System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to take a picture of a room and upload it; means for a server to input the received picture into a AI model for image analysis and perform analysis to identify locations and types of furnishings and items in the room; means for the server to list current issues based on the analysis results; means for the server to generate solutions to the issues and provide suggestions to the user; and means for the server to generate simulation images to visually show the solutions.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] As people continue to work from home, they often spend long periods of time in the same room, which makes them want to change the atmosphere of the room or feel the need to tidy up. However, rearranging furniture and tidying up by themselves can be time-consuming and can sometimes be unsuccessful. Therefore, there is a need for a system that allows users to easily get ideas for redecorating and tidying up their room. [Means for solving the problem]

[0005] The present invention solves the above problem by providing a system that includes a means for taking photos of a room and uploading them to an application, a means for the server to input the photos received into an AI model for image analysis and analyze them to identify the location and type of furniture and items in the room, a means for the server to list current issues based on the analysis results, a means for the server to generate solutions to the issues and provide suggestions to the user, a means for the server to generate simulation images to visually show the solutions, and a means for the server to send the suggestions and simulation images from the server to the user's terminal and display them on the terminal.

[0006] A "user" refers to a person who uses this system and is responsible for taking photos of the room and uploading them to the application.

[0007] "Device" means the electronic device used by the User to upload photos and receive and display suggestions from the Server.

[0008] "Server" refers to the central processing unit that receives photos sent from the user's device, uses the AI ​​model to analyze the images and generate suggestions, and then sends the results to the user's device.

[0009] "Image analysis" refers to the process of using an AI model to identify the location and type of furniture and items in a room from input photo data.

[0010] An "AI model" is a computational model that uses artificial intelligence and is used to analyze image data to perform object recognition and semantic segmentation.

[0011] "Issues" refer to areas for improvement or problems in the room identified as a result of image analysis.

[0012] "Proposal" refers to a specific action or proposed change generated by the server to resolve an identified issue.

[0013] A "simulation image" is an image generated as a visual representation of a suggestion, showing what the room will look like if the user implements the suggestion.

[0014] "Photo upload" refers to the act of sending a photo of a room taken by a user to a server via an application.

[0015] "Redecorating" refers to the act of rearranging furniture and decorations in a room to create a new atmosphere.

[0016] "Tidying up" refers to the act of arranging the items in a room neatly so that they are easy to see and use.

[0017] "Current issues" refer to areas that need improvement in furniture arrangement and organization, as identified by the server through photo analysis of the room.

[0018] "Solution" refers to a specific improvement method or proposal that the server generates for the current problem. [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] This invention relates to a system that provides suggestions for redecorating and tidying up a room. A user takes a photo of the room, and AI analyzes the photo to identify current issues and generate specific suggestions for improvement. The processing content of the system's program and specific operation examples are explained below.

[0041] Overall system overview

[0042] 1. User operations

[0043] Users take photos of their rooms with their smartphones or digital cameras and upload them to the application by tapping the "Upload Photos" button within the app.

[0044] 2. Server Processing

[0045] The server receives the photo sent from the user's device, verifies the resolution and file format of the photo, and if there are no problems, proceeds to the next processing step.

[0046] The server calls the AI ​​model, providing it with the received photo as input, which then analyzes the image to identify the location and type of furniture (sofa, table, bookshelf, etc.) and other items (books, decorations, etc.) in the room.

[0047] 3. Identifying the issue

[0048] Based on the results of image analysis, the server lists current issues, such as cramped furniture arrangement, cluttered bookshelves, blocked windows, etc.

[0049] 4. Proposal Generation

[0050] Based on the analysis, the server generates specific suggestions for solving the problem, such as how to organize bookshelves, rearrange furniture, or recommend new furniture or decorative items to purchase.

[0051] Based on the proposal, the server generates a simulation image that visually shows the proposal.

[0052] 5. Displaying the results

[0053] The terminal displays the proposals and simulation images received from the server on the user interface, and the user can view them and decide whether to redecorate the room as proposed.

[0054] Specific examples

[0055] User Action:

[0056] A user takes a photo of their living room with their smartphone and taps the "Upload Photo" button in the application to send it to the server, where it is verified that the photo has the correct resolution and is in JPEG format.

