system

Abstract

The system according to the embodiment aims to efficiently design and assemble buildings based on the user's wishes. [Solution] The system according to the embodiment comprises a hearing unit, a generation unit, and an assembly unit. The hearing unit hears the user's wishes. The generation unit generates building drawings based on the information collected by the hearing unit. The assembly unit assembles building parts based on the drawings generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies presented challenges in that the process of creating building plans based on user requests and then assembling the building based on those plans was complex and difficult to perform efficiently.

[0005] The system according to the embodiment aims to efficiently design and assemble buildings based on the user's wishes. [Means for solving the problem]

[0006] The system according to the embodiment includes a hearing unit, a generation unit, and an assembly unit. The hearing unit hears the user's wishes. The generation unit generates a drawing of the building based on the information collected by the hearing unit. The assembly unit assembles parts of the building based on the drawing generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently design and assemble buildings based on the user's wishes. [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 of form 1) An architectural system according to an embodiment of the present invention integrates a 3D printer and a generative AI to provide a new approach to architecture. In this architectural system, the generative AI first interviews the user to determine what kind of house they would like to live in or what kind of building they would like to build. The generative AI then creates a blueprint of the building based on the interview results. These blueprints are finalized after multiple rounds of discussions with the user. Finally, a 3D printer assembles the actual building parts based on the blueprints created by the generative AI. This system efficiently creates buildings that meet the user's needs. For example, the generative AI interviews the user to determine what kind of house they would like to live in or what kind of building they would like to build. During this process, the generative AI listens in detail to the user's wishes and requests and inputs them as prompts. For example, specific requests such as "I want a spacious living room" or "I want a house with lots of natural light" are collected. The generative AI then creates a blueprint of the building based on the results of the interview. The generative AI then generates a blueprint that reflects the user's requests and presents it to the user. The user reviews the blueprint and makes any necessary corrections or additions. The generative AI then creates a new blueprint incorporating these requests and continues the process of discussing the blueprint with the user. For example, if a user expresses a request such as "I want the living room to be a little larger," the generative AI will create a new blueprint that reflects that request. Finally, once the user is satisfied with the blueprint, a 3D printer will assemble the actual building parts based on that blueprint. The 3D printer accurately prints and assembles each part of the building based on the blueprint created by the generative AI. For example, parts such as walls, floors, and roofs are created using a 3D printer, and then the building is completed by assembling them. This system allows buildings to be created efficiently according to the user's wishes. Users can obtain their ideal building by communicating their wishes and requests in detail. In addition, by linking the generative AI with the 3D printer, the process from building design to assembly can be streamlined, reducing time and costs. This allows the architectural system to efficiently create buildings that meet the user's wishes.

[0029] The building system according to this embodiment comprises a hearing unit, a generation unit, and an assembly unit. The hearing unit hears the user's wishes. The hearing unit collects detailed wishes and requests, such as what kind of house the user wants to live in or what kind of building they want to build. For example, the hearing unit can collect specific requests from the user, such as "I want a large living room" or "I want a house that gets lots of natural light." The hearing unit can also input the user's wishes and requests as prompts into the generation AI. The generation unit uses the generation AI to generate building drawings based on the information collected by the hearing unit. For example, the generation unit generates drawings that reflect the user's requests and presents them to the user. The generation unit allows the user to review the drawings and provide requests for modifications or additional information as needed. The generation unit then creates new drawings based on this feedback and repeats the process of brainstorming with the user. For example, if the user requests that "the living room be a little larger," the generation unit can create new drawings that reflect this request. The assembly unit assembles the building parts based on the drawings created by the generation AI. The assembly unit, for example, uses a 3D printer to accurately print and assemble each part of the building based on drawings created by the generation AI. For example, the assembly unit can create parts such as walls, floors, and roofs using a 3D printer and assemble them to complete the building. This allows the building system according to the embodiment to efficiently create buildings that meet the user's needs. Some or all of the above-described processes in the assembly unit may be performed using AI, for example, or without AI. For example, the assembly unit can accurately print and assemble each part of the building based on drawings created by the generation AI. This allows the building system to efficiently create buildings that meet the user's needs.

[0030] The hearing unit can specifically collect the user's wishes and requests. The hearing unit collects the user's wishes and requests in detail, such as what kind of house the user wants to live in or what kind of building the user wants to build. For example, the hearing unit can collect the user's specific requests, such as "I want a spacious living room" or "I want a house with lots of natural light." The hearing unit can also input the user's wishes and requests as prompts to the generation AI. This allows the user's specific requests to be collected in detail. Some or all of the above-described processing in the hearing unit may be performed using, or without, the generation AI. For example, the hearing unit can input the user's wishes and requests as prompts to the generation AI, and the generation AI can collect the user's requests based on the prompts.

[0031] The generation unit can generate a drawing that reflects the user's requests. For example, the generation unit generates a drawing that reflects the user's requests and presents it to the user. The user can then review the drawing and, if necessary, request corrections or additions. The generation unit then creates a new drawing that reflects the requests and repeatedly discusses them with the user. For example, if the user requests that the living room be a little larger, the generation unit can create a new drawing that reflects the request. This allows a drawing that reflects the user's requests to be generated. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's requests as prompts to the generation AI, and the generation AI can generate a drawing based on the prompts.

