System

Abstract

An object of a system according to an embodiment is to specifically propose a reconstruction plan reflecting a user's desire.SOLUTION: A system includes an information input unit, a question generation unit, a reconstruction plan generation unit, and a design drawing creation unit. The information input unit inputs information on a room from a user. The question generation unit generates a question based on the information input by the information input unit. The reconstruction plan generation unit generates a reconstruction plan on the basis of a user's answer to the question generated by the question generation unit. The design drawing creation unit compiles the reconstruction plan generated by the reconstruction plan generation unit into a design drawing.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to grasp a concrete image of large-scale renovations or remodeling, and there is room for improvement in proposing renovation plans that reflect the user's wishes to the greatest extent possible.

[0005] The system according to the embodiment aims to propose specific renovation plans that reflect the user's wishes. [Means for solving the problem]

[0006] The system according to the embodiment includes an information input unit, a question generation unit, a renovation plan generation unit, and a design drawing creation unit. The information input unit receives room information from a user. The question generation unit generates questions based on the information input by the information input unit. The renovation plan generation unit generates renovation plans based on the user's answers to the questions generated by the question generation unit. The design drawing creation unit compiles the renovation plans generated by the renovation plan generation unit into a design drawing. [Effects of the Invention]

[0007] The system according to the embodiment can specifically propose a renovation plan that reflects the user's wishes. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The renovation and remodeling system according to an embodiment of the present invention is a system that helps users to form a concrete image and streamlines the renovation and remodeling process. This system visually represents the image after removing pillars and walls or large-scale renovations based on actual buildings and rooms. This allows the renovation and remodeling system to help users form a concrete image and streamlines the renovation and remodeling process.

[0029] A renovation / remodeling system according to an embodiment includes an information input unit, a question generation unit, a renovation plan generation unit, and a blueprint creation unit. The information input unit receives room information from a user. For example, the user can input the room's size, layout, and interior photos. The information input unit also allows the user to input the room information using a smartphone or tablet. The question generation unit generates questions based on the information input by the information input unit. For example, the generation AI asks the user questions such as "What kind of room do you want?" and "What are you struggling with?" The renovation plan generation unit generates renovation plans based on the user's answers to the questions generated by the question generation unit. For example, the generation AI suggests removing pillars or walls or installing new walls and furniture based on the user's requests. The blueprint creation unit compiles the renovation plans generated by the renovation plan generation unit into blueprints. For example, the generation AI generates specific blueprints based on the renovation plans and provides them to contractors. This allows the renovation / remodeling system to help users visualize the renovation and streamline the renovation / remodeling process. For example, if a user likes a proposed renovation plan, they can use that information to place an order with a contractor. The generation AI will compile the renovation plan into a blueprint and provide it to the contractor. For example, by generating a specific blueprint based on the renovation plan and sending it to the contractor, a smooth order process can be achieved.

[0030] The information input unit uses 3D scanning technology to automatically generate a detailed model of the room, allowing users to easily input information. For example, the information input unit automatically generates a 3D model by allowing users to scan the room using a smartphone or tablet camera. For example, by simply taking a photo of the four corners of the room with the camera, the system automatically calculates the size and layout of the room and creates a 3D model. This allows users to easily input information about the room.

[0031] The information input unit can compare the information with a database of past renovation examples and automatically suggest similar examples. For example, when a user inputs information about a room, the information input unit causes the system to search the database of past renovation examples and automatically suggest similar examples. For example, renovation examples of rooms with the same size and layout can be displayed for reference. This allows the user to refer to past examples.

[0032] The information input unit uses voice recognition technology to input room information, allowing the user to provide the information verbally. The information input unit constructs a system that uses voice recognition technology to input information so that the user can provide room information verbally. For example, if the user says, "The room size is 20 square meters," the system automatically inputs that information. This allows the user to provide information verbally.

