System

Abstract

An object of the system according to the embodiment is to provide a house conforming to the Building Standard Law at a reasonable price in a short time.SOLUTION: A system includes a reception part, a generation part, a print part, a confirmation part, and a correction part. The reception unit receives a request or a condition of a site from a user. The generation unit generates design data of the house on the basis of the information received by the reception unit. The printing unit prints the house based on the design data generated by the generation unit. The confirmation unit confirms whether the design data generated by the generation unit complies with the Building Standards Ordinance. The correction unit corrects the design data based on the result confirmed by the confirmation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, the process from designing to constructing a house was time-consuming and costly, and it was difficult to verify compliance with the Building Standards Act.

[0005] The system according to the embodiment aims to provide a house that complies with the Building Standards Act in a short time and at a reasonable price. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a printing unit, a confirmation unit, and a correction unit. The reception unit receives requests or site conditions from a user. The generation unit generates design data for a house based on the information received by the reception unit. The printing unit prints the house based on the design data generated by the generation unit. The confirmation unit confirms whether the design data generated by the generation unit complies with the Building Standards Act. The correction unit corrects the design data based on the results confirmed by the confirmation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a house that complies with the Building Standards Act in a short time and at a reasonable price. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A 3D printer housing system according to an embodiment of the present invention utilizes generative AI to provide homes that can be constructed in a short time. This system accepts user requests and site conditions, generates design data for the home using the generative AI, and then a 3D printer prints the home based on that design data. Furthermore, the generative AI creates a design that complies with the Building Standards Act, eliminating legal issues. For example, the 3D printer creates an optimal design taking into account the user's requests and site conditions. The 3D printer then prints the home based on the generated design data. This process also simultaneously prints amenities such as a kitchen, bathroom, and toilet. The printed home is the ideal size for a two-person household and can be completed in a short time. This allows the 3D printer housing system to provide affordable homes that can be constructed in a short time. For example, compared to conventional building methods, construction time is significantly reduced, thereby reducing costs. Furthermore, the generative AI creates an optimal design, resulting in a lean, efficient home. Furthermore, the generative AI creates a design that complies with the Building Standards Act, eliminating legal issues. For example, homes are provided that are earthquake-resistant and fireproof in accordance with the Building Standards Act, so you can live in them with peace of mind.

[0029] A 3D printer housing system according to an embodiment includes a reception unit, a generation unit, a printing unit, a verification unit, and a correction unit. The reception unit receives user requests or site conditions. For example, it can receive information such as the user's desired design and functions, the site's topography, and area. The generation unit uses a generation AI to generate design data for a house based on the information received by the reception unit. For example, the generation AI creates an optimal design taking into account the user's requests and site conditions. The generation AI can generate design data using a text generation AI (e.g., LLM) or a multimodal generation AI. The printing unit prints a house based on the design data generated by the generation unit. For example, a 3D printer can be used to simultaneously print fixtures such as a kitchen, bathroom, and toilet. The verification unit verifies whether the design data generated by the generation unit complies with the Building Standards Act. For example, it verifies legal requirements such as earthquake resistance standards and fire safety standards of the Building Standards Act. The correction unit modifies the design data based on the results of the verification unit. For example, the design data can be modified to satisfy legal requirements. As a result, the 3D printer housing system of the embodiment makes it possible to construct a house that complies with the Building Standards Act in a short period of time, based on the user's requests and site conditions.

[0030] The generation unit can create an optimal design based on the user's requests or site conditions. The generation unit, for example, creates an optimal design taking into account the user's requests. For example, it can create a design that reflects the design and functions desired by the user. The generation unit can also create an optimal design taking into account the site conditions. For example, it can create a design that suits the site's topography and area. Furthermore, the generation unit can use a generation AI to create an optimal design based on the user's requests and site conditions. For example, the generation AI receives the user's requests and site conditions as input and generates optimal design data. As a result, the generation unit can provide a home that provides greater satisfaction by creating an optimal design based on the user's requests and site conditions.

[0031] The printing unit can simultaneously print kitchen or bathroom / toilet fixtures. For example, the printing unit can simultaneously print a kitchen sink and stove. For example, a kitchen sink and stove can be printed as an integrated unit using a 3D printer. The printing unit can also simultaneously print bathroom / toilet fixtures. For example, a bathroom bathtub and toilet can be printed as an integrated unit. Furthermore, by simultaneously printing kitchen and bathroom / toilet fixtures, construction time can be significantly reduced. For example, the time required to install the fixtures can be significantly reduced compared to conventional construction methods. This allows the printing unit to significantly reduce construction time by simultaneously printing the fixtures.

[0032] The confirmation unit can confirm the legal requirements for creating a design that complies with the Building Standards Act. The confirmation unit can, for example, confirm the earthquake resistance standards of the Building Standards Act. For example, it can confirm whether the design data meets the earthquake resistance standards. The confirmation unit can also confirm fire safety standards. For example, it can confirm whether the design data meets the fire safety standards. Furthermore, the confirmation unit can also confirm other legal requirements of the Building Standards Act. For example, it can confirm whether the design data meets all the requirements of the Building Standards Act. In this way, the confirmation unit can provide a home that complies with the Building Standards Act by confirming the legal requirements.

[0033] The correction unit can correct the design data based on the results confirmed by the confirmation unit. The correction unit can, for example, correct the design data based on earthquake resistance standards confirmed by the confirmation unit. For example, the correction unit corrects the design data so that it satisfies the earthquake resistance standards. The correction unit can also correct the design data based on fire safety standards. For example, the correction unit can correct the design data so that it satisfies the fire safety standards. Furthermore, the correction unit can also correct the design data based on other legal requirements of the Building Standards Act. For example, the correction unit can correct the design data so that it satisfies all requirements of the Building Standards Act. In this way, the correction unit can provide a house that meets legal requirements by correcting the design data.

[0034] The printing department can complete printing a house in one day. The printing department can complete printing a house in one day, for example, using a 3D printer. For example, by increasing the printing speed, the printing department can complete printing a house in one day. The printing department can also complete printing a house in one day by optimizing the materials used. For example, by using a material that hardens quickly, the printing time can be shortened. Furthermore, the printing department can complete printing a house in one day by streamlining the printing process. For example, by optimizing the printing order and method, the printing time can be shortened. As a result, the printing department can complete construction of the house in a short time and make it ready for occupancy quickly.

[0035] The reception unit can analyze the user's past request history and select the optimal reception method. For example, the reception unit can prioritize reception of requests that the user has frequently requested in the past. For example, the past request history can be stored in a database and the most frequent requests can be processed preferentially. The reception unit can also find specific patterns from the user's past request history and suggest the optimal reception method. For example, an algorithm can be used to analyze the past request history and extract specific patterns. Furthermore, the reception unit can select the most efficient reception method based on the user's past request history. For example, the reception unit can suggest the optimal reception procedure based on the past request history. In this way, the reception unit can provide a more efficient reception method by analyzing the past request history.

[0036] When receiving a request, the reception unit can filter requests based on the user's current living situation and areas of interest. The reception unit can, for example, take into account the user's current living situation (family composition, occupation, etc.) and preferentially receive related requests. For example, the user's living situation is stored in a database and related requests are filtered. The reception unit can also filter related requests based on the user's areas of interest (hobbies, interests, etc.). For example, the user's areas of interest can be stored in a database and an algorithm can be used to extract related requests. Furthermore, the reception unit can suggest optimal requests based on the user's living situation and areas of interest. For example, an algorithm can be used to suggest optimal requests based on the user's living situation and areas of interest. This allows the reception unit to preferentially receive related requests based on the user's living situation and areas of interest.

[0037] When receiving a request, the reception unit can select the optimal reception means depending on the user's input method. For example, when the user uses voice input, the reception unit can receive the request using voice recognition technology. For example, voice recognition software automatically analyzes the voice and saves it as text. Furthermore, when the user uses text input, the reception unit can also receive the request using text analysis technology. For example, a text analysis algorithm can be used to analyze the user's request and provide an appropriate response. Furthermore, when the user uses image input, the reception unit can also receive the request using image recognition technology. For example, an image recognition algorithm can be used to analyze the image sent by the user and provide an appropriate response. This allows the reception unit to select the optimal reception means depending on the user's input method, enabling smoother reception.

[0038] When receiving a request, the reception unit can prioritize receiving highly relevant requests by taking into account the user's geographical location information. The reception unit can, for example, prioritize receiving requests related to places close to the user's current location. For example, the reception unit acquires the user's geographical location information as GPS data and filters out related requests. The reception unit can also filter out related requests based on the user's geographical location information. For example, the reception unit can store the user's address information in a database and use an algorithm to extract related requests. Furthermore, the reception unit can also suggest optimal requests by taking into account the user's geographical location information. For example, the reception unit can use an algorithm to suggest optimal requests based on the user's geographical location information. In this way, the reception unit can prioritize receiving highly relevant requests by taking into account the user's geographical location information.