[0057] Server image analysis:

[0058] The server inputs the received photos into the AI ​​model and begins analysis. The AI ​​model recognizes objects such as sofas, tables, bookshelves, books, and decorations, and identifies their locations and types.

[0059] Identifying the issue:

[0060] Based on the analysis results, the server lists problems such as the sofa in the living room being located in the center of the room and obstructing traffic flow, or the bookshelf being disorganized.

[0061] Proposal generation:

[0062] The server moves the sofa to a window wall, organizes bookshelves by color, generates suggestions for purchasing additional storage boxes, and even recommends new curtains.

[0063] The server generates a visual simulation of the proposal, allowing the user to visually see how the room will change.

[0064] Viewing Results:

[0065] The device displays the proposal and a simulation image to the user, who then reviews the proposal and decides whether to redecorate the room.

[0066] This system allows users to easily receive specific suggestions for redecorating and tidying up their rooms, and gives them creative ideas that they would not have thought of on their own, making it easier to maintain a comfortable living environment.

[0067] The processing flow will be explained below.

[0068] Step 1:

[0069] The user takes a photo of the room with a smartphone or digital camera, opens the application, selects the photo, and taps the "Upload Photo" button.

[0070] Step 2:

[0071] The device uploads the selected photo to the server, verifies the resolution and file format of the photo, and sends it to the server if there are no problems.

[0072] Step 3:

[0073] The server receives the received photos and prepares them for input into the AI ​​model, checking that the photo resolution and file format are suitable for analysis.

[0074] Step 4:

[0075] The server calls the AI ​​model and provides the photo as input data, which then initiates object recognition and semantic segmentation to identify the location and type of furniture (e.g., sofa, table, bookshelf) and items (e.g., books, decorations) in the room.

[0076] Step 5:

[0077] The server receives the analysis results from the AI ​​model and lists current issues, identifying specific problems such as inefficient furniture placement or cluttered bookshelves.

[0078] Step 6:

[0079] The server generates solutions based on the identified problems, such as how to organize bookshelves, rearrange furniture, or recommend new furniture or decorative items to purchase.

[0080] Step 7:

[0081] The server generates a simulation image to visually represent the generated solution, allowing the user to visually confirm the proposed changes.

[0082] Step 8:

[0083] The server sends the proposal and the simulated image to the user's terminal. The proposal content and the visualized simulated image are sent together.

[0084] Step 9:

[0085] The device displays the received suggestions and simulation images on the user interface, allowing the user to decide whether to carry out the suggested rearrangements and tidying up.

[0086] This allows users to come up with creative ideas that they would not have thought of on their own, making it easier to improve their rooms.

[0087] Example 1

[0088] 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."

[0089] In modern homes, rearranging and tidying rooms requires a lot of time and effort. It can be difficult for individual users to come up with an efficient and aesthetically pleasing arrangement and tidying method. Furthermore, the lack of a way to visually confirm the results of rearranging furniture and items in a room before rearranging them can easily leave users feeling anxious and dissatisfied. The present invention aims to solve these problems and provide a system that allows users to rearrange and tidy their rooms easily and effectively.

[0090] 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.

[0091] In this invention, the server includes: [means for a user to take and upload photos of the room; [means for the server to input the photos received by the server into a generative AI model for image analysis and analyze them to identify the locations and types of furniture and items in the room; [means for the server to list current issues based on the analysis results; [means for the server to generate solutions to the issues and provide suggestions to the user; [means for the server to generate simulation images to visually show the solutions; and [means for the server to send the suggestions and simulation images from the server to the user's terminal and display them on the terminal.] This enables users to efficiently and effectively rearrange and tidy up their rooms, and provides an environment in which specific changes to the placement of furniture and items can be visually confirmed.

[0092] A "user" refers to a person who uses the system to receive suggestions for redecorating and tidying up their room.

[0093] "Server" refers to the computer system that receives photos uploaded by users, analyzes the images using a generative AI model, identifies issues, and generates solutions.

[0094] "Device" means the device used by the User to take and upload photos and to view proposals and simulated images received from the Server, such as a smartphone or tablet.

[0095] "Generative AI model" refers to the artificial intelligence algorithm used by the server, a machine learning model used to analyze photos of a room and identify the location and type of furniture and items.