[0032] The generation unit can generate a final drawing by repeatedly exchanging opinions with the user. For example, the generation unit can generate a drawing that reflects the user's requests and present it to the user. The user can then review the drawing and provide requests for modifications or additional information as needed. The generation unit can then create the drawing again, incorporating this feedback, and repeat the process of brainstorming with the user. For example, if the user requests that the living room be made a little larger, the generation unit can create a new drawing that reflects this request. In this way, the final drawing can be generated by repeatedly exchanging opinions with the user. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's requests as prompts to the generation AI, and the generation AI can generate a drawing based on those prompts.

[0033] The assembly unit can accurately print and assemble each part of a building based on drawings created by a generation AI. For example, the assembly unit can use a 3D printer to accurately print and assemble each part of a building based on drawings created by a generation AI. For instance, the assembly unit can create parts such as walls, floors, and roofs using a 3D printer and assemble them to complete the building. This allows for the accurate printing and assembly of each part of a building based on drawings created by a generation AI. Some or all of the above-described processes in the assembly unit may be performed using AI, or without AI. For example, the assembly unit can accurately print and assemble each part of a building based on drawings created by a generation AI.

[0034] The hearing unit can analyze the user's past architectural wishes and requests and select an appropriate hearing method. The hearing unit, for example, asks related questions based on the architectural style the user previously desired. The hearing unit can also analyze the user's past requests, find commonalities, and customize the questions. Furthermore, the hearing unit can ask specific questions by referring to a wish list previously submitted by the user. This makes it possible to select the optimal hearing method by analyzing the user's past wishes and requests. Some or all of the above-described processing in the hearing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the hearing unit can input the user's past wishes and requests into the generation AI as prompts, and the generation AI can select a hearing method based on the prompts.

[0035] The hearing unit can customize questions during the hearing based on the user's lifestyle and family composition. For example, if the user lives with his or her family, the hearing unit asks questions to hear the requests of all family members. Furthermore, if the user lives alone, the hearing unit can ask questions tailored to the user's personal lifestyle. Furthermore, if the user has a pet, the hearing unit can ask questions about pet facilities. This allows for more appropriate questions to be asked by customizing the questions based on the user's lifestyle and family composition. Some or all of the above-described processing in the hearing unit may be performed using, or without, a generation AI. For example, the hearing unit can input the user's lifestyle and family composition into the generation AI as prompts, and the generation AI can customize questions based on the prompts.

[0036] The interviewing unit can prioritize asking highly relevant questions based on the user's geographical location during the interview. For example, if the user lives in an urban area, the interviewing unit can ask questions about buildings suitable for urban living. If the user lives in a suburban area, the interviewing unit can ask questions about buildings suitable for the natural environment. Furthermore, if the user lives in a specific region, the interviewing unit can ask questions about buildings suitable for the climate and environment of that region. In this way, by considering the user's geographical location, highly relevant questions can be prioritized. Some or all of the above processing in the interviewing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the interviewing unit can input the user's geographical location information as a prompt to the generative AI, and the generative AI can prioritize asking highly relevant questions based on that prompt.

[0037] The interviewing unit can analyze the user's social media activity during the interview and add relevant questions. For example, the interviewing unit can ask specific questions based on architecture-related posts the user has shared on social media. It can also ask questions based on architects and design trends the user follows. Furthermore, the interviewing unit can ask relevant questions based on information about architecture-related communities the user participates in. In this way, relevant questions can be added by analyzing the user's social media activity. Some or all of the above processing in the interviewing unit may be performed using, for example, generative AI, or not using generative AI. For example, the interviewing unit can input the user's social media activity as a prompt to the generative AI, and the generative AI can add relevant questions based on that prompt.

[0038] When generating a drawing, the generation unit can adjust the level of detail of the drawing based on the priority of the building. For example, the generation unit can generate detailed drawings for major rooms and spaces. The generation unit can also generate simplified drawings for auxiliary rooms and spaces. Furthermore, the generation unit can generate particularly detailed drawings for parts that the user particularly values. In this way, by adjusting the level of detail of the drawing based on the importance of the building, more appropriate drawings can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the importance of the building to the generation AI as a prompt, and the generation AI can adjust the level of detail of the drawing based on the prompt.

[0039] When generating a blueprint, the generation unit can apply different generation algorithms depending on the category of the building. For example, the generation unit can apply an algorithm that emphasizes livability to residential buildings. The generation unit can also apply an algorithm that emphasizes functionality to commercial buildings. The generation unit can also apply an algorithm that emphasizes safety to public facility buildings. This allows for the generation of more appropriate blueprints by applying different generation algorithms depending on the category of the building. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the building category to the generation AI as a prompt, and the generation AI can apply different generation algorithms based on the prompt.

[0040] When generating blueprints, the generation unit can determine the priority of the blueprints based on the building's submission deadline. For example, the generation unit can generate blueprints with the highest priority for urgent buildings. The generation unit can also generate blueprints with priority for buildings with an upcoming submission deadline. Furthermore, the generation unit can postpone generating blueprints for buildings with a distant submission deadline. This allows blueprints to be generated more efficiently by determining the priority of the blueprints based on the building's submission time. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input the building's submission deadline as a prompt to the generation AI, and the generation AI can determine the priority of the blueprints based on the prompt.

[0041] When generating a blueprint, the generation unit can adjust the order of the blueprints based on related information about the building. For example, the generation unit can generate blueprints for major rooms and spaces first. The generation unit can also generate blueprints for auxiliary rooms and spaces later. Furthermore, the generation unit can generate blueprints first for parts that the user particularly values. This allows blueprints to be generated more efficiently by adjusting the order of the blueprints based on the relevance of the building. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input related information about the building to the generation AI as a prompt, and the generation AI can adjust the order of the blueprints based on the prompt.

