System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving as input a picture of a room from a user; means for receiving as input a drawing from the user; means for receiving as input a request for a reform from the user; means for generating a reformed image using a generative artificial intelligence model based on the received picture of the room, the drawing, and the request for the reform; means for determining whether the generated reformed image is workable; and means for providing the generated reformed image and the result of the workability determination to the user.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] When planning a renovation, it is necessary to accurately reflect the user's requests and quickly determine whether the project is feasible. However, the traditional renovation proposal process requires a lot of manual work, which is time-consuming and labor-intensive, and can lead to lower customer satisfaction and delays in planning. To solve this problem, there is a need for a system that uses advanced technology to automate and streamline the process of proposing renovations and making decisions about construction. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides a system with the following features: It includes a means for receiving room photos, blueprints, and renovation requests as input from a user. It also includes a means for generating renovation images using a generative artificial intelligence model based on this input data. It also includes a means for determining whether the generated renovation images are technically feasible, and a means for providing the user with the final generated renovation images and the results of the determination of feasibility. This automates and streamlines processes that previously relied on manual labor, enabling the system to quickly and accurately reflect user requests.

[0006] "User" refers to an individual or corporation that uses the renovation system to input photos, drawings, and renovation requests for a room.

[0007] "Room Photos" means a series of digital images showing the current state of the room to be renovated.

[0008] "Drawings" refers to blueprints or architectural drawings that show the structure, dimensions, and layout of the room to be renovated.

[0009] "Renovation requests" refers to information that describes the specific improvements, design, and functionality that the user wishes to have renovated.

[0010] A "generative artificial intelligence model" refers to a machine learning algorithm or deep learning model that generates renovation images based on input data provided by the user.

[0011] "Renovation image" refers to a visual plan of the room after renovation that reflects the user's wishes, created by a generative AI model.

[0012] "Constructibility assessment" refers to the process of evaluating whether the generated renovation image is actually technically and structurally feasible.

[0013] A "total coordination plan" refers to a detailed plan and instructions that includes each step of the renovation, and provides specific construction procedures and a timeline based on the renovation image.

[0014] "System" refers to an integrated system for proposing renovations and supporting construction, consisting of a user terminal, a server, a generative AI model, an algorithm for determining whether construction is possible, and software and hardware for linking these together. [Brief explanation of the drawings]

[0015] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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, a 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), and an APU (Accelerated Processing Unit).

[0019] 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.

[0020] 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.

[0021] 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), Bluetooth (registered trademark), etc.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. 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).

[0027] 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.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] This invention is a system that proposes renovations based on the user's requests, assesses the feasibility of construction, and provides a total coordinated plan. This system is an integrated system that includes a user terminal, a server, a generative AI model, and a construction feasibility assessment algorithm.

[0037] System Overview

[0038] User device:

[0039] The user terminal is a device that allows users to input photos of the room, drawings, and renovation requests. Users use their smartphones or PCs to enter information into a dedicated web form and send it to the server.

[0040] server:

[0041] The server plays a central role in receiving data sent by users, generating renovation images using generative AI models, and determining the feasibility of construction. To achieve these functions, the server is equipped with advanced data processing capabilities and algorithms.

[0042] Generative AI models:

[0043] The generative AI model is an artificial intelligence model that generates visual images of the remodeled home based on photos, drawings, and renovation requests provided by the user. This model uses machine learning algorithms and deep learning technology.

[0044] Constructability determination algorithm:

[0045] The constructability algorithm is responsible for assessing whether the generated renovation image is technically and structurally feasible. The algorithm analyzes past construction data and technical constraints.

[0046] Processing steps

[0047] User:

[0048] Users take photos of their own rooms and prepare existing blueprints. Next, they enter the photos, blueprints, and renovation requests into a dedicated web form. For example, they write requests such as "I want a modern design for the kitchen" in the text field. The entered data is then sent from the device to the server.

[0049] Device:

[0050] The terminal sends the data entered by the user to the server in bulk, typically by using an HTTP POST request.

[0051] server:

[0052] The server analyzes the received data, obtaining photos of the room, blueprints, and renovation requests. This data is then input into a generative AI model to generate a renovation image. Based on the generated renovation image, a feasibility assessment algorithm performs technical and structural evaluations.

[0053] Generative AI model and constructability algorithm:

[0054] The generative AI model automatically creates remodeling images based on the user's requirements, and then a constructability algorithm evaluates the images to determine whether they are technically and structurally feasible.

[0055] server:

[0056] Based on the results of the feasibility assessment, the server creates a final renovation plan, which includes 3D rendered images of the renovation, specific construction procedures, and a timeline. The final plan is then sent from the server to the user's device.

[0057] User device:

[0058] The terminal receives the final renovation plan sent from the server and displays it to the user, who can then check the final plan and request any necessary revisions.

[0059] Specific examples

[0060] For example, if a user wants to "modernize their kitchen," they can take photos of the current state of the kitchen and upload the blueprints. This data is sent to the server, and the generative AI model generates an image of the "modern kitchen." Next, a constructability assessment algorithm evaluates whether the generated image is feasible. If the evaluation result is favorable, the server creates a final renovation plan and provides it to the user.

[0061] As described above, the system of the present invention efficiently and accurately reflects the user's requests and optimizes the renovation process.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] Users take photos of the room they want to remodel and prepare existing blueprint data. They also enter their specific requests for the remodel in text format. For example, they might enter a request such as, "I want the kitchen design to be modern."

[0065] Step 2:

[0066] Users access a dedicated web form on their smartphone or computer and upload the photos, blueprints, and renovation requests they have prepared. Each piece of data is entered into the corresponding field.

[0067] Step 3:

[0068] The device sends the photos, drawings, and renovation requests entered by the user to the server all at once, using an HTTP POST request.

[0069] Step 4:

[0070] The server receives the data sent by the user, analyzes the room photos, blueprints, and renovation requests, checks whether the received data is in the correct format, and converts the data if necessary.

[0071] Step 5:

[0072] The server calls a generative AI model based on the received data and generates a renovation image. The generative AI model receives photos, blueprints, and customer requests as input, and automatically generates an image of the user's desired renovation based on these.

[0073] Step 6:

[0074] The server then inputs the generated renovation images into a constructability assessment algorithm to evaluate whether the proposed renovation is technically and structurally feasible, which includes detailed analysis based on past construction data and technical constraints.

[0075] Step 7:

[0076] Based on the confirmed feasibility of the renovation, the server creates a final renovation plan, which includes 3D renderings of the renovation, specific construction procedures, and a timeline for each step.

[0077] Step 8:

[0078] The server sends the data to the terminal to provide the final renovation plan to the user. The plan data is sent using an HTTP response.

[0079] Step 9:

[0080] The terminal receives the final renovation plan sent from the server and displays it to the user. The user checks the contents of the final plan and provides feedback to the server on any corrections or additions they may require, if necessary.

[0081] Step 10:

[0082] The user checks the final renovation plan and, if there are no problems, prepares to start construction. At this time, the server may also coordinate with the construction company.

[0083] Through the above processing steps, the system of the present invention efficiently and accurately proposes renovations that reflect the user's requests and determines the feasibility of construction. The user can receive consistent support throughout the renovation process.

[0084] Example 1

[0085] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0086] Conventional renovation proposal systems have had difficulty generating renovation images that accurately reflect the user's requests and quickly and accurately assessing the feasibility of the renovation. Furthermore, when proposing a specific total coordination plan based on the room images and blueprints submitted by the user, manual evaluation and planning are time-consuming and inefficient.

[0087] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0088] In this invention, the server includes means for receiving a room image as input from a user, means for receiving a structure blueprint as input from a user, means for receiving a remodeling request as input from a user, means for generating a remodeling image using a generative AI model based on the received room image, structure blueprint, and remodeling request, means for determining whether the generated remodeling image is technically and structurally feasible, and means for providing the generated remodeling image and the technical and structural determination results to the user. This makes it possible to accurately reflect the user's requests, evaluate the feasibility of construction, and efficiently and accurately provide remodeling proposals and total coordination plans.

[0089] A "room image" is a photograph or digital image taken by a user that shows the current state of a room.

[0090] A "structural blueprint" is a drawing that shows the structure and layout of a room or building, including the location and dimensions of walls, floors, ceilings, windows, doors, etc.

[0091] The "renovation request" is information describing the details of the renovation or remodeling desired by the user in text or other formats. For example, it includes a specific request such as "I want the kitchen to have a modern design."

[0092] A "generative artificial intelligence model" is an artificial intelligence model that uses machine learning algorithms and deep learning technology to generate renovation images based on user requests.

[0093] The "renovation image" is a visual image of the renovated room generated by the generative AI model, which reflects the user's wishes.

[0094] "Means for determining technical and structural feasibility" refers to algorithms and methods that evaluate whether the generated renovation image can actually be constructed based on past construction data and existing technical constraints.

[0095] The "overall coordination plan" is an integrated plan for each element of the renovation, including 3D rendered images of the renovation, specific construction procedures, and a timeline.

[0096] This invention is a system that proposes renovations based on the user's requests, assesses the feasibility of construction, and provides a total coordinated plan. This system is an integrated system that includes a user terminal, a server, a generative AI model, and a construction feasibility assessment algorithm.

[0097] User terminal

[0098] The user terminal is a device that allows users to input images of rooms, blueprints of structures, and renovation requests. Users access a dedicated web form using a smartphone or PC and enter the necessary information.

[0099] server

[0100] The server receives data sent by users, generates renovation images using generative AI models, and plays a central role in determining whether construction is possible. The server requires advanced data processing capabilities, and the construction feasibility determination algorithm also runs on the server.

[0101] Generative AI Models

[0102] The generative AI model is an artificial intelligence model that generates visual images of the renovated building based on user-provided room images, structural blueprints, and renovation requests. This model uses machine learning algorithms and deep learning technology to generate realistic visual images.

[0103] Constructibility determination algorithm

[0104] The constructability algorithm is responsible for assessing whether the generated renovation images are technically and structurally feasible. The algorithm performs an analysis based on past construction data and current technical constraints.

[0105] Specifically, the user first takes a photo of their room and prepares a blueprint for the structure. Next, they enter the photos, blueprints, and renovation requests into a dedicated web form and send it from their device to the server. The device then sends the data entered by the user to the server using an HTTP POST request.

[0106] The server analyzes the received data and obtains images of the room, blueprints, and renovation requirements. This data is then input into a generative AI model to generate a renovation image. Based on the generated renovation image, a feasibility assessment algorithm performs a technical and structural evaluation.

[0107] As a concrete example, if a user wishes to have a modern design for their kitchen, they can take photos of the current state of the kitchen and upload the blueprint. This data is sent to the server, and a generative AI model generates an image of the "modern kitchen." Next, a constructability assessment algorithm evaluates whether the generated image is feasible. If the evaluation result is favorable, the server creates a final renovation plan and provides it to the user.

[0108] An example of a prompt for a generative AI model is as follows:

[0109] "Please generate an image of a modern kitchen renovation. I've attached photos of the current situation and blueprints."

[0110] "Please create a design that will make this living room look larger."

[0111] The system of the present invention efficiently and accurately reflects the user's requests and optimizes the renovation process.

[0112] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0113] Step 1:

[0114] User: The user accesses a dedicated web form using their smartphone or PC. They take a picture of the room and prepare a blueprint of the structure. They then enter their renovation requests into a text field. This includes specific requests such as "I want the kitchen to have a modern design." After this, they press the send button to send the entered data from their device to the server.

[0115] Input: Room image, structure blueprint, renovation request

[0116] Output: Data collected by the user and ready to be sent

[0117] Step 2:

[0118] Terminal: The terminal collects the data entered by the user in bulk and sends it to the server using an HTTP POST request, including a function to check the accuracy and format of the data. It notifies the user that the data has been sent.

[0119] Input: Data collected by the user

[0120] Output: Data sent to the server via an HTTP POST request

[0121] Step 3:

[0122] Server: The server receives the HTTP POST request and analyzes the data contained in the request body. The server extracts the room images, the building blueprints, and the renovation requests separately and checks the data format of each. The analysis results are temporarily stored for use in the next step.

[0123] Input: Data sent to the server as an HTTP POST request

[0124] Output: Images of the analyzed rooms, blueprints of the structure, and renovation requests

[0125] Step 4:

[0126] Server: The server creates a prompt to be input to the generative AI model based on the analyzed data. For example, it generates a prompt such as, "Please generate an image of a modern kitchen renovation. I have attached a photo of the current state and a blueprint." The server then sends this prompt along with the image and blueprint as input data to the generative AI model.

[0127] Input: Parsed room image, structure blueprint, and prompt text

[0128] Output: The data and prompts that are fed into the generative AI model

[0129] Step 5:

[0130] Generative AI Model: The generative AI model uses machine learning algorithms and deep learning techniques to generate a realistic, high-quality visual image of the renovation based on the received data and prompts. The generated image is then returned to the server.

[0131] Input: Room image, structure blueprint, and prompt

[0132] Output: Generated remodeled image

[0133] Step 6:

[0134] Server: The server receives the remodeling image returned from the generative AI model and launches the constructability judgment algorithm. The server provides the received remodeling image as input.

[0135] Input: Generated remodeled image

[0136] Output: Construction feasibility judgment result

[0137] Step 7:

[0138] Constructability Determination Algorithm: The algorithm determines whether the renovation image is technically and structurally feasible. This is done based on past construction data and existing technical constraints. The evaluation results are returned to the server.

[0139] Input: Generated remodeled image

[0140] Output: Technical and structural feasibility assessment results

[0141] Step 8:

[0142] Server: The server creates a final renovation plan based on the evaluation results. The final plan includes 3D rendered renovation images, specific construction procedures, and a timeline. The server then sends the final renovation plan to the user's device.

[0143] Input: Technical and structural feasibility assessment results

[0144] Output: Final renovation plan

[0145] Step 9:

[0146] User terminal: The user terminal receives the final renovation plan sent from the server. The received final plan is displayed to the user and prompted for confirmation. The user can review the final plan and use the option to request revisions if necessary.

[0147] Input: Final renovation plan

[0148] Output: The final renovation plan displayed to the user

[0149] (Application example 1)

[0150] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0151] With current renovation proposal systems, when users select furniture and interior items in a physical store, it is difficult for them to check in real time how the selected items will be arranged. Furthermore, there is also the issue of not being able to immediately make a technical judgment as to whether the selected furniture and interior items can actually be arranged. This often causes inconvenience to users, as they are unable to get a concrete image of how the room will look after renovation before purchasing. Furthermore, insufficient confirmation of feasibility increases the risk of problems occurring after purchase.

[0152] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0153] In this invention, the server includes means for receiving a room photo as input from a user, means for receiving a blueprint from the user as input, means for receiving a remodeling request from the user as input, means for generating a remodeling image using a generative artificial intelligence model based on the received room photo, blueprint, and remodeling request, means for determining whether the generated remodeling image is feasible, means for providing the generated remodeling image and the determination result of feasibility to the user, means for arranging the items in the room image in real time when the user selects furniture and interior items on site to generate a visual image, and means for determining whether these images are technically feasible to arrange. This allows the user to check the arrangement image of the items selected in the physical store in real time and instantly determine the technical feasibility of arranging the items.

[0154] A "user terminal" is an information processing device that allows a user to input requests for renovations and furniture selection.

[0155] A "server" is a central information processing system that receives, analyzes, and processes data sent from user terminals.

[0156] A "generative AI model" is an artificial intelligence model that uses deep learning and machine learning algorithms to generate visual images of the renovated home based on the user's requests.

[0157] The "construction feasibility determination algorithm" is an algorithm for evaluating whether the generated renovation image is technically and structurally feasible.

[0158] A "renovation image" is a visual image of the room after renovation, generated based on photos, drawings, and requests of the room provided by the user.

[0159] "Means for determining whether the furniture or interior can be technically arranged" refers to a means for making a technical judgment as to whether the selected furniture or interior can actually be arranged.

[0160] "Means for arranging products in real time and generating visual images" refers to means for instantly arranging products in an image of a room and generating a visual image when the product is selected in a physical store.

[0161] "Furniture and interior" is a general term for household goods and decorative items placed inside a room.

[0162] As an embodiment of the present invention, we will explain a method for supporting furniture and interior design selection in a brick-and-mortar store using a renovation proposal system. This system consists of a user terminal, a server, a generative AI model, and a construction feasibility determination algorithm.

[0163] The user terminal is an information processing device such as a smartphone or smart glasses, and the user uses it to input their requests for renovations and furniture selection. The user takes a photo of the room with the terminal, inputs a blueprint, and enters their renovation requests in text. The user terminal sends this data to the server. An HTTP POST request is used to send the data.

[0164] The server receives and analyzes data sent from the user's device. It acquires photos of the room, blueprints, and renovation requests and inputs them into the generative AI model. The generative AI model uses deep learning algorithms such as TENSORFLOW (registered trademark) to generate a visual image of the room after renovation. The generated renovation image is evaluated by a feasibility assessment algorithm to determine whether it is technically and structurally feasible.

[0165] The generative AI model and constructability assessment algorithm evaluate whether the generated renovation image is feasible based on past construction data and technical constraints. The generated renovation image and the constructability assessment results are sent to the user's device and provided to the user.

[0166] When a user selects furniture or interior items in a physical store, they use smart glasses or a smartphone to select the items and input the information into the terminal in real time. At this time, the items are placed on an image of the room in real time, generating a visual image. Furthermore, the system has the function of instantly determining whether these images are technically possible to arrange.

[0167] As a concrete example, consider a user in a furniture store considering a new dining set. The user holds up smart glasses to select a dining set and see in real time how it will be placed in their current kitchen. The server uses a generative AI model to generate a visual image of a "modern dining set" placed in the current kitchen and determines whether the placement is technically possible. This information is sent to the user's device, allowing the user to instantly see how the room will look after the renovation.

[0168] An example of a prompt is as follows:

[0169] "Please input a photo of the user's current room and generate an image of a new modern dining set. Specifically, please create a visual of how it would blend with the current kitchen, including a white modern table and four chairs."

[0170] With this system, when a user selects a product in a physical store, they can instantly see how the product will be positioned, and can also instantly check whether that placement is technically feasible.

[0171] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0172] Step 1:

[0173] The user uses a smartphone or smart glasses to take a photo of the room, input the blueprint, and enter the renovation request as text. This data is sent from the user's device to the server. The input data consists of image data (photo of the room) and text data (blueprint, renovation request), and the server receives this data.

[0174] Step 2:

[0175] The server analyzes the received photos of the room, blueprints, and renovation requests. The analyzed data is input into a generative AI model. The photos of the room are preprocessed using an image processing library (e.g., OpenCV), and the blueprints and renovation requests are converted into an appropriate format using a text processing library (e.g., NLTK).

[0176] Step 3:

[0177] The server uses a generative AI model to generate a visual image of the remodeled room based on the user's requests. The generative AI model (e.g., a model trained using TensorFlow) takes the preprocessed data as input and outputs a 3D rendering of the remodeled room. This output is the initial version of the remodeled image provided to the user.

[0178] Step 4:

[0179] The server inputs the generated renovation image into a constructability judgment algorithm. The constructability judgment algorithm evaluates whether the generated image is technically and structurally feasible based on a database of past construction projects and technical constraints. The evaluation results are output in text format.

[0180] Step 5:

[0181] The server then creates a final renovation plan based on the generated renovation images and the results of the construction feasibility assessment. This plan includes 3D rendered renovation images, specific construction procedures, and a timeline. The created plan is then sent to the user's device.

[0182] Step 6:

[0183] The user terminal receives the final renovation plan sent from the server and displays it to the user. The user checks the plan and requests revisions as necessary.

[0184] Step 7:

[0185] When a user selects furniture or interior items in a physical store, they scan the item using a camera on their smartphone or smart glasses. The user device then sends this product information to the server. The product information is sent to the server as image data and text data.

[0186] Step 8:

[0187] The server analyzes the received product information in real time and obtains detailed data on the selected product (size, color, material, etc.). Based on this, the generative AI model is used again to place the product in the image of the user's room in real time and generate a visual image.

[0188] Step 9:

[0189] The server evaluates the generated visual image to determine whether it is technically possible to arrange the product using a construction feasibility determination algorithm, and the product arrangement image including this evaluation result is sent to the user's terminal.

[0190] Step 10:

[0191] The user terminal displays the product layout image and technical evaluation results generated in real time to the user, allowing the user to make a decision on purchasing the product based on this.

[0192] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0193] This invention relates to a system that proposes renovations based on the user's requests, judges the feasibility of construction, and provides a total coordinated plan, and also combines it with an emotion engine that recognizes the user's emotions and reflects them in the renovation proposal. This system is an integrated system that includes a user terminal, a server, a generative AI model, a construction feasibility judgment algorithm, and an emotion engine.

[0194] System Overview

[0195] User device:

[0196] The user terminal is a device that allows users to input photos of the room, blueprints, and renovation requests. Users use their smartphones or PCs to enter information into a dedicated web form and send it to the server. Furthermore, the device is equipped with an emotion recognition function using a camera and microphone to detect the user's emotions in real time.

[0197] server:

[0198] The server receives data sent by users, generates renovation images using a generative AI model, and plays a central role in determining the feasibility of construction. In addition, the server uses an emotion engine to analyze users' emotions and adjusts renovation proposals based on the analysis results.

[0199] Generative AI models:

[0200] The generative AI model is an artificial intelligence model that generates visual images of the remodeled home based on photos, drawings, and renovation requests provided by the user. This model uses machine learning algorithms and deep learning technology.

[0201] Constructability determination algorithm:

[0202] The constructability algorithm is responsible for assessing whether the generated renovation image is technically and structurally feasible. The algorithm analyzes past construction data and technical constraints.

[0203] Emotion Engine:

[0204] The emotion engine analyzes the user's facial expressions, tone of voice, and other factors to recognize their emotions. This allows the system to understand in real time how the user feels about the renovation proposal. This emotional information is used to influence the renovation image generation process and to make proposals that will further increase user satisfaction.