[0033] The information input unit can automatically tag interior photos selected by the user, making it easier to search for them later in the process. For example, when a user uploads an interior photo, the information input unit automatically analyzes the photo and tags it. For example, it can recognize furniture and decorations in the photo and tag them with tags such as "sofa" or "painting." This makes it easier to search for interior photos.

[0034] The question generation unit personalizes questions to match the user's lifestyle and hobbies and preferences, allowing it to elicit more specific requests. For example, the question generation unit uses a generation AI to analyze the user's lifestyle and hobbies and preferences and personalize questions based on that. For example, a user who likes the outdoors could be asked questions about how to use their garden or balcony. This allows it to elicit the user's specific requests.

[0035] The question generation unit can perform the question and answer process in a chatbot format, allowing the user to answer in an interactive format. The question generation unit, for example, performs the question and answer process in a chatbot format, building a system that allows the user to answer in an interactive format. For example, the user answers questions as if they were having a conversation with the chatbot. This allows the user to answer in an interactive format.

[0036] The question generation unit can automatically translate the content of the question into different languages ​​to achieve multilingual support. The question generation unit, for example, automatically translates the content of the question into different languages ​​to build a system that achieves multilingual support. For example, it supports multiple languages ​​such as English, French, and Chinese. This allows multilingual support to be achieved.

[0037] The renovation plan generator can incorporate designs that take energy efficiency and environmental impact into consideration. The renovation plan generator can incorporate designs that take energy efficiency into consideration in renovation plans proposed by the generation AI. For example, it can suggest the use of insulation materials and the placement of energy-efficient appliances. This makes it possible to design with energy efficiency and environmental impact in mind.

[0038] The renovation plan generation unit can present the optimal plan, taking into account the user's budget and construction period. The renovation plan generation unit, for example, builds a system in which the generation AI considers the user's budget and construction period and proposes the optimal renovation plan. For example, it presents a plan that will achieve the maximum effect within the budget. This makes it possible to provide the optimal plan based on the user's budget and construction period.

[0039] The renovation plan generation unit can use AR technology to enable the user to check the renovation plan in the actual space. For example, the renovation plan generation unit uses AR technology to build a system that allows the user to check the proposed renovation plan in the actual space. For example, the room after the renovation is displayed using AR on a smartphone or tablet. This allows the user to check the renovation plan in the actual space.

[0040] The renovation proposal generator can present different design styles and themes as options, allowing the user to freely select. For example, the renovation proposal generator constructs a system that presents different design styles and themes as options when proposing renovation proposals. For example, it proposes styles such as modern, classic, and minimalist. This allows the user to freely select a design style or theme.

[0041] The blueprint creation unit can add a function that reflects the progress of construction in real time. For example, the blueprint creation unit builds a system that adds a function that reflects the progress of construction in real time to the blueprints created by the generation AI. For example, the progress of construction can be monitored using GPS or sensors and reflected in the blueprints. This allows the progress of construction to be reflected in real time.

[0042] The blueprint creation unit can automatically select the most suitable contractor based on their past construction performance and evaluations when placing an order with a contractor. The blueprint creation unit, for example, builds a system that automatically selects the most suitable contractor based on their past construction performance and evaluations when placing an order with a contractor. For example, it ranks contractors based on their construction performance and evaluation scores. This allows the most suitable contractor to be automatically selected.

[0043] The blueprint creation unit can create a blueprint and output it as a physical model using 3D printing technology, allowing the user to check it. The blueprint creation unit, for example, builds a system that creates a blueprint and outputs it as a physical model using 3D printing technology. For example, a miniature model of a room is created using a 3D printer based on the blueprint. This allows the user to check the physical model.

[0044] The renovation proposal generation unit can incorporate ergonomic design into the proposed interior and furniture layout. The renovation proposal generation unit, for example, builds a system that incorporates ergonomic design into the interior and furniture layout proposed by the generation AI. For example, it optimizes the height and layout of furniture based on ergonomics. This makes it possible to arrange interior and furniture in an ergonomic way.