[0039] When receiving a request, the reception unit can analyze the user's social media activity and receive related requests. The reception unit can, for example, analyze the content of the user's social media posts and receive related requests. For example, the reception unit can store the content of the social media posts in a database and use an algorithm to extract related requests. The reception unit can also receive related requests by referring to the activities of the user's friends on social media. For example, the reception unit can analyze the friends' activity data and extract related requests. Furthermore, the reception unit can receive related requests based on the user's check-in information on social media. For example, the check-in information can be stored in a database and use an algorithm to extract related requests. In this way, the reception unit can analyze the user's social media activity and receive related requests preferentially.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a request. The reception unit can, for example, propose an optimal reception method based on the user's past feedback. For example, the past feedback can be stored in a database and an algorithm can be used to propose an optimal reception method. The reception unit can also improve the reception procedure by reflecting the user's past feedback. For example, the reception procedure can be optimized by analyzing the past feedback. Furthermore, the reception unit can propose an optimal request by taking the user's past feedback into consideration. For example, an algorithm can be used to propose an optimal request based on the past feedback. In this way, the reception unit can provide a more optimal reception method by reflecting the user's past feedback.

[0041] When generating a design, the generation unit can adjust the level of detail of the design based on the importance of the house. The generation unit can, for example, design major rooms (such as the living room and kitchen) in detail. For example, the generation AI can design the living room and kitchen in detail based on the user's requests. The generation unit can also simplify the design of rooms with less importance (such as storage spaces). For example, the generation AI can simplify the design of storage spaces based on the user's requests. Furthermore, the generation unit can adjust the level of detail of the design based on the importance of the entire house. For example, the generation AI can adjust the level of detail of the design of the entire house based on the user's requests. This allows the generation unit to adjust the level of detail of the design based on the importance of the house, thereby enabling efficient design.

[0042] The generation unit can apply different design algorithms depending on the category of the house when generating the design. For example, in the case of a detached house, the generation unit can apply a design algorithm that makes use of a large lot. For example, when the generation AI designs a detached house, it applies a design algorithm that makes use of a large lot. In addition, in the case of an apartment building, the generation unit can also apply a design algorithm that makes effective use of a limited space. For example, when the generation AI designs an apartment building, it can apply a design algorithm that makes effective use of a limited space. Furthermore, in the case of a commercial facility, the generation unit can also apply a design algorithm that takes into account the flow of customers. For example, when the generation AI designs a commercial facility, it can apply a design algorithm that takes into account the flow of customers. This allows the generation unit to apply an appropriate design algorithm depending on the category of the house, enabling an optimal design.

[0043] When generating a design, the generation unit can improve the accuracy of the design by referring to the user's past design results. The generation unit can, for example, create a new design by referring to the user's preferred design style in the past. For example, the generation AI can analyze the user's past design data and create a new design that reflects the user's preferred design style. The generation unit can also extract improvements from the user's past design results to improve the accuracy of the design. For example, the generation AI can analyze the past design results and extract improvements to improve the accuracy of the design. Furthermore, the generation unit can propose an optimal design based on the user's past design results. For example, the generation AI can use an algorithm that proposes an optimal design based on the past design results. In this way, the generation unit improves the accuracy of the design by referring to the user's past design results.

[0044] When generating designs, the generation unit can determine the priority of designs based on the submission date of the house. For example, the generation unit can prioritize the design of houses with an upcoming submission deadline. For example, the generation AI can store the submission deadline in a database and prioritize the design of houses with an upcoming deadline. The generation unit can also postpone the design of houses with a distant submission deadline. For example, the generation AI can adjust the priority of designs based on the submission deadline. Furthermore, the generation unit can adjust the priority of designs based on the submission date. For example, the generation AI can use an algorithm that determines the priority of designs based on the submission date. This allows the generation unit to determine the priority of designs based on the submission date, enabling efficient design.

[0045] The generation unit can adjust the order of designs based on the relevance of the houses when generating designs. For example, the generation unit can prioritize designs when the relevance of the houses is high. For example, the generation AI stores the relevance of the houses in a database and prioritizes the design of houses with high relevance. The generation unit can also postpone designs when the relevance of the houses is low. For example, the generation AI can adjust the order of designs based on the relevance of the houses. Furthermore, the generation unit can also adjust the order of designs based on the relevance of the houses. For example, the generation AI can use an algorithm that determines the order of designs based on the relevance of the houses. This allows the generation unit to adjust the order of designs based on the relevance of the houses, thereby enabling efficient designs.

[0046] When generating a design, the generation unit can adjust the use of design terminology according to the user's level of expertise. For example, if the user has specialized knowledge, the generation unit can use detailed terminology. For example, the generation AI can evaluate the user's level of expertise based on survey results or past experience and use detailed terminology. The generation unit can also provide explanations in simpler terms if the user does not have specialized knowledge. For example, the generation AI can use an algorithm that provides explanations in simpler terms based on the user's level of expertise. Furthermore, the generation unit can also adjust the use of design terminology according to the user's level of expertise. For example, the generation AI can adjust the use of design terminology based on the user's level of expertise. This allows the generation unit to adjust the use of design terminology according to the user's level of expertise, thereby enabling a more understandable design.

[0047] The print unit can adjust the level of detail of the print based on the importance of the house when printing. For example, the print unit can print major rooms (such as the living room and kitchen) in detail. For example, the print unit prints the living room and kitchen in detail based on the user's request via the generation AI. The print unit can also simplify the print of rooms with less importance (such as storage spaces). For example, the print unit can simplify the print of the storage space based on the user's request via the generation AI. Furthermore, the print unit can adjust the level of detail of the print based on the importance of the entire house. For example, the print unit can adjust the level of detail of the print of the entire house based on the user's request via the generation AI. This allows the print unit to adjust the level of detail of the print based on the importance of the house, enabling efficient printing.

[0048] When printing, the printing unit can apply different print algorithms depending on the category of the home. For example, in the case of a detached home, the printing unit can apply a print algorithm that makes use of the large lot. For example, when the generation AI prints a detached home, the printing unit applies a print algorithm that makes use of the large lot. In addition, in the case of an apartment building, the printing unit can apply a print algorithm that makes effective use of the limited space. For example, when the generation AI prints an apartment building, the printing unit can apply a print algorithm that makes effective use of the limited space. Furthermore, in the case of a commercial facility, the printing unit can apply a print algorithm that takes into account the customer's flow. For example, when the generation AI prints a commercial facility, the printing unit can apply a print algorithm that takes into account the customer's flow. This allows the printing unit to apply an appropriate print algorithm depending on the category of the home, enabling optimal printing.

[0049] When printing, the printing unit can improve the accuracy of the print by referring to the user's past print results. The printing unit can, for example, create a new print by referring to the user's preferred print style in the past. For example, the printing unit uses a generation AI to analyze the user's past print data and create a new print that reflects the user's preferred print style. The printing unit can also extract areas for improvement from the user's past print results and improve the accuracy of the print. For example, the printing unit can use a generation AI to analyze past print results and extract areas for improvement to improve the accuracy of the print. Furthermore, the printing unit can suggest the optimal print based on the user's past print results. For example, the printing unit can use an algorithm that uses the generation AI to suggest the optimal print based on the past print results. This allows the printing unit to improve the accuracy of the print by referring to the user's past print results.

[0050] When printing, the printing department can determine the priority of printing based on the submission date of the house. For example, the printing department can prioritize printing of houses with an upcoming submission deadline. For example, the generation AI stores the submission deadline in a database, and the printing department prioritizes printing of houses with an upcoming deadline. The printing department can also postpone printing of houses with a more distant submission deadline. For example, the printing department can adjust the priority of printing based on the submission deadline of the generation AI. Furthermore, the printing department can adjust the priority of printing based on the submission date. For example, the printing department can use an algorithm in which the generation AI determines the priority of printing based on the submission date. This allows the printing department to prioritize printing based on the submission date, enabling efficient printing.

[0051] The printing unit can adjust the order of printing based on the relevance of the houses when printing. For example, the printing unit can prioritize printing when the relevance of the houses is high. For example, the generation AI stores the relevance of the houses in a database, and the printing unit prioritizes printing of houses with high relevance. The printing unit can also postpone printing when the relevance of the houses is low. For example, the printing unit can adjust the order of printing based on the relevance of the houses by the generation AI. Furthermore, the printing unit can adjust the order of printing based on the relevance of the houses. For example, the printing unit can use an algorithm that determines the order of printing based on the relevance of the houses by the generation AI. This allows the printing unit to adjust the order of printing based on the relevance of the houses, thereby enabling efficient printing.