[0205] Processing steps

[0206] User:

[0207] Users take photos of their rooms and prepare existing blueprints. They also enter their specific requests for the renovation in text format. For example, they might enter a request such as "I want the kitchen to have a modern design." Furthermore, the system's emotion recognition function analyzes emotions in real time based on facial expressions and voice.

[0208] Device:

[0209] The device sends the photos, drawings, renovation requests, and emotional information entered by the user to the server all at once, using an HTTP POST request.

[0210] server:

[0211] The server receives the data sent by the user and analyzes the room photos, blueprints, and renovation requests. It checks whether the received data is in the correct format and converts the data if necessary. The server also analyzes the user's emotional data in parallel.

[0212] Generative AI models and emotion engines:

[0213] The generative AI model takes photos, blueprints, and user requests as input and automatically generates an image of the desired remodeled home. The emotion engine analyzes the user's emotions and adjusts the remodeled image based on the results.

[0214] Constructability determination algorithm:

[0215] The generated renovation images are then fed into a constructability assessment algorithm to assess whether they are technically and structurally feasible, which involves advanced analysis.

[0216] server:

[0217] The server then creates a final renovation plan based on the renovation image whose feasibility has been confirmed. This plan includes 3D renderings of the renovation, specific construction procedures, and a timeline for each step. Adjustments can also be made based on the user's emotions. The final plan is then sent from the server to the user's device.

[0218] Device:

[0219] The terminal receives the final renovation plan sent from the server and displays it to the user. The user checks the contents of the final plan and provides feedback to the server on any corrections or additions they may require, if necessary.

[0220] User:

[0221] The user checks the final renovation plan and, if there are no problems, prepares to start construction. At this time, the server may also coordinate with the construction company.

[0222] Specific examples

[0223] For example, if a user wishes to have a modern design for their kitchen, they can take photos of the current state of the kitchen and upload the blueprints. This data is sent to the server, and the generative AI model generates an image of a "modern kitchen." Meanwhile, the emotion engine analyzes the user's emotions and determines whether the user is satisfied with the generated image. If the user is not satisfied, the server calls the AI ​​model again to adjust the image. Once a satisfactory renovation image is generated, a feasibility assessment algorithm performs a technical evaluation, and if construction feasibility is confirmed, a final renovation plan is created and provided to the user.

[0224] As described above, the system of the present invention efficiently and accurately reflects the user's wishes and feelings, optimizing the renovation process, and the user can receive consistent support throughout the entire renovation process.

[0225] The processing flow will be explained below.

[0226] Step 1:

[0227] The user takes a photo of their room and prepares existing blueprint data. They also enter their specific requests for the renovation in text format. For example, they might enter a request such as "I want the kitchen design to be modern." Furthermore, emotion data is recorded using a camera and microphone with emotion recognition capabilities.

[0228] Step 2:

[0229] Users access a dedicated web form on their smartphone or PC and upload the photos, blueprints, and renovation requests they have prepared. Emotional data is also sent at the same time.

[0230] Step 3:

[0231] The device sends the photos, drawings, renovation requests, and emotion data entered by the user to the server all at once using an HTTP POST request.

[0232] Step 4:

[0233] The server receives the data sent by the user, analyzes the photos, blueprints, and renovation requests, verifies that the received data is in the correct format, and converts the data if necessary.

[0234] Step 5:

[0235] The server calls a generative AI model based on the received data and generates a renovation image. The generative AI model receives photos, blueprints, and customer requests as input, and automatically generates an image of the user's desired renovation based on these.

[0236] Step 6:

[0237] The server analyzes the user's emotions using an emotion engine, which recognizes the user's emotions in real time from the transmitted audio and video data and analyzes the results.

[0238] Step 7:

[0239] The server adjusts the generated renovation image based on the emotional data obtained from the emotion engine. For example, if the user expresses dissatisfaction with the generated image, it provides feedback to the AI ​​model to regenerate a more satisfying image.

[0240] Step 8:

[0241] The server then inputs the highly satisfying renovation images evaluated by the emotion engine into a constructability judgment algorithm, which evaluates whether the generated renovation images are technically and structurally feasible.

[0242] Step 9:

[0243] Based on the confirmed feasibility of the renovation, the server creates a final renovation plan, which includes 3D renderings of the renovation, specific construction procedures, and a timeline for each step.

[0244] Step 10:

[0245] The server sends the data to the terminal to provide the final renovation plan to the user. The plan data is sent using an HTTP response.

[0246] Step 11:

[0247] The terminal receives the final renovation plan sent from the server and displays it to the user. The user checks the contents of the final plan and provides feedback to the server on any corrections or additions they may require, if necessary.

[0248] Step 12:

[0249] The user checks the final renovation plan and, if there are no problems, prepares to start construction. At this time, the server may also coordinate with the construction company.

[0250] As a result, the system of the present invention efficiently and accurately reflects the user's wishes and feelings, optimizing the renovation process, allowing the user to receive consistent support throughout the renovation process and achieving a high level of satisfaction with the renovation.

[0251] Example 2

[0252] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0253] Current renovation proposal systems provide renovation plans based on user requests, but do not take the user's emotions into consideration, which can result in low user satisfaction. Furthermore, there is a risk that the plan will not be realized due to uncertainty about technical feasibility. Therefore, there is a need for a renovation proposal system that takes into consideration both the user's emotions and the technical feasibility of construction.

[0254] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a room photo from the user as input, means for receiving a blueprint from the user as input, means for receiving a renovation request from the user as input, means for generating a renovation image using a generative artificial intelligence model based on the received room photo, blueprint, and renovation request, means for detecting the user's emotions in real time, means for using an emotion engine to analyze the detected user's emotion data, means for adjusting the renovation image based on the analysis results, means for determining whether the generated renovation image is feasible, and means for providing the generated renovation image and the determination result of feasibility to the user. This enables highly accurate renovation proposals that take into account the user's emotions and technical feasibility.

[0255] A "user" is an individual or group who wishes to remodel and provides the system with data such as photographs, drawings, and requests for remodeling.

[0256] A "room photo" is a recorded image of the current state of a room that the user wishes to remodel.

[0257] "Drawings" are design drawings that show the structure and dimensions of the room to be renovated.

[0258] "Renovation requests" is information that a user inputs into the system in text format describing the changes and design they would like to make to the room to be renovated.

[0259] A "generative artificial intelligence model" is an algorithm that uses machine learning and deep learning technologies to generate a visual image of the renovated home based on data provided by the user.

[0260] A "renovation image" is a visual image of a room after renovation, generated by a generative artificial intelligence model.

[0261] The "emotion engine" is an algorithm that detects and analyzes the user's emotions in real time.

[0262] The "construction feasibility determination algorithm" is an algorithm that determines whether the generated renovation image is technically and structurally feasible.

[0263] A "total coordination plan" is a final plan that includes 3D rendering images, construction procedures, and a timeline for each step required for the renovation.

[0264] A "device" is an appliance used by a user to input photos, drawings, and requests into the system and to receive renovation proposals from the system.

[0265] This invention relates to a system that proposes renovations based on user requests, determines the feasibility of construction, and provides a total coordinated plan, as well as a system that combines an emotion engine that recognizes the user's emotions and reflects them in the renovation proposal. This system is an integrated system that includes a user terminal, a server, a generative AI model, a construction feasibility determination algorithm, and an emotion engine.

[0266] User device:

[0267] The user terminal is a device that allows users to input photos of their rooms, blueprints, and renovation requests. Users use their smartphones or PCs to enter information into a dedicated web form and send it to the server. Furthermore, the terminal is equipped with an emotion recognition function using a camera and microphone to detect the user's emotions in real time.

[0268] server:

[0269] The server receives data sent by users, uses a generative AI model to generate renovation images and plays a central role in determining feasibility. Additionally, the server uses an emotion engine to analyze users' emotions and adjust renovation proposals based on the analysis results. Specific software includes frameworks for machine learning and deep learning.

[0270] Generative AI models:

[0271] A generative AI model is an artificial intelligence model that generates visual images of the remodeled home based on photos, blueprints, and renovation requests provided by the user. This model uses machine learning algorithms and deep learning technology. For example, if a user inputs a request such as "I want a modern kitchen design," the generative AI model will generate a visual image of a modern kitchen based on this request.

[0272] Emotion Engine:

[0273] The emotion engine analyzes the user's facial expressions, tone of voice, and other factors to recognize the user's emotions. For this purpose, an emotion recognition algorithm is used. This allows the system to understand in real time how the user feels about the renovation proposal. For example, if the user is dissatisfied with the generated renovation image, the emotion engine analyzes this and sends feedback to the server.

[0274] Constructability determination algorithm:

[0275] The constructability algorithm evaluates whether the generated renovation image is technically and structurally feasible. This algorithm analyzes past construction data and technical constraints. For example, it determines whether the user's desired design is compatible with the actual building structure.

[0276] Total coordination plan:

[0277] The server then creates a final renovation plan based on the confirmed feasibility of the renovation. The plan includes 3D renderings of the renovation, specific construction steps, and a timeline for each step. It can also make adjustments based on the user's emotions.

[0278] Specific examples

[0279] For example, if a user wishes to have a modern design for their kitchen, they can take photos of the current state of the kitchen and upload the blueprints. This data is sent to the server, and the generative AI model generates an image of a "modern kitchen." Meanwhile, the emotion engine analyzes the user's emotions and determines whether the user is satisfied with the generated image. If the user is not satisfied, the server calls the AI ​​model again to adjust the image. Once a satisfactory renovation image is generated, a feasibility assessment algorithm performs a technical evaluation, and if construction feasibility is confirmed, a final renovation plan is created and provided to the user.

[0280] For example, by entering a prompt such as "I want to remodel my kitchen into a modern design," the generative AI model will generate a visual image of a modern kitchen, and the emotion engine will analyze the user's emotions and adjust the proposal. Finally, a remodeling plan that has undergone technical evaluation will be provided.

[0281] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0282] Step 1:

[0283] The user inputs their renovation requests into the device. First, the user uses a smartphone or PC to enter photos of their room, blueprints, and specific renovation requests into a dedicated web form. For example, they can enter a request such as "I want my kitchen to have a modern design" in text format. The device's camera and microphone are then used to detect the user's emotions in real time and collect emotional data. The input data includes photos of the room, blueprints, text of the renovation requests, and emotional data.

[0284] Step 2:

[0285] The device sends the input data to the server. The device then sends the photos, drawings, renovation requests, and emotional information entered by the user to the server all at once. Specifically, the data is sent to the server using an HTTP POST request, and the series of data reaches the server. The input data is sent according to the format (JPEG, PNG, text, etc.).

[0286] Step 3:

[0287] The server receives the data and checks the format. The server receives the data sent by the user. First, it checks whether the format of the received data is correct. For example, it checks whether photo data is in JPEG format and whether drawing data is in a compatible format. If necessary, it converts the data into the appropriate format. The input to this step is the various data sent by the user, and the output is the data whose format has been checked.

[0288] Step 4:

[0289] The server calls the generative AI model to generate a visual image of the renovation. The server inputs the received photos, blueprints, and renovation requests of the room into the generative AI model. This model uses machine learning and deep learning technology to generate a visual image of the renovated state. Specifically, it understands the room structure and the user's requests and outputs the optimal design proposal. The input data are photos, blueprints, and renovation requests, and the output is the generated visual image.

[0290] Step 5:

[0291] The server analyzes the user's emotions using an emotion engine. The server inputs the user's emotion data into the emotion engine. The emotion engine analyzes facial expressions and voice to grasp the user's emotional state in real time. For example, it determines whether the user is happy or dissatisfied. The input data is emotion information, and the output is the analyzed emotional state.

[0292] Step 6:

[0293] The server adjusts the renovation image based on the analysis results. It receives the emotion engine's analysis results and adjusts the renovation visual image. For example, if the user's satisfaction is low, it uses the generative AI model again to generate a new image. This step uses the analyzed emotional state and the initial visual image, and outputs the adjusted final visual image.

[0294] Step 7:

[0295] The server applies an algorithm to determine constructability. The server inputs the adjusted renovation image into the constructability algorithm to evaluate whether it is technically and structurally feasible. The analysis is performed by referencing past construction data and technical constraints. The input data is the final visual image, and the output is the constructability judgment result.

[0296] Step 8:

[0297] The server creates the final renovation plan. Based on the renovation image whose constructability has been confirmed, the server creates the final renovation plan. This plan includes 3D rendering images of the renovation, specific construction procedures, and a timeline for each step. The input data is the renovation image whose constructability has been confirmed, and the output is the final renovation plan.

[0298] Step 9:

[0299] The server sends the final plan to the terminal. The created final renovation plan is then sent from the server to the terminal. This is done using an HTTP POST request as well. The input data is the final renovation plan, and the output is the plan information sent to the user's terminal.

[0300] Step 10:

[0301] The terminal displays the final renovation plan to the user. The terminal receives the final renovation plan sent from the server and displays it to the user. The user checks the proposed plan in detail. The input data is the final renovation plan, and the output is plan information that can be visually confirmed.

[0302] Step 11:

[0303] The user may check the plan and send a request for revision. If the user has any requests for revisions or additions to the final plan, they can feed this back to the server from their terminal. For example, they may send a request such as "I want the kitchen counter to be a little wider." The input data is the revision request, and the output is the new data requesting the revision.

[0304] Step 12:

[0305] The user finalizes the plan and prepares for construction. If the user is satisfied with the final plan and there are no problems, they prepare to begin construction of the renovation. At this time, the server coordinates with the contractor as necessary to support a smooth construction process. The data input is the confirmation result of the final plan, and the data output is the state of construction preparation.

[0306] (Application example 2)

[0307] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0308] In systems for proposing renovations and assessing the feasibility of construction, there is a need to reflect user emotions in real time and make proposals that will increase satisfaction. In addition, when proposing entertainment content for autonomous vehicles, it is also important to provide optimal content that reflects the passengers' emotions, thereby increasing passenger comfort.

[0309] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0310] In this invention, the server includes means for receiving a room photo as input from the user, means for receiving a blueprint from the user as input, means for receiving a remodeling request from the user as input, means for generating a remodeling image using a generative artificial intelligence model based on the received room photo, blueprint, and remodeling request, means for determining whether the generated remodeling image is feasible, means for providing the generated remodeling image and the determination result of feasibility to the user, means for using an emotion engine to detect the user's emotions and reflect them in remodeling proposals, and means for proposing in-vehicle entertainment content and recognizing the user's emotions to adjust the proposal content. This makes it possible to provide optimal remodeling proposals and in-vehicle entertainment content that reflect the user's emotions.

[0311] The "means for receiving a room photo as an input from a user" is a device or interface for collecting a room photo specified by the user in digital form.

[0312] The "means for receiving drawing data from a user as input" is a device or interface that receives drawing data of a room or building provided by a user.

[0313] The "means for receiving input of renovation requests from the user" is an interface that allows the user to input specific renovation requests and wishes in text or voice format.

[0314] "Means for generating a remodeling image using a generative artificial intelligence model based on received room photos, drawings, and remodeling requests" refers to an artificial intelligence system that uses data provided by the user to automatically create a visual image of the remodeled room.

[0315] The "means for determining whether the generated renovation image is feasible" refers to an algorithm or system that evaluates whether the created renovation image is technically and structurally feasible.

[0316] The "means for providing the user with the generated renovation image and the judgement result of the feasibility of construction" is a system or interface that notifies the user of the evaluated renovation image and information about its feasibility of construction.

[0317] "Means using an emotion engine to detect user emotions and reflect them in renovation proposals" refers to an artificial intelligence system that analyzes emotions from the user's facial expressions, voice, etc., and reflects the results in renovation proposals.

[0318] The "means for suggesting in-vehicle entertainment content and adjusting the suggested content based on the user's emotions" refers to a system that provides entertainment content exclusively for passengers in autonomous vehicles and changes the content in real time according to the passengers' emotions.

[0319] System in general

[0320] This system receives photos, blueprints, and renovation requests from users, generates renovation images using a generative AI model, and determines the feasibility of the renovation. It also includes an emotion engine that detects the user's emotions and reflects them in the renovation proposals. It also includes a function to suggest in-vehicle entertainment content and adjust the proposals based on the user's emotions.

[0321] Hardware and Software

[0322] 1. User Device

[0323] Devices such as smartphones and computers are used.

[0324] This allows users to enter photos of the room, blueprint data, and renovation requests.

[0325] The user device is equipped with a camera and a microphone to collect data for emotion recognition.

[0326] 2. Server

[0327] A server with high-performance data processing capabilities is required.

[0328] The server is equipped with hardware (e.g., GPU) and software (e.g., TensorFlow) for implementing generative AI models.

[0329] For example, Google (registered trademark) Cloud Vision API or IBM Watson (registered trademark) is used as an emotion engine for emotion recognition.

[0330] Data Processing and Data Calculation

[0331] 1. Data collection and input

[0332] Photos, drawings, and renovation requests for the room sent from the user terminal are received by the server.

[0333] Send this data to the server using an HTTP POST request.

[0334] 2. Creating a Renovation Image

[0335] The server analyzes the received data and inputs it into a generative AI model.

[0336] The model generates a visual image of the renovated area based on the received data.

[0337] This generative AI model uses machine learning algorithms and deep learning techniques.

[0338] 3. Judgment of construction feasibility

[0339] The generated renovation images are then run through an algorithm to determine the technical and structural feasibility of construction.

[0340] The algorithm includes historical construction data and technical constraints.

[0341] 4. Emotion recognition and regulation

[0342] Using an emotion engine, the user's emotional data (facial expressions and tone of voice) is analyzed and reflected in the renovation image.

[0343] The renovation image is readjusted based on the emotional data.

[0344] 5. Entertainment content proposals

[0345] The server proposes entertainment content for the vehicle.

[0346] Here too, the emotion engine works, tailoring content suggestions based on the user's emotions.

[0347] Specific examples

[0348] If a user has a desire to "modernize their kitchen," they can take photos of the current state of the kitchen and upload the blueprint. The server receives this data and uses a generative AI model to generate a "modern kitchen" renovation image. At the same time, an emotion engine analyzes the user's emotions and determines their satisfaction with the generated image. If the emotion is not positive, the server will adjust the renovation image again.

[0349] Additionally, cameras and microphones capture passengers' facial expressions and voices as they board the vehicle. For example, if a passenger has a relaxed facial expression, the system can use this information to suggest relaxing music or movies.

[0350] Prompt Sentence Examples

[0351] "The user appears relaxed. Please suggest entertainment content appropriate for this situation."

[0352] "The user's facial expression and voice indicate that they are stressed. Please suggest entertainment content to ease this situation."

[0353] As described above, the present invention is a system that makes it possible to provide optimal renovation proposals and in-vehicle entertainment content that reflect the user's emotions.

[0354] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0355] Step 1:

[0356] The user uses the terminal to input and send photos and drawings of the room and requests for renovation.

[0357] Input: Room photos, blueprints, renovation requests (e.g., "I want a modern kitchen design")

[0358] Specific operation: The user uses a smartphone or computer to enter photos of the room, drawings, and renovation requests into a dedicated web form and press the submit button.

[0359] Output: The submitted data is sent to the server.

[0360] Step 2:

[0361] The server analyzes the data received from the terminal and checks whether the data format is correct.

[0362] Input: Received data (room photos, drawings, renovation requests)

[0363] Specific operation: The server analyzes the format of the data received and performs data conversion as necessary.

[0364] Output: Parsed data, transformed data

[0365] Step 3:

[0366] The server inputs the analyzed data into a generative AI model to generate a renovation image.

[0367] Input: Photos of the analyzed room, drawings, and renovation requests

[0368] Specific operation: The server uses a generative artificial intelligence model to generate a visual image of the remodeled home based on the input data.

[0369] Output: Generated renovation image

[0370] Step 4:

[0371] The server inputs the generated renovation image into a construction feasibility determination algorithm and performs a technical evaluation.

[0372] Input: Generated renovation image

[0373] Specific operation: The server executes a constructability determination algorithm to evaluate whether the generated renovation image is technically and structurally feasible.

[0374] Output: Construction feasibility judgment result

[0375] Step 5:

[0376] The server collects the user's emotional data and analyzes it using an emotion engine.

[0377] Input: User's facial expression data, voice data

[0378] Specific operation: The device's camera and microphone are used to collect the user's facial expressions and voice, which are then sent to the server, where they are analyzed using an emotion engine.

[0379] Output: Emotion analysis results

[0380] Step 6:

[0381] The server adjusts the renovation image based on the results of emotion analysis.

[0382] Input: User sentiment analysis results, generated renovation image

[0383] Specific operation: The server uses the results of emotion analysis to readjust the generated renovation image.

[0384] Output: Adjusted renovation image

[0385] Step 7:

[0386] The server provides the user with the final renovation image and construction feasibility results.

[0387] Input: Adjusted renovation image, construction feasibility assessment results

[0388] Specific operation: The server compiles the final renovation image and the results of the feasibility of construction and sends them to the user's terminal.

[0389] Output: The final renovation plan provided to the user

[0390] Step 8:

[0391] The user reviews the final renovation plan and provides feedback to make adjustments as needed.

[0392] Input: Final renovation plan, user feedback

[0393] Specific operation: The user reviews the final renovation plan, enters any corrections or additions they would like to make, and sends feedback to the server.

[0394] Output: User feedback, requests for further proposals

[0395] Step 9:

[0396] The server receives feedback from the user and adjusts the renovation image again.

[0397] Input: User feedback

[0398] How it works: The server analyzes the feedback and uses the generative AI model and emotion engine again to refine the renovation image.

[0399] Output: Re-adjusted renovation image

[0400] Step 10:

[0401] The server proposes in-vehicle entertainment content and adjusts it based on the user's emotions.

[0402] Input: User's facial expression data, voice data

[0403] Specific operation: The system uses cameras and microphones inside the vehicle to collect the user's facial expressions and voice, which are then analyzed by the emotion engine. Based on the analysis results, entertainment content is suggested and adjusted as necessary.

