System

Abstract

A system is provided.SOLUTION: Means for a user inputting a room layout request in a chat interface, means for the user uploading real environment data of a target room, means for a server analyzing the uploaded real environment data and extracting dimensions and shapes of the room and an arrangement of existing furniture, means for the server analyzing a request from the user using a natural language processing technology, means for the server creating a plurality of layout plans using an AI generated based on the analysis result, and means for the server generating the created layout plans in a 3D model or image format; A system comprising: means for presenting to a user; and means for displaying detailed information and a purchase link for a suggested piece of furniture or appliance.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] In the fields of modern interior design and layout planning, users are required to efficiently and effectively determine the layout of their rooms based on their own preferences. However, traditional methods require specialized knowledge and time, making it difficult to plan everything on your own. It is also not easy to accurately grasp the dimensions of a room and the layout of existing furniture and then propose an appropriate layout based on that. Furthermore, retailers face the challenge of optimally arranging products while taking into account customer purchasing behavior. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system that includes: a means for a user to input their desired room layout into a chat interface; a means for the user to upload real-world data for the target room; a means for a server to analyze the uploaded real-world data and extract the room's dimensions, shape, and existing furniture layout; a means for the server to analyze the user's requests using natural language processing technology; a means for the server to create multiple layout proposals based on the analysis results using generative AI; a means for the server to generate the generated layout proposals in 3D model or image format and present them to the user; and a means for the server to display detailed information about the proposed furniture and appliances and a link to purchase them. The system also solves the problems faced by retailers by adding a means for proposing optimal product placement based on behavioral economics and a means for presenting the proposals to retailers as 3D models and simulating practical layout proposals and their effects. The system also includes a means for the server to use an object detection algorithm to analyze LiDAR data and image data and a means for the user to input their desired room layout into the chat interface. This enables efficient and effective layout proposals that meet the user's preferences.

[0006] "User" refers to an individual or organization that uses the system to design a room layout.

[0007] "Chat interface" refers to a user interface that allows a user to interact with the system by entering text.

[0008] "Room layout preference" refers to the user's requests and wishes regarding the use and design of the room.

[0009] "Real-world data" refers to the physical information of a room captured in the form of LiDAR data, images, videos, etc.

[0010] "Upload" refers to the act of a user sending data from their device to a server.

[0011] "Server" refers to a computer that acts as the central processing unit of the system, analyzes data, and generates and presents layout proposals.

[0012] "Analysis" refers to the process by which the server extracts and understands information from the data it receives.

[0013] "Generative AI" refers to algorithms or systems that use artificial intelligence to create new layout ideas based on data.

[0014] A "3D model" refers to a digital model that represents a physical space or object in three dimensions.

[0015] "Purchase Link" means a web link or hyperlink that enables a User to directly purchase the suggested furniture or appliance.

[0016] "Behavioral economics" refers to a branch of economics that deals with consumer purchasing behavior and decision-making.

[0017] "Optimal placement" refers to optimizing product placement by taking into account consumer behavior and efficiency.

[0018] "Object detection algorithm" refers to computer vision techniques used to recognize objects from images and LiDAR data and determine their location and dimensions. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] As an embodiment of the present invention, a program will be described below in which the entire system functions in cooperation with the user, server, and terminal each playing a specific role.

[0041] First, a user uses the chat interface to input their room layout preferences. For example, if a user wants to know how to most efficiently arrange newly purchased furniture, they can input a specific preference into the chat interface, such as "I'd like to put the sofa on one side of the living room and move the TV around."

[0042] Next, users upload real-world data of the target room, including LiDAR data, images, videos, etc. Users can use their smartphones or dedicated devices to capture 360-degree images and LiDAR scans and upload them to the system.

[0043] The server analyzes the real-world data uploaded by the user. It uses LiDAR data and images to determine the dimensions, shape, and layout of existing furniture in the room. For example, it generates a detailed 3D model from the LiDAR data and applies object detection algorithms to the image data to extract the location and dimensions of furniture.

[0044] The server then analyzes the user's preferences using natural language processing technology. It analyzes the user's input, extracts keywords such as "sofa," "television," and "living room," and identifies the desired layout characteristics based on these. The analysis results provide the information needed to design a layout that specifically reflects the user's preferences.

[0045] The server uses generative AI to create multiple layout proposals based on the analysis results. At this time, the server considers the optimal layout based on the user's preferences and the actual environment of the room. For example, it suggests placing a sofa in the optimal position in the living room and arranging related interior items around it.

[0046] The generated layout proposals are rendered by the server in the form of 3D models or images and presented to the user. The user can visually check multiple layout proposals and choose the one they like best. For example, by rotating multiple 3D models and viewing them from different angles, the user can get a concrete idea of ​​how they will look when actually installed in a room.

[0047] In addition, the server displays detailed information and a link to purchase the suggested furniture or home appliance, allowing the user to immediately purchase the suggested item. For example, a link to purchase a sofa or TV may be displayed, and when the user clicks, the link will take them directly to the sales site.

[0048] For example, if a user wants to set up a new office space in their living room, they first enter their request into the chat interface and upload a LiDAR scan of the space to the system. The server analyzes the data and generates layout proposals that suggest the optimal placement of existing furniture and new furniture. These layout proposals are then presented to the user as 3D models, along with links to purchase the proposed desks and chairs.

[0049] This system allows users to quickly obtain efficient, visually verifiable layout plans that reflect their preferences, and also allows them to easily purchase the suggested furniture and appliances.

[0050] The processing flow will be explained below.

[0051] Step 1:

[0052] The user enters their desired room layout into the chat interface, for example, "I want to turn my living room into a comfortable office space."

[0053] Step 2:

[0054] The user uploads real-world data of the target room, such as a 360-degree image taken with a smartphone or LiDAR scan data, to the system.

[0055] Step 3:

[0056] The server receives uploaded real-world data, which can include LiDAR data, images, and videos.

[0057] Step 4:

[0058] The server analyzes the received real-world data, extracting the dimensions and shape of the room as a 3D model from the LiDAR data, and using object detection algorithms to identify the position and dimensions of furniture from the image data.

[0059] Step 5:

[0060] The server analyzes the user's input using natural language processing technology. It extracts keywords and important requirements from the user's input and identifies the information necessary for layout design. For example, if the user uses the term "office space," it identifies the appropriate layout characteristics.

[0061] Step 6:

[0062] The server uses AI to generate multiple layout proposals based on the analysis results, such as the placement of sofas and desks and chairs, to generate layout proposals optimized to the user's preferences.

[0063] Step 7:

[0064] The server generates layout proposals visually in the form of 3D models or images, and presents the data to the user. For example, it provides functions that allow the user to rotate, enlarge, and reduce the 3D models so that multiple layout proposals can be compared.

[0065] Step 8:

[0066] The server displays detailed information about the proposed furniture and home appliances and a link to purchase them. For example, the server provides the user with detailed product information about the proposed desk or chair, along with a link to an online shop to purchase the item.

[0067] Step 9:

[0068] Users can review the proposed layouts and choose the one they like best. They can then purchase their favorite furniture and appliances directly through the purchase link.

[0069] Step 10:

[0070] The server records purchase logs and accumulates data for future layout suggestions and user feedback, which improves the accuracy of future layout suggestions.

[0071] Example 1

[0072] 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."

[0073] Many modern users seek specific advice on room layout changes and new furniture arrangements, but the process is time-consuming and laborious, and visual confirmation methods are limited. Therefore, a system that allows users to obtain layout proposals efficiently and visually easily understandable is needed. There is also a need for a system that allows users to easily purchase the proposed furniture and appliances.

[0074] 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.

[0075] In this invention, the server includes: a means for a user to input a desired room layout into a chat interface; a means for the user to upload real-world data of the target room; a means for the server to analyze the uploaded real-world data and extract the room's dimensions, shape, and existing furniture layout; a means for the server to analyze the user's requests using natural language processing technology; a means for the server to create multiple layout proposals using a generative AI model based on the analysis results; a means for the server to generate the generated layout proposals in 3D model or image format and present them to the user; and a means for the server to display detailed information and purchase links for the proposed furniture and home appliances. This allows the user to easily obtain efficient and visually confirmable layout proposals that reflect their wishes, and also enables the user to easily purchase the proposed furniture and home appliances.

[0076] A "chat interface" is an interface that allows a user to input text-based information and communicate with the system.

[0077] "Real-world data" is a general term for data such as 360-degree images and LiDAR data that includes the dimensions, shape, and furniture placement of a user's room.

[0078] A "server" is a computer system that performs key processes such as analyzing data, running generative AI, natural language processing, and generating 3D models.

[0079] "Natural language processing technology" is a technology for analyzing text data entered by a user and understanding its meaning.

[0080] A "generative AI model" is an artificial intelligence model that generates new content or suggestions based on input data and prompts.

[0081] A "3D model" is a digital model that represents an analyzed space, furniture arrangement, etc. in three dimensions.

[0082] An "object detection algorithm" is an algorithm that detects specific objects from images or LiDAR data and identifies their location and dimensions.

[0083] A "prompt sentence" is text data that is input into a generative AI model, and the AI ​​generates output based on this.

[0084] As an embodiment of the present invention, a program will be described in which a user, a server, and a terminal each play a specific role and function in cooperation as a system.

[0085] First, a user uses the chat interface to input their room layout preferences. For example, if a user is asking how to most efficiently arrange newly purchased furniture, they might input a specific preference into the chat interface, such as "I want to put the sofa on one side of the living room and move the TV around."

[0086] Next, the user uses a smartphone or dedicated device to capture real-world data, such as 360-degree images of the room or LiDAR data, and uploads it to the system, which then captures information such as the actual room dimensions, shape, and existing furniture placement.

[0087] The server analyzes the real-world data uploaded by the user, using LiDAR data to generate a detailed 3D model of the room and applying object detection algorithms to the image data to identify the location and dimensions of furniture. For example, an object detection algorithm can be used to identify the location and dimensions of furniture such as a "sofa" or a "television" from an image.

[0088] The server then uses natural language processing techniques (e.g., spaCy or BERT) to analyze the user's preferences. Keywords such as "sofa," "television," and "living room" are extracted from the user's input, and the desired layout characteristics are identified based on these. The analysis results provide the information needed to design a layout that specifically reflects the user's preferences.

[0089] The server uses a generative AI model (such as DALL-E or GPT-3 (registered trademark)) based on the analysis results to create multiple layout proposals. For example, the server generates a specific layout proposal such as "Place the sofa by the window in the living room and hang the TV on the opposite wall."

[0090] The generated layout proposals are converted into 3D models and images and presented to the user, who can visually check the multiple layout proposals provided and rotate and zoom in on the 3D models to view details from different angles.

[0091] Additionally, the server displays detailed information about the suggested furniture or appliances along with a link to purchase them. When the user clicks on the link, they are taken directly to the sales site, where they can conveniently purchase the suggested item. For example, the server might display, "This sofa is made by XYZ company and can be purchased here," and the user can click on the link to begin the purchase process immediately.

[0092] For example, if a user wants to set up a new office space in their living room, they first enter their request into the chat interface and upload a LiDAR scan of the space to the system. The server analyzes the data and generates layout proposals that include the arrangement of existing furniture and the optimal placement of new desks and chairs. These layout proposals are then presented to the user as 3D models, along with links to purchase the proposed desks and chairs.

[0093] An example of a prompt to input to the generative AI model is, "Based on 360-degree images of the room and LiDAR data, please generate the optimal layout proposal for a new desk and chair in the living room. The user's preference is 'I want the desk to be placed by the window, with the chair in front of it.'" This system allows users to obtain efficient, visually verifiable layout proposals that reflect their preferences, and also allows them to easily purchase the suggested furniture and appliances.

[0094] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0095] Step 1: User preference input

[0096] The user uses the chat interface to input their preferences for the room layout. For example, they can input specific requests such as "I want to put a sofa on one side of the living room and change the position of the TV." This input is sent as text data from the device to the server, which receives and temporarily stores this text data.

[0097] Step 2: Acquire and upload real-world data

[0098] Users use their smartphones or dedicated devices to capture 360-degree images and LiDAR data of their rooms, which are then uploaded to a cloud server. The uploaded data is then stored on the server as a resource containing information such as the room's dimensions, shape, and the layout of existing furniture.

[0099] Step 3: Data analysis

[0100] The server analyzes the uploaded real-world data. It generates a detailed 3D model of the room based on the LiDAR data, and applies an object detection algorithm to the image data to identify the location and dimensions of furniture. Specifically, the object detection algorithm identifies the location and dimensions of a "sofa" or "television" and reflects this in the 3D model. The input data is LiDAR data and image data, and the output is dimensional information and a 3D model of the furniture arrangement.

[0101] Step 4: Natural Language Analysis

[0102] The server uses natural language processing techniques (e.g., spaCy or BERT) to analyze the user's preferences. It extracts keywords such as "sofa," "television," and "living room" from the user's text data and identifies the required layout characteristics based on them. The input is the user's text data, and the output is the extracted keywords and the layout requirements based on them.

[0103] Step 5: Generate layout proposals

[0104] The server generates multiple layout proposals using a generative AI model (e.g., DALL-E or GPT-3) based on the analysis results. For example, a prompt might be, "Based on 360-degree images of the room and LiDAR data, generate the optimal layout proposal for placing a new desk and chair in the living room. The user's preference is 'I want the desk to be placed by the window, with the chair in front of it.'" Based on this prompt, the AI ​​model generates multiple layout proposals, and specific layout proposals are obtained as output.

[0105] Step 6: Present layout plan

[0106] The generated layout proposals are converted into 3D models or images by the server and presented to the user. The user can rotate these 3D models on their device and zoom in to check the details. This allows the user to visually check multiple layout proposals and choose the best one.

[0107] Step 7: Provide a purchase link

[0108] The server displays detailed information about the suggested furniture or home appliances and a link to purchase them. When the user clicks on the link for the suggested item, they are taken directly to the sales site and can begin the purchase process. This allows the user to easily purchase the suggested item. The input is information about the suggested furniture or home appliance, and the output is a purchase link and its detailed information.

[0109] In this way, each step works seamlessly together to provide an efficient, visually verifiable layout plan that reflects the user's preferences, and the process of purchasing the proposed furniture and home appliances is also simplified.

[0110] (Application example 1)

[0111] 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."

[0112] Conventional furniture and fixture layout planning often requires specialized knowledge and is time-consuming. Furthermore, there are limited ways to visually check the layout beforehand, making changes to the store layout risky. Furthermore, it is difficult to consider efficient layouts because the effects of layout plans cannot be simulated in advance. The challenge is to solve these problems and provide a method that allows store owners and designers to easily determine optimal layouts.

[0113] 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.

[0114] In this invention, the server includes: a means for a user to input their desired room layout into a chat interface; a means for uploading real-world data for the target room; a means for analyzing the uploaded real-world data and extracting the room's dimensions, shape, and existing furniture layout; a means for analyzing the user's requests using natural language processing technology; a means for using a generation AI to create multiple layout proposals based on the analysis results; a means for generating the generated layout proposals in 3D model or image format and presenting them to the user; a means for displaying detailed information and purchase links for the proposed furniture and appliances; and a means for optimizing the store layout and visually confirming them using augmented reality (AR). This allows store owners and designers to quickly obtain efficient, visually confirmable layout proposals that reflect their preferences. Furthermore, detailed information and purchase links for the proposed fixtures and decorations are provided, facilitating the purchasing process and facilitating improvements to store designs.

[0115] "Means for users to input their room layout preferences into a chat interface" refers to an interface where users input their preferences regarding the layout of a room or shop in text format.

[0116] "Means for users to upload real-world environment data for the target room" refers to the means by which users send information about the real-world environment, such as 360-degree images and LiDAR data acquired with their smartphones or dedicated devices, to a server.

[0117] "Means for the server to analyze the uploaded real-world data and extract the room's dimensions, shape, and existing furniture layout" refers to means for the server to process the real-world data submitted by the user and use algorithms to identify the physical details of the room and the layout of existing furniture.

[0118] "Means for the server to analyze requests from users using natural language processing technology" refers to means for the server to use natural language processing technology to analyze the text entered by the user and extract important elements and keywords from it.

[0119] "Means for the server to create multiple layout proposals using a generation AI based on the analysis results" refers to a means for the server to use a generation AI (artificial intelligence) to generate multiple layout proposals based on the data analyzed by the server in response to the user's requests.

[0120] "Means for generating layout plans generated by the server in the form of 3D models or images and presenting them to users" refers to means for creating layout plans generated by the server as 3D models or still images and presenting them visually to users.

[0121] "Means for the server to display detailed information about the proposed furniture and home appliances and links to purchase them" refers to means for displaying detailed information about the furniture and home appliances included in the layout proposal generated by the server to the user and links to purchase them.

[0122] "Means for optimizing the store layout and visually confirming it using AR (Augmented Reality)" refers to a means in which the server efficiently proposes the layout of the store and allows the user to visually confirm that layout using AR (Augmented Reality) technology.

[0123] "Means for a server to suggest optimal product placement based on the principles of behavioral economics" refers to means for a server to suggest optimal product placement locations using the theory of behavioral economics.

[0124] "A means of presenting proposals generated by the server to contractors as 3D models and simulating practical layout plans and their effects" refers to a means of creating layout proposals generated by the server as 3D models and presenting them to contractors to simulate actual layout methods and their effects.

[0125] "Means by which the server uses an object detection algorithm to analyze the LiDAR data or image data" means means by which the server uses an object detection algorithm to analyze the LiDAR data or image data.

[0126] "Means for visually checking proposed store layouts using AR displays on a smartphone or smart glasses" refers to means that enables users to visually check proposed store layouts using AR technology on a smartphone or smart glasses.

[0127] A system for realizing the present invention is configured as follows.

[0128] First, a user uses a chat interface to input their preferences for the layout of a room or store. For example, if a user wants to know how to most efficiently arrange newly purchased furniture, they can input a specific preference into the chat interface, such as "I want to put the sofa on one side of the living room and change the position of the TV."

[0129] Next, users upload real-world data of the target room or store, including LiDAR data, images, and videos. Users use their smartphones or smart glasses to capture 360-degree images and LiDAR scans and upload them to the system.

[0130] The server analyzes real-world data uploaded by users. It uses LiDAR data and image data to determine the dimensions, shape, and layout of existing furniture in a room or store. For example, it generates a detailed 3D model from the LiDAR data, and applies object detection algorithms to the image data to extract the location and dimensions of furniture. Open-source libraries and proprietary algorithms may be used for the analysis.

[0131] The server then analyzes the user's preferences using natural language processing technology. It analyzes the user's input, extracts keywords such as "sofa," "television," and "living room," and identifies the desired layout characteristics based on these. The analysis results provide the information needed to design a layout that specifically reflects the user's preferences.

[0132] Based on the analysis results, the server uses generative AI to create multiple layout proposals. At this time, the server considers the optimal layout based on the user's preferences and the actual environment of the room or store. For example, it may suggest placing a sofa in the optimal position in the living room and arranging related interior items around it. The generative AI may use APIs from OpenAI (registered trademark), etc.

[0133] The generated layout proposals are rendered as 3D models or images by the server and presented to the user. The user can visually check multiple layout proposals using AR displays on their smartphone or smart glasses. For example, by rotating multiple 3D models and viewing them from different angles, the user can get a concrete idea of ​​how they will look when actually installed in a room or store.

[0134] In addition, the server displays detailed information and a link to purchase the suggested furniture or home appliance, allowing the user to immediately purchase the suggested item. For example, a link to purchase a sofa or TV may be displayed, and when the user clicks, the link will take them directly to the sales site.

[0135] As a specific example, consider the case where a store owner wishes to place new furniture in their store. The store owner first enters their wishes into a chat interface, providing specific instructions such as "I want to place a counter near the entrance and rearrange the shelves in the back room." They then acquire 360-degree images and LiDAR scans of the store and upload them to the system. The server analyzes the data and generates layout proposals that suggest optimal locations for existing fixtures and new fixtures. These layout proposals are then presented to the user in an AR display so that they can be visually confirmed. Detailed information about the proposed fixtures and decorations, along with a link to purchase them, is also displayed, allowing the user to proceed with the purchase process.

[0136] In this way, this system not only allows store owners and designers to quickly obtain efficient, visually verifiable layout plans that reflect their wishes, but also allows them to easily purchase the proposed fixtures and decorations.

[0137] To illustrate, the following is an example of a prompt sentence that users can use to input their preferences: "Please identify the layout characteristics based on the following preferences: I would like the counter to be located near the entrance and the shelves in the back room to be repositioned."

[0138] This allows the store layout to be optimized and visually confirmed using AR (augmented reality).

[0139] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0140] Step 1:

[0141] The user inputs the desired room layout into the chat interface.

[0142] Specific actions: The user types into the chat interface something like, "I want to place the counter near the entrance and rearrange the shelves in the back room."

[0143] Input: User text input (desired layout)

[0144] Output: Layout desired text

[0145] Step 2:

[0146] The user uploads real-world environment data for the target room.