[0096] "Image analysis" refers to the process by which a generative AI model takes a photo as input and identifies the furniture and items in the room, determining their location and type.

[0097] "Issues" refer to problems with the current state of the room that were identified as a result of image analysis. Examples include obstructions to traffic flow and disorganized storage.

[0098] "Solutions" refer to specific methods the server suggests to resolve the issues it has identified, such as rearranging furniture, organizing storage, or suggesting the purchase of additional items.

[0099] A "simulation image" is an image created to visually show the content of a proposal generated by the server, and is used by the user to check the effect of the proposal in advance.

[0100] "Uploading" refers to the act of sending a photo of a room taken by a user to a server.

[0101] "Photo" refers to an image file taken by the user of the current state of the room.

[0102] This invention relates to a system that provides suggestions for redecorating and tidying up a room. A user takes a photo of the room, and a generative AI model on a server analyzes the photo to identify current issues and generate specific suggestions for improvement. The processing content of the system's program and specific operation examples are explained below.

[0103] Hardware and software used

[0104] To implement this system, the following hardware and software is required:

[0105] User devices: smartphones, tablets, digital cameras, etc. Used to upload photos taken to the server.

[0106] Server: A powerful computer system used to receive photos from users, perform analysis, generate suggestions, and create simulated images.

[0107] Generative AI model: An AI model built using machine learning libraries such as TENSORFLOW® or PyTorch, used to analyze photos of a room and identify the location and type of furniture and items.

[0108] Data processing and calculation details

[0109] The system operates as follows.

[0110] 1. Take and upload a photo:

[0111] The user takes a photo of the room using a smartphone or digital camera and uploads the photo to the server through the application, for example, by tapping the "Upload Photo" button.

[0112] 2. Photo Receipt and Verification:

[0113] The server receives the photo sent by the user. After receiving it, it verifies the photo's resolution (for example, 300 dpi or higher) and file format (JPEG format), and if there are no problems, it proceeds to the next step.

[0114] 3. Image Analysis:

[0115] The server calls a built-in generative AI model (for example, a TensorFlow object detection model) and provides the received photo as input. The AI ​​model analyzes the image and identifies the location and type of furniture, such as sofas, tables, bookshelves, and other items, such as books and decorations. This process uses image processing technology to recognize objects.

[0116] 4. Identify the problem:

[0117] Based on the results of the image analysis, the server lists the current issues in the room, pointing out specific problems such as "the sofa is placed in the center of the room and obstructs traffic flow," "the bookshelf is cluttered," and "the window is blocked by furniture."

[0118] 5. Proposal generation:

[0119] Based on the analysis results, the server generates specific suggestions for solving the problem, such as "move the sofa to the window," "organize the bookshelf by color," or "purchase additional storage boxes." It also generates a simulated image that visually shows the suggestions. Image synthesis technology is used to simulate what the proposed layout will look like.

[0120] 6. Displaying the results:

[0121] The terminal displays the proposals and simulation images received from the server on the user interface. The user checks the proposals and decides whether to redecorate or tidy up the room.

[0122] Examples and prompts

[0123] User Action:

[0124] A user takes a photo of their living room with their smartphone and taps the "Upload Photo" button in the application, which validates the photo's resolution and file format.

[0125] Server image analysis:

[0126] The server inputs the received photos into the AI ​​model and begins analysis. The AI ​​model recognizes objects such as sofas, tables, bookshelves, books, and decorations, and identifies their locations and types.

[0127] Identifying the issue:

[0128] The server lists problems based on the analysis results, such as the sofa in the living room being in the middle, which makes it difficult to move around, or the bookshelf being cluttered and in need of organization.

[0129] Proposal generation:

[0130] The server generates suggestions such as moving the sofa to the window, arranging the bookshelf by color, purchasing additional storage boxes, purchasing new curtains, etc. It also generates simulated images that reflect these suggestions so that the user can visually confirm them.

[0131] Viewing Results:

[0132] The device displays the proposal and a simulation image to the user, who can then review the proposal and decide whether or not to actually redecorate the room.