[0404] Output: Suggested entertainment content, adjustments

[0405] In this way, the present invention makes it possible to provide optimal renovation proposals and in-vehicle entertainment content that reflect the user's emotions.

[0406] 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.

[0407] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0408] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0409] [Second embodiment]

[0410] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0411] 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.

[0412] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. 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).

[0413] 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.

[0414] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0415] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0416] 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. 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.

[0417] 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.

[0418] 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 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.

[0419] 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.

[0420] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0421] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0422] This invention is a system that proposes renovations based on the user's requests, assesses the feasibility of construction, and provides a total coordinated plan. This system is an integrated system that includes a user terminal, a server, a generative AI model, and a construction feasibility assessment algorithm.

[0423] System Overview

[0424] User device:

[0425] The user terminal is a device that allows users to input photos of the room, drawings, and renovation requests. Users use their smartphones or PCs to enter information into a dedicated web form and send it to the server.

[0426] server:

[0427] The server plays a central role in receiving data sent by users, generating renovation images using generative AI models, and determining the feasibility of construction. To achieve these functions, the server is equipped with advanced data processing capabilities and algorithms.

[0428] Generative AI models:

[0429] The generative AI model is an artificial intelligence model that generates visual images of the remodeled home based on photos, drawings, and renovation requests provided by the user. This model uses machine learning algorithms and deep learning technology.

[0430] Constructability determination algorithm:

[0431] The constructability algorithm is responsible for assessing whether the generated renovation image is technically and structurally feasible. The algorithm analyzes past construction data and technical constraints.

[0432] Processing steps

[0433] User:

[0434] Users take photos of their own rooms and prepare existing blueprints. Next, they enter the photos, blueprints, and renovation requests into a dedicated web form. For example, they write requests such as "I want a modern design for the kitchen" in the text field. The entered data is then sent from the device to the server.

[0435] Device:

[0436] The terminal sends the data entered by the user to the server in bulk, typically by using an HTTP POST request.

[0437] server:

[0438] The server analyzes the received data, obtaining photos of the room, blueprints, and renovation requests. This data is then input into a generative AI model to generate a renovation image. Based on the generated renovation image, a feasibility assessment algorithm performs technical and structural evaluations.

[0439] Generative AI model and constructability algorithm:

[0440] The generative AI model automatically creates remodeling images based on the user's requirements, and then a constructability algorithm evaluates the images to determine whether they are technically and structurally feasible.

[0441] server:

[0442] Based on the results of the feasibility assessment, the server creates a final renovation plan, which includes 3D rendered images of the renovation, specific construction procedures, and a timeline. The final plan is then sent from the server to the user's device.

[0443] User device:

[0444] The terminal receives the final renovation plan sent from the server and displays it to the user, who can then check the final plan and request any necessary revisions.

[0445] Specific examples

[0446] For example, if a user wants to "modernize their kitchen," they can take photos of the current state of the kitchen and upload the blueprints. This data is sent to the server, and the generative AI model generates an image of the "modern kitchen." Next, a constructability assessment algorithm evaluates whether the generated image is feasible. If the evaluation result is favorable, the server creates a final renovation plan and provides it to the user.

[0447] As described above, the system of the present invention efficiently and accurately reflects the user's requests and optimizes the renovation process.

[0448] The processing flow will be explained below.

[0449] Step 1:

[0450] Users take photos of the room they want to remodel and prepare existing blueprint data. They also enter their specific requests for the remodel in text format. For example, they might enter a request such as, "I want the kitchen design to be modern."

[0451] Step 2:

[0452] Users access a dedicated web form on their smartphone or computer and upload the photos, blueprints, and renovation requests they have prepared. Each piece of data is entered into the corresponding field.

[0453] Step 3:

[0454] The device sends the photos, drawings, and renovation requests entered by the user to the server all at once, using an HTTP POST request.

[0455] Step 4:

[0456] The server receives the data sent by the user, analyzes the room photos, blueprints, and renovation requests, checks whether the received data is in the correct format, and converts the data if necessary.

[0457] Step 5:

[0458] The server calls a generative AI model based on the received data and generates a renovation image. The generative AI model receives photos, blueprints, and customer requests as input, and automatically generates an image of the user's desired renovation based on these.

[0459] Step 6:

[0460] The server then inputs the generated renovation images into a constructability assessment algorithm to evaluate whether the proposed renovation is technically and structurally feasible, which includes detailed analysis based on past construction data and technical constraints.

[0461] Step 7:

[0462] Based on the confirmed feasibility of the renovation, the server creates a final renovation plan, which includes 3D renderings of the renovation, specific construction procedures, and a timeline for each step.

[0463] Step 8:

[0464] The server sends the data to the terminal to provide the final renovation plan to the user. The plan data is sent using an HTTP response.

[0465] Step 9:

[0466] The terminal receives the final renovation plan sent from the server and displays it to the user. The user checks the contents of the final plan and provides feedback to the server on any corrections or additions they may require, if necessary.

[0467] Step 10:

[0468] The user checks the final renovation plan and, if there are no problems, prepares to start construction. At this time, the server may also coordinate with the construction company.

[0469] Through the above processing steps, the system of the present invention efficiently and accurately proposes renovations that reflect the user's requests and determines the feasibility of construction. The user can receive consistent support throughout the renovation process.

[0470] Example 1

[0471] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0472] Conventional renovation proposal systems have had difficulty generating renovation images that accurately reflect the user's requests and quickly and accurately assessing the feasibility of the renovation. Furthermore, when proposing a specific total coordination plan based on the room images and blueprints submitted by the user, manual evaluation and planning are time-consuming and inefficient.

[0473] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0474] In this invention, the server includes means for receiving a room image as input from a user, means for receiving a structure blueprint as input from a user, means for receiving a remodeling request as input from a user, means for generating a remodeling image using a generative AI model based on the received room image, structure blueprint, and remodeling request, means for determining whether the generated remodeling image is technically and structurally feasible, and means for providing the generated remodeling image and the technical and structural determination results to the user. This makes it possible to accurately reflect the user's requests, evaluate the feasibility of construction, and efficiently and accurately provide remodeling proposals and total coordination plans.

[0475] A "room image" is a photograph or digital image taken by a user that shows the current state of a room.

[0476] A "structural blueprint" is a drawing that shows the structure and layout of a room or building, including the location and dimensions of walls, floors, ceilings, windows, doors, etc.

[0477] The "renovation request" is information describing the details of the renovation or remodeling desired by the user in text or other formats. For example, it includes a specific request such as "I want the kitchen to have a modern design."

[0478] A "generative artificial intelligence model" is an artificial intelligence model that uses machine learning algorithms and deep learning technology to generate renovation images based on user requests.

[0479] The "renovation image" is a visual image of the renovated room generated by the generative AI model, which reflects the user's wishes.

[0480] "Means for determining technical and structural feasibility" refers to algorithms and methods that evaluate whether the generated renovation image can actually be constructed based on past construction data and existing technical constraints.

[0481] The "overall coordination plan" is an integrated plan for each element of the renovation, including 3D rendered images of the renovation, specific construction procedures, and a timeline.

[0482] This invention is a system that proposes renovations based on the user's requests, assesses the feasibility of construction, and provides a total coordinated plan. This system is an integrated system that includes a user terminal, a server, a generative AI model, and a construction feasibility assessment algorithm.

[0483] User terminal

[0484] The user terminal is a device that allows users to input images of rooms, blueprints of structures, and renovation requests. Users access a dedicated web form using a smartphone or PC and enter the necessary information.

[0485] server

[0486] The server receives data sent by users, generates renovation images using generative AI models, and plays a central role in determining whether construction is possible. The server requires advanced data processing capabilities, and the construction feasibility determination algorithm also runs on the server.

[0487] Generative AI Models

[0488] The generative AI model is an artificial intelligence model that generates visual images of the renovated building based on user-provided room images, structural blueprints, and renovation requests. This model uses machine learning algorithms and deep learning technology to generate realistic visual images.

[0489] Constructibility determination algorithm

[0490] The constructability algorithm is responsible for assessing whether the generated renovation images are technically and structurally feasible. The algorithm performs an analysis based on past construction data and current technical constraints.

[0491] Specifically, the user first takes a photo of their room and prepares a blueprint for the structure. Next, they enter the photos, blueprints, and renovation requests into a dedicated web form and send it from their device to the server. The device then sends the data entered by the user to the server using an HTTP POST request.

[0492] The server analyzes the received data and obtains images of the room, blueprints, and renovation requirements. This data is then input into a generative AI model to generate a renovation image. Based on the generated renovation image, a feasibility assessment algorithm performs a technical and structural evaluation.

[0493] As a concrete example, if a user wishes to have a modern design for their kitchen, they can take photos of the current state of the kitchen and upload the blueprint. This data is sent to the server, and a generative AI model generates an image of the "modern kitchen." Next, a constructability assessment algorithm evaluates whether the generated image is feasible. If the evaluation result is favorable, the server creates a final renovation plan and provides it to the user.

[0494] An example of a prompt for a generative AI model is as follows:

[0495] "Please generate an image of a modern kitchen renovation. I've attached photos of the current situation and blueprints."

[0496] "Please create a design that will make this living room look larger."

[0497] The system of the present invention efficiently and accurately reflects the user's requests and optimizes the renovation process.

[0498] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0499] Step 1:

[0500] User: The user accesses a dedicated web form using their smartphone or PC. They take a picture of the room and prepare a blueprint of the structure. They then enter their renovation requests into a text field. This includes specific requests such as "I want the kitchen to have a modern design." After this, they press the send button to send the entered data from their device to the server.

[0501] Input: Room image, structure blueprint, renovation request

[0502] Output: Data collected by the user and ready to be sent

[0503] Step 2:

[0504] Terminal: The terminal collects the data entered by the user in bulk and sends it to the server using an HTTP POST request, including a function to check the accuracy and format of the data. It notifies the user that the data has been sent.

[0505] Input: Data collected by the user

[0506] Output: Data sent to the server via an HTTP POST request

[0507] Step 3:

[0508] Server: The server receives the HTTP POST request and analyzes the data contained in the request body. The server extracts the room images, the building blueprints, and the renovation requests separately and checks the data format of each. The analysis results are temporarily stored for use in the next step.

[0509] Input: Data sent to the server as an HTTP POST request

[0510] Output: Images of the analyzed rooms, blueprints of the structure, and renovation requests

[0511] Step 4:

[0512] Server: The server creates a prompt to be input to the generative AI model based on the analyzed data. For example, it generates a prompt such as, "Please generate an image of a modern kitchen renovation. I have attached a photo of the current state and a blueprint." The server then sends this prompt along with the image and blueprint as input data to the generative AI model.

[0513] Input: Parsed room image, structure blueprint, and prompt text

[0514] Output: The data and prompts that are fed into the generative AI model

[0515] Step 5:

[0516] Generative AI Model: The generative AI model uses machine learning algorithms and deep learning techniques to generate a realistic, high-quality visual image of the renovation based on the received data and prompts. The generated image is then returned to the server.

[0517] Input: Room image, structure blueprint, and prompt

[0518] Output: Generated remodeled image

[0519] Step 6:

[0520] Server: The server receives the remodeling image returned from the generative AI model and launches the constructability judgment algorithm. The server provides the received remodeling image as input.

[0521] Input: Generated remodeled image

[0522] Output: Construction feasibility judgment result

[0523] Step 7:

[0524] Constructability Determination Algorithm: The algorithm determines whether the renovation image is technically and structurally feasible. This is done based on past construction data and existing technical constraints. The evaluation results are returned to the server.

[0525] Input: Generated remodeled image

[0526] Output: Technical and structural feasibility assessment results

[0527] Step 8:

[0528] Server: The server creates a final renovation plan based on the evaluation results. The final plan includes 3D rendered renovation images, specific construction procedures, and a timeline. The server then sends the final renovation plan to the user's device.

[0529] Input: Technical and structural feasibility assessment results

[0530] Output: Final renovation plan

[0531] Step 9:

[0532] User terminal: The user terminal receives the final renovation plan sent from the server. The received final plan is displayed to the user and prompted for confirmation. The user can review the final plan and use the option to request revisions if necessary.

[0533] Input: Final renovation plan

[0534] Output: The final renovation plan displayed to the user

[0535] (Application example 1)

[0536] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0537] With current renovation proposal systems, when users select furniture and interior items in a physical store, it is difficult for them to check in real time how the selected items will be arranged. Furthermore, there is also the issue of not being able to immediately make a technical judgment as to whether the selected furniture and interior items can actually be arranged. This often causes inconvenience to users, as they are unable to get a concrete image of how the room will look after renovation before purchasing. Furthermore, insufficient confirmation of feasibility increases the risk of problems occurring after purchase.

[0538] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0539] In this invention, the server includes means for receiving a room photo as input from a user, means for receiving a blueprint from the user as input, means for receiving a remodeling request from the user as input, means for generating a remodeling image using a generative artificial intelligence model based on the received room photo, blueprint, and remodeling request, means for determining whether the generated remodeling image is feasible, means for providing the generated remodeling image and the determination result of feasibility to the user, means for arranging the items in the room image in real time when the user selects furniture and interior items on site to generate a visual image, and means for determining whether these images are technically feasible to arrange. This allows the user to check the arrangement image of the items selected in the physical store in real time and instantly determine the technical feasibility of arranging the items.

[0540] A "user terminal" is an information processing device that allows a user to input requests for renovations and furniture selection.

[0541] A "server" is a central information processing system that receives, analyzes, and processes data sent from user terminals.

[0542] A "generative AI model" is an artificial intelligence model that uses deep learning and machine learning algorithms to generate visual images of the renovated home based on the user's requests.

[0543] The "construction feasibility determination algorithm" is an algorithm for evaluating whether the generated renovation image is technically and structurally feasible.

[0544] A "renovation image" is a visual image of the room after renovation, generated based on photos, drawings, and requests of the room provided by the user.

[0545] "Means for determining whether the furniture or interior can be technically arranged" refers to a means for making a technical judgment as to whether the selected furniture or interior can actually be arranged.

[0546] "Means for arranging products in real time and generating visual images" refers to means for instantly arranging products in an image of a room and generating a visual image when the product is selected in a physical store.

[0547] "Furniture and interior" is a general term for household goods and decorative items placed inside a room.

[0548] As an embodiment of the present invention, we will explain a method for supporting furniture and interior design selection in a brick-and-mortar store using a renovation proposal system. This system consists of a user terminal, a server, a generative AI model, and a construction feasibility determination algorithm.

[0549] The user terminal is an information processing device such as a smartphone or smart glasses, and the user uses it to input their requests for renovations and furniture selection. The user takes a photo of the room with the terminal, inputs a blueprint, and enters their renovation requests in text. The user terminal sends this data to the server. An HTTP POST request is used to send the data.

[0550] The server receives and analyzes data sent from the user's device. It acquires photos of the room, blueprints, and renovation requests and inputs them into a generative AI model. The generative AI model uses deep learning algorithms such as TensorFlow to generate a visual image of the room after renovation. The generated renovation image is evaluated by a feasibility assessment algorithm to determine whether it is technically and structurally feasible.

[0551] The generative AI model and constructability assessment algorithm evaluate whether the generated renovation image is feasible based on past construction data and technical constraints. The generated renovation image and the constructability assessment results are sent to the user's device and provided to the user.

[0552] When a user selects furniture or interior items in a physical store, they use smart glasses or a smartphone to select the items and input the information into the terminal in real time. At this time, the items are placed on an image of the room in real time, generating a visual image. Furthermore, the system has the function of instantly determining whether these images are technically possible to arrange.

[0553] As a concrete example, consider a user in a furniture store considering a new dining set. The user holds up smart glasses to select a dining set and see in real time how it will be placed in their current kitchen. The server uses a generative AI model to generate a visual image of a "modern dining set" placed in the current kitchen and determines whether the placement is technically possible. This information is sent to the user's device, allowing the user to instantly see how the room will look after the renovation.

[0554] An example of a prompt is as follows:

[0555] "Please input a photo of the user's current room and generate an image of a new modern dining set. Specifically, please create a visual of how it would blend with the current kitchen, including a white modern table and four chairs."

[0556] With this system, when a user selects a product in a physical store, they can instantly see how the product will be positioned, and can also instantly check whether that placement is technically feasible.

[0557] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0558] Step 1:

[0559] The user uses a smartphone or smart glasses to take a photo of the room, input the blueprint, and enter the renovation request as text. This data is sent from the user's device to the server. The input data consists of image data (photo of the room) and text data (blueprint, renovation request), and the server receives this data.

[0560] Step 2:

[0561] The server analyzes the received photos of the room, blueprints, and renovation requests. The analyzed data is input into a generative AI model. The photos of the room are preprocessed using an image processing library (e.g., OpenCV), and the blueprints and renovation requests are converted into an appropriate format using a text processing library (e.g., NLTK).

[0562] Step 3:

[0563] The server uses a generative AI model to generate a visual image of the remodeled room based on the user's requests. The generative AI model (e.g., a model trained using TensorFlow) takes the preprocessed data as input and outputs a 3D rendering of the remodeled room. This output is the initial version of the remodeled image provided to the user.

[0564] Step 4:

[0565] The server inputs the generated renovation image into a constructability judgment algorithm. The constructability judgment algorithm evaluates whether the generated image is technically and structurally feasible based on a database of past construction projects and technical constraints. The evaluation results are output in text format.

[0566] Step 5:

[0567] The server then creates a final renovation plan based on the generated renovation images and the results of the construction feasibility assessment. This plan includes 3D rendered renovation images, specific construction procedures, and a timeline. The created plan is then sent to the user's device.

[0568] Step 6:

[0569] The user terminal receives the final renovation plan sent from the server and displays it to the user. The user checks the plan and requests revisions as necessary.

[0570] Step 7:

[0571] When a user selects furniture or interior items in a physical store, they scan the item using a camera on their smartphone or smart glasses. The user device then sends this product information to the server. The product information is sent to the server as image data and text data.

[0572] Step 8:

[0573] The server analyzes the received product information in real time and obtains detailed data on the selected product (size, color, material, etc.). Based on this, the generative AI model is used again to place the product in the image of the user's room in real time and generate a visual image.

[0574] Step 9:

[0575] The server evaluates the generated visual image to determine whether it is technically possible to arrange the product using a construction feasibility determination algorithm, and the product arrangement image including this evaluation result is sent to the user's terminal.

[0576] Step 10:

[0577] The user terminal displays the product layout image and technical evaluation results generated in real time to the user, allowing the user to make a decision on purchasing the product based on this.

[0578] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0579] This invention relates to a system that proposes renovations based on the user's requests, judges the feasibility of construction, and provides a total coordinated plan, and also combines it with an emotion engine that recognizes the user's emotions and reflects them in the renovation proposal. This system is an integrated system that includes a user terminal, a server, a generative AI model, a construction feasibility judgment algorithm, and an emotion engine.

[0580] System Overview

[0581] User device:

[0582] The user terminal is a device that allows users to input photos of the room, blueprints, and renovation requests. Users use their smartphones or PCs to enter information into a dedicated web form and send it to the server. Furthermore, the device is equipped with an emotion recognition function using a camera and microphone to detect the user's emotions in real time.

[0583] server:

[0584] The server receives data sent by users, generates renovation images using a generative AI model, and plays a central role in determining the feasibility of construction. In addition, the server uses an emotion engine to analyze users' emotions and adjusts renovation proposals based on the analysis results.

[0585] Generative AI models:

[0586] The generative AI model is an artificial intelligence model that generates visual images of the remodeled home based on photos, drawings, and renovation requests provided by the user. This model uses machine learning algorithms and deep learning technology.

[0587] Constructability determination algorithm:

[0588] The constructability algorithm is responsible for assessing whether the generated renovation image is technically and structurally feasible. The algorithm analyzes past construction data and technical constraints.

[0589] Emotion Engine:

[0590] The emotion engine analyzes the user's facial expressions, tone of voice, and other factors to recognize their emotions. This allows the system to understand in real time how the user feels about the renovation proposal. This emotional information is used to influence the renovation image generation process and to make proposals that will further increase user satisfaction.

[0591] Processing steps

[0592] User:

[0593] Users take photos of their rooms and prepare existing blueprints. They also enter their specific requests for the renovation in text format. For example, they might enter a request such as "I want the kitchen to have a modern design." Furthermore, the system's emotion recognition function analyzes emotions in real time based on facial expressions and voice.

[0594] Device:

[0595] The device sends the photos, drawings, renovation requests, and emotional information entered by the user to the server all at once, using an HTTP POST request.

[0596] server:

[0597] The server receives the data sent by the user and analyzes the room photos, blueprints, and renovation requests. It checks whether the received data is in the correct format and converts the data if necessary. The server also analyzes the user's emotional data in parallel.

[0598] Generative AI models and emotion engines:

[0599] The generative AI model takes photos, blueprints, and user requests as input and automatically generates an image of the desired remodeled home. The emotion engine analyzes the user's emotions and adjusts the remodeled image based on the results.

[0600] Constructability determination algorithm:

[0601] The generated renovation images are then fed into a constructability assessment algorithm to assess whether they are technically and structurally feasible, which involves advanced analysis.

[0602] server:

[0603] The server then creates a final renovation plan based on the renovation image whose feasibility has been confirmed. This plan includes 3D renderings of the renovation, specific construction procedures, and a timeline for each step. Adjustments can also be made based on the user's emotions. The final plan is then sent from the server to the user's device.

[0604] Device:

[0605] The terminal receives the final renovation plan sent from the server and displays it to the user. The user checks the contents of the final plan and provides feedback to the server on any corrections or additions they may require, if necessary.

[0606] User:

[0607] The user checks the final renovation plan and, if there are no problems, prepares to start construction. At this time, the server may also coordinate with the construction company.