[0147] How it works: Users capture 360-degree images and LiDAR data using their smartphones or smart glasses and upload them to the system.

[0148] Input: Real-world data such as 360-degree images and LiDAR data

[0149] Output: Real-world data uploaded to the server

[0150] Step 3:

[0151] The server analyzes the uploaded real-world data and extracts the room's dimensions, shape, and existing furniture layout.

[0152] What it does: The server converts the LiDAR data into a 3D model and analyzes the image data with object detection algorithms to determine the location and dimensions of the furniture.

[0153] Input: Real-world data (360-degree images, LiDAR data)

[0154] Output: Analyzed dimensional data, shape data, and layout data of existing furniture

[0155] Step 4:

[0156] The server analyzes the user's wishes using natural language processing technology.

[0157] How it works: The server extracts keywords from the user's input text and identifies layout characteristics. Here, it uses OpenAI's API to analyze the keywords.

[0158] Input: Desired layout text

[0159] Output: extracted keywords, parsed layout features

[0160] Step 5:

[0161] The server uses generative AI to create multiple layout proposals based on the analysis results.

[0162] How it works: The server uses generative AI to generate multiple layout proposals based on the analysis results and real-world data.

[0163] Input: Analysis results (layout characteristics, dimension data, shape data, existing furniture layout data)

[0164] Output: Multiple layout options

[0165] Step 6:

[0166] The server generates the layout proposal in the form of a 3D model or image and presents it to the user.

[0167] How it works: The server uses 3D modeling software to create 3D models and images of the layout proposals it generates, and presents them to the user, who then visually checks them using a smartphone or smart glasses.

[0168] Input: Multiple layout ideas

[0169] Output: 3D model, layout plan in image format

[0170] Step 7:

[0171] The server displays detailed information and purchase links for the suggested furniture and appliances.

[0172] Specific operation: Based on the layout plan, the server displays detailed information about the proposed furniture and appliances to the user, along with links to purchase each item.

[0173] Input: Layout plan, furniture and appliance information

[0174] Output: Detailed information, purchase link

[0175] Step 8:

[0176] Users can use their smartphones or smart glasses to visually check the proposed store layout using an AR display.

[0177] Specific operation: Using a smartphone or smart glasses, users can use AR technology to visually check layout proposals by overlaying them on the actual space.

[0178] Input: 3D model, AR software

[0179] Output: Visual confirmation of layout plan using AR display

[0180] 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.

[0181] As an embodiment of the present invention, a program in which the entire system functions in cooperation with the user, server, and terminal each performing their own role will be described below.

[0182] First, the user uses the chat interface to input their preferences for the room layout. For example, the user might say, "I want to turn my living room into a comfortable office space." Then, they upload LiDAR data, images, and videos as real-world data for the room via their device.

[0183] The server receives the real-world data uploaded by the user and begins analyzing it. It applies object detection algorithms to LiDAR and image data to extract the room's dimensions, shape, and existing furniture layout. Additionally, it uses natural language processing technology to analyze the user's preferences obtained through the chat interface and converts them into specific layout characteristics.

[0184] The server then uses generative AI to create multiple layout proposals based on the analyzed data. The proposed layouts are presented to the user in the form of 3D models or images. The user can visually check these models and select the optimal layout. For example, the system suggests new desk and chair arrangements, optimization of storage space, etc.

[0185] Furthermore, the present invention incorporates an emotion engine that recognizes emotions from user input and facial and voice data. This emotion engine monitors the user's emotional state in real time and evaluates the user's reaction when a layout proposal is presented. For example, if the user expresses joy in response to a proposed layout proposal, the emotion engine can record that information and reflect it in future proposals.

[0186] As a concrete example, suppose a user enters their desire to "create a relaxing space" into the chat interface and uploads LiDAR scan data of their room. The server analyzes the room data and generates multiple relaxation space layout proposals. Furthermore, an emotion engine evaluates the user's facial expressions and voice to see if they are relaxed, and presents the most suitable layout proposal to the user. The user can review the layout proposals and select and purchase the proposed furniture and appliances via detailed information and a purchase link.

[0187] This system can efficiently present visually verifiable layout proposals based on the user's preferences, and can make optimal proposals that take the user's emotions into consideration. Furthermore, by providing links to purchase the proposed furniture and home appliances, the user can easily complete the purchasing process.

[0188] The processing flow will be explained below.

[0189] Step 1:

[0190] The user enters their desired room layout into the chat interface, for example, "I want to turn my living room into a comfortable office space."

[0191] Step 2:

[0192] The user uploads real-world data of the target room, specifically 360-degree images taken with a smartphone or LiDAR scan data, to the system.

[0193] Step 3:

[0194] The server receives uploaded real-world data, which can include LiDAR data, images, and videos.

[0195] Step 4:

[0196] The server analyzes the received data, for example extracting the dimensions and shape of the room as a 3D model from the LiDAR data, and using object detection algorithms to identify the position and dimensions of furniture from the image data.

[0197] Step 5:

[0198] The server uses an emotion engine to recognize emotions from user input and facial and voice data. For example, a webcam and microphone are used to capture facial data and tone of voice.

[0199] Step 6:

[0200] The server analyzes the user's input using natural language processing technology, extracting keywords and important requests from the user's input and identifying the information necessary for layout design. For example, it identifies keywords such as "office space" and "living room."

[0201] Step 7:

[0202] The server uses generative AI to create multiple layout proposals based on the analysis results and emotion data. For example, it generates layout proposals optimized for the user's preferences and emotions, such as the placement of sofas and desks and chairs.

[0203] Step 8:

[0204] The server generates layout plans visually in the form of 3D models or images, and presents the data to the user. For example, it provides functions to rotate, enlarge, and reduce the 3D models.

[0205] Step 9:

[0206] The server uses an emotion engine to evaluate the user's reaction to the proposed layout. For example, if the user smiles, the server records this information and determines that the proposal is favorable.

[0207] Step 10:

[0208] The server displays detailed information about the proposed furniture and home appliances and a link to purchase them. For example, the server provides the user with detailed product information about the proposed desk or chair, along with a link to an online shop to purchase the item.

[0209] Step 11:

[0210] Users can review the proposed layouts and choose the one they like best. They can then purchase their favorite furniture and appliances directly through the purchase link.

[0211] Step 12:

[0212] The server records purchase logs and accumulates data for future layout suggestions and user feedback, which improves the accuracy of future layout suggestions.

[0213] Example 2

[0214] 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."

[0215] Conventional room layout design systems were unable to fully consider the user's preferences or actual environmental data, making it difficult to present efficient layout proposals. Furthermore, there was no mechanism to propose optimal layouts that took the user's emotions into consideration, resulting in low user satisfaction.

[0216] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0217] In this invention, the server includes means for using emotion recognition technology that analyzes the user's facial expressions and voice data to evaluate their emotions and takes the user's reaction into consideration when presenting a layout proposal, means for the user to input their desired room layout into a chat interface, and means for uploading actual environment data for the target room. This makes it possible to present efficient, visually verifiable layout proposals that meet the user's wishes, and to make optimal proposals that take the user's emotions into consideration.

[0218] "User" refers to a person who uses the system to design a room layout.

[0219] "Chat interface" refers to an interactive interface through which users input requests to the system via text.

[0220] "Real-world data" refers to data about the physical attributes of a room, including the dimensions and shape of the room, existing furniture layout, etc.

[0221] "Server" refers to the core computer system of the system that receives and analyzes data from users, generates and presents layout proposals using generative AI models.

[0222] "Object detection algorithm" refers to technology for recognizing the position and shape of objects from visual information such as images and LiDAR data.

[0223] "Natural language processing technology" refers to technology for analyzing text data and understanding user requests.

[0224] A "generative AI model" refers to an artificial intelligence model that generates new content (e.g., layout proposals) based on input data.

[0225] A "3D model" refers to a digital model that visualizes the proposed room layout in three dimensions.

[0226] "Emotion recognition technology" refers to technology for evaluating a user's emotions by analyzing their facial expressions and voice data.

[0227] "Purchase Link" refers to a web link provided along with detailed information about the proposed furniture or home appliances to facilitate the purchase process.

[0228] "Principles of behavioral economics" refers to an economic theory that optimizes the placement and offering of products and services by taking into account human psychology and behavior.

[0229] This invention is a system that optimally designs room layouts, with the user, server, and terminal all fulfilling their respective roles. Specifically, the user inputs their requests into the terminal, and the server generates an appropriate layout plan based on those requests and presents it to the user.

[0230] User Input

[0231] Users use the chat interface to input their desired room layout. For example, a user might input, "I want to turn my living room into a comfortable office space." Additionally, the user uploads real-world environmental data (such as LiDAR data, images, and videos) of the target room via their device. This allows the system to obtain the user's specific requirements and current room data.

[0232] Data analysis by server

[0233] The server receives the uploaded real-world data and analyzes it. This analysis uses object detection algorithms (e.g., YOLO, Mask R-CNN) to extract the room's dimensions, shape, and existing furniture layout. Furthermore, the server uses natural language processing techniques (e.g., BERT, GPT-3) to analyze the user's preferences and convert them into specific layout characteristics.

[0234] Generate layout proposals

[0235] Based on the analyzed data, the server uses a generative AI model (e.g., DALL-E, Stable Diffusion, etc.) to create multiple layout proposals. At this stage, an example prompt, "Please generate a living room layout proposal that will allow the user to relax," is input into the generative AI model. The resulting layout proposals are then rendered in the form of a three-dimensional model or image.

[0236] Proposal and Evaluation

[0237] The generated layout proposals are sent from the server to the device, which then presents them to the user. The user can visually check the proposed layout proposals and select the most appropriate one. Furthermore, the system incorporates emotion recognition technology (e.g., Microsoft® Emotion API, Face++) to analyze the user's facial expressions and voice data to evaluate their emotions. As a result, it is possible to propose optimal layout proposals based on the user's reactions.

[0238] Specific examples

[0239] For example, if a user enters "I want to create a space where I can relax" into the chat interface and uploads LiDAR scan data of their room, the server analyzes the room data and generates multiple layout proposals for relaxation spaces. At this time, the prompt text is entered as "Please generate a layout proposal for a living room where the user can relax." The generated layout proposals are presented to the user in the form of 3D models or images, and emotion recognition technology evaluates whether the user's facial expressions and voice are relaxing, and the most suitable layout proposal is ultimately presented to the user.

[0240] This system provides efficient and visually verifiable layout proposals based on the user's preferences, enabling optimal proposals that take the user's emotions into consideration. Furthermore, by providing links to purchase the proposed furniture and home appliances, the user can easily complete the purchasing process.

[0241] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0242] Step 1:

[0243] The user opens the chat interface on their device and enters information about their desired room layout, such as "I want to turn my living room into a comfortable office space." This input information is the starting point for the system and is important for clarifying the user's intentions.

[0244] input:

[0245] User layout preference (text format)

[0246] output:

[0247] User preference data

[0248] Specific behavior:

[0249] The user enters their preference in the chat box.

[0250] The terminal receives the user's desired data.

[0251] Step 2:

[0252] Users upload real-world environmental data (LiDAR data, images, videos, etc.) of the target room through their device, which is used to understand the current state of the room.

[0253] input:

[0254] LiDAR data, images, and videos

[0255] output:

[0256] Uploaded real-world data

[0257] Specific behavior:

[0258] The user scans the room using the LiDAR scanner on their device.

[0259] Upload scanned data and images to the server.

[0260] Step 3:

[0261] The server receives the uploaded real-world data and begins analyzing it. This analysis involves using object detection algorithms (e.g., YOLO, Mask R-CNN) to extract the room's dimensions, shape, and existing furniture layout. Next, it uses natural language processing techniques (e.g., BERT, GPT-3) to translate the user's preferences into specific layout characteristics.

[0262] input:

[0263] Uploaded real-world data

[0264] User preference data

[0265] output:

[0266] Room dimensions, shape, and furniture layout data

[0267] Analyzed Layout Characteristics

[0268] Specific behavior:

[0269] The server analyzes the LiDAR data to determine the dimensions of the room.

[0270] The server extracts the furniture layout from the image data.

[0271] The server analyzes the user's preferences and extracts layout characteristics.

[0272] Step 4:

[0273] Based on the analysis results, the server uses a generative AI model (e.g., DALL-E, Stable Diffusion) to create multiple layout proposals. At this stage, the prompt "Please generate a living room layout proposal that will allow the user to relax" is input to the generative AI model.

[0274] input:

[0275] Analyzed room data

[0276] Layout Characteristics

[0277] output:

[0278] Generated layout plan (3D model, image format)

[0279] Specific behavior:

[0280] The server inputs a prompt sentence into the generative AI model.

[0281] Generative AI generates multiple layout proposals.

[0282] Step 5:

[0283] The terminal receives the layout proposals sent from the server and presents them to the user, who can visually check these layout proposals and select the most suitable one.

[0284] input:

[0285] Generated layout plan

[0286] output:

[0287] Present layout proposals to users

[0288] Specific behavior:

[0289] The terminal receives the generated layout plan.

[0290] The terminal displays the layout proposal to the user.

[0291] Step 6:

[0292] The server is equipped with emotion recognition technology (e.g., Microsoft's Emotion API, Face++) that analyzes the user's facial expressions and voice data to evaluate their emotions. This allows it to ultimately propose an optimal layout plan that takes the user's reaction into account.

[0293] input:

[0294] User's facial expression data, voice data

[0295] output:

[0296] Emotional evaluation data

[0297] Optimal layout plan

[0298] Specific behavior:

[0299] The server analyzes the user's facial expressions and voice data.

[0300] Emotion recognition technology assesses the user's emotions and records the information.

[0301] The server presents the optimal layout proposal to the user.

[0302] (Application example 2)

[0303] 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."

[0304] In traditional store operations, it is difficult to instantly optimize the effective display and placement of products, and selecting an appropriate layout requires a great deal of time and effort. Furthermore, there is a lack of a way to receive real-time feedback on staff and customer reactions and reflect that feedback, which results in delays in improving sales and customer satisfaction.

[0305] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input a desired room layout into a chat interface; a means for the user to upload real-world environment data of the target room; a means for the server to analyze the uploaded real-world environment data and extract the room's dimensions, shape, and existing furniture layout; a means for the server to analyze the user's requests using natural language processing technology; a means for the server to create multiple layout proposals using a generation AI based on the analysis results; a means for the server to generate the generated layout proposals in 3D model or image format and present them to the user; a means for the server to display detailed information and purchase links for the proposed furniture and home appliances; and a means for evaluating user reactions using an emotion engine and improving the proposals. This makes it possible to quickly and efficiently propose effective layouts for displays and products and provide optimal layouts that take into account the emotions of staff and customers.

[0306] A "chat interface" is an interactive user interface that allows users to input their wishes and requests through text input in natural language.

[0307] "Real-world data" refers to data that includes the physical dimensions, shape, and current furniture layout of the target room or space, and includes LiDAR data, images, videos, etc.

[0308] A "server" is a computer system that receives data uploaded or information entered by users, analyzes it, and processes it.

[0309] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate text in natural language, and is used to analyze user requests.

[0310] A "generative AI model" is an artificial intelligence model that generates optimal layout proposals based on user input and environmental data, and uses a generative model, for example.

[0311] A "3D model" is a computer graphics model constructed in three-dimensional space, and is used by users to visually check layout proposals.

[0312] An "emotion engine" is an algorithm that analyzes emotions from a user's facial expressions, voice, etc. and evaluates their reaction.

[0313] "Display" refers to a display device for visually presenting products and information, and in this context refers particularly to the arrangement of products in a store.

[0314] An "object detection algorithm" is a technology that detects the position and shape of objects from images and LiDAR data.

[0315] A "layout plan" is a proposal for optimizing the layout of a room or space, showing what furniture and products should be placed where.

[0316] The present invention is a system in which users, servers, and terminals work together to optimize the placement and display of products in a physical store. An embodiment of the system will be described in detail below.

[0317] System configuration

[0318] 1. User Device

[0319] Users can use devices such as smartphones or tablets to input their preferences and requests for product placement into a chat interface, and can also upload LiDAR data and images, providing the system with information about the physical dimensions and shape of the physical store and the current product placement situation.

[0320] 2. Server

[0321] The server receives and analyzes real-world data (LiDAR data, images, and videos) uploaded by users. Specifically, it uses an object detection algorithm (e.g., OpenCV) to extract the dimensions and shape of the room and the layout of existing products. It then analyzes the user's requests using natural language processing technology (e.g., GPT-3). Based on the analysis results, it then uses a generative AI model to generate multiple layout proposals.

[0322] 3. Proposal Generation and Display

[0323] The layout proposals generated by the server are presented to the user in the form of 3D models or images, allowing the user to visually check them and select the most suitable layout. Detailed information and purchase links for the proposed products and appliances are also displayed, allowing the user to easily complete the purchase process.

[0324] 4. Emotion Engine

[0325] Additionally, it has a built-in emotion engine that can monitor and evaluate the reactions of users, staff, and customers in real time. For example, if a user expresses delight in a proposed layout, this information can be recorded and reflected in future proposals. This emotion data analysis uses a sentiment analysis pipeline such as Transformers.

[0326] Specific examples

[0327] Display placement in physical stores

[0328] A store manager uses a tablet to input, "I want to create a visually appealing display for a new product," and uploads LiDAR scan data and photos of the store. The server analyzes this and generates an optimal layout proposal. Staff members view the layout and provide their feedback, and the system makes a final proposal based on their reactions. The following prompts are used in this process:

[0329] Prompt Sentence Examples

[0330] User desire: Create a visually appealing display for a new product

[0331] Room dimensions: 10m wide, 8m deep, 3m high

[0332] Existing furniture layout: 3 shelves along the wall, 1 table in the middle

[0333] Optimal layout ideas:

[0334] This system not only allows for efficient optimization of store display layout, but also enables continuous improvement based on feedback from staff and customers.

[0335] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0336] Step 1:

[0337] Users use a chat interface to enter their product placement preferences in text, and then upload LiDAR data and images of the store using a smartphone or tablet.

[0338] Input: User's desired text, LiDAR data, image data

[0339] Output: Chat request data, LiDAR data, image data

[0340] Specific operation: The user inputs the desired information into the device, selects the captured LiDAR data and image data, and uploads them.

[0341] Step 2:

[0342] The server analyzes the real-world data received from the user (LiDAR data, image data, video) and uses an object detection algorithm (e.g., OpenCV) to extract the dimensions and shape of the store and the existing product layout.

[0343] Input: LiDAR data, image data

[0344] Output: Room dimensions, shape, and existing product placement data

[0345] How it works: The server applies object detection algorithms to automatically extract store dimensions, shape, and product placement information from LiDAR and image data.

[0346] Step 3:

[0347] The server analyzes the desired text from the user using natural language processing technology (e.g., GPT-3) and converts the user's requests into specific layout characteristics.

[0348] Input: User-defined text

[0349] Output: Specific layout characteristics

[0350] What it does: The server uses the generative AI model to analyze text data received from the chat interface and extract layout characteristics.

[0351] Step 4:

[0352] Based on the analyzed data, the server uses a generative AI model to generate multiple layout proposals.

[0353] Input: Room dimensions, shape, existing product placement data, specific layout characteristics

[0354] Output: Multiple layout options

[0355] Specific operation: The server inputs a prompt sentence into the generative AI model and generates multiple optimal layout proposals. Examples of prompt sentences used are as follows:

[0356] Prompt Sentence Examples

[0357] User desire: Create a visually appealing display for a new product

[0358] Room dimensions: 10m wide, 8m deep, 3m high

[0359] Existing furniture layout: 3 shelves along the wall, 1 table in the middle

[0360] Optimal layout ideas:

[0361] Step 5:

[0362] The server generates the layout proposal in the form of a 3D model or image and presents it to the user.

[0363] Input: Multiple layout ideas

[0364] Output: 3D model, layout plan in image format

[0365] Specific operation: The layout proposal generated by the server is converted into a visually displayable format using 3D modeling software and presented to the user.

[0366] Step 6:

[0367] The user visually checks the proposed layouts and selects the most suitable one. The user's selection and reaction data (facial expressions and voice) are input into the system.

[0368] Input: 3D model, layout plan in image format, user reaction data

[0369] Output: Selected layout plan, user reaction data

[0370] Specific operation: The user selects the best layout from the layout proposals visually reviewed and inputs their selection and reaction into the system.

[0371] Step 7:

[0372] The server-based emotion engine analyzes the user's reaction data, evaluates the user's emotional state, and adjusts the final layout proposal based on the reaction.

[0373] Input: User reaction data

[0374] Output: Final layout proposal adjustments

[0375] How it works: The emotion engine analyzes the reaction data to identify the user's emotional state, and then optimizes and adjusts the generated layout proposals based on the results.

[0376] Step 8:

[0377] The server then presents the final adjusted layout proposal to the user again, and displays detailed information and purchase links for the proposed products, allowing the user to select products and complete the purchase process.