[0052] When printing, the printing unit can adjust the use of technical terms in the print according to the user's level of expertise. For example, if the user has technical expertise, the printing unit can use detailed technical terms. For example, in the printing unit, the generation AI evaluates the user's level of expertise based on survey results and past experience and uses detailed technical terms. In addition, if the user does not have technical expertise, the printing unit can provide explanations in simple terms. For example, the printing unit can use an algorithm in which the generation AI provides explanations in simple terms based on the user's level of expertise. In addition, the printing unit can adjust the use of technical terms in the print according to the user's level of expertise. For example, in the printing unit, the generation AI can adjust the use of technical terms in the print based on the user's level of expertise. This allows the printing unit to adjust the use of technical terms in the print according to the user's level of expertise, thereby enabling prints that are easier to understand.

[0053] During confirmation, the confirmation unit can adjust the level of detail of the confirmation based on the importance of the house. For example, the confirmation unit can perform a detailed confirmation of major rooms (such as the living room and kitchen). For example, the generation AI can perform a detailed confirmation of the living room and kitchen based on the user's request. The confirmation unit can also simplify the confirmation of rooms of less importance (such as storage spaces). For example, the generation AI can simplify the confirmation of storage spaces based on the user's request. Furthermore, the confirmation unit can adjust the level of detail of the confirmation based on the importance of the entire house. For example, the generation AI can adjust the level of detail of the confirmation of the entire house based on the user's request. This allows the confirmation unit to adjust the level of detail of the confirmation based on the importance of the house, thereby enabling efficient confirmation.

[0054] The verification unit can apply different verification algorithms depending on the category of the home during verification. For example, in the case of a detached home, the verification unit can apply a verification algorithm that makes use of the large lot. For example, when the generation AI is verifying a detached home, the verification unit applies a verification algorithm that makes use of the large lot. In addition, in the case of an apartment building, the verification unit can apply a verification algorithm that makes effective use of the limited space. For example, when the generation AI is verifying an apartment building, the verification unit can apply a verification algorithm that makes effective use of the limited space. In addition, in the case of a commercial facility, the verification unit can apply a verification algorithm that takes into account the customer's movement lines. For example, when the generation AI is verifying a commercial facility, the verification unit can apply a verification algorithm that takes into account the customer's movement lines. This allows the verification unit to perform optimal verification by applying an appropriate verification algorithm depending on the category of the home.

[0055] The confirmation unit can improve the accuracy of confirmation by referring to the user's past confirmation results when confirming. The confirmation unit can, for example, perform a new confirmation by referring to the user's preferred confirmation style in the past. For example, the confirmation unit uses the generation AI to analyze the user's past confirmation data and perform a new confirmation that reflects the user's preferred confirmation style. The confirmation unit can also extract areas for improvement from the user's past confirmation results and improve the accuracy of the confirmation. For example, the confirmation unit can use the generation AI to analyze the past confirmation results and extract areas for improvement to improve the accuracy of the confirmation. Furthermore, the confirmation unit can suggest optimal confirmation based on the user's past confirmation results. For example, the confirmation unit can use an algorithm that uses the generation AI to suggest optimal confirmation based on the past confirmation results. As a result, the confirmation unit improves the accuracy of the confirmation by referring to the user's past confirmation results.

[0056] During confirmation, the confirmation unit can determine confirmation priorities based on the submission date of the home. For example, the confirmation unit can prioritize the confirmation of homes with an upcoming submission deadline. For example, the generation AI stores the submission deadline in a database, and the confirmation unit prioritizes the confirmation of homes with an upcoming deadline. The confirmation unit can also postpone the confirmation of homes with a distant submission deadline. For example, the confirmation unit can adjust the confirmation priority based on the submission deadline of the generation AI. Furthermore, the confirmation unit can adjust the confirmation priority based on the submission date. For example, the confirmation unit can use an algorithm in which the generation AI determines the confirmation priority based on the submission date. This allows the confirmation unit to determine the confirmation priority based on the submission date, thereby enabling efficient confirmation.

[0057] The confirmation unit can adjust the order of confirmation based on the relevance of the homes during confirmation. For example, the confirmation unit can prioritize confirmation when the relevance of the homes is high. For example, the generation AI stores the relevance of the homes in a database, and the confirmation unit prioritizes confirmation of homes with high relevance. The confirmation unit can also postpone confirmation when the relevance of the homes is low. For example, the confirmation unit can adjust the order of confirmation based on the relevance of the homes by the generation AI. Furthermore, the confirmation unit can adjust the order of confirmation based on the relevance of the homes. For example, the confirmation unit can use an algorithm that determines the order of confirmation based on the relevance of the homes by the generation AI. This allows the confirmation unit to adjust the order of confirmation based on the relevance of the homes, thereby enabling efficient confirmation.

[0058] During confirmation, the verification unit can adjust the use of technical terminology in the confirmation according to the user's level of expertise. For example, if the user has technical expertise, the verification unit can use detailed technical terminology. For example, the verification unit uses detailed technical terminology by having the generation AI evaluate the user's level of expertise based on questionnaire results or past experience. The verification unit can also provide explanations in simple terms if the user does not have technical expertise. For example, the verification unit can use an algorithm in which the generation AI provides explanations in simple terms based on the user's level of expertise. Furthermore, the verification unit can adjust the use of technical terminology in the confirmation according to the user's level of expertise. For example, the verification unit can adjust the use of technical terminology in the confirmation according to the user's level of expertise. This allows the verification unit to adjust the use of technical terminology in the confirmation according to the user's level of expertise, thereby enabling a more understandable confirmation.

[0059] The correction unit can adjust the level of detail of the correction based on the importance of the house when making corrections. For example, the correction unit can make detailed corrections to major rooms (such as the living room and kitchen). For example, the correction unit allows the generation AI to make detailed corrections to the living room and kitchen based on a user request. The correction unit can also simplify the correction of rooms with less importance (such as storage spaces). For example, the correction unit allows the generation AI to simplify the correction of storage spaces based on a user request. Furthermore, the correction unit can adjust the level of detail of the correction based on the importance of the entire house. For example, the correction unit allows the generation AI to adjust the level of detail of the correction for the entire house based on a user request. This allows the correction unit to adjust the level of detail of the correction based on the importance of the house, thereby enabling efficient corrections.

[0060] The correction unit can apply different correction algorithms depending on the category of the home during correction. For example, in the case of a detached home, the correction unit can apply a correction algorithm that makes use of the large lot. For example, when the generation AI corrects a detached home, the correction unit applies a correction algorithm that makes use of the large lot. In addition, in the case of an apartment building, the correction unit can apply a correction algorithm that makes effective use of limited space. For example, when the generation AI corrects an apartment building, the correction unit can apply a correction algorithm that makes effective use of limited space. In addition, in the case of a commercial facility, the correction unit can apply a correction algorithm that takes into account customer movement paths. For example, when the generation AI corrects a commercial facility, the correction unit can apply a correction algorithm that takes into account customer movement paths. This allows the correction unit to perform optimal correction by applying an appropriate correction algorithm depending on the category of the home.

[0061] When making corrections, the correction unit can improve the accuracy of the corrections by referring to the user's past correction results. The correction unit can, for example, make new corrections by referring to the user's preferred correction style in the past. For example, the correction unit causes the generation AI to analyze the user's past correction data and make new corrections that reflect the user's preferred correction style. The correction unit can also extract areas for improvement from the user's past correction results and improve the accuracy of the corrections. For example, the correction unit causes the generation AI to analyze the past correction results and extract areas for improvement to improve the accuracy of the corrections. Furthermore, the correction unit can suggest optimal corrections based on the user's past correction results. For example, the correction unit can use an algorithm that causes the generation AI to suggest optimal corrections based on the past correction results. In this way, the correction unit improves the accuracy of the corrections by referring to the user's past correction results.

[0062] When making corrections, the correction unit can determine the priority of corrections based on the submission date of the house. For example, the correction unit can prioritize corrections to houses with an upcoming submission deadline. For example, the generation AI stores the submission deadline in a database, and the correction unit prioritizes corrections to houses with an upcoming deadline. The correction unit can also postpone corrections to houses with a distant submission deadline. For example, the correction unit can adjust the priority of corrections based on the submission deadline of the generation AI. Furthermore, the correction unit can adjust the priority of corrections based on the submission date. For example, the correction unit can use an algorithm that determines the priority of corrections based on the submission date of the generation AI. This allows the correction unit to determine the priority of corrections based on the submission date, thereby enabling efficient corrections.