[0608] Specific examples

[0609] For example, if a user wishes to have a modern design for their kitchen, they can take photos of the current state of the kitchen and upload the blueprints. This data is sent to the server, and the generative AI model generates an image of a "modern kitchen." Meanwhile, the emotion engine analyzes the user's emotions and determines whether the user is satisfied with the generated image. If the user is not satisfied, the server calls the AI ​​model again to adjust the image. Once a satisfactory renovation image is generated, a feasibility assessment algorithm performs a technical evaluation, and if construction feasibility is confirmed, a final renovation plan is created and provided to the user.

[0610] As described above, the system of the present invention efficiently and accurately reflects the user's wishes and feelings, optimizing the renovation process, and the user can receive consistent support throughout the entire renovation process.

[0611] The processing flow will be explained below.

[0612] Step 1:

[0613] The user takes a photo of their room and prepares existing blueprint data. They also enter their specific requests for the renovation in text format. For example, they might enter a request such as "I want the kitchen design to be modern." Furthermore, emotion data is recorded using a camera and microphone with emotion recognition capabilities.

[0614] Step 2:

[0615] Users access a dedicated web form on their smartphone or PC and upload the photos, blueprints, and renovation requests they have prepared. Emotional data is also sent at the same time.

[0616] Step 3:

[0617] The device sends the photos, drawings, renovation requests, and emotion data entered by the user to the server all at once using an HTTP POST request.

[0618] Step 4:

[0619] The server receives the data sent by the user, analyzes the photos, blueprints, and renovation requests, verifies that the received data is in the correct format, and converts the data if necessary.

[0620] Step 5:

[0621] The server calls a generative AI model based on the received data and generates a renovation image. The generative AI model receives photos, blueprints, and customer requests as input, and automatically generates an image of the user's desired renovation based on these.

[0622] Step 6:

[0623] The server analyzes the user's emotions using an emotion engine, which recognizes the user's emotions in real time from the transmitted audio and video data and analyzes the results.

[0624] Step 7:

[0625] The server adjusts the generated renovation image based on the emotional data obtained from the emotion engine. For example, if the user expresses dissatisfaction with the generated image, it provides feedback to the AI ​​model to regenerate a more satisfying image.

[0626] Step 8:

[0627] The server then inputs the highly satisfying renovation images evaluated by the emotion engine into a constructability judgment algorithm, which evaluates whether the generated renovation images are technically and structurally feasible.

[0628] Step 9:

[0629] Based on the confirmed feasibility of the renovation, the server creates a final renovation plan, which includes 3D renderings of the renovation, specific construction procedures, and a timeline for each step.

[0630] Step 10:

[0631] The server sends the data to the terminal to provide the final renovation plan to the user. The plan data is sent using an HTTP response.

[0632] Step 11:

[0633] The terminal receives the final renovation plan sent from the server and displays it to the user. The user checks the contents of the final plan and provides feedback to the server on any corrections or additions they may require, if necessary.

[0634] Step 12:

[0635] The user checks the final renovation plan and, if there are no problems, prepares to start construction. At this time, the server may also coordinate with the construction company.

[0636] As a result, the system of the present invention efficiently and accurately reflects the user's wishes and feelings, optimizing the renovation process, allowing the user to receive consistent support throughout the renovation process and achieving a high level of satisfaction with the renovation.

[0637] Example 2

[0638] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0639] Current renovation proposal systems provide renovation plans based on user requests, but do not take the user's emotions into consideration, which can result in low user satisfaction. Furthermore, there is a risk that the plan will not be realized due to uncertainty about technical feasibility. Therefore, there is a need for a renovation proposal system that takes into consideration both the user's emotions and the technical feasibility of construction.

[0640] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a room photo from the user as input, means for receiving a blueprint from the user as input, means for receiving a renovation request from the user as input, means for generating a renovation image using a generative artificial intelligence model based on the received room photo, blueprint, and renovation request, means for detecting the user's emotions in real time, means for using an emotion engine to analyze the detected user's emotion data, means for adjusting the renovation image based on the analysis results, means for determining whether the generated renovation image is feasible, and means for providing the generated renovation image and the determination result of feasibility to the user. This enables highly accurate renovation proposals that take into account the user's emotions and technical feasibility.

[0641] A "user" is an individual or group who wishes to remodel and provides the system with data such as photographs, drawings, and requests for remodeling.

[0642] A "room photo" is a recorded image of the current state of a room that the user wishes to remodel.

[0643] "Drawings" are design drawings that show the structure and dimensions of the room to be renovated.

[0644] "Renovation requests" is information that a user inputs into the system in text format describing the changes and design they would like to make to the room to be renovated.

[0645] A "generative artificial intelligence model" is an algorithm that uses machine learning and deep learning technologies to generate a visual image of the renovated home based on data provided by the user.

[0646] A "renovation image" is a visual image of a room after renovation, generated by a generative artificial intelligence model.

[0647] The "emotion engine" is an algorithm that detects and analyzes the user's emotions in real time.

[0648] The "construction feasibility determination algorithm" is an algorithm that determines whether the generated renovation image is technically and structurally feasible.

[0649] A "total coordination plan" is a final plan that includes 3D rendering images, construction procedures, and a timeline for each step required for the renovation.

[0650] A "device" is an appliance used by a user to input photos, drawings, and requests into the system and to receive renovation proposals from the system.

[0651] This invention relates to a system that proposes renovations based on user requests, determines the feasibility of construction, and provides a total coordinated plan, as well as a system that combines an emotion engine that recognizes the user's emotions and reflects them in the renovation proposal. This system is an integrated system that includes a user terminal, a server, a generative AI model, a construction feasibility determination algorithm, and an emotion engine.

[0652] User device:

[0653] The user terminal is a device that allows users to input photos of their rooms, blueprints, and renovation requests. Users use their smartphones or PCs to enter information into a dedicated web form and send it to the server. Furthermore, the terminal is equipped with an emotion recognition function using a camera and microphone to detect the user's emotions in real time.

[0654] server:

[0655] The server receives data sent by users, uses a generative AI model to generate renovation images and plays a central role in determining feasibility. Additionally, the server uses an emotion engine to analyze users' emotions and adjust renovation proposals based on the analysis results. Specific software includes frameworks for machine learning and deep learning.

[0656] Generative AI models:

[0657] A generative AI model is an artificial intelligence model that generates visual images of the remodeled home based on photos, blueprints, and renovation requests provided by the user. This model uses machine learning algorithms and deep learning technology. For example, if a user inputs a request such as "I want a modern kitchen design," the generative AI model will generate a visual image of a modern kitchen based on this request.

[0658] Emotion Engine:

[0659] The emotion engine analyzes the user's facial expressions, tone of voice, and other factors to recognize the user's emotions. For this purpose, an emotion recognition algorithm is used. This allows the system to understand in real time how the user feels about the renovation proposal. For example, if the user is dissatisfied with the generated renovation image, the emotion engine analyzes this and sends feedback to the server.

[0660] Constructability determination algorithm:

[0661] The constructability algorithm evaluates whether the generated renovation image is technically and structurally feasible. This algorithm analyzes past construction data and technical constraints. For example, it determines whether the user's desired design is compatible with the actual building structure.

[0662] Total coordination plan:

[0663] The server then creates a final renovation plan based on the confirmed feasibility of the renovation. The plan includes 3D renderings of the renovation, specific construction steps, and a timeline for each step. It can also make adjustments based on the user's emotions.

[0664] Specific examples

[0665] For example, if a user wishes to have a modern design for their kitchen, they can take photos of the current state of the kitchen and upload the blueprints. This data is sent to the server, and the generative AI model generates an image of a "modern kitchen." Meanwhile, the emotion engine analyzes the user's emotions and determines whether the user is satisfied with the generated image. If the user is not satisfied, the server calls the AI ​​model again to adjust the image. Once a satisfactory renovation image is generated, a feasibility assessment algorithm performs a technical evaluation, and if construction feasibility is confirmed, a final renovation plan is created and provided to the user.

[0666] For example, by entering a prompt such as "I want to remodel my kitchen into a modern design," the generative AI model will generate a visual image of a modern kitchen, and the emotion engine will analyze the user's emotions and adjust the proposal. Finally, a remodeling plan that has undergone technical evaluation will be provided.

[0667] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0668] Step 1:

[0669] The user inputs their renovation requests into the device. First, the user uses a smartphone or PC to enter photos of their room, blueprints, and specific renovation requests into a dedicated web form. For example, they can enter a request such as "I want my kitchen to have a modern design" in text format. The device's camera and microphone are then used to detect the user's emotions in real time and collect emotional data. The input data includes photos of the room, blueprints, text of the renovation requests, and emotional data.

[0670] Step 2:

[0671] The device sends the input data to the server. The device then sends the photos, drawings, renovation requests, and emotional information entered by the user to the server all at once. Specifically, the data is sent to the server using an HTTP POST request, and the series of data reaches the server. The input data is sent according to the format (JPEG, PNG, text, etc.).

[0672] Step 3:

[0673] The server receives the data and checks the format. The server receives the data sent by the user. First, it checks whether the format of the received data is correct. For example, it checks whether photo data is in JPEG format and whether drawing data is in a compatible format. If necessary, it converts the data into the appropriate format. The input to this step is the various data sent by the user, and the output is the data whose format has been checked.

[0674] Step 4:

[0675] The server calls the generative AI model to generate a visual image of the renovation. The server inputs the received photos, blueprints, and renovation requests of the room into the generative AI model. This model uses machine learning and deep learning technology to generate a visual image of the renovated state. Specifically, it understands the room structure and the user's requests and outputs the optimal design proposal. The input data are photos, blueprints, and renovation requests, and the output is the generated visual image.

[0676] Step 5:

[0677] The server analyzes the user's emotions using an emotion engine. The server inputs the user's emotion data into the emotion engine. The emotion engine analyzes facial expressions and voice to grasp the user's emotional state in real time. For example, it determines whether the user is happy or dissatisfied. The input data is emotion information, and the output is the analyzed emotional state.

[0678] Step 6:

[0679] The server adjusts the renovation image based on the analysis results. It receives the emotion engine's analysis results and adjusts the renovation visual image. For example, if the user's satisfaction is low, it uses the generative AI model again to generate a new image. This step uses the analyzed emotional state and the initial visual image, and outputs the adjusted final visual image.

[0680] Step 7:

[0681] The server applies an algorithm to determine constructability. The server inputs the adjusted renovation image into the constructability algorithm to evaluate whether it is technically and structurally feasible. The analysis is performed by referencing past construction data and technical constraints. The input data is the final visual image, and the output is the constructability judgment result.

[0682] Step 8:

[0683] The server creates the final renovation plan. Based on the renovation image whose constructability has been confirmed, the server creates the final renovation plan. This plan includes 3D rendering images of the renovation, specific construction procedures, and a timeline for each step. The input data is the renovation image whose constructability has been confirmed, and the output is the final renovation plan.

[0684] Step 9:

[0685] The server sends the final plan to the terminal. The created final renovation plan is then sent from the server to the terminal. This is done using an HTTP POST request as well. The input data is the final renovation plan, and the output is the plan information sent to the user's terminal.

[0686] Step 10:

[0687] The terminal displays the final renovation plan to the user. The terminal receives the final renovation plan sent from the server and displays it to the user. The user checks the proposed plan in detail. The input data is the final renovation plan, and the output is plan information that can be visually confirmed.

[0688] Step 11:

[0689] The user may check the plan and send a request for revision. If the user has any requests for revisions or additions to the final plan, they can feed this back to the server from their terminal. For example, they may send a request such as "I want the kitchen counter to be a little wider." The input data is the revision request, and the output is the new data requesting the revision.

[0690] Step 12:

[0691] The user finalizes the plan and prepares for construction. If the user is satisfied with the final plan and there are no problems, they prepare to begin construction of the renovation. At this time, the server coordinates with the contractor as necessary to support a smooth construction process. The data input is the confirmation result of the final plan, and the data output is the state of construction preparation.

[0692] (Application example 2)

[0693] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0694] In systems for proposing renovations and assessing the feasibility of construction, there is a need to reflect user emotions in real time and make proposals that will increase satisfaction. In addition, when proposing entertainment content for autonomous vehicles, it is also important to provide optimal content that reflects the passengers' emotions, thereby increasing passenger comfort.

[0695] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0696] In this invention, the server includes means for receiving a room photo as input from the user, means for receiving a blueprint from the user as input, means for receiving a remodeling request from the user as input, means for generating a remodeling image using a generative artificial intelligence model based on the received room photo, blueprint, and remodeling request, means for determining whether the generated remodeling image is feasible, means for providing the generated remodeling image and the determination result of feasibility to the user, means for using an emotion engine to detect the user's emotions and reflect them in remodeling proposals, and means for proposing in-vehicle entertainment content and recognizing the user's emotions to adjust the proposal content. This makes it possible to provide optimal remodeling proposals and in-vehicle entertainment content that reflect the user's emotions.

[0697] The "means for receiving a room photo as an input from a user" is a device or interface for collecting a room photo specified by the user in digital form.

[0698] The "means for receiving drawing data from a user as input" is a device or interface that receives drawing data of a room or building provided by a user.

[0699] The "means for receiving input of renovation requests from the user" is an interface that allows the user to input specific renovation requests and wishes in text or voice format.

[0700] "Means for generating a remodeling image using a generative artificial intelligence model based on received room photos, drawings, and remodeling requests" refers to an artificial intelligence system that uses data provided by the user to automatically create a visual image of the remodeled room.

[0701] The "means for determining whether the generated renovation image is feasible" refers to an algorithm or system that evaluates whether the created renovation image is technically and structurally feasible.

[0702] The "means for providing the user with the generated renovation image and the judgement result of the feasibility of construction" is a system or interface that notifies the user of the evaluated renovation image and information about its feasibility of construction.

[0703] "Means using an emotion engine to detect user emotions and reflect them in renovation proposals" refers to an artificial intelligence system that analyzes emotions from the user's facial expressions, voice, etc., and reflects the results in renovation proposals.

[0704] The "means for suggesting in-vehicle entertainment content and adjusting the suggested content based on the user's emotions" refers to a system that provides entertainment content exclusively for passengers in autonomous vehicles and changes the content in real time according to the passengers' emotions.

[0705] System in general

[0706] This system receives photos, blueprints, and renovation requests from users, generates renovation images using a generative AI model, and determines the feasibility of the renovation. It also includes an emotion engine that detects the user's emotions and reflects them in the renovation proposals. It also includes a function to suggest in-vehicle entertainment content and adjust the proposals based on the user's emotions.

[0707] Hardware and Software

[0708] 1. User Device

[0709] Devices such as smartphones and computers are used.

[0710] This allows users to enter photos of the room, blueprint data, and renovation requests.

[0711] The user device is equipped with a camera and a microphone to collect data for emotion recognition.

[0712] 2. Server

[0713] A server with high-performance data processing capabilities is required.

[0714] The server is equipped with hardware (e.g., GPU) and software (e.g., TensorFlow) for implementing generative AI models.

[0715] Emotion engines for emotion recognition include, for example, Google's Cloud Vision API and IBM Watson.

[0716] Data Processing and Data Calculation

[0717] 1. Data collection and input

[0718] Photos, drawings, and renovation requests for the room sent from the user terminal are received by the server.

[0719] Send this data to the server using an HTTP POST request.

[0720] 2. Creating a Renovation Image

[0721] The server analyzes the received data and inputs it into a generative AI model.

[0722] The model generates a visual image of the renovated area based on the received data.

[0723] This generative AI model uses machine learning algorithms and deep learning techniques.

[0724] 3. Judgment of construction feasibility

[0725] The generated renovation images are then run through an algorithm to determine the technical and structural feasibility of construction.

[0726] The algorithm includes historical construction data and technical constraints.

[0727] 4. Emotion recognition and regulation

[0728] Using an emotion engine, the user's emotional data (facial expressions and tone of voice) is analyzed and reflected in the renovation image.

[0729] The renovation image is readjusted based on the emotional data.

[0730] 5. Entertainment content proposals

[0731] The server proposes entertainment content for the vehicle.

[0732] Here too, the emotion engine works, tailoring content suggestions based on the user's emotions.

[0733] Specific examples

[0734] If a user has a desire to "modernize their kitchen," they can take photos of the current state of the kitchen and upload the blueprint. The server receives this data and uses a generative AI model to generate a "modern kitchen" renovation image. At the same time, an emotion engine analyzes the user's emotions and determines their satisfaction with the generated image. If the emotion is not positive, the server will adjust the renovation image again.

[0735] Additionally, cameras and microphones capture passengers' facial expressions and voices as they board the vehicle. For example, if a passenger has a relaxed facial expression, the system can use this information to suggest relaxing music or movies.

[0736] Prompt Sentence Examples

[0737] "The user appears relaxed. Please suggest entertainment content appropriate for this situation."

[0738] "The user's facial expression and voice indicate that they are stressed. Please suggest entertainment content to ease this situation."

[0739] As described above, the present invention is a system that makes it possible to provide optimal renovation proposals and in-vehicle entertainment content that reflect the user's emotions.

[0740] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0741] Step 1:

[0742] The user uses the terminal to input and send photos and drawings of the room and requests for renovation.

[0743] Input: Room photos, blueprints, renovation requests (e.g., "I want a modern kitchen design")

[0744] Specific operation: The user uses a smartphone or computer to enter photos of the room, drawings, and renovation requests into a dedicated web form and press the submit button.

[0745] Output: The submitted data is sent to the server.

[0746] Step 2:

[0747] The server analyzes the data received from the terminal and checks whether the data format is correct.

[0748] Input: Received data (room photos, drawings, renovation requests)

[0749] Specific operation: The server analyzes the format of the data received and performs data conversion as necessary.

[0750] Output: Parsed data, transformed data

[0751] Step 3:

[0752] The server inputs the analyzed data into a generative AI model to generate a renovation image.

[0753] Input: Photos of the analyzed room, drawings, and renovation requests

[0754] Specific operation: The server uses a generative artificial intelligence model to generate a visual image of the remodeled home based on the input data.

[0755] Output: Generated renovation image

[0756] Step 4:

[0757] The server inputs the generated renovation image into a construction feasibility determination algorithm and performs a technical evaluation.

[0758] Input: Generated renovation image

[0759] Specific operation: The server executes a constructability determination algorithm to evaluate whether the generated renovation image is technically and structurally feasible.

[0760] Output: Construction feasibility judgment result

[0761] Step 5:

[0762] The server collects the user's emotional data and analyzes it using an emotion engine.

[0763] Input: User's facial expression data, voice data

[0764] Specific operation: The device's camera and microphone are used to collect the user's facial expressions and voice, which are then sent to the server, where they are analyzed using an emotion engine.

[0765] Output: Emotion analysis results

[0766] Step 6:

[0767] The server adjusts the renovation image based on the results of emotion analysis.

[0768] Input: User sentiment analysis results, generated renovation image

[0769] Specific operation: The server uses the results of emotion analysis to readjust the generated renovation image.

[0770] Output: Adjusted renovation image

[0771] Step 7:

[0772] The server provides the user with the final renovation image and construction feasibility results.

[0773] Input: Adjusted renovation image, construction feasibility assessment results

[0774] Specific operation: The server compiles the final renovation image and the results of the feasibility of construction and sends them to the user's terminal.

[0775] Output: The final renovation plan provided to the user

[0776] Step 8:

[0777] The user reviews the final renovation plan and provides feedback to make adjustments as needed.

[0778] Input: Final renovation plan, user feedback

[0779] Specific operation: The user reviews the final renovation plan, enters any corrections or additions they would like to make, and sends feedback to the server.

[0780] Output: User feedback, requests for further proposals

[0781] Step 9:

[0782] The server receives feedback from the user and adjusts the renovation image again.

[0783] Input: User feedback

[0784] How it works: The server analyzes the feedback and uses the generative AI model and emotion engine again to refine the renovation image.

[0785] Output: Re-adjusted renovation image

[0786] Step 10:

[0787] The server proposes in-vehicle entertainment content and adjusts it based on the user's emotions.

[0788] Input: User's facial expression data, voice data

[0789] Specific operation: The system uses cameras and microphones inside the vehicle to collect the user's facial expressions and voice, which are then analyzed by the emotion engine. Based on the analysis results, entertainment content is suggested and adjusted as necessary.

[0790] Output: Suggested entertainment content, adjustments

[0791] In this way, the present invention makes it possible to provide optimal renovation proposals and in-vehicle entertainment content that reflect the user's emotions.

[0792] 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.

[0793] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0794] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0795] [Third embodiment]

[0796] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0797] 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.

[0798] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. 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).

[0799] 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.

[0800] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0801] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0802] 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. 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.

[0803] 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.

[0804] 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 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.

[0805] 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.

[0806] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0807] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0808] This invention is a system that proposes renovations based on the user's requests, assesses the feasibility of construction, and provides a total coordinated plan. This system is an integrated system that includes a user terminal, a server, a generative AI model, and a construction feasibility assessment algorithm.

[0809] System Overview

[0810] User device:

[0811] The user terminal is a device that allows users to input photos of the room, drawings, and renovation requests. Users use their smartphones or PCs to enter information into a dedicated web form and send it to the server.

[0812] server:

[0813] The server plays a central role in receiving data sent by users, generating renovation images using generative AI models, and determining the feasibility of construction. To achieve these functions, the server is equipped with advanced data processing capabilities and algorithms.

[0814] Generative AI models:

[0815] The generative AI model is an artificial intelligence model that generates visual images of the remodeled home based on photos, drawings, and renovation requests provided by the user. This model uses machine learning algorithms and deep learning technology.

[0816] Constructability determination algorithm:

[0817] The constructability algorithm is responsible for assessing whether the generated renovation image is technically and structurally feasible. The algorithm analyzes past construction data and technical constraints.

[0818] Processing steps

[0819] User:

[0820] Users take photos of their own rooms and prepare existing blueprints. Next, they enter the photos, blueprints, and renovation requests into a dedicated web form. For example, they write requests such as "I want a modern design for the kitchen" in the text field. The entered data is then sent from the device to the server.

[0821] Device:

[0822] The terminal sends the data entered by the user to the server in bulk, typically by using an HTTP POST request.