[0378] Input: Final layout proposal adjustments

[0379] Output: Final layout with adjustments, product details, and purchase links

[0380] Specific operation: The server generates the final layout plan, along with detailed product information and purchase links for the corresponding products, and presents them to the user.

[0381] 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.

[0382] 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.

[0383] 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.

[0384] [Second embodiment]

[0385] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0386] 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.

[0387] 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).

[0388] 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.

[0389] 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.

[0390] 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).

[0391] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0392] 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.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] 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."

[0397] As an embodiment of the present invention, a program will be described below in which the entire system functions in cooperation with the user, server, and terminal each playing a specific role.

[0398] First, a user uses the chat interface to input their room layout preferences. For example, if a user wants to know how to most efficiently arrange newly purchased furniture, they can input a specific preference into the chat interface, such as "I'd like to put the sofa on one side of the living room and move the TV around."

[0399] Next, users upload real-world data of the target room, including LiDAR data, images, videos, etc. Users can use their smartphones or dedicated devices to capture 360-degree images and LiDAR scans and upload them to the system.

[0400] The server analyzes the real-world data uploaded by the user. It uses LiDAR data and images to determine the dimensions, shape, and layout of existing furniture in the room. For example, it generates a detailed 3D model from the LiDAR data and applies object detection algorithms to the image data to extract the location and dimensions of furniture.

[0401] The server then analyzes the user's preferences using natural language processing technology. It analyzes the user's input, extracts keywords such as "sofa," "television," and "living room," and identifies the desired layout characteristics based on these. The analysis results provide the information needed to design a layout that specifically reflects the user's preferences.

[0402] The server uses generative AI to create multiple layout proposals based on the analysis results. At this time, the server considers the optimal layout based on the user's preferences and the actual environment of the room. For example, it suggests placing a sofa in the optimal position in the living room and arranging related interior items around it.

[0403] The generated layout proposals are rendered by the server in the form of 3D models or images and presented to the user. The user can visually check multiple layout proposals and choose the one they like best. For example, by rotating multiple 3D models and viewing them from different angles, the user can get a concrete idea of ​​how they will look when actually installed in a room.

[0404] In addition, the server displays detailed information and a link to purchase the suggested furniture or home appliance, allowing the user to immediately purchase the suggested item. For example, a link to purchase a sofa or TV may be displayed, and when the user clicks, the link will take them directly to the sales site.

[0405] For example, if a user wants to set up a new office space in their living room, they first enter their request into the chat interface and upload a LiDAR scan of the space to the system. The server analyzes the data and generates layout proposals that suggest the optimal placement of existing furniture and new furniture. These layout proposals are then presented to the user as 3D models, along with links to purchase the proposed desks and chairs.

[0406] This system allows users to quickly obtain efficient, visually verifiable layout plans that reflect their preferences, and also allows them to easily purchase the suggested furniture and appliances.

[0407] The processing flow will be explained below.

[0408] Step 1:

[0409] The user enters their desired room layout into the chat interface, for example, "I want to turn my living room into a comfortable office space."

[0410] Step 2:

[0411] The user uploads real-world data of the target room, such as a 360-degree image taken with a smartphone or LiDAR scan data, to the system.

[0412] Step 3:

[0413] The server receives uploaded real-world data, which can include LiDAR data, images, and videos.

[0414] Step 4:

[0415] The server analyzes the received real-world data, extracting the dimensions and shape of the room as a 3D model from the LiDAR data, and using object detection algorithms to identify the position and dimensions of furniture from the image data.

[0416] Step 5:

[0417] The server analyzes the user's input using natural language processing technology. It extracts keywords and important requirements from the user's input and identifies the information necessary for layout design. For example, if the user uses the term "office space," it identifies the appropriate layout characteristics.

[0418] Step 6:

[0419] The server uses AI to generate multiple layout proposals based on the analysis results, such as the placement of sofas and desks and chairs, to generate layout proposals optimized to the user's preferences.

[0420] Step 7:

[0421] The server generates layout proposals visually in the form of 3D models or images, and presents the data to the user. For example, it provides functions that allow the user to rotate, enlarge, and reduce the 3D models so that multiple layout proposals can be compared.

[0422] Step 8:

[0423] The server displays detailed information about the proposed furniture and home appliances and a link to purchase them. For example, the server provides the user with detailed product information about the proposed desk or chair, along with a link to an online shop to purchase the item.

[0424] Step 9:

[0425] Users can review the proposed layouts and choose the one they like best. They can then purchase their favorite furniture and appliances directly through the purchase link.

[0426] Step 10:

[0427] The server records purchase logs and accumulates data for future layout suggestions and user feedback, which improves the accuracy of future layout suggestions.

[0428] Example 1

[0429] 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."

[0430] Many modern users seek specific advice on room layout changes and new furniture arrangements, but the process is time-consuming and laborious, and visual confirmation methods are limited. Therefore, a system that allows users to obtain layout proposals efficiently and visually easily understandable is needed. There is also a need for a system that allows users to easily purchase the proposed furniture and appliances.

[0431] 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.

[0432] In this invention, the server includes: a means for a user to input a desired room layout into a chat interface; a means for the user to upload real-world data of the target room; a means for the server to analyze the uploaded real-world data and extract the room's dimensions, shape, and existing furniture layout; a means for the server to analyze the user's requests using natural language processing technology; a means for the server to create multiple layout proposals using a generative AI model based on the analysis results; a means for the server to generate the generated layout proposals in 3D model or image format and present them to the user; and a means for the server to display detailed information and purchase links for the proposed furniture and home appliances. This allows the user to easily obtain efficient and visually confirmable layout proposals that reflect their wishes, and also enables the user to easily purchase the proposed furniture and home appliances.

[0433] A "chat interface" is an interface that allows a user to input text-based information and communicate with the system.

[0434] "Real-world data" is a general term for data such as 360-degree images and LiDAR data that includes the dimensions, shape, and furniture placement of a user's room.

[0435] A "server" is a computer system that performs key processes such as analyzing data, running generative AI, natural language processing, and generating 3D models.

[0436] "Natural language processing technology" is a technology for analyzing text data entered by a user and understanding its meaning.

[0437] A "generative AI model" is an artificial intelligence model that generates new content or suggestions based on input data and prompts.

[0438] A "3D model" is a digital model that represents an analyzed space, furniture arrangement, etc. in three dimensions.

[0439] An "object detection algorithm" is an algorithm that detects specific objects from images or LiDAR data and identifies their location and dimensions.

[0440] A "prompt sentence" is text data that is input into a generative AI model, and the AI ​​generates output based on this.

[0441] As an embodiment of the present invention, a program will be described in which a user, a server, and a terminal each play a specific role and function in cooperation as a system.

[0442] First, a user uses the chat interface to input their room layout preferences. For example, if a user is asking how to most efficiently arrange newly purchased furniture, they might input a specific preference into the chat interface, such as "I want to put the sofa on one side of the living room and move the TV around."

[0443] Next, the user uses a smartphone or dedicated device to capture real-world data, such as 360-degree images of the room or LiDAR data, and uploads it to the system, which then captures information such as the actual room dimensions, shape, and existing furniture placement.

[0444] The server analyzes the real-world data uploaded by the user, using LiDAR data to generate a detailed 3D model of the room and applying object detection algorithms to the image data to identify the location and dimensions of furniture. For example, an object detection algorithm can be used to identify the location and dimensions of furniture such as a "sofa" or a "television" from an image.

[0445] The server then uses natural language processing techniques (e.g., spaCy or BERT) to analyze the user's preferences. Keywords such as "sofa," "television," and "living room" are extracted from the user's input, and the desired layout characteristics are identified based on these. The analysis results provide the information needed to design a layout that specifically reflects the user's preferences.

[0446] The server uses a generative AI model (such as DALL-E or GPT-3) to create multiple layout proposals based on the analysis results. For example, the server might generate a specific layout proposal such as "Place the sofa by the window in the living room and hang the TV on the opposite wall."

[0447] The generated layout proposals are converted into 3D models and images and presented to the user, who can visually check the multiple layout proposals provided and rotate and zoom in on the 3D models to view details from different angles.

[0448] Additionally, the server displays detailed information about the suggested furniture or appliances along with a link to purchase them. When the user clicks on the link, they are taken directly to the sales site, where they can conveniently purchase the suggested item. For example, the server might display, "This sofa is made by XYZ company and can be purchased here," and the user can click on the link to begin the purchase process immediately.

[0449] For example, if a user wants to set up a new office space in their living room, they first enter their request into the chat interface and upload a LiDAR scan of the space to the system. The server analyzes the data and generates layout proposals that include the arrangement of existing furniture and the optimal placement of new desks and chairs. These layout proposals are then presented to the user as 3D models, along with links to purchase the proposed desks and chairs.

[0450] An example of a prompt to input to the generative AI model is, "Based on 360-degree images of the room and LiDAR data, please generate the optimal layout proposal for a new desk and chair in the living room. The user's preference is 'I want the desk to be placed by the window, with the chair in front of it.'" This system allows users to obtain efficient, visually verifiable layout proposals that reflect their preferences, and also allows them to easily purchase the suggested furniture and appliances.

[0451] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0452] Step 1: User preference input

[0453] The user uses the chat interface to input their preferences for the room layout. For example, they can input specific requests such as "I want to put a sofa on one side of the living room and change the position of the TV." This input is sent as text data from the device to the server, which receives and temporarily stores this text data.

[0454] Step 2: Acquire and upload real-world data

[0455] Users use their smartphones or dedicated devices to capture 360-degree images and LiDAR data of their rooms, which are then uploaded to a cloud server. The uploaded data is then stored on the server as a resource containing information such as the room's dimensions, shape, and the layout of existing furniture.

[0456] Step 3: Data analysis

[0457] The server analyzes the uploaded real-world data. It generates a detailed 3D model of the room based on the LiDAR data, and applies an object detection algorithm to the image data to identify the location and dimensions of furniture. Specifically, the object detection algorithm identifies the location and dimensions of a "sofa" or "television" and reflects this in the 3D model. The input data is LiDAR data and image data, and the output is dimensional information and a 3D model of the furniture arrangement.

[0458] Step 4: Natural Language Analysis

[0459] The server uses natural language processing techniques (e.g., spaCy or BERT) to analyze the user's preferences. It extracts keywords such as "sofa," "television," and "living room" from the user's text data and identifies the required layout characteristics based on them. The input is the user's text data, and the output is the extracted keywords and the layout requirements based on them.

[0460] Step 5: Generate layout proposals

[0461] The server generates multiple layout proposals using a generative AI model (e.g., DALL-E or GPT-3) based on the analysis results. For example, a prompt might be, "Based on 360-degree images of the room and LiDAR data, generate the optimal layout proposal for placing a new desk and chair in the living room. The user's preference is 'I want the desk to be placed by the window, with the chair in front of it.'" Based on this prompt, the AI ​​model generates multiple layout proposals, and specific layout proposals are obtained as output.

[0462] Step 6: Present layout plan

[0463] The generated layout proposals are converted into 3D models or images by the server and presented to the user. The user can rotate these 3D models on their device and zoom in to check the details. This allows the user to visually check multiple layout proposals and choose the best one.

[0464] Step 7: Provide a purchase link

[0465] The server displays detailed information about the suggested furniture or home appliances and a link to purchase them. When the user clicks on the link for the suggested item, they are taken directly to the sales site and can begin the purchase process. This allows the user to easily purchase the suggested item. The input is information about the suggested furniture or home appliance, and the output is a purchase link and its detailed information.

[0466] In this way, each step works seamlessly together to provide an efficient, visually verifiable layout plan that reflects the user's preferences, and the process of purchasing the proposed furniture and home appliances is also simplified.

[0467] (Application example 1)

[0468] 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."

[0469] Conventional furniture and fixture layout planning often requires specialized knowledge and is time-consuming. Furthermore, there are limited ways to visually check the layout beforehand, making changes to the store layout risky. Furthermore, it is difficult to consider efficient layouts because the effects of layout plans cannot be simulated in advance. The challenge is to solve these problems and provide a method that allows store owners and designers to easily determine optimal layouts.

[0470] 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.

[0471] In this invention, the server includes: a means for a user to input their desired room layout into a chat interface; a means for uploading real-world data for the target room; a means for analyzing the uploaded real-world data and extracting the room's dimensions, shape, and existing furniture layout; a means for analyzing the user's requests using natural language processing technology; a means for using a generation AI to create multiple layout proposals based on the analysis results; a means for generating the generated layout proposals in 3D model or image format and presenting them to the user; a means for displaying detailed information and purchase links for the proposed furniture and appliances; and a means for optimizing the store layout and visually confirming them using augmented reality (AR). This allows store owners and designers to quickly obtain efficient, visually confirmable layout proposals that reflect their preferences. Furthermore, detailed information and purchase links for the proposed fixtures and decorations are provided, facilitating the purchasing process and facilitating improvements to store designs.

[0472] "Means for users to input their room layout preferences into a chat interface" refers to an interface where users input their preferences regarding the layout of a room or shop in text format.

[0473] "Means for users to upload real-world environment data for the target room" refers to the means by which users send information about the real-world environment, such as 360-degree images and LiDAR data acquired with their smartphones or dedicated devices, to a server.

[0474] "Means for the server to analyze the uploaded real-world data and extract the room's dimensions, shape, and existing furniture layout" refers to means for the server to process the real-world data submitted by the user and use algorithms to identify the physical details of the room and the layout of existing furniture.

[0475] "Means for the server to analyze requests from users using natural language processing technology" refers to means for the server to use natural language processing technology to analyze the text entered by the user and extract important elements and keywords from it.

[0476] "Means for the server to create multiple layout proposals using a generation AI based on the analysis results" refers to a means for the server to use a generation AI (artificial intelligence) to generate multiple layout proposals based on the data analyzed by the server in response to the user's requests.

[0477] "Means for generating layout plans generated by the server in the form of 3D models or images and presenting them to users" refers to means for creating layout plans generated by the server as 3D models or still images and presenting them visually to users.

[0478] "Means for the server to display detailed information about the proposed furniture and home appliances and links to purchase them" refers to means for displaying detailed information about the furniture and home appliances included in the layout proposal generated by the server to the user and links to purchase them.

[0479] "Means for optimizing the store layout and visually confirming it using AR (Augmented Reality)" refers to a means in which the server efficiently proposes the layout of the store and allows the user to visually confirm that layout using AR (Augmented Reality) technology.

[0480] "Means for a server to suggest optimal product placement based on the principles of behavioral economics" refers to means for a server to suggest optimal product placement locations using the theory of behavioral economics.

[0481] "A means of presenting proposals generated by the server to contractors as 3D models and simulating practical layout plans and their effects" refers to a means of creating layout proposals generated by the server as 3D models and presenting them to contractors to simulate actual layout methods and their effects.

[0482] "Means by which the server uses an object detection algorithm to analyze the LiDAR data or image data" means means by which the server uses an object detection algorithm to analyze the LiDAR data or image data.

[0483] "Means for visually checking proposed store layouts using AR displays on a smartphone or smart glasses" refers to means that enables users to visually check proposed store layouts using AR technology on a smartphone or smart glasses.

[0484] A system for realizing the present invention is configured as follows.

[0485] First, a user uses a chat interface to input their preferences for the layout of a room or store. For example, if a user wants to know how to most efficiently arrange newly purchased furniture, they can input a specific preference into the chat interface, such as "I want to put the sofa on one side of the living room and change the position of the TV."

[0486] Next, users upload real-world data of the target room or store, including LiDAR data, images, and videos. Users use their smartphones or smart glasses to capture 360-degree images and LiDAR scans and upload them to the system.

[0487] The server analyzes real-world data uploaded by users. It uses LiDAR data and image data to determine the dimensions, shape, and layout of existing furniture in a room or store. For example, it generates a detailed 3D model from the LiDAR data, and applies object detection algorithms to the image data to extract the location and dimensions of furniture. Open-source libraries and proprietary algorithms may be used for the analysis.

[0488] The server then analyzes the user's preferences using natural language processing technology. It analyzes the user's input, extracts keywords such as "sofa," "television," and "living room," and identifies the desired layout characteristics based on these. The analysis results provide the information needed to design a layout that specifically reflects the user's preferences.

[0489] Based on the analysis results, the server uses generative AI to create multiple layout proposals. At this time, the server considers the optimal layout based on the user's preferences and the actual environment of the room or store. For example, it may suggest placing a sofa in the optimal position in the living room and arranging related interior items around it. OpenAI's APIs and other similar services may be used for generative AI.

[0490] The generated layout proposals are rendered as 3D models or images by the server and presented to the user. The user can visually check multiple layout proposals using AR displays on their smartphone or smart glasses. For example, by rotating multiple 3D models and viewing them from different angles, the user can get a concrete idea of ​​how they will look when actually installed in a room or store.

[0491] In addition, the server displays detailed information and a link to purchase the suggested furniture or home appliance, allowing the user to immediately purchase the suggested item. For example, a link to purchase a sofa or TV may be displayed, and when the user clicks, the link will take them directly to the sales site.

[0492] As a specific example, consider the case where a store owner wishes to place new furniture in their store. The store owner first enters their wishes into a chat interface, providing specific instructions such as "I want to place a counter near the entrance and rearrange the shelves in the back room." They then acquire 360-degree images and LiDAR scans of the store and upload them to the system. The server analyzes the data and generates layout proposals that suggest optimal locations for existing fixtures and new fixtures. These layout proposals are then presented to the user in an AR display so that they can be visually confirmed. Detailed information about the proposed fixtures and decorations, along with a link to purchase them, is also displayed, allowing the user to proceed with the purchase process.

[0493] In this way, this system not only allows store owners and designers to quickly obtain efficient, visually verifiable layout plans that reflect their wishes, but also allows them to easily purchase the proposed fixtures and decorations.

[0494] To illustrate, the following is an example of a prompt sentence that users can use to input their preferences: "Please identify the layout characteristics based on the following preferences: I would like the counter to be located near the entrance and the shelves in the back room to be repositioned."

[0495] This allows the store layout to be optimized and visually confirmed using AR (augmented reality).

[0496] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0497] Step 1:

[0498] The user inputs the desired room layout into the chat interface.

[0499] Specific actions: The user types into the chat interface something like, "I want to place the counter near the entrance and rearrange the shelves in the back room."

[0500] Input: User text input (desired layout)

[0501] Output: Layout desired text

[0502] Step 2:

[0503] The user uploads real-world environment data for the target room.

[0504] How it works: Users capture 360-degree images and LiDAR data using their smartphones or smart glasses and upload them to the system.

[0505] Input: Real-world data such as 360-degree images and LiDAR data

[0506] Output: Real-world data uploaded to the server

[0507] Step 3:

[0508] The server analyzes the uploaded real-world data and extracts the room's dimensions, shape, and existing furniture layout.

[0509] What it does: The server converts the LiDAR data into a 3D model and analyzes the image data with object detection algorithms to determine the location and dimensions of the furniture.

[0510] Input: Real-world data (360-degree images, LiDAR data)

[0511] Output: Analyzed dimensional data, shape data, and layout data of existing furniture

[0512] Step 4:

[0513] The server analyzes the user's wishes using natural language processing technology.

[0514] How it works: The server extracts keywords from the user's input text and identifies layout characteristics. Here, it uses OpenAI's API to analyze the keywords.

[0515] Input: Desired layout text

[0516] Output: extracted keywords, parsed layout features

[0517] Step 5:

[0518] The server uses generative AI to create multiple layout proposals based on the analysis results.

[0519] How it works: The server uses generative AI to generate multiple layout proposals based on the analysis results and real-world data.

[0520] Input: Analysis results (layout characteristics, dimension data, shape data, existing furniture layout data)

[0521] Output: Multiple layout options

[0522] Step 6:

[0523] The server generates the layout proposal in the form of a 3D model or image and presents it to the user.

[0524] How it works: The server uses 3D modeling software to create 3D models and images of the layout proposals it generates, and presents them to the user, who then visually checks them using a smartphone or smart glasses.

[0525] Input: Multiple layout ideas

[0526] Output: 3D model, layout plan in image format

[0527] Step 7:

[0528] The server displays detailed information and purchase links for the suggested furniture and appliances.

[0529] Specific operation: Based on the layout plan, the server displays detailed information about the proposed furniture and appliances to the user, along with links to purchase each item.

[0530] Input: Layout plan, furniture and appliance information

[0531] Output: Detailed information, purchase link

[0532] Step 8:

[0533] Users can use their smartphones or smart glasses to visually check the proposed store layout using an AR display.

[0534] Specific operation: Using a smartphone or smart glasses, users can use AR technology to visually check layout proposals by overlaying them on the actual space.

[0535] Input: 3D model, AR software

[0536] Output: Visual confirmation of layout plan using AR display

[0537] 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.

[0538] As an embodiment of the present invention, a program in which the entire system functions in cooperation with the user, server, and terminal each performing their own role will be described below.

[0539] First, the user uses the chat interface to input their preferences for the room layout. For example, the user might say, "I want to turn my living room into a comfortable office space." Then, they upload LiDAR data, images, and videos as real-world data for the room via their device.

