system

Abstract

The system according to this embodiment aims to propose an optimal design plan tailored to the user's preferences and lifestyle, and to provide consistent support from planning to construction. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a display unit, and a construction support unit. The reception unit receives information such as the user's preferences, lifestyle, and budget. The generation unit generates an optimal design plan based on the information received by the reception unit. The display unit displays the design plan generated by the generation unit in a virtual space using 3D modeling and AR technology. The construction support unit supports coordination with construction contractors based on the design plan generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to propose an optimal design plan according to the user's preferences and lifestyle and consistently support it until construction.

[0005] The system according to the embodiment aims to propose an optimal design plan according to the user's preferences and lifestyle and consistently support it until construction.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a display unit, and a construction support unit. The reception unit receives information such as the user's preferences, lifestyle, and budget. The generation unit generates an optimal design plan based on the information received by the reception unit. The display unit displays the design plan generated by the generation unit in a virtual space using 3D modeling and AR technology. The construction support unit supports collaboration with construction contractors based on the design plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose an optimal design plan tailored to the user's preferences and lifestyle, and provide consistent support from planning to construction. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) 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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​Home Design Concierge according to an embodiment of the present invention is an application that utilizes generative AI to support the design and renovation of living spaces for individuals and families. The AI ​​Home Design Concierge proposes an optimal design plan tailored to the user's preferences, lifestyle, and budget, and supports the process up to actual construction. For example, the user inputs information such as their preferences, lifestyle, and budget into the AI ​​Home Design Concierge. Next, the generative AI generates an optimal design plan based on past design data and the user's input data. The generated design plan is displayed in a virtual space using 3D modeling and AR technology, allowing the user to experience the design in the virtual space using a smartphone or tablet. Furthermore, the AI ​​Home Design Concierge also supports actual construction, assisting in coordination with contractors based on the design plan selected by the user. This mechanism allows users to easily obtain an optimal design plan tailored to their preferences and lifestyle and experience it in a virtual space. In addition, the construction process can proceed smoothly, improving user satisfaction. As a result, the AI ​​Home Design Concierge can efficiently support the design and renovation of users' living spaces.

[0029] The AI ​​home design concierge according to this embodiment comprises a reception unit, a generation unit, a display unit, and a construction support unit. The reception unit receives information such as the user's preferences, lifestyle, and budget. For example, the user can log in to the application and input information such as their preferred colors, style, and budget. The reception unit can also save and reuse information previously entered by the user. The generation unit uses a generation AI to generate an optimal design plan based on the information received by the reception unit. For example, the generation unit analyzes past design data to generate a design plan that best suits the user's preferences and lifestyle. The generation unit can also use the generation AI to generate a new design plan based on the user's input data. The display unit displays the design plan generated by the generation unit in a virtual space using 3D modeling and AR technology. For example, the display unit allows the user to experience the design in a virtual space using a smartphone or tablet. The display unit can also change the design in real time, allowing the user to check the design in the virtual space. The construction support unit supports coordination with construction contractors based on the design plan generated by the generation unit. The construction support department, for example, shares the design plan selected by the user with the construction company and provides a construction schedule and cost estimate. Furthermore, the construction support department facilitates communication with the construction company, ensuring a smooth construction process. As a result, the AI ​​home design concierge according to this embodiment generates an optimal design plan tailored to the user's preferences and lifestyle, which can then be experienced in a virtual space.

[0030] The reception desk receives information such as the user's preferences, lifestyle, and budget. Specifically, users can log in to the application and input information such as their preferred colors, styles, and budget. For example, users can select their favorite color palette and interior style (modern, classic, minimalist, etc.) through the application interface. Users can also input detailed information about their lifestyle, such as family structure, whether they have pets, hobbies, and daily routines. This allows the reception desk to collect basic data to provide design plans tailored to the user's individual needs and preferences. Furthermore, the reception desk can save and reuse information previously entered by the user. This saves users the trouble of re-entering information they have already entered, allowing for a smoother design plan generation process. For example, a new design plan can be proposed based on the user's previously entered preferred colors, styles, and budget. The reception desk can also collect user feedback and use it as data to improve the accuracy of the design plans. In this way, the reception desk can play a crucial role in providing customized design plans that meet the user's needs.

[0031] The generation unit uses a generation AI to generate the optimal design plan based on the information received by the reception unit. Specifically, the generation AI analyzes information such as the user's preferred colors, style, lifestyle, and budget, and generates a design plan based on that. The generation AI learns from past design data and can propose a design plan that is best suited to the user's preferences and lifestyle. For example, the generation AI will determine the room layout, furniture placement, and selection of decorative items based on the color palette and interior style chosen by the user. The generation AI can also propose functional designs that suit the user's lifestyle. For example, it will suggest pet-friendly designs and materials for users with pets, and a user-friendly kitchen layout for users who enjoy cooking. Furthermore, the generation AI can generate cost-effective design plans that fit the user's budget. This allows the generation unit to quickly and accurately generate the optimal design plan that meets the user's individual needs and preferences. The generation unit can also use the generation AI to generate new design plans based on the user's input data. This ensures that users always receive plans that incorporate the latest design trends and technologies.