[0823] server:

[0824] The server analyzes the received data, obtaining photos of the room, blueprints, and renovation requests. This data is then input into a generative AI model to generate a renovation image. Based on the generated renovation image, a feasibility assessment algorithm performs technical and structural evaluations.

[0825] Generative AI model and constructability algorithm:

[0826] The generative AI model automatically creates remodeling images based on the user's requirements, and then a constructability algorithm evaluates the images to determine whether they are technically and structurally feasible.

[0827] server:

[0828] Based on the results of the feasibility assessment, the server creates a final renovation plan, which includes 3D rendered images of the renovation, specific construction procedures, and a timeline. The final plan is then sent from the server to the user's device.

[0829] User device:

[0830] The terminal receives the final renovation plan sent from the server and displays it to the user, who can then check the final plan and request any necessary revisions.

[0831] Specific examples

[0832] For example, if a user wants to "modernize their kitchen," they can take photos of the current state of the kitchen and upload the blueprints. This data is sent to the server, and the generative AI model generates an image of the "modern kitchen." Next, a constructability assessment algorithm evaluates whether the generated image is feasible. If the evaluation result is favorable, the server creates a final renovation plan and provides it to the user.

[0833] As described above, the system of the present invention efficiently and accurately reflects the user's requests and optimizes the renovation process.

[0834] The processing flow will be explained below.

[0835] Step 1:

[0836] Users take photos of the room they want to remodel and prepare existing blueprint data. They also enter their specific requests for the remodel in text format. For example, they might enter a request such as, "I want the kitchen design to be modern."

[0837] Step 2:

[0838] Users access a dedicated web form on their smartphone or computer and upload the photos, blueprints, and renovation requests they have prepared. Each piece of data is entered into the corresponding field.

[0839] Step 3:

[0840] The device sends the photos, drawings, and renovation requests entered by the user to the server all at once, using an HTTP POST request.

[0841] Step 4:

[0842] The server receives the data sent by the user, analyzes the room photos, blueprints, and renovation requests, checks whether the received data is in the correct format, and converts the data if necessary.

[0843] Step 5:

[0844] The server calls a generative AI model based on the received data and generates a renovation image. The generative AI model receives photos, blueprints, and customer requests as input, and automatically generates an image of the user's desired renovation based on these.

[0845] Step 6:

[0846] The server then inputs the generated renovation images into a constructability assessment algorithm to evaluate whether the proposed renovation is technically and structurally feasible, which includes detailed analysis based on past construction data and technical constraints.

[0847] Step 7:

[0848] Based on the confirmed feasibility of the renovation, the server creates a final renovation plan, which includes 3D renderings of the renovation, specific construction procedures, and a timeline for each step.

[0849] Step 8:

[0850] The server sends the data to the terminal to provide the final renovation plan to the user. The plan data is sent using an HTTP response.

[0851] Step 9:

[0852] The terminal receives the final renovation plan sent from the server and displays it to the user. The user checks the contents of the final plan and provides feedback to the server on any corrections or additions they may require, if necessary.

[0853] Step 10:

[0854] The user checks the final renovation plan and, if there are no problems, prepares to start construction. At this time, the server may also coordinate with the construction company.

[0855] Through the above processing steps, the system of the present invention efficiently and accurately proposes renovations that reflect the user's requests and determines the feasibility of construction. The user can receive consistent support throughout the renovation process.

[0856] Example 1

[0857] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0858] Conventional renovation proposal systems have had difficulty generating renovation images that accurately reflect the user's requests and quickly and accurately assessing the feasibility of the renovation. Furthermore, when proposing a specific total coordination plan based on the room images and blueprints submitted by the user, manual evaluation and planning are time-consuming and inefficient.

[0859] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0860] In this invention, the server includes means for receiving a room image as input from a user, means for receiving a structure blueprint as input from a user, means for receiving a remodeling request as input from a user, means for generating a remodeling image using a generative AI model based on the received room image, structure blueprint, and remodeling request, means for determining whether the generated remodeling image is technically and structurally feasible, and means for providing the generated remodeling image and the technical and structural determination results to the user. This makes it possible to accurately reflect the user's requests, evaluate the feasibility of construction, and efficiently and accurately provide remodeling proposals and total coordination plans.

[0861] A "room image" is a photograph or digital image taken by a user that shows the current state of a room.

[0862] A "structural blueprint" is a drawing that shows the structure and layout of a room or building, including the location and dimensions of walls, floors, ceilings, windows, doors, etc.

[0863] The "renovation request" is information describing the details of the renovation or remodeling desired by the user in text or other formats. For example, it includes a specific request such as "I want the kitchen to have a modern design."

[0864] A "generative artificial intelligence model" is an artificial intelligence model that uses machine learning algorithms and deep learning technology to generate renovation images based on user requests.

[0865] The "renovation image" is a visual image of the renovated room generated by the generative AI model, which reflects the user's wishes.

[0866] "Means for determining technical and structural feasibility" refers to algorithms and methods that evaluate whether the generated renovation image can actually be constructed based on past construction data and existing technical constraints.

[0867] The "overall coordination plan" is an integrated plan for each element of the renovation, including 3D rendered images of the renovation, specific construction procedures, and a timeline.

[0868] This invention is a system that proposes renovations based on the user's requests, assesses the feasibility of construction, and provides a total coordinated plan. This system is an integrated system that includes a user terminal, a server, a generative AI model, and a construction feasibility assessment algorithm.

[0869] User terminal

[0870] The user terminal is a device that allows users to input images of rooms, blueprints of structures, and renovation requests. Users access a dedicated web form using a smartphone or PC and enter the necessary information.

[0871] server

[0872] The server receives data sent by users, generates renovation images using generative AI models, and plays a central role in determining whether construction is possible. The server requires advanced data processing capabilities, and the construction feasibility determination algorithm also runs on the server.

[0873] Generative AI Models

[0874] The generative AI model is an artificial intelligence model that generates visual images of the renovated building based on user-provided room images, structural blueprints, and renovation requests. This model uses machine learning algorithms and deep learning technology to generate realistic visual images.

[0875] Constructibility determination algorithm

[0876] The constructability algorithm is responsible for assessing whether the generated renovation images are technically and structurally feasible. The algorithm performs an analysis based on past construction data and current technical constraints.

[0877] Specifically, the user first takes a photo of their room and prepares a blueprint for the structure. Next, they enter the photos, blueprints, and renovation requests into a dedicated web form and send it from their device to the server. The device then sends the data entered by the user to the server using an HTTP POST request.

[0878] The server analyzes the received data and obtains images of the room, blueprints, and renovation requirements. This data is then input into a generative AI model to generate a renovation image. Based on the generated renovation image, a feasibility assessment algorithm performs a technical and structural evaluation.

[0879] As a concrete example, if a user wishes to have a modern design for their kitchen, they can take photos of the current state of the kitchen and upload the blueprint. This data is sent to the server, and a generative AI model generates an image of the "modern kitchen." Next, a constructability assessment algorithm evaluates whether the generated image is feasible. If the evaluation result is favorable, the server creates a final renovation plan and provides it to the user.

[0880] An example of a prompt for a generative AI model is as follows:

[0881] "Please generate an image of a modern kitchen renovation. I've attached photos of the current situation and blueprints."

[0882] "Please create a design that will make this living room look larger."

[0883] The system of the present invention efficiently and accurately reflects the user's requests and optimizes the renovation process.

[0884] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0885] Step 1:

[0886] User: The user accesses a dedicated web form using their smartphone or PC. They take a picture of the room and prepare a blueprint of the structure. They then enter their renovation requests into a text field. This includes specific requests such as "I want the kitchen to have a modern design." After this, they press the send button to send the entered data from their device to the server.

[0887] Input: Room image, structure blueprint, renovation request

[0888] Output: Data collected by the user and ready to be sent

[0889] Step 2:

[0890] Terminal: The terminal collects the data entered by the user in bulk and sends it to the server using an HTTP POST request, including a function to check the accuracy and format of the data. It notifies the user that the data has been sent.

[0891] Input: Data collected by the user

[0892] Output: Data sent to the server via an HTTP POST request

[0893] Step 3:

[0894] Server: The server receives the HTTP POST request and analyzes the data contained in the request body. The server extracts the room images, the building blueprints, and the renovation requests separately and checks the data format of each. The analysis results are temporarily stored for use in the next step.

[0895] Input: Data sent to the server as an HTTP POST request

[0896] Output: Images of the analyzed rooms, blueprints of the structure, and renovation requests

[0897] Step 4:

[0898] Server: The server creates a prompt to be input to the generative AI model based on the analyzed data. For example, it generates a prompt such as, "Please generate an image of a modern kitchen renovation. I have attached a photo of the current state and a blueprint." The server then sends this prompt along with the image and blueprint as input data to the generative AI model.

[0899] Input: Parsed room image, structure blueprint, and prompt text

[0900] Output: The data and prompts that are fed into the generative AI model

[0901] Step 5:

[0902] Generative AI Model: The generative AI model uses machine learning algorithms and deep learning techniques to generate a realistic, high-quality visual image of the renovation based on the received data and prompts. The generated image is then returned to the server.

[0903] Input: Room image, structure blueprint, and prompt

[0904] Output: Generated remodeled image

[0905] Step 6:

[0906] Server: The server receives the remodeling image returned from the generative AI model and launches the constructability judgment algorithm. The server provides the received remodeling image as input.

[0907] Input: Generated remodeled image

[0908] Output: Construction feasibility judgment result

[0909] Step 7:

[0910] Constructability Determination Algorithm: The algorithm determines whether the renovation image is technically and structurally feasible. This is done based on past construction data and existing technical constraints. The evaluation results are returned to the server.

[0911] Input: Generated remodeled image

[0912] Output: Technical and structural feasibility assessment results

[0913] Step 8:

[0914] Server: The server creates a final renovation plan based on the evaluation results. The final plan includes 3D rendered renovation images, specific construction procedures, and a timeline. The server then sends the final renovation plan to the user's device.

[0915] Input: Technical and structural feasibility assessment results

[0916] Output: Final renovation plan

[0917] Step 9:

[0918] User terminal: The user terminal receives the final renovation plan sent from the server. The received final plan is displayed to the user and prompted for confirmation. The user can review the final plan and use the option to request revisions if necessary.

[0919] Input: Final renovation plan

[0920] Output: The final renovation plan displayed to the user

[0921] (Application example 1)

[0922] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0923] With current renovation proposal systems, when users select furniture and interior items in a physical store, it is difficult for them to check in real time how the selected items will be arranged. Furthermore, there is also the issue of not being able to immediately make a technical judgment as to whether the selected furniture and interior items can actually be arranged. This often causes inconvenience to users, as they are unable to get a concrete image of how the room will look after renovation before purchasing. Furthermore, insufficient confirmation of feasibility increases the risk of problems occurring after purchase.

[0924] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0925] In this invention, the server includes means for receiving a room photo as input from a user, means for receiving a blueprint from the user as input, means for receiving a remodeling request from the user as input, means for generating a remodeling image using a generative artificial intelligence model based on the received room photo, blueprint, and remodeling request, means for determining whether the generated remodeling image is feasible, means for providing the generated remodeling image and the determination result of feasibility to the user, means for arranging the items in the room image in real time when the user selects furniture and interior items on site to generate a visual image, and means for determining whether these images are technically feasible to arrange. This allows the user to check the arrangement image of the items selected in the physical store in real time and instantly determine the technical feasibility of arranging the items.

[0926] A "user terminal" is an information processing device that allows a user to input requests for renovations and furniture selection.

[0927] A "server" is a central information processing system that receives, analyzes, and processes data sent from user terminals.

[0928] A "generative AI model" is an artificial intelligence model that uses deep learning and machine learning algorithms to generate visual images of the renovated home based on the user's requests.

[0929] The "construction feasibility determination algorithm" is an algorithm for evaluating whether the generated renovation image is technically and structurally feasible.

[0930] A "renovation image" is a visual image of the room after renovation, generated based on photos, drawings, and requests of the room provided by the user.

[0931] "Means for determining whether the furniture or interior can be technically arranged" refers to a means for making a technical judgment as to whether the selected furniture or interior can actually be arranged.

[0932] "Means for arranging products in real time and generating visual images" refers to means for instantly arranging products in an image of a room and generating a visual image when the product is selected in a physical store.

[0933] "Furniture and interior" is a general term for household goods and decorative items placed inside a room.

[0934] As an embodiment of the present invention, we will explain a method for supporting furniture and interior design selection in a brick-and-mortar store using a renovation proposal system. This system consists of a user terminal, a server, a generative AI model, and a construction feasibility determination algorithm.

[0935] The user terminal is an information processing device such as a smartphone or smart glasses, and the user uses it to input their requests for renovations and furniture selection. The user takes a photo of the room with the terminal, inputs a blueprint, and enters their renovation requests in text. The user terminal sends this data to the server. An HTTP POST request is used to send the data.

[0936] The server receives and analyzes data sent from the user's device. It acquires photos of the room, blueprints, and renovation requests and inputs them into a generative AI model. The generative AI model uses deep learning algorithms such as TensorFlow to generate a visual image of the room after renovation. The generated renovation image is evaluated by a feasibility assessment algorithm to determine whether it is technically and structurally feasible.

[0937] The generative AI model and constructability assessment algorithm evaluate whether the generated renovation image is feasible based on past construction data and technical constraints. The generated renovation image and the constructability assessment results are sent to the user's device and provided to the user.

[0938] When a user selects furniture or interior items in a physical store, they use smart glasses or a smartphone to select the items and input the information into the terminal in real time. At this time, the items are placed on an image of the room in real time, generating a visual image. Furthermore, the system has the function of instantly determining whether these images are technically possible to arrange.

[0939] As a concrete example, consider a user in a furniture store considering a new dining set. The user holds up smart glasses to select a dining set and see in real time how it will be placed in their current kitchen. The server uses a generative AI model to generate a visual image of a "modern dining set" placed in the current kitchen and determines whether the placement is technically possible. This information is sent to the user's device, allowing the user to instantly see how the room will look after the renovation.

[0940] An example of a prompt is as follows:

[0941] "Please input a photo of the user's current room and generate an image of a new modern dining set. Specifically, please create a visual of how it would blend with the current kitchen, including a white modern table and four chairs."

[0942] With this system, when a user selects a product in a physical store, they can instantly see how the product will be positioned, and can also instantly check whether that placement is technically feasible.

[0943] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0944] Step 1:

[0945] The user uses a smartphone or smart glasses to take a photo of the room, input the blueprint, and enter the renovation request as text. This data is sent from the user's device to the server. The input data consists of image data (photo of the room) and text data (blueprint, renovation request), and the server receives this data.

[0946] Step 2:

[0947] The server analyzes the received photos of the room, blueprints, and renovation requests. The analyzed data is input into a generative AI model. The photos of the room are preprocessed using an image processing library (e.g., OpenCV), and the blueprints and renovation requests are converted into an appropriate format using a text processing library (e.g., NLTK).

[0948] Step 3:

[0949] The server uses a generative AI model to generate a visual image of the remodeled room based on the user's requests. The generative AI model (e.g., a model trained using TensorFlow) takes the preprocessed data as input and outputs a 3D rendering of the remodeled room. This output is the initial version of the remodeled image provided to the user.

[0950] Step 4:

[0951] The server inputs the generated renovation image into a constructability judgment algorithm. The constructability judgment algorithm evaluates whether the generated image is technically and structurally feasible based on a database of past construction projects and technical constraints. The evaluation results are output in text format.

[0952] Step 5:

[0953] The server then creates a final renovation plan based on the generated renovation images and the results of the construction feasibility assessment. This plan includes 3D rendered renovation images, specific construction procedures, and a timeline. The created plan is then sent to the user's device.

[0954] Step 6:

[0955] The user terminal receives the final renovation plan sent from the server and displays it to the user. The user checks the plan and requests revisions as necessary.

[0956] Step 7:

[0957] When a user selects furniture or interior items in a physical store, they scan the item using a camera on their smartphone or smart glasses. The user device then sends this product information to the server. The product information is sent to the server as image data and text data.

[0958] Step 8:

[0959] The server analyzes the received product information in real time and obtains detailed data on the selected product (size, color, material, etc.). Based on this, the generative AI model is used again to place the product in the image of the user's room in real time and generate a visual image.

[0960] Step 9:

[0961] The server evaluates the generated visual image to determine whether it is technically possible to arrange the product using a construction feasibility determination algorithm, and the product arrangement image including this evaluation result is sent to the user's terminal.

[0962] Step 10:

[0963] The user terminal displays the product layout image and technical evaluation results generated in real time to the user, allowing the user to make a decision on purchasing the product based on this.

[0964] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0965] This invention relates to a system that proposes renovations based on the user's requests, judges the feasibility of construction, and provides a total coordinated plan, and also combines it with an emotion engine that recognizes the user's emotions and reflects them in the renovation proposal. This system is an integrated system that includes a user terminal, a server, a generative AI model, a construction feasibility judgment algorithm, and an emotion engine.

[0966] System Overview

[0967] User device:

[0968] The user terminal is a device that allows users to input photos of the room, blueprints, and renovation requests. Users use their smartphones or PCs to enter information into a dedicated web form and send it to the server. Furthermore, the device is equipped with an emotion recognition function using a camera and microphone to detect the user's emotions in real time.

[0969] server:

[0970] The server receives data sent by users, generates renovation images using a generative AI model, and plays a central role in determining the feasibility of construction. In addition, the server uses an emotion engine to analyze users' emotions and adjusts renovation proposals based on the analysis results.

[0971] Generative AI models:

[0972] The generative AI model is an artificial intelligence model that generates visual images of the remodeled home based on photos, drawings, and renovation requests provided by the user. This model uses machine learning algorithms and deep learning technology.

[0973] Constructability determination algorithm:

[0974] The constructability algorithm is responsible for assessing whether the generated renovation image is technically and structurally feasible. The algorithm analyzes past construction data and technical constraints.

[0975] Emotion Engine:

[0976] The emotion engine analyzes the user's facial expressions, tone of voice, and other factors to recognize their emotions. This allows the system to understand in real time how the user feels about the renovation proposal. This emotional information is used to influence the renovation image generation process and to make proposals that will further increase user satisfaction.

[0977] Processing steps

[0978] User:

[0979] Users take photos of their rooms and prepare existing blueprints. They also enter their specific requests for the renovation in text format. For example, they might enter a request such as "I want the kitchen to have a modern design." Furthermore, the system's emotion recognition function analyzes emotions in real time based on facial expressions and voice.

[0980] Device:

[0981] The device sends the photos, drawings, renovation requests, and emotional information entered by the user to the server all at once, using an HTTP POST request.

[0982] server:

[0983] The server receives the data sent by the user and analyzes the room photos, blueprints, and renovation requests. It checks whether the received data is in the correct format and converts the data if necessary. The server also analyzes the user's emotional data in parallel.

[0984] Generative AI models and emotion engines:

[0985] The generative AI model takes photos, blueprints, and user requests as input and automatically generates an image of the desired remodeled home. The emotion engine analyzes the user's emotions and adjusts the remodeled image based on the results.

[0986] Constructability determination algorithm:

[0987] The generated renovation images are then fed into a constructability assessment algorithm to assess whether they are technically and structurally feasible, which involves advanced analysis.

[0988] server:

[0989] The server then creates a final renovation plan based on the renovation image whose feasibility has been confirmed. This plan includes 3D renderings of the renovation, specific construction procedures, and a timeline for each step. Adjustments can also be made based on the user's emotions. The final plan is then sent from the server to the user's device.

[0990] Device:

[0991] The terminal receives the final renovation plan sent from the server and displays it to the user. The user checks the contents of the final plan and provides feedback to the server on any corrections or additions they may require, if necessary.

[0992] User:

[0993] The user checks the final renovation plan and, if there are no problems, prepares to start construction. At this time, the server may also coordinate with the construction company.

[0994] Specific examples

[0995] For example, if a user wishes to have a modern design for their kitchen, they can take photos of the current state of the kitchen and upload the blueprints. This data is sent to the server, and the generative AI model generates an image of a "modern kitchen." Meanwhile, the emotion engine analyzes the user's emotions and determines whether the user is satisfied with the generated image. If the user is not satisfied, the server calls the AI ​​model again to adjust the image. Once a satisfactory renovation image is generated, a feasibility assessment algorithm performs a technical evaluation, and if construction feasibility is confirmed, a final renovation plan is created and provided to the user.

[0996] As described above, the system of the present invention efficiently and accurately reflects the user's wishes and feelings, optimizing the renovation process, and the user can receive consistent support throughout the entire renovation process.

[0997] The processing flow will be explained below.

[0998] Step 1:

[0999] The user takes a photo of their room and prepares existing blueprint data. They also enter their specific requests for the renovation in text format. For example, they might enter a request such as "I want the kitchen design to be modern." Furthermore, emotion data is recorded using a camera and microphone with emotion recognition capabilities.

[1000] Step 2:

[1001] Users access a dedicated web form on their smartphone or PC and upload the photos, blueprints, and renovation requests they have prepared. Emotional data is also sent at the same time.

[1002] Step 3:

[1003] The device sends the photos, drawings, renovation requests, and emotion data entered by the user to the server all at once using an HTTP POST request.

[1004] Step 4:

[1005] The server receives the data sent by the user, analyzes the photos, blueprints, and renovation requests, verifies that the received data is in the correct format, and converts the data if necessary.

[1006] Step 5:

[1007] The server calls a generative AI model based on the received data and generates a renovation image. The generative AI model receives photos, blueprints, and customer requests as input, and automatically generates an image of the user's desired renovation based on these.

[1008] Step 6:

[1009] The server analyzes the user's emotions using an emotion engine, which recognizes the user's emotions in real time from the transmitted audio and video data and analyzes the results.

[1010] Step 7:

[1011] The server adjusts the generated renovation image based on the emotional data obtained from the emotion engine. For example, if the user expresses dissatisfaction with the generated image, it provides feedback to the AI ​​model to regenerate a more satisfying image.