[0540] The server receives the real-world data uploaded by the user and begins analyzing it. It applies object detection algorithms to LiDAR and image data to extract the room's dimensions, shape, and existing furniture layout. Additionally, it uses natural language processing technology to analyze the user's preferences obtained through the chat interface and converts them into specific layout characteristics.

[0541] The server then uses generative AI to create multiple layout proposals based on the analyzed data. The proposed layouts are presented to the user in the form of 3D models or images. The user can visually check these models and select the optimal layout. For example, the system suggests new desk and chair arrangements, optimization of storage space, etc.

[0542] Furthermore, the present invention incorporates an emotion engine that recognizes emotions from user input and facial and voice data. This emotion engine monitors the user's emotional state in real time and evaluates the user's reaction when a layout proposal is presented. For example, if the user expresses joy in response to a proposed layout proposal, the emotion engine can record that information and reflect it in future proposals.

[0543] As a concrete example, suppose a user enters their desire to "create a relaxing space" into the chat interface and uploads LiDAR scan data of their room. The server analyzes the room data and generates multiple relaxation space layout proposals. Furthermore, an emotion engine evaluates the user's facial expressions and voice to see if they are relaxed, and presents the most suitable layout proposal to the user. The user can review the layout proposals and select and purchase the proposed furniture and appliances via detailed information and a purchase link.

[0544] This system can efficiently present visually verifiable layout proposals based on the user's preferences, and can make optimal proposals that take the user's emotions into consideration. Furthermore, by providing links to purchase the proposed furniture and home appliances, the user can easily complete the purchasing process.

[0545] The processing flow will be explained below.

[0546] Step 1:

[0547] The user enters their desired room layout into the chat interface, for example, "I want to turn my living room into a comfortable office space."

[0548] Step 2:

[0549] The user uploads real-world data of the target room, specifically 360-degree images taken with a smartphone or LiDAR scan data, to the system.

[0550] Step 3:

[0551] The server receives uploaded real-world data, which can include LiDAR data, images, and videos.

[0552] Step 4:

[0553] The server analyzes the received data, for example extracting the dimensions and shape of the room as a 3D model from the LiDAR data, and using object detection algorithms to identify the position and dimensions of furniture from the image data.

[0554] Step 5:

[0555] The server uses an emotion engine to recognize emotions from user input and facial and voice data. For example, a webcam and microphone are used to capture facial data and tone of voice.

[0556] Step 6:

[0557] The server analyzes the user's input using natural language processing technology, extracting keywords and important requests from the user's input and identifying the information necessary for layout design. For example, it identifies keywords such as "office space" and "living room."

[0558] Step 7:

[0559] The server uses generative AI to create multiple layout proposals based on the analysis results and emotion data. For example, it generates layout proposals optimized for the user's preferences and emotions, such as the placement of sofas and desks and chairs.

[0560] Step 8:

[0561] The server generates layout plans visually in the form of 3D models or images, and presents the data to the user. For example, it provides functions to rotate, enlarge, and reduce the 3D models.

[0562] Step 9:

[0563] The server uses an emotion engine to evaluate the user's reaction to the proposed layout. For example, if the user smiles, the server records this information and determines that the proposal is favorable.

[0564] Step 10:

[0565] The server displays detailed information about the proposed furniture and home appliances and a link to purchase them. For example, the server provides the user with detailed product information about the proposed desk or chair, along with a link to an online shop to purchase the item.

[0566] Step 11:

[0567] Users can review the proposed layouts and choose the one they like best. They can then purchase their favorite furniture and appliances directly through the purchase link.

[0568] Step 12:

[0569] The server records purchase logs and accumulates data for future layout suggestions and user feedback, which improves the accuracy of future layout suggestions.

[0570] Example 2

[0571] 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."

[0572] Conventional room layout design systems were unable to fully consider the user's preferences or actual environmental data, making it difficult to present efficient layout proposals. Furthermore, there was no mechanism to propose optimal layouts that took the user's emotions into consideration, resulting in low user satisfaction.

[0573] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0574] In this invention, the server includes means for using emotion recognition technology that analyzes the user's facial expressions and voice data to evaluate their emotions and takes the user's reaction into consideration when presenting a layout proposal, means for the user to input their desired room layout into a chat interface, and means for uploading actual environment data for the target room. This makes it possible to present efficient, visually verifiable layout proposals that meet the user's wishes, and to make optimal proposals that take the user's emotions into consideration.

[0575] "User" refers to a person who uses the system to design a room layout.

[0576] "Chat interface" refers to an interactive interface through which users input requests to the system via text.

[0577] "Real-world data" refers to data about the physical attributes of a room, including the dimensions and shape of the room, existing furniture layout, etc.

[0578] "Server" refers to the core computer system of the system that receives and analyzes data from users, generates and presents layout proposals using generative AI models.

[0579] "Object detection algorithm" refers to technology for recognizing the position and shape of objects from visual information such as images and LiDAR data.

[0580] "Natural language processing technology" refers to technology for analyzing text data and understanding user requests.

[0581] A "generative AI model" refers to an artificial intelligence model that generates new content (e.g., layout proposals) based on input data.

[0582] A "3D model" refers to a digital model that visualizes the proposed room layout in three dimensions.

[0583] "Emotion recognition technology" refers to technology for evaluating a user's emotions by analyzing their facial expressions and voice data.

[0584] "Purchase Link" refers to a web link provided along with detailed information about the proposed furniture or home appliances to facilitate the purchase process.

[0585] "Principles of behavioral economics" refers to an economic theory that optimizes the placement and offering of products and services by taking into account human psychology and behavior.

[0586] This invention is a system that optimally designs room layouts, with the user, server, and terminal all fulfilling their respective roles. Specifically, the user inputs their requests into the terminal, and the server generates an appropriate layout plan based on those requests and presents it to the user.

[0587] User Input

[0588] Users use the chat interface to input their desired room layout. For example, a user might input, "I want to turn my living room into a comfortable office space." Additionally, the user uploads real-world environmental data (such as LiDAR data, images, and videos) of the target room via their device. This allows the system to obtain the user's specific requirements and current room data.

[0589] Data analysis by server

[0590] The server receives the uploaded real-world data and analyzes it. This analysis uses object detection algorithms (e.g., YOLO, Mask R-CNN) to extract the room's dimensions, shape, and existing furniture layout. Furthermore, the server uses natural language processing techniques (e.g., BERT, GPT-3) to analyze the user's preferences and convert them into specific layout characteristics.

[0591] Generate layout proposals

[0592] Based on the analyzed data, the server uses a generative AI model (e.g., DALL-E, Stable Diffusion, etc.) to create multiple layout proposals. At this stage, an example prompt, "Please generate a living room layout proposal that will allow the user to relax," is input into the generative AI model. The resulting layout proposals are then rendered in the form of a three-dimensional model or image.

[0593] Proposal and Evaluation

[0594] The generated layout proposals are sent from the server to the device, which then presents them to the user. The user can visually review the proposed layout proposals and select the most appropriate one. Furthermore, the system incorporates emotion recognition technology (e.g., Microsoft's Emotion API, Face++) to analyze the user's facial expressions and voice data to evaluate their emotions. As a result, it is possible to propose optimal layout proposals based on the user's reactions.

[0595] Specific examples

[0596] For example, if a user enters "I want to create a space where I can relax" into the chat interface and uploads LiDAR scan data of their room, the server analyzes the room data and generates multiple layout proposals for relaxation spaces. At this time, the prompt text is entered as "Please generate a layout proposal for a living room where the user can relax." The generated layout proposals are presented to the user in the form of 3D models or images, and emotion recognition technology evaluates whether the user's facial expressions and voice are relaxing, and the most suitable layout proposal is ultimately presented to the user.

[0597] This system provides efficient and visually verifiable layout proposals based on the user's preferences, enabling optimal proposals that take the user's emotions into consideration. Furthermore, by providing links to purchase the proposed furniture and home appliances, the user can easily complete the purchasing process.

[0598] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0599] Step 1:

[0600] The user opens the chat interface on their device and enters information about their desired room layout, such as "I want to turn my living room into a comfortable office space." This input information is the starting point for the system and is important for clarifying the user's intentions.

[0601] input:

[0602] User layout preference (text format)

[0603] output:

[0604] User preference data

[0605] Specific behavior:

[0606] The user enters their preference in the chat box.

[0607] The terminal receives the user's desired data.

[0608] Step 2:

[0609] Users upload real-world environmental data (LiDAR data, images, videos, etc.) of the target room through their device, which is used to understand the current state of the room.

[0610] input:

[0611] LiDAR data, images, and videos

[0612] output:

[0613] Uploaded real-world data

[0614] Specific behavior:

[0615] The user scans the room using the LiDAR scanner on their device.

[0616] Upload scanned data and images to the server.

[0617] Step 3:

[0618] The server receives the uploaded real-world data and begins analyzing it. This analysis involves using object detection algorithms (e.g., YOLO, Mask R-CNN) to extract the room's dimensions, shape, and existing furniture layout. Next, it uses natural language processing techniques (e.g., BERT, GPT-3) to translate the user's preferences into specific layout characteristics.

[0619] input:

[0620] Uploaded real-world data

[0621] User preference data

[0622] output:

[0623] Room dimensions, shape, and furniture layout data

[0624] Analyzed Layout Characteristics

[0625] Specific behavior:

[0626] The server analyzes the LiDAR data to determine the dimensions of the room.

[0627] The server extracts the furniture layout from the image data.

[0628] The server analyzes the user's preferences and extracts layout characteristics.

[0629] Step 4:

[0630] Based on the analysis results, the server uses a generative AI model (e.g., DALL-E, Stable Diffusion) to create multiple layout proposals. At this stage, the prompt "Please generate a living room layout proposal that will allow the user to relax" is input to the generative AI model.

[0631] input:

[0632] Analyzed room data

[0633] Layout Characteristics

[0634] output:

[0635] Generated layout plan (3D model, image format)

[0636] Specific behavior:

[0637] The server inputs a prompt sentence into the generative AI model.

[0638] Generative AI generates multiple layout proposals.

[0639] Step 5:

[0640] The terminal receives the layout proposals sent from the server and presents them to the user, who can visually check these layout proposals and select the most suitable one.

[0641] input:

[0642] Generated layout plan

[0643] output:

[0644] Present layout proposals to users

[0645] Specific behavior:

[0646] The terminal receives the generated layout plan.

[0647] The terminal displays the layout proposal to the user.

[0648] Step 6:

[0649] The server is equipped with emotion recognition technology (e.g., Microsoft's Emotion API, Face++) that analyzes the user's facial expressions and voice data to evaluate their emotions. This allows it to ultimately propose an optimal layout plan that takes the user's reaction into account.

[0650] input:

[0651] User's facial expression data, voice data

[0652] output:

[0653] Emotional evaluation data

[0654] Optimal layout plan

[0655] Specific behavior:

[0656] The server analyzes the user's facial expressions and voice data.

[0657] Emotion recognition technology assesses the user's emotions and records the information.

[0658] The server presents the optimal layout proposal to the user.

[0659] (Application example 2)

[0660] 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."

[0661] In traditional store operations, it is difficult to instantly optimize the effective display and placement of products, and selecting an appropriate layout requires a great deal of time and effort. Furthermore, there is a lack of a way to receive real-time feedback on staff and customer reactions and reflect that feedback, which results in delays in improving sales and customer satisfaction.

[0662] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input a desired room layout into a chat interface; a means for the user to upload real-world environment data of the target room; a means for the server to analyze the uploaded real-world environment data and extract the room's dimensions, shape, and existing furniture layout; a means for the server to analyze the user's requests using natural language processing technology; a means for the server to create multiple layout proposals using a generation AI based on the analysis results; a means for the server to generate the generated layout proposals in 3D model or image format and present them to the user; a means for the server to display detailed information and purchase links for the proposed furniture and home appliances; and a means for evaluating user reactions using an emotion engine and improving the proposals. This makes it possible to quickly and efficiently propose effective layouts for displays and products and provide optimal layouts that take into account the emotions of staff and customers.

[0663] A "chat interface" is an interactive user interface that allows users to input their wishes and requests through text input in natural language.

[0664] "Real-world data" refers to data that includes the physical dimensions, shape, and current furniture layout of the target room or space, and includes LiDAR data, images, videos, etc.

[0665] A "server" is a computer system that receives data uploaded or information entered by users, analyzes it, and processes it.

[0666] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate text in natural language, and is used to analyze user requests.

[0667] A "generative AI model" is an artificial intelligence model that generates optimal layout proposals based on user input and environmental data, and uses a generative model, for example.

[0668] A "3D model" is a computer graphics model constructed in three-dimensional space, and is used by users to visually check layout proposals.

[0669] An "emotion engine" is an algorithm that analyzes emotions from a user's facial expressions, voice, etc. and evaluates their reaction.

[0670] "Display" refers to a display device for visually presenting products and information, and in this context refers particularly to the arrangement of products in a store.

[0671] An "object detection algorithm" is a technology that detects the position and shape of objects from images and LiDAR data.

[0672] A "layout plan" is a proposal for optimizing the layout of a room or space, showing what furniture and products should be placed where.

[0673] The present invention is a system in which users, servers, and terminals work together to optimize the placement and display of products in a physical store. An embodiment of the system will be described in detail below.

[0674] System configuration

[0675] 1. User Device

[0676] Users can use devices such as smartphones or tablets to input their preferences and requests for product placement into a chat interface, and can also upload LiDAR data and images, providing the system with information about the physical dimensions and shape of the physical store and the current product placement situation.

[0677] 2. Server

[0678] The server receives and analyzes real-world data (LiDAR data, images, and videos) uploaded by users. Specifically, it uses an object detection algorithm (e.g., OpenCV) to extract the dimensions and shape of the room and the layout of existing products. It then analyzes the user's requests using natural language processing technology (e.g., GPT-3). Based on the analysis results, it then uses a generative AI model to generate multiple layout proposals.

[0679] 3. Proposal Generation and Display

[0680] The layout proposals generated by the server are presented to the user in the form of 3D models or images, allowing the user to visually check them and select the most suitable layout. Detailed information and purchase links for the proposed products and appliances are also displayed, allowing the user to easily complete the purchase process.

[0681] 4. Emotion Engine

[0682] Additionally, it has a built-in emotion engine that can monitor and evaluate the reactions of users, staff, and customers in real time. For example, if a user expresses delight in a proposed layout, this information can be recorded and reflected in future proposals. This emotion data analysis uses a sentiment analysis pipeline such as Transformers.

[0683] Specific examples

[0684] Display placement in physical stores

[0685] A store manager uses a tablet to input, "I want to create a visually appealing display for a new product," and uploads LiDAR scan data and photos of the store. The server analyzes this and generates an optimal layout proposal. Staff members view the layout and provide their feedback, and the system makes a final proposal based on their reactions. The following prompts are used in this process:

[0686] Prompt Sentence Examples

[0687] User desire: Create a visually appealing display for a new product

[0688] Room dimensions: 10m wide, 8m deep, 3m high

[0689] Existing furniture layout: 3 shelves along the wall, 1 table in the middle

[0690] Optimal layout ideas:

[0691] This system not only allows for efficient optimization of store display layout, but also enables continuous improvement based on feedback from staff and customers.

[0692] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0693] Step 1:

[0694] Users use a chat interface to enter their product placement preferences in text, and then upload LiDAR data and images of the store using a smartphone or tablet.

[0695] Input: User's desired text, LiDAR data, image data

[0696] Output: Chat request data, LiDAR data, image data

[0697] Specific operation: The user inputs the desired information into the device, selects the captured LiDAR data and image data, and uploads them.

[0698] Step 2:

[0699] The server analyzes the real-world data received from the user (LiDAR data, image data, video) and uses an object detection algorithm (e.g., OpenCV) to extract the dimensions and shape of the store and the existing product layout.

[0700] Input: LiDAR data, image data

[0701] Output: Room dimensions, shape, and existing product placement data

[0702] How it works: The server applies object detection algorithms to automatically extract store dimensions, shape, and product placement information from LiDAR and image data.

[0703] Step 3:

[0704] The server analyzes the desired text from the user using natural language processing technology (e.g., GPT-3) and converts the user's requests into specific layout characteristics.

[0705] Input: User-defined text

[0706] Output: Specific layout characteristics

[0707] What it does: The server uses the generative AI model to analyze text data received from the chat interface and extract layout characteristics.

[0708] Step 4:

[0709] Based on the analyzed data, the server uses a generative AI model to generate multiple layout proposals.

[0710] Input: Room dimensions, shape, existing product placement data, specific layout characteristics

[0711] Output: Multiple layout options

[0712] Specific operation: The server inputs a prompt sentence into the generative AI model and generates multiple optimal layout proposals. Examples of prompt sentences used are as follows:

[0713] Prompt Sentence Examples

[0714] User desire: Create a visually appealing display for a new product

[0715] Room dimensions: 10m wide, 8m deep, 3m high

[0716] Existing furniture layout: 3 shelves along the wall, 1 table in the middle

[0717] Optimal layout ideas:

[0718] Step 5:

[0719] The server generates the layout proposal in the form of a 3D model or image and presents it to the user.

[0720] Input: Multiple layout ideas

[0721] Output: 3D model, layout plan in image format

[0722] Specific operation: The layout proposal generated by the server is converted into a visually displayable format using 3D modeling software and presented to the user.

[0723] Step 6:

[0724] The user visually checks the proposed layouts and selects the most suitable one. The user's selection and reaction data (facial expressions and voice) are input into the system.

[0725] Input: 3D model, layout plan in image format, user reaction data

[0726] Output: Selected layout plan, user reaction data

[0727] Specific operation: The user selects the best layout from the layout proposals visually reviewed and inputs their selection and reaction into the system.

[0728] Step 7:

[0729] The server-based emotion engine analyzes the user's reaction data, evaluates the user's emotional state, and adjusts the final layout proposal based on the reaction.

[0730] Input: User reaction data

[0731] Output: Final layout proposal adjustments

[0732] How it works: The emotion engine analyzes the reaction data to identify the user's emotional state, and then optimizes and adjusts the generated layout proposals based on the results.

[0733] Step 8:

[0734] The server then presents the final adjusted layout proposal to the user again, and displays detailed information and purchase links for the proposed products, allowing the user to select products and complete the purchase process.

[0735] Input: Final layout proposal adjustments

[0736] Output: Final layout with adjustments, product details, and purchase links

[0737] Specific operation: The server generates the final layout plan, along with detailed product information and purchase links for the corresponding products, and presents them to the user.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] [Third embodiment]

[0742] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0743] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0744] 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).

[0745] 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.

[0746] 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.

[0747] 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).

[0748] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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."

[0754] As an embodiment of the present invention, a program will be described below in which the entire system functions in cooperation with the user, server, and terminal each playing a specific role.

[0755] First, a user uses the chat interface to input their room layout preferences. For example, if a user wants to know how to most efficiently arrange newly purchased furniture, they can input a specific preference into the chat interface, such as "I'd like to put the sofa on one side of the living room and move the TV around."

[0756] Next, users upload real-world data of the target room, including LiDAR data, images, videos, etc. Users can use their smartphones or dedicated devices to capture 360-degree images and LiDAR scans and upload them to the system.

[0757] The server analyzes the real-world data uploaded by the user. It uses LiDAR data and images to determine the dimensions, shape, and layout of existing furniture in the room. For example, it generates a detailed 3D model from the LiDAR data and applies object detection algorithms to the image data to extract the location and dimensions of furniture.

[0758] The server then analyzes the user's preferences using natural language processing technology. It analyzes the user's input, extracts keywords such as "sofa," "television," and "living room," and identifies the desired layout characteristics based on these. The analysis results provide the information needed to design a layout that specifically reflects the user's preferences.

[0759] The server uses generative AI to create multiple layout proposals based on the analysis results. At this time, the server considers the optimal layout based on the user's preferences and the actual environment of the room. For example, it suggests placing a sofa in the optimal position in the living room and arranging related interior items around it.

[0760] The generated layout proposals are rendered by the server in the form of 3D models or images and presented to the user. The user can visually check multiple layout proposals and choose the one they like best. For example, by rotating multiple 3D models and viewing them from different angles, the user can get a concrete idea of ​​how they will look when actually installed in a room.

[0761] In addition, the server displays detailed information and a link to purchase the suggested furniture or home appliance, allowing the user to immediately purchase the suggested item. For example, a link to purchase a sofa or TV may be displayed, and when the user clicks, the link will take them directly to the sales site.

[0762] For example, if a user wants to set up a new office space in their living room, they first enter their request into the chat interface and upload a LiDAR scan of the space to the system. The server analyzes the data and generates layout proposals that suggest the optimal placement of existing furniture and new furniture. These layout proposals are then presented to the user as 3D models, along with links to purchase the proposed desks and chairs.

[0763] This system allows users to quickly obtain efficient, visually verifiable layout plans that reflect their preferences, and also allows them to easily purchase the suggested furniture and appliances.

