system

Abstract

The system according to this embodiment aims to enable seamless changes to building designs during business negotiations. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a verification unit, and a provision unit. The reception unit receives customer requests. The generation unit generates design drawings based on the requests received by the reception unit. The verification unit verifies whether the design drawings generated by the generation unit comply with the law. The provision unit provides the design drawings generated by the generation unit to the customer.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 conventional technology, there has been a problem that it is difficult to seamlessly make design changes to a building during negotiations.

[0005] The system according to an embodiment aims to seamlessly make design changes to a building during negotiations.

Means for Solving the Problems

[0006] The system according to an embodiment includes a reception unit, a generation unit, a confirmation unit, and a provision unit. The reception unit receives customer requests. The generation unit generates a design drawing based on the requests received by the reception unit. The confirmation unit confirms whether the design drawing generated by the generation unit complies with laws and regulations. The provision unit provides the design drawing generated by the generation unit to the customer. [Effects of the Invention]

[0007] The system according to this embodiment allows for seamless changes to the building's design during business negotiations. [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) The building design system according to an embodiment of the present invention is a system that seamlessly handles design changes during negotiations by replacing building design with AI. The building design system immediately reflects customer requests and generates 2D and 3D drawings. This eliminates the repetitive process of negotiation → drawing revision → subsequent negotiation → drawing revision in the conventional design process, contributing to the short-term conclusion of negotiations. The building design system learns various knowledge required of a first-class architect and generates buildings that comply with the land and regulations. For example, it installs various regulations and performs design based on information about candidate land (dimensions, orientation, etc.). When a customer inputs their requests on the spot during negotiations, the building design system generates an output in a few seconds and provides an estimated quote. Next, the design drawings generated by the building design system are immediately revised according to the customer's requests. For example, if a customer requests a "high ceiling," the building design system immediately changes the floor plan to reflect that request. This reduces the burden on both the customer and the architect and contributes to the short-term conclusion of negotiations. Furthermore, after the contract is signed, final confirmations and applications to government offices are carried out manually as before. This allows the building design system to verify whether the generated design drawings comply with regulations and to make necessary corrections. This mechanism streamlines the building design process and enables quick responses to customer requests. For example, if a customer requests to add a living room staircase during negotiations, the building design system can immediately generate design drawings reflecting that request and present them to the customer. This enables quicker negotiations and is expected to boost sales volume and the number of contracts. Furthermore, for corporate clients, there is the benefit of being able to hire a dedicated architect for 100,000 yen per branch per month. This also leads to faster negotiations and can boost sales volume and the number of contracts. In summary, the building design system can efficiently receive customer requests, generate design drawings, verify compliance with regulations, and deliver them.

[0029] The building design system according to this embodiment comprises a reception unit, a generation unit, a verification unit, and a provision unit. The reception unit receives customer requests. Customer requests include, but are not limited to, design details, functional requirements, and budget constraints. The reception unit receives customer requests, for example, through an online form. The reception unit can also receive requests by telephone or in person. Furthermore, the reception unit can store customer requests as digital data and use them for subsequent processing. The generation unit generates design drawings based on the requests received by the reception unit. The generation unit generates 2D and 3D drawings, for example, using CAD software. The generation unit can also create detailed architectural models using 3D modeling tools. Furthermore, the generation unit can automatically generate design drawings that reflect customer requests using AI. The verification unit verifies whether the design drawings generated by the generation unit comply with laws and regulations. The verification unit checks the design drawings based on various laws and regulations, for example, the Building Standards Act, the Industrial Safety and Health Act, and the Environmental Protection Act. The verification unit can also automatically verify compliance with laws and regulations using AI. Furthermore, if the verification unit needs to modify the design drawings, it feeds the modification details back to the generation unit. The delivery unit provides the design drawings generated by the generation unit to the customer. For example, the delivery unit sends the design drawings to the customer in PDF format. Alternatively, the delivery unit can print the design drawings and mail them to the customer. Furthermore, the delivery unit can immediately modify the design drawings according to the customer's request and provide them again. As a result, the building design system according to this embodiment can efficiently receive customer requests, generate design drawings, verify compliance with laws and regulations, and provide them.

[0030] The reception department receives customer requests. These requests may include, but are not limited to, design details, functional requirements, and budget constraints. The reception department may receive customer requests through online forms, for example. These online forms have a user-friendly interface and are designed to allow customers to easily enter the necessary information. The forms include text boxes, dropdown menus, and checkboxes, allowing for the collection of detailed customer requests. The reception department can also receive requests by phone or in person. In phone inquiries, a professional operator will listen to the customer's requests and record them as digital data. In face-to-face inquiries, a design engineer will meet with the customer directly to hear detailed requests. Furthermore, the reception department can save customer requests as digital data and use them for subsequent processing. The digital data is stored in cloud storage and is secured, minimizing the risk of data leakage or loss. This allows the reception department to efficiently collect diverse customer requests and smoothly transition them into the subsequent design process.

[0031] The generation unit generates design drawings based on requests received by the reception unit. For example, the generation unit generates 2D and 3D drawings using CAD software. The CAD software has advanced design capabilities and can accurately depict the detailed structure and layout of a building. The generation unit can also create detailed architectural models using 3D modeling tools. These 3D modeling tools realistically reproduce the building's exterior and interior structure, allowing customers to visually confirm the completed image. Furthermore, the generation unit can automatically generate design drawings that reflect customer requests using AI. The AI ​​analyzes customer requests using natural language processing technology and proposes the optimal design pattern. For example, if a customer requests a "large living room," the AI ​​generates the optimal layout based on past design data and automatically adjusts the size and placement of the living room. This allows the generation unit to respond quickly and accurately to customer requests and provide high-quality design drawings.

[0032] The verification unit checks whether the design drawings generated by the generation unit comply with the law. The verification unit checks the design drawings based on various laws, such as the Building Standards Act, the Industrial Safety and Health Act, and the Environmental Protection Act. Staff with specialized legal knowledge are in charge of verifying legal compliance, and each item of the design drawing is examined in detail. The verification unit can also automatically verify legal compliance using AI. The AI ​​refers to a legal database and quickly determines whether each element of the design drawing complies with the law. For example, it automatically checks items such as building height, seismic standards, and environmental impact, and issues a warning if there is a problem. Furthermore, if the design drawing needs to be modified, the verification unit feeds the modification details back to the generation unit. The feedback clearly indicates specific areas for modification and improvement, enabling the generation unit to respond quickly. In this way, the verification unit can improve the efficiency of the design process while ensuring legal compliance.

[0033] The service provider provides customers with design drawings generated by the design generation service. For example, the service provider sends the design drawings to customers in PDF format. PDF is a widely used file format that customers can easily view and print. The service provider can also print the design drawings and mail them to customers. The printed design drawings are printed on high-quality paper, and detailed drawings and annotations are clearly displayed. Furthermore, the service provider can immediately revise the design drawings according to customer requests and resend them. For example, if a customer wants to change part of a design drawing, the service provider will quickly make the revisions and resend the latest design drawing. This allows the service provider to respond flexibly to customer requests and increase customer satisfaction. In addition, the service provider also provides explanations and support to customers when providing design drawings. For example, they will explain the content and changes in the design drawings to help customers understand them easily. This allows the service provider to facilitate communication with customers and build trust.

[0034] The generation unit can generate 2D and 3D drawings based on customer requests. For example, the generation unit can generate 2D floor plans using CAD software. It can also generate 3D models using 3D modeling tools. For example, the generation unit can create detailed 3D models of the building's exterior and interior based on customer requests. Furthermore, the generation unit can use AI to automatically generate 2D and 3D drawings that reflect customer requests. For example, if a customer requests "a large window in the living room," the generation unit can instantly generate 2D and 3D drawings that reflect that request. This improves design accuracy by generating 2D and 3D drawings based on customer requests. Some or all of the above-described processes in the generation unit may be performed using, for example, generation AI, or without generation AI. For example, the generation unit can input customer requests as prompts into the generation AI, causing the generation AI to generate 2D and 3D drawings.

