0067] The acquisition unit can analyze past policy change history and select the optimal acquisition method when acquiring policies. For example, the acquisition unit can analyze past policy change history and prioritize the acquisition of policies that are frequently changed. For example, the acquisition unit can analyze policy change patterns and acquire policies at times when changes are predicted. For example, the acquisition unit can identify high-priority policies from past policy change history and prioritize their acquisition. This allows for efficient policy acquisition by selecting the optimal policy acquisition method through analysis of past policy change history. Some or all of the above processes in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input past policy change history data into a generating AI and have the generating AI select the optimal acquisition method.

[0068] The acquisition unit can filter policies by considering regional building codes and disaster prevention standards when acquiring them. For example, the acquisition unit can consider regional building codes and acquire only the policies for the relevant region. For example, the acquisition unit can consider regional disaster prevention standards and acquire only the policies for the relevant region. For example, the acquisition unit can filter regional standards and acquire only the necessary policies. This improves the accuracy of the design by acquiring policies suitable for the relevant region by considering regional standards. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input regional standard data into a generating AI and have the generating AI perform the filtering.

[0069] The acquisition unit can estimate the user's emotions and determine the priority of policies to acquire based on the estimated user emotions. For example, if the user is stressed, the acquisition unit will prioritize acquiring high-priority policies and postpone lower-priority policies. For example, if the user is relaxed, the acquisition unit will acquire all policies equally. For example, if the user is in a hurry, the acquisition unit will prioritize acquiring the most important policies. This enables efficient policy acquisition by determining policy priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or not using AI. For example, the acquisition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0070] The acquisition unit can prioritize the acquisition of highly relevant policies by considering the user's geographical location information when acquiring policies. For example, the acquisition unit can prioritize the acquisition of policies for a given region based on the user's current location. For example, the acquisition unit can acquire highly relevant regional policies based on the user's past travel history. For example, the acquisition unit can acquire highly relevant regional policies by considering the user's future travel plans. This improves the accuracy of the design by prioritizing the acquisition of highly relevant policies by considering the user's geographical location information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's geographical location information data into a generating AI and have the generating AI select highly relevant policies.

[0071] The acquisition unit can analyze the user's social media activity and acquire relevant policies when acquiring policies. For example, the acquisition unit can analyze the content of the user's social media posts and acquire relevant policies. For example, the acquisition unit can analyze the user's followers and followed accounts on social media and acquire relevant policies. For example, the acquisition unit can analyze the user's social media activity time and acquire policies at the optimal time. This allows for efficient acquisition of relevant policies by analyzing the user's social media activity, improving the accuracy of the design. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's social media data into a generating AI and have the generating AI select relevant policies.

[0072] The design department can estimate the user's emotions and adjust the design's presentation based on those emotions. For example, if the user is stressed, the design department provides a simple and highly visual design. If the user is relaxed, the design department provides a detailed design. If the user is in a hurry, the design department provides a concise design. By adjusting the design's presentation according to the user's emotions, a user-friendly design is provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the design department may be performed using AI or not. For example, the design department can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The design department can adjust the level of detail in the design based on the importance of the policies during the design process. For example, the design department can perform a detailed design based on high-importance policies. For example, the design department can perform a simplified design based on low-importance policies. For example, the design department can adjust the level of detail in the design stepwise according to the importance of the policies. This allows for efficient design by adjusting the level of detail in the design based on the importance of the policies. Some or all of the above processes in the design department may be performed using AI, for example, or not using AI. For example, the design department can input policy data into a generating AI and have the generating AI perform the adjustment of the level of detail in the design.

[0074] The design department can apply different design algorithms to buildings depending on their intended use during the design phase. For example, the design department might apply a design algorithm that prioritizes livability to residential buildings, a design algorithm that prioritizes profitability to commercial buildings, or a design algorithm that prioritizes safety to public facilities. By applying a design algorithm appropriate to the building's intended use, it becomes possible to create designs suitable for that purpose. Some or all of the above-described processes in the design department may be performed using AI, for example, or without AI. For example, the design department can input building use data into a generating AI and have the generating AI select the design algorithm to apply.