[0133] Prompt Sentence Examples

[0134] "You take a photo of your living room and upload it to the server through the app. The server analyzes the photo and provides specific suggestions for furniture arrangement and organization. For example, you might move the sofa to a window, organize your bookshelves by color, or buy additional storage boxes. You can visually review the suggestions and then decide whether to implement them."

[0135] This system allows users to receive specific suggestions for easily rearranging and tidying their rooms, and gives them creative ideas that they would not have thought of on their own, making it easier to maintain a comfortable living environment.

[0136] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0137] Step 1:

[0138] The user takes a photo of the room with a smartphone or digital camera and then taps the "Upload Photo" button in the application to send the photo to the server.

[0139] Input: Room photo (JPEG format)

[0140] Output: Uploaded photo

[0141] Specific behavior: Check the resolution of the photo taken by the user and upload it to the server through the application.

[0142] Step 2:

[0143] The server receives the photo sent by the user. The server verifies the resolution and file format of the received photo, and if there are no problems, it proceeds to the next step.

[0144] Input: A photo submitted by the user

[0145] Output: Verification result (whether the photo is appropriate or not)

[0146] Specific operation: Check whether the resolution of the received photo is 300 dpi or higher and whether the file format is JPEG.

[0147] Step 3:

[0148] The server invokes a generative AI model (for example, a TensorFlow object detection model) and provides the received photo as input. The AI ​​model analyzes the image and identifies the furniture and items in the room.

[0149] Input: A photo that passes validation

[0150] Output: Image analysis results (furniture and item locations and types)

[0151] What it does: It uses AI models to analyze images and identify objects such as sofas, tables, bookshelves, books, and decorative items, and their contents.

[0152] Step 4:

[0153] Based on the results of image analysis, the server lists the current issues in the room.

[0154] Input: Image analysis results

[0155] Output: Issue list (current issues)

[0156] Specific operation: Based on the analysis results, it makes a list of problems such as cramped furniture layout, poor traffic flow, and cluttered bookshelves.

[0157] Step 5:

[0158] The server generates specific proposals to address the identified issues, such as rearranging the sofa, reorganizing the bookshelf, or adding new items, and generates a visual simulation image.

[0159] Input: Issues list

[0160] Output: Proposal and simulation images

[0161] Specific actions: Using machine learning models, the system generates specific rearrangement and suggestions, such as moving the sofa to a window, organizing bookshelves by color, or purchasing additional storage boxes, and then uses image synthesis technology to create simulated images.

[0162] Step 6:

[0163] The generated proposal and simulation image are sent from the server to the user, who then displays them on the terminal.

[0164] Input: Proposal content and simulation image

[0165] Output: Proposal and simulation images displayed on the user's device

[0166] Specific operation: The proposals and simulation images are sent to the user's device and displayed in an application on the device. The user reviews the proposals and decides whether to redecorate and organize the room.

[0167] (Application example 1)

[0168] 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."

[0169] In modern factory environments, work efficiency and space optimization are important challenges. However, achieving these goals requires specialized knowledge and time, and there are limitations to how much manual work can be done by on-site personnel. Furthermore, it is difficult to accurately identify current problems and propose specific improvement measures. This can result in unnecessary traffic flow and inappropriate machine placement, leading to reduced productivity. Therefore, a system that can automatically and efficiently solve these issues is needed.

[0170] 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.

[0171] In this invention, the server includes: [means for a user to take a photo of the environment and upload it; [means for the server to input the received photo into an AI model for image analysis and analyze it to identify the location and type of objects and items in the environment; [means for the server to list current issues based on the analysis results; [means for the server to generate solutions to the issues and provide proposals to the user; [means for the server to generate simulation images to visually show the solutions; [means for the server to send the proposals and simulation images from the server to the user's terminal and display them on the terminal; and [means for the server to generate proposals to optimize the factory's work area and machine layout.] This makes it possible to automatically identify current problems in the factory environment and visually show specific improvement measures, thereby improving work efficiency and optimizing space.

[0172] "Environment" refers to the place where machines and equipment are installed, such as a factory or work site.

[0173] "Placement" refers to objects in the environment, such as machines, equipment, workbenches, storage facilities, etc.

[0174] "Items" refer to small objects present in the environment, such as tools, materials, products, and decorations.

[0175] "Location" refers to the specific location that a placement or item occupies within the environment.