[0042] During assembly, the assembly unit can select the optimal assembly method by analyzing the user's past building experience. For example, the assembly unit selects the optimal method based on assembly methods the user has previously experienced. The assembly unit can also suggest an efficient assembly method based on the user's past building experience. Furthermore, the assembly unit can analyze the user's past building experience and select the most appropriate assembly method. In this way, the optimal assembly method can be selected by analyzing the user's past building experience. Some or all of the above-described processing in the assembly unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the assembly unit can input the user's past building experience into the generation AI as a prompt, and the generation AI can select the optimal assembly method based on the prompt.

[0043] During assembly, the assembly unit can customize the assembly means based on the user's current living situation. For example, if the user is busy, the assembly unit can provide a means that allows assembly to be completed quickly. Furthermore, if the user is relaxed, the assembly unit can provide a means that includes detailed instructions. Furthermore, if the user has specific requests, the assembly unit can provide a means that meets those requests. This allows for customizing the assembly means based on the user's current living situation to provide a more appropriate assembly means. Some or all of the above-described processing in the assembly unit may be performed, for example, using or without the generation AI. For example, the assembly unit can input the user's current living situation into the generation AI as a prompt, and the generation AI can customize the assembly means based on the prompt.

[0044] The assembly unit can select the optimal assembly method during assembly, taking into account the user's geographical location information. For example, if the user lives in an urban area, the assembly unit can provide an assembly method suitable for urban living. If the user lives in a suburban area, the assembly unit can provide an assembly method suitable for the natural environment. Furthermore, if the user lives in a specific region, the assembly unit can provide an assembly method suitable for the climate and local conditions of that region. In this way, the optimal assembly method can be selected by taking into account the user's geographical location information. Some or all of the above processing in the assembly unit may be performed using, for example, a generative AI, or without a generative AI. For example, the assembly unit can input the user's geographical location information as a prompt to the generative AI, and the generative AI can select the optimal assembly method based on that prompt.

[0045] The assembly unit can analyze the user's social media activity during assembly and propose assembly methods. For example, the assembly unit can propose specific assembly methods based on architecture-related posts shared by the user on social media. It can also propose assembly methods by referencing architects and design trends followed by the user. Furthermore, it can propose relevant assembly methods based on information from architecture-related communities the user participates in. Thus, by analyzing the user's social media activity, it can propose relevant assembly methods. Some or all of the above processing in the assembly unit may be performed using, for example, a generative AI, or without one. For example, the assembly unit can input the user's social media activity as a prompt to the generative AI, which can then propose assembly methods based on that prompt.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The hearing unit can analyze the user's past architectural wishes and requests and select an appropriate hearing method. For example, the hearing unit can ask related questions based on the architectural style the user previously desired. The hearing unit can also analyze the user's past requests, find commonalities, and customize the questions. Furthermore, the hearing unit can ask specific questions by referring to a wish list previously submitted by the user. This makes it possible to select the optimal hearing method by analyzing the user's past wishes and requests. Some or all of the above-mentioned processing in the hearing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the hearing unit can input the user's past wishes and requests into the generation AI as prompts, and the generation AI can select a hearing method based on the prompts.

[0048] When generating a drawing, the generation unit can adjust the level of detail of the drawing based on the priority of the building. For example, the generation unit can generate detailed drawings for major rooms and spaces. The generation unit can also generate simplified drawings for auxiliary rooms and spaces. Furthermore, the generation unit can generate particularly detailed drawings for parts that the user particularly values. In this way, by adjusting the level of detail of the drawing based on the importance of the building, more appropriate drawings can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the importance of the building to the generation AI as a prompt, and the generation AI can adjust the level of detail of the drawing based on the prompt.

[0049] The assembly unit can analyze the user's past construction experience during assembly to select the optimal assembly method. For example, it can select the optimal method based on the user's past experience with assembly methods. Furthermore, the assembly unit can propose an efficient assembly method based on the user's past construction experience. In addition, the assembly unit can analyze the user's past construction experience and select the most suitable assembly method. Thus, by analyzing the user's past construction experience, the optimal assembly method can be selected. Some or all of the above-described processes in the assembly unit may be performed using, for example, a generative AI, or without a generative AI. For example, the assembly unit can input the user's past construction experience as a prompt to the generative AI, which can then select the optimal assembly method based on that prompt.

[0050] The interviewing unit can customize questions during the interview based on the user's lifestyle and family structure. For example, if the user lives with family, it will ask questions to gather information on the needs of all family members. If the user lives alone, the interviewing unit can ask questions tailored to the individual's lifestyle. Furthermore, if the user has pets, the interviewing unit can ask questions about pet-related equipment. By customizing questions based on the user's lifestyle and family structure, more appropriate questions can be asked. Some or all of the above processing in the interviewing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the interviewing unit can input the user's lifestyle and family structure as prompts into the generative AI, which can then customize questions based on those prompts.

[0051] When generating a drawing, the generation unit can apply different generation algorithms depending on the category of the building. For example, an algorithm that emphasizes livability can be applied to a residential building. The generation unit can also apply an algorithm that emphasizes functionality to a commercial building. Furthermore, the generation unit can apply an algorithm that emphasizes safety to a public facility building. In this way, by applying different generation algorithms depending on the category of the building, more appropriate drawings can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the building category to the generation AI as a prompt, and the generation AI can apply different generation algorithms based on the prompt.