[0764] The processing flow will be explained below.

[0765] Step 1:

[0766] The user enters their desired room layout into the chat interface, for example, "I want to turn my living room into a comfortable office space."

[0767] Step 2:

[0768] The user uploads real-world data of the target room, such as a 360-degree image taken with a smartphone or LiDAR scan data, to the system.

[0769] Step 3:

[0770] The server receives uploaded real-world data, which can include LiDAR data, images, and videos.

[0771] Step 4:

[0772] The server analyzes the received real-world data, extracting the dimensions and shape of the room as a 3D model from the LiDAR data, and using object detection algorithms to identify the position and dimensions of furniture from the image data.

[0773] Step 5:

[0774] The server analyzes the user's input using natural language processing technology. It extracts keywords and important requirements from the user's input and identifies the information necessary for layout design. For example, if the user uses the term "office space," it identifies the appropriate layout characteristics.

[0775] Step 6:

[0776] The server uses AI to generate multiple layout proposals based on the analysis results, such as the placement of sofas and desks and chairs, to generate layout proposals optimized to the user's preferences.

[0777] Step 7:

[0778] The server generates layout proposals visually in the form of 3D models or images, and presents the data to the user. For example, it provides functions that allow the user to rotate, enlarge, and reduce the 3D models so that multiple layout proposals can be compared.

[0779] Step 8:

[0780] The server displays detailed information about the proposed furniture and home appliances and a link to purchase them. For example, the server provides the user with detailed product information about the proposed desk or chair, along with a link to an online shop to purchase the item.

[0781] Step 9:

[0782] Users can review the proposed layouts and choose the one they like best. They can then purchase their favorite furniture and appliances directly through the purchase link.

[0783] Step 10:

[0784] The server records purchase logs and accumulates data for future layout suggestions and user feedback, which improves the accuracy of future layout suggestions.

[0785] Example 1

[0786] 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."

[0787] Many modern users seek specific advice on room layout changes and new furniture arrangements, but the process is time-consuming and laborious, and visual confirmation methods are limited. Therefore, a system that allows users to obtain layout proposals efficiently and visually easily understandable is needed. There is also a need for a system that allows users to easily purchase the proposed furniture and appliances.

[0788] 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.

[0789] In this invention, the server includes: a means for a user to input a desired room layout into a chat interface; a means for the user to upload real-world data of the target room; a means for the server to analyze the uploaded real-world data and extract the room's dimensions, shape, and existing furniture layout; a means for the server to analyze the user's requests using natural language processing technology; a means for the server to create multiple layout proposals using a generative AI model based on the analysis results; a means for the server to generate the generated layout proposals in 3D model or image format and present them to the user; and a means for the server to display detailed information and purchase links for the proposed furniture and home appliances. This allows the user to easily obtain efficient and visually confirmable layout proposals that reflect their wishes, and also enables the user to easily purchase the proposed furniture and home appliances.

[0790] A "chat interface" is an interface that allows a user to input text-based information and communicate with the system.

[0791] "Real-world data" is a general term for data such as 360-degree images and LiDAR data that includes the dimensions, shape, and furniture placement of a user's room.

[0792] A "server" is a computer system that performs key processes such as analyzing data, running generative AI, natural language processing, and generating 3D models.

[0793] "Natural language processing technology" is a technology for analyzing text data entered by a user and understanding its meaning.

[0794] A "generative AI model" is an artificial intelligence model that generates new content or suggestions based on input data and prompts.

[0795] A "3D model" is a digital model that represents an analyzed space, furniture arrangement, etc. in three dimensions.

[0796] An "object detection algorithm" is an algorithm that detects specific objects from images or LiDAR data and identifies their location and dimensions.

[0797] A "prompt sentence" is text data that is input into a generative AI model, and the AI ​​generates output based on this.

[0798] As an embodiment of the present invention, a program will be described in which a user, a server, and a terminal each play a specific role and function in cooperation as a system.

[0799] First, a user uses the chat interface to input their room layout preferences. For example, if a user is asking how to most efficiently arrange newly purchased furniture, they might input a specific preference into the chat interface, such as "I want to put the sofa on one side of the living room and move the TV around."

[0800] Next, the user uses a smartphone or dedicated device to capture real-world data, such as 360-degree images of the room or LiDAR data, and uploads it to the system, which then captures information such as the actual room dimensions, shape, and existing furniture placement.

[0801] The server analyzes the real-world data uploaded by the user, using LiDAR data to generate a detailed 3D model of the room and applying object detection algorithms to the image data to identify the location and dimensions of furniture. For example, an object detection algorithm can be used to identify the location and dimensions of furniture such as a "sofa" or a "television" from an image.

[0802] The server then uses natural language processing techniques (e.g., spaCy or BERT) to analyze the user's preferences. Keywords such as "sofa," "television," and "living room" are extracted from the user's input, and the desired layout characteristics are identified based on these. The analysis results provide the information needed to design a layout that specifically reflects the user's preferences.

[0803] The server uses a generative AI model (such as DALL-E or GPT-3) to create multiple layout proposals based on the analysis results. For example, the server might generate a specific layout proposal such as "Place the sofa by the window in the living room and hang the TV on the opposite wall."

[0804] The generated layout proposals are converted into 3D models and images and presented to the user, who can visually check the multiple layout proposals provided and rotate and zoom in on the 3D models to view details from different angles.

[0805] Additionally, the server displays detailed information about the suggested furniture or appliances along with a link to purchase them. When the user clicks on the link, they are taken directly to the sales site, where they can conveniently purchase the suggested item. For example, the server might display, "This sofa is made by XYZ company and can be purchased here," and the user can click on the link to begin the purchase process immediately.

[0806] For example, if a user wants to set up a new office space in their living room, they first enter their request into the chat interface and upload a LiDAR scan of the space to the system. The server analyzes the data and generates layout proposals that include the arrangement of existing furniture and the optimal placement of new desks and chairs. These layout proposals are then presented to the user as 3D models, along with links to purchase the proposed desks and chairs.

[0807] An example of a prompt to input to the generative AI model is, "Based on 360-degree images of the room and LiDAR data, please generate the optimal layout proposal for a new desk and chair in the living room. The user's preference is 'I want the desk to be placed by the window, with the chair in front of it.'" This system allows users to obtain efficient, visually verifiable layout proposals that reflect their preferences, and also allows them to easily purchase the suggested furniture and appliances.

[0808] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0809] Step 1: User preference input

[0810] The user uses the chat interface to input their preferences for the room layout. For example, they can input specific requests such as "I want to put a sofa on one side of the living room and change the position of the TV." This input is sent as text data from the device to the server, which receives and temporarily stores this text data.

[0811] Step 2: Acquire and upload real-world data

[0812] Users use their smartphones or dedicated devices to capture 360-degree images and LiDAR data of their rooms, which are then uploaded to a cloud server. The uploaded data is then stored on the server as a resource containing information such as the room's dimensions, shape, and the layout of existing furniture.

[0813] Step 3: Data analysis

[0814] The server analyzes the uploaded real-world data. It generates a detailed 3D model of the room based on the LiDAR data, and applies an object detection algorithm to the image data to identify the location and dimensions of furniture. Specifically, the object detection algorithm identifies the location and dimensions of a "sofa" or "television" and reflects this in the 3D model. The input data is LiDAR data and image data, and the output is dimensional information and a 3D model of the furniture arrangement.

[0815] Step 4: Natural Language Analysis

[0816] The server uses natural language processing techniques (e.g., spaCy or BERT) to analyze the user's preferences. It extracts keywords such as "sofa," "television," and "living room" from the user's text data and identifies the required layout characteristics based on them. The input is the user's text data, and the output is the extracted keywords and the layout requirements based on them.

[0817] Step 5: Generate layout proposals

[0818] The server generates multiple layout proposals using a generative AI model (e.g., DALL-E or GPT-3) based on the analysis results. For example, a prompt might be, "Based on 360-degree images of the room and LiDAR data, generate the optimal layout proposal for placing a new desk and chair in the living room. The user's preference is 'I want the desk to be placed by the window, with the chair in front of it.'" Based on this prompt, the AI ​​model generates multiple layout proposals, and specific layout proposals are obtained as output.

[0819] Step 6: Present layout plan

[0820] The generated layout proposals are converted into 3D models or images by the server and presented to the user. The user can rotate these 3D models on their device and zoom in to check the details. This allows the user to visually check multiple layout proposals and choose the best one.

[0821] Step 7: Provide a purchase link

[0822] The server displays detailed information about the suggested furniture or home appliances and a link to purchase them. When the user clicks on the link for the suggested item, they are taken directly to the sales site and can begin the purchase process. This allows the user to easily purchase the suggested item. The input is information about the suggested furniture or home appliance, and the output is a purchase link and its detailed information.

[0823] In this way, each step works seamlessly together to provide an efficient, visually verifiable layout plan that reflects the user's preferences, and the process of purchasing the proposed furniture and home appliances is also simplified.

[0824] (Application example 1)

[0825] 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."

[0826] Conventional furniture and fixture layout planning often requires specialized knowledge and is time-consuming. Furthermore, there are limited ways to visually check the layout beforehand, making changes to the store layout risky. Furthermore, it is difficult to consider efficient layouts because the effects of layout plans cannot be simulated in advance. The challenge is to solve these problems and provide a method that allows store owners and designers to easily determine optimal layouts.

[0827] 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.

[0828] In this invention, the server includes: a means for a user to input their desired room layout into a chat interface; a means for uploading real-world data for the target room; a means for analyzing the uploaded real-world data and extracting the room's dimensions, shape, and existing furniture layout; a means for analyzing the user's requests using natural language processing technology; a means for using a generation AI to create multiple layout proposals based on the analysis results; a means for generating the generated layout proposals in 3D model or image format and presenting them to the user; a means for displaying detailed information and purchase links for the proposed furniture and appliances; and a means for optimizing the store layout and visually confirming them using augmented reality (AR). This allows store owners and designers to quickly obtain efficient, visually confirmable layout proposals that reflect their preferences. Furthermore, detailed information and purchase links for the proposed fixtures and decorations are provided, facilitating the purchasing process and facilitating improvements to store designs.

[0829] "Means for users to input their room layout preferences into a chat interface" refers to an interface where users input their preferences regarding the layout of a room or shop in text format.

[0830] "Means for users to upload real-world environment data for the target room" refers to the means by which users send information about the real-world environment, such as 360-degree images and LiDAR data acquired with their smartphones or dedicated devices, to a server.

[0831] "Means for the server to analyze the uploaded real-world data and extract the room's dimensions, shape, and existing furniture layout" refers to means for the server to process the real-world data submitted by the user and use algorithms to identify the physical details of the room and the layout of existing furniture.

[0832] "Means for the server to analyze requests from users using natural language processing technology" refers to means for the server to use natural language processing technology to analyze the text entered by the user and extract important elements and keywords from it.

[0833] "Means for the server to create multiple layout proposals using a generation AI based on the analysis results" refers to a means for the server to use a generation AI (artificial intelligence) to generate multiple layout proposals based on the data analyzed by the server in response to the user's requests.

[0834] "Means for generating layout plans generated by the server in the form of 3D models or images and presenting them to users" refers to means for creating layout plans generated by the server as 3D models or still images and presenting them visually to users.

[0835] "Means for the server to display detailed information about the proposed furniture and home appliances and links to purchase them" refers to means for displaying detailed information about the furniture and home appliances included in the layout proposal generated by the server to the user and links to purchase them.

[0836] "Means for optimizing the store layout and visually confirming it using AR (Augmented Reality)" refers to a means in which the server efficiently proposes the layout of the store and allows the user to visually confirm that layout using AR (Augmented Reality) technology.

[0837] "Means for a server to suggest optimal product placement based on the principles of behavioral economics" refers to means for a server to suggest optimal product placement locations using the theory of behavioral economics.

[0838] "A means of presenting proposals generated by the server to contractors as 3D models and simulating practical layout plans and their effects" refers to a means of creating layout proposals generated by the server as 3D models and presenting them to contractors to simulate actual layout methods and their effects.

[0839] "Means by which the server uses an object detection algorithm to analyze the LiDAR data or image data" means means by which the server uses an object detection algorithm to analyze the LiDAR data or image data.

[0840] "Means for visually checking proposed store layouts using AR displays on a smartphone or smart glasses" refers to means that enables users to visually check proposed store layouts using AR technology on a smartphone or smart glasses.

[0841] A system for realizing the present invention is configured as follows.

[0842] First, a user uses a chat interface to input their preferences for the layout of a room or store. For example, if a user wants to know how to most efficiently arrange newly purchased furniture, they can input a specific preference into the chat interface, such as "I want to put the sofa on one side of the living room and change the position of the TV."

[0843] Next, users upload real-world data of the target room or store, including LiDAR data, images, and videos. Users use their smartphones or smart glasses to capture 360-degree images and LiDAR scans and upload them to the system.

[0844] The server analyzes real-world data uploaded by users. It uses LiDAR data and image data to determine the dimensions, shape, and layout of existing furniture in a room or store. For example, it generates a detailed 3D model from the LiDAR data, and applies object detection algorithms to the image data to extract the location and dimensions of furniture. Open-source libraries and proprietary algorithms may be used for the analysis.

[0845] The server then analyzes the user's preferences using natural language processing technology. It analyzes the user's input, extracts keywords such as "sofa," "television," and "living room," and identifies the desired layout characteristics based on these. The analysis results provide the information needed to design a layout that specifically reflects the user's preferences.

[0846] Based on the analysis results, the server uses generative AI to create multiple layout proposals. At this time, the server considers the optimal layout based on the user's preferences and the actual environment of the room or store. For example, it may suggest placing a sofa in the optimal position in the living room and arranging related interior items around it. OpenAI's APIs and other similar services may be used for generative AI.

[0847] The generated layout proposals are rendered as 3D models or images by the server and presented to the user. The user can visually check multiple layout proposals using AR displays on their smartphone or smart glasses. For example, by rotating multiple 3D models and viewing them from different angles, the user can get a concrete idea of ​​how they will look when actually installed in a room or store.

[0848] In addition, the server displays detailed information and a link to purchase the suggested furniture or home appliance, allowing the user to immediately purchase the suggested item. For example, a link to purchase a sofa or TV may be displayed, and when the user clicks, the link will take them directly to the sales site.

[0849] As a specific example, consider the case where a store owner wishes to place new furniture in their store. The store owner first enters their wishes into a chat interface, providing specific instructions such as "I want to place a counter near the entrance and rearrange the shelves in the back room." They then acquire 360-degree images and LiDAR scans of the store and upload them to the system. The server analyzes the data and generates layout proposals that suggest optimal locations for existing fixtures and new fixtures. These layout proposals are then presented to the user in an AR display so that they can be visually confirmed. Detailed information about the proposed fixtures and decorations, along with a link to purchase them, is also displayed, allowing the user to proceed with the purchase process.

[0850] In this way, this system not only allows store owners and designers to quickly obtain efficient, visually verifiable layout plans that reflect their wishes, but also allows them to easily purchase the proposed fixtures and decorations.

[0851] To illustrate, the following is an example of a prompt sentence that users can use to input their preferences: "Please identify the layout characteristics based on the following preferences: I would like the counter to be located near the entrance and the shelves in the back room to be repositioned."

[0852] This allows the store layout to be optimized and visually confirmed using AR (augmented reality).

[0853] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0854] Step 1:

[0855] The user inputs the desired room layout into the chat interface.

[0856] Specific actions: The user types into the chat interface something like, "I want to place the counter near the entrance and rearrange the shelves in the back room."

[0857] Input: User text input (desired layout)

[0858] Output: Layout desired text

[0859] Step 2:

[0860] The user uploads real-world environment data for the target room.

[0861] How it works: Users capture 360-degree images and LiDAR data using their smartphones or smart glasses and upload them to the system.

[0862] Input: Real-world data such as 360-degree images and LiDAR data

[0863] Output: Real-world data uploaded to the server

[0864] Step 3:

[0865] The server analyzes the uploaded real-world data and extracts the room's dimensions, shape, and existing furniture layout.

[0866] What it does: The server converts the LiDAR data into a 3D model and analyzes the image data with object detection algorithms to determine the location and dimensions of the furniture.

[0867] Input: Real-world data (360-degree images, LiDAR data)

[0868] Output: Analyzed dimensional data, shape data, and layout data of existing furniture

[0869] Step 4:

[0870] The server analyzes the user's wishes using natural language processing technology.

[0871] How it works: The server extracts keywords from the user's input text and identifies layout characteristics. Here, it uses OpenAI's API to analyze the keywords.

[0872] Input: Desired layout text

[0873] Output: extracted keywords, parsed layout features

[0874] Step 5:

[0875] The server uses generative AI to create multiple layout proposals based on the analysis results.

[0876] How it works: The server uses generative AI to generate multiple layout proposals based on the analysis results and real-world data.

[0877] Input: Analysis results (layout characteristics, dimension data, shape data, existing furniture layout data)

[0878] Output: Multiple layout options

[0879] Step 6:

[0880] The server generates the layout proposal in the form of a 3D model or image and presents it to the user.

[0881] How it works: The server uses 3D modeling software to create 3D models and images of the layout proposals it generates, and presents them to the user, who then visually checks them using a smartphone or smart glasses.

[0882] Input: Multiple layout ideas

[0883] Output: 3D model, layout plan in image format

[0884] Step 7:

[0885] The server displays detailed information and purchase links for the suggested furniture and appliances.

[0886] Specific operation: Based on the layout plan, the server displays detailed information about the proposed furniture and appliances to the user, along with links to purchase each item.

[0887] Input: Layout plan, furniture and appliance information

[0888] Output: Detailed information, purchase link

[0889] Step 8:

[0890] Users can use their smartphones or smart glasses to visually check the proposed store layout using an AR display.

[0891] Specific operation: Using a smartphone or smart glasses, users can use AR technology to visually check layout proposals by overlaying them on the actual space.

[0892] Input: 3D model, AR software

[0893] Output: Visual confirmation of layout plan using AR display

[0894] 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.

[0895] As an embodiment of the present invention, a program in which the entire system functions in cooperation with the user, server, and terminal each performing their own role will be described below.

[0896] First, the user uses the chat interface to input their preferences for the room layout. For example, the user might say, "I want to turn my living room into a comfortable office space." Then, they upload LiDAR data, images, and videos as real-world data for the room via their device.

[0897] The server receives the real-world data uploaded by the user and begins analyzing it. It applies object detection algorithms to LiDAR and image data to extract the room's dimensions, shape, and existing furniture layout. Additionally, it uses natural language processing technology to analyze the user's preferences obtained through the chat interface and converts them into specific layout characteristics.

[0898] The server then uses generative AI to create multiple layout proposals based on the analyzed data. The proposed layouts are presented to the user in the form of 3D models or images. The user can visually check these models and select the optimal layout. For example, the system suggests new desk and chair arrangements, optimization of storage space, etc.

[0899] Furthermore, the present invention incorporates an emotion engine that recognizes emotions from user input and facial and voice data. This emotion engine monitors the user's emotional state in real time and evaluates the user's reaction when a layout proposal is presented. For example, if the user expresses joy in response to a proposed layout proposal, the emotion engine can record that information and reflect it in future proposals.

[0900] As a concrete example, suppose a user enters their desire to "create a relaxing space" into the chat interface and uploads LiDAR scan data of their room. The server analyzes the room data and generates multiple relaxation space layout proposals. Furthermore, an emotion engine evaluates the user's facial expressions and voice to see if they are relaxed, and presents the most suitable layout proposal to the user. The user can review the layout proposals and select and purchase the proposed furniture and appliances via detailed information and a purchase link.

[0901] This system can efficiently present visually verifiable layout proposals based on the user's preferences, and can make optimal proposals that take the user's emotions into consideration. Furthermore, by providing links to purchase the proposed furniture and home appliances, the user can easily complete the purchasing process.

[0902] The processing flow will be explained below.

[0903] Step 1:

[0904] The user enters their desired room layout into the chat interface, for example, "I want to turn my living room into a comfortable office space."

[0905] Step 2:

[0906] The user uploads real-world data of the target room, specifically 360-degree images taken with a smartphone or LiDAR scan data, to the system.

[0907] Step 3:

[0908] The server receives uploaded real-world data, which can include LiDAR data, images, and videos.

[0909] Step 4:

[0910] The server analyzes the received data, for example extracting the dimensions and shape of the room as a 3D model from the LiDAR data, and using object detection algorithms to identify the position and dimensions of furniture from the image data.

[0911] Step 5:

[0912] The server uses an emotion engine to recognize emotions from user input and facial and voice data. For example, a webcam and microphone are used to capture facial data and tone of voice.

[0913] Step 6:

[0914] The server analyzes the user's input using natural language processing technology, extracting keywords and important requests from the user's input and identifying the information necessary for layout design. For example, it identifies keywords such as "office space" and "living room."