[0176] "Type" refers to an attribute that indicates the characteristics and classification of a placement or item.

[0177] "Challenges" refer to problems within the environment that hinder work efficiency or space optimization.

[0178] "Solution" refers to specific measures or proposals to resolve the identified issues.

[0179] "Simulation image" refers to a virtual image that visually represents a solution.

[0180] "Work area" refers to the specific location or space where work is performed.

[0181] "Machine layout" refers to the way machines and equipment are arranged within a work area.

[0182] "Device" refers to the device (e.g., smartphone, tablet, head-mounted display, etc.) that a user uses to upload photos and view suggestions.

[0183] "Receiving" refers to the server acquiring the photo data sent from the user's terminal.

[0184] "Analysis" refers to the process of using AI models to process photo data and identify the location and type of objects and items.

[0185] This invention relates to a system for optimizing work efficiency and space in a factory environment. Users take photos of the factory interior using a smartphone or head-mounted display (HMD) and upload them to a dedicated application. Based on the photos, a server uses an AI model to perform image analysis, identify current problems, and generate specific improvement proposals. The processing content of the system's program and specific operation examples are explained below.

[0186] Overall system overview

[0187] User operations

[0188] Users use their smartphones or HMDs to take photos of the current state of the factory, and then tap the "Upload Photos" button in the dedicated application to send the photo data to the server.

[0189] Server Processing

[0190] The server verifies the received photo data and, if there are no problems, inputs it into an AI model for image analysis. This AI model uses TensorFlow and PyTorch. The AI ​​identifies the location and type of objects (machines, equipment, workbenches, storage facilities) and items (tools, materials, products, decorations) in the image.

[0191] Identifying the issue

[0192] Based on the analysis results, the server lists current problems such as narrowing of the work area and obstructions to traffic flow.

[0193] Proposal Generation

[0194] The server generates specific solutions to the identified problems, such as rearranging work areas or organizing storage facilities, and also generates simulation images that visually show these proposals.

[0195] Displaying the results

[0196] The proposals and simulation images generated by the server are sent to the user's device (smartphone or HMD) and displayed on the user interface. The user can review these and decide whether to make layout changes or improvements as proposed.

[0197] Hardware and Software Used

[0198] Hardware:

[0199] Smartphones (e.g. iPhone(R) 12, Samsung Galaxy S21)

[0200] Head-mounted displays (e.g., Microsoft(R) HoloLens(R) 2, Oculus Quest 2)

[0201] Server (e.g., Google Cloud Platform, Amazon Web Services)

[0202] software:

[0203] Dedicated app for smartphone or HMD (development environment: ANDROID (registered trademark) Studio, Xcode)

[0204] AI models for image analysis (e.g., TensorFlow, PyTorch)

[0205] Server-side processing (e.g., Node.js, Python)

[0206] Specific examples

[0207] A user puts on a HoloLens 2 and scans the layout of machines and equipment in a factory, uploading the data to an application. The server analyzes the received images using TensorFlow to detect problems such as overly narrow work areas and blocked traffic lines. Specific proposals for rearranging machines, installing new storage facilities, and streamlining traffic lines are then generated, and simulated images of these are displayed on the HoloLens for the user to check on the spot.

[0208] Example of input prompt for generative AI model:

[0209] Upload a photo of your current factory environment and we'll generate suggestions to optimize work areas and machine locations.

[0210] This will enable factory managers to improve work efficiency and make layout changes without waste based on expert advice.

[0211] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0212] Step 1:

[0213] Users use a smartphone or head-mounted display to take photos of the factory and upload them into the application.

[0214] Input: Photographic data of the physical environment

[0215] Output: Photo data sent to the server

[0216] Specific behavior: When the user taps the "Upload Photo" button in the application, the photo they took is sent to the server.

[0217] Step 2:

[0218] The server validates the received photo data and checks whether the resolution and file format are appropriate.

[0219] Input: Photo data sent by the user

[0220] Output: Analysis request to the configured AI model

[0221] Specific operation: The server checks the resolution and file format (e.g., JPEG, PNG) of the photo data, and if there are no problems, it proceeds to the next processing step. If not, it returns an error message.

[0222] Step 3:

[0223] The photo data received by the server is input into the AI ​​model and image analysis is performed.