[0052] During assembly, the assembly unit can select the optimal assembly method by taking into account the user's geographical location information. For example, if the user lives in an urban area, the assembly unit can provide an assembly method suitable for urban life. Furthermore, if the user lives in the suburbs, the assembly unit can provide an assembly method suitable for the natural environment. Furthermore, if the user lives in a specific region, the assembly unit can provide an assembly method suitable for the climate and environment of that region. This makes it possible to select the optimal assembly method by taking into account the user's geographical location information. Some or all of the above-described processing in the assembly unit may be performed using, or without, a generation AI. For example, the assembly unit can input the user's geographical location information into the generation AI as a prompt, and the generation AI can select the optimal assembly method based on the prompt.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The hearing department listens to the user's wishes. For example, it collects detailed requests and desires, such as what kind of house the user wants to live in or what kind of building they want to build. Specifically, it collects requests such as "I want a spacious living room" or "I want a house with lots of natural light." The hearing department can also input the user's wishes and desires as prompts into the generation AI. Step 2: The generation unit uses the generation AI to generate a drawing of the building based on the information collected by the hearing unit. For example, it generates a drawing that reflects the user's requests and presents it to the user. The user can review the drawing and, if necessary, make corrections or additions. The generation unit then reflects this and creates a new drawing, repeatedly bouncing ideas off the user. For example, it can create a new drawing that reflects requests such as "I'd like the living room to be a little larger." Step 3: The assembly department assembles the building parts based on the drawings created by the generative AI. For example, using a 3D printer, each part of the building is accurately printed and assembled based on the drawings created by the generative AI. Specifically, parts such as walls, floors, and roofs can be created using a 3D printer, and then assembled to complete the building. This makes it possible to efficiently create buildings that meet the user's wishes.

[0055] (Example 2) An architectural system according to an embodiment of the present invention integrates a 3D printer and a generative AI to provide a new approach to architecture. In this architectural system, the generative AI first interviews the user to determine what kind of house they would like to live in or what kind of building they would like to build. The generative AI then creates a blueprint of the building based on the interview results. These blueprints are finalized after multiple rounds of discussions with the user. Finally, a 3D printer assembles the actual building parts based on the blueprints created by the generative AI. This system efficiently creates buildings that meet the user's needs. For example, the generative AI interviews the user to determine what kind of house they would like to live in or what kind of building they would like to build. During this process, the generative AI listens in detail to the user's wishes and requests and inputs them as prompts. For example, specific requests such as "I want a spacious living room" or "I want a house with lots of natural light" are collected. The generative AI then creates a blueprint of the building based on the results of the interview. The generative AI then generates a blueprint that reflects the user's requests and presents it to the user. The user reviews the blueprint and makes any necessary corrections or additions. The generative AI then creates a new blueprint incorporating these requests and continues the process of discussing the blueprint with the user. For example, if a user expresses a request such as "I want the living room to be a little larger," the generative AI will create a new blueprint that reflects that request. Finally, once the user is satisfied with the blueprint, a 3D printer will assemble the actual building parts based on that blueprint. The 3D printer accurately prints and assembles each part of the building based on the blueprint created by the generative AI. For example, parts such as walls, floors, and roofs are created using a 3D printer, and then the building is completed by assembling them. This system allows buildings to be created efficiently according to the user's wishes. Users can obtain their ideal building by communicating their wishes and requests in detail. In addition, by linking the generative AI with the 3D printer, the process from building design to assembly can be streamlined, reducing time and costs. This allows the architectural system to efficiently create buildings that meet the user's wishes.

[0056] The building system according to this embodiment comprises a hearing unit, a generation unit, and an assembly unit. The hearing unit hears the user's wishes. The hearing unit collects detailed wishes and requests, such as what kind of house the user wants to live in or what kind of building they want to build. For example, the hearing unit can collect specific requests from the user, such as "I want a large living room" or "I want a house that gets lots of natural light." The hearing unit can also input the user's wishes and requests as prompts into the generation AI. The generation unit uses the generation AI to generate building drawings based on the information collected by the hearing unit. For example, the generation unit generates drawings that reflect the user's requests and presents them to the user. The generation unit allows the user to review the drawings and provide requests for modifications or additional information as needed. The generation unit then creates new drawings based on this feedback and repeats the process of brainstorming with the user. For example, if the user requests that "the living room be a little larger," the generation unit can create new drawings that reflect this request. The assembly unit assembles the building parts based on the drawings created by the generation AI. The assembly unit, for example, uses a 3D printer to accurately print and assemble each part of the building based on drawings created by the generation AI. For example, the assembly unit can create parts such as walls, floors, and roofs using a 3D printer and assemble them to complete the building. This allows the building system according to the embodiment to efficiently create buildings that meet the user's needs. Some or all of the above-described processes in the assembly unit may be performed using AI, for example, or without AI. For example, the assembly unit can accurately print and assemble each part of the building based on drawings created by the generation AI. This allows the building system to efficiently create buildings that meet the user's needs.

[0057] The hearing unit can specifically collect the user's wishes and requests. The hearing unit collects the user's wishes and requests in detail, such as what kind of house the user wants to live in or what kind of building the user wants to build. For example, the hearing unit can collect the user's specific requests, such as "I want a spacious living room" or "I want a house with lots of natural light." The hearing unit can also input the user's wishes and requests as prompts to the generation AI. This allows the user's specific requests to be collected in detail. Some or all of the above-described processing in the hearing unit may be performed using, or without, the generation AI. For example, the hearing unit can input the user's wishes and requests as prompts to the generation AI, and the generation AI can collect the user's requests based on the prompts.