[0035] The verification unit can confirm whether the design drawings conform to various laws and regulations. For example, the verification unit checks the design drawings based on the Building Standards Act. The verification unit can also confirm the design drawings based on the Industrial Safety and Health Act. Furthermore, the verification unit can also confirm the design drawings based on the Environmental Protection Act. For example, the verification unit confirms whether the height and structure of the building are appropriate based on the Building Standards Act. It confirms whether the safety equipment inside the building is properly installed based on the Industrial Safety and Health Act. It evaluates the impact of the building on the environment based on the Environmental Protection Act. By confirming whether the design drawings conform to various laws and regulations, compliance with laws and regulations is ensured. 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 design drawings into AI and have the AI ​​perform the legal compliance check.

[0036] The service provider can provide design drawings to customers and make immediate modifications according to customer requests. For example, the service provider can send design drawings to customers in PDF format. Alternatively, the service provider can print the design drawings and mail them to customers. Furthermore, the service provider can immediately modify the design drawings according to customer requests and provide them again. For example, if a customer requests that a large window be installed in the living room, the service provider can immediately modify the design drawings to reflect that request and provide them to the customer. This improves customer satisfaction by providing design drawings to customers and making immediate modifications according to their requests. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input customer requests as prompts into a generating AI and have the generating AI perform modifications to the design drawings.

[0037] The Learning Department is the department where the AI ​​learns. For example, the Learning Department can use supervised learning to teach the AI ​​architectural knowledge. The Learning Department can also use unsupervised learning to teach the AI ​​data patterns. Furthermore, the Learning Department can use reinforcement learning to teach the AI ​​the optimal design method. For example, as supervised learning, the Learning Department can teach the AI ​​laws such as the Building Standards Act and the Industrial Safety and Health Act. As unsupervised learning, it can teach the AI ​​data patterns using past design data. As reinforcement learning, it can have the AI ​​perform trial and error in design to learn the optimal design method. As a result, the accuracy of the system improves as the AI ​​learns. Some or all of the above processes in the Learning Department may be performed using the AI, or they may not. For example, the Learning Department can input architectural knowledge as a prompt to the Generating AI and have the Generating AI perform the learning.

[0038] The Estimation Department is the department that provides preliminary estimates. The Estimation Department estimates, for example, material costs, labor costs, and time. The Estimation Department can also provide preliminary estimates quickly using AI. Furthermore, the Estimation Department can adjust the content of the estimate according to the customer's requests. For example, the Estimation Department estimates material costs based on the unit price of materials necessary for construction. For labor costs, it estimates the personnel costs necessary for construction. For time estimates, it estimates the construction period necessary for construction. This enables a quick response to customers by providing preliminary estimates. Some or all of the above processes in the Estimation Department may be performed using AI, for example, or not using AI. For example, the Estimation Department can input estimates of material costs, labor costs, and time as prompts into a generating AI and have the generating AI execute the estimate.

[0039] The reception desk can analyze a customer's past request history and select the most suitable reception method. For example, the reception desk can automatically display requests that the customer has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the customer has used in the past. Furthermore, the reception desk can predict and suggest requests to be used during specific time periods based on the customer's past request history. For example, the reception desk can store the customer's past requests in a database and select the most suitable reception method based on that data. This allows the reception desk to select the most suitable reception method by analyzing the customer's past request history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the customer's past request history into a generating AI and have the generating AI select the most suitable reception method.

[0040] The reception department can filter requests based on the customer's current projects and areas of interest when receiving them. For example, the reception department can prioritize requests related to projects the customer is currently working on. The reception department can also filter requests based on the customer's areas of interest. Furthermore, the reception department can filter requests based on areas the customer has shown interest in in the past. For example, the reception department can store the customer's current project information in a database and filter based on that data. This streamlines the request reception process by filtering based on the customer's current projects and areas of interest. Some or all of the above processing in the reception department may be performed using AI, for example, or not. For example, the reception department can input the customer's project information into a generating AI and have the generating AI perform the filtering.

[0041] The reception desk can prioritize requests based on their relevance, taking into account the customer's geographical location. For example, it can prioritize requests related to the customer's current location. It can also filter requests based on the customer's geographical location. Furthermore, it can filter requests based on places the customer has visited in the past. For example, the reception desk can store the customer's geographical location in a database and perform filtering based on that data. This allows for prioritizing requests based on the customer's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the customer's geographical location into a generating AI and have the generating AI perform the filtering.

[0042] The reception department can analyze a customer's social media activity when receiving a request and accept relevant requests. For example, the reception department can prioritize requests related to the customer's current interests based on their social media activity. The reception department can also automatically accept requests mentioned by the customer on social media. Furthermore, the reception department can analyze the customer's social media activity and filter the requests to accept them. For example, the reception department can store the customer's social media activity in a database and perform filtering based on that data. This allows for efficient acceptance of relevant requests by analyzing the customer's social media activity. Some or all of the above processing in the reception department may be performed using AI, for example, or not. For example, the reception department can input the customer's social media activity into a generating AI and have the generating AI perform the filtering.

[0043] The generation unit can adjust the level of detail generated based on the importance of customer requests when generating design drawings. For example, the generation unit can generate detailed design drawings for important requests. It can also generate simplified design drawings for lower-priority requests. Furthermore, the generation unit can adjust the level of detail of the design drawings according to the importance of customer requests. For example, the generation unit can store the importance of customer requests in a database and adjust the level of detail based on that data. This improves the accuracy of the design drawings by adjusting the level of detail based on the importance of customer requests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of customer requests into a generation AI and have the generation AI perform the level of detail adjustment.

[0044] The generation unit can apply different generation algorithms depending on the customer's requirements when generating design drawings. For example, in the case of residential design, the generation unit applies a generation algorithm specifically for residential buildings. Similarly, in the case of commercial building design, the generation unit can apply a generation algorithm specifically for commercial buildings. Furthermore, in the case of public building design, the generation unit can apply a generation algorithm specifically for public buildings. For example, the generation unit can store the customer's requirements in a database and apply a generation algorithm based on that data. This improves the accuracy of the design drawings by applying different generation algorithms depending on the customer's requirements. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the customer's requirements into a generation AI and have the generation AI execute the application of the generation algorithm.

[0045] The generation unit can determine the generation priority based on when customer requests are submitted when generating design drawings. For example, the generation unit can prioritize requests submitted early. It can also prioritize urgent requests. Furthermore, the generation unit can determine the priority of requests based on the submission date. For example, the generation unit can store the submission dates of customer requests in a database and determine the priority based on that data. This enables efficient generation of design drawings by determining the generation priority based on the submission dates of customer requests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the submission dates of customer requests into a generation AI and have the generation AI perform the priority determination.

[0046] The generation unit can adjust the generation order based on the relevance of customer requests when generating design drawings. For example, the generation unit can prioritize processing requests with high relevance. It can also postpone requests with low relevance. Furthermore, the generation unit can adjust the generation order based on the relevance of customer requests. For example, the generation unit can store the relevance of customer requests in a database and adjust the order based on that data. This allows for efficient generation of design drawings by adjusting the generation order based on the relevance of customer requests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of customer requests into a generation AI and have the generation AI perform the order adjustment.

[0047] The verification unit can improve the accuracy of legal compliance verification by considering the interrelationships of design drawings. For example, the verification unit performs legal compliance verification by considering the interrelationships of each part of the design drawings. The verification unit can also improve the accuracy of verification by analyzing the interrelationships of design drawings. Furthermore, the verification unit can adjust the criteria for legal compliance verification by considering the interrelationships of design drawings. For example, the verification unit can store the interrelationships of design drawings in a database and perform verification based on that data. This improves the accuracy of legal compliance verification by considering the interrelationships of design drawings. 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 interrelationships of design drawings into a generating AI and have the generating AI perform the verification accuracy improvement.

[0048] The verification unit can perform compliance checks by considering the attribute information of the design drawing submitter. For example, the verification unit can perform compliance checks by considering the attribute information of the design drawing submitter. The verification unit can also improve the accuracy of the checks based on the submitter's attribute information. Furthermore, the verification unit can adjust the criteria for compliance checks by considering the submitter's attribute information. For example, the verification unit can store the attribute information of the design drawing submitter in a database and perform checks based on that data. This improves the accuracy of compliance checks by considering the attribute information of the design drawing submitter. 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 submitter's attribute information into a generating AI and have the generating AI perform the task of improving the accuracy of the checks.