[0224] Input: Photo data

[0225] Output: A list of placements and items, with their locations and types.

[0226] Specific operation: The server uses TensorFlow and PyTorch to call AI models, analyze the photo data, and identify the location and type of objects (machines, equipment) and items (tools, materials).

[0227] Step 4:

[0228] Based on the analysis results, the server lists current problems.

[0229] Input: List of positions and types

[0230] Output: A list of issues

[0231] Specific operation: The server extracts problems such as obstructions to traffic flow and narrowing of work areas from the analysis results and creates a list of problems.

[0232] Step 5:

[0233] The server generates solutions and visual simulation images for the listed problems.

[0234] Input: List of issues

[0235] Output: Solution proposal and simulation images

[0236] Specific operation: The server generates specific solutions to the problem, such as proposing machine relocation or the introduction of new storage facilities. It also generates simulation images so that the user can visually confirm the proposed solutions.

[0237] Step 6:

[0238] The generated proposal and simulation image are sent from the server to the user's terminal, which displays them.

[0239] Input: Solution proposal and simulated images

[0240] Output: Proposal and simulation images displayed

[0241] Specific operation: The server sends the generated proposal and simulation images to the user's smartphone or HMD, where they are displayed on the application. The user then checks the proposal and decides whether to make layout changes or carry out improvements.

[0242] This is the flow of the system program's processing to optimize the factory environment. By performing appropriate data processing and calculations at each step, the system provides specific proposals and visual simulations.

[0243] 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.

[0244] This invention relates to a system that provides suggestions for redecorating and tidying up a room. Users take photos of the room, and AI analyzes the photos to identify current issues and generate specific suggestions for improvement. Furthermore, the system incorporates an emotion engine that can recognize the user's emotional state and reflect it in the suggestions.

[0245] Overall system overview

[0246] 1. User operations

[0247] Users take photos of their rooms with their smartphones or digital cameras and upload them to the application by tapping the "Upload Photos" button within the app.

[0248] 2. User Emotion Recognition

[0249] The device uses an emotion engine to recognize emotions from the user's facial expressions and voice, using the smartphone's camera and microphone.

[0250] The device transmits the recognized emotion data to the server.

[0251] 3. Server Processing

[0252] The server receives the photo and emotion data sent from the user's device, verifies the photo's resolution and file format, and if there are no problems, proceeds to the next processing step.

[0253] 4. Image Analysis

[0254] The server calls the AI ​​model and provides the photo as input data, which then initiates object recognition and semantic segmentation to identify the location and type of furniture (e.g., sofa, table, bookshelf) and items (e.g., books, decorations) in the room.

[0255] 5. Identifying the Issues

[0256] The server receives the analysis results from the AI ​​model and lists current issues, identifying specific problems such as inefficient furniture placement or cluttered bookshelves.

[0257] 6. Proposal Generation

[0258] The server generates solutions based on the identified problems, such as how to organize bookshelves, rearrange furniture, or recommend new furniture or decorative items to purchase.

[0259] The server adjusts the suggestions based on the user's emotional data. For example, if the user is feeling stressed, it will recommend placements and items that will help them relax.

[0260] Based on the proposal, the server generates a simulation image that visually shows the proposal.

[0261] 7. Displaying the results

[0262] The terminal displays the proposals and simulation images received from the server on the user interface, and the user can view them and decide whether to redecorate the room as proposed.

[0263] Specific examples

[0264] User Action:

[0265] A user takes a photo of their living room with their smartphone and taps the "Upload Photo" button in the application to send it to the server, where it is verified that the photo has the correct resolution and is in JPEG format.

[0266] User Emotion Recognition:

[0267] The device uses the smartphone camera to analyze the user's facial expressions, and the emotion engine recognizes the user's emotional state (e.g., stress, joy).

[0268] The device transmits the recognized emotion data to the server.

[0269] Server image analysis:

[0270] The server inputs the received photos into the AI ​​model and begins analysis. The AI ​​model recognizes objects such as sofas, tables, bookshelves, books, and decorations, and identifies their locations and types.

[0271] Identifying the issue:

[0272] Based on the analysis results, the server lists problems such as the sofa in the living room being located in the center of the room and obstructing traffic flow, or the bookshelf being disorganized.