[0915] Step 7:

[0916] The server uses generative AI to create multiple layout proposals based on the analysis results and emotion data. For example, it generates layout proposals optimized for the user's preferences and emotions, such as the placement of sofas and desks and chairs.

[0917] Step 8:

[0918] The server generates layout plans visually in the form of 3D models or images, and presents the data to the user. For example, it provides functions to rotate, enlarge, and reduce the 3D models.

[0919] Step 9:

[0920] The server uses an emotion engine to evaluate the user's reaction to the proposed layout. For example, if the user smiles, the server records this information and determines that the proposal is favorable.

[0921] Step 10:

[0922] The server displays detailed information about the proposed furniture and home appliances and a link to purchase them. For example, the server provides the user with detailed product information about the proposed desk or chair, along with a link to an online shop to purchase the item.

[0923] Step 11:

[0924] Users can review the proposed layouts and choose the one they like best. They can then purchase their favorite furniture and appliances directly through the purchase link.

[0925] Step 12:

[0926] The server records purchase logs and accumulates data for future layout suggestions and user feedback, which improves the accuracy of future layout suggestions.

[0927] Example 2

[0928] 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."

[0929] Conventional room layout design systems were unable to fully consider the user's preferences or actual environmental data, making it difficult to present efficient layout proposals. Furthermore, there was no mechanism to propose optimal layouts that took the user's emotions into consideration, resulting in low user satisfaction.

[0930] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0931] In this invention, the server includes means for using emotion recognition technology that analyzes the user's facial expressions and voice data to evaluate their emotions and takes the user's reaction into consideration when presenting a layout proposal, means for the user to input their desired room layout into a chat interface, and means for uploading actual environment data for the target room. This makes it possible to present efficient, visually verifiable layout proposals that meet the user's wishes, and to make optimal proposals that take the user's emotions into consideration.

[0932] "User" refers to a person who uses the system to design a room layout.

[0933] "Chat interface" refers to an interactive interface through which users input requests to the system via text.

[0934] "Real-world data" refers to data about the physical attributes of a room, including the dimensions and shape of the room, existing furniture layout, etc.

[0935] "Server" refers to the core computer system of the system that receives and analyzes data from users, generates and presents layout proposals using generative AI models.

[0936] "Object detection algorithm" refers to technology for recognizing the position and shape of objects from visual information such as images and LiDAR data.

[0937] "Natural language processing technology" refers to technology for analyzing text data and understanding user requests.

[0938] A "generative AI model" refers to an artificial intelligence model that generates new content (e.g., layout proposals) based on input data.

[0939] A "3D model" refers to a digital model that visualizes the proposed room layout in three dimensions.

[0940] "Emotion recognition technology" refers to technology for evaluating a user's emotions by analyzing their facial expressions and voice data.

[0941] "Purchase Link" refers to a web link provided along with detailed information about the proposed furniture or home appliances to facilitate the purchase process.

[0942] "Principles of behavioral economics" refers to an economic theory that optimizes the placement and offering of products and services by taking into account human psychology and behavior.

[0943] This invention is a system that optimally designs room layouts, with the user, server, and terminal all fulfilling their respective roles. Specifically, the user inputs their requests into the terminal, and the server generates an appropriate layout plan based on those requests and presents it to the user.

[0944] User Input

[0945] Users use the chat interface to input their desired room layout. For example, a user might input, "I want to turn my living room into a comfortable office space." Additionally, the user uploads real-world environmental data (such as LiDAR data, images, and videos) of the target room via their device. This allows the system to obtain the user's specific requirements and current room data.

[0946] Data analysis by server

[0947] The server receives the uploaded real-world data and analyzes it. This analysis uses object detection algorithms (e.g., YOLO, Mask R-CNN) to extract the room's dimensions, shape, and existing furniture layout. Furthermore, the server uses natural language processing techniques (e.g., BERT, GPT-3) to analyze the user's preferences and convert them into specific layout characteristics.

[0948] Generate layout proposals

[0949] Based on the analyzed data, the server uses a generative AI model (e.g., DALL-E, Stable Diffusion, etc.) to create multiple layout proposals. At this stage, an example prompt, "Please generate a living room layout proposal that will allow the user to relax," is input into the generative AI model. The resulting layout proposals are then rendered in the form of a three-dimensional model or image.

[0950] Proposal and Evaluation

[0951] The generated layout proposals are sent from the server to the device, which then presents them to the user. The user can visually review the proposed layout proposals and select the most appropriate one. Furthermore, the system incorporates emotion recognition technology (e.g., Microsoft's Emotion API, Face++) to analyze the user's facial expressions and voice data to evaluate their emotions. As a result, it is possible to propose optimal layout proposals based on the user's reactions.

[0952] Specific examples

[0953] For example, if a user enters "I want to create a space where I can relax" into the chat interface and uploads LiDAR scan data of their room, the server analyzes the room data and generates multiple layout proposals for relaxation spaces. At this time, the prompt text is entered as "Please generate a layout proposal for a living room where the user can relax." The generated layout proposals are presented to the user in the form of 3D models or images, and emotion recognition technology evaluates whether the user's facial expressions and voice are relaxing, and the most suitable layout proposal is ultimately presented to the user.

[0954] This system provides efficient and visually verifiable layout proposals based on the user's preferences, enabling optimal proposals that take the user's emotions into consideration. Furthermore, by providing links to purchase the proposed furniture and home appliances, the user can easily complete the purchasing process.

[0955] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0956] Step 1:

[0957] The user opens the chat interface on their device and enters information about their desired room layout, such as "I want to turn my living room into a comfortable office space." This input information is the starting point for the system and is important for clarifying the user's intentions.

[0958] input:

[0959] User layout preference (text format)

[0960] output:

[0961] User preference data

[0962] Specific behavior:

[0963] The user enters their preference in the chat box.

[0964] The terminal receives the user's desired data.

[0965] Step 2:

[0966] Users upload real-world environmental data (LiDAR data, images, videos, etc.) of the target room through their device, which is used to understand the current state of the room.

[0967] input:

[0968] LiDAR data, images, and videos

[0969] output:

[0970] Uploaded real-world data

[0971] Specific behavior:

[0972] The user scans the room using the LiDAR scanner on their device.

[0973] Upload scanned data and images to the server.

[0974] Step 3:

[0975] The server receives the uploaded real-world data and begins analyzing it. This analysis involves using object detection algorithms (e.g., YOLO, Mask R-CNN) to extract the room's dimensions, shape, and existing furniture layout. Next, it uses natural language processing techniques (e.g., BERT, GPT-3) to translate the user's preferences into specific layout characteristics.

[0976] input:

[0977] Uploaded real-world data

[0978] User preference data

[0979] output:

[0980] Room dimensions, shape, and furniture layout data

[0981] Analyzed Layout Characteristics

[0982] Specific behavior:

[0983] The server analyzes the LiDAR data to determine the dimensions of the room.

[0984] The server extracts the furniture layout from the image data.

[0985] The server analyzes the user's preferences and extracts layout characteristics.

[0986] Step 4:

[0987] Based on the analysis results, the server uses a generative AI model (e.g., DALL-E, Stable Diffusion) to create multiple layout proposals. At this stage, the prompt "Please generate a living room layout proposal that will allow the user to relax" is input to the generative AI model.

[0988] input:

[0989] Analyzed room data

[0990] Layout Characteristics

[0991] output:

[0992] Generated layout plan (3D model, image format)

[0993] Specific behavior:

[0994] The server inputs a prompt sentence into the generative AI model.

[0995] Generative AI generates multiple layout proposals.

[0996] Step 5:

[0997] The terminal receives the layout proposals sent from the server and presents them to the user, who can visually check these layout proposals and select the most suitable one.

[0998] input:

[0999] Generated layout plan

[1000] output:

[1001] Present layout proposals to users

[1002] Specific behavior:

[1003] The terminal receives the generated layout plan.

[1004] The terminal displays the layout proposal to the user.

[1005] Step 6:

[1006] The server is equipped with emotion recognition technology (e.g., Microsoft's Emotion API, Face++) that analyzes the user's facial expressions and voice data to evaluate their emotions. This allows it to ultimately propose an optimal layout plan that takes the user's reaction into account.

[1007] input:

[1008] User's facial expression data, voice data

[1009] output:

[1010] Emotional evaluation data

[1011] Optimal layout plan

[1012] Specific behavior:

[1013] The server analyzes the user's facial expressions and voice data.

[1014] Emotion recognition technology assesses the user's emotions and records the information.

[1015] The server presents the optimal layout proposal to the user.

[1016] (Application example 2)

[1017] 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."

[1018] In traditional store operations, it is difficult to instantly optimize the effective display and placement of products, and selecting an appropriate layout requires a great deal of time and effort. Furthermore, there is a lack of a way to receive real-time feedback on staff and customer reactions and reflect that feedback, which results in delays in improving sales and customer satisfaction.

[1019] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input a desired room layout into a chat interface; a means for the user to upload real-world environment data of the target room; a means for the server to analyze the uploaded real-world environment data and extract the room's dimensions, shape, and existing furniture layout; a means for the server to analyze the user's requests using natural language processing technology; a means for the server to create multiple layout proposals using a generation AI based on the analysis results; a means for the server to generate the generated layout proposals in 3D model or image format and present them to the user; a means for the server to display detailed information and purchase links for the proposed furniture and home appliances; and a means for evaluating user reactions using an emotion engine and improving the proposals. This makes it possible to quickly and efficiently propose effective layouts for displays and products and provide optimal layouts that take into account the emotions of staff and customers.

[1020] A "chat interface" is an interactive user interface that allows users to input their wishes and requests through text input in natural language.

[1021] "Real-world data" refers to data that includes the physical dimensions, shape, and current furniture layout of the target room or space, and includes LiDAR data, images, videos, etc.

[1022] A "server" is a computer system that receives data uploaded or information entered by users, analyzes it, and processes it.

[1023] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate text in natural language, and is used to analyze user requests.

[1024] A "generative AI model" is an artificial intelligence model that generates optimal layout proposals based on user input and environmental data, and uses a generative model, for example.

[1025] A "3D model" is a computer graphics model constructed in three-dimensional space, and is used by users to visually check layout proposals.

[1026] An "emotion engine" is an algorithm that analyzes emotions from a user's facial expressions, voice, etc. and evaluates their reaction.

[1027] "Display" refers to a display device for visually presenting products and information, and in this context refers particularly to the arrangement of products in a store.

[1028] An "object detection algorithm" is a technology that detects the position and shape of objects from images and LiDAR data.

[1029] A "layout plan" is a proposal for optimizing the layout of a room or space, showing what furniture and products should be placed where.

[1030] The present invention is a system in which users, servers, and terminals work together to optimize the placement and display of products in a physical store. An embodiment of the system will be described in detail below.

[1031] System configuration

[1032] 1. User Device

[1033] Users can use devices such as smartphones or tablets to input their preferences and requests for product placement into a chat interface, and can also upload LiDAR data and images, providing the system with information about the physical dimensions and shape of the physical store and the current product placement situation.

[1034] 2. Server

[1035] The server receives and analyzes real-world data (LiDAR data, images, and videos) uploaded by users. Specifically, it uses an object detection algorithm (e.g., OpenCV) to extract the dimensions and shape of the room and the layout of existing products. It then analyzes the user's requests using natural language processing technology (e.g., GPT-3). Based on the analysis results, it then uses a generative AI model to generate multiple layout proposals.

[1036] 3. Proposal Generation and Display

[1037] The layout proposals generated by the server are presented to the user in the form of 3D models or images, allowing the user to visually check them and select the most suitable layout. Detailed information and purchase links for the proposed products and appliances are also displayed, allowing the user to easily complete the purchase process.

[1038] 4. Emotion Engine

[1039] Additionally, it has a built-in emotion engine that can monitor and evaluate the reactions of users, staff, and customers in real time. For example, if a user expresses delight in a proposed layout, this information can be recorded and reflected in future proposals. This emotion data analysis uses a sentiment analysis pipeline such as Transformers.

[1040] Specific examples

[1041] Display placement in physical stores

[1042] A store manager uses a tablet to input, "I want to create a visually appealing display for a new product," and uploads LiDAR scan data and photos of the store. The server analyzes this and generates an optimal layout proposal. Staff members view the layout and provide their feedback, and the system makes a final proposal based on their reactions. The following prompts are used in this process:

[1043] Prompt Sentence Examples

[1044] User desire: Create a visually appealing display for a new product

[1045] Room dimensions: 10m wide, 8m deep, 3m high

[1046] Existing furniture layout: 3 shelves along the wall, 1 table in the middle

[1047] Optimal layout ideas:

[1048] This system not only allows for efficient optimization of store display layout, but also enables continuous improvement based on feedback from staff and customers.

[1049] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1050] Step 1:

[1051] Users use a chat interface to enter their product placement preferences in text, and then upload LiDAR data and images of the store using a smartphone or tablet.

[1052] Input: User's desired text, LiDAR data, image data

[1053] Output: Chat request data, LiDAR data, image data

[1054] Specific operation: The user inputs the desired information into the device, selects the captured LiDAR data and image data, and uploads them.

[1055] Step 2:

[1056] The server analyzes the real-world data received from the user (LiDAR data, image data, video) and uses an object detection algorithm (e.g., OpenCV) to extract the dimensions and shape of the store and the existing product layout.

[1057] Input: LiDAR data, image data

[1058] Output: Room dimensions, shape, and existing product placement data

[1059] How it works: The server applies object detection algorithms to automatically extract store dimensions, shape, and product placement information from LiDAR and image data.

[1060] Step 3:

[1061] The server analyzes the desired text from the user using natural language processing technology (e.g., GPT-3) and converts the user's requests into specific layout characteristics.

[1062] Input: User-defined text

[1063] Output: Specific layout characteristics

[1064] What it does: The server uses the generative AI model to analyze text data received from the chat interface and extract layout characteristics.

[1065] Step 4:

[1066] Based on the analyzed data, the server uses a generative AI model to generate multiple layout proposals.

[1067] Input: Room dimensions, shape, existing product placement data, specific layout characteristics

[1068] Output: Multiple layout options

[1069] Specific operation: The server inputs a prompt sentence into the generative AI model and generates multiple optimal layout proposals. Examples of prompt sentences used are as follows:

[1070] Prompt Sentence Examples

[1071] User desire: Create a visually appealing display for a new product

[1072] Room dimensions: 10m wide, 8m deep, 3m high

[1073] Existing furniture layout: 3 shelves along the wall, 1 table in the middle

[1074] Optimal layout ideas:

[1075] Step 5:

[1076] The server generates the layout proposal in the form of a 3D model or image and presents it to the user.

[1077] Input: Multiple layout ideas

[1078] Output: 3D model, layout plan in image format

[1079] Specific operation: The layout proposal generated by the server is converted into a visually displayable format using 3D modeling software and presented to the user.

[1080] Step 6:

[1081] The user visually checks the proposed layouts and selects the most suitable one. The user's selection and reaction data (facial expressions and voice) are input into the system.

[1082] Input: 3D model, layout plan in image format, user reaction data

[1083] Output: Selected layout plan, user reaction data

[1084] Specific operation: The user selects the best layout from the layout proposals visually reviewed and inputs their selection and reaction into the system.

[1085] Step 7:

[1086] The server-based emotion engine analyzes the user's reaction data, evaluates the user's emotional state, and adjusts the final layout proposal based on the reaction.

[1087] Input: User reaction data

[1088] Output: Final layout proposal adjustments

[1089] How it works: The emotion engine analyzes the reaction data to identify the user's emotional state, and then optimizes and adjusts the generated layout proposals based on the results.

[1090] Step 8:

[1091] The server then presents the final adjusted layout proposal to the user again, and displays detailed information and purchase links for the proposed products, allowing the user to select products and complete the purchase process.

[1092] Input: Final layout proposal adjustments

[1093] Output: Final layout with adjustments, product details, and purchase links

[1094] Specific operation: The server generates the final layout plan, along with detailed product information and purchase links for the corresponding products, and presents them to the user.

[1095] 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.

[1096] 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.

[1097] 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.

[1098] [Fourth embodiment]

[1099] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1100] 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.

[1101] 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).

[1102] 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.

[1103] 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.

[1104] 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).

[1105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1106] 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.

[1107] 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.

[1108] 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.

[1109] 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.

[1110] 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.

[1111] 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."

[1112] As an embodiment of the present invention, a program will be described below in which the entire system functions in cooperation with the user, server, and terminal each playing a specific role.

[1113] First, a user uses the chat interface to input their room layout preferences. For example, if a user wants to know how to most efficiently arrange newly purchased furniture, they can input a specific preference into the chat interface, such as "I'd like to put the sofa on one side of the living room and move the TV around."

[1114] Next, users upload real-world data of the target room, including LiDAR data, images, videos, etc. Users can use their smartphones or dedicated devices to capture 360-degree images and LiDAR scans and upload them to the system.

[1115] The server analyzes the real-world data uploaded by the user. It uses LiDAR data and images to determine the dimensions, shape, and layout of existing furniture in the room. For example, it generates a detailed 3D model from the LiDAR data and applies object detection algorithms to the image data to extract the location and dimensions of furniture.

[1116] The server then analyzes the user's preferences using natural language processing technology. It analyzes the user's input, extracts keywords such as "sofa," "television," and "living room," and identifies the desired layout characteristics based on these. The analysis results provide the information needed to design a layout that specifically reflects the user's preferences.

[1117] The server uses generative AI to create multiple layout proposals based on the analysis results. At this time, the server considers the optimal layout based on the user's preferences and the actual environment of the room. For example, it suggests placing a sofa in the optimal position in the living room and arranging related interior items around it.

[1118] The generated layout proposals are rendered by the server in the form of 3D models or images and presented to the user. The user can visually check multiple layout proposals and choose the one they like best. For example, by rotating multiple 3D models and viewing them from different angles, the user can get a concrete idea of ​​how they will look when actually installed in a room.

[1119] In addition, the server displays detailed information and a link to purchase the suggested furniture or home appliance, allowing the user to immediately purchase the suggested item. For example, a link to purchase a sofa or TV may be displayed, and when the user clicks, the link will take them directly to the sales site.

[1120] For example, if a user wants to set up a new office space in their living room, they first enter their request into the chat interface and upload a LiDAR scan of the space to the system. The server analyzes the data and generates layout proposals that suggest the optimal placement of existing furniture and new furniture. These layout proposals are then presented to the user as 3D models, along with links to purchase the proposed desks and chairs.

[1121] This system allows users to quickly obtain efficient, visually verifiable layout plans that reflect their preferences, and also allows them to easily purchase the suggested furniture and appliances.

[1122] The processing flow will be explained below.

[1123] Step 1:

[1124] The user enters their desired room layout into the chat interface, for example, "I want to turn my living room into a comfortable office space."

[1125] Step 2:

[1126] The user uploads real-world data of the target room, such as a 360-degree image taken with a smartphone or LiDAR scan data, to the system.

[1127] Step 3:

[1128] The server receives uploaded real-world data, which can include LiDAR data, images, and videos.

[1129] Step 4:

[1130] The server analyzes the received real-world data, extracting the dimensions and shape of the room as a 3D model from the LiDAR data, and using object detection algorithms to identify the position and dimensions of furniture from the image data.

[1131] Step 5:

[1132] The server analyzes the user's input using natural language processing technology. It extracts keywords and important requirements from the user's input and identifies the information necessary for layout design. For example, if the user uses the term "office space," it identifies the appropriate layout characteristics.

[1133] Step 6:

[1134] The server uses AI to generate multiple layout proposals based on the analysis results, such as the placement of sofas and desks and chairs, to generate layout proposals optimized to the user's preferences.

[1135] Step 7:

[1136] The server generates layout proposals visually in the form of 3D models or images, and presents the data to the user. For example, it provides functions that allow the user to rotate, enlarge, and reduce the 3D models so that multiple layout proposals can be compared.

[1137] Step 8:

[1138] The server displays detailed information about the proposed furniture and home appliances and a link to purchase them. For example, the server provides the user with detailed product information about the proposed desk or chair, along with a link to an online shop to purchase the item.

[1139] Step 9:

[1140] Users can review the proposed layouts and choose the one they like best. They can then purchase their favorite furniture and appliances directly through the purchase link.

[1141] Step 10:

[1142] The server records purchase logs and accumulates data for future layout suggestions and user feedback, which improves the accuracy of future layout suggestions.

[1143] Example 1

[1144] 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."

[1145] Many modern users seek specific advice on room layout changes and new furniture arrangements, but the process is time-consuming and laborious, and visual confirmation methods are limited. Therefore, a system that allows users to obtain layout proposals efficiently and visually easily understandable is needed. There is also a need for a system that allows users to easily purchase the proposed furniture and appliances.

[1146] 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.