[0032] The display unit displays the design plan generated by the generation unit in a virtual space using 3D modeling and AR technology. Specifically, users can experience the design in a virtual space using their smartphones or tablets. For example, through the application, users can view the generated design plan as a 3D model and freely walk around the room. This allows users to check the design as if they were actually in that space. The display unit can also overlay the design plan onto the real space using AR technology. For example, if a user takes a picture of their room at home with their smartphone camera, the generated design plan will be overlaid on the image in real time, allowing them to see how it fits into the actual room. Furthermore, the display unit allows users to change the design in real time and check the design in the virtual space. For example, if a user wants to change the colors or furniture arrangement, they can easily make changes through the application and instantly check the results in the virtual space. In this way, the display unit provides an environment in which users can intuitively understand the design plan and adjust it until they are satisfied.

[0033] The Construction Support Department supports collaboration with construction contractors based on the design plans generated by the Generation Department. Specifically, it shares the design plan selected by the user with the construction contractor and provides construction schedules and cost estimates. For example, the Construction Support Department can output the generated design plan as detailed drawings and specifications and provide them to the construction contractor. This allows the construction contractor to carry out construction accurately in accordance with the user's wishes. The Construction Support Department also supports communication with the construction contractor, enabling a smooth construction process. For example, it can quickly share information between the user and the construction contractor regarding problems or changes that arise during construction and take appropriate action. Furthermore, the Construction Support Department reports the progress of construction to the user in real time, providing an environment where the user can entrust the construction to the department with peace of mind. In this way, the Construction Support Department acts as a bridge between the user and the construction contractor, realizing a smooth construction process and increasing user satisfaction.

[0034] The reception desk can analyze the user's past design preference history and suggest the optimal method of information input. For example, the reception desk can automatically display relevant design options based on the design style the user has previously selected. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest design options to be used at a specific time of day based on the user's past preference history. This allows the reception desk to provide the user with the optimal method of information input based on their past design preference history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past design preference history data into a generating AI and have the generating AI suggest the optimal method of information input.

[0035] The reception desk can customize input fields based on the user's current lifestyle and areas of interest during information entry. For example, if the user is planning to welcome a new family member, the reception desk will prioritize displaying design options for children's rooms and family spaces. It can also suggest home office design options if the user is working remotely. Furthermore, if the user enjoys gardening as a hobby, the reception desk can customize and display garden and balcony design options. This improves the accuracy of information entry by providing input fields tailored to the user's lifestyle and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For instance, the reception desk can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI customize the input fields.

[0036] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location during information input. For example, if the user lives in an urban area, the reception desk will prioritize displaying design options suitable for urban areas. If the user lives in a suburban area, the reception desk can also suggest design options for large gardens or garages. Furthermore, if the user lives in a specific region, the reception desk can display design options suitable for the climate and culture of that region. This allows for optimal information input by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI prioritize inputting highly relevant information.

[0037] The reception desk can analyze the user's social media activity during information input and automatically input relevant information. For example, the reception desk can suggest relevant design options based on design images the user has shared on social media. It can also display trending design options based on information from design accounts the user follows on social media. Furthermore, the reception desk can automatically input relevant design options based on design styles the user has "liked" on social media. This reduces the user's effort by basing information input on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI automatically input relevant information.

[0038] The generation unit can prioritize referencing the most successful examples from past design data when generating design plans. For example, the generation unit can generate a new design plan based on a design plan that has received high ratings in the past. The generation unit can also prioritize referencing successful examples from past design data that match the user's preferences. Furthermore, the generation unit can analyze past design data and generate a design plan based on the most successful examples. This improves the accuracy of design plan generation by basing it on past successful examples. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input past design data into a generation AI and have the generation AI generate a design plan based on the most successful examples.

[0039] The generation unit can apply different design algorithms to the user's lifestyle when generating design plans. For example, if the user works remotely, the generation unit can apply a design algorithm specialized for home offices. If the user enjoys the outdoors, the generation unit can also apply an algorithm specialized for garden and balcony designs. Furthermore, if the user spends a lot of time with family, the generation unit can apply a design algorithm specialized for family spaces. This improves user satisfaction by providing design plans tailored to the user's lifestyle. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input user lifestyle data into a generation AI and have the generation AI apply different design algorithms.

[0040] The generation unit can determine design priorities based on the user's submission deadline when generating design plans. For example, if the user is in a hurry, the generation unit will prioritize generating design plans that can be quickly implemented. If the user has ample time, the generation unit can also generate detailed design plans. Furthermore, if the user has set a specific deadline, the generation unit can generate a design plan that aligns with that deadline. This allows for a response tailored to the user's needs by generating design plans based on the submission deadline. Some or all of the above processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input user submission deadline data into the generation AI and have the generation AI determine the design priorities.

[0041] The generation unit can adjust the order of designs based on user relevance when generating design plans. For example, if the user prioritizes the living room design, the generation unit will generate the living room design plan first. It can also prioritize the kitchen design plan if the user values ​​the kitchen design. Furthermore, if the user values ​​the overall design, the generation unit can generate a design plan that considers the overall balance. This improves user satisfaction by generating design plans based on user relevance. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input user relevance data into the generation AI and have the generation AI adjust the order of designs.

[0042] The display unit can select the optimal display method when displaying a virtual space by referring to the user's past design experience history. For example, the display unit can adjust the display method of the virtual space based on the design style the user has preferred in the past. The display unit can also select the optimal display method based on the settings of devices the user has used in the past. Furthermore, the display unit can prioritize selecting the display method that gave the user the highest satisfaction from their past design experience history. This improves user satisfaction by using a display method based on past design experience history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past design experience history data into a generating AI and have the generating AI select the optimal display method.