[0045] The renovation plan generation unit can personalize the proposed interior and furniture layout to suit the user's lifestyle and hobbies and preferences. For example, the renovation plan generation unit constructs a system in which a generation AI analyzes the user's lifestyle and hobbies and preferences and personalizes the interior and furniture layout based on that. For example, a reading space can be suggested for a user who enjoys reading. This makes it possible to arrange the interior and furniture to suit the user's lifestyle and hobbies and preferences.

[0046] The renovation plan generator can use AR technology to enable the user to check the proposed interior and furniture layout in the actual space. The renovation plan generator, for example, builds a system that allows the user to check the proposed interior and furniture layout in the actual space using AR technology. For example, the interior and furniture layout is displayed using AR using a smartphone or tablet. This allows the user to check the interior and furniture layout in the actual space.

[0047] The renovation plan generator can present different design styles and themes as options for the proposed interior and furniture arrangements, allowing the user to freely select. The renovation plan generator, for example, constructs a system that presents different design styles and themes as options for the proposed interior and furniture arrangements. For example, it proposes styles such as modern, classic, and minimalist. This allows the user to freely select a design style or theme.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] When a user inputs information about a room, the information input unit can compare it with a database of past renovation examples and automatically suggest similar examples. For example, if a user inputs the size and layout of a room, the system will search the database of past renovation examples and display renovation examples of rooms with the same size and layout. This makes it easier for users to refer to past examples. It can also make more detailed suggestions based on the example selected by the user. This makes it easier for users to get a concrete image.

[0050] The information input unit uses 3D scanning technology to automatically generate a detailed model of the room, allowing users to easily input information. For example, a user can scan the room using the camera on their smartphone or tablet and automatically generate a 3D model. This allows the user to simply take a photo of the four corners of the room with the camera, and the system will automatically calculate the size and layout of the room and create a 3D model. This allows users to easily input room information.

[0051] The information input unit uses voice recognition technology to input room information, allowing the user to provide the information verbally. For example, a system is constructed that uses voice recognition technology to input information so that the user can provide room information verbally. For example, if the user says, "The room is 20 square meters," the system automatically inputs that information. This allows the user to provide information verbally.

[0052] The information input unit can automatically tag interior photos selected by the user, making it easier to search for them later in the process. For example, when a user uploads an interior photo, the system automatically analyzes the photo and tags it. For example, it can recognize furniture and decorations in the photo and tag them as "sofa" or "painting." This makes it easier to search for interior photos.

[0053] The question generation unit can personalize questions to match the user's lifestyle and hobbies and preferences, eliciting more specific requests. For example, the generation AI can analyze the user's lifestyle and hobbies and preferences and personalize the questions based on that. For example, if a user likes the outdoors, it can ask questions about how to use their garden or balcony. This allows the user's specific requests to be elicited.

[0054] The question generation unit can perform the question and answer process in a chatbot format, allowing the user to answer in an interactive format. For example, a system can be constructed in which the question and answer process is performed in a chatbot format, allowing the user to answer in an interactive format. For example, the user answers questions as if they were having a conversation with the chatbot. This allows the user to answer in an interactive format.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The information input unit receives room information from the user. For example, the user can input the room size, layout, interior photos, etc. The user can also input room information using a smartphone or tablet. Step 2: The question generator generates questions based on the information input by the information input unit. For example, the generation AI asks the user questions such as "What kind of room do you want to create?" and "What are you having trouble with?" Step 3: The renovation plan generator generates renovation plans based on the user's answers to the questions generated by the question generator. For example, the generation AI may suggest removing pillars or walls, or installing new walls or furniture, based on the user's requests. Step 4: The blueprint creation unit compiles the renovation plans generated by the renovation plan generation unit into blueprints. For example, the generation AI generates specific blueprints based on the renovation plans and provides them to contractors.