[0075] The design unit can estimate the user's emotions and adjust the length of the design based on the estimated emotions. For example, if the user is stressed, the design unit can shorten the design length and provide a concise design. For example, if the user is relaxed, the design unit can provide a detailed design. For example, if the user is in a hurry, the design unit can provide a concise design. In this way, by adjusting the length of the design according to the user's emotions, an appropriate design is provided for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the design unit may be performed using AI or not using AI. For example, the design unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The design department can determine design priorities based on policy update timings during the design phase. For example, the design department can determine design priorities based on the latest policies. For example, the design department can prioritize the incorporation of high-priority policies into the design, taking policy update timings into consideration. For example, the design department can adjust design priorities based on policy update frequency. This allows for design based on the latest standards by determining design priorities based on policy update timings. Some or all of the above processes in the design department may be performed using AI, for example, or not. For example, the design department can input policy update timing data into a generating AI and have the generating AI perform the determination of design priorities.

[0077] The design department can adjust the design sequence based on the relationships between buildings during the design process. For example, the design department can adjust the design sequence based on the building's use. For example, the design department can adjust the design sequence based on the building's size. For example, the design department can adjust the design sequence based on the building's location. This allows for efficient design by adjusting the design sequence based on the relationships between buildings. Some or all of the above processes in the design department may be performed using AI, for example, or without AI. For example, the design department can input building relationship data into a generating AI and have the generating AI perform the adjustment of the design sequence.

[0078] The verification unit can estimate the user's emotions and adjust the verification criteria based on the estimated user emotions. For example, if the user is stressed, the verification unit provides concise verification criteria. For example, if the user is relaxed, the verification unit provides detailed verification criteria. For example, if the user is in a hurry, the verification unit provides concise verification criteria. By adjusting the verification criteria according to the user's emotions, appropriate verification is provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The verification unit can improve the accuracy of verification by considering the interrelationships of the design during verification. For example, the verification unit analyzes the interrelationships of the design and confirms consistency. For example, the verification unit detects inconsistencies by considering the interrelationships of the design. For example, the verification unit selects the optimal verification method based on the interrelationships of the design. As a result, the accuracy of verification is improved by considering the interrelationships of the design. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the interrelationship data of the design into a generating AI and have the generating AI perform the verification accuracy improvement.

[0080] The verification unit can perform verification while considering the designer's attribute information. For example, the verification unit can adjust the rigor of the verification by considering the designer's years of experience. For example, the verification unit can apply appropriate verification criteria by considering the designer's field of expertise. For example, the verification unit can adjust the focus of the verification based on the designer's past performance. In this way, appropriate verification is performed by considering the designer's attribute information. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the designer's attribute information data into a generating AI and have the generating AI perform the verification adjustments.

[0081] The verification unit can estimate the user's emotions and adjust the order in which the verification results are displayed based on the estimated emotions. For example, if the user is stressed, the verification unit will display important results first. If the user is relaxed, the verification unit will display detailed results sequentially. If the user is in a hurry, the verification unit will display concise results first. By adjusting the display order of the verification results according to the user's emotions, the system provides results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the verification unit may be performed using AI, or not using AI. For example, the verification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The verification unit can perform verification while considering the geographical distribution of the design. For example, the verification unit can perform verification based on regional criteria, taking into account the geographical distribution of the design. For example, the verification unit can analyze the geographical distribution of the design and perform verification while considering the characteristics of each region. For example, the verification unit can select the optimal verification method based on the geographical distribution of the design. As a result, verification that conforms to regional criteria is performed by considering the geographical distribution of the design. Some or all of the above processes in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input geographical distribution data of the design into a generating AI and have the generating AI perform the verification adjustments.

[0083] The verification unit can improve the accuracy of the verification by referring to relevant design literature during the verification process. For example, the verification unit refers to relevant design literature to confirm compliance with the standards. For example, the verification unit improves the accuracy of the verification based on the relevant design literature. For example, the verification unit analyzes the relevant design literature and selects the optimal verification method. As a result, the accuracy of the verification is improved by referring to the relevant design literature. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the relevant design literature data into a generating AI and have the generating AI perform the verification accuracy improvement.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The automated design system may also include a history analysis unit that optimizes the design by considering the user's past design history. The history analysis unit, for example, analyzes data from past design projects to identify successful and unsuccessful design patterns. It also collects user feedback from past design projects to identify areas for design improvement. Furthermore, it proposes the optimal design methodology based on data from past design projects. This improves the accuracy and efficiency of the design by considering past design history, providing a design that meets the user's needs.

[0086] The design automation system may also include a progress monitoring unit that monitors the design progress in real time and notifies the user. The progress monitoring unit, for example, monitors each step of the design process and notifies the user of the progress. For example, the progress monitoring unit sends an alert to the user if a design delay occurs. For example, the progress monitoring unit visually displays the design progress using graphs or charts, making it easy for the user to understand. This improves the efficiency of the design process and reduces user stress by allowing real-time monitoring of the design progress.