[0049] The verification unit can perform compliance checks while considering the geographical distribution of design drawings. For example, the verification unit can perform compliance checks while considering the geographical distribution of design drawings. The verification unit can also improve the accuracy of the checks based on the geographical distribution. Furthermore, the verification unit can adjust the criteria for compliance checks while considering the geographical distribution. For example, the verification unit can store the geographical distribution of design drawings in a database and perform checks based on that data. This improves the accuracy of compliance checks by considering the geographical distribution of design drawings. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input the geographical distribution of design drawings into a generating AI and have the generating AI perform the task of improving the accuracy of the checks.

[0050] The verification unit can improve the accuracy of its verification by referring to relevant documents for the design drawings when verifying compliance with laws and regulations. For example, the verification unit can perform a legal compliance verification by referring to relevant documents for the design drawings. The verification unit can also improve the accuracy of the verification based on the relevant documents. Furthermore, the verification unit can adjust the criteria for legal compliance verification by referring to relevant documents. For example, the verification unit can store the relevant documents for the design drawings in a database and perform the verification based on that data. This improves the accuracy of legal compliance verification by referring to the relevant documents for the design drawings. 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 the relevant documents for the design drawings into a generating AI and have the generating AI perform the verification accuracy improvement.

[0051] The service provider can select the optimal delivery method when providing design drawings by referring to the customer's past request history. For example, the service provider can automatically display requests that the customer has frequently entered in the past as candidates. The service provider can also prioritize suggesting delivery methods (voice, text, etc.) that the customer has used in the past. Furthermore, the service provider can predict and suggest delivery methods to be used at specific times based on the customer's past request history. For example, the service provider can store the customer's past request history in a database and select the optimal delivery method based on that data. This allows the service provider to select the optimal delivery method by referring to the customer's past request history. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the customer's past request history into a generating AI and have the generating AI select the optimal delivery method.

[0052] The service provider can customize the content of the design drawings provided based on the customer's current projects and areas of interest. For example, the service provider can prioritize providing design drawings related to the customer's current projects. Furthermore, the service provider can customize and provide relevant design drawings based on the customer's areas of interest. In addition, the service provider can customize and provide design drawings based on areas the customer has shown interest in in the past. For example, the service provider can store the customer's current project information in a database and customize the content of the design drawings based on that data. This improves customer satisfaction by customizing the content of the design drawings based on the customer's current projects and areas of interest. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the customer's project information into a generating AI and have the generating AI perform the customization of the design drawings.

[0053] The service provider can select the optimal delivery method when providing design drawings, taking into account the customer's geographical location information. For example, the service provider can prioritize providing design drawings related to the customer's current location. Furthermore, the service provider can filter and provide relevant design drawings based on the customer's geographical location information. It can also filter and provide design drawings based on places the customer has visited in the past. For example, the service provider can store the customer's geographical location information in a database and select the optimal delivery method based on that data. This allows for the selection of the optimal delivery method by considering the customer's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the customer's geographical location information into a generating AI and have the generating AI select the optimal delivery method.

[0054] The service provider can analyze the customer's social media activity and adjust the content provided when delivering design drawings. For example, the service provider can prioritize providing design drawings related to the customer's current interests based on their social media activity. The service provider can also automatically provide design drawings that reflect requests mentioned by the customer on social media. Furthermore, the service provider can analyze the customer's social media activity and filter the relevant design drawings before providing them. For example, the service provider can store the customer's social media activity in a database and adjust the content provided based on that data. This allows for the efficient provision of relevant design drawings by analyzing the customer's social media activity. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the customer's social media activity into a generating AI and have the generating AI perform the adjustment of the content provided.

[0055] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data to improve the accuracy of the learning algorithm. Furthermore, the learning unit can adjust the parameters of the learning algorithm by referring to past learning data. For example, the learning unit can save past learning data to a database and optimize the learning algorithm based on that data. This improves the accuracy of the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0056] The learning unit can weight the learning data based on the submission timing of design drawings during the learning process. For example, the learning unit can prioritize learning from data of design drawings submitted early. It can also prioritize learning from data of urgent design drawings. Furthermore, the learning unit can weight the learning data based on the submission timing. For example, the learning unit can store the submission timing of design drawings in a database and perform weighting based on that data. This improves the accuracy of learning by weighting the learning data based on the submission timing of design drawings. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the submission timing of design drawings into a generating AI and have the generating AI perform the weighting.

[0057] The estimation department can select the optimal delivery method by referring to the customer's past estimation history when providing an estimate. For example, the estimation department can automatically display estimates that the customer has frequently entered in the past as candidates. The estimation department can also prioritize suggesting delivery methods (voice, text, etc.) that the customer has used in the past. Furthermore, the estimation department can predict and suggest delivery methods to be used at specific times based on the customer's past estimation history. For example, the estimation department can store the customer's past estimation history in a database and select the optimal delivery method based on that data. This allows the optimal delivery method to be selected by referring to the customer's past estimation history. Some or all of the above processes in the estimation department may be performed using AI, for example, or not using AI. For example, the estimation department can input the customer's past estimation history into a generating AI and have the generating AI select the optimal delivery method.

[0058] The estimation department can select the optimal delivery method when providing an estimate, taking into account the customer's geographical location information. For example, the estimation department may prioritize providing estimates related to the customer's current location. It can also filter and provide relevant estimates based on the customer's geographical location information. Furthermore, it can filter and provide estimates based on places the customer has visited in the past. For example, the estimation department can store the customer's geographical location information in a database and select the optimal delivery method based on that data. This allows for the selection of the optimal delivery method by considering the customer's geographical location information. Some or all of the above processing in the estimation department may be performed using AI, for example, or not. For example, the estimation department can input the customer's geographical location information into a generating AI and have the generating AI select the optimal delivery method.

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

[0060] The reception desk can refer to the customer's past request history when receiving a customer request and suggest the most suitable reception method. For example, it can automatically display requests that the customer has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the customer has used in the past. Furthermore, the reception desk can predict and suggest requests to be used during specific time periods based on the customer's past request history. In this way, the optimal reception method can be selected by analyzing the customer's past request history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the customer's past request history into a generating AI and have the generating AI select the optimal reception method.

[0061] The generation unit can adjust the level of detail generated based on the importance of the customer's requests when generating design drawings. For example, it can generate detailed design drawings for important requests, and simplified design drawings for lower-priority requests. Furthermore, the generation unit can adjust the level of detail of the design drawings according to the importance of the customer's requests. This improves the accuracy of the design drawings by adjusting the level of detail based on the importance of the customer's requests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the customer's requests into the generation AI and have the generation AI perform the level of detail adjustment.

[0062] The verification unit can improve the accuracy of legal compliance verification by considering the interrelationships of design drawings. For example, it can perform legal compliance verification by considering the interrelationships of each part of the design drawings. It can also improve the accuracy of verification by analyzing the interrelationships of the design drawings. Furthermore, the verification unit can adjust the criteria for legal compliance verification by considering the interrelationships of the design drawings. As a result, the accuracy of legal compliance verification is improved by considering the interrelationships of the design drawings. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input the interrelationships of the design drawings into a generating AI and have the generating AI perform the verification accuracy improvement.

[0063] The service delivery unit can select the optimal delivery method by referring to the customer's past request history when providing design drawings. For example, it can automatically display requests that the customer has frequently entered in the past as candidates. It can also prioritize suggesting delivery methods (voice, text, etc.) that the customer has used in the past. Furthermore, the service delivery unit can predict and suggest delivery methods to be used at specific times based on the customer's past request history. In this way, the optimal delivery method can be selected by referring to the customer's past request history. Some or all of the above processing in the service delivery unit may be performed using AI, for example, or not using AI. For example, the service delivery unit can input the customer's past request history into a generating AI and have the generating AI select the optimal delivery method.