[0043] The display unit can adjust the display resolution and interaction based on the user's device information when displaying a virtual space. For example, if the user is using a high-resolution device, the display unit can set a higher display resolution for the virtual space. Conversely, if the user is using a low-resolution device, the display unit can set a lower display resolution to provide smoother interaction. Furthermore, if the user is using a touchscreen device, the display unit can provide interaction optimized for touch operation. This improves user satisfaction through a display method based on device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's device information into a generating AI and have the generating AI perform adjustments to the display resolution and interaction.

[0044] The display unit can select the optimal display method when displaying a virtual space, taking into account the user's geographical location information. For example, if the user lives in an urban area, the display unit can provide a virtual space display method suitable for urban areas. Furthermore, if the user lives in a suburban area, the display unit can provide a virtual space display method that takes advantage of the wider space. Additionally, if the user lives in a specific region, the display unit can provide a virtual space display method suitable for the climate and culture of that region. This improves user satisfaction by considering geographical location information in the display method. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal display method.

[0045] The display unit can analyze the user's social media activity and display relevant designs when displaying in a virtual space. For example, the display unit can display relevant designs in the virtual space based on design images shared by the user on social media. The display unit can also display trending designs in the virtual space based on information from design accounts followed by the user on social media. Furthermore, the display unit can display relevant designs in the virtual space based on design styles that the user has "liked" on social media. This improves user satisfaction by displaying designs based on social media activity. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's social media activity data into a generating AI and have the generating AI execute the display of relevant designs.

[0046] The construction support department can analyze the user's past construction history and propose the most suitable contractor during construction support. For example, the construction support department can propose the most suitable contractor based on the user's evaluation of contractors they have used in the past. The construction support department can also prioritize proposing specific contractors based on the user's past construction history. Furthermore, the construction support department can analyze the user's past construction history and propose the contractor with the highest satisfaction rating. This improves user satisfaction by proposing contractors based on past construction history. Some or all of the above processes in the construction support department may be performed using AI, for example, or not. For example, the construction support department can input the user's past construction history data into a generating AI and have the generating AI propose the most suitable contractor.

[0047] The construction support department can customize the construction schedule based on the user's current lifestyle during construction support. For example, if the user is busy with work, the construction support department can suggest a schedule for construction on weekends or evenings. If the user spends a lot of time with family, the construction support department can also suggest a schedule that fits the family's schedule. Furthermore, if the user is traveling, the construction support department can suggest a schedule for construction during the trip. This improves user satisfaction by providing a construction schedule that suits the user's lifestyle. Some or all of the above processes in the construction support department may be performed using AI, for example, or not. For example, the construction support department can input user lifestyle data into a generating AI and have the generating AI perform the customization of the construction schedule.

[0048] The construction support department can select the most suitable contractor during construction support, taking into account the user's geographical location. For example, if the user lives in an urban area, the construction support department can suggest a contractor suitable for urban areas. If the user lives in a suburban area, the construction support department can also suggest a contractor suitable for suburban areas. Furthermore, if the user lives in a specific region, the construction support department can prioritize suggesting contractors in that region. This improves user satisfaction by selecting a contractor that takes geographical location into account. Some or all of the above processing in the construction support department may be performed using AI, for example, or without AI. For example, the construction support department can input the user's geographical location information into a generating AI and have the generating AI select the most suitable contractor.

[0049] The Construction Support Department can analyze a user's social media activity during construction support and provide relevant construction information. For example, the Construction Support Department can suggest relevant contractors based on construction information shared by the user on social media. It can also suggest the most suitable contractor based on information about contractors followed by the user on social media. Furthermore, the Construction Support Department can suggest relevant contractors based on construction styles that the user has "liked" on social media. This improves user satisfaction by providing construction information based on social media activity. Some or all of the above processing in the Construction Support Department may be performed using AI, for example, or not. For example, the Construction Support Department can input user social media activity data into a generating AI and have the generating AI provide relevant construction information.

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

[0051] The generation unit can analyze the user's past design preference history and generate the optimal design plan. For example, it can automatically display relevant design options based on the design style the user has previously selected. It can also prioritize suggesting colors and materials the user has used in the past. Furthermore, it can generate design plans tailored to specific seasons or events based on the user's past preference history. This improves user satisfaction by generating design plans based on past design preference history.

[0052] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location. For example, if a user lives in an urban area, design options suitable for urban areas will be displayed preferentially. If a user lives in a suburban area, design options for large gardens or garages can be suggested. Furthermore, if a user lives in a specific region, design options suitable for the climate and culture of that region can be displayed. In this way, by considering geographical location, it becomes possible to input information that is optimal for the user.

[0053] The generation unit can apply different design algorithms to the user's lifestyle when generating design plans. For example, if the user works remotely, a design algorithm specialized for home offices can be applied. If the user enjoys the outdoors, an algorithm specialized for garden and balcony designs can be applied. Furthermore, if the user spends a lot of time with family, a design algorithm specialized for family spaces can be applied. By providing design plans tailored to the user's lifestyle, user satisfaction is improved.

[0054] The display unit can adjust the display resolution and interaction based on the user's device information when displaying a virtual space. For example, if the user is using a high-resolution device, the display resolution of the virtual space can be set higher. Conversely, if the user is using a low-resolution device, the display resolution can be set lower to provide smoother interaction. Furthermore, if the user is using a touchscreen device, interaction optimized for touch operation can be provided. This improves user satisfaction through a display method based on device information.

[0055] The reception desk can analyze users' social media activity and automatically input relevant information. For example, it can suggest relevant design options based on design images shared by users on social media. It can also display trending design options based on design accounts that users follow on social media. Furthermore, it can automatically input relevant design options based on design styles that users have "liked" on social media. This reduces the effort required from users by automating information input based on their social media activity.