[0087] The automated design system may further include a quality evaluation unit that evaluates the quality of the design. The quality evaluation unit, for example, evaluates each element of the design and calculates a quality score. It may also evaluate the durability and safety of the design and provide feedback to the user. Furthermore, it may evaluate the aesthetics and functionality of the design and suggest areas for improvement. By evaluating the quality of the design, the accuracy and reliability of the design are improved, resulting in a valuable design for the user.

[0088] The automated design system can further incorporate a cost optimization unit to optimize design costs. This unit, for example, analyzes the cost of each design element and proposes cost reductions. It also suggests cost-effective options in areas such as material selection and construction method selection. Furthermore, it monitors the overall design cost in real time to support budget-based design. By optimizing design costs, this enables more economical design and reduces the user's cost burden.

[0089] The automated design system may also include an environmental assessment unit that evaluates the environmental impact of the design. For example, the environmental assessment unit evaluates the environmental impact of each design element and proposes ways to minimize the environmental burden. For example, the environmental assessment unit evaluates energy efficiency and resource usage and recommends environmentally friendly designs. For example, the environmental assessment unit evaluates the environmental impact throughout the entire design lifecycle and supports sustainable design. This allows for sustainable design through the evaluation of the environmental impact of the design, contributing to environmental protection.

[0090] The automated design system can further estimate user emotions and adjust the design interface based on those emotions. For example, if a user is stressed, it can provide a simple and intuitive interface. If a user is relaxed, it can provide an interface that displays detailed information. If a user is in a hurry, it can provide an interface that prioritizes displaying important information. By adjusting the interface according to the user's emotions, user experience is improved and stress is reduced.

[0091] The automated design system can further estimate user emotions and adjust design feedback based on those emotions. For example, if a user is stressed, it prioritizes providing positive feedback. If a user is relaxed, it provides detailed feedback. If a user is in a hurry, it provides concise feedback. By adjusting feedback according to user emotions, user satisfaction is improved and the design process proceeds more smoothly.

[0092] The automated design system can further estimate the user's emotions and adjust the design process based on those emotions. For example, if the user is stressed, the design process will slow down to a pace that is easy for the user to understand. If the user is relaxed, the design process will speed up for greater efficiency. If the user is in a hurry, the most important parts will be prioritized. By adjusting the design process according to the user's emotions, user stress is reduced and the design process becomes more efficient.

[0093] The automated design system can further estimate the user's emotions and adjust the design notification method based on those emotions. For example, if the user is stressed, notifications will be less frequent, and only important notifications will be sent. If the user is relaxed, detailed notifications will be sent. If the user is in a hurry, immediate notifications will be sent to encourage quick action. By adjusting notification methods according to the user's emotions, user stress is reduced and the design process proceeds more smoothly.

[0094] The automated design system can further estimate user emotions and adjust the design review method based on those emotions. For example, if the user is stressed, it can provide a concise review; if the user is relaxed, it can provide a detailed review; and if the user is in a hurry, it can provide a to-the-point review. By adjusting the review method according to the user's emotions, it deepens the user's understanding and makes the design process more efficient.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The acquisition unit acquires the latest policies. The acquisition unit regularly acquires the latest policies, such as the Building Standards Act and disaster prevention standards. The acquisition unit automatically acquires the latest policies via the internet and monitors policy update information, so it can acquire the latest policies as soon as they are published. In addition, the acquisition unit allows users to set the frequency of policy acquisition, such as daily, weekly, or monthly. Step 2: The design department automatically performs the design based on the policies acquired by the acquisition department. The design department performs the design considering seismic resistance and fire resistance, and designs the seismic resistance in accordance with the Building Standards Act, and the placement of evacuation routes and emergency equipment in accordance with disaster prevention standards. The design department uses AI to analyze the policies and generate a design that meets the standards. Step 3: The Verification Unit verifies the design generated by the Design Unit. The Verification Unit verifies whether the generated design meets the standards, analyzes the design using AI, and verifies the design based on the Building Standards Act and disaster prevention standards.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0099] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0100] Each of the multiple elements described above, including the acquisition unit, design unit, and verification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit can acquire the latest policy from the internet via the communication I / F 44 of the smart device 14. The design unit is implemented in the specific processing unit 290 of the data processing unit 12 and performs design using AI based on the acquired policy. The verification unit is implemented in the specific processing unit 290 of the data processing unit 12 and verifies using AI whether the generated design meets the criteria. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0107] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0108] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0109] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0110] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0111] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0115] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the acquisition unit, design unit, and verification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit can acquire the latest policy from the internet via the communication I / F 44 of the smart glasses 214. The design unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and performs design using AI based on the acquired policy. The verification unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and verifies, for example, whether the generated design meets the criteria using AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the acquisition unit, design unit, and verification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit can acquire the latest policy from the internet via the communication I / F 44 of the headset terminal 314. The design unit is implemented in the specific processing unit 290 of the data processing unit 12 and performs design using AI based on the acquired policy. The verification unit is implemented in the specific processing unit 290 of the data processing unit 12 and verifies using AI whether the generated design meets the criteria. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0141] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the acquisition unit, design unit, and verification unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit can acquire the latest policy from the internet via the communication I / F 44 of the robot 414. The design unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12, and performs design using AI based on the acquired policy. The verification unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12, and verifies using AI whether the generated design meets the criteria. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0150] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0152] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0153] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0154] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0158] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0159] 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.