[0063] The correction unit can adjust the order of corrections based on the relevance of the houses when making corrections. For example, the correction unit can prioritize corrections when the relevance of the houses is high. For example, the correction unit stores the relevance of the houses in a database by the generation AI, and prioritizes corrections to houses with high relevance. The correction unit can also postpone corrections when the relevance of the houses is low. For example, the correction unit can adjust the order of corrections based on the relevance of the houses by the generation AI. Furthermore, the correction unit can adjust the order of corrections based on the relevance of the houses. For example, the correction unit can use an algorithm that determines the order of corrections based on the relevance of the houses by the generation AI. This allows the correction unit to adjust the order of corrections based on the relevance of the houses, thereby enabling efficient corrections.

[0064] When making corrections, the correction unit can adjust the use of technical terminology in the corrections depending on the user's level of expertise. For example, if the user has technical expertise, the correction unit can use detailed technical terminology. For example, the correction unit uses detailed technical terminology by having the generation AI evaluate the user's level of expertise based on survey results or past experience. The correction unit can also provide explanations in simple terms if the user does not have technical expertise. For example, the correction unit can use an algorithm in which the generation AI provides explanations in simple terms based on the user's level of expertise. Furthermore, the correction unit can also adjust the use of technical terminology in the corrections depending on the user's level of expertise. For example, the correction unit can adjust the use of technical terminology in the corrections depending on the user's level of expertise. This allows the correction unit to adjust the use of technical terminology in the corrections depending on the user's level of expertise, thereby making the corrections easier to understand.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The generation unit can create an energy-efficient design based on the user's requests. For example, the generation unit can optimize the placement of solar panels to create a design that maximizes energy efficiency. The generation unit can also optimize the use of insulation materials to create a design that reduces energy consumption. Furthermore, the generation unit can design a window placement that maximizes natural light to reduce lighting energy consumption. In this way, the generation unit can provide a highly energy-efficient home.

[0067] The reception unit can make optimal design proposals based on the user's past request history. For example, it can make design proposals that reflect the designs and functions that the user has preferred in the past. It can also extract points that the user values ​​from the past request history and make optimal design proposals. It can also analyze the past request history and make new proposals that match the user's preferences. In this way, the reception unit can make design proposals that will provide greater satisfaction by utilizing the user's past request history.

[0068] The generation unit can create an environmentally friendly design when generating a design. For example, a design that utilizes renewable energy can be created. Also, a design that uses eco-friendly building materials can be created. Furthermore, a design that minimizes energy consumption can be created. In this way, the generation unit can provide an environmentally friendly home.

[0069] The printing unit can adjust the printing process depending on the type of material being used. For example, when using a metal material, it can print at the appropriate temperature and speed. When using a plastic material, it can also incorporate an appropriate cooling process. Furthermore, when using a composite material, it can apply a printing process that suits the characteristics of each material. This allows the printing unit to provide the optimal printing process depending on the material being used.

[0070] The verification unit can evaluate the quality of the design data and propose corrections as necessary. For example, if there is an inconsistency in the design data, the verification unit can propose corrections. Also, if the design data is not optimized, the verification unit can propose improvements. Furthermore, if the design data does not meet legal requirements, the verification unit can propose necessary corrections. This allows the verification unit to improve the quality of the design data.

[0071] The correction unit can improve the accuracy of corrections based on the user's past feedback. For example, it can analyze past feedback, extract common problems, and make corrections. It can also make corrections that reflect the points that the user values ​​based on past feedback. Furthermore, it can make new correction suggestions by utilizing past feedback. This allows the correction unit to make more accurate corrections by utilizing the user's past feedback.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The reception unit receives requests or site conditions from the user. For example, the reception unit can receive information such as the user's desired design and functions, the topography and area of ​​the site, etc. Step 2: The generation unit uses the generation AI to generate design data for the house based on the information received by the reception unit. For example, the generation AI takes into consideration the user's requests and the site conditions to create an optimal design. The generation AI can generate design data using a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The printing department prints the house based on the design data generated by the generation department. For example, a 3D printer can be used to simultaneously print fixtures such as the kitchen, bathroom, and toilet. Step 4: The verification unit verifies whether the design data generated by the generation unit conforms to the Building Standards Act, for example, by verifying legal requirements such as earthquake resistance standards and fire safety standards of the Building Standards Act. Step 5: The correction unit corrects the design data based on the results of the check by the check unit. For example, the design data can be corrected to satisfy legal requirements.

[0074] (Example 2) A 3D printer housing system according to an embodiment of the present invention utilizes generative AI to provide homes that can be constructed in a short time. This system accepts user requests and site conditions, generates design data for the home using the generative AI, and then a 3D printer prints the home based on that design data. Furthermore, the generative AI creates a design that complies with the Building Standards Act, eliminating legal issues. For example, the 3D printer creates an optimal design taking into account the user's requests and site conditions. The 3D printer then prints the home based on the generated design data. This process also simultaneously prints amenities such as a kitchen, bathroom, and toilet. The printed home is the ideal size for a two-person household and can be completed in a short time. This allows the 3D printer housing system to provide affordable homes that can be constructed in a short time. For example, compared to conventional building methods, construction time is significantly reduced, thereby reducing costs. Furthermore, the generative AI creates an optimal design, resulting in a lean, efficient home. Furthermore, the generative AI creates a design that complies with the Building Standards Act, eliminating legal issues. For example, homes are provided that are earthquake-resistant and fireproof in accordance with the Building Standards Act, so you can live in them with peace of mind.

[0075] A 3D printer housing system according to an embodiment includes a reception unit, a generation unit, a printing unit, a verification unit, and a correction unit. The reception unit receives user requests or site conditions. For example, it can receive information such as the user's desired design and functions, the site's topography, and area. The generation unit uses a generation AI to generate design data for a house based on the information received by the reception unit. For example, the generation AI creates an optimal design taking into account the user's requests and site conditions. The generation AI can generate design data using a text generation AI (e.g., LLM) or a multimodal generation AI. The printing unit prints a house based on the design data generated by the generation unit. For example, a 3D printer can be used to simultaneously print fixtures such as a kitchen, bathroom, and toilet. The verification unit verifies whether the design data generated by the generation unit complies with the Building Standards Act. For example, it verifies legal requirements such as earthquake resistance standards and fire safety standards of the Building Standards Act. The correction unit modifies the design data based on the results of the verification unit. For example, the design data can be modified to satisfy legal requirements. As a result, the 3D printer housing system of the embodiment makes it possible to construct a house that complies with the Building Standards Act in a short period of time, based on the user's requests and site conditions.

[0076] The generation unit can create an optimal design based on the user's requests or site conditions. The generation unit, for example, creates an optimal design taking into account the user's requests. For example, it can create a design that reflects the design and functions desired by the user. The generation unit can also create an optimal design taking into account the site conditions. For example, it can create a design that suits the site's topography and area. Furthermore, the generation unit can use a generation AI to create an optimal design based on the user's requests and site conditions. For example, the generation AI receives the user's requests and site conditions as input and generates optimal design data. As a result, the generation unit can provide a home that provides greater satisfaction by creating an optimal design based on the user's requests and site conditions.

[0077] The printing unit can simultaneously print kitchen or bathroom / toilet fixtures. For example, the printing unit can simultaneously print a kitchen sink and stove. For example, a kitchen sink and stove can be printed as an integrated unit using a 3D printer. The printing unit can also simultaneously print bathroom / toilet fixtures. For example, a bathroom bathtub and toilet can be printed as an integrated unit. Furthermore, by simultaneously printing kitchen and bathroom / toilet fixtures, construction time can be significantly reduced. For example, the time required to install the fixtures can be significantly reduced compared to conventional construction methods. This allows the printing unit to significantly reduce construction time by simultaneously printing the fixtures.

[0078] The confirmation unit can confirm the legal requirements for creating a design that complies with the Building Standards Act. The confirmation unit can, for example, confirm the earthquake resistance standards of the Building Standards Act. For example, it can confirm whether the design data meets the earthquake resistance standards. The confirmation unit can also confirm fire safety standards. For example, it can confirm whether the design data meets the fire safety standards. Furthermore, the confirmation unit can also confirm other legal requirements of the Building Standards Act. For example, it can confirm whether the design data meets all the requirements of the Building Standards Act. In this way, the confirmation unit can provide a home that complies with the Building Standards Act by confirming the legal requirements.