[0056] The construction support department can analyze a user's past construction history to propose the most suitable contractor during construction support. For example, it can propose the best contractor based on the user's evaluation of contractors they have used in the past. It can also prioritize proposing specific contractors based on the user's past construction history. Furthermore, it can analyze the user's past construction history and propose the contractor who received the highest satisfaction rating. As a result, user satisfaction is improved by proposing contractors based on past construction history.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The reception desk receives information such as the user's preferences, lifestyle, and budget. For example, a user can log in to the application and enter information such as their preferred colors, styles, and budget. The reception desk can also save and reuse information that the user has previously entered. Step 2: The generation unit uses generation AI to generate the optimal design plan based on the information received by the reception unit. For example, it analyzes past design data to generate a design plan that best suits the user's preferences and lifestyle. The generation unit can also use generation AI to generate new design plans based on user input data. Step 3: The display unit displays the design plan generated by the generation unit in a virtual space using 3D modeling and AR technology. For example, a user can experience the design in a virtual space using a smartphone or tablet. The display unit can also change the design in real time, allowing the user to check the design in the virtual space. Step 4: The Construction Support Department supports coordination with contractors based on the design plan generated by the Generation Department. For example, it shares the design plan selected by the user with the contractor and provides a construction schedule and cost estimate. The Construction Support Department also supports communication with the contractor, ensuring a smooth construction process.

[0059] (Example of form 2) The AI ​​Home Design Concierge according to an embodiment of the present invention is an application that utilizes generative AI to support the design and renovation of living spaces for individuals and families. The AI ​​Home Design Concierge proposes an optimal design plan tailored to the user's preferences, lifestyle, and budget, and supports the process up to actual construction. For example, the user inputs information such as their preferences, lifestyle, and budget into the AI ​​Home Design Concierge. Next, the generative AI generates an optimal design plan based on past design data and the user's input data. The generated design plan is displayed in a virtual space using 3D modeling and AR technology, allowing the user to experience the design in the virtual space using a smartphone or tablet. Furthermore, the AI ​​Home Design Concierge also supports actual construction, assisting in coordination with contractors based on the design plan selected by the user. This mechanism allows users to easily obtain an optimal design plan tailored to their preferences and lifestyle and experience it in a virtual space. In addition, the construction process can proceed smoothly, improving user satisfaction. As a result, the AI ​​Home Design Concierge can efficiently support the design and renovation of users' living spaces.

[0060] The AI ​​home design concierge according to this embodiment comprises a reception unit, a generation unit, a display unit, and a construction support unit. The reception unit receives information such as the user's preferences, lifestyle, and budget. For example, the user can log in to the application and input information such as their preferred colors, style, and budget. The reception unit can also save and reuse information previously entered by the user. The generation unit uses a generation AI to generate an optimal design plan based on the information received by the reception unit. For example, the generation unit analyzes past design data to generate a design plan that best suits the user's preferences and lifestyle. The generation unit can also use the generation AI to generate a new design plan based on the user's input data. The display unit displays the design plan generated by the generation unit in a virtual space using 3D modeling and AR technology. For example, the display unit allows the user to experience the design in a virtual space using a smartphone or tablet. The display unit can also change the design in real time, allowing the user to check the design in the virtual space. The construction support unit supports coordination with construction contractors based on the design plan generated by the generation unit. The construction support department, for example, shares the design plan selected by the user with the construction company and provides a construction schedule and cost estimate. Furthermore, the construction support department facilitates communication with the construction company, ensuring a smooth construction process. As a result, the AI ​​home design concierge according to this embodiment generates an optimal design plan tailored to the user's preferences and lifestyle, which can then be experienced in a virtual space.

[0061] The reception desk receives information such as the user's preferences, lifestyle, and budget. Specifically, users can log in to the application and input information such as their preferred colors, styles, and budget. For example, users can select their favorite color palette and interior style (modern, classic, minimalist, etc.) through the application interface. Users can also input detailed information about their lifestyle, such as family structure, whether they have pets, hobbies, and daily routines. This allows the reception desk to collect basic data to provide design plans tailored to the user's individual needs and preferences. Furthermore, the reception desk can save and reuse information previously entered by the user. This saves users the trouble of re-entering information they have already entered, allowing for a smoother design plan generation process. For example, a new design plan can be proposed based on the user's previously entered preferred colors, styles, and budget. The reception desk can also collect user feedback and use it as data to improve the accuracy of the design plans. In this way, the reception desk can play a crucial role in providing customized design plans that meet the user's needs.

[0062] The generation unit uses a generation AI to generate the optimal design plan based on the information received by the reception unit. Specifically, the generation AI analyzes information such as the user's preferred colors, style, lifestyle, and budget, and generates a design plan based on that. The generation AI learns from past design data and can propose a design plan that is best suited to the user's preferences and lifestyle. For example, the generation AI will determine the room layout, furniture placement, and selection of decorative items based on the color palette and interior style chosen by the user. The generation AI can also propose functional designs that suit the user's lifestyle. For example, it will suggest pet-friendly designs and materials for users with pets, and a user-friendly kitchen layout for users who enjoy cooking. Furthermore, the generation AI can generate cost-effective design plans that fit the user's budget. This allows the generation unit to quickly and accurately generate the optimal design plan that meets the user's individual needs and preferences. The generation unit can also use the generation AI to generate new design plans based on the user's input data. This ensures that users always receive plans that incorporate the latest design trends and technologies.