[0160] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0161] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0162] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0163] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0165] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0166] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0167] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0168] (Note 1) The acquisition unit retrieves the latest policy, A design unit that automatically performs design based on the policy acquired by the acquisition unit, The system comprises a verification unit that verifies the design generated by the design unit. A system characterized by the following features. (Note 2) The acquisition unit is, Regularly obtain the latest policies regarding building codes, disaster prevention standards, etc. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned design department, Based on the acquired policies, the design will take into account earthquake resistance and fire prevention. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned design department, Based on disaster prevention standards, design evacuation routes and the placement of emergency equipment. The system described in Appendix 1, characterized by the features described herein. (Note 5) The verification unit, Verify whether the generated design meets the criteria. The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, It estimates the user's sentiment and adjusts the timing of policy acquisition based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, When retrieving policies, the system analyzes past policy change history and selects the optimal retrieval method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, When acquiring policies, filtering is performed to take into account the specific building codes and disaster prevention standards of each region. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, It estimates the user's sentiment and determines the priority of policies to acquire based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, When retrieving policies, the system prioritizes retrieving the most relevant policies by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When retrieving policies, the system analyzes the user's social media activity and retrieves relevant policies. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned design department, We estimate the user's emotions and adjust the design's presentation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned design department, During the design phase, adjust the level of detail in the design based on the importance of the policy. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned design department, During the design phase, different design algorithms are applied depending on the building's intended use. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned design department, The system estimates the user's emotions and adjusts the design length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned design department, Prioritize design based on policy update timing during the design phase. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned design department, During the design phase, adjust the order of design elements based on their relationships to the building. The system described in Appendix 1, characterized by the features described herein. (Note 18) The verification unit, We estimate the user's emotions and adjust the validation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The verification unit, During verification, consider the interrelationships of the design to improve the accuracy of the verification. The system described in Appendix 1, characterized by the features described herein. (Note 20) The verification unit, During verification, the designer's attribute information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The verification unit, It estimates the user's sentiment and adjusts the order in which the verification results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The verification unit, During verification, the geographical distribution of the design will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The verification unit, During verification, we improve the accuracy of the verification by referring to relevant design literature. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The acquisition unit retrieves the latest policy, A design unit that automatically performs design based on the policy acquired by the acquisition unit, The system comprises a verification unit that verifies the design generated by the design unit. A system characterized by the following features.

2. The acquisition unit is, Regularly obtain the latest policies regarding building codes, disaster prevention standards, etc. The system according to feature 1.

3. The aforementioned design department, Based on the acquired policies, the design will take into account earthquake resistance and fire prevention. The system according to feature 1.

4. The aforementioned design department, Based on disaster prevention standards, design evacuation routes and the placement of emergency equipment. The system according to feature 1.

5. The verification unit, Verify whether the generated design meets the criteria. The system according to feature 1.

6. The acquisition unit is, It estimates the user's sentiment and adjusts the timing of policy acquisition based on the estimated user sentiment. The system according to feature 1.

7. The acquisition unit is, When retrieving policies, the system analyzes past policy change history and selects the optimal retrieval method. The system according to feature 1.

8. The acquisition unit is, When acquiring policies, filtering is performed to take into account the specific building codes and disaster prevention standards of each region. The system according to feature 1.

9. The acquisition unit is, It estimates the user's sentiment and determines the priority of policies to acquire based on the estimated user sentiment. The system according to feature 1.

10. The acquisition unit is, When retrieving policies, the system prioritizes retrieving the most relevant policies by considering the user's geographical location. The system according to feature 1.

Images