[0079] The correction unit can correct the design data based on the results confirmed by the confirmation unit. The correction unit can, for example, correct the design data based on earthquake resistance standards confirmed by the confirmation unit. For example, the correction unit corrects the design data so that it satisfies the earthquake resistance standards. The correction unit can also correct the design data based on fire safety standards. For example, the correction unit can correct the design data so that it satisfies the fire safety standards. Furthermore, the correction unit can also correct the design data based on other legal requirements of the Building Standards Act. For example, the correction unit can correct the design data so that it satisfies all requirements of the Building Standards Act. In this way, the correction unit can provide a house that meets legal requirements by correcting the design data.

[0080] The printing department can complete printing a house in one day. The printing department can complete printing a house in one day, for example, using a 3D printer. For example, by increasing the printing speed, the printing department can complete printing a house in one day. The printing department can also complete printing a house in one day by optimizing the materials used. For example, by using a material that hardens quickly, the printing time can be shortened. Furthermore, the printing department can complete printing a house in one day by streamlining the printing process. For example, by optimizing the printing order and method, the printing time can be shortened. As a result, the printing department can complete construction of the house in a short time and make it ready for occupancy quickly.

[0081] The reception unit can estimate the user's emotions and adjust the priority of requests based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit can prioritize important requests and respond quickly. For example, the reception unit can capture the user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. Furthermore, when the user is relaxed, the reception unit can carefully listen to detailed requests and provide optimal suggestions. For example, the reception unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, when the user is in a hurry, the reception unit can prioritize brief requests and respond quickly. For example, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the reception unit to adjust the priority of requests according to the user's emotions, enabling more appropriate responses. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] The reception unit can analyze the user's past request history and select the optimal reception method. For example, the reception unit can prioritize reception of requests that the user has frequently requested in the past. For example, the past request history can be stored in a database and the most frequent requests can be processed preferentially. The reception unit can also find specific patterns from the user's past request history and suggest the optimal reception method. For example, an algorithm can be used to analyze the past request history and extract specific patterns. Furthermore, the reception unit can select the most efficient reception method based on the user's past request history. For example, the reception unit can suggest the optimal reception procedure based on the past request history. In this way, the reception unit can provide a more efficient reception method by analyzing the past request history.

[0083] When receiving a request, the reception unit can filter requests based on the user's current living situation and areas of interest. The reception unit can, for example, take into account the user's current living situation (family composition, occupation, etc.) and preferentially receive related requests. For example, the user's living situation is stored in a database and related requests are filtered. The reception unit can also filter related requests based on the user's areas of interest (hobbies, interests, etc.). For example, the user's areas of interest can be stored in a database and an algorithm can be used to extract related requests. Furthermore, the reception unit can suggest optimal requests based on the user's living situation and areas of interest. For example, an algorithm can be used to suggest optimal requests based on the user's living situation and areas of interest. This allows the reception unit to preferentially receive related requests based on the user's living situation and areas of interest.

[0084] When receiving a request, the reception unit can select the optimal reception means depending on the user's input method. For example, when the user uses voice input, the reception unit can receive the request using voice recognition technology. For example, voice recognition software automatically analyzes the voice and saves it as text. Furthermore, when the user uses text input, the reception unit can also receive the request using text analysis technology. For example, a text analysis algorithm can be used to analyze the user's request and provide an appropriate response. Furthermore, when the user uses image input, the reception unit can also receive the request using image recognition technology. For example, an image recognition algorithm can be used to analyze the image sent by the user and provide an appropriate response. This allows the reception unit to select the optimal reception means depending on the user's input method, enabling smoother reception.

[0085] The reception unit can estimate the user's emotions and determine the priority of requests to be received based on the estimated user emotions. For example, when the user is stressed, the reception unit can prioritize important requests. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, when the user is relaxed, the reception unit can also carefully receive detailed requests. For example, the reception unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, when the user is in a hurry, the reception unit can prioritize brief requests. For example, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the reception unit to prioritize requests based on the user's emotions, enabling more appropriate responses. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] When receiving a request, the reception unit can prioritize receiving highly relevant requests by taking into account the user's geographical location information. The reception unit can, for example, prioritize receiving requests related to places close to the user's current location. For example, the reception unit acquires the user's geographical location information as GPS data and filters out related requests. The reception unit can also filter out related requests based on the user's geographical location information. For example, the reception unit can store the user's address information in a database and use an algorithm to extract related requests. Furthermore, the reception unit can also suggest optimal requests by taking into account the user's geographical location information. For example, the reception unit can use an algorithm to suggest optimal requests based on the user's geographical location information. In this way, the reception unit can prioritize receiving highly relevant requests by taking into account the user's geographical location information.

[0087] When receiving a request, the reception unit can analyze the user's social media activity and receive related requests. The reception unit can, for example, analyze the content of the user's social media posts and receive related requests. For example, the reception unit can store the content of the social media posts in a database and use an algorithm to extract related requests. The reception unit can also receive related requests by referring to the activities of the user's friends on social media. For example, the reception unit can analyze the friends' activity data and extract related requests. Furthermore, the reception unit can receive related requests based on the user's check-in information on social media. For example, the check-in information can be stored in a database and use an algorithm to extract related requests. In this way, the reception unit can analyze the user's social media activity and receive related requests preferentially.

[0088] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a request. The reception unit can, for example, propose an optimal reception method based on the user's past feedback. For example, the past feedback can be stored in a database and an algorithm can be used to propose an optimal reception method. The reception unit can also improve the reception procedure by reflecting the user's past feedback. For example, the reception procedure can be optimized by analyzing the past feedback. Furthermore, the reception unit can propose an optimal request by taking the user's past feedback into consideration. For example, an algorithm can be used to propose an optimal request based on the past feedback. In this way, the reception unit can provide a more optimal reception method by reflecting the user's past feedback.

[0089] The generation unit can estimate the user's emotions and adjust the design expression method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a design that makes extensive use of soft colors and curves. For example, the generation AI can capture the user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. Furthermore, if the user is stressed, the generation unit can generate a simple, linear design. For example, the generation AI can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is excited, the generation unit can generate a visually stimulating design. For example, the generation AI can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the generation unit to adjust the design expression method according to the user's emotions, thereby providing a more satisfying design. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] When generating a design, the generation unit can adjust the level of detail of the design based on the importance of the house. The generation unit can, for example, design major rooms (such as the living room and kitchen) in detail. For example, the generation AI can design the living room and kitchen in detail based on the user's requests. The generation unit can also simplify the design of rooms with less importance (such as storage spaces). For example, the generation AI can simplify the design of storage spaces based on the user's requests. Furthermore, the generation unit can adjust the level of detail of the design based on the importance of the entire house. For example, the generation AI can adjust the level of detail of the design of the entire house based on the user's requests. This allows the generation unit to adjust the level of detail of the design based on the importance of the house, thereby enabling efficient design.

[0091] The generation unit can apply different design algorithms depending on the category of the house when generating the design. For example, in the case of a detached house, the generation unit can apply a design algorithm that makes use of a large lot. For example, when the generation AI designs a detached house, it applies a design algorithm that makes use of a large lot. In addition, in the case of an apartment building, the generation unit can also apply a design algorithm that makes effective use of a limited space. For example, when the generation AI designs an apartment building, it can apply a design algorithm that makes effective use of a limited space. Furthermore, in the case of a commercial facility, the generation unit can also apply a design algorithm that takes into account the flow of customers. For example, when the generation AI designs a commercial facility, it can apply a design algorithm that takes into account the flow of customers. This allows the generation unit to apply an appropriate design algorithm depending on the category of the house, enabling an optimal design.

[0092] When generating a design, the generation unit can improve the accuracy of the design by referring to the user's past design results. The generation unit can, for example, create a new design by referring to the user's preferred design style in the past. For example, the generation AI can analyze the user's past design data and create a new design that reflects the user's preferred design style. The generation unit can also extract improvements from the user's past design results to improve the accuracy of the design. For example, the generation AI can analyze the past design results and extract improvements to improve the accuracy of the design. Furthermore, the generation unit can propose an optimal design based on the user's past design results. For example, the generation AI can use an algorithm that proposes an optimal design based on the past design results. In this way, the generation unit improves the accuracy of the design by referring to the user's past design results.

[0093] The generation unit can estimate the user's emotions and adjust the length of the design based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can complete the design in a short time. For example, the generation AI can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The generation unit can also create a detailed design when the user is relaxed. For example, the generation AI can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, if the user is excited, the generation unit can quickly generate a visually stimulating design. For example, the generation AI can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the generation unit to adjust the length of the design according to the user's emotions, thereby enabling a more appropriate design. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0094] When generating designs, the generation unit can determine the priority of designs based on the submission date of the house. For example, the generation unit can prioritize the design of houses with an upcoming submission deadline. For example, the generation AI can store the submission deadline in a database and prioritize the design of houses with an upcoming deadline. The generation unit can also postpone the design of houses with a distant submission deadline. For example, the generation AI can adjust the priority of designs based on the submission deadline. Furthermore, the generation unit can adjust the priority of designs based on the submission date. For example, the generation AI can use an algorithm that determines the priority of designs based on the submission date. This allows the generation unit to determine the priority of designs based on the submission date, enabling efficient design.