[0064] The estimation department can select the optimal delivery method by referring to the customer's past estimation history when providing an estimate. For example, it can automatically display estimates that the customer has frequently entered in the past as candidates. It can also prioritize suggesting delivery methods (voice, text, etc.) that the customer has used in the past. Furthermore, the estimation department can predict and suggest delivery methods to be used at specific times based on the customer's past estimation history. This allows for the selection of the optimal delivery method by referring to the customer's past estimation history. Some or all of the above processes in the estimation department may be performed using AI, for example, or not. For example, the estimation department can input the customer's past estimation history into a generating AI and have the generating AI select the optimal delivery method.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception department receives customer requests. These requests include design details, functional requirements, and budget constraints. The reception department receives requests via online forms, telephone, and in person, and stores the customer requests as digital data. Step 2: The generation unit generates design drawings based on the requests received by the reception unit. The generation unit can generate 2D and 3D drawings using CAD software and 3D modeling tools, and can also automatically generate design drawings that reflect customer requests using AI. Step 3: The verification unit checks whether the design drawings generated by the generation unit comply with the law. The verification unit checks the design drawings based on various laws and regulations such as the Building Standards Act, the Industrial Safety and Health Act, and the Environmental Protection Act, and can also automatically verify compliance using AI. If necessary, it feeds back any modifications to the design drawings to the generation unit. Step 4: The supply unit provides the customer with the design drawings generated by the generation unit. The supply unit can send the design drawings in PDF format or print and mail them. Furthermore, they can immediately revise the design drawings according to the customer's requests and provide them again.

[0067] (Example of form 2) The building design system according to an embodiment of the present invention is a system that seamlessly handles design changes during negotiations by replacing building design with AI. The building design system immediately reflects customer requests and generates 2D and 3D drawings. This eliminates the repetitive process of negotiation → drawing revision → subsequent negotiation → drawing revision in the conventional design process, contributing to the short-term conclusion of negotiations. The building design system learns various knowledge required of a first-class architect and generates buildings that comply with the land and regulations. For example, it installs various regulations and performs design based on information about candidate land (dimensions, orientation, etc.). When a customer inputs their requests on the spot during negotiations, the building design system generates an output in a few seconds and provides an estimated quote. Next, the design drawings generated by the building design system are immediately revised according to the customer's requests. For example, if a customer requests a "high ceiling," the building design system immediately changes the floor plan to reflect that request. This reduces the burden on both the customer and the architect and contributes to the short-term conclusion of negotiations. Furthermore, after the contract is signed, final confirmations and applications to government offices are carried out manually as before. This allows the building design system to verify whether the generated design drawings comply with regulations and to make necessary corrections. This mechanism streamlines the building design process and enables quick responses to customer requests. For example, if a customer requests to add a living room staircase during negotiations, the building design system can immediately generate design drawings reflecting that request and present them to the customer. This enables quicker negotiations and is expected to boost sales volume and the number of contracts. Furthermore, for corporate clients, there is the benefit of being able to hire a dedicated architect for 100,000 yen per branch per month. This also leads to faster negotiations and can boost sales volume and the number of contracts. In summary, the building design system can efficiently receive customer requests, generate design drawings, verify compliance with regulations, and deliver them.

[0068] The building design system according to this embodiment comprises a reception unit, a generation unit, a verification unit, and a provision unit. The reception unit receives customer requests. Customer requests include, but are not limited to, design details, functional requirements, and budget constraints. The reception unit receives customer requests, for example, through an online form. The reception unit can also receive requests by telephone or in person. Furthermore, the reception unit can store customer requests as digital data and use them for subsequent processing. The generation unit generates design drawings based on the requests received by the reception unit. The generation unit generates 2D and 3D drawings, for example, using CAD software. The generation unit can also create detailed architectural models using 3D modeling tools. Furthermore, the generation unit can automatically generate design drawings that reflect customer requests using AI. The verification unit verifies whether the design drawings generated by the generation unit comply with laws and regulations. The verification unit checks the design drawings based on various laws and regulations, for example, the Building Standards Act, the Industrial Safety and Health Act, and the Environmental Protection Act. The verification unit can also automatically verify compliance with laws and regulations using AI. Furthermore, if the verification unit needs to modify the design drawings, it feeds the modification details back to the generation unit. The delivery unit provides the design drawings generated by the generation unit to the customer. For example, the delivery unit sends the design drawings to the customer in PDF format. Alternatively, the delivery unit can print the design drawings and mail them to the customer. Furthermore, the delivery unit can immediately modify the design drawings according to the customer's request and provide them again. As a result, the building design system according to this embodiment can efficiently receive customer requests, generate design drawings, verify compliance with laws and regulations, and provide them.

[0069] The reception department receives customer requests. These requests may include, but are not limited to, design details, functional requirements, and budget constraints. The reception department may receive customer requests through online forms, for example. These online forms have a user-friendly interface and are designed to allow customers to easily enter the necessary information. The forms include text boxes, dropdown menus, and checkboxes, allowing for the collection of detailed customer requests. The reception department can also receive requests by phone or in person. In phone inquiries, a professional operator will listen to the customer's requests and record them as digital data. In face-to-face inquiries, a design engineer will meet with the customer directly to hear detailed requests. Furthermore, the reception department can save customer requests as digital data and use them for subsequent processing. The digital data is stored in cloud storage and is secured, minimizing the risk of data leakage or loss. This allows the reception department to efficiently collect diverse customer requests and smoothly transition them into the subsequent design process.

[0070] The generation unit generates design drawings based on requests received by the reception unit. For example, the generation unit generates 2D and 3D drawings using CAD software. The CAD software has advanced design capabilities and can accurately depict the detailed structure and layout of a building. The generation unit can also create detailed architectural models using 3D modeling tools. These 3D modeling tools realistically reproduce the building's exterior and interior structure, allowing customers to visually confirm the completed image. Furthermore, the generation unit can automatically generate design drawings that reflect customer requests using AI. The AI ​​analyzes customer requests using natural language processing technology and proposes the optimal design pattern. For example, if a customer requests a "large living room," the AI ​​generates the optimal layout based on past design data and automatically adjusts the size and placement of the living room. This allows the generation unit to respond quickly and accurately to customer requests and provide high-quality design drawings.

[0071] The verification unit checks whether the design drawings generated by the generation unit comply with the law. The verification unit checks the design drawings based on various laws, such as the Building Standards Act, the Industrial Safety and Health Act, and the Environmental Protection Act. Staff with specialized legal knowledge are in charge of verifying legal compliance, and each item of the design drawing is examined in detail. The verification unit can also automatically verify legal compliance using AI. The AI ​​refers to a legal database and quickly determines whether each element of the design drawing complies with the law. For example, it automatically checks items such as building height, seismic standards, and environmental impact, and issues a warning if there is a problem. Furthermore, if the design drawing needs to be modified, the verification unit feeds the modification details back to the generation unit. The feedback clearly indicates specific areas for modification and improvement, enabling the generation unit to respond quickly. In this way, the verification unit can improve the efficiency of the design process while ensuring legal compliance.

[0072] The service provider provides customers with design drawings generated by the design generation service. For example, the service provider sends the design drawings to customers in PDF format. PDF is a widely used file format that customers can easily view and print. The service provider can also print the design drawings and mail them to customers. The printed design drawings are printed on high-quality paper, and detailed drawings and annotations are clearly displayed. Furthermore, the service provider can immediately revise the design drawings according to customer requests and resend them. For example, if a customer wants to change part of a design drawing, the service provider will quickly make the revisions and resend the latest design drawing. This allows the service provider to respond flexibly to customer requests and increase customer satisfaction. In addition, the service provider also provides explanations and support to customers when providing design drawings. For example, they will explain the content and changes in the design drawings to help customers understand them easily. This allows the service provider to facilitate communication with customers and build trust.

[0073] The generation unit can generate 2D and 3D drawings based on customer requests. For example, the generation unit can generate 2D floor plans using CAD software. It can also generate 3D models using 3D modeling tools. For example, the generation unit can create detailed 3D models of the building's exterior and interior based on customer requests. Furthermore, the generation unit can use AI to automatically generate 2D and 3D drawings that reflect customer requests. For example, if a customer requests "a large window in the living room," the generation unit can instantly generate 2D and 3D drawings that reflect that request. This improves design accuracy by generating 2D and 3D drawings based on customer requests. Some or all of the above-described processes in the generation unit may be performed using, for example, generation AI, or without generation AI. For example, the generation unit can input customer requests as prompts into the generation AI, causing the generation AI to generate 2D and 3D drawings.