[1012] Step 8:

[1013] The server then inputs the highly satisfying renovation images evaluated by the emotion engine into a constructability judgment algorithm, which evaluates whether the generated renovation images are technically and structurally feasible.

[1014] Step 9:

[1015] Based on the confirmed feasibility of the renovation, the server creates a final renovation plan, which includes 3D renderings of the renovation, specific construction procedures, and a timeline for each step.

[1016] Step 10:

[1017] The server sends the data to the terminal to provide the final renovation plan to the user. The plan data is sent using an HTTP response.

[1018] Step 11:

[1019] The terminal receives the final renovation plan sent from the server and displays it to the user. The user checks the contents of the final plan and provides feedback to the server on any corrections or additions they may require, if necessary.

[1020] Step 12:

[1021] The user checks the final renovation plan and, if there are no problems, prepares to start construction. At this time, the server may also coordinate with the construction company.

[1022] As a result, the system of the present invention efficiently and accurately reflects the user's wishes and feelings, optimizing the renovation process, allowing the user to receive consistent support throughout the renovation process and achieving a high level of satisfaction with the renovation.

[1023] Example 2

[1024] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1025] Current renovation proposal systems provide renovation plans based on user requests, but do not take the user's emotions into consideration, which can result in low user satisfaction. Furthermore, there is a risk that the plan will not be realized due to uncertainty about technical feasibility. Therefore, there is a need for a renovation proposal system that takes into consideration both the user's emotions and the technical feasibility of construction.

[1026] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a room photo from the user as input, means for receiving a blueprint from the user as input, means for receiving a renovation request from the user as input, means for generating a renovation image using a generative artificial intelligence model based on the received room photo, blueprint, and renovation request, means for detecting the user's emotions in real time, means for using an emotion engine to analyze the detected user's emotion data, means for adjusting the renovation image based on the analysis results, means for determining whether the generated renovation image is feasible, and means for providing the generated renovation image and the determination result of feasibility to the user. This enables highly accurate renovation proposals that take into account the user's emotions and technical feasibility.

[1027] A "user" is an individual or group who wishes to remodel and provides the system with data such as photographs, drawings, and requests for remodeling.

[1028] A "room photo" is a recorded image of the current state of a room that the user wishes to remodel.

[1029] "Drawings" are design drawings that show the structure and dimensions of the room to be renovated.

[1030] "Renovation requests" is information that a user inputs into the system in text format describing the changes and design they would like to make to the room to be renovated.

[1031] A "generative artificial intelligence model" is an algorithm that uses machine learning and deep learning technologies to generate a visual image of the renovated home based on data provided by the user.

[1032] A "renovation image" is a visual image of a room after renovation, generated by a generative artificial intelligence model.

[1033] The "emotion engine" is an algorithm that detects and analyzes the user's emotions in real time.

[1034] The "construction feasibility determination algorithm" is an algorithm that determines whether the generated renovation image is technically and structurally feasible.

[1035] A "total coordination plan" is a final plan that includes 3D rendering images, construction procedures, and a timeline for each step required for the renovation.

[1036] A "device" is an appliance used by a user to input photos, drawings, and requests into the system and to receive renovation proposals from the system.

[1037] This invention relates to a system that proposes renovations based on user requests, determines the feasibility of construction, and provides a total coordinated plan, as well as a system that combines an emotion engine that recognizes the user's emotions and reflects them in the renovation proposal. This system is an integrated system that includes a user terminal, a server, a generative AI model, a construction feasibility determination algorithm, and an emotion engine.

[1038] User device:

[1039] The user terminal is a device that allows users to input photos of their rooms, blueprints, and renovation requests. Users use their smartphones or PCs to enter information into a dedicated web form and send it to the server. Furthermore, the terminal is equipped with an emotion recognition function using a camera and microphone to detect the user's emotions in real time.

[1040] server:

[1041] The server receives data sent by users, uses a generative AI model to generate renovation images and plays a central role in determining feasibility. Additionally, the server uses an emotion engine to analyze users' emotions and adjust renovation proposals based on the analysis results. Specific software includes frameworks for machine learning and deep learning.

[1042] Generative AI models:

[1043] A generative AI model is an artificial intelligence model that generates visual images of the remodeled home based on photos, blueprints, and renovation requests provided by the user. This model uses machine learning algorithms and deep learning technology. For example, if a user inputs a request such as "I want a modern kitchen design," the generative AI model will generate a visual image of a modern kitchen based on this request.

[1044] Emotion Engine:

[1045] The emotion engine analyzes the user's facial expressions, tone of voice, and other factors to recognize the user's emotions. For this purpose, an emotion recognition algorithm is used. This allows the system to understand in real time how the user feels about the renovation proposal. For example, if the user is dissatisfied with the generated renovation image, the emotion engine analyzes this and sends feedback to the server.

[1046] Constructability determination algorithm:

[1047] The constructability algorithm evaluates whether the generated renovation image is technically and structurally feasible. This algorithm analyzes past construction data and technical constraints. For example, it determines whether the user's desired design is compatible with the actual building structure.

[1048] Total coordination plan:

[1049] The server then creates a final renovation plan based on the confirmed feasibility of the renovation. The plan includes 3D renderings of the renovation, specific construction steps, and a timeline for each step. It can also make adjustments based on the user's emotions.

[1050] Specific examples

[1051] For example, if a user wishes to have a modern design for their kitchen, they can take photos of the current state of the kitchen and upload the blueprints. This data is sent to the server, and the generative AI model generates an image of a "modern kitchen." Meanwhile, the emotion engine analyzes the user's emotions and determines whether the user is satisfied with the generated image. If the user is not satisfied, the server calls the AI ​​model again to adjust the image. Once a satisfactory renovation image is generated, a feasibility assessment algorithm performs a technical evaluation, and if construction feasibility is confirmed, a final renovation plan is created and provided to the user.

[1052] For example, by entering a prompt such as "I want to remodel my kitchen into a modern design," the generative AI model will generate a visual image of a modern kitchen, and the emotion engine will analyze the user's emotions and adjust the proposal. Finally, a remodeling plan that has undergone technical evaluation will be provided.

[1053] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1054] Step 1:

[1055] The user inputs their renovation requests into the device. First, the user uses a smartphone or PC to enter photos of their room, blueprints, and specific renovation requests into a dedicated web form. For example, they can enter a request such as "I want my kitchen to have a modern design" in text format. The device's camera and microphone are then used to detect the user's emotions in real time and collect emotional data. The input data includes photos of the room, blueprints, text of the renovation requests, and emotional data.

[1056] Step 2:

[1057] The device sends the input data to the server. The device then sends the photos, drawings, renovation requests, and emotional information entered by the user to the server all at once. Specifically, the data is sent to the server using an HTTP POST request, and the series of data reaches the server. The input data is sent according to the format (JPEG, PNG, text, etc.).

[1058] Step 3:

[1059] The server receives the data and checks the format. The server receives the data sent by the user. First, it checks whether the format of the received data is correct. For example, it checks whether photo data is in JPEG format and whether drawing data is in a compatible format. If necessary, it converts the data into the appropriate format. The input to this step is the various data sent by the user, and the output is the data whose format has been checked.

[1060] Step 4:

[1061] The server calls the generative AI model to generate a visual image of the renovation. The server inputs the received photos, blueprints, and renovation requests of the room into the generative AI model. This model uses machine learning and deep learning technology to generate a visual image of the renovated state. Specifically, it understands the room structure and the user's requests and outputs the optimal design proposal. The input data are photos, blueprints, and renovation requests, and the output is the generated visual image.

[1062] Step 5:

[1063] The server analyzes the user's emotions using an emotion engine. The server inputs the user's emotion data into the emotion engine. The emotion engine analyzes facial expressions and voice to grasp the user's emotional state in real time. For example, it determines whether the user is happy or dissatisfied. The input data is emotion information, and the output is the analyzed emotional state.

[1064] Step 6:

[1065] The server adjusts the renovation image based on the analysis results. It receives the emotion engine's analysis results and adjusts the renovation visual image. For example, if the user's satisfaction is low, it uses the generative AI model again to generate a new image. This step uses the analyzed emotional state and the initial visual image, and outputs the adjusted final visual image.

[1066] Step 7:

[1067] The server applies an algorithm to determine constructability. The server inputs the adjusted renovation image into the constructability algorithm to evaluate whether it is technically and structurally feasible. The analysis is performed by referencing past construction data and technical constraints. The input data is the final visual image, and the output is the constructability judgment result.

[1068] Step 8:

[1069] The server creates the final renovation plan. Based on the renovation image whose constructability has been confirmed, the server creates the final renovation plan. This plan includes 3D rendering images of the renovation, specific construction procedures, and a timeline for each step. The input data is the renovation image whose constructability has been confirmed, and the output is the final renovation plan.

[1070] Step 9:

[1071] The server sends the final plan to the terminal. The created final renovation plan is then sent from the server to the terminal. This is done using an HTTP POST request as well. The input data is the final renovation plan, and the output is the plan information sent to the user's terminal.

[1072] Step 10:

[1073] The terminal displays the final renovation plan to the user. The terminal receives the final renovation plan sent from the server and displays it to the user. The user checks the proposed plan in detail. The input data is the final renovation plan, and the output is plan information that can be visually confirmed.

[1074] Step 11:

[1075] The user may check the plan and send a request for revision. If the user has any requests for revisions or additions to the final plan, they can feed this back to the server from their terminal. For example, they may send a request such as "I want the kitchen counter to be a little wider." The input data is the revision request, and the output is the new data requesting the revision.

[1076] Step 12:

[1077] The user finalizes the plan and prepares for construction. If the user is satisfied with the final plan and there are no problems, they prepare to begin construction of the renovation. At this time, the server coordinates with the contractor as necessary to support a smooth construction process. The data input is the confirmation result of the final plan, and the data output is the state of construction preparation.

[1078] (Application example 2)

[1079] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1080] In systems for proposing renovations and assessing the feasibility of construction, there is a need to reflect user emotions in real time and make proposals that will increase satisfaction. In addition, when proposing entertainment content for autonomous vehicles, it is also important to provide optimal content that reflects the passengers' emotions, thereby increasing passenger comfort.

[1081] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1082] In this invention, the server includes means for receiving a room photo as input from the user, means for receiving a blueprint from the user as input, means for receiving a remodeling request from the user as input, means for generating a remodeling image using a generative artificial intelligence model based on the received room photo, blueprint, and remodeling request, means for determining whether the generated remodeling image is feasible, means for providing the generated remodeling image and the determination result of feasibility to the user, means for using an emotion engine to detect the user's emotions and reflect them in remodeling proposals, and means for proposing in-vehicle entertainment content and recognizing the user's emotions to adjust the proposal content. This makes it possible to provide optimal remodeling proposals and in-vehicle entertainment content that reflect the user's emotions.

[1083] The "means for receiving a room photo as an input from a user" is a device or interface for collecting a room photo specified by the user in digital form.

[1084] The "means for receiving drawing data from a user as input" is a device or interface that receives drawing data of a room or building provided by a user.

[1085] The "means for receiving input of renovation requests from the user" is an interface that allows the user to input specific renovation requests and wishes in text or voice format.

[1086] "Means for generating a remodeling image using a generative artificial intelligence model based on received room photos, drawings, and remodeling requests" refers to an artificial intelligence system that uses data provided by the user to automatically create a visual image of the remodeled room.

[1087] The "means for determining whether the generated renovation image is feasible" refers to an algorithm or system that evaluates whether the created renovation image is technically and structurally feasible.

[1088] The "means for providing the user with the generated renovation image and the judgement result of the feasibility of construction" is a system or interface that notifies the user of the evaluated renovation image and information about its feasibility of construction.

[1089] "Means using an emotion engine to detect user emotions and reflect them in renovation proposals" refers to an artificial intelligence system that analyzes emotions from the user's facial expressions, voice, etc., and reflects the results in renovation proposals.

[1090] The "means for suggesting in-vehicle entertainment content and adjusting the suggested content based on the user's emotions" refers to a system that provides entertainment content exclusively for passengers in autonomous vehicles and changes the content in real time according to the passengers' emotions.

[1091] System in general

[1092] This system receives photos, blueprints, and renovation requests from users, generates renovation images using a generative AI model, and determines the feasibility of the renovation. It also includes an emotion engine that detects the user's emotions and reflects them in the renovation proposals. It also includes a function to suggest in-vehicle entertainment content and adjust the proposals based on the user's emotions.

[1093] Hardware and Software

[1094] 1. User Device

[1095] Devices such as smartphones and computers are used.

[1096] This allows users to enter photos of the room, blueprint data, and renovation requests.

[1097] The user device is equipped with a camera and a microphone to collect data for emotion recognition.

[1098] 2. Server

[1099] A server with high-performance data processing capabilities is required.

[1100] The server is equipped with hardware (e.g., GPU) and software (e.g., TensorFlow) for implementing generative AI models.

[1101] Emotion engines for emotion recognition include, for example, Google's Cloud Vision API and IBM Watson.

[1102] Data Processing and Data Calculation

[1103] 1. Data collection and input

[1104] Photos, drawings, and renovation requests for the room sent from the user terminal are received by the server.

[1105] Send this data to the server using an HTTP POST request.

[1106] 2. Creating a Renovation Image

[1107] The server analyzes the received data and inputs it into a generative AI model.

[1108] The model generates a visual image of the renovated area based on the received data.

[1109] This generative AI model uses machine learning algorithms and deep learning techniques.

[1110] 3. Judgment of construction feasibility

[1111] The generated renovation images are then run through an algorithm to determine the technical and structural feasibility of construction.

[1112] The algorithm includes historical construction data and technical constraints.

[1113] 4. Emotion recognition and regulation

[1114] Using an emotion engine, the user's emotional data (facial expressions and tone of voice) is analyzed and reflected in the renovation image.

[1115] The renovation image is readjusted based on the emotional data.

[1116] 5. Entertainment content proposals

[1117] The server proposes entertainment content for the vehicle.

[1118] Here too, the emotion engine works, tailoring content suggestions based on the user's emotions.

[1119] Specific examples

[1120] If a user has a desire to "modernize their kitchen," they can take photos of the current state of the kitchen and upload the blueprint. The server receives this data and uses a generative AI model to generate a "modern kitchen" renovation image. At the same time, an emotion engine analyzes the user's emotions and determines their satisfaction with the generated image. If the emotion is not positive, the server will adjust the renovation image again.

[1121] Additionally, cameras and microphones capture passengers' facial expressions and voices as they board the vehicle. For example, if a passenger has a relaxed facial expression, the system can use this information to suggest relaxing music or movies.

[1122] Prompt Sentence Examples

[1123] "The user appears relaxed. Please suggest entertainment content appropriate for this situation."

[1124] "The user's facial expression and voice indicate that they are stressed. Please suggest entertainment content to ease this situation."

[1125] As described above, the present invention is a system that makes it possible to provide optimal renovation proposals and in-vehicle entertainment content that reflect the user's emotions.

[1126] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1127] Step 1:

[1128] The user uses the terminal to input and send photos and drawings of the room and requests for renovation.

[1129] Input: Room photos, blueprints, renovation requests (e.g., "I want a modern kitchen design")

[1130] Specific operation: The user uses a smartphone or computer to enter photos of the room, drawings, and renovation requests into a dedicated web form and press the submit button.

[1131] Output: The submitted data is sent to the server.

[1132] Step 2:

[1133] The server analyzes the data received from the terminal and checks whether the data format is correct.

[1134] Input: Received data (room photos, drawings, renovation requests)

[1135] Specific operation: The server analyzes the format of the data received and performs data conversion as necessary.

[1136] Output: Parsed data, transformed data

[1137] Step 3:

[1138] The server inputs the analyzed data into a generative AI model to generate a renovation image.

[1139] Input: Photos of the analyzed room, drawings, and renovation requests

[1140] Specific operation: The server uses a generative artificial intelligence model to generate a visual image of the remodeled home based on the input data.

[1141] Output: Generated renovation image

[1142] Step 4:

[1143] The server inputs the generated renovation image into a construction feasibility determination algorithm and performs a technical evaluation.

[1144] Input: Generated renovation image

[1145] Specific operation: The server executes a constructability determination algorithm to evaluate whether the generated renovation image is technically and structurally feasible.

[1146] Output: Construction feasibility judgment result

[1147] Step 5:

[1148] The server collects the user's emotional data and analyzes it using an emotion engine.

[1149] Input: User's facial expression data, voice data

[1150] Specific operation: The device's camera and microphone are used to collect the user's facial expressions and voice, which are then sent to the server, where they are analyzed using an emotion engine.

[1151] Output: Emotion analysis results

[1152] Step 6:

[1153] The server adjusts the renovation image based on the results of emotion analysis.

[1154] Input: User sentiment analysis results, generated renovation image

[1155] Specific operation: The server uses the results of emotion analysis to readjust the generated renovation image.

[1156] Output: Adjusted renovation image

[1157] Step 7:

[1158] The server provides the user with the final renovation image and construction feasibility results.

[1159] Input: Adjusted renovation image, construction feasibility assessment results

[1160] Specific operation: The server compiles the final renovation image and the results of the feasibility of construction and sends them to the user's terminal.

[1161] Output: The final renovation plan provided to the user

[1162] Step 8:

[1163] The user reviews the final renovation plan and provides feedback to make adjustments as needed.

[1164] Input: Final renovation plan, user feedback

[1165] Specific operation: The user reviews the final renovation plan, enters any corrections or additions they would like to make, and sends feedback to the server.

[1166] Output: User feedback, requests for further proposals

[1167] Step 9:

[1168] The server receives feedback from the user and adjusts the renovation image again.

[1169] Input: User feedback

[1170] How it works: The server analyzes the feedback and uses the generative AI model and emotion engine again to refine the renovation image.

[1171] Output: Re-adjusted renovation image

[1172] Step 10:

[1173] The server proposes in-vehicle entertainment content and adjusts it based on the user's emotions.

[1174] Input: User's facial expression data, voice data

[1175] Specific operation: The system uses cameras and microphones inside the vehicle to collect the user's facial expressions and voice, which are then analyzed by the emotion engine. Based on the analysis results, entertainment content is suggested and adjusted as necessary.

[1176] Output: Suggested entertainment content, adjustments

[1177] In this way, the present invention makes it possible to provide optimal renovation proposals and in-vehicle entertainment content that reflect the user's emotions.

[1178] 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.

[1179] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1180] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1181] [Fourth embodiment]

[1182] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1183] 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.

[1184] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. 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).

[1185] 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.

[1186] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1187] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1188] 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. 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.

[1189] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1190] 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.

[1191] 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 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.

[1192] 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.

[1193] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1194] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1195] This invention is a system that proposes renovations based on the user's requests, assesses the feasibility of construction, and provides a total coordinated plan. This system is an integrated system that includes a user terminal, a server, a generative AI model, and a construction feasibility assessment algorithm.

[1196] System Overview

[1197] User device:

[1198] The user terminal is a device that allows users to input photos of the room, drawings, and renovation requests. Users use their smartphones or PCs to enter information into a dedicated web form and send it to the server.

[1199] server:

[1200] The server plays a central role in receiving data sent by users, generating renovation images using generative AI models, and determining the feasibility of construction. To achieve these functions, the server is equipped with advanced data processing capabilities and algorithms.

[1201] Generative AI models:

[1202] The generative AI model is an artificial intelligence model that generates visual images of the remodeled home based on photos, drawings, and renovation requests provided by the user. This model uses machine learning algorithms and deep learning technology.

[1203] Constructability determination algorithm:

[1204] The constructability algorithm is responsible for assessing whether the generated renovation image is technically and structurally feasible. The algorithm analyzes past construction data and technical constraints.

[1205] Processing steps

[1206] User:

[1207] Users take photos of their own rooms and prepare existing blueprints. Next, they enter the photos, blueprints, and renovation requests into a dedicated web form. For example, they write requests such as "I want a modern design for the kitchen" in the text field. The entered data is then sent from the device to the server.

[1208] Device:

[1209] The terminal sends the data entered by the user to the server in bulk, typically by using an HTTP POST request.

[1210] server:

[1211] The server analyzes the received data, obtaining photos of the room, blueprints, and renovation requests. This data is then input into a generative AI model to generate a renovation image. Based on the generated renovation image, a feasibility assessment algorithm performs technical and structural evaluations.

[1212] Generative AI model and constructability algorithm:

[1213] The generative AI model automatically creates remodeling images based on the user's requirements, and then a constructability algorithm evaluates the images to determine whether they are technically and structurally feasible.

[1214] server:

[1215] Based on the results of the feasibility assessment, the server creates a final renovation plan, which includes 3D rendered images of the renovation, specific construction procedures, and a timeline. The final plan is then sent from the server to the user's device.

[1216] User device:

[1217] The terminal receives the final renovation plan sent from the server and displays it to the user, who can then check the final plan and request any necessary revisions.

[1218] Specific examples

[1219] For example, if a user wants to "modernize their kitchen," they can take photos of the current state of the kitchen and upload the blueprints. This data is sent to the server, and the generative AI model generates an image of the "modern kitchen." Next, a constructability assessment algorithm evaluates whether the generated image is feasible. If the evaluation result is favorable, the server creates a final renovation plan and provides it to the user.

[1220] As described above, the system of the present invention efficiently and accurately reflects the user's requests and optimizes the renovation process.

[1221] The processing flow will be explained below.

[1222] Step 1:

[1223] Users take photos of the room they want to remodel and prepare existing blueprint data. They also enter their specific requests for the remodel in text format. For example, they might enter a request such as, "I want the kitchen design to be modern."

[1224] Step 2:

[1225] Users access a dedicated web form on their smartphone or computer and upload the photos, blueprints, and renovation requests they have prepared. Each piece of data is entered into the corresponding field.

[1226] Step 3:

[1227] The device sends the photos, drawings, and renovation requests entered by the user to the server all at once, using an HTTP POST request.

[1228] Step 4:

[1229] The server receives the data sent by the user, analyzes the room photos, blueprints, and renovation requests, checks whether the received data is in the correct format, and converts the data if necessary.