[0057] (Example 2) The renovation and remodeling system according to an embodiment of the present invention is a system that helps users to form a concrete image and streamlines the renovation and remodeling process. This system visually represents the image after removing pillars and walls or large-scale renovations based on actual buildings and rooms. This allows the renovation and remodeling system to help users form a concrete image and streamlines the renovation and remodeling process.

[0058] A renovation / remodeling system according to an embodiment includes an information input unit, a question generation unit, a renovation plan generation unit, and a blueprint creation unit. The information input unit receives room information from a user. For example, the user can input the room's size, layout, and interior photos. The information input unit also allows the user to input the room information using a smartphone or tablet. The question generation unit generates questions based on the information input by the information input unit. For example, the generation AI asks the user questions such as "What kind of room do you want?" and "What are you struggling with?" The renovation plan generation unit generates renovation plans based on the user's answers to the questions generated by the question generation unit. For example, the generation AI suggests removing pillars or walls or installing new walls and furniture based on the user's requests. The blueprint creation unit compiles the renovation plans generated by the renovation plan generation unit into blueprints. For example, the generation AI generates specific blueprints based on the renovation plans and provides them to contractors. This allows the renovation / remodeling system to help users visualize the renovation and streamline the renovation / remodeling process. For example, if a user likes a proposed renovation plan, they can use that information to place an order with a contractor. The generation AI will compile the renovation plan into a blueprint and provide it to the contractor. For example, by generating a specific blueprint based on the renovation plan and sending it to the contractor, a smooth order process can be achieved.

[0059] The information input unit uses 3D scanning technology to automatically generate a detailed model of the room, allowing users to easily input information. For example, the information input unit automatically generates a 3D model by allowing users to scan the room using a smartphone or tablet camera. For example, by simply taking a photo of the four corners of the room with the camera, the system automatically calculates the size and layout of the room and creates a 3D model. This allows users to easily input information about the room.

[0060] The information input unit can compare the information with a database of past renovation examples and automatically suggest similar examples. For example, when a user inputs information about a room, the information input unit causes the system to search the database of past renovation examples and automatically suggest similar examples. For example, renovation examples of rooms with the same size and layout can be displayed for reference. This allows the user to refer to past examples.

[0061] The information input unit can use the emotion estimation function to analyze the emotion expressed when the user inputs room information and provide an interface for reducing stress. For example, when the user inputs room information, the information input unit can analyze the emotion expressed in real time using a camera or microphone and provide an interface for reducing stress. For example, if the user is feeling stressed, the system can play relaxing music. This can reduce the user's stress.

[0062] The information input unit uses voice recognition technology to input room information, allowing the user to provide the information verbally. The information input unit constructs a system that uses voice recognition technology to input information so that the user can provide room information verbally. For example, if the user says, "The room size is 20 square meters," the system automatically inputs that information. This allows the user to provide information verbally.

[0063] The information input unit can automatically tag interior photos selected by the user, making it easier to search for them later in the process. For example, when a user uploads an interior photo, the information input unit automatically analyzes the photo and tags it. For example, it can recognize furniture and decorations in the photo and tag them with tags such as "sofa" or "painting." This makes it easier to search for interior photos.

[0064] The information input unit uses the emotion estimation function to suggest an optimal input method based on information input by a user, thereby improving user satisfaction. The information input unit, for example, uses the emotion estimation function to build a system that suggests an optimal input method based on information input by a user. For example, if a user is feeling stressed, the system suggests voice input. This improves user satisfaction.

[0065] The question generation unit personalizes questions to match the user's lifestyle and hobbies and preferences, allowing it to elicit more specific requests. For example, the question generation unit uses a generation AI to analyze the user's lifestyle and hobbies and preferences and personalize questions based on that. For example, a user who likes the outdoors could be asked questions about how to use their garden or balcony. This allows it to elicit the user's specific requests.

[0066] The question generation unit can use the emotion estimation function to analyze the emotion of the user when answering and generate questions that elicit positive emotions. The question generation unit, for example, uses the emotion estimation function to analyze the emotion of the user when answering in real time and build a system that generates questions that elicit positive emotions. For example, questions that make the user feel happy are given priority. This can elicit positive emotions from the user.