[0058] The generation unit can generate a drawing that reflects the user's requests. For example, the generation unit generates a drawing that reflects the user's requests and presents it to the user. The user can then review the drawing and, if necessary, request corrections or additions. The generation unit then creates a new drawing that reflects the requests and repeatedly discusses them with the user. For example, if the user requests that the living room be a little larger, the generation unit can create a new drawing that reflects the request. This allows a drawing that reflects the user's requests to be generated. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's requests as prompts to the generation AI, and the generation AI can generate a drawing based on the prompts.

[0059] The generation unit can generate a final drawing by repeatedly exchanging opinions with the user. For example, the generation unit can generate a drawing that reflects the user's requests and present it to the user. The user can then review the drawing and provide requests for modifications or additional information as needed. The generation unit can then create the drawing again, incorporating this feedback, and repeat the process of brainstorming with the user. For example, if the user requests that the living room be made a little larger, the generation unit can create a new drawing that reflects this request. In this way, the final drawing can be generated by repeatedly exchanging opinions with the user. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's requests as prompts to the generation AI, and the generation AI can generate a drawing based on those prompts.

[0060] The assembly unit can accurately print and assemble each part of a building based on drawings created by a generation AI. For example, the assembly unit can use a 3D printer to accurately print and assemble each part of a building based on drawings created by a generation AI. For instance, the assembly unit can create parts such as walls, floors, and roofs using a 3D printer and assemble them to complete the building. This allows for the accurate printing and assembly of each part of a building based on drawings created by a generation AI. Some or all of the above-described processes in the assembly unit may be performed using AI, or without AI. For example, the assembly unit can accurately print and assemble each part of a building based on drawings created by a generation AI.

[0061] The interviewing unit can estimate the user's emotions and adjust the interview questions based on the estimated emotions. For example, if the user is relaxed, the interviewing unit can ask detailed questions to elicit specific requests. If the user is nervous, the interviewing unit can start with simple questions and gradually move to more detailed ones. Furthermore, if the user is in a hurry, the interviewing unit can ask concise questions to quickly gather requests. In this way, by adjusting the interview questions according to the user's emotions, more appropriate questions can be asked. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the interviewing unit may be performed using a generative AI, or not using a generative AI. For example, the interviewing unit can input the user's emotions as a prompt to the generative AI, and the generative AI can adjust the questions based on that prompt.

[0062] The hearing unit can analyze the user's past architectural wishes and requests and select an appropriate hearing method. The hearing unit, for example, asks related questions based on the architectural style the user previously desired. The hearing unit can also analyze the user's past requests, find commonalities, and customize the questions. Furthermore, the hearing unit can ask specific questions by referring to a wish list previously submitted by the user. This makes it possible to select the optimal hearing method by analyzing the user's past wishes and requests. Some or all of the above-described processing in the hearing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the hearing unit can input the user's past wishes and requests into the generation AI as prompts, and the generation AI can select a hearing method based on the prompts.

[0063] The hearing unit can customize questions during the hearing based on the user's lifestyle and family composition. For example, if the user lives with his or her family, the hearing unit asks questions to hear the requests of all family members. Furthermore, if the user lives alone, the hearing unit can ask questions tailored to the user's personal lifestyle. Furthermore, if the user has a pet, the hearing unit can ask questions about pet facilities. This allows for more appropriate questions to be asked by customizing the questions based on the user's lifestyle and family composition. Some or all of the above-described processing in the hearing unit may be performed using, or without, a generation AI. For example, the hearing unit can input the user's lifestyle and family composition into the generation AI as prompts, and the generation AI can customize questions based on the prompts.

[0064] The hearing unit can estimate the user's emotions and adjust the order of hearings based on the estimated user emotions. For example, if the user is relaxed, the hearing unit can ask detailed questions first to elicit specific requests. Furthermore, if the user is nervous, the hearing unit can start with simple questions and gradually move on to more detailed questions. Furthermore, if the user is in a hurry, the hearing unit can ask questions that focus on the main points first to quickly collect requests. This allows the questions to be asked in a more appropriate order by adjusting the order of hearings based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as 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. Some or all of the above-described processing in the hearing unit can be performed using, for example, the generation AI. For example, the hearing unit can input the user's emotions as prompts to the generation AI, and the generation AI can adjust the order of hearings based on the prompts.

[0065] The interviewing unit can prioritize asking highly relevant questions based on the user's geographical location during the interview. For example, if the user lives in an urban area, the interviewing unit can ask questions about buildings suitable for urban living. If the user lives in a suburban area, the interviewing unit can ask questions about buildings suitable for the natural environment. Furthermore, if the user lives in a specific region, the interviewing unit can ask questions about buildings suitable for the climate and environment of that region. In this way, by considering the user's geographical location, highly relevant questions can be prioritized. Some or all of the above processing in the interviewing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the interviewing unit can input the user's geographical location information as a prompt to the generative AI, and the generative AI can prioritize asking highly relevant questions based on that prompt.

[0066] The interviewing unit can analyze the user's social media activity during the interview and add relevant questions. For example, the interviewing unit can ask specific questions based on architecture-related posts the user has shared on social media. It can also ask questions based on architects and design trends the user follows. Furthermore, the interviewing unit can ask relevant questions based on information about architecture-related communities the user participates in. In this way, relevant questions can be added by analyzing the user's social media activity. Some or all of the above processing in the interviewing unit may be performed using, for example, generative AI, or not using generative AI. For example, the interviewing unit can input the user's social media activity as a prompt to the generative AI, and the generative AI can add relevant questions based on that prompt.