[0095] The generation unit can adjust the order of designs based on the relevance of the houses when generating designs. For example, the generation unit can prioritize designs when the relevance of the houses is high. For example, the generation AI stores the relevance of the houses in a database and prioritizes the design of houses with high relevance. The generation unit can also postpone designs when the relevance of the houses is low. For example, the generation AI can adjust the order of designs based on the relevance of the houses. Furthermore, the generation unit can also adjust the order of designs based on the relevance of the houses. For example, the generation AI can use an algorithm that determines the order of designs based on the relevance of the houses. This allows the generation unit to adjust the order of designs based on the relevance of the houses, thereby enabling efficient designs.

[0096] When generating a design, the generation unit can adjust the use of design terminology according to the user's level of expertise. For example, if the user has specialized knowledge, the generation unit can use detailed terminology. For example, the generation AI can evaluate the user's level of expertise based on survey results or past experience and use detailed terminology. The generation unit can also provide explanations in simpler terms if the user does not have specialized knowledge. For example, the generation AI can use an algorithm that provides explanations in simpler terms based on the user's level of expertise. Furthermore, the generation unit can also adjust the use of design terminology according to the user's level of expertise. For example, the generation AI can adjust the use of design terminology based on the user's level of expertise. This allows the generation unit to adjust the use of design terminology according to the user's level of expertise, thereby enabling a more understandable design.

[0097] The printing unit can estimate the user's emotions and adjust the timing of printing based on the estimated user emotions. For example, if the user is in a hurry, the printing unit can start printing quickly. For example, the printing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the printing unit can start printing after detailed confirmation. For example, the printing unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, if the user is feeling stressed, the printing unit can prioritize printing of important parts. For example, the printing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the printing unit to adjust the timing of printing according to the user's emotions, thereby enabling printing at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0098] The print unit can adjust the level of detail of the print based on the importance of the house when printing. For example, the print unit can print major rooms (such as the living room and kitchen) in detail. For example, the print unit prints the living room and kitchen in detail based on the user's request via the generation AI. The print unit can also simplify the print of rooms with less importance (such as storage spaces). For example, the print unit can simplify the print of the storage space based on the user's request via the generation AI. Furthermore, the print unit can adjust the level of detail of the print based on the importance of the entire house. For example, the print unit can adjust the level of detail of the print of the entire house based on the user's request via the generation AI. This allows the print unit to adjust the level of detail of the print based on the importance of the house, enabling efficient printing.

[0099] When printing, the printing unit can apply different print algorithms depending on the category of the home. For example, in the case of a detached home, the printing unit can apply a print algorithm that makes use of the large lot. For example, when the generation AI prints a detached home, the printing unit applies a print algorithm that makes use of the large lot. In addition, in the case of an apartment building, the printing unit can apply a print algorithm that makes effective use of the limited space. For example, when the generation AI prints an apartment building, the printing unit can apply a print algorithm that makes effective use of the limited space. Furthermore, in the case of a commercial facility, the printing unit can apply a print algorithm that takes into account the customer's flow. For example, when the generation AI prints a commercial facility, the printing unit can apply a print algorithm that takes into account the customer's flow. This allows the printing unit to apply an appropriate print algorithm depending on the category of the home, enabling optimal printing.

[0100] When printing, the printing unit can improve the accuracy of the print by referring to the user's past print results. The printing unit can, for example, create a new print by referring to the user's preferred print style in the past. For example, the printing unit uses a generation AI to analyze the user's past print data and create a new print that reflects the user's preferred print style. The printing unit can also extract areas for improvement from the user's past print results and improve the accuracy of the print. For example, the printing unit can use a generation AI to analyze past print results and extract areas for improvement to improve the accuracy of the print. Furthermore, the printing unit can suggest the optimal print based on the user's past print results. For example, the printing unit can use an algorithm that uses the generation AI to suggest the optimal print based on the past print results. This allows the printing unit to improve the accuracy of the print by referring to the user's past print results.

[0101] The printing unit can estimate the user's emotions and adjust the length of the print based on the estimated user emotions. For example, if the user is in a hurry, the printing unit can complete the print in a short time. For example, the printing unit can capture the user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the printing unit can perform detailed printing. For example, the printing unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is excited, the printing unit can print a visually stimulating design in a short time. For example, the printing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the printing unit to adjust the length of the print according to the user's emotions, enabling more appropriate printing. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0102] When printing, the printing department can determine the priority of printing based on the submission date of the house. For example, the printing department can prioritize printing of houses with an upcoming submission deadline. For example, the generation AI stores the submission deadline in a database, and the printing department prioritizes printing of houses with an upcoming deadline. The printing department can also postpone printing of houses with a more distant submission deadline. For example, the printing department can adjust the priority of printing based on the submission deadline of the generation AI. Furthermore, the printing department can adjust the priority of printing based on the submission date. For example, the printing department can use an algorithm in which the generation AI determines the priority of printing based on the submission date. This allows the printing department to prioritize printing based on the submission date, enabling efficient printing.

[0103] The printing unit can adjust the order of printing based on the relevance of the houses when printing. For example, the printing unit can prioritize printing when the relevance of the houses is high. For example, the generation AI stores the relevance of the houses in a database, and the printing unit prioritizes printing of houses with high relevance. The printing unit can also postpone printing when the relevance of the houses is low. For example, the printing unit can adjust the order of printing based on the relevance of the houses by the generation AI. Furthermore, the printing unit can adjust the order of printing based on the relevance of the houses. For example, the printing unit can use an algorithm that determines the order of printing based on the relevance of the houses by the generation AI. This allows the printing unit to adjust the order of printing based on the relevance of the houses, thereby enabling efficient printing.

[0104] When printing, the printing unit can adjust the use of technical terms in the print according to the user's level of expertise. For example, if the user has technical expertise, the printing unit can use detailed technical terms. For example, in the printing unit, the generation AI evaluates the user's level of expertise based on survey results and past experience and uses detailed technical terms. In addition, if the user does not have technical expertise, the printing unit can provide explanations in simple terms. For example, the printing unit can use an algorithm in which the generation AI provides explanations in simple terms based on the user's level of expertise. In addition, the printing unit can adjust the use of technical terms in the print according to the user's level of expertise. For example, in the printing unit, the generation AI can adjust the use of technical terms in the print based on the user's level of expertise. This allows the printing unit to adjust the use of technical terms in the print according to the user's level of expertise, thereby enabling prints that are easier to understand.

[0105] The confirmation unit can estimate the user's emotions and adjust the confirmation method based on the estimated user emotions. For example, if the user is nervous, the confirmation unit can provide a simple, highly visible confirmation method. For example, the confirmation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the confirmation unit can provide a confirmation method that includes detailed information. For example, the confirmation unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, if the user is in a hurry, the confirmation unit can provide a confirmation method that focuses on the key points. For example, the confirmation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the confirmation unit to adjust the confirmation method according to the user's emotions, enabling more appropriate confirmation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0106] During confirmation, the confirmation unit can adjust the level of detail of the confirmation based on the importance of the house. For example, the confirmation unit can perform a detailed confirmation of major rooms (such as the living room and kitchen). For example, the generation AI can perform a detailed confirmation of the living room and kitchen based on the user's request. The confirmation unit can also simplify the confirmation of rooms of less importance (such as storage spaces). For example, the generation AI can simplify the confirmation of storage spaces based on the user's request. Furthermore, the confirmation unit can adjust the level of detail of the confirmation based on the importance of the entire house. For example, the generation AI can adjust the level of detail of the confirmation of the entire house based on the user's request. This allows the confirmation unit to adjust the level of detail of the confirmation based on the importance of the house, thereby enabling efficient confirmation.

[0107] The verification unit can apply different verification algorithms depending on the category of the home during verification. For example, in the case of a detached home, the verification unit can apply a verification algorithm that makes use of the large lot. For example, when the generation AI is verifying a detached home, the verification unit applies a verification algorithm that makes use of the large lot. In addition, in the case of an apartment building, the verification unit can apply a verification algorithm that makes effective use of the limited space. For example, when the generation AI is verifying an apartment building, the verification unit can apply a verification algorithm that makes effective use of the limited space. In addition, in the case of a commercial facility, the verification unit can apply a verification algorithm that takes into account the customer's movement lines. For example, when the generation AI is verifying a commercial facility, the verification unit can apply a verification algorithm that takes into account the customer's movement lines. This allows the verification unit to perform optimal verification by applying an appropriate verification algorithm depending on the category of the home.