[0067] The question generation unit can perform the question and answer process in a chatbot format, allowing the user to answer in an interactive format. The question generation unit, for example, performs the question and answer process in a chatbot format, building a system that allows the user to answer in an interactive format. For example, the user answers questions as if they were having a conversation with the chatbot. This allows the user to answer in an interactive format.

[0068] The question generation unit can automatically translate the content of the question into different languages ​​to achieve multilingual support. The question generation unit, for example, automatically translates the content of the question into different languages ​​to build a system that achieves multilingual support. For example, it supports multiple languages ​​such as English, French, and Chinese. This allows multilingual support to be achieved.

[0069] The question generator uses the emotion estimation function to provide real-time feedback on the user's answers, thereby improving the quality of the answers. For example, the question generator uses the emotion estimation function to build a system that provides real-time feedback on the user's answers. For example, if the user is feeling anxious, the system displays an encouraging message, thereby improving the quality of the answers.

[0070] The renovation plan generator can incorporate designs that take energy efficiency and environmental impact into consideration. The renovation plan generator can incorporate designs that take energy efficiency into consideration in renovation plans proposed by the generation AI. For example, it can suggest the use of insulation materials and the placement of energy-efficient appliances. This makes it possible to design with energy efficiency and environmental impact in mind.

[0071] The renovation plan generation unit can present the optimal plan, taking into account the user's budget and construction period. The renovation plan generation unit, for example, builds a system in which the generation AI considers the user's budget and construction period and proposes the optimal renovation plan. For example, it presents a plan that will achieve the maximum effect within the budget. This makes it possible to provide the optimal plan based on the user's budget and construction period.

[0072] The renovation plan generation unit can use AR technology to enable the user to check the renovation plan in the actual space. For example, the renovation plan generation unit uses AR technology to build a system that allows the user to check the proposed renovation plan in the actual space. For example, the room after the renovation is displayed using AR on a smartphone or tablet. This allows the user to check the renovation plan in the actual space.

[0073] The renovation proposal generator can present different design styles and themes as options, allowing the user to freely select. For example, the renovation proposal generator constructs a system that presents different design styles and themes as options when proposing renovation proposals. For example, it proposes styles such as modern, classic, and minimalist. This allows the user to freely select a design style or theme.

[0074] The renovation plan generator uses the emotion estimation function to monitor the user's emotional response to proposed renovation plans in real time, and can continuously make optimal proposals. The renovation plan generator, for example, uses the emotion estimation function to build a system that monitors the user's emotional response to proposed renovation plans in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. This allows the generator to continuously make optimal proposals based on the user's emotional response.

[0075] The blueprint creation unit can add a function that reflects the progress of construction in real time. For example, the blueprint creation unit builds a system that adds a function that reflects the progress of construction in real time to the blueprints created by the generation AI. For example, the progress of construction can be monitored using GPS or sensors and reflected in the blueprints. This allows the progress of construction to be reflected in real time.

[0076] The blueprint creation unit can automatically select the most suitable contractor based on their past construction performance and evaluations when placing an order with a contractor. The blueprint creation unit, for example, builds a system that automatically selects the most suitable contractor based on their past construction performance and evaluations when placing an order with a contractor. For example, it ranks contractors based on their construction performance and evaluation scores. This allows the most suitable contractor to be automatically selected.

[0077] The blueprint creation unit can use the emotion estimation function to provide support to alleviate the anxiety and doubts that the user feels during the ordering process. For example, the blueprint creation unit uses the emotion estimation function to build a system that provides support to alleviate the anxiety and doubts that the user feels during the ordering process. For example, if the user feels anxious, the system provides a detailed explanation. This can alleviate the user's anxiety and doubts.