[0063] The display unit displays the design plan generated by the generation unit in a virtual space using 3D modeling and AR technology. Specifically, users can experience the design in a virtual space using their smartphones or tablets. For example, through the application, users can view the generated design plan as a 3D model and freely walk around the room. This allows users to check the design as if they were actually in that space. The display unit can also overlay the design plan onto the real space using AR technology. For example, if a user takes a picture of their room at home with their smartphone camera, the generated design plan will be overlaid on the image in real time, allowing them to see how it fits into the actual room. Furthermore, the display unit allows users to change the design in real time and check the design in the virtual space. For example, if a user wants to change the colors or furniture arrangement, they can easily make changes through the application and instantly check the results in the virtual space. In this way, the display unit provides an environment in which users can intuitively understand the design plan and adjust it until they are satisfied.

[0064] The Construction Support Department supports collaboration with construction contractors based on the design plans generated by the Generation Department. Specifically, it shares the design plan selected by the user with the construction contractor and provides construction schedules and cost estimates. For example, the Construction Support Department can output the generated design plan as detailed drawings and specifications and provide them to the construction contractor. This allows the construction contractor to carry out construction accurately in accordance with the user's wishes. The Construction Support Department also supports communication with the construction contractor, enabling a smooth construction process. For example, it can quickly share information between the user and the construction contractor regarding problems or changes that arise during construction and take appropriate action. Furthermore, the Construction Support Department reports the progress of construction to the user in real time, providing an environment where the user can entrust the construction to the department with peace of mind. In this way, the Construction Support Department acts as a bridge between the user and the construction contractor, realizing a smooth construction process and increasing user satisfaction.

[0065] The reception desk can estimate the user's emotions and adjust the information input interface based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick information entry. This improves the efficiency of information input by providing an interface that responds 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 reception desk may be performed using AI or not using AI. For example, the reception desk can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0066] The reception desk can analyze the user's past design preference history and suggest the optimal method of information input. For example, the reception desk can automatically display relevant design options based on the design style the user has previously selected. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest design options to be used at a specific time of day based on the user's past preference history. This allows the reception desk to provide the user with the optimal method of information input based on their past design preference history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past design preference history data into a generating AI and have the generating AI suggest the optimal method of information input.

[0067] The reception desk can customize input fields based on the user's current lifestyle and areas of interest during information entry. For example, if the user is planning to welcome a new family member, the reception desk will prioritize displaying design options for children's rooms and family spaces. It can also suggest home office design options if the user is working remotely. Furthermore, if the user enjoys gardening as a hobby, the reception desk can customize and display garden and balcony design options. This improves the accuracy of information entry by providing input fields tailored to the user's lifestyle and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For instance, the reception desk can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI customize the input fields.

[0068] The reception desk can estimate the user's emotions and prioritize input information based on the estimated emotions. For example, if the user is stressed, the reception desk can prioritize inputting the most important information. If the user is relaxed, the reception desk can also prioritize inputting detailed information. Furthermore, if the user is in a hurry, the reception desk can prioritize inputting only the minimum necessary information. This enables efficient information input by prioritizing information 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 reception desk may be performed using AI, or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0069] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location during information input. For example, if the user lives in an urban area, the reception desk will prioritize displaying design options suitable for urban areas. If the user lives in a suburban area, the reception desk can also suggest design options for large gardens or garages. Furthermore, if the user lives in a specific region, the reception desk can display design options suitable for the climate and culture of that region. This allows for optimal information input by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI prioritize inputting highly relevant information.

[0070] The reception desk can analyze the user's social media activity during information input and automatically input relevant information. For example, the reception desk can suggest relevant design options based on design images the user has shared on social media. It can also display trending design options based on information from design accounts the user follows on social media. Furthermore, the reception desk can automatically input relevant design options based on design styles the user has "liked" on social media. This reduces the user's effort by basing information input on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI automatically input relevant information.

[0071] The generation unit can estimate the user's emotions and adjust the design plan generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a relaxed design plan. If the user is in a hurry, the generation unit can also generate a simple and quickly implementable design plan. Furthermore, if the user is excited, the generation unit can generate a visually stimulating design plan. This improves user satisfaction by generating design plans that match the user's emotions. 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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0072] The generation unit can prioritize referencing the most successful examples from past design data when generating design plans. For example, the generation unit can generate a new design plan based on a design plan that has received high ratings in the past. The generation unit can also prioritize referencing successful examples from past design data that match the user's preferences. Furthermore, the generation unit can analyze past design data and generate a design plan based on the most successful examples. This improves the accuracy of design plan generation by basing it on past successful examples. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input past design data into a generation AI and have the generation AI generate a design plan based on the most successful examples.

[0073] The generation unit can apply different design algorithms to the user's lifestyle when generating design plans. For example, if the user works remotely, the generation unit can apply a design algorithm specialized for home offices. If the user enjoys the outdoors, the generation unit can also apply an algorithm specialized for garden and balcony designs. Furthermore, if the user spends a lot of time with family, the generation unit can apply a design algorithm specialized for family spaces. This improves user satisfaction by providing design plans tailored to the user's lifestyle. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input user lifestyle data into a generation AI and have the generation AI apply different design algorithms.

[0074] The generation unit can estimate the user's emotions and adjust the level of detail in the design plan based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a detailed design plan. If the user is in a hurry, the generation unit can also generate a concise design plan. Furthermore, if the user is excited, the generation unit can generate a visually stimulating design plan. This improves user satisfaction by providing design plans with levels of detail that match the user's emotions. 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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0075] The generation unit can determine design priorities based on the user's submission deadline when generating design plans. For example, if the user is in a hurry, the generation unit will prioritize generating design plans that can be quickly implemented. If the user has ample time, the generation unit can also generate detailed design plans. Furthermore, if the user has set a specific deadline, the generation unit can generate a design plan that aligns with that deadline. This allows for a response tailored to the user's needs by generating design plans based on the submission deadline. Some or all of the above processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input user submission deadline data into the generation AI and have the generation AI determine the design priorities.