[0074] The verification unit can confirm whether the design drawings conform to various laws and regulations. For example, the verification unit checks the design drawings based on the Building Standards Act. The verification unit can also confirm the design drawings based on the Industrial Safety and Health Act. Furthermore, the verification unit can also confirm the design drawings based on the Environmental Protection Act. For example, the verification unit confirms whether the height and structure of the building are appropriate based on the Building Standards Act. It confirms whether the safety equipment inside the building is properly installed based on the Industrial Safety and Health Act. It evaluates the impact of the building on the environment based on the Environmental Protection Act. By confirming whether the design drawings conform to various laws and regulations, compliance with laws and regulations is ensured. 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 design drawings into AI and have the AI ​​perform the legal compliance check.

[0075] The service provider can provide design drawings to customers and make immediate modifications according to customer requests. For example, the service provider can send design drawings to customers in PDF format. Alternatively, the service provider can print the design drawings and mail them to customers. Furthermore, the service provider can immediately modify the design drawings according to customer requests and provide them again. For example, if a customer requests that a large window be installed in the living room, the service provider can immediately modify the design drawings to reflect that request and provide them to the customer. This improves customer satisfaction by providing design drawings to customers and making immediate modifications according to their requests. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input customer requests as prompts into a generating AI and have the generating AI perform modifications to the design drawings.

[0076] The Learning Department is the department where the AI ​​learns. For example, the Learning Department can use supervised learning to teach the AI ​​architectural knowledge. The Learning Department can also use unsupervised learning to teach the AI ​​data patterns. Furthermore, the Learning Department can use reinforcement learning to teach the AI ​​the optimal design method. For example, as supervised learning, the Learning Department can teach the AI ​​laws such as the Building Standards Act and the Industrial Safety and Health Act. As unsupervised learning, it can teach the AI ​​data patterns using past design data. As reinforcement learning, it can have the AI ​​perform trial and error in design to learn the optimal design method. As a result, the accuracy of the system improves as the AI ​​learns. Some or all of the above processes in the Learning Department may be performed using the AI, or they may not. For example, the Learning Department can input architectural knowledge as a prompt to the Generating AI and have the Generating AI perform the learning.

[0077] The Estimation Department is the department that provides preliminary estimates. The Estimation Department estimates, for example, material costs, labor costs, and time. The Estimation Department can also provide preliminary estimates quickly using AI. Furthermore, the Estimation Department can adjust the content of the estimate according to the customer's requests. For example, the Estimation Department estimates material costs based on the unit price of materials necessary for construction. For labor costs, it estimates the personnel costs necessary for construction. For time estimates, it estimates the construction period necessary for construction. This enables a quick response to customers by providing preliminary estimates. Some or all of the above processes in the Estimation Department may be performed using AI, for example, or not using AI. For example, the Estimation Department can input estimates of material costs, labor costs, and time as prompts into a generating AI and have the generating AI execute the estimate.

[0078] The reception desk can estimate the customer's emotions and adjust the way requests are received based on those emotions. For example, if the customer is stressed, the reception desk can provide a simple interface and minimize the input steps. If the customer is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the customer is in a hurry, the reception desk can prioritize voice input to allow for quick request entry. For example, the reception desk can capture the customer's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the customer's voice and estimate their emotions using voice analysis technology. It can also collect the customer's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This improves customer satisfaction by adjusting the way requests are received based on the customer's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input customer emotion data into a generating AI and have the generating AI perform emotion estimation.

[0079] The reception desk can analyze a customer's past request history and select the most suitable reception method. For example, the reception desk can automatically display requests that the customer has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the customer has used in the past. Furthermore, the reception desk can predict and suggest requests to be used during specific time periods based on the customer's past request history. For example, the reception desk can store the customer's past requests in a database and select the most suitable reception method based on that data. This allows the reception desk to select the most suitable reception method by analyzing the customer's past request history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the customer's past request history into a generating AI and have the generating AI select the most suitable reception method.

[0080] The reception department can filter requests based on the customer's current projects and areas of interest when receiving them. For example, the reception department can prioritize requests related to projects the customer is currently working on. The reception department can also filter requests based on the customer's areas of interest. Furthermore, the reception department can filter requests based on areas the customer has shown interest in in the past. For example, the reception department can store the customer's current project information in a database and filter based on that data. This streamlines the request reception process by filtering based on the customer's current projects and areas of interest. Some or all of the above processing in the reception department may be performed using AI, for example, or not. For example, the reception department can input the customer's project information into a generating AI and have the generating AI perform the filtering.

[0081] The reception desk can estimate the customer's emotions and prioritize requests based on those emotions. For example, if the customer is stressed, the reception desk will prioritize important requests. If the customer is relaxed, the reception desk may also prioritize detailed requests. Furthermore, if the customer is in a hurry, the reception desk may prioritize requests that require a quick response. For example, the reception desk can capture the customer's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the customer's voice and estimate their emotions using voice analysis technology. It can also collect the customer's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for prioritizing requests based on the customer's emotions, thus prioritizing important requests. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input customer emotion data into a generating AI and have the generating AI perform emotion estimation.

[0082] The reception desk can prioritize requests based on their relevance, taking into account the customer's geographical location. For example, it can prioritize requests related to the customer's current location. It can also filter requests based on the customer's geographical location. Furthermore, it can filter requests based on places the customer has visited in the past. For example, the reception desk can store the customer's geographical location in a database and perform filtering based on that data. This allows for prioritizing requests based on the customer's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the customer's geographical location into a generating AI and have the generating AI perform the filtering.

[0083] The reception department can analyze a customer's social media activity when receiving a request and accept relevant requests. For example, the reception department can prioritize requests related to the customer's current interests based on their social media activity. The reception department can also automatically accept requests mentioned by the customer on social media. Furthermore, the reception department can analyze the customer's social media activity and filter the requests to accept them. For example, the reception department can store the customer's social media activity in a database and perform filtering based on that data. This allows for efficient acceptance of relevant requests by analyzing the customer's social media activity. Some or all of the above processing in the reception department may be performed using AI, for example, or not. For example, the reception department can input the customer's social media activity into a generating AI and have the generating AI perform the filtering.

[0084] The generation unit can estimate the customer's emotions and adjust the method of generating design drawings based on the estimated emotions. For example, if the customer is relaxed, the generation unit will generate design drawings that proceed at a leisurely pace. If the customer is in a hurry, the generation unit can also generate design drawings that emphasize the shortest route. Furthermore, if the customer is excited, the generation unit can generate design drawings with visually stimulating effects. For example, the generation unit can capture the customer's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the customer's voice and estimate their emotions using voice analysis technology. It can also collect the customer's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This improves customer satisfaction by adjusting the method of generating design drawings based on the customer'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-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input customer emotion data into a generation AI and have the generation AI perform emotion estimation.

[0085] The generation unit can adjust the level of detail generated based on the importance of customer requests when generating design drawings. For example, the generation unit can generate detailed design drawings for important requests. It can also generate simplified design drawings for lower-priority requests. Furthermore, the generation unit can adjust the level of detail of the design drawings according to the importance of customer requests. For example, the generation unit can store the importance of customer requests in a database and adjust the level of detail based on that data. This improves the accuracy of the design drawings by adjusting the level of detail based on the importance of customer requests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of customer requests into a generation AI and have the generation AI perform the level of detail adjustment.

[0086] The generation unit can apply different generation algorithms depending on the customer's requirements when generating design drawings. For example, in the case of residential design, the generation unit applies a generation algorithm specifically for residential buildings. Similarly, in the case of commercial building design, the generation unit can apply a generation algorithm specifically for commercial buildings. Furthermore, in the case of public building design, the generation unit can apply a generation algorithm specifically for public buildings. For example, the generation unit can store the customer's requirements in a database and apply a generation algorithm based on that data. This improves the accuracy of the design drawings by applying different generation algorithms depending on the customer's requirements. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the customer's requirements into a generation AI and have the generation AI execute the application of the generation algorithm.

[0087] The generation unit can estimate the customer's emotions and adjust the length of the design drawing based on those emotions. For example, if the customer is in a hurry, the generation unit can generate a short, concise design drawing. If the customer is relaxed, the generation unit can also generate a longer design drawing with detailed explanations. Furthermore, if the customer is excited, the generation unit can generate a design drawing with visually stimulating effects. For example, the generation unit can capture the customer's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the customer's voice and estimate their emotions using voice analysis technology. It can also collect the customer's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. By adjusting the length of the design drawing based on the customer's emotions, customer satisfaction can be improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input customer emotion data into a generation AI and have the generation AI perform emotion estimation.