[0078] The blueprint creation unit can create a blueprint and output it as a physical model using 3D printing technology, allowing the user to check it. The blueprint creation unit, for example, builds a system that creates a blueprint and outputs it as a physical model using 3D printing technology. For example, a miniature model of a room is created using a 3D printer based on the blueprint. This allows the user to check the physical model.

[0079] The blueprint creation unit can use the emotion estimation function to analyze the emotions felt by the user when placing an order and provide optimal support. The blueprint creation unit, for example, uses the emotion estimation function to analyze the emotions felt by the user when placing an order and builds a system that provides optimal support. For example, if the user is feeling anxious, the system provides detailed explanations and support. This makes it possible to provide optimal support based on the user's emotions.

[0080] The renovation proposal generation unit can incorporate ergonomic design into the proposed interior and furniture layout. The renovation proposal generation unit, for example, builds a system that incorporates ergonomic design into the interior and furniture layout proposed by the generation AI. For example, it optimizes the height and layout of furniture based on ergonomics. This makes it possible to arrange interior and furniture in an ergonomic way.

[0081] The renovation plan generation unit can personalize the proposed interior and furniture layout to suit the user's lifestyle and hobbies and preferences. For example, the renovation plan generation unit constructs a system in which a generation AI analyzes the user's lifestyle and hobbies and preferences and personalizes the interior and furniture layout based on that. For example, a reading space can be suggested for a user who enjoys reading. This makes it possible to arrange the interior and furniture to suit the user's lifestyle and hobbies and preferences.

[0082] The remodeling plan generator can use the emotion estimation function to preferentially suggest interior and furniture layouts that will evoke the most positive emotions in the user. The remodeling plan generator, for example, uses the emotion estimation function to build a system that preferentially suggests interior and furniture layouts that will evoke the most positive emotions in the user. For example, layouts that make the user feel happy are preferentially displayed. This makes it possible to provide interior and furniture layouts that will evoke the most positive emotions in the user.

[0083] The renovation plan generator can use AR technology to enable the user to check the proposed interior and furniture layout in the actual space. The renovation plan generator, for example, builds a system that allows the user to check the proposed interior and furniture layout in the actual space using AR technology. For example, the interior and furniture layout is displayed using AR using a smartphone or tablet. This allows the user to check the interior and furniture layout in the actual space.

[0084] The renovation plan generator can present different design styles and themes as options for the proposed interior and furniture arrangements, allowing the user to freely select. The renovation plan generator, for example, constructs a system that presents different design styles and themes as options for the proposed interior and furniture arrangements. For example, it proposes styles such as modern, classic, and minimalist. This allows the user to freely select a design style or theme.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] When a user inputs information about a room, the information input unit can compare it with a database of past renovation examples and automatically suggest similar examples. For example, if a user inputs the size and layout of a room, the system will search the database of past renovation examples and display renovation examples of rooms with the same size and layout. This makes it easier for users to refer to past examples. It can also make more detailed suggestions based on the example selected by the user. This makes it easier for users to get a concrete image.

[0087] The information input unit uses 3D scanning technology to automatically generate a detailed model of the room, allowing users to easily input information. For example, a user can scan the room using the camera on their smartphone or tablet and automatically generate a 3D model. This allows the user to simply take a photo of the four corners of the room with the camera, and the system will automatically calculate the size and layout of the room and create a 3D model. This allows users to easily input room information.

[0088] The information input unit can use the emotion estimation function to analyze the emotion of the user when entering room information and provide an interface for reducing stress. For example, when the user enters room information, the system can analyze the emotion in real time using a camera or microphone and provide an interface for reducing stress. For example, if the user is feeling stressed, the system can play relaxing music. This can reduce the user's stress.

[0089] The information input unit uses voice recognition technology to input room information, allowing the user to provide the information verbally. For example, a system is constructed that uses voice recognition technology to input information so that the user can provide room information verbally. For example, if the user says, "The room is 20 square meters," the system automatically inputs that information. This allows the user to provide information verbally.