[1230] Step 5:

[1231] The server calls a generative AI model based on the received data and generates a renovation image. The generative AI model receives photos, blueprints, and customer requests as input, and automatically generates an image of the user's desired renovation based on these.

[1232] Step 6:

[1233] The server then inputs the generated renovation images into a constructability assessment algorithm to evaluate whether the proposed renovation is technically and structurally feasible, which includes detailed analysis based on past construction data and technical constraints.

[1234] Step 7:

[1235] Based on the confirmed feasibility of the renovation, the server creates a final renovation plan, which includes 3D renderings of the renovation, specific construction procedures, and a timeline for each step.

[1236] Step 8:

[1237] The server sends the data to the terminal to provide the final renovation plan to the user. The plan data is sent using an HTTP response.

[1238] Step 9:

[1239] The terminal receives the final renovation plan sent from the server and displays it to the user. The user checks the contents of the final plan and provides feedback to the server on any corrections or additions they may require, if necessary.

[1240] Step 10:

[1241] The user checks the final renovation plan and, if there are no problems, prepares to start construction. At this time, the server may also coordinate with the construction company.

[1242] Through the above processing steps, the system of the present invention efficiently and accurately proposes renovations that reflect the user's requests and determines the feasibility of construction. The user can receive consistent support throughout the renovation process.

[1243] Example 1

[1244] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1245] Conventional renovation proposal systems have had difficulty generating renovation images that accurately reflect the user's requests and quickly and accurately assessing the feasibility of the renovation. Furthermore, when proposing a specific total coordination plan based on the room images and blueprints submitted by the user, manual evaluation and planning are time-consuming and inefficient.

[1246] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1247] In this invention, the server includes means for receiving a room image as input from a user, means for receiving a structure blueprint as input from a user, means for receiving a remodeling request as input from a user, means for generating a remodeling image using a generative AI model based on the received room image, structure blueprint, and remodeling request, means for determining whether the generated remodeling image is technically and structurally feasible, and means for providing the generated remodeling image and the technical and structural determination results to the user. This makes it possible to accurately reflect the user's requests, evaluate the feasibility of construction, and efficiently and accurately provide remodeling proposals and total coordination plans.

[1248] A "room image" is a photograph or digital image taken by a user that shows the current state of a room.

[1249] A "structural blueprint" is a drawing that shows the structure and layout of a room or building, including the location and dimensions of walls, floors, ceilings, windows, doors, etc.

[1250] The "renovation request" is information describing the details of the renovation or remodeling desired by the user in text or other formats. For example, it includes a specific request such as "I want the kitchen to have a modern design."

[1251] A "generative artificial intelligence model" is an artificial intelligence model that uses machine learning algorithms and deep learning technology to generate renovation images based on user requests.

[1252] The "renovation image" is a visual image of the renovated room generated by the generative AI model, which reflects the user's wishes.

[1253] "Means for determining technical and structural feasibility" refers to algorithms and methods that evaluate whether the generated renovation image can actually be constructed based on past construction data and existing technical constraints.

[1254] The "overall coordination plan" is an integrated plan for each element of the renovation, including 3D rendered images of the renovation, specific construction procedures, and a timeline.

[1255] This invention is a system that proposes renovations based on the user's requests, assesses the feasibility of construction, and provides a total coordinated plan. This system is an integrated system that includes a user terminal, a server, a generative AI model, and a construction feasibility assessment algorithm.

[1256] User terminal

[1257] The user terminal is a device that allows users to input images of rooms, blueprints of structures, and renovation requests. Users access a dedicated web form using a smartphone or PC and enter the necessary information.

[1258] server

[1259] The server receives data sent by users, generates renovation images using generative AI models, and plays a central role in determining whether construction is possible. The server requires advanced data processing capabilities, and the construction feasibility determination algorithm also runs on the server.

[1260] Generative AI Models

[1261] The generative AI model is an artificial intelligence model that generates visual images of the renovated building based on user-provided room images, structural blueprints, and renovation requests. This model uses machine learning algorithms and deep learning technology to generate realistic visual images.

[1262] Constructibility determination algorithm

[1263] The constructability algorithm is responsible for assessing whether the generated renovation images are technically and structurally feasible. The algorithm performs an analysis based on past construction data and current technical constraints.

[1264] Specifically, the user first takes a photo of their room and prepares a blueprint for the structure. Next, they enter the photos, blueprints, and renovation requests into a dedicated web form and send it from their device to the server. The device then sends the data entered by the user to the server using an HTTP POST request.

[1265] The server analyzes the received data and obtains images of the room, blueprints, and renovation requirements. This data is then input into a generative AI model to generate a renovation image. Based on the generated renovation image, a feasibility assessment algorithm performs a technical and structural evaluation.

[1266] As a concrete example, if a user wishes to have a modern design for their kitchen, they can take photos of the current state of the kitchen and upload the blueprint. This data is sent to the server, and a generative AI model generates an image of the "modern kitchen." Next, a constructability assessment algorithm evaluates whether the generated image is feasible. If the evaluation result is favorable, the server creates a final renovation plan and provides it to the user.

[1267] An example of a prompt for a generative AI model is as follows:

[1268] "Please generate an image of a modern kitchen renovation. I've attached photos of the current situation and blueprints."

[1269] "Please create a design that will make this living room look larger."

[1270] The system of the present invention efficiently and accurately reflects the user's requests and optimizes the renovation process.

[1271] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1272] Step 1:

[1273] User: The user accesses a dedicated web form using their smartphone or PC. They take a picture of the room and prepare a blueprint of the structure. They then enter their renovation requests into a text field. This includes specific requests such as "I want the kitchen to have a modern design." After this, they press the send button to send the entered data from their device to the server.

[1274] Input: Room image, structure blueprint, renovation request

[1275] Output: Data collected by the user and ready to be sent

[1276] Step 2:

[1277] Terminal: The terminal collects the data entered by the user in bulk and sends it to the server using an HTTP POST request, including a function to check the accuracy and format of the data. It notifies the user that the data has been sent.

[1278] Input: Data collected by the user

[1279] Output: Data sent to the server via an HTTP POST request

[1280] Step 3:

[1281] Server: The server receives the HTTP POST request and analyzes the data contained in the request body. The server extracts the room images, the building blueprints, and the renovation requests separately and checks the data format of each. The analysis results are temporarily stored for use in the next step.

[1282] Input: Data sent to the server as an HTTP POST request

[1283] Output: Images of the analyzed rooms, blueprints of the structure, and renovation requests

[1284] Step 4:

[1285] Server: The server creates a prompt to be input to the generative AI model based on the analyzed data. For example, it generates a prompt such as, "Please generate an image of a modern kitchen renovation. I have attached a photo of the current state and a blueprint." The server then sends this prompt along with the image and blueprint as input data to the generative AI model.

[1286] Input: Parsed room image, structure blueprint, and prompt text

[1287] Output: The data and prompts that are fed into the generative AI model

[1288] Step 5:

[1289] Generative AI Model: The generative AI model uses machine learning algorithms and deep learning techniques to generate a realistic, high-quality visual image of the renovation based on the received data and prompts. The generated image is then returned to the server.

[1290] Input: Room image, structure blueprint, and prompt

[1291] Output: Generated remodeled image

[1292] Step 6:

[1293] Server: The server receives the remodeling image returned from the generative AI model and launches the constructability judgment algorithm. The server provides the received remodeling image as input.

[1294] Input: Generated remodeled image

[1295] Output: Construction feasibility judgment result

[1296] Step 7:

[1297] Constructability Determination Algorithm: The algorithm determines whether the renovation image is technically and structurally feasible. This is done based on past construction data and existing technical constraints. The evaluation results are returned to the server.

[1298] Input: Generated remodeled image

[1299] Output: Technical and structural feasibility assessment results

[1300] Step 8:

[1301] Server: The server creates a final renovation plan based on the evaluation results. The final plan includes 3D rendered renovation images, specific construction procedures, and a timeline. The server then sends the final renovation plan to the user's device.

[1302] Input: Technical and structural feasibility assessment results

[1303] Output: Final renovation plan

[1304] Step 9:

[1305] User terminal: The user terminal receives the final renovation plan sent from the server. The received final plan is displayed to the user and prompted for confirmation. The user can review the final plan and use the option to request revisions if necessary.

[1306] Input: Final renovation plan

[1307] Output: The final renovation plan displayed to the user

[1308] (Application example 1)

[1309] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1310] With current renovation proposal systems, when users select furniture and interior items in a physical store, it is difficult for them to check in real time how the selected items will be arranged. Furthermore, there is also the issue of not being able to immediately make a technical judgment as to whether the selected furniture and interior items can actually be arranged. This often causes inconvenience to users, as they are unable to get a concrete image of how the room will look after renovation before purchasing. Furthermore, insufficient confirmation of feasibility increases the risk of problems occurring after purchase.

[1311] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1312] In this invention, the server includes means for receiving a room photo as input from a user, means for receiving a blueprint from the user as input, means for receiving a remodeling request from the user as input, means for generating a remodeling image using a generative artificial intelligence model based on the received room photo, blueprint, and remodeling request, means for determining whether the generated remodeling image is feasible, means for providing the generated remodeling image and the determination result of feasibility to the user, means for arranging the items in the room image in real time when the user selects furniture and interior items on site to generate a visual image, and means for determining whether these images are technically feasible to arrange. This allows the user to check the arrangement image of the items selected in the physical store in real time and instantly determine the technical feasibility of arranging the items.

[1313] A "user terminal" is an information processing device that allows a user to input requests for renovations and furniture selection.

[1314] A "server" is a central information processing system that receives, analyzes, and processes data sent from user terminals.

[1315] A "generative AI model" is an artificial intelligence model that uses deep learning and machine learning algorithms to generate visual images of the renovated home based on the user's requests.

[1316] The "construction feasibility determination algorithm" is an algorithm for evaluating whether the generated renovation image is technically and structurally feasible.

[1317] A "renovation image" is a visual image of the room after renovation, generated based on photos, drawings, and requests of the room provided by the user.

[1318] "Means for determining whether the furniture or interior can be technically arranged" refers to a means for making a technical judgment as to whether the selected furniture or interior can actually be arranged.

[1319] "Means for arranging products in real time and generating visual images" refers to means for instantly arranging products in an image of a room and generating a visual image when the product is selected in a physical store.

[1320] "Furniture and interior" is a general term for household goods and decorative items placed inside a room.

[1321] As an embodiment of the present invention, we will explain a method for supporting furniture and interior design selection in a brick-and-mortar store using a renovation proposal system. This system consists of a user terminal, a server, a generative AI model, and a construction feasibility determination algorithm.

[1322] The user terminal is an information processing device such as a smartphone or smart glasses, and the user uses it to input their requests for renovations and furniture selection. The user takes a photo of the room with the terminal, inputs a blueprint, and enters their renovation requests in text. The user terminal sends this data to the server. An HTTP POST request is used to send the data.

[1323] The server receives and analyzes data sent from the user's device. It acquires photos of the room, blueprints, and renovation requests and inputs them into a generative AI model. The generative AI model uses deep learning algorithms such as TensorFlow to generate a visual image of the room after renovation. The generated renovation image is evaluated by a feasibility assessment algorithm to determine whether it is technically and structurally feasible.

[1324] The generative AI model and constructability assessment algorithm evaluate whether the generated renovation image is feasible based on past construction data and technical constraints. The generated renovation image and the constructability assessment results are sent to the user's device and provided to the user.

[1325] When a user selects furniture or interior items in a physical store, they use smart glasses or a smartphone to select the items and input the information into the terminal in real time. At this time, the items are placed on an image of the room in real time, generating a visual image. Furthermore, the system has the function of instantly determining whether these images are technically possible to arrange.

[1326] As a concrete example, consider a user in a furniture store considering a new dining set. The user holds up smart glasses to select a dining set and see in real time how it will be placed in their current kitchen. The server uses a generative AI model to generate a visual image of a "modern dining set" placed in the current kitchen and determines whether the placement is technically possible. This information is sent to the user's device, allowing the user to instantly see how the room will look after the renovation.

[1327] An example of a prompt is as follows:

[1328] "Please input a photo of the user's current room and generate an image of a new modern dining set. Specifically, please create a visual of how it would blend with the current kitchen, including a white modern table and four chairs."

[1329] With this system, when a user selects a product in a physical store, they can instantly see how the product will be positioned, and can also instantly check whether that placement is technically feasible.

[1330] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1331] Step 1:

[1332] The user uses a smartphone or smart glasses to take a photo of the room, input the blueprint, and enter the renovation request as text. This data is sent from the user's device to the server. The input data consists of image data (photo of the room) and text data (blueprint, renovation request), and the server receives this data.

[1333] Step 2:

[1334] The server analyzes the received photos of the room, blueprints, and renovation requests. The analyzed data is input into a generative AI model. The photos of the room are preprocessed using an image processing library (e.g., OpenCV), and the blueprints and renovation requests are converted into an appropriate format using a text processing library (e.g., NLTK).

[1335] Step 3:

[1336] The server uses a generative AI model to generate a visual image of the remodeled room based on the user's requests. The generative AI model (e.g., a model trained using TensorFlow) takes the preprocessed data as input and outputs a 3D rendering of the remodeled room. This output is the initial version of the remodeled image provided to the user.

[1337] Step 4:

[1338] The server inputs the generated renovation image into a constructability judgment algorithm. The constructability judgment algorithm evaluates whether the generated image is technically and structurally feasible based on a database of past construction projects and technical constraints. The evaluation results are output in text format.

[1339] Step 5:

[1340] The server then creates a final renovation plan based on the generated renovation images and the results of the construction feasibility assessment. This plan includes 3D rendered renovation images, specific construction procedures, and a timeline. The created plan is then sent to the user's device.

[1341] Step 6:

[1342] The user terminal receives the final renovation plan sent from the server and displays it to the user. The user checks the plan and requests revisions as necessary.

[1343] Step 7:

[1344] When a user selects furniture or interior items in a physical store, they scan the item using a camera on their smartphone or smart glasses. The user device then sends this product information to the server. The product information is sent to the server as image data and text data.

[1345] Step 8:

[1346] The server analyzes the received product information in real time and obtains detailed data on the selected product (size, color, material, etc.). Based on this, the generative AI model is used again to place the product in the image of the user's room in real time and generate a visual image.

[1347] Step 9:

[1348] The server evaluates the generated visual image to determine whether it is technically possible to arrange the product using a construction feasibility determination algorithm, and the product arrangement image including this evaluation result is sent to the user's terminal.

[1349] Step 10:

[1350] The user terminal displays the product layout image and technical evaluation results generated in real time to the user, allowing the user to make a decision on purchasing the product based on this.

[1351] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1352] This invention relates to a system that proposes renovations based on the user's requests, judges the feasibility of construction, and provides a total coordinated plan, and also combines it with an emotion engine that recognizes the user's emotions and reflects them in the renovation proposal. This system is an integrated system that includes a user terminal, a server, a generative AI model, a construction feasibility judgment algorithm, and an emotion engine.

[1353] System Overview

[1354] User device:

[1355] The user terminal is a device that allows users to input photos of the room, blueprints, and renovation requests. Users use their smartphones or PCs to enter information into a dedicated web form and send it to the server. Furthermore, the device is equipped with an emotion recognition function using a camera and microphone to detect the user's emotions in real time.

[1356] server:

[1357] The server receives data sent by users, generates renovation images using a generative AI model, and plays a central role in determining the feasibility of construction. In addition, the server uses an emotion engine to analyze users' emotions and adjusts renovation proposals based on the analysis results.

[1358] Generative AI models:

[1359] The generative AI model is an artificial intelligence model that generates visual images of the remodeled home based on photos, drawings, and renovation requests provided by the user. This model uses machine learning algorithms and deep learning technology.

[1360] Constructability determination algorithm:

[1361] The constructability algorithm is responsible for assessing whether the generated renovation image is technically and structurally feasible. The algorithm analyzes past construction data and technical constraints.

[1362] Emotion Engine:

[1363] The emotion engine analyzes the user's facial expressions, tone of voice, and other factors to recognize their emotions. This allows the system to understand in real time how the user feels about the renovation proposal. This emotional information is used to influence the renovation image generation process and to make proposals that will further increase user satisfaction.

[1364] Processing steps

[1365] User:

[1366] Users take photos of their rooms and prepare existing blueprints. They also enter their specific requests for the renovation in text format. For example, they might enter a request such as "I want the kitchen to have a modern design." Furthermore, the system's emotion recognition function analyzes emotions in real time based on facial expressions and voice.

[1367] Device:

[1368] The device sends the photos, drawings, renovation requests, and emotional information entered by the user to the server all at once, using an HTTP POST request.

[1369] server:

[1370] The server receives the data sent by the user and analyzes the room photos, blueprints, and renovation requests. It checks whether the received data is in the correct format and converts the data if necessary. The server also analyzes the user's emotional data in parallel.

[1371] Generative AI models and emotion engines:

[1372] The generative AI model takes photos, blueprints, and user requests as input and automatically generates an image of the desired remodeled home. The emotion engine analyzes the user's emotions and adjusts the remodeled image based on the results.

[1373] Constructability determination algorithm:

[1374] The generated renovation images are then fed into a constructability assessment algorithm to assess whether they are technically and structurally feasible, which involves advanced analysis.

[1375] server:

[1376] The server then creates a final renovation plan based on the renovation image whose feasibility has been confirmed. This plan includes 3D renderings of the renovation, specific construction procedures, and a timeline for each step. Adjustments can also be made based on the user's emotions. The final plan is then sent from the server to the user's device.

[1377] Device:

[1378] The terminal receives the final renovation plan sent from the server and displays it to the user. The user checks the contents of the final plan and provides feedback to the server on any corrections or additions they may require, if necessary.

[1379] User:

[1380] The user checks the final renovation plan and, if there are no problems, prepares to start construction. At this time, the server may also coordinate with the construction company.

[1381] Specific examples

[1382] For example, if a user wishes to have a modern design for their kitchen, they can take photos of the current state of the kitchen and upload the blueprints. This data is sent to the server, and the generative AI model generates an image of a "modern kitchen." Meanwhile, the emotion engine analyzes the user's emotions and determines whether the user is satisfied with the generated image. If the user is not satisfied, the server calls the AI ​​model again to adjust the image. Once a satisfactory renovation image is generated, a feasibility assessment algorithm performs a technical evaluation, and if construction feasibility is confirmed, a final renovation plan is created and provided to the user.

[1383] As described above, the system of the present invention efficiently and accurately reflects the user's wishes and feelings, optimizing the renovation process, and the user can receive consistent support throughout the entire renovation process.

[1384] The processing flow will be explained below.

[1385] Step 1:

[1386] The user takes a photo of their room and prepares existing blueprint data. They also enter their specific requests for the renovation in text format. For example, they might enter a request such as "I want the kitchen design to be modern." Furthermore, emotion data is recorded using a camera and microphone with emotion recognition capabilities.

[1387] Step 2:

[1388] Users access a dedicated web form on their smartphone or PC and upload the photos, blueprints, and renovation requests they have prepared. Emotional data is also sent at the same time.

[1389] Step 3:

[1390] The device sends the photos, drawings, renovation requests, and emotion data entered by the user to the server all at once using an HTTP POST request.

[1391] Step 4:

[1392] The server receives the data sent by the user, analyzes the photos, blueprints, and renovation requests, verifies that the received data is in the correct format, and converts the data if necessary.

[1393] Step 5:

[1394] The server calls a generative AI model based on the received data and generates a renovation image. The generative AI model receives photos, blueprints, and customer requests as input, and automatically generates an image of the user's desired renovation based on these.

[1395] Step 6:

[1396] The server analyzes the user's emotions using an emotion engine, which recognizes the user's emotions in real time from the transmitted audio and video data and analyzes the results.

[1397] Step 7:

[1398] The server adjusts the generated renovation image based on the emotional data obtained from the emotion engine. For example, if the user expresses dissatisfaction with the generated image, it provides feedback to the AI ​​model to regenerate a more satisfying image.

[1399] Step 8:

[1400] The server then inputs the highly satisfying renovation images evaluated by the emotion engine into a constructability judgment algorithm, which evaluates whether the generated renovation images are technically and structurally feasible.

[1401] Step 9:

[1402] Based on the confirmed feasibility of the renovation, the server creates a final renovation plan, which includes 3D renderings of the renovation, specific construction procedures, and a timeline for each step.

[1403] Step 10:

[1404] The server sends the data to the terminal to provide the final renovation plan to the user. The plan data is sent using an HTTP response.

[1405] Step 11:

[1406] The terminal receives the final renovation plan sent from the server and displays it to the user. The user checks the contents of the final plan and provides feedback to the server on any corrections or additions they may require, if necessary.

[1407] Step 12:

[1408] The user checks the final renovation plan and, if there are no problems, prepares to start construction. At this time, the server may also coordinate with the construction company.

[1409] As a result, the system of the present invention efficiently and accurately reflects the user's wishes and feelings, optimizing the renovation process, allowing the user to receive consistent support throughout the renovation process and achieving a high level of satisfaction with the renovation.

[1410] Example 2

[1411] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1412] Current renovation proposal systems provide renovation plans based on user requests, but do not take the user's emotions into consideration, which can result in low user satisfaction. Furthermore, there is a risk that the plan will not be realized due to uncertainty about technical feasibility. Therefore, there is a need for a renovation proposal system that takes into consideration both the user's emotions and the technical feasibility of construction.

[1413] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a room photo from the user as input, means for receiving a blueprint from the user as input, means for receiving a renovation request from the user as input, means for generating a renovation image using a generative artificial intelligence model based on the received room photo, blueprint, and renovation request, means for detecting the user's emotions in real time, means for using an emotion engine to analyze the detected user's emotion data, means for adjusting the renovation image based on the analysis results, means for determining whether the generated renovation image is feasible, and means for providing the generated renovation image and the determination result of feasibility to the user. This enables highly accurate renovation proposals that take into account the user's emotions and technical feasibility.