[0088] The generation unit can determine the generation priority based on when customer requests are submitted when generating design drawings. For example, the generation unit can prioritize requests submitted early. It can also prioritize urgent requests. Furthermore, the generation unit can determine the priority of requests based on the submission date. For example, the generation unit can store the submission dates of customer requests in a database and determine the priority based on that data. This enables efficient generation of design drawings by determining the generation priority based on the submission dates of customer requests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the submission dates of customer requests into a generation AI and have the generation AI perform the priority determination.

[0089] The generation unit can adjust the generation order based on the relevance of customer requests when generating design drawings. For example, the generation unit can prioritize processing requests with high relevance. It can also postpone requests with low relevance. Furthermore, the generation unit can adjust the generation order based on the relevance of customer requests. For example, the generation unit can store the relevance of customer requests in a database and adjust the order based on that data. This allows for efficient generation of design drawings by adjusting the generation order based on the relevance of customer requests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of customer requests into a generation AI and have the generation AI perform the order adjustment.

[0090] The verification unit can estimate the customer's emotions and adjust the compliance verification criteria based on the estimated emotions. For example, if the customer is nervous, the verification unit can provide a simple and highly visible display method. If the customer is relaxed, the verification unit can also provide a display method that includes detailed information. Furthermore, if the customer is in a hurry, the verification unit can provide a concise display method. For example, the verification unit can capture the customer's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the customer's voice and estimate their emotions using voice analysis technology. It can also collect the customer's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This improves customer satisfaction by adjusting the compliance verification criteria based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 customer emotion data into the generating AI and have the generating AI perform emotion estimation.

[0091] The verification unit can improve the accuracy of legal compliance verification by considering the interrelationships of design drawings. For example, the verification unit performs legal compliance verification by considering the interrelationships of each part of the design drawings. The verification unit can also improve the accuracy of verification by analyzing the interrelationships of design drawings. Furthermore, the verification unit can adjust the criteria for legal compliance verification by considering the interrelationships of design drawings. For example, the verification unit can store the interrelationships of design drawings in a database and perform verification based on that data. This improves the accuracy of legal compliance verification by considering the interrelationships of design drawings. 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 interrelationships of design drawings into a generating AI and have the generating AI perform the verification accuracy improvement.

[0092] The verification unit can perform compliance checks by considering the attribute information of the design drawing submitter. For example, the verification unit can perform compliance checks by considering the attribute information of the design drawing submitter. The verification unit can also improve the accuracy of the checks based on the submitter's attribute information. Furthermore, the verification unit can adjust the criteria for compliance checks by considering the submitter's attribute information. For example, the verification unit can store the attribute information of the design drawing submitter in a database and perform checks based on that data. This improves the accuracy of compliance checks by considering the attribute information of the design drawing submitter. 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 submitter's attribute information into a generating AI and have the generating AI perform the task of improving the accuracy of the checks.

[0093] The verification unit can estimate the customer's emotions and adjust the order in which the compliance verification results are displayed based on the estimated emotions. For example, if the customer is nervous, the verification unit can provide a simple and highly visible display method. If the customer is relaxed, the verification unit can also provide a display method that includes detailed information. Furthermore, if the customer is in a hurry, the verification unit can provide a concise display method. For example, the verification unit can capture the customer's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the customer's voice and estimate their emotions using voice analysis technology. It can also collect the customer's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This improves customer satisfaction by adjusting the order in which the compliance verification results are displayed based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input customer emotion data into a generating AI and have the generating AI perform emotion estimation.

[0094] The verification unit can perform compliance checks while considering the geographical distribution of design drawings. For example, the verification unit can perform compliance checks while considering the geographical distribution of design drawings. The verification unit can also improve the accuracy of the checks based on the geographical distribution. Furthermore, the verification unit can adjust the criteria for compliance checks while considering the geographical distribution. For example, the verification unit can store the geographical distribution of design drawings in a database and perform checks based on that data. This improves the accuracy of compliance checks by considering the geographical distribution of design drawings. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input the geographical distribution of design drawings into a generating AI and have the generating AI perform the task of improving the accuracy of the checks.

[0095] The verification unit can improve the accuracy of its verification by referring to relevant documents for the design drawings when verifying compliance with laws and regulations. For example, the verification unit can perform a legal compliance verification by referring to relevant documents for the design drawings. The verification unit can also improve the accuracy of the verification based on the relevant documents. Furthermore, the verification unit can adjust the criteria for legal compliance verification by referring to relevant documents. For example, the verification unit can store the relevant documents for the design drawings in a database and perform the verification based on that data. This improves the accuracy of legal compliance verification by referring to the relevant documents for the design drawings. 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 the relevant documents for the design drawings into a generating AI and have the generating AI perform the verification accuracy improvement.

[0096] The service provider can estimate the customer's emotions and adjust the way the design drawings are delivered based on those emotions. For example, if the customer is relaxed, the service provider can provide a delivery method that includes detailed explanations. If the customer is in a hurry, the service provider can provide a concise and to-the-point delivery method. Furthermore, if the customer is excited, the service provider can provide a delivery method that includes visually stimulating effects. For example, the service provider can capture the customer's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the customer's voice and estimate their emotions using voice analysis technology. It can also collect the customer's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. By adjusting the way the design drawings are delivered based on the customer's emotions, customer satisfaction can be improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provision unit may be performed using AI, for example, or without AI. For example, the service provision unit can input customer emotion data into a generating AI and have the generating AI perform emotion estimation.

[0097] The service provider can select the optimal delivery method when providing design drawings by referring to the customer's past request history. For example, the service provider can automatically display requests that the customer has frequently entered in the past as candidates. The service provider can also prioritize suggesting delivery methods (voice, text, etc.) that the customer has used in the past. Furthermore, the service provider can predict and suggest delivery methods to be used at specific times based on the customer's past request history. For example, the service provider can store the customer's past request history in a database and select the optimal delivery method based on that data. This allows the service provider to select the optimal delivery method by referring to the customer's past request history. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the customer's past request history into a generating AI and have the generating AI select the optimal delivery method.

[0098] The service provider can customize the content of the design drawings provided based on the customer's current projects and areas of interest. For example, the service provider can prioritize providing design drawings related to the customer's current projects. Furthermore, the service provider can customize and provide relevant design drawings based on the customer's areas of interest. In addition, the service provider can customize and provide design drawings based on areas the customer has shown interest in in the past. For example, the service provider can store the customer's current project information in a database and customize the content of the design drawings based on that data. This improves customer satisfaction by customizing the content of the design drawings based on the customer's current projects and areas of interest. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the customer's project information into a generating AI and have the generating AI perform the customization of the design drawings.

[0099] The service provider can estimate the customer's emotions and determine the order in which design drawings are provided based on those estimated emotions. For example, if the customer is stressed, the service provider can prioritize providing important design drawings. Similarly, if the customer is relaxed, the service provider can prioritize providing detailed design drawings. Furthermore, if the customer is in a hurry, the service provider can prioritize providing design drawings that require immediate attention. For example, the service provider can capture the customer's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the customer's voice and estimate their emotions using voice analysis technology. It can also collect the customer's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the service provider to prioritize the provision of important design drawings by determining the order in which they are provided based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provision unit may be performed using AI, for example, or without AI. For example, the service provision unit can input customer emotion data into a generating AI and have the generating AI perform emotion estimation.

[0100] The service provider can select the optimal delivery method when providing design drawings, taking into account the customer's geographical location information. For example, the service provider can prioritize providing design drawings related to the customer's current location. Furthermore, the service provider can filter and provide relevant design drawings based on the customer's geographical location information. It can also filter and provide design drawings based on places the customer has visited in the past. For example, the service provider can store the customer's geographical location information in a database and select the optimal delivery method based on that data. This allows for the selection of the optimal delivery method by considering the customer's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the customer's geographical location information into a generating AI and have the generating AI select the optimal delivery method.

[0101] The service provider can analyze the customer's social media activity and adjust the content provided when delivering design drawings. For example, the service provider can prioritize providing design drawings related to the customer's current interests based on their social media activity. The service provider can also automatically provide design drawings that reflect requests mentioned by the customer on social media. Furthermore, the service provider can analyze the customer's social media activity and filter the relevant design drawings before providing them. For example, the service provider can store the customer's social media activity in a database and adjust the content provided based on that data. This allows for the efficient provision of relevant design drawings by analyzing the customer's social media activity. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the customer's social media activity into a generating AI and have the generating AI perform the adjustment of the content provided.