[1147] In this invention, the server includes: a means for a user to input a desired room layout into a chat interface; a means for the user to upload real-world data of the target room; a means for the server to analyze the uploaded real-world data and extract the room's dimensions, shape, and existing furniture layout; a means for the server to analyze the user's requests using natural language processing technology; a means for the server to create multiple layout proposals using a generative AI model based on the analysis results; a means for the server to generate the generated layout proposals in 3D model or image format and present them to the user; and a means for the server to display detailed information and purchase links for the proposed furniture and home appliances. This allows the user to easily obtain efficient and visually confirmable layout proposals that reflect their wishes, and also enables the user to easily purchase the proposed furniture and home appliances.

[1148] A "chat interface" is an interface that allows a user to input text-based information and communicate with the system.

[1149] "Real-world data" is a general term for data such as 360-degree images and LiDAR data that includes the dimensions, shape, and furniture placement of a user's room.

[1150] A "server" is a computer system that performs key processes such as analyzing data, running generative AI, natural language processing, and generating 3D models.

[1151] "Natural language processing technology" is a technology for analyzing text data entered by a user and understanding its meaning.

[1152] A "generative AI model" is an artificial intelligence model that generates new content or suggestions based on input data and prompts.

[1153] A "3D model" is a digital model that represents an analyzed space, furniture arrangement, etc. in three dimensions.

[1154] An "object detection algorithm" is an algorithm that detects specific objects from images or LiDAR data and identifies their location and dimensions.

[1155] A "prompt sentence" is text data that is input into a generative AI model, and the AI ​​generates output based on this.

[1156] As an embodiment of the present invention, a program will be described in which a user, a server, and a terminal each play a specific role and function in cooperation as a system.

[1157] First, a user uses the chat interface to input their room layout preferences. For example, if a user is asking how to most efficiently arrange newly purchased furniture, they might input a specific preference into the chat interface, such as "I want to put the sofa on one side of the living room and move the TV around."

[1158] Next, the user uses a smartphone or dedicated device to capture real-world data, such as 360-degree images of the room or LiDAR data, and uploads it to the system, which then captures information such as the actual room dimensions, shape, and existing furniture placement.

[1159] The server analyzes the real-world data uploaded by the user, using LiDAR data to generate a detailed 3D model of the room and applying object detection algorithms to the image data to identify the location and dimensions of furniture. For example, an object detection algorithm can be used to identify the location and dimensions of furniture such as a "sofa" or a "television" from an image.

[1160] The server then uses natural language processing techniques (e.g., spaCy or BERT) to analyze the user's preferences. Keywords such as "sofa," "television," and "living room" are extracted from the user's input, and the desired layout characteristics are identified based on these. The analysis results provide the information needed to design a layout that specifically reflects the user's preferences.

[1161] The server uses a generative AI model (such as DALL-E or GPT-3) to create multiple layout proposals based on the analysis results. For example, the server might generate a specific layout proposal such as "Place the sofa by the window in the living room and hang the TV on the opposite wall."

[1162] The generated layout proposals are converted into 3D models and images and presented to the user, who can visually check the multiple layout proposals provided and rotate and zoom in on the 3D models to view details from different angles.

[1163] Additionally, the server displays detailed information about the suggested furniture or appliances along with a link to purchase them. When the user clicks on the link, they are taken directly to the sales site, where they can conveniently purchase the suggested item. For example, the server might display, "This sofa is made by XYZ company and can be purchased here," and the user can click on the link to begin the purchase process immediately.

[1164] For example, if a user wants to set up a new office space in their living room, they first enter their request into the chat interface and upload a LiDAR scan of the space to the system. The server analyzes the data and generates layout proposals that include the arrangement of existing furniture and the optimal placement of new desks and chairs. These layout proposals are then presented to the user as 3D models, along with links to purchase the proposed desks and chairs.

[1165] An example of a prompt to input to the generative AI model is, "Based on 360-degree images of the room and LiDAR data, please generate the optimal layout proposal for a new desk and chair in the living room. The user's preference is 'I want the desk to be placed by the window, with the chair in front of it.'" This system allows users to obtain efficient, visually verifiable layout proposals that reflect their preferences, and also allows them to easily purchase the suggested furniture and appliances.

[1166] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1167] Step 1: User preference input

[1168] The user uses the chat interface to input their preferences for the room layout. For example, they can input specific requests such as "I want to put a sofa on one side of the living room and change the position of the TV." This input is sent as text data from the device to the server, which receives and temporarily stores this text data.

[1169] Step 2: Acquire and upload real-world data

[1170] Users use their smartphones or dedicated devices to capture 360-degree images and LiDAR data of their rooms, which are then uploaded to a cloud server. The uploaded data is then stored on the server as a resource containing information such as the room's dimensions, shape, and the layout of existing furniture.

[1171] Step 3: Data analysis

[1172] The server analyzes the uploaded real-world data. It generates a detailed 3D model of the room based on the LiDAR data, and applies an object detection algorithm to the image data to identify the location and dimensions of furniture. Specifically, the object detection algorithm identifies the location and dimensions of a "sofa" or "television" and reflects this in the 3D model. The input data is LiDAR data and image data, and the output is dimensional information and a 3D model of the furniture arrangement.

[1173] Step 4: Natural Language Analysis

[1174] The server uses natural language processing techniques (e.g., spaCy or BERT) to analyze the user's preferences. It extracts keywords such as "sofa," "television," and "living room" from the user's text data and identifies the required layout characteristics based on them. The input is the user's text data, and the output is the extracted keywords and the layout requirements based on them.

[1175] Step 5: Generate layout proposals

[1176] The server generates multiple layout proposals using a generative AI model (e.g., DALL-E or GPT-3) based on the analysis results. For example, a prompt might be, "Based on 360-degree images of the room and LiDAR data, generate the optimal layout proposal for placing a new desk and chair in the living room. The user's preference is 'I want the desk to be placed by the window, with the chair in front of it.'" Based on this prompt, the AI ​​model generates multiple layout proposals, and specific layout proposals are obtained as output.

[1177] Step 6: Present layout plan

[1178] The generated layout proposals are converted into 3D models or images by the server and presented to the user. The user can rotate these 3D models on their device and zoom in to check the details. This allows the user to visually check multiple layout proposals and choose the best one.

[1179] Step 7: Provide a purchase link

[1180] The server displays detailed information about the suggested furniture or home appliances and a link to purchase them. When the user clicks on the link for the suggested item, they are taken directly to the sales site and can begin the purchase process. This allows the user to easily purchase the suggested item. The input is information about the suggested furniture or home appliance, and the output is a purchase link and its detailed information.

[1181] In this way, each step works seamlessly together to provide an efficient, visually verifiable layout plan that reflects the user's preferences, and the process of purchasing the proposed furniture and home appliances is also simplified.

[1182] (Application example 1)

[1183] 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."

[1184] Conventional furniture and fixture layout planning often requires specialized knowledge and is time-consuming. Furthermore, there are limited ways to visually check the layout beforehand, making changes to the store layout risky. Furthermore, it is difficult to consider efficient layouts because the effects of layout plans cannot be simulated in advance. The challenge is to solve these problems and provide a method that allows store owners and designers to easily determine optimal layouts.

[1185] 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.

[1186] In this invention, the server includes: a means for a user to input their desired room layout into a chat interface; a means for uploading real-world data for the target room; a means for analyzing the uploaded real-world data and extracting the room's dimensions, shape, and existing furniture layout; a means for analyzing the user's requests using natural language processing technology; a means for using a generation AI to create multiple layout proposals based on the analysis results; a means for generating the generated layout proposals in 3D model or image format and presenting them to the user; a means for displaying detailed information and purchase links for the proposed furniture and appliances; and a means for optimizing the store layout and visually confirming them using augmented reality (AR). This allows store owners and designers to quickly obtain efficient, visually confirmable layout proposals that reflect their preferences. Furthermore, detailed information and purchase links for the proposed fixtures and decorations are provided, facilitating the purchasing process and facilitating improvements to store designs.

[1187] "Means for users to input their room layout preferences into a chat interface" refers to an interface where users input their preferences regarding the layout of a room or shop in text format.

[1188] "Means for users to upload real-world environment data for the target room" refers to the means by which users send information about the real-world environment, such as 360-degree images and LiDAR data acquired with their smartphones or dedicated devices, to a server.

[1189] "Means for the server to analyze the uploaded real-world data and extract the room's dimensions, shape, and existing furniture layout" refers to means for the server to process the real-world data submitted by the user and use algorithms to identify the physical details of the room and the layout of existing furniture.

[1190] "Means for the server to analyze requests from users using natural language processing technology" refers to means for the server to use natural language processing technology to analyze the text entered by the user and extract important elements and keywords from it.

[1191] "Means for the server to create multiple layout proposals using a generation AI based on the analysis results" refers to a means for the server to use a generation AI (artificial intelligence) to generate multiple layout proposals based on the data analyzed by the server in response to the user's requests.

[1192] "Means for generating layout plans generated by the server in the form of 3D models or images and presenting them to users" refers to means for creating layout plans generated by the server as 3D models or still images and presenting them visually to users.

[1193] "Means for the server to display detailed information about the proposed furniture and home appliances and links to purchase them" refers to means for displaying detailed information about the furniture and home appliances included in the layout proposal generated by the server to the user and links to purchase them.

[1194] "Means for optimizing the store layout and visually confirming it using AR (Augmented Reality)" refers to a means in which the server efficiently proposes the layout of the store and allows the user to visually confirm that layout using AR (Augmented Reality) technology.

[1195] "Means for a server to suggest optimal product placement based on the principles of behavioral economics" refers to means for a server to suggest optimal product placement locations using the theory of behavioral economics.

[1196] "A means of presenting proposals generated by the server to contractors as 3D models and simulating practical layout plans and their effects" refers to a means of creating layout proposals generated by the server as 3D models and presenting them to contractors to simulate actual layout methods and their effects.

[1197] "Means by which the server uses an object detection algorithm to analyze the LiDAR data or image data" means means by which the server uses an object detection algorithm to analyze the LiDAR data or image data.

[1198] "Means for visually checking proposed store layouts using AR displays on a smartphone or smart glasses" refers to means that enables users to visually check proposed store layouts using AR technology on a smartphone or smart glasses.

[1199] A system for realizing the present invention is configured as follows.

[1200] First, a user uses a chat interface to input their preferences for the layout of a room or store. For example, if a user wants to know how to most efficiently arrange newly purchased furniture, they can input a specific preference into the chat interface, such as "I want to put the sofa on one side of the living room and change the position of the TV."

[1201] Next, users upload real-world data of the target room or store, including LiDAR data, images, and videos. Users use their smartphones or smart glasses to capture 360-degree images and LiDAR scans and upload them to the system.

[1202] The server analyzes real-world data uploaded by users. It uses LiDAR data and image data to determine the dimensions, shape, and layout of existing furniture in a room or store. For example, it generates a detailed 3D model from the LiDAR data, and applies object detection algorithms to the image data to extract the location and dimensions of furniture. Open-source libraries and proprietary algorithms may be used for the analysis.

[1203] The server then analyzes the user's preferences using natural language processing technology. It analyzes the user's input, extracts keywords such as "sofa," "television," and "living room," and identifies the desired layout characteristics based on these. The analysis results provide the information needed to design a layout that specifically reflects the user's preferences.

[1204] Based on the analysis results, the server uses generative AI to create multiple layout proposals. At this time, the server considers the optimal layout based on the user's preferences and the actual environment of the room or store. For example, it may suggest placing a sofa in the optimal position in the living room and arranging related interior items around it. OpenAI's APIs and other similar services may be used for generative AI.

[1205] The generated layout proposals are rendered as 3D models or images by the server and presented to the user. The user can visually check multiple layout proposals using AR displays on their smartphone or smart glasses. For example, by rotating multiple 3D models and viewing them from different angles, the user can get a concrete idea of ​​how they will look when actually installed in a room or store.

[1206] In addition, the server displays detailed information and a link to purchase the suggested furniture or home appliance, allowing the user to immediately purchase the suggested item. For example, a link to purchase a sofa or TV may be displayed, and when the user clicks, the link will take them directly to the sales site.

[1207] As a specific example, consider the case where a store owner wishes to place new furniture in their store. The store owner first enters their wishes into a chat interface, providing specific instructions such as "I want to place a counter near the entrance and rearrange the shelves in the back room." They then acquire 360-degree images and LiDAR scans of the store and upload them to the system. The server analyzes the data and generates layout proposals that suggest optimal locations for existing fixtures and new fixtures. These layout proposals are then presented to the user in an AR display so that they can be visually confirmed. Detailed information about the proposed fixtures and decorations, along with a link to purchase them, is also displayed, allowing the user to proceed with the purchase process.

[1208] In this way, this system not only allows store owners and designers to quickly obtain efficient, visually verifiable layout plans that reflect their wishes, but also allows them to easily purchase the proposed fixtures and decorations.

[1209] To illustrate, the following is an example of a prompt sentence that users can use to input their preferences: "Please identify the layout characteristics based on the following preferences: I would like the counter to be located near the entrance and the shelves in the back room to be repositioned."

[1210] This allows the store layout to be optimized and visually confirmed using AR (augmented reality).

[1211] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1212] Step 1:

[1213] The user inputs the desired room layout into the chat interface.

[1214] Specific actions: The user types into the chat interface something like, "I want to place the counter near the entrance and rearrange the shelves in the back room."

[1215] Input: User text input (desired layout)

[1216] Output: Layout desired text

[1217] Step 2:

[1218] The user uploads real-world environment data for the target room.

[1219] How it works: Users capture 360-degree images and LiDAR data using their smartphones or smart glasses and upload them to the system.

[1220] Input: Real-world data such as 360-degree images and LiDAR data

[1221] Output: Real-world data uploaded to the server

[1222] Step 3:

[1223] The server analyzes the uploaded real-world data and extracts the room's dimensions, shape, and existing furniture layout.

[1224] What it does: The server converts the LiDAR data into a 3D model and analyzes the image data with object detection algorithms to determine the location and dimensions of the furniture.

[1225] Input: Real-world data (360-degree images, LiDAR data)

[1226] Output: Analyzed dimensional data, shape data, and layout data of existing furniture

[1227] Step 4:

[1228] The server analyzes the user's wishes using natural language processing technology.

[1229] How it works: The server extracts keywords from the user's input text and identifies layout characteristics. Here, it uses OpenAI's API to analyze the keywords.

[1230] Input: Desired layout text

[1231] Output: extracted keywords, parsed layout features

[1232] Step 5:

[1233] The server uses generative AI to create multiple layout proposals based on the analysis results.

[1234] How it works: The server uses generative AI to generate multiple layout proposals based on the analysis results and real-world data.

[1235] Input: Analysis results (layout characteristics, dimension data, shape data, existing furniture layout data)

[1236] Output: Multiple layout options

[1237] Step 6:

[1238] The server generates the layout proposal in the form of a 3D model or image and presents it to the user.

[1239] How it works: The server uses 3D modeling software to create 3D models and images of the layout proposals it generates, and presents them to the user, who then visually checks them using a smartphone or smart glasses.

[1240] Input: Multiple layout ideas

[1241] Output: 3D model, layout plan in image format

[1242] Step 7:

[1243] The server displays detailed information and purchase links for the suggested furniture and appliances.

[1244] Specific operation: Based on the layout plan, the server displays detailed information about the proposed furniture and appliances to the user, along with links to purchase each item.

[1245] Input: Layout plan, furniture and appliance information

[1246] Output: Detailed information, purchase link

[1247] Step 8:

[1248] Users can use their smartphones or smart glasses to visually check the proposed store layout using an AR display.

[1249] Specific operation: Using a smartphone or smart glasses, users can use AR technology to visually check layout proposals by overlaying them on the actual space.

[1250] Input: 3D model, AR software

[1251] Output: Visual confirmation of layout plan using AR display

[1252] 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.

[1253] As an embodiment of the present invention, a program in which the entire system functions in cooperation with the user, server, and terminal each performing their own role will be described below.

[1254] First, the user uses the chat interface to input their preferences for the room layout. For example, the user might say, "I want to turn my living room into a comfortable office space." Then, they upload LiDAR data, images, and videos as real-world data for the room via their device.

[1255] The server receives the real-world data uploaded by the user and begins analyzing it. It applies object detection algorithms to LiDAR and image data to extract the room's dimensions, shape, and existing furniture layout. Additionally, it uses natural language processing technology to analyze the user's preferences obtained through the chat interface and converts them into specific layout characteristics.

[1256] The server then uses generative AI to create multiple layout proposals based on the analyzed data. The proposed layouts are presented to the user in the form of 3D models or images. The user can visually check these models and select the optimal layout. For example, the system suggests new desk and chair arrangements, optimization of storage space, etc.

[1257] Furthermore, the present invention incorporates an emotion engine that recognizes emotions from user input and facial and voice data. This emotion engine monitors the user's emotional state in real time and evaluates the user's reaction when a layout proposal is presented. For example, if the user expresses joy in response to a proposed layout proposal, the emotion engine can record that information and reflect it in future proposals.

[1258] As a concrete example, suppose a user enters their desire to "create a relaxing space" into the chat interface and uploads LiDAR scan data of their room. The server analyzes the room data and generates multiple relaxation space layout proposals. Furthermore, an emotion engine evaluates the user's facial expressions and voice to see if they are relaxed, and presents the most suitable layout proposal to the user. The user can review the layout proposals and select and purchase the proposed furniture and appliances via detailed information and a purchase link.

[1259] This system can efficiently present visually verifiable layout proposals based on the user's preferences, and can make optimal proposals that take the user's emotions into consideration. Furthermore, by providing links to purchase the proposed furniture and home appliances, the user can easily complete the purchasing process.

[1260] The processing flow will be explained below.

[1261] Step 1:

[1262] The user enters their desired room layout into the chat interface, for example, "I want to turn my living room into a comfortable office space."

[1263] Step 2:

[1264] The user uploads real-world data of the target room, specifically 360-degree images taken with a smartphone or LiDAR scan data, to the system.

[1265] Step 3:

[1266] The server receives uploaded real-world data, which can include LiDAR data, images, and videos.

[1267] Step 4:

[1268] The server analyzes the received data, for example extracting the dimensions and shape of the room as a 3D model from the LiDAR data, and using object detection algorithms to identify the position and dimensions of furniture from the image data.

[1269] Step 5:

[1270] The server uses an emotion engine to recognize emotions from user input and facial and voice data. For example, a webcam and microphone are used to capture facial data and tone of voice.

[1271] Step 6:

[1272] The server analyzes the user's input using natural language processing technology, extracting keywords and important requests from the user's input and identifying the information necessary for layout design. For example, it identifies keywords such as "office space" and "living room."

[1273] Step 7:

[1274] The server uses generative AI to create multiple layout proposals based on the analysis results and emotion data. For example, it generates layout proposals optimized for the user's preferences and emotions, such as the placement of sofas and desks and chairs.

[1275] Step 8:

[1276] The server generates layout plans visually in the form of 3D models or images, and presents the data to the user. For example, it provides functions to rotate, enlarge, and reduce the 3D models.

[1277] Step 9:

[1278] The server uses an emotion engine to evaluate the user's reaction to the proposed layout. For example, if the user smiles, the server records this information and determines that the proposal is favorable.

[1279] Step 10:

[1280] The server displays detailed information about the proposed furniture and home appliances and a link to purchase them. For example, the server provides the user with detailed product information about the proposed desk or chair, along with a link to an online shop to purchase the item.

[1281] Step 11:

[1282] Users can review the proposed layouts and choose the one they like best. They can then purchase their favorite furniture and appliances directly through the purchase link.

[1283] Step 12:

[1284] The server records purchase logs and accumulates data for future layout suggestions and user feedback, which improves the accuracy of future layout suggestions.

[1285] Example 2

[1286] 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."

[1287] Conventional room layout design systems were unable to fully consider the user's preferences or actual environmental data, making it difficult to present efficient layout proposals. Furthermore, there was no mechanism to propose optimal layouts that took the user's emotions into consideration, resulting in low user satisfaction.

[1288] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1289] In this invention, the server includes means for using emotion recognition technology that analyzes the user's facial expressions and voice data to evaluate their emotions and takes the user's reaction into consideration when presenting a layout proposal, means for the user to input their desired room layout into a chat interface, and means for uploading actual environment data for the target room. This makes it possible to present efficient, visually verifiable layout proposals that meet the user's wishes, and to make optimal proposals that take the user's emotions into consideration.

[1290] "User" refers to a person who uses the system to design a room layout.

[1291] "Chat interface" refers to an interactive interface through which users input requests to the system via text.

[1292] "Real-world data" refers to data about the physical attributes of a room, including the dimensions and shape of the room, existing furniture layout, etc.

[1293] "Server" refers to the core computer system of the system that receives and analyzes data from users, generates and presents layout proposals using generative AI models.

[1294] "Object detection algorithm" refers to technology for recognizing the position and shape of objects from visual information such as images and LiDAR data.

[1295] "Natural language processing technology" refers to technology for analyzing text data and understanding user requests.

[1296] A "generative AI model" refers to an artificial intelligence model that generates new content (e.g., layout proposals) based on input data.

[1297] A "3D model" refers to a digital model that visualizes the proposed room layout in three dimensions.