[1414] A "user" is an individual or group who wishes to remodel and provides the system with data such as photographs, drawings, and requests for remodeling.

[1415] A "room photo" is a recorded image of the current state of a room that the user wishes to remodel.

[1416] "Drawings" are design drawings that show the structure and dimensions of the room to be renovated.

[1417] "Renovation requests" is information that a user inputs into the system in text format describing the changes and design they would like to make to the room to be renovated.

[1418] A "generative artificial intelligence model" is an algorithm that uses machine learning and deep learning technologies to generate a visual image of the renovated home based on data provided by the user.

[1419] A "renovation image" is a visual image of a room after renovation, generated by a generative artificial intelligence model.

[1420] The "emotion engine" is an algorithm that detects and analyzes the user's emotions in real time.

[1421] The "construction feasibility determination algorithm" is an algorithm that determines whether the generated renovation image is technically and structurally feasible.

[1422] A "total coordination plan" is a final plan that includes 3D rendering images, construction procedures, and a timeline for each step required for the renovation.

[1423] A "device" is an appliance used by a user to input photos, drawings, and requests into the system and to receive renovation proposals from the system.

[1424] This invention relates to a system that proposes renovations based on user requests, determines the feasibility of construction, and provides a total coordinated plan, as well as a system that combines an emotion engine that recognizes the user's emotions and reflects them in the renovation proposal. This system is an integrated system that includes a user terminal, a server, a generative AI model, a construction feasibility determination algorithm, and an emotion engine.

[1425] User device:

[1426] The user terminal is a device that allows users to input photos of their rooms, blueprints, and renovation requests. Users use their smartphones or PCs to enter information into a dedicated web form and send it to the server. Furthermore, the terminal is equipped with an emotion recognition function using a camera and microphone to detect the user's emotions in real time.

[1427] server:

[1428] The server receives data sent by users, uses a generative AI model to generate renovation images and plays a central role in determining feasibility. Additionally, the server uses an emotion engine to analyze users' emotions and adjust renovation proposals based on the analysis results. Specific software includes frameworks for machine learning and deep learning.

[1429] Generative AI models:

[1430] A generative AI model is an artificial intelligence model that generates visual images of the remodeled home based on photos, blueprints, and renovation requests provided by the user. This model uses machine learning algorithms and deep learning technology. For example, if a user inputs a request such as "I want a modern kitchen design," the generative AI model will generate a visual image of a modern kitchen based on this request.

[1431] Emotion Engine:

[1432] The emotion engine analyzes the user's facial expressions, tone of voice, and other factors to recognize the user's emotions. For this purpose, an emotion recognition algorithm is used. This allows the system to understand in real time how the user feels about the renovation proposal. For example, if the user is dissatisfied with the generated renovation image, the emotion engine analyzes this and sends feedback to the server.

[1433] Constructability determination algorithm:

[1434] The constructability algorithm evaluates whether the generated renovation image is technically and structurally feasible. This algorithm analyzes past construction data and technical constraints. For example, it determines whether the user's desired design is compatible with the actual building structure.

[1435] Total coordination plan:

[1436] The server then creates a final renovation plan based on the confirmed feasibility of the renovation. The plan includes 3D renderings of the renovation, specific construction steps, and a timeline for each step. It can also make adjustments based on the user's emotions.

[1437] Specific examples

[1438] For example, if a user wishes to have a modern design for their kitchen, they can take photos of the current state of the kitchen and upload the blueprints. This data is sent to the server, and the generative AI model generates an image of a "modern kitchen." Meanwhile, the emotion engine analyzes the user's emotions and determines whether the user is satisfied with the generated image. If the user is not satisfied, the server calls the AI ​​model again to adjust the image. Once a satisfactory renovation image is generated, a feasibility assessment algorithm performs a technical evaluation, and if construction feasibility is confirmed, a final renovation plan is created and provided to the user.

[1439] For example, by entering a prompt such as "I want to remodel my kitchen into a modern design," the generative AI model will generate a visual image of a modern kitchen, and the emotion engine will analyze the user's emotions and adjust the proposal. Finally, a remodeling plan that has undergone technical evaluation will be provided.

[1440] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1441] Step 1:

[1442] The user inputs their renovation requests into the device. First, the user uses a smartphone or PC to enter photos of their room, blueprints, and specific renovation requests into a dedicated web form. For example, they can enter a request such as "I want my kitchen to have a modern design" in text format. The device's camera and microphone are then used to detect the user's emotions in real time and collect emotional data. The input data includes photos of the room, blueprints, text of the renovation requests, and emotional data.

[1443] Step 2:

[1444] The device sends the input data to the server. The device then sends the photos, drawings, renovation requests, and emotional information entered by the user to the server all at once. Specifically, the data is sent to the server using an HTTP POST request, and the series of data reaches the server. The input data is sent according to the format (JPEG, PNG, text, etc.).

[1445] Step 3:

[1446] The server receives the data and checks the format. The server receives the data sent by the user. First, it checks whether the format of the received data is correct. For example, it checks whether photo data is in JPEG format and whether drawing data is in a compatible format. If necessary, it converts the data into the appropriate format. The input to this step is the various data sent by the user, and the output is the data whose format has been checked.

[1447] Step 4:

[1448] The server calls the generative AI model to generate a visual image of the renovation. The server inputs the received photos, blueprints, and renovation requests of the room into the generative AI model. This model uses machine learning and deep learning technology to generate a visual image of the renovated state. Specifically, it understands the room structure and the user's requests and outputs the optimal design proposal. The input data are photos, blueprints, and renovation requests, and the output is the generated visual image.

[1449] Step 5:

[1450] The server analyzes the user's emotions using an emotion engine. The server inputs the user's emotion data into the emotion engine. The emotion engine analyzes facial expressions and voice to grasp the user's emotional state in real time. For example, it determines whether the user is happy or dissatisfied. The input data is emotion information, and the output is the analyzed emotional state.

[1451] Step 6:

[1452] The server adjusts the renovation image based on the analysis results. It receives the emotion engine's analysis results and adjusts the renovation visual image. For example, if the user's satisfaction is low, it uses the generative AI model again to generate a new image. This step uses the analyzed emotional state and the initial visual image, and outputs the adjusted final visual image.

[1453] Step 7:

[1454] The server applies an algorithm to determine constructability. The server inputs the adjusted renovation image into the constructability algorithm to evaluate whether it is technically and structurally feasible. The analysis is performed by referencing past construction data and technical constraints. The input data is the final visual image, and the output is the constructability judgment result.

[1455] Step 8:

[1456] The server creates the final renovation plan. Based on the renovation image whose constructability has been confirmed, the server creates the final renovation plan. This plan includes 3D rendering images of the renovation, specific construction procedures, and a timeline for each step. The input data is the renovation image whose constructability has been confirmed, and the output is the final renovation plan.

[1457] Step 9:

[1458] The server sends the final plan to the terminal. The created final renovation plan is then sent from the server to the terminal. This is done using an HTTP POST request as well. The input data is the final renovation plan, and the output is the plan information sent to the user's terminal.

[1459] Step 10:

[1460] The terminal displays the final renovation plan to the user. The terminal receives the final renovation plan sent from the server and displays it to the user. The user checks the proposed plan in detail. The input data is the final renovation plan, and the output is plan information that can be visually confirmed.

[1461] Step 11:

[1462] The user may check the plan and send a request for revision. If the user has any requests for revisions or additions to the final plan, they can feed this back to the server from their terminal. For example, they may send a request such as "I want the kitchen counter to be a little wider." The input data is the revision request, and the output is the new data requesting the revision.

[1463] Step 12:

[1464] The user finalizes the plan and prepares for construction. If the user is satisfied with the final plan and there are no problems, they prepare to begin construction of the renovation. At this time, the server coordinates with the contractor as necessary to support a smooth construction process. The data input is the confirmation result of the final plan, and the data output is the state of construction preparation.

[1465] (Application example 2)

[1466] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1467] In systems for proposing renovations and assessing the feasibility of construction, there is a need to reflect user emotions in real time and make proposals that will increase satisfaction. In addition, when proposing entertainment content for autonomous vehicles, it is also important to provide optimal content that reflects the passengers' emotions, thereby increasing passenger comfort.

[1468] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1469] In this invention, the server includes means for receiving a room photo as input from the user, means for receiving a blueprint from the user as input, means for receiving a remodeling request from the user as input, means for generating a remodeling image using a generative artificial intelligence model based on the received room photo, blueprint, and remodeling request, means for determining whether the generated remodeling image is feasible, means for providing the generated remodeling image and the determination result of feasibility to the user, means for using an emotion engine to detect the user's emotions and reflect them in remodeling proposals, and means for proposing in-vehicle entertainment content and recognizing the user's emotions to adjust the proposal content. This makes it possible to provide optimal remodeling proposals and in-vehicle entertainment content that reflect the user's emotions.

[1470] The "means for receiving a room photo as an input from a user" is a device or interface for collecting a room photo specified by the user in digital form.

[1471] The "means for receiving drawing data from a user as input" is a device or interface that receives drawing data of a room or building provided by a user.

[1472] The "means for receiving input of renovation requests from the user" is an interface that allows the user to input specific renovation requests and wishes in text or voice format.

[1473] "Means for generating a remodeling image using a generative artificial intelligence model based on received room photos, drawings, and remodeling requests" refers to an artificial intelligence system that uses data provided by the user to automatically create a visual image of the remodeled room.

[1474] The "means for determining whether the generated renovation image is feasible" refers to an algorithm or system that evaluates whether the created renovation image is technically and structurally feasible.

[1475] The "means for providing the user with the generated renovation image and the judgement result of the feasibility of construction" is a system or interface that notifies the user of the evaluated renovation image and information about its feasibility of construction.

[1476] "Means using an emotion engine to detect user emotions and reflect them in renovation proposals" refers to an artificial intelligence system that analyzes emotions from the user's facial expressions, voice, etc., and reflects the results in renovation proposals.

[1477] The "means for suggesting in-vehicle entertainment content and adjusting the suggested content based on the user's emotions" refers to a system that provides entertainment content exclusively for passengers in autonomous vehicles and changes the content in real time according to the passengers' emotions.

[1478] System in general

[1479] This system receives photos, blueprints, and renovation requests from users, generates renovation images using a generative AI model, and determines the feasibility of the renovation. It also includes an emotion engine that detects the user's emotions and reflects them in the renovation proposals. It also includes a function to suggest in-vehicle entertainment content and adjust the proposals based on the user's emotions.

[1480] Hardware and Software

[1481] 1. User Device

[1482] Devices such as smartphones and computers are used.

[1483] This allows users to enter photos of the room, blueprint data, and renovation requests.

[1484] The user device is equipped with a camera and a microphone to collect data for emotion recognition.

[1485] 2. Server

[1486] A server with high-performance data processing capabilities is required.

[1487] The server is equipped with hardware (e.g., GPU) and software (e.g., TensorFlow) for implementing generative AI models.

[1488] Emotion engines for emotion recognition include, for example, Google's Cloud Vision API and IBM Watson.

[1489] Data Processing and Data Calculation

[1490] 1. Data collection and input

[1491] Photos, drawings, and renovation requests for the room sent from the user terminal are received by the server.

[1492] Send this data to the server using an HTTP POST request.

[1493] 2. Creating a Renovation Image

[1494] The server analyzes the received data and inputs it into a generative AI model.

[1495] The model generates a visual image of the renovated area based on the received data.

[1496] This generative AI model uses machine learning algorithms and deep learning techniques.

[1497] 3. Judgment of construction feasibility

[1498] The generated renovation images are then run through an algorithm to determine the technical and structural feasibility of construction.

[1499] The algorithm includes historical construction data and technical constraints.

[1500] 4. Emotion recognition and regulation

[1501] Using an emotion engine, the user's emotional data (facial expressions and tone of voice) is analyzed and reflected in the renovation image.

[1502] The renovation image is readjusted based on the emotional data.

[1503] 5. Entertainment content proposals

[1504] The server proposes entertainment content for the vehicle.

[1505] Here too, the emotion engine works, tailoring content suggestions based on the user's emotions.

[1506] Specific examples

[1507] If a user has a desire to "modernize their kitchen," they can take photos of the current state of the kitchen and upload the blueprint. The server receives this data and uses a generative AI model to generate a "modern kitchen" renovation image. At the same time, an emotion engine analyzes the user's emotions and determines their satisfaction with the generated image. If the emotion is not positive, the server will adjust the renovation image again.

[1508] Additionally, cameras and microphones capture passengers' facial expressions and voices as they board the vehicle. For example, if a passenger has a relaxed facial expression, the system can use this information to suggest relaxing music or movies.

[1509] Prompt Sentence Examples

[1510] "The user appears relaxed. Please suggest entertainment content appropriate for this situation."

[1511] "The user's facial expression and voice indicate that they are stressed. Please suggest entertainment content to ease this situation."

[1512] As described above, the present invention is a system that makes it possible to provide optimal renovation proposals and in-vehicle entertainment content that reflect the user's emotions.

[1513] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1514] Step 1:

[1515] The user uses the terminal to input and send photos and drawings of the room and requests for renovation.

[1516] Input: Room photos, blueprints, renovation requests (e.g., "I want a modern kitchen design")

[1517] Specific operation: The user uses a smartphone or computer to enter photos of the room, drawings, and renovation requests into a dedicated web form and press the submit button.

[1518] Output: The submitted data is sent to the server.

[1519] Step 2:

[1520] The server analyzes the data received from the terminal and checks whether the data format is correct.

[1521] Input: Received data (room photos, drawings, renovation requests)

[1522] Specific operation: The server analyzes the format of the data received and performs data conversion as necessary.

[1523] Output: Parsed data, transformed data

[1524] Step 3:

[1525] The server inputs the analyzed data into a generative AI model to generate a renovation image.

[1526] Input: Photos of the analyzed room, drawings, and renovation requests

[1527] Specific operation: The server uses a generative artificial intelligence model to generate a visual image of the remodeled home based on the input data.

[1528] Output: Generated renovation image

[1529] Step 4:

[1530] The server inputs the generated renovation image into a construction feasibility determination algorithm and performs a technical evaluation.

[1531] Input: Generated renovation image

[1532] Specific operation: The server executes a constructability determination algorithm to evaluate whether the generated renovation image is technically and structurally feasible.

[1533] Output: Construction feasibility judgment result

[1534] Step 5:

[1535] The server collects the user's emotional data and analyzes it using an emotion engine.

[1536] Input: User's facial expression data, voice data

[1537] Specific operation: The device's camera and microphone are used to collect the user's facial expressions and voice, which are then sent to the server, where they are analyzed using an emotion engine.

[1538] Output: Emotion analysis results

[1539] Step 6:

[1540] The server adjusts the renovation image based on the results of emotion analysis.

[1541] Input: User sentiment analysis results, generated renovation image

[1542] Specific operation: The server uses the results of emotion analysis to readjust the generated renovation image.

[1543] Output: Adjusted renovation image

[1544] Step 7:

[1545] The server provides the user with the final renovation image and construction feasibility results.

[1546] Input: Adjusted renovation image, construction feasibility assessment results

[1547] Specific operation: The server compiles the final renovation image and the results of the feasibility of construction and sends them to the user's terminal.

[1548] Output: The final renovation plan provided to the user

[1549] Step 8:

[1550] The user reviews the final renovation plan and provides feedback to make adjustments as needed.

[1551] Input: Final renovation plan, user feedback

[1552] Specific operation: The user reviews the final renovation plan, enters any corrections or additions they would like to make, and sends feedback to the server.

[1553] Output: User feedback, requests for further proposals

[1554] Step 9:

[1555] The server receives feedback from the user and adjusts the renovation image again.

[1556] Input: User feedback

[1557] How it works: The server analyzes the feedback and uses the generative AI model and emotion engine again to refine the renovation image.

[1558] Output: Re-adjusted renovation image

[1559] Step 10:

[1560] The server proposes in-vehicle entertainment content and adjusts it based on the user's emotions.

[1561] Input: User's facial expression data, voice data

[1562] Specific operation: The system uses cameras and microphones inside the vehicle to collect the user's facial expressions and voice, which are then analyzed by the emotion engine. Based on the analysis results, entertainment content is suggested and adjusted as necessary.

[1563] Output: Suggested entertainment content, adjustments

[1564] In this way, the present invention makes it possible to provide optimal renovation proposals and in-vehicle entertainment content that reflect the user's emotions.

[1565] 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.

[1566] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1567] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1568] 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.

[1569] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1570] 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.

[1571] 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).

[1572] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1573] 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."

[1574] 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.

[1575] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1576] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1577] 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.

[1578] 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.

[1579] 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.

[1580] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1581] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1582] 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.

[1583] 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.

[1584] 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.

[1585] 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.

[1586] The following is further disclosed regarding the above embodiment.

[1587] (Claim 1)

[1588] means for receiving a photo of a room as input from a user;

[1589] means for receiving a drawing as input from a user;

[1590] A means for receiving a renovation request as input from a user;

[1591] A means for generating a renovation image using a generative artificial intelligence model based on the received room photos, drawings, and renovation requests;

[1592] A means for determining whether the generated renovation image is feasible;

[1593] A means for providing the generated renovation image and the result of the judgment on the feasibility of construction to the user;

[1594] A system including:

[1595] (Claim 2)

[1596] The system of claim 1 uses drawings and past renovation data to determine whether a renovation image generated based on a received renovation request is technically feasible.

[1597] (Claim 3)

[1598] The system according to claim 1, wherein a total coordination plan for renovation is created based on the generated renovation image and provided to the user.

[1599] "Example 1"

[1600] (Claim 1)

[1601] means for receiving an input image of a room from a user;

[1602] means for receiving a design drawing of a structure as input from a user;

[1603] means for receiving a renovation request as input from a user;

[1604] A means for generating a renovation image using a generative artificial intelligence model based on the received room image, the structure blueprint, and the renovation request;

[1605] A means of determining whether the generated renovation images are technically and structurally feasible;

[1606] A means for providing the generated renovation image and technical and structural judgment results to a user;

[1607] A system including:

[1608] (Claim 2)

[1609] The system according to claim 1, wherein the system uses the design drawings of the structure and past renovation data to determine whether the renovation image generated based on the received renovation request is technically feasible.

[1610] (Claim 3)

[1611] The system according to claim 1, wherein an overall coordination plan for the renovation is created based on the generated renovation image and provided to the user.

[1612] "Application Example 1"

[1613] (Claim 1)

[1614] means for receiving a photo of a room as input from a user;

[1615] means for receiving a drawing as input from a user;

[1616] A means for receiving a renovation request as input from a user;

[1617] A means for generating a renovation image using a generative artificial intelligence model based on the received room photos, drawings, and renovation requests;

[1618] A means for determining whether the generated renovation image is feasible;

[1619] A means for providing the generated renovation image and the result of the judgment on the feasibility of construction to the user;

[1620] When a user selects furniture or interior items on-site, a means for placing the items on a room image in real time to generate a visual image;

[1621] A means of determining whether these images are technically feasible to deploy;

[1622] A system including:

[1623] (Claim 2)

[1624] The system of claim 1 uses drawings and past renovation data to determine whether a renovation image generated based on a received renovation request is technically feasible.

[1625] (Claim 3)

[1626] The system according to claim 1, wherein a total coordination plan for renovation is created based on the generated renovation image and provided to the user.

[1627] "Example 2: Combining Emotion Engines"

[1628] (Claim 1)

[1629] means for receiving a photo of a room as input from a user;

[1630] means for receiving a drawing as input from a user;

[1631] A means for receiving a renovation request as input from a user;

[1632] A means for generating a renovation image using a generative artificial intelligence model based on the received room photos, drawings, and renovation requests;

[1633] means for detecting user emotions in real time;

[1634] means for using an emotion engine to analyze the detected emotion data of the user;

[1635] A means for adjusting the image of the renovation based on the analysis results;

[1636] A means for determining whether the generated renovation image is feasible;

[1637] A means for providing the generated renovation image and the result of the judgment on the feasibility of construction to the user;

[1638] A system including:

[1639] (Claim 2)

[1640] The system of claim 1 uses drawings and past renovation data to determine whether a renovation image generated based on a received renovation request is technically feasible.

[1641] (Claim 3)

[1642] The system according to claim 1, wherein a total coordination plan for renovation is created based on the generated renovation image and provided to the user.

[1643] "Application example 2 when combining emotion engines"

[1644] (Claim 1)

[1645] means for receiving a photo of a room as input from a user;

[1646] means for receiving a drawing as input from a user;

[1647] A means for receiving a renovation request as input from a user;

[1648] A means for generating a renovation image using a generative artificial intelligence model based on the received room photos, drawings, and renovation requests;

[1649] A means for determining whether the generated renovation image is feasible;

[1650] A means for providing the generated renovation image and the result of the judgment on the feasibility of construction to the user;

[1651] A means for detecting user emotions and using an emotion engine to reflect the emotions in the renovation proposal;

[1652] means for suggesting in-vehicle entertainment content and adjusting the suggestions based on user emotions;

[1653] A system including:

[1654] (Claim 2)

[1655] The system of claim 1 uses drawings and past renovation data to determine whether a renovation image generated based on a received renovation request is technically feasible.

[1656] (Claim 3)

[1657] The system according to claim 1, wherein a total coordination plan for renovation is created based on the generated renovation image and provided to the user. [Explanation of symbols]

[1658] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving a photo of a room as input from a user; means for receiving a drawing as input from a user; A means for receiving a renovation request as input from a user; A means for generating a renovation image using a generative artificial intelligence model based on the received room photos, drawings, and renovation requests; A means for determining whether the generated renovation image is feasible; A means for providing the generated renovation image and the result of the judgment on the feasibility of construction to the user; A system including:

2. The system according to claim 1, wherein the system uses drawings and past renovation data to determine whether a renovation image generated based on a received renovation request is technically feasible.

3. 2. The system according to claim 1, wherein a total coordination plan for renovation is created based on the created renovation image and provided to the user.

Images