[0273] Proposal generation:

[0274] The server moves the sofa to a window wall, organizes bookshelves by color, generates suggestions for purchasing additional storage boxes, and even recommends new curtains.

[0275] The server references the user's emotional data and suggests placements and items that will help users who are feeling stressed to relax.

[0276] The server generates a visual simulation of the proposal, allowing the user to visually see how the room will change.

[0277] Viewing Results:

[0278] The device displays the proposal and a simulation image to the user, who then reviews the proposal and decides whether to redecorate the room.

[0279] This system allows users to easily receive specific suggestions for redecorating and tidying up their rooms, and can provide creative ideas that they would not have thought of on their own. In addition, by receiving appropriate suggestions based on the user's emotional state, it becomes easier to maintain a more comfortable living environment.

[0280] The processing flow will be explained below.

[0281] Step 1:

[0282] The user takes a photo of the room with a smartphone or digital camera, opens the application, selects the photo, and taps the "Upload Photo" button.

[0283] Step 2:

[0284] The device uploads the selected photo to the server, verifies the resolution and file format of the photo, and sends it to the server if there are no problems.

[0285] Step 3:

[0286] The device captures the user's facial expressions using the smartphone's camera and captures their voice using the microphone. The emotion engine analyzes this data to identify the user's emotional state.

[0287] Step 4:

[0288] The device transmits the recognized emotion data to the server.

[0289] Step 5:

[0290] The server receives the photo and emotion data sent from the user's device. It verifies the photo's resolution and file format again, and if there are no problems, it proceeds to the next step.

[0291] Step 6:

[0292] The server calls the AI ​​model and provides the photo as input data, which then initiates object recognition and semantic segmentation to identify the location and type of furniture (sofa, table, bookshelf, etc.) and items (books, decorations, etc.) in the room.

[0293] Step 7:

[0294] The server receives the analysis results from the AI ​​model and lists current issues, identifying specific problems such as inefficient furniture placement or cluttered bookshelves.

[0295] Step 8:

[0296] The server generates solutions based on the identified problems, such as how to organize bookshelves, rearrange furniture, or recommend new furniture or decorative items to purchase.

[0297] Step 9:

[0298] The server adjusts the suggestions based on the user's emotional data. For example, if the user is feeling stressed, it will recommend placements and items that will help them relax.

[0299] Step 10:

[0300] The server generates a simulated image to visually represent the proposed changes, allowing the user to visually confirm the proposed changes.

[0301] Step 11:

[0302] The server sends the proposal and the simulated image to the user's terminal. The proposal content and the visualized simulated image are sent together.

[0303] Step 12:

[0304] The device displays the received proposal and the simulation image on the user interface, and the user can view it and decide whether to redecorate the room according to the proposal.

[0305] This processing flow allows users to come up with creative ideas that they would not have thought of on their own, and by receiving appropriate suggestions based on their emotional state, it becomes easier to maintain a more comfortable living environment.

[0306] Example 2

[0307] 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."

[0308] Redecorating and tidying a room is generally time-consuming, and it is often difficult for users to find the optimal arrangement. Furthermore, if the suggestions are not appropriate for the user's emotional state, they are unlikely to accept them. Therefore, there is a need for a system that can provide specific suggestions for efficiently redecorating and tidying a room and give appropriate advice based on the user's emotional state.

[0309] 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.

[0310] In this invention, the server includes a means for a user to take a photo of their room and upload it, a means for the terminal to collect the user's facial expression and voice data and recognize their emotional state using an emotion analysis engine, and a means for the terminal to transmit the recognized emotional data to the server. This allows the user to receive optimal suggestions for redecorating and tidying up their room based on the photo of their room and their emotional state at the time. Furthermore, because the suggestions are suited to the user's emotional state, they are more likely to be accepted, ensuring a more comfortable living environment.

[0311] A "user" refers to an individual who uses the system to take photos of their room and receive suggestions for redecorating and tidying up.

[0312] A "terminal" is an information processing device such as a smartphone or tablet that is operated by a user.

[0313] A "server" is a remote computer that performs the analysis processing that is the core of the system.

[0314] An "emotion analysis engine" refers to a program or algorithm that analyzes a user's facial expressions and voice data to recognize their emotional state.