[1298] "Emotion recognition technology" refers to technology for evaluating a user's emotions by analyzing their facial expressions and voice data.

[1299] "Purchase Link" refers to a web link provided along with detailed information about the proposed furniture or home appliances to facilitate the purchase process.

[1300] "Principles of behavioral economics" refers to an economic theory that optimizes the placement and offering of products and services by taking into account human psychology and behavior.

[1301] This invention is a system that optimally designs room layouts, with the user, server, and terminal all fulfilling their respective roles. Specifically, the user inputs their requests into the terminal, and the server generates an appropriate layout plan based on those requests and presents it to the user.

[1302] User Input

[1303] Users use the chat interface to input their desired room layout. For example, a user might input, "I want to turn my living room into a comfortable office space." Additionally, the user uploads real-world environmental data (such as LiDAR data, images, and videos) of the target room via their device. This allows the system to obtain the user's specific requirements and current room data.

[1304] Data analysis by server

[1305] The server receives the uploaded real-world data and analyzes it. This analysis uses object detection algorithms (e.g., YOLO, Mask R-CNN) to extract the room's dimensions, shape, and existing furniture layout. Furthermore, the server uses natural language processing techniques (e.g., BERT, GPT-3) to analyze the user's preferences and convert them into specific layout characteristics.

[1306] Generate layout proposals

[1307] Based on the analyzed data, the server uses a generative AI model (e.g., DALL-E, Stable Diffusion, etc.) to create multiple layout proposals. At this stage, an example prompt, "Please generate a living room layout proposal that will allow the user to relax," is input into the generative AI model. The resulting layout proposals are then rendered in the form of a three-dimensional model or image.

[1308] Proposal and Evaluation

[1309] The generated layout proposals are sent from the server to the device, which then presents them to the user. The user can visually review the proposed layout proposals and select the most appropriate one. Furthermore, the system incorporates emotion recognition technology (e.g., Microsoft's Emotion API, Face++) to analyze the user's facial expressions and voice data to evaluate their emotions. As a result, it is possible to propose optimal layout proposals based on the user's reactions.

[1310] Specific examples

[1311] For example, if a user enters "I want to create a space where I can relax" into the chat interface and uploads LiDAR scan data of their room, the server analyzes the room data and generates multiple layout proposals for relaxation spaces. At this time, the prompt text is entered as "Please generate a layout proposal for a living room where the user can relax." The generated layout proposals are presented to the user in the form of 3D models or images, and emotion recognition technology evaluates whether the user's facial expressions and voice are relaxing, and the most suitable layout proposal is ultimately presented to the user.

[1312] This system provides efficient and visually verifiable layout proposals based on the user's preferences, enabling optimal proposals that take the user's emotions into consideration. Furthermore, by providing links to purchase the proposed furniture and home appliances, the user can easily complete the purchasing process.

[1313] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1314] Step 1:

[1315] The user opens the chat interface on their device and enters information about their desired room layout, such as "I want to turn my living room into a comfortable office space." This input information is the starting point for the system and is important for clarifying the user's intentions.

[1316] input:

[1317] User layout preference (text format)

[1318] output:

[1319] User preference data

[1320] Specific behavior:

[1321] The user enters their preference in the chat box.

[1322] The terminal receives the user's desired data.

[1323] Step 2:

[1324] Users upload real-world environmental data (LiDAR data, images, videos, etc.) of the target room through their device, which is used to understand the current state of the room.

[1325] input:

[1326] LiDAR data, images, and videos

[1327] output:

[1328] Uploaded real-world data

[1329] Specific behavior:

[1330] The user scans the room using the LiDAR scanner on their device.

[1331] Upload scanned data and images to the server.

[1332] Step 3:

[1333] The server receives the uploaded real-world data and begins analyzing it. This analysis involves using object detection algorithms (e.g., YOLO, Mask R-CNN) to extract the room's dimensions, shape, and existing furniture layout. Next, it uses natural language processing techniques (e.g., BERT, GPT-3) to translate the user's preferences into specific layout characteristics.

[1334] input:

[1335] Uploaded real-world data

[1336] User preference data

[1337] output:

[1338] Room dimensions, shape, and furniture layout data

[1339] Analyzed Layout Characteristics

[1340] Specific behavior:

[1341] The server analyzes the LiDAR data to determine the dimensions of the room.

[1342] The server extracts the furniture layout from the image data.

[1343] The server analyzes the user's preferences and extracts layout characteristics.

[1344] Step 4:

[1345] Based on the analysis results, the server uses a generative AI model (e.g., DALL-E, Stable Diffusion) to create multiple layout proposals. At this stage, the prompt "Please generate a living room layout proposal that will allow the user to relax" is input to the generative AI model.

[1346] input:

[1347] Analyzed room data

[1348] Layout Characteristics

[1349] output:

[1350] Generated layout plan (3D model, image format)

[1351] Specific behavior:

[1352] The server inputs a prompt sentence into the generative AI model.

[1353] Generative AI generates multiple layout proposals.

[1354] Step 5:

[1355] The terminal receives the layout proposals sent from the server and presents them to the user, who can visually check these layout proposals and select the most suitable one.

[1356] input:

[1357] Generated layout plan

[1358] output:

[1359] Present layout proposals to users

[1360] Specific behavior:

[1361] The terminal receives the generated layout plan.

[1362] The terminal displays the layout proposal to the user.

[1363] Step 6:

[1364] The server is equipped with emotion recognition technology (e.g., Microsoft's Emotion API, Face++) that analyzes the user's facial expressions and voice data to evaluate their emotions. This allows it to ultimately propose an optimal layout plan that takes the user's reaction into account.

[1365] input:

[1366] User's facial expression data, voice data

[1367] output:

[1368] Emotional evaluation data

[1369] Optimal layout plan

[1370] Specific behavior:

[1371] The server analyzes the user's facial expressions and voice data.

[1372] Emotion recognition technology assesses the user's emotions and records the information.

[1373] The server presents the optimal layout proposal to the user.

[1374] (Application example 2)

[1375] 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."

[1376] In traditional store operations, it is difficult to instantly optimize the effective display and placement of products, and selecting an appropriate layout requires a great deal of time and effort. Furthermore, there is a lack of a way to receive real-time feedback on staff and customer reactions and reflect that feedback, which results in delays in improving sales and customer satisfaction.

[1377] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input a desired room layout into a chat interface; a means for the user to upload real-world environment data of the target room; a means for the server to analyze the uploaded real-world environment data and extract the room's dimensions, shape, and existing furniture layout; a means for the server to analyze the user's requests using natural language processing technology; a means for the server to create multiple layout proposals using a generation AI based on the analysis results; a means for the server to generate the generated layout proposals in 3D model or image format and present them to the user; a means for the server to display detailed information and purchase links for the proposed furniture and home appliances; and a means for evaluating user reactions using an emotion engine and improving the proposals. This makes it possible to quickly and efficiently propose effective layouts for displays and products and provide optimal layouts that take into account the emotions of staff and customers.

[1378] A "chat interface" is an interactive user interface that allows users to input their wishes and requests through text input in natural language.

[1379] "Real-world data" refers to data that includes the physical dimensions, shape, and current furniture layout of the target room or space, and includes LiDAR data, images, videos, etc.

[1380] A "server" is a computer system that receives data uploaded or information entered by users, analyzes it, and processes it.

[1381] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate text in natural language, and is used to analyze user requests.

[1382] A "generative AI model" is an artificial intelligence model that generates optimal layout proposals based on user input and environmental data, and uses a generative model, for example.

[1383] A "3D model" is a computer graphics model constructed in three-dimensional space, and is used by users to visually check layout proposals.

[1384] An "emotion engine" is an algorithm that analyzes emotions from a user's facial expressions, voice, etc. and evaluates their reaction.

[1385] "Display" refers to a display device for visually presenting products and information, and in this context refers particularly to the arrangement of products in a store.

[1386] An "object detection algorithm" is a technology that detects the position and shape of objects from images and LiDAR data.

[1387] A "layout plan" is a proposal for optimizing the layout of a room or space, showing what furniture and products should be placed where.

[1388] The present invention is a system in which users, servers, and terminals work together to optimize the placement and display of products in a physical store. An embodiment of the system will be described in detail below.

[1389] System configuration

[1390] 1. User Device

[1391] Users can use devices such as smartphones or tablets to input their preferences and requests for product placement into a chat interface, and can also upload LiDAR data and images, providing the system with information about the physical dimensions and shape of the physical store and the current product placement situation.

[1392] 2. Server

[1393] The server receives and analyzes real-world data (LiDAR data, images, and videos) uploaded by users. Specifically, it uses an object detection algorithm (e.g., OpenCV) to extract the dimensions and shape of the room and the layout of existing products. It then analyzes the user's requests using natural language processing technology (e.g., GPT-3). Based on the analysis results, it then uses a generative AI model to generate multiple layout proposals.

[1394] 3. Proposal Generation and Display

[1395] The layout proposals generated by the server are presented to the user in the form of 3D models or images, allowing the user to visually check them and select the most suitable layout. Detailed information and purchase links for the proposed products and appliances are also displayed, allowing the user to easily complete the purchase process.

[1396] 4. Emotion Engine

[1397] Additionally, it has a built-in emotion engine that can monitor and evaluate the reactions of users, staff, and customers in real time. For example, if a user expresses delight in a proposed layout, this information can be recorded and reflected in future proposals. This emotion data analysis uses a sentiment analysis pipeline such as Transformers.

[1398] Specific examples

[1399] Display placement in physical stores

[1400] A store manager uses a tablet to input, "I want to create a visually appealing display for a new product," and uploads LiDAR scan data and photos of the store. The server analyzes this and generates an optimal layout proposal. Staff members view the layout and provide their feedback, and the system makes a final proposal based on their reactions. The following prompts are used in this process:

[1401] Prompt Sentence Examples

[1402] User desire: Create a visually appealing display for a new product

[1403] Room dimensions: 10m wide, 8m deep, 3m high

[1404] Existing furniture layout: 3 shelves along the wall, 1 table in the middle

[1405] Optimal layout ideas:

[1406] This system not only allows for efficient optimization of store display layout, but also enables continuous improvement based on feedback from staff and customers.

[1407] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1408] Step 1:

[1409] Users use a chat interface to enter their product placement preferences in text, and then upload LiDAR data and images of the store using a smartphone or tablet.

[1410] Input: User's desired text, LiDAR data, image data

[1411] Output: Chat request data, LiDAR data, image data

[1412] Specific operation: The user inputs the desired information into the device, selects the captured LiDAR data and image data, and uploads them.

[1413] Step 2:

[1414] The server analyzes the real-world data received from the user (LiDAR data, image data, video) and uses an object detection algorithm (e.g., OpenCV) to extract the dimensions and shape of the store and the existing product layout.

[1415] Input: LiDAR data, image data

[1416] Output: Room dimensions, shape, and existing product placement data

[1417] How it works: The server applies object detection algorithms to automatically extract store dimensions, shape, and product placement information from LiDAR and image data.

[1418] Step 3:

[1419] The server analyzes the desired text from the user using natural language processing technology (e.g., GPT-3) and converts the user's requests into specific layout characteristics.

[1420] Input: User-defined text

[1421] Output: Specific layout characteristics

[1422] What it does: The server uses the generative AI model to analyze text data received from the chat interface and extract layout characteristics.

[1423] Step 4:

[1424] Based on the analyzed data, the server uses a generative AI model to generate multiple layout proposals.

[1425] Input: Room dimensions, shape, existing product placement data, specific layout characteristics

[1426] Output: Multiple layout options

[1427] Specific operation: The server inputs a prompt sentence into the generative AI model and generates multiple optimal layout proposals. Examples of prompt sentences used are as follows:

[1428] Prompt Sentence Examples

[1429] User desire: Create a visually appealing display for a new product

[1430] Room dimensions: 10m wide, 8m deep, 3m high

[1431] Existing furniture layout: 3 shelves along the wall, 1 table in the middle

[1432] Optimal layout ideas:

[1433] Step 5:

[1434] The server generates the layout proposal in the form of a 3D model or image and presents it to the user.

[1435] Input: Multiple layout ideas

[1436] Output: 3D model, layout plan in image format

[1437] Specific operation: The layout proposal generated by the server is converted into a visually displayable format using 3D modeling software and presented to the user.

[1438] Step 6:

[1439] The user visually checks the proposed layouts and selects the most suitable one. The user's selection and reaction data (facial expressions and voice) are input into the system.

[1440] Input: 3D model, layout plan in image format, user reaction data

[1441] Output: Selected layout plan, user reaction data

[1442] Specific operation: The user selects the best layout from the layout proposals visually reviewed and inputs their selection and reaction into the system.

[1443] Step 7:

[1444] The server-based emotion engine analyzes the user's reaction data, evaluates the user's emotional state, and adjusts the final layout proposal based on the reaction.

[1445] Input: User reaction data

[1446] Output: Final layout proposal adjustments

[1447] How it works: The emotion engine analyzes the reaction data to identify the user's emotional state, and then optimizes and adjusts the generated layout proposals based on the results.

[1448] Step 8:

[1449] The server then presents the final adjusted layout proposal to the user again, and displays detailed information and purchase links for the proposed products, allowing the user to select products and complete the purchase process.

[1450] Input: Final layout proposal adjustments

[1451] Output: Final layout with adjustments, product details, and purchase links

[1452] Specific operation: The server generates the final layout plan, along with detailed product information and purchase links for the corresponding products, and presents them to the user.

[1453] 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.

[1454] 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.

[1455] 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.

[1456] 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.

[1457] 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.

[1458] 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.

[1459] 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).

[1460] 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.

[1461] 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."

[1462] 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.

[1463] 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).

[1464] 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.

[1465] 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.

[1466] 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.

[1467] 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.

[1468] 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.

[1469] 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.

[1470] 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.

[1471] 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.

[1472] 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.

[1473] 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.

[1474] The following is further disclosed regarding the above embodiment.

[1475] (Claim 1)

[1476] a means for a user to input room layout preferences into a chat interface;

[1477] A means for a user to upload real-world environment data of a target room;

[1478] A means for the server to analyze the uploaded real-world data and extract the dimensions, shape, and existing furniture layout of the room;

[1479] A means for the server to analyze requests from users using natural language processing technology;

[1480] The server uses AI to generate multiple layout plans based on the analysis results.

[1481] A means for generating the layout proposal generated by the server in the form of a 3D model or image and presenting it to the user;

[1482] A means for the server to display detailed information and a link to purchase the suggested furniture or appliances;

[1483] A system including:

[1484] (Claim 2)

[1485] A means for the server to propose optimal product placement based on the principles of behavioral economics;

[1486] The proposals generated by the server are presented to the contractor as 3D models, and a means of simulating practical layout plans and their effects is provided.

[1487] The system of claim 1 further comprising:

[1488] (Claim 3)

[1489] a server using an object detection algorithm to analyze the LiDAR data and image data;

[1490] means for providing a way for a user to input room layout preferences into a chat interface;

[1491] The system of claim 1 further comprising:

[1492] "Example 1"

[1493] (Claim 1)

[1494] a means for a user to input room layout preferences into a chat interface;

[1495] A means for a user to upload real-world environment data of a target room;

[1496] A means for the server to analyze the uploaded real-world data and extract the dimensions, shape, and existing furniture layout of the room;

[1497] A means for the server to analyze the user's request using natural language processing technology;

[1498] A means for the server to create multiple layout proposals using a generative AI model based on the analysis results;

[1499] A means for generating the layout proposal generated by the server in the form of a 3D model or image and presenting it to the user;

[1500] A means for the server to display detailed information and a link to purchase the suggested furniture or appliances;

[1501] A system including:

[1502] (Claim 2)

[1503] A means for the server to generate a 3D model of the room based on the real-world data uploaded by the user;

[1504] A means for the server to input a prompt sentence into the generation AI model and generate a layout proposal;

[1505] A means for the server to present multiple 3D models to the user with visual effects such as rotation and zoom-in.

[1506] The system of claim 1 further comprising:

[1507] (Claim 3)

[1508] a means for the server to analyze the LiDAR data and image data using an object detection algorithm;

[1509] A way for users to upload 360-degree images or LiDAR scans to determine the dimensions and shape of a room;

[1510] The system of claim 1 further comprising:

[1511] "Application Example 1"

[1512] (Claim 1)

[1513] a means for a user to input room layout preferences into a chat interface;

[1514] A means for a user to upload real-world environment data of a target room;

[1515] A means for the server to analyze the uploaded real-world data and extract the dimensions, shape, and existing furniture layout of the room;

[1516] A means for the server to analyze requests from users using natural language processing technology;

[1517] The server uses AI to generate multiple layout plans based on the analysis results.

[1518] A means for generating the layout proposal generated by the server in the form of a 3D model or image and presenting it to the user;

[1519] A means for the server to display detailed information and a link to purchase the suggested furniture or appliances;

[1520] Optimize the store layout and visually check it using AR (Augmented Reality),

[1521] A system including:

[1522] (Claim 2)

[1523] A means for the server to propose optimal product placement based on the principles of behavioral economics;

[1524] The proposals generated by the server are presented to the contractor as 3D models, and a means of simulating practical layout plans and their effects is provided.

[1525] The system of claim 1 further comprising:

[1526] (Claim 3)

[1527] a server using an object detection algorithm to analyze the LiDAR data and image data;

[1528] means for providing a way for a user to input room layout preferences into a chat interface;

[1529] A method to visually check the store layout plan using AR display on a smartphone or smart glasses,

[1530] The system of claim 1 further comprising:

[1531] "Example 2: Combining Emotion Engines"

[1532] (Claim 1)

[1533] a means for a user to input room layout preferences into a chat interface;

[1534] A means for a user to upload real-world environment data of a target room;

[1535] A means for the server to analyze the uploaded real-world data and extract the dimensions, shape, and existing furniture layout of the room;

[1536] A means for the server to analyze requests from users using natural language processing technology;

[1537] A means for the server to create multiple layout plans using artificial intelligence based on the analysis results;

[1538] A means for generating the layout plan generated by the server in a three-dimensional model or image format and presenting it to the user;

[1539] A means for the server to display detailed information and a link to purchase the suggested furniture or appliances;

[1540] A method using emotion recognition technology in which the server analyzes the user's facial expressions and voice data to evaluate their emotions and takes the user's reaction into consideration when presenting layout proposals;

[1541] A system including:

[1542] (Claim 2)

[1543] A means for the server to propose optimal product placement based on the principles of behavioral economics;

[1544] The proposals generated by the server are presented to the contractor as 3D models, and a means for simulating practical layout plans and their effects is provided.

[1545] The system of claim 1 further comprising:

[1546] (Claim 3)

[1547] a server using an object detection algorithm to analyze the light detection and ranging data and image data;

[1548] means for providing a way for a user to input room layout preferences into a chat interface;

[1549] The system of claim 1 further comprising:

[1550] "Application example 2 when combining emotion engines"

[1551] (Claim 1)

[1552] a means for a user to input room layout preferences into a chat interface;

[1553] A means for a user to upload real-world environment data of a target room;

[1554] A means for the server to analyze the uploaded real-world data and extract the dimensions, shape, and existing furniture layout of the room;

[1555] A means for the server to analyze requests from users using natural language processing technology;

[1556] The server uses AI to generate multiple layout plans based on the analysis results.

[1557] A means for generating the layout proposal generated by the server in the form of a 3D model or image and presenting it to the user;

[1558] A means for the server to display detailed information and a link to purchase the suggested furniture or appliances;

[1559] A means to evaluate user responses and improve the suggestions through an emotion engine;

[1560] A system including:

[1561] (Claim 2)

[1562] A means for generating layout proposals for optimizing store displays and product placement;

[1563] A means for presenting the proposals generated by the server to store operators as 3D models and simulating the layout proposals and their effects;

[1564] A means of analyzing staff and customer sentiment data to tailor proposals;

[1565] The system of claim 1 further comprising:

[1566] (Claim 3)

[1567] a server using an object detection algorithm to analyze the LiDAR data and image data;

[1568] means for providing a way for a user to input room layout preferences into a chat interface;

[1569] The system of claim 1 further comprising: [Explanation of symbols]

[1570] 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. a means for a user to input room layout preferences into a chat interface; A means for a user to upload real-world environment data of a target room; A means for the server to analyze the uploaded real-world data and extract the dimensions, shape, and existing furniture layout of the room; A means for the server to analyze requests from users using natural language processing technology; The server uses AI to generate multiple layout plans based on the analysis results. A means for generating the layout proposal generated by the server in the form of a 3D model or image and presenting it to the user; A means for the server to display detailed information and a link to purchase the suggested furniture or appliances; A system including:

2. A means for the server to propose optimal product placement based on the principles of behavioral economics; The proposals generated by the server are presented to the contractor as 3D models, and a means of simulating practical layout plans and their effects is provided. The system of claim 1 further comprising:

3. a server using an object detection algorithm to analyze the LiDAR data and image data; means for providing a way for a user to input room layout preferences into a chat interface; The system of claim 1 further comprising:

Images