[0067] The generation unit can estimate the user's emotions and adjust the way the drawing is presented based on those emotions. For example, if the user is relaxed, the generation unit can generate a detailed drawing that reflects their specific needs. If the user is stressed, the generation unit can generate a simple drawing that focuses on the essentials. Furthermore, if the user is in a hurry, the generation unit can generate a concise drawing that allows for quick review. By adjusting the way the drawing is presented according to the user's emotions, a more appropriate drawing can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the user's emotions as a prompt to the generation AI, which can then adjust the way the drawing is presented based on that prompt.

[0068] When generating a drawing, the generation unit can adjust the level of detail of the drawing based on the priority of the building. For example, the generation unit can generate detailed drawings for major rooms and spaces. The generation unit can also generate simplified drawings for auxiliary rooms and spaces. Furthermore, the generation unit can generate particularly detailed drawings for parts that the user particularly values. In this way, by adjusting the level of detail of the drawing based on the importance of the building, more appropriate drawings can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the importance of the building to the generation AI as a prompt, and the generation AI can adjust the level of detail of the drawing based on the prompt.

[0069] When generating a blueprint, the generation unit can apply different generation algorithms depending on the category of the building. For example, the generation unit can apply an algorithm that emphasizes livability to residential buildings. The generation unit can also apply an algorithm that emphasizes functionality to commercial buildings. The generation unit can also apply an algorithm that emphasizes safety to public facility buildings. This allows for the generation of more appropriate blueprints by applying different generation algorithms depending on the category of the building. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the building category to the generation AI as a prompt, and the generation AI can apply different generation algorithms based on the prompt.

[0070] The generation unit can estimate the user's emotions and adjust the length of the drawing based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a longer drawing with detailed explanations. If the user is in a hurry, the generation unit can generate a shorter, more concise drawing. Furthermore, if the user is excited, the generation unit can generate a drawing with visually stimulating effects. By adjusting the length of the drawing according to the user's emotions, a more appropriate drawing can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the user's emotions as a prompt to the generation AI, and the generation AI can adjust the length of the drawing based on that prompt.

[0071] When generating blueprints, the generation unit can determine the priority of the blueprints based on the building's submission deadline. For example, the generation unit can generate blueprints with the highest priority for urgent buildings. The generation unit can also generate blueprints with priority for buildings with an upcoming submission deadline. Furthermore, the generation unit can postpone generating blueprints for buildings with a distant submission deadline. This allows blueprints to be generated more efficiently by determining the priority of the blueprints based on the building's submission time. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input the building's submission deadline as a prompt to the generation AI, and the generation AI can determine the priority of the blueprints based on the prompt.

[0072] When generating a blueprint, the generation unit can adjust the order of the blueprints based on related information about the building. For example, the generation unit can generate blueprints for major rooms and spaces first. The generation unit can also generate blueprints for auxiliary rooms and spaces later. Furthermore, the generation unit can generate blueprints first for parts that the user particularly values. This allows blueprints to be generated more efficiently by adjusting the order of the blueprints based on the relevance of the building. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input related information about the building to the generation AI as a prompt, and the generation AI can adjust the order of the blueprints based on the prompt.

[0073] The assembly unit can estimate the user's emotions and adjust the assembly method based on the estimated emotions. For example, if the user is relaxed, the assembly unit can provide an assembly method that includes detailed explanations. If the user is in a hurry, the assembly unit can provide a method that allows for quick assembly completion. Furthermore, if the user is excited, the assembly unit can provide an assembly method that includes visually stimulating effects. This allows for the provision of a more appropriate assembly method by adjusting the assembly method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the assembly unit may be performed using a generative AI, or not. For example, the assembly unit can input the user's emotions as a prompt to the generative AI, which can then adjust the assembly method based on that prompt.

[0074] During assembly, the assembly unit can select the optimal assembly method by analyzing the user's past building experience. For example, the assembly unit selects the optimal method based on assembly methods the user has previously experienced. The assembly unit can also suggest an efficient assembly method based on the user's past building experience. Furthermore, the assembly unit can analyze the user's past building experience and select the most appropriate assembly method. In this way, the optimal assembly method can be selected by analyzing the user's past building experience. Some or all of the above-described processing in the assembly unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the assembly unit can input the user's past building experience into the generation AI as a prompt, and the generation AI can select the optimal assembly method based on the prompt.

[0075] During assembly, the assembly unit can customize the assembly means based on the user's current living situation. For example, if the user is busy, the assembly unit can provide a means that allows assembly to be completed quickly. Furthermore, if the user is relaxed, the assembly unit can provide a means that includes detailed instructions. Furthermore, if the user has specific requests, the assembly unit can provide a means that meets those requests. This allows for customizing the assembly means based on the user's current living situation to provide a more appropriate assembly means. Some or all of the above-described processing in the assembly unit may be performed, for example, using or without the generation AI. For example, the assembly unit can input the user's current living situation into the generation AI as a prompt, and the generation AI can customize the assembly means based on the prompt.

[0076] The assembly unit can estimate the user's emotions and determine assembly priorities based on those emotions. For example, if the user is in a hurry, the assembly unit can start assembling from the most important part. If the user is relaxed, the assembly unit can provide an assembly method that includes detailed explanations. Furthermore, if the user is excited, the assembly unit can provide an assembly method that includes visually stimulating effects. This allows for assembly to be performed in a more appropriate order by determining assembly priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the assembly unit may be performed using a generative AI, or not. For example, the assembly unit can input the user's emotions as a prompt to the generative AI, which can then determine assembly priorities based on that prompt.