[0076] The generation unit can adjust the order of designs based on user relevance when generating design plans. For example, if the user prioritizes the living room design, the generation unit will generate the living room design plan first. It can also prioritize the kitchen design plan if the user values ​​the kitchen design. Furthermore, if the user values ​​the overall design, the generation unit can generate a design plan that considers the overall balance. This improves user satisfaction by generating design plans based on user relevance. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input user relevance data into the generation AI and have the generation AI adjust the order of designs.

[0077] The display unit can estimate the user's emotions and adjust the display method of the virtual space based on the estimated user emotions. For example, if the user is relaxed, the display unit can display a virtual space with soft colors. If the user is excited, the display unit can also display a virtual space with vivid colors. Furthermore, if the user is stressed, the display unit can also display a virtual space with calm colors. This improves user satisfaction by providing a virtual space display method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0078] The display unit can select the optimal display method when displaying a virtual space by referring to the user's past design experience history. For example, the display unit can adjust the display method of the virtual space based on the design style the user has preferred in the past. The display unit can also select the optimal display method based on the settings of devices the user has used in the past. Furthermore, the display unit can prioritize selecting the display method that gave the user the highest satisfaction from their past design experience history. This improves user satisfaction by using a display method based on past design experience history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past design experience history data into a generating AI and have the generating AI select the optimal display method.

[0079] The display unit can adjust the display resolution and interaction based on the user's device information when displaying a virtual space. For example, if the user is using a high-resolution device, the display unit can set a higher display resolution for the virtual space. Conversely, if the user is using a low-resolution device, the display unit can set a lower display resolution to provide smoother interaction. Furthermore, if the user is using a touchscreen device, the display unit can provide interaction optimized for touch operation. This improves user satisfaction through a display method based on device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's device information into a generating AI and have the generating AI perform adjustments to the display resolution and interaction.

[0080] The display unit can estimate the user's emotions and adjust the virtual space operation procedures based on the estimated emotions. For example, if the user is relaxed, the display unit can provide detailed operation procedures. If the user is in a hurry, the display unit can also provide concise operation procedures. Furthermore, if the user is stressed, the display unit can provide simple and intuitive operation procedures. By providing operation procedures that match the user's emotions, user satisfaction is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or not using AI. For example, the display unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0081] The display unit can select the optimal display method when displaying a virtual space, taking into account the user's geographical location information. For example, if the user lives in an urban area, the display unit can provide a virtual space display method suitable for urban areas. Furthermore, if the user lives in a suburban area, the display unit can provide a virtual space display method that takes advantage of the wider space. Additionally, if the user lives in a specific region, the display unit can provide a virtual space display method suitable for the climate and culture of that region. This improves user satisfaction by considering geographical location information in the display method. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal display method.

[0082] The display unit can analyze the user's social media activity and display relevant designs when displaying in a virtual space. For example, the display unit can display relevant designs in the virtual space based on design images shared by the user on social media. The display unit can also display trending designs in the virtual space based on information from design accounts followed by the user on social media. Furthermore, the display unit can display relevant designs in the virtual space based on design styles that the user has "liked" on social media. This improves user satisfaction by displaying designs based on social media activity. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's social media activity data into a generating AI and have the generating AI execute the display of relevant designs.

[0083] The construction support unit can estimate the user's emotions and adjust its construction support methods based on those emotions. For example, if the user is relaxed, the construction support unit can provide a detailed construction schedule. If the user is in a hurry, the construction support unit can also support the rapid progress of the construction. Furthermore, if the user is stressed, the construction support unit can provide simple and easy-to-understand construction support. By providing construction support tailored to the user's emotions, user satisfaction is improved. Emotion estimation is achieved using an emotion estimation function, for example, using 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 construction support unit may be performed using AI, or not using AI. For example, the construction support unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0084] The construction support department can analyze the user's past construction history and propose the most suitable contractor during construction support. For example, the construction support department can propose the most suitable contractor based on the user's evaluation of contractors they have used in the past. The construction support department can also prioritize proposing specific contractors based on the user's past construction history. Furthermore, the construction support department can analyze the user's past construction history and propose the contractor with the highest satisfaction rating. This improves user satisfaction by proposing contractors based on past construction history. Some or all of the above processes in the construction support department may be performed using AI, for example, or not. For example, the construction support department can input the user's past construction history data into a generating AI and have the generating AI propose the most suitable contractor.

[0085] The construction support department can customize the construction schedule based on the user's current lifestyle during construction support. For example, if the user is busy with work, the construction support department can suggest a schedule for construction on weekends or evenings. If the user spends a lot of time with family, the construction support department can also suggest a schedule that fits the family's schedule. Furthermore, if the user is traveling, the construction support department can suggest a schedule for construction during the trip. This improves user satisfaction by providing a construction schedule that suits the user's lifestyle. Some or all of the above processes in the construction support department may be performed using AI, for example, or not. For example, the construction support department can input user lifestyle data into a generating AI and have the generating AI perform the customization of the construction schedule.