[0102] The learning unit can estimate the customer's emotions and select training data based on the estimated emotions. For example, if the customer is relaxed, the learning unit can select detailed training data. If the customer is in a hurry, the learning unit can also select concise and to-the-point training data. Furthermore, if the customer is excited, the learning unit can select visually stimulating training data. For example, the learning unit can capture the customer's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the customer's voice and estimate their emotions using voice analysis technology. It can also collect the customer's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This improves the accuracy of learning by selecting training data based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input customer emotion data into a generating AI and have the generating AI perform emotion estimation.

[0103] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data to improve the accuracy of the learning algorithm. Furthermore, the learning unit can adjust the parameters of the learning algorithm by referring to past learning data. For example, the learning unit can save past learning data to a database and optimize the learning algorithm based on that data. This improves the accuracy of the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0104] The learning unit can estimate the customer's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit will learn more frequently if the customer is relaxed. It can also reduce the learning frequency if the customer is in a hurry. Furthermore, it can adjust the learning frequency if the customer is excited. For example, the learning unit can capture the customer's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the customer's voice and estimate their emotions using voice analysis technology. It can also collect the customer's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This improves the efficiency of learning by adjusting the learning frequency based on the customer'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 learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input customer emotion data into a generating AI and have the generating AI perform emotion estimation.

[0105] The learning unit can weight the learning data based on the submission timing of design drawings during the learning process. For example, the learning unit can prioritize learning from data of design drawings submitted early. It can also prioritize learning from data of urgent design drawings. Furthermore, the learning unit can weight the learning data based on the submission timing. For example, the learning unit can store the submission timing of design drawings in a database and perform weighting based on that data. This improves the accuracy of learning by weighting the learning data based on the submission timing of design drawings. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the submission timing of design drawings into a generating AI and have the generating AI perform the weighting.

[0106] The estimation department can estimate the customer's emotions and adjust the way estimates are provided based on those emotions. For example, if the customer is relaxed, the estimation department can provide a detailed estimate. If the customer is in a hurry, it can provide a concise and to-the-point estimate. Furthermore, if the customer is excited, it can provide a visually stimulating estimate. For example, the estimation department can capture the customer's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the customer's voice and estimate their emotions using voice analysis technology. It can also collect the customer's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. By adjusting the way estimates are provided based on the customer's emotions, customer satisfaction can be improved. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the estimation department may be performed using AI, for example, or without AI. For example, the estimation department can input customer sentiment data into a generating AI and have the generating AI perform sentiment estimation.

[0107] The estimation department can select the optimal delivery method by referring to the customer's past estimation history when providing an estimate. For example, the estimation department can automatically display estimates that the customer has frequently entered in the past as candidates. The estimation department can also prioritize suggesting delivery methods (voice, text, etc.) that the customer has used in the past. Furthermore, the estimation department can predict and suggest delivery methods to be used at specific times based on the customer's past estimation history. For example, the estimation department can store the customer's past estimation history in a database and select the optimal delivery method based on that data. This allows the optimal delivery method to be selected by referring to the customer's past estimation history. Some or all of the above processes in the estimation department may be performed using AI, for example, or not using AI. For example, the estimation department can input the customer's past estimation history into a generating AI and have the generating AI select the optimal delivery method.

[0108] The estimation department can estimate customer emotions and prioritize estimates based on those emotions. For example, if a customer is stressed, the estimation department can prioritize important estimates. Conversely, if a customer is relaxed, the estimation department can prioritize detailed estimates. Furthermore, if a customer is in a hurry, the estimation department can prioritize estimates requiring immediate attention. For example, the estimation department can capture a customer's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. It can also record a customer's voice and estimate their emotions using voice analysis technology. Alternatively, it can collect customer biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for prioritizing estimates based on customer emotions, thereby prioritizing important estimates. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the estimation department may be performed using AI, for example, or without AI. For example, the estimation department can input customer sentiment data into a generating AI and have the generating AI perform sentiment estimation.

[0109] The estimation department can select the optimal delivery method when providing an estimate, taking into account the customer's geographical location information. For example, the estimation department may prioritize providing estimates related to the customer's current location. It can also filter and provide relevant estimates based on the customer's geographical location information. Furthermore, it can filter and provide estimates based on places the customer has visited in the past. For example, the estimation department can store the customer's geographical location information in a database and select the optimal delivery method based on that data. This allows for the selection of the optimal delivery method by considering the customer's geographical location information. Some or all of the above processing in the estimation department may be performed using AI, for example, or not. For example, the estimation department can input the customer's geographical location information into a generating AI and have the generating AI select the optimal delivery method.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The reception desk can refer to the customer's past request history when receiving a customer request and suggest the most suitable reception method. For example, it can automatically display requests that the customer has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the customer has used in the past. Furthermore, the reception desk can predict and suggest requests to be used during specific time periods based on the customer's past request history. In this way, the optimal reception method can be selected by analyzing the customer's past request history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the customer's past request history into a generating AI and have the generating AI select the optimal reception method.

[0112] The generation unit can adjust the level of detail generated based on the importance of the customer's requests when generating design drawings. For example, it can generate detailed design drawings for important requests, and simplified design drawings for lower-priority requests. Furthermore, the generation unit can adjust the level of detail of the design drawings according to the importance of the customer's requests. This improves the accuracy of the design drawings by adjusting the level of detail based on the importance of the customer's requests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the customer's requests into the generation AI and have the generation AI perform the level of detail adjustment.

[0113] The verification unit can improve the accuracy of legal compliance verification by considering the interrelationships of design drawings. For example, it can perform legal compliance verification by considering the interrelationships of each part of the design drawings. It can also improve the accuracy of verification by analyzing the interrelationships of the design drawings. Furthermore, the verification unit can adjust the criteria for legal compliance verification by considering the interrelationships of the design drawings. As a result, the accuracy of legal compliance verification is improved by considering the interrelationships of the design drawings. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input the interrelationships of the design drawings into a generating AI and have the generating AI perform the verification accuracy improvement.

[0114] The service delivery unit can select the optimal delivery method by referring to the customer's past request history when providing design drawings. For example, it can automatically display requests that the customer has frequently entered in the past as candidates. It can also prioritize suggesting delivery methods (voice, text, etc.) that the customer has used in the past. Furthermore, the service delivery unit can predict and suggest delivery methods to be used at specific times based on the customer's past request history. In this way, the optimal delivery method can be selected by referring to the customer's past request history. Some or all of the above processing in the service delivery unit may be performed using AI, for example, or not using AI. For example, the service delivery unit can input the customer's past request history into a generating AI and have the generating AI select the optimal delivery method.

[0115] The estimation department can select the optimal delivery method by referring to the customer's past estimation history when providing an estimate. For example, it can automatically display estimates that the customer has frequently entered in the past as candidates. It can also prioritize suggesting delivery methods (voice, text, etc.) that the customer has used in the past. Furthermore, the estimation department can predict and suggest delivery methods to be used at specific times based on the customer's past estimation history. This allows for the selection of the optimal delivery method by referring to the customer's past estimation history. Some or all of the above processes in the estimation department may be performed using AI, for example, or not. For example, the estimation department can input the customer's past estimation history into a generating AI and have the generating AI select the optimal delivery method.

[0116] The reception desk can estimate the customer's emotions and adjust the way requests are received based on those emotions. For example, if the customer is stressed, a simple interface can be provided, minimizing the input steps. If the customer is relaxed, detailed input options can be provided, and customizable input methods can be suggested. Furthermore, if the customer is in a hurry, voice input can be prioritized to allow for quick request entry. This improves customer satisfaction by adjusting the way requests are received based on the customer's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input customer emotion data into a generative AI and have the generative AI perform emotion estimation.

[0117] The generation unit can estimate the customer's emotions and adjust the method of generating design drawings based on the estimated customer emotions. For example, if the customer is relaxed, it can generate design drawings that proceed at a leisurely pace. If the customer is in a hurry, it can also generate design drawings that emphasize the shortest route. Furthermore, if the customer is excited, it can generate design drawings with visually stimulating effects. By adjusting the method of generating design drawings based on the customer's emotions, customer satisfaction is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The 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 generation unit may be performed using AI or not using AI. For example, the generation unit can input customer emotion data into the generative AI and have the generative AI perform emotion estimation.