[0077] The assembly unit can select the optimal assembly method during assembly, taking into account the user's geographical location information. For example, if the user lives in an urban area, the assembly unit can provide an assembly method suitable for urban living. If the user lives in a suburban area, the assembly unit can provide an assembly method suitable for the natural environment. Furthermore, if the user lives in a specific region, the assembly unit can provide an assembly method suitable for the climate and local conditions of that region. In this way, the optimal assembly method can be selected by taking into account the user's geographical location information. Some or all of the above processing in the assembly unit may be performed using, for example, a generative AI, or without a generative AI. For example, the assembly unit can input the user's geographical location information as a prompt to the generative AI, and the generative AI can select the optimal assembly method based on that prompt.

[0078] The assembly unit can analyze the user's social media activity during assembly and propose assembly methods. For example, the assembly unit can propose specific assembly methods based on architecture-related posts shared by the user on social media. It can also propose assembly methods by referencing architects and design trends followed by the user. Furthermore, it can propose relevant assembly methods based on information from architecture-related communities the user participates in. Thus, by analyzing the user's social media activity, it can propose relevant assembly methods. Some or all of the above processing in the assembly unit may be performed using, for example, a generative AI, or without one. For example, the assembly unit can input the user's social media activity as a prompt to the generative AI, which can then propose assembly methods based on that prompt. === Hard Collateral 1-1 === Each of the multiple elements described above, including the hearing unit, generation unit, and assembly unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the hearing unit collects user requests using the microphone 38B of the smart device 14 and inputs them as prompts to the generation AI via the control unit 46A. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates building drawings based on the information from the hearing unit. The assembly unit is implemented, for example, by the control unit 46A of the smart device 14 and assembles building parts using a 3D printer based on the drawings created by the generation AI. === Hard Collateral 1-2 === Each of the multiple elements described above, including the hearing unit, generation unit, and assembly unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the hearing unit collects the user's wishes using the microphone 238 of the smart glasses 214 and inputs them as prompts to the generation AI via the control unit 46A. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates building plans based on the information from the hearing unit. The assembly unit is implemented, for example, by the control unit 46A of the smart glasses 214 and assembles building parts using a 3D printer based on the plans created by the generation AI. === Hard Collateral 1-3 === Each of the multiple elements described above, including the hearing unit, generation unit, and assembly unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the hearing unit collects user requests using the microphone 238 of the headset terminal 314 and inputs them as prompts to the generation AI via the control unit 46A. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates building drawings based on the information from the hearing unit. The assembly unit is implemented, for example, by the control unit 46A of the headset terminal 314 and assembles building parts using a 3D printer based on the drawings created by the generation AI. === Hard Collateral 1-4 === Each of the multiple elements described above, including the hearing unit, generation unit, and assembly unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the hearing unit collects user requests using the microphone 238 of the robot 414 and inputs them as prompts to the generation AI via the control unit 46A. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and generates building drawings based on the information from the hearing unit. The assembly unit is implemented by, for example, the control unit 46A of the robot 414 and assembles building parts using a 3D printer based on the drawings created by the generation AI.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The interviewing unit can estimate the user's emotions and adjust the interview questions based on the estimated emotions. For example, if the user is relaxed, it can ask detailed questions to elicit specific requests. If the user is nervous, the interviewing unit can start with simple questions and gradually move to more detailed ones. Furthermore, if the user is in a hurry, the interviewing unit can ask concise questions to quickly gather requests. By adjusting the interview questions according to the user's emotions, more appropriate questions can be asked. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interviewing unit may be performed using a generative AI, or not. For example, the interviewing unit can input the user's emotions as a prompt to the generative AI, and the generative AI can adjust the questions based on that prompt.

[0081] The generation unit can estimate the user's emotions and adjust the way the drawing is presented based on those emotions. For example, if the user is relaxed, it can generate a detailed drawing that reflects their specific needs. If the user is stressed, the generation unit can generate a simple drawing that focuses on the essentials. Furthermore, if the user is in a hurry, the generation unit can generate a concise drawing for quick review. By adjusting the way the drawing is presented according to the user's emotions, a more appropriate drawing can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the user's emotions as a prompt to the generation AI, which can then adjust the way the drawing is presented based on that prompt.

[0082] The assembly unit can estimate the user's emotions and adjust the assembly method based on the estimated emotions. For example, if the user is relaxed, it can provide an assembly method that includes detailed explanations. If the user is in a hurry, the assembly unit can provide a method that allows for quick assembly. Furthermore, if the user is excited, the assembly unit can provide an assembly method that includes visually stimulating effects. By adjusting the assembly method according to the user's emotions, a more appropriate assembly method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the assembly unit may be performed using a generative AI, or not using a generative AI. For example, the assembly unit can input the user's emotions as a prompt to the generative AI, and the generative AI can adjust the assembly method based on that prompt.

[0083] The hearing unit can analyze the user's past architectural wishes and requests and select an appropriate hearing method. For example, the hearing unit can ask related questions based on the architectural style the user previously desired. The hearing unit can also analyze the user's past requests, find commonalities, and customize the questions. Furthermore, the hearing unit can ask specific questions by referring to a wish list previously submitted by the user. This makes it possible to select the optimal hearing method by analyzing the user's past wishes and requests. Some or all of the above-mentioned processing in the hearing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the hearing unit can input the user's past wishes and requests into the generation AI as prompts, and the generation AI can select a hearing method based on the prompts.