[0086] The construction support unit can estimate the user's emotions and prioritize construction support based on those emotions. For example, if the user is in a hurry, the construction support unit can provide support to ensure the construction proceeds quickly. If the user is relaxed, the construction support unit can also provide a detailed construction schedule. Furthermore, if the user is stressed, the construction support unit can provide simple and easy-to-understand construction support. By prioritizing construction support according to the user's emotions, user satisfaction is improved. 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 construction support unit may be performed using AI or not. For example, the construction support unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0087] The construction support department can select the most suitable contractor during construction support, taking into account the user's geographical location. For example, if the user lives in an urban area, the construction support department can suggest a contractor suitable for urban areas. If the user lives in a suburban area, the construction support department can also suggest a contractor suitable for suburban areas. Furthermore, if the user lives in a specific region, the construction support department can prioritize suggesting contractors in that region. This improves user satisfaction by selecting a contractor that takes geographical location into account. Some or all of the above processing in the construction support department may be performed using AI, for example, or without AI. For example, the construction support department can input the user's geographical location information into a generating AI and have the generating AI select the most suitable contractor.

[0088] The Construction Support Department can analyze a user's social media activity during construction support and provide relevant construction information. For example, the Construction Support Department can suggest relevant contractors based on construction information shared by the user on social media. It can also suggest the most suitable contractor based on information about contractors followed by the user on social media. Furthermore, the Construction Support Department can suggest relevant contractors based on construction styles that the user has "liked" on social media. This improves user satisfaction by providing construction information based on social media activity. Some or all of the above processing in the Construction Support Department may be performed using AI, for example, or not. For example, the Construction Support Department can input user social media activity data into a generating AI and have the generating AI provide relevant construction information.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] The reception desk can analyze the user's tone of voice, estimate their emotions, and adjust the interface accordingly. For example, if the user is excited, it can provide a colorful and interactive interface. If the user is calm, it can provide a simple and calming interface. Furthermore, if the user is stressed, it can provide an interface that collects information with minimal input steps. This adjustment of the interface based on the user's tone of voice improves user satisfaction.

[0091] The generation unit can analyze the user's past design preference history and generate the optimal design plan. For example, it can automatically display relevant design options based on the design style the user has previously selected. It can also prioritize suggesting colors and materials the user has used in the past. Furthermore, it can generate design plans tailored to specific seasons or events based on the user's past preference history. This improves user satisfaction by generating design plans based on past design preference history.

[0092] The display unit can estimate the user's emotions and adjust the display method of the virtual space based on the estimated emotions. For example, if the user is relaxed, a virtual space with soft colors can be displayed. If the user is excited, a virtual space with vibrant colors can be displayed. Furthermore, if the user is stressed, a virtual space with calming colors can be displayed. By providing a virtual space display method that responds to the user's emotions, user satisfaction can be improved.

[0093] The construction support department can estimate the user's emotions and adjust the construction support method based on those emotions. For example, if the user is relaxed, it can provide a detailed construction schedule. If the user is in a hurry, it can also provide support to ensure the construction proceeds quickly. Furthermore, if the user is stressed, it can provide simple and easy-to-understand construction support. By providing construction support that is tailored to the user's emotions, user satisfaction is improved.

[0094] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location. For example, if a user lives in an urban area, design options suitable for urban areas will be displayed preferentially. If a user lives in a suburban area, design options for large gardens or garages can be suggested. Furthermore, if a user lives in a specific region, design options suitable for the climate and culture of that region can be displayed. In this way, by considering geographical location, it becomes possible to input information that is optimal for the user.

[0095] The generation unit can apply different design algorithms to the user's lifestyle when generating design plans. For example, if the user works remotely, a design algorithm specialized for home offices can be applied. If the user enjoys the outdoors, an algorithm specialized for garden and balcony designs can be applied. Furthermore, if the user spends a lot of time with family, a design algorithm specialized for family spaces can be applied. By providing design plans tailored to the user's lifestyle, user satisfaction is improved.

[0096] The display unit can adjust the display resolution and interaction based on the user's device information when displaying a virtual space. For example, if the user is using a high-resolution device, the display resolution of the virtual space can be set higher. Conversely, if the user is using a low-resolution device, the display resolution can be set lower to provide smoother interaction. Furthermore, if the user is using a touchscreen device, interaction optimized for touch operation can be provided. This improves user satisfaction through a display method based on device information.

[0097] The reception desk can analyze users' social media activity and automatically input relevant information. For example, it can suggest relevant design options based on design images shared by users on social media. It can also display trending design options based on design accounts that users follow on social media. Furthermore, it can automatically input relevant design options based on design styles that users have "liked" on social media. This reduces the effort required from users by automating information input based on their social media activity.

[0098] The generation unit can estimate the user's emotions and adjust the design plan generation method based on the estimated emotions. For example, if the user is relaxed, it can generate a relaxed design plan. If the user is in a hurry, it can generate a simple and quickly implementable design plan. Furthermore, if the user is excited, it can generate a visually stimulating design plan. By generating design plans that respond to the user's emotions, user satisfaction is improved.