[0108] The confirmation unit can improve the accuracy of confirmation by referring to the user's past confirmation results when confirming. The confirmation unit can, for example, perform a new confirmation by referring to the user's preferred confirmation style in the past. For example, the confirmation unit uses the generation AI to analyze the user's past confirmation data and perform a new confirmation that reflects the user's preferred confirmation style. The confirmation unit can also extract areas for improvement from the user's past confirmation results and improve the accuracy of the confirmation. For example, the confirmation unit can use the generation AI to analyze the past confirmation results and extract areas for improvement to improve the accuracy of the confirmation. Furthermore, the confirmation unit can suggest optimal confirmation based on the user's past confirmation results. For example, the confirmation unit can use an algorithm that uses the generation AI to suggest optimal confirmation based on the past confirmation results. As a result, the confirmation unit improves the accuracy of the confirmation by referring to the user's past confirmation results.

[0109] The confirmation unit can estimate the user's emotions and adjust the length of the confirmation based on the estimated user emotions. For example, if the user is in a hurry, the confirmation unit can complete the confirmation in a short time. For example, the confirmation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the confirmation unit can perform detailed confirmation. For example, the confirmation unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, if the user is excited, the confirmation unit can quickly confirm a visually stimulating design. For example, the confirmation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the confirmation unit to adjust the length of the confirmation according to the user's emotions, enabling more appropriate confirmation. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0110] During confirmation, the confirmation unit can determine confirmation priorities based on the submission date of the home. For example, the confirmation unit can prioritize the confirmation of homes with an upcoming submission deadline. For example, the generation AI stores the submission deadline in a database, and the confirmation unit prioritizes the confirmation of homes with an upcoming deadline. The confirmation unit can also postpone the confirmation of homes with a distant submission deadline. For example, the confirmation unit can adjust the confirmation priority based on the submission deadline of the generation AI. Furthermore, the confirmation unit can adjust the confirmation priority based on the submission date. For example, the confirmation unit can use an algorithm in which the generation AI determines the confirmation priority based on the submission date. This allows the confirmation unit to determine the confirmation priority based on the submission date, thereby enabling efficient confirmation.

[0111] The confirmation unit can adjust the order of confirmation based on the relevance of the homes during confirmation. For example, the confirmation unit can prioritize confirmation when the relevance of the homes is high. For example, the generation AI stores the relevance of the homes in a database, and the confirmation unit prioritizes confirmation of homes with high relevance. The confirmation unit can also postpone confirmation when the relevance of the homes is low. For example, the confirmation unit can adjust the order of confirmation based on the relevance of the homes by the generation AI. Furthermore, the confirmation unit can adjust the order of confirmation based on the relevance of the homes. For example, the confirmation unit can use an algorithm that determines the order of confirmation based on the relevance of the homes by the generation AI. This allows the confirmation unit to adjust the order of confirmation based on the relevance of the homes, thereby enabling efficient confirmation.

[0112] During confirmation, the verification unit can adjust the use of technical terminology in the confirmation according to the user's level of expertise. For example, if the user has technical expertise, the verification unit can use detailed technical terminology. For example, the verification unit uses detailed technical terminology by having the generation AI evaluate the user's level of expertise based on questionnaire results or past experience. The verification unit can also provide explanations in simple terms if the user does not have technical expertise. For example, the verification unit can use an algorithm in which the generation AI provides explanations in simple terms based on the user's level of expertise. Furthermore, the verification unit can adjust the use of technical terminology in the confirmation according to the user's level of expertise. For example, the verification unit can adjust the use of technical terminology in the confirmation according to the user's level of expertise. This allows the verification unit to adjust the use of technical terminology in the confirmation according to the user's level of expertise, thereby enabling a more understandable confirmation.

[0113] The correction unit can estimate the user's emotions and adjust the correction method based on the estimated user emotions. For example, if the user is nervous, the correction unit can provide a simple and highly visible correction method. For example, the correction unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the correction unit can provide a correction method that includes detailed information. For example, the correction unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, if the user is in a hurry, the correction unit can provide a correction method that focuses on the key points. For example, the correction unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the correction unit to adjust the correction method according to the user's emotions, enabling more appropriate correction. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0114] The correction unit can adjust the level of detail of the correction based on the importance of the house when making corrections. For example, the correction unit can make detailed corrections to major rooms (such as the living room and kitchen). For example, the correction unit allows the generation AI to make detailed corrections to the living room and kitchen based on a user request. The correction unit can also simplify the correction of rooms with less importance (such as storage spaces). For example, the correction unit allows the generation AI to simplify the correction of storage spaces based on a user request. Furthermore, the correction unit can adjust the level of detail of the correction based on the importance of the entire house. For example, the correction unit allows the generation AI to adjust the level of detail of the correction for the entire house based on a user request. This allows the correction unit to adjust the level of detail of the correction based on the importance of the house, thereby enabling efficient corrections.

[0115] The correction unit can apply different correction algorithms depending on the category of the home during correction. For example, in the case of a detached home, the correction unit can apply a correction algorithm that makes use of the large lot. For example, when the generation AI corrects a detached home, the correction unit applies a correction algorithm that makes use of the large lot. In addition, in the case of an apartment building, the correction unit can apply a correction algorithm that makes effective use of limited space. For example, when the generation AI corrects an apartment building, the correction unit can apply a correction algorithm that makes effective use of limited space. In addition, in the case of a commercial facility, the correction unit can apply a correction algorithm that takes into account customer movement paths. For example, when the generation AI corrects a commercial facility, the correction unit can apply a correction algorithm that takes into account customer movement paths. This allows the correction unit to perform optimal correction by applying an appropriate correction algorithm depending on the category of the home.

[0116] When making corrections, the correction unit can improve the accuracy of the corrections by referring to the user's past correction results. The correction unit can, for example, make new corrections by referring to the user's preferred correction style in the past. For example, the correction unit causes the generation AI to analyze the user's past correction data and make new corrections that reflect the user's preferred correction style. The correction unit can also extract areas for improvement from the user's past correction results and improve the accuracy of the corrections. For example, the correction unit causes the generation AI to analyze the past correction results and extract areas for improvement to improve the accuracy of the corrections. Furthermore, the correction unit can suggest optimal corrections based on the user's past correction results. For example, the correction unit can use an algorithm that causes the generation AI to suggest optimal corrections based on the past correction results. In this way, the correction unit improves the accuracy of the corrections by referring to the user's past correction results.

[0117] The correction unit can estimate the user's emotion and adjust the length of the correction based on the estimated user emotion. For example, if the user is in a hurry, the correction unit can complete the correction in a short time. For example, the correction unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the correction unit can perform detailed correction. For example, the correction unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, if the user is excited, the correction unit can quickly modify a visually stimulating design. For example, the correction unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the correction unit to adjust the length of the correction according to the user's emotion, thereby enabling more appropriate correction. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0118] When making corrections, the correction unit can determine the priority of corrections based on the submission date of the house. For example, the correction unit can prioritize corrections to houses with an upcoming submission deadline. For example, the generation AI stores the submission deadline in a database, and the correction unit prioritizes corrections to houses with an upcoming deadline. The correction unit can also postpone corrections to houses with a distant submission deadline. For example, the correction unit can adjust the priority of corrections based on the submission deadline of the generation AI. Furthermore, the correction unit can adjust the priority of corrections based on the submission date. For example, the correction unit can use an algorithm that determines the priority of corrections based on the submission date of the generation AI. This allows the correction unit to determine the priority of corrections based on the submission date, thereby enabling efficient corrections.

[0119] The correction unit can adjust the order of corrections based on the relevance of the houses when making corrections. For example, the correction unit can prioritize corrections when the relevance of the houses is high. For example, the correction unit stores the relevance of the houses in a database by the generation AI, and prioritizes corrections to houses with high relevance. The correction unit can also postpone corrections when the relevance of the houses is low. For example, the correction unit can adjust the order of corrections based on the relevance of the houses by the generation AI. Furthermore, the correction unit can adjust the order of corrections based on the relevance of the houses. For example, the correction unit can use an algorithm that determines the order of corrections based on the relevance of the houses by the generation AI. This allows the correction unit to adjust the order of corrections based on the relevance of the houses, thereby enabling efficient corrections.