[0118] The verification unit can estimate the customer's emotions and adjust the compliance verification criteria based on the estimated emotions. For example, if the customer is nervous, it can provide a simple and highly visible display method. If the customer is relaxed, it can provide a display method that includes detailed information. Furthermore, if the customer is in a hurry, it can provide a display method that gets straight to the point. By adjusting the compliance verification criteria based on the customer's emotions, customer satisfaction is improved. 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 or not using AI. For example, the verification unit can input customer emotion data into a generative AI and have the generative AI perform emotion estimation.

[0119] The service provider can estimate the customer's emotions and adjust the way the design drawings are presented based on those emotions. For example, if the customer is relaxed, the service provider can provide a presentation that includes detailed explanations. If the customer is in a hurry, the service provider can provide a concise and to-the-point presentation. Furthermore, if the customer is excited, the service provider can provide a presentation that includes visually stimulating effects. By adjusting the way the design drawings are presented based on the customer's emotions, customer satisfaction is improved. 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 service provider may be performed using AI or not using AI. For example, the service provider can input customer emotion data into a generative AI and have the generative AI perform emotion estimation.

[0120] The estimation unit can estimate the customer's emotions and adjust the way estimates are provided based on those emotions. For example, if the customer is relaxed, a detailed estimate can be provided. If the customer is in a hurry, a concise and to-the-point estimate can be provided. Furthermore, if the customer is excited, a visually stimulating estimate can be provided. By adjusting the way estimates are provided based on the customer's emotions, customer satisfaction is improved. Emotion estimation is achieved using an emotion estimation function, such as 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 estimation unit may be performed using AI or not. For example, the estimation unit can input customer emotion data into a generative AI and have the generative AI perform emotion estimation.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception department receives customer requests. These requests include design details, functional requirements, and budget constraints. The reception department receives requests via online forms, telephone, and in person, and stores the customer requests as digital data. Step 2: The generation unit generates design drawings based on the requests received by the reception unit. The generation unit can generate 2D and 3D drawings using CAD software and 3D modeling tools, and can also automatically generate design drawings that reflect customer requests using AI. Step 3: The verification unit checks whether the design drawings generated by the generation unit comply with the law. The verification unit checks the design drawings based on various laws and regulations such as the Building Standards Act, the Industrial Safety and Health Act, and the Environmental Protection Act, and can also automatically verify compliance using AI. If necessary, it feeds back any modifications to the design drawings to the generation unit. Step 4: The supply unit provides the customer with the design drawings generated by the generation unit. The supply unit can send the design drawings in PDF format or print and mail them. Furthermore, they can immediately revise the design drawings according to the customer's requests and provide them again.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the reception unit, generation unit, verification unit, provision unit, learning unit, and estimation unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives customer requests. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates design drawings. The verification unit is implemented by the specific processing unit 290 of the data processing unit 12 and verifies compliance with laws and regulations. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the design drawings to the customer. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and trains the AI ​​on architectural knowledge. The estimation unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides an estimated quote. 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.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, generation unit, confirmation unit, provision unit, learning unit, and estimation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives customer requests. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates design drawings. The confirmation unit is implemented by the specific processing unit 290 of the data processing unit 12 and verifies compliance with laws and regulations. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the design drawings to the customer. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and trains the AI ​​on architectural knowledge. The estimation unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides an estimated quote. 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.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the reception unit, generation unit, confirmation unit, provision unit, learning unit, and estimation unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives customer requests. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates design drawings. The confirmation unit is implemented by the specific processing unit 290 of the data processing unit 12 and verifies compliance with laws and regulations. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides design drawings to the customer. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and trains the AI ​​on architectural knowledge. The estimation unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides an estimated quote. 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.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the reception unit, generation unit, verification unit, provision unit, learning unit, and estimation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives customer requests. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates design drawings. The verification unit is implemented by the specific processing unit 290 of the data processing unit 12 and verifies compliance with laws and regulations. The provision unit is implemented by the control unit 46A of the robot 414 and provides the design drawings to the customer. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and allows the AI ​​to learn architectural knowledge. The estimation unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides an estimated quote. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A reception desk that handles customer requests, A generation unit that generates design drawings based on requests received by the reception unit, A verification unit that verifies whether the design drawings generated by the generation unit comply with the law, The system includes a supply unit that provides design drawings generated by the generation unit to the customer. A system characterized by the following features. (Note 2) The generating unit is We generate 2D and 3D drawings based on customer requirements. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned verification unit is Verify whether the design drawings comply with various laws and regulations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We provide design drawings to the customer and make immediate modifications according to the customer's requests. The system described in Appendix 1, characterized by the features described herein. (Note 5) Equipped with a learning unit where AI learns. The system described in Appendix 1, characterized by the features described herein. (Note 6) We have a quotation department that provides estimated quotes. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We estimate customer emotions and adjust how requests are received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the customer's past request history and select the most suitable method of receiving their request. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving requests, we filter them based on the customer's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the customer's emotions and prioritizes the requests to be accepted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving requests, we prioritize requests that are highly relevant, taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving a request, we analyze the customer's social media activity and accept relevant requests. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is We estimate customer emotions and adjust the design drawing generation method based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating design drawings, the level of detail is adjusted based on the importance of the customer's requests. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating design drawings, different generation algorithms are applied depending on the category of customer requests. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is Estimate customer emotions and adjust the length of design drawings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating design drawings, the priority of generation is determined based on when the customer's requests were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating design drawings, the generation order is adjusted based on the relevance of customer requests. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned verification unit is We estimate customer sentiment and adjust compliance criteria based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned verification unit is When verifying compliance with regulations, we improve the accuracy of the verification by considering the interrelationships between design drawings. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned verification unit is When verifying compliance with regulations, the attribute information of the person who submitted the design drawings will be taken into consideration during the verification process. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned verification unit is The system estimates customer sentiment and adjusts the order in which compliance check results are displayed based on the estimated customer sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned verification unit is When verifying compliance with regulations, the geographical distribution of design drawings will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned verification unit is When verifying compliance with regulations, we improve the accuracy of the verification by referring to relevant documents for the design drawings. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We estimate customer emotions and adjust the method of providing design drawings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing design drawings, we select the most suitable delivery method by referring to the customer's past request history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing design drawings, we customize the content based on the customer's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates customer emotions and determines the order in which design drawings are provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing design drawings, we select the most suitable delivery method considering the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing design drawings, we analyze the customer's social media activity and adjust the content accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned learning unit, The system estimates customer emotions and selects training data based on the estimated customer emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned learning unit, It estimates customer emotions and adjusts the learning frequency based on the estimated customer emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned learning unit, During training, the training data is weighted based on the submission dates of the design drawings. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned estimation section, We estimate customer sentiment and adjust how we provide quotes based on that estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned estimation section, When providing a quote, we refer to the customer's past quote history to select the most suitable delivery method. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned estimation section, Estimate customer sentiment and prioritize quotes based on that estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned estimation section, When providing a quote, we select the most suitable delivery method considering the customer's geographical location. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0195] 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. A reception desk that handles customer requests, A generation unit that generates design drawings based on requests received by the reception unit, A verification unit that verifies whether the design drawings generated by the generation unit comply with the law, The system includes a supply unit that provides design drawings generated by the generation unit to the customer. A system characterized by the following features.

2. The generating unit is We generate 2D and 3D drawings based on customer requirements. The system according to feature 1.

3. The aforementioned verification unit is Verify whether the design drawings comply with various laws and regulations. The system according to feature 1.

4. The aforementioned supply unit is, We provide design drawings to the customer and make immediate modifications according to the customer's requests. The system according to feature 1.

5. Equipped with a learning unit where AI learns. The system according to feature 1.

6. We have a quotation department that provides estimated quotes. The system according to feature 1.

7. The aforementioned reception unit is We estimate customer emotions and adjust how requests are received based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the customer's past request history and select the most suitable method of receiving their request. The system according to feature 1.

9. The aforementioned reception unit is When receiving requests, we filter them based on the customer's current projects and areas of interest. The system according to feature 1.

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