[0099] The construction support department can analyze a user's past construction history to propose the most suitable contractor during construction support. For example, it can propose the best contractor based on the user's evaluation of contractors they have used in the past. It can also prioritize proposing specific contractors based on the user's past construction history. Furthermore, it can analyze the user's past construction history and propose the contractor who received the highest satisfaction rating. As a result, user satisfaction is improved by proposing contractors based on past construction history.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The reception desk receives information such as the user's preferences, lifestyle, and budget. For example, a user can log in to the application and enter information such as their preferred colors, styles, and budget. The reception desk can also save and reuse information that the user has previously entered. Step 2: The generation unit uses generation AI to generate the optimal design plan based on the information received by the reception unit. For example, it analyzes past design data to generate a design plan that best suits the user's preferences and lifestyle. The generation unit can also use generation AI to generate new design plans based on user input data. Step 3: The display unit displays the design plan generated by the generation unit in a virtual space using 3D modeling and AR technology. For example, a user can experience the design in a virtual space using a smartphone or tablet. The display unit can also change the design in real time, allowing the user to check the design in the virtual space. Step 4: The Construction Support Department supports coordination with contractors based on the design plan generated by the Generation Department. For example, it shares the design plan selected by the user with the contractor and provides a construction schedule and cost estimate. The Construction Support Department also supports communication with the contractor, ensuring a smooth construction process.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0104] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] Each of the multiple elements described above, including the reception unit, generation unit, display unit, and construction support unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives information such as the user's preferences, lifestyle, and budget. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates an optimal design plan using generation AI. The display unit is implemented by, for example, the control unit 46A of the smart device 14 and displays the design in a virtual space using 3D modeling and AR technology. The construction support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and supports cooperation with construction contractors. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0106] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0107] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0112] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0113] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0114] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0115] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0116] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] Each of the multiple elements described above, including the reception unit, generation unit, display unit, and construction support unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives information such as the user's preferences, lifestyle, and budget. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates an optimal design plan using generation AI. The display unit is implemented, for example, by the control unit 46A of the smart glasses 214 and displays the design in a virtual space using 3D modeling and AR technology. The construction support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and supports cooperation with construction contractors. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0125] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0126] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0128] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0129] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0130] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0131] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0132] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] Each of the multiple elements described above, including the reception unit, generation unit, display unit, and construction support unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives information such as the user's preferences, lifestyle, and budget. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates an optimal design plan using generation AI. The display unit is implemented by, for example, the control unit 46A of the headset terminal 314 and displays the design in a virtual space using 3D modeling and AR technology. The construction support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and supports cooperation with construction contractors. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0139] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0145] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0146] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the reception unit, generation unit, display unit, and construction support unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives information such as the user's preferences, lifestyle, and budget. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates an optimal design plan using generation AI. The display unit is implemented by, for example, the control unit 46A of the robot 414 and displays the design in a virtual space using 3D modeling and AR technology. The construction support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and supports cooperation with construction contractors. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0155] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0157] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0158] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0159] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0163] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0164] 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.

[0165] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0166] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0167] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0168] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0170] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0171] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0172] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0173] (Note 1) A reception desk that receives information such as user preferences, lifestyle, and budget, A generation unit that generates an optimal design plan based on the information received by the reception unit, A display unit that displays the design plan generated by the generation unit in a virtual space using 3D modeling and AR technology, The system includes a construction support unit that supports coordination with construction contractors based on the design plan generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and adjusts the information input interface based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is It analyzes the user's past design preference history and suggests the optimal method for information input. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When entering information, the input fields are customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and prioritizes input information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When users enter information, the system prioritizes inputting highly relevant information by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When users enter information, the system analyzes their social media activity and automatically fills in relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is We estimate the user's emotions and adjust the design plan generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is When generating design plans, the system prioritizes referencing the most successful examples from past design data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is When generating a design plan, different design algorithms are applied depending on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is We estimate the user's emotions and adjust the level of detail in the design plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating a design plan, prioritize the designs based on when the user submitted their design. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating a design plan, adjust the order of designs based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned display unit is It estimates the user's emotions and adjusts how the virtual space is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned display unit is When displaying a virtual space, the system selects the optimal display method by referring to the user's past design experience history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned display unit is When displaying a virtual space, the display resolution and interactions are adjusted based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned display unit is It estimates the user's emotions and adjusts the virtual space operation procedures based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned display unit is When displaying a virtual space, the optimal display method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is When displaying the virtual space, the system analyzes the user's social media activity and displays relevant designs. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned construction support section is The system estimates the user's emotions and adjusts the construction support method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned construction support section is During construction support, we analyze the user's past construction history and propose the most suitable contractor. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned construction support section is During construction support, the construction schedule is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned construction support section is The system estimates the user's emotions and determines the priority of construction support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned construction support section is During construction support, the system selects the most suitable contractor by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned construction support section is During construction support, we analyze the user's social media activity and provide relevant construction information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives information such as user preferences, lifestyle, and budget, A generation unit that generates an optimal design plan based on the information received by the reception unit, A display unit that displays the design plan generated by the generation unit in a virtual space using 3D modeling and AR technology, The system includes a construction support unit that supports coordination with construction contractors based on the design plan generated by the generation unit. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and adjusts the information input interface based on the estimated user emotions. The system according to feature 1.

3. The aforementioned reception unit is It analyzes the user's past design preference history and suggests the optimal method for information input. The system according to feature 1.

4. The aforementioned reception unit is When entering information, the input fields are customized based on the user's current lifestyle and areas of interest. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and prioritizes input information based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is When users enter information, the system prioritizes inputting highly relevant information by considering their geographical location. The system according to feature 1.

7. The aforementioned reception unit is When users enter information, the system analyzes their social media activity and automatically fills in relevant information. The system according to feature 1.

8. The generating unit is We estimate the user's emotions and adjust the design plan generation method based on the estimated user emotions. The system according to feature 1.

9. The generating unit is When generating design plans, the system prioritizes referencing the most successful examples from past design data. The system according to feature 1.

10. The generating unit is When generating a design plan, different design algorithms are applied depending on the user's lifestyle. The system according to feature 1.

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