[0090] The information input unit can automatically tag interior photos selected by the user, making it easier to search for them later in the process. For example, when a user uploads an interior photo, the system automatically analyzes the photo and tags it. For example, it can recognize furniture and decorations in the photo and tag them as "sofa" or "painting." This makes it easier to search for interior photos.

[0091] The information input unit can use the emotion estimation function to suggest an optimal input method based on information input by a user, thereby improving user satisfaction. For example, a system can be constructed that uses the emotion estimation function to suggest an optimal input method based on information input by a user. For example, if a user is feeling stressed, the system can suggest voice input. This improves user satisfaction.

[0092] The question generation unit can personalize questions to match the user's lifestyle and hobbies and preferences, eliciting more specific requests. For example, the generation AI can analyze the user's lifestyle and hobbies and preferences and personalize the questions based on that. For example, if a user likes the outdoors, it can ask questions about how to use their garden or balcony. This allows the user's specific requests to be elicited.

[0093] The question generation unit can use the emotion estimation function to analyze the emotion of the user when answering a question and generate questions that elicit positive emotions. For example, a system can be constructed that uses the emotion estimation function to analyze the emotion of the user when answering a question in real time and generate questions that elicit positive emotions. For example, questions that make the user feel happy can be given priority. This can elicit positive emotions from the user.

[0094] The question generation unit can perform the question and answer process in a chatbot format, allowing the user to answer in an interactive format. For example, a system can be constructed in which the question and answer process is performed in a chatbot format, allowing the user to answer in an interactive format. For example, the user answers questions as if they were having a conversation with the chatbot. This allows the user to answer in an interactive format.

[0095] The question generator can use the emotion estimation function to provide real-time feedback to the user's answers, thereby improving the quality of the answers. For example, a system can be built that uses the emotion estimation function to provide real-time feedback to the user's answers. For example, if the user is feeling anxious, the system can display an encouraging message, thereby improving the quality of the answers.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The information input unit receives room information from the user. For example, the user can input the room size, layout, interior photos, etc. The user can also input room information using a smartphone or tablet. Step 2: The question generator generates questions based on the information input by the information input unit. For example, the generation AI asks the user questions such as "What kind of room do you want to create?" and "What are you having trouble with?" Step 3: The renovation plan generator generates renovation plans based on the user's answers to the questions generated by the question generator. For example, the generation AI may suggest removing pillars or walls, or installing new walls or furniture, based on the user's requests. Step 4: The blueprint creation unit compiles the renovation plans generated by the renovation plan generation unit into blueprints. For example, the generation AI generates specific blueprints based on the renovation plans and provides them to contractors.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

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

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

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

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, a 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.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0139] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0142] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0149] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0150] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0155] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0156] 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.

[0157] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0158] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0159] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0160] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0162] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an information input unit for inputting room information from a user; a question generation unit that generates a question based on the information input by the information input unit; a renovation plan generation unit that generates renovation plans based on the user's answers to the questions generated by the question generation unit; a design drawing creation unit that compiles the renovation plans generated by the renovation plan generation unit into a design drawing; A system characterized by:

2. The information input unit The 3D scanning technology is used to automatically generate a detailed model of the room, allowing users to easily input information.

2. The system of claim 1.

3. The question generation unit The process of questioning and answering is carried out in a chatbot format, allowing the user to answer in a dialogue format.

2. The system of claim 1.

4. The renovation plan generation unit Incorporating energy efficiency or environmentally friendly design 2. The system of claim 1.

5. The design drawing creation unit Add a function that reflects construction progress in real time 2. The system of claim 1.

6. The information input unit Analyzing emotions when users input room information and providing an interface to reduce stress 2. The system of claim 1.

7. The question generation unit Analyze the user's emotions when answering questions and generate questions that elicit positive emotions 2. The system of claim 1.

8. The renovation plan generation unit Monitor the user's emotional response to the proposed renovation plan in real time and continuously make optimal proposals.

2. The system of claim 1.

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