[0120] When making corrections, the correction unit can adjust the use of technical terminology in the corrections depending on the user's level of expertise. For example, if the user has technical expertise, the correction unit can use detailed technical terminology. For example, the correction unit uses detailed technical terminology by having the generation AI evaluate the user's level of expertise based on survey results or past experience. The correction unit can also provide explanations in simple terms if the user does not have technical expertise. For example, the correction unit can use an algorithm in which the generation AI provides explanations in simple terms based on the user's level of expertise. Furthermore, the correction unit can also adjust the use of technical terminology in the corrections depending on the user's level of expertise. For example, the correction unit can adjust the use of technical terminology in the corrections depending on the user's level of expertise. This allows the correction unit to adjust the use of technical terminology in the corrections depending on the user's level of expertise, thereby making the corrections easier to understand. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, printing unit, confirmation unit, and correction unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive user requests and site conditions via the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates design data using a generation AI. The printing unit is realized by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing device 12 and controls a 3D printer to print the house. The confirmation unit is realized by the specific processing unit 290 of the data processing device 12 and checks whether the design data complies with the Building Standards Act. The correction unit is realized by the specific processing unit 290 of the data processing device 12 and corrects the design data to satisfy legal requirements. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, printing unit, confirmation unit, and correction unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive user requests and site conditions via the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates design data using a generation AI. The printing unit is realized by the speaker 240 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and controls a 3D printer to print a house. The confirmation unit is realized by the specific processing unit 290 of the data processing device 12 and confirms whether the design data complies with the Building Standards Act. The correction unit is realized by the specific processing unit 290 of the data processing device 12 and corrects the design data to satisfy legal requirements. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, printing unit, confirmation unit, and correction unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can receive user requests and site conditions via the microphone 238 of the headset-type terminal 314 or the communication I / F 26 of the data processing device 12. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates design data using a generation AI. The printing unit is realized by the display 343 of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12 and controls a 3D printer to print a house. The confirmation unit is realized by the specific processing unit 290 of the data processing device 12 and checks whether the design data complies with the Building Standards Act. The correction unit is realized by the specific processing unit 290 of the data processing device 12 and corrects the design data to satisfy legal requirements. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, printing unit, confirmation unit, and correction unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive user requests and site conditions via the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates design data using a generation AI. The printing unit is realized by the control object 443 of the robot 414 or the specific processing unit 290 of the data processing device 12 and controls a 3D printer to print a house. The confirmation unit is realized by the specific processing unit 290 of the data processing device 12 and checks whether the design data complies with the Building Standards Act. The correction unit is realized by the specific processing unit 290 of the data processing device 12 and corrects the design data to satisfy legal requirements.

[0121] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0122] The generation unit can create an energy-efficient design based on the user's requests. For example, the generation unit can optimize the placement of solar panels to create a design that maximizes energy efficiency. The generation unit can also optimize the use of insulation materials to create a design that reduces energy consumption. Furthermore, the generation unit can design a window placement that maximizes natural light to reduce lighting energy consumption. In this way, the generation unit can provide a highly energy-efficient home.

[0123] The print unit can estimate the user's emotions and notify the user of the print progress status in real time based on the estimated user emotions. For example, if the user is feeling anxious, the print unit can provide frequent detailed progress status notifications to provide a sense of security. If the user is relaxed, the print unit can notify the user of only important progress status notifications to reduce the user's stress. Furthermore, if the user is excited, the print unit can notify the user of the progress status in a visually appealing format to maintain the user's excitement. This allows the print unit to provide appropriate notifications according to the user's emotions.

[0124] The confirmation unit can estimate the user's emotions and adjust the timing of confirmation based on the estimated user emotions. For example, if the user is in a hurry, the confirmation can be performed quickly and the results can be notified immediately. If the user is relaxed, the confirmation can be performed in detail and the results can be explained carefully. Furthermore, if the user is feeling stressed, the confirmation unit can prioritize checking important points and respond quickly. This allows the confirmation unit to adjust the timing of confirmation according to the user's emotions, enabling more appropriate responses.

[0125] The correction unit can estimate the user's emotions and adjust the priority of corrections based on the estimated user's emotions. For example, if the user is feeling anxious, important corrections can be made with priority and a quick response can be made. Also, if the user is relaxed, detailed corrections can be made carefully and the user's satisfaction can be increased. Furthermore, if the user is in a hurry, simple corrections can be made with priority and a quick response can be made. In this way, the correction unit can adjust the priority of corrections according to the user's emotions, thereby enabling a more appropriate response.

[0126] The generation unit can estimate the user's emotions and provide design feedback based on the estimated user's emotions. For example, if the user feels anxious, detailed feedback can be provided to give the user a sense of security. If the user feels relaxed, brief feedback can be provided to reduce the user's stress. Furthermore, if the user feels excited, visually appealing feedback can be provided to maintain the user's excitement. In this way, the generation unit can provide appropriate feedback according to the user's emotions.

[0127] The reception unit can make optimal design proposals based on the user's past request history. For example, it can make design proposals that reflect the designs and functions that the user has preferred in the past. It can also extract points that the user values ​​from the past request history and make optimal design proposals. It can also analyze the past request history and make new proposals that match the user's preferences. In this way, the reception unit can make design proposals that will provide greater satisfaction by utilizing the user's past request history.

[0128] The generation unit can create an environmentally friendly design when generating a design. For example, a design that utilizes renewable energy can be created. Also, a design that uses eco-friendly building materials can be created. Furthermore, a design that minimizes energy consumption can be created. In this way, the generation unit can provide an environmentally friendly home.

[0129] The printing unit can adjust the printing process depending on the type of material being used. For example, when using a metal material, it can print at the appropriate temperature and speed. When using a plastic material, it can also incorporate an appropriate cooling process. Furthermore, when using a composite material, it can apply a printing process that suits the characteristics of each material. This allows the printing unit to provide the optimal printing process depending on the material being used.

[0130] The verification unit can evaluate the quality of the design data and propose corrections as necessary. For example, if there is an inconsistency in the design data, the verification unit can propose corrections. Also, if the design data is not optimized, the verification unit can propose improvements. Furthermore, if the design data does not meet legal requirements, the verification unit can propose necessary corrections. This allows the verification unit to improve the quality of the design data.

[0131] The correction unit can improve the accuracy of corrections based on the user's past feedback. For example, it can analyze past feedback, extract common problems, and make corrections. It can also make corrections that reflect the points that the user values ​​based on past feedback. Furthermore, it can make new correction suggestions by utilizing past feedback. This allows the correction unit to make more accurate corrections by utilizing the user's past feedback.

[0132] The processing flow of the second embodiment will be briefly explained below.

[0133] Step 1: The reception unit receives requests or site conditions from the user. For example, the reception unit can receive information such as the user's desired design and functions, the topography and area of ​​the site, etc. Step 2: The generation unit uses the generation AI to generate design data for the house based on the information received by the reception unit. For example, the generation AI takes into consideration the user's requests and the site conditions to create an optimal design. The generation AI can generate design data using a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The printing department prints the house based on the design data generated by the generation department. For example, a 3D printer can be used to simultaneously print fixtures such as the kitchen, bathroom, and toilet. Step 4: The verification unit verifies whether the design data generated by the generation unit conforms to the Building Standards Act, for example, by verifying legal requirements such as earthquake resistance standards and fire safety standards of the Building Standards Act. Step 5: The correction unit corrects the design data based on the results of the check by the check unit. For example, the design data can be corrected to satisfy legal requirements.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0139] 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.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0155] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0171] 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.

[0172] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0173] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0174] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0175] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0176] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0177] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0178] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0179] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0180] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0181] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0182] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0183] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0184] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0185] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0188] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0189] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0190] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0191] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0192] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0193] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0194] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0195] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0196] 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.

[0197] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0198] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0199] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0200] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0201] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0202] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0203] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0204] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0205] [Explanation of symbols]

[0206] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives requests or site conditions from users; a generation unit that generates design data for a house based on the information received by the reception unit; a printing unit that prints a house based on the design data generated by the generation unit; a verification unit that verifies whether the design data generated by the generation unit conforms to the Building Standards Act; a correction unit that corrects the design data based on the result of the confirmation by the confirmation unit. A system characterized by:

2. The generation unit Create the optimal design based on the user's requests or site conditions 2. The system of claim 1.

3. The printing unit Print kitchen or bathroom fixtures at the same time 2. The system of claim 1.

4. The confirmation unit Identify legal requirements for building code compliant designs 2. The system of claim 1.

5. The correction unit The design data is corrected based on the result of the confirmation by the confirmation unit.

2. The system of claim 1.

6. The printing unit Complete house printing in one day 2. The system of claim 1.

7. The reception unit Estimate user emotions and adjust the priority of requests based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyze the user's past request history and select the optimal reception method 2. The system of claim 1.

Images