[0084] When generating a drawing, the generation unit can adjust the level of detail of the drawing based on the priority of the building. For example, the generation unit can generate detailed drawings for major rooms and spaces. The generation unit can also generate simplified drawings for auxiliary rooms and spaces. Furthermore, the generation unit can generate particularly detailed drawings for parts that the user particularly values. In this way, by adjusting the level of detail of the drawing based on the importance of the building, more appropriate drawings can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the importance of the building to the generation AI as a prompt, and the generation AI can adjust the level of detail of the drawing based on the prompt.

[0085] The assembly unit can analyze the user's past construction experience during assembly to select the optimal assembly method. For example, it can select the optimal method based on the user's past experience with assembly methods. Furthermore, the assembly unit can propose an efficient assembly method based on the user's past construction experience. In addition, the assembly unit can analyze the user's past construction experience and select the most suitable assembly method. Thus, by analyzing the user's past construction experience, the optimal assembly method can be selected. Some or all of the above-described processes in the assembly unit may be performed using, for example, a generative AI, or without a generative AI. For example, the assembly unit can input the user's past construction experience as a prompt to the generative AI, which can then select the optimal assembly method based on that prompt.

[0086] The interviewing unit can customize questions during the interview based on the user's lifestyle and family structure. For example, if the user lives with family, it will ask questions to gather information on the needs of all family members. If the user lives alone, the interviewing unit can ask questions tailored to the individual's lifestyle. Furthermore, if the user has pets, the interviewing unit can ask questions about pet-related equipment. By customizing questions based on the user's lifestyle and family structure, more appropriate questions can be asked. Some or all of the above processing in the interviewing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the interviewing unit can input the user's lifestyle and family structure as prompts into the generative AI, which can then customize questions based on those prompts.

[0087] When generating a drawing, the generation unit can apply different generation algorithms depending on the category of the building. For example, an algorithm that emphasizes livability can be applied to a residential building. The generation unit can also apply an algorithm that emphasizes functionality to a commercial building. Furthermore, the generation unit can apply an algorithm that emphasizes safety to a public facility building. In this way, by applying different generation algorithms depending on the category of the building, more appropriate drawings can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the building category to the generation AI as a prompt, and the generation AI can apply different generation algorithms based on the prompt.

[0088] The assembly unit can estimate the user's emotions and determine assembly priorities based on those emotions. For example, if the user is in a hurry, it can start assembling from the most important parts. If the user is relaxed, the assembly unit can provide an assembly method that includes detailed explanations. Furthermore, if the user is excited, the assembly unit can provide an assembly method that includes visually stimulating effects. This allows for more appropriate assembly by determining assembly priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the assembly unit may be performed using or without a generative AI. For example, the assembly unit can input the user's emotions as a prompt to the generative AI, which can then determine assembly priorities based on that prompt.

[0089] During assembly, the assembly unit can select the optimal assembly method by taking into account the user's geographical location information. For example, if the user lives in an urban area, the assembly unit can provide an assembly method suitable for urban life. Furthermore, if the user lives in the suburbs, the assembly unit can provide an assembly method suitable for the natural environment. Furthermore, if the user lives in a specific region, the assembly unit can provide an assembly method suitable for the climate and environment of that region. This makes it possible to select the optimal assembly method by taking into account the user's geographical location information. Some or all of the above-described processing in the assembly unit may be performed using, or without, a generation AI. For example, the assembly unit can input the user's geographical location information into the generation AI as a prompt, and the generation AI can select the optimal assembly method based on the prompt.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The hearing department listens to the user's wishes. For example, it collects detailed requests and desires, such as what kind of house the user wants to live in or what kind of building they want to build. Specifically, it collects requests such as "I want a spacious living room" or "I want a house with lots of natural light." The hearing department can also input the user's wishes and desires as prompts into the generation AI. Step 2: The generation unit uses the generation AI to generate a drawing of the building based on the information collected by the hearing unit. For example, it generates a drawing that reflects the user's requests and presents it to the user. The user can review the drawing and, if necessary, make corrections or additions. The generation unit then reflects this and creates a new drawing, repeatedly bouncing ideas off the user. For example, it can create a new drawing that reflects requests such as "I'd like the living room to be a little larger." Step 3: The assembly department assembles the building parts based on the drawings created by the generative AI. For example, using a 3D printer, each part of the building is accurately printed and assembled based on the drawings created by the generative AI. Specifically, parts such as walls, floors, and roofs can be created using a 3D printer, and then assembled to complete the building. This makes it possible to efficiently create buildings that meet the user's wishes.

[0092] 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.

[0093] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0094] 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.

[0095] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0110] 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.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] [Explanation of symbols]

[0164] 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 hearing section for hearing user requests; a generation unit that generates a drawing of a building based on the information collected by the hearing unit; an assembly unit that assembles parts of a building based on the drawings generated by the generation unit. A system characterized by:

2. The hearing section Collect specific user wishes and requests 2. The system of claim 1.

3. The generation unit Generate drawings that reflect the user's needs 2. The system of claim 1.

4. The generation unit Repeated exchanges of opinions with the user to generate the final drawing 2. The system of claim 1.

5. The assembly includes: Accurately print and assemble each part of a building based on drawings created by generative AI 2. The system of claim 1.

6. The hearing section Estimate the user's emotions and adjust the interview questions based on the estimated user emotions 2. The system of claim 1.

7. The hearing section Analyze the user's past architectural wishes and requests and select the appropriate interview method 2. The system of claim 1.

8. The hearing section During interviews, customize questions based on the user's lifestyle and family structure.

2. The system of claim 1.

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