[0315] An "AI model" is a trained program that uses artificial intelligence to analyze photos and identify the location and type of furniture and items in a room.

[0316] "Image analysis" refers to the process of using AI models to recognize objects in a photo and identify their location and type.

[0317] "Issues" refer to areas or situations within a room that need improvement and are identified from the analysis results.

[0318] "Solution" refers to a specific suggestion for improvement generated by the server for an identified issue.

[0319] A "simulation image" is a virtual image created to visually show the proposal content generated by the server.

[0320] "Suggestion" refers to specific instructions or advice provided by the server on how to rearrange or tidy up the user's room.

[0321] This invention relates to a system that provides suggestions for redecorating and tidying up a room. The system allows users to take and upload photos of their room, analyzes the photos using an AI model, identifies current issues, and proposes specific solutions. Furthermore, the system incorporates an emotion analysis engine that can recognize the user's emotional state and reflect it in the suggestions.

[0322] This system consists of a photographic device such as a smartphone or digital camera, a terminal for user operation (such as a smartphone or tablet), a server that performs the analysis processing, and an AI model and emotion analysis engine.

[0323] Hardware and Software Configuration

[0324] 1. Photo device: The user uses a smartphone or digital camera to take photos of the room. The photos are saved on the device and uploaded to the server via the application.

[0325] 2. Device: A smartphone or tablet on which users upload photos and run an emotion analysis engine. The device collects facial and voice data from users and analyzes their emotional state. The emotion analysis engine is implemented using, for example, Google's Face API or Amazon's Rekognition.

[0326] 3. Server: A remote computer that analyzes the received photos and emotion data. The server uses AI models (such as TensorFlow or PyTorch) to analyze the photos and perform object recognition and semantic segmentation. It then identifies issues based on the analysis results and generates specific suggestions and visual simulation images.

[0327] Data Processing and Computation

[0328] The server receives the photo and emotion data sent from the user's device. It verifies the photo's resolution and file format, and if there are no problems, it proceeds to the next analysis step. The server then calls the AI ​​model and provides the photo as input data. The AI ​​model then performs object recognition and semantic segmentation to identify the location and type of furniture and items in the room. Based on the analysis results, it lists current issues and generates solutions. These include how to organize bookshelves, rearrange furniture, and recommend new furniture or decorative items to purchase. The suggestions are adjusted based on the user's emotion data. For example, if the user is feeling stressed, it will recommend placements and items that will help them relax. Finally, a visual simulation image is generated based on the suggestions and sent to the user's device.

[0329] Examples of concrete examples and prompts

[0330] For example, a user takes a photo of their living room with their smartphone and taps the "Upload Photo" button in the application to send it to the server. The device uses the smartphone's camera to analyze the user's facial expressions, and the emotion engine recognizes the user's emotional state (e.g., stress, joy). The device then sends the recognized emotion data to the server. The server then inputs the received photo into an AI model and begins analysis. The AI ​​model recognizes objects such as sofas, tables, bookshelves, books, and decorative items, and identifies their respective positions and types. Based on the analysis results, the server lists problems such as the sofa being in the middle of the room and blocking traffic flow, or the bookshelf being cluttered. The server then generates suggestions to move the sofa to the window, organize the bookshelf by color, and purchase additional storage boxes. It also recommends the purchase of new curtains. The suggestions are generated as simulated images and ultimately sent to the user's device.

[0331] An example prompt might be, "I'd like some suggestions for redecorating my room. Please generate suggestions based on the photos and emotion data below."

[0332] Photo: [livingroom.jpg]

[0333] Emotional data: Feeling stressed

[0334] Please provide specific suggestions and visual simulation images."

[0335] According to the present invention, a user can easily receive specific suggestions for rearranging and tidying a room, and by receiving appropriate suggestions according to the user's emotional state, the user can maintain a comfortable living environment.

[0336] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0337] Step 1:

[0338] Taking and uploading photos

[0339] A user takes a photo of a room using a smartphone or digital camera.

[0340] The user takes a photo and taps the "Upload Photo" button in the application to send it to the server.

[0341] Input: A photo of the room (JPEG format).

[0342] Output: Photo data uploaded to the server.

[0343] Specific behavior: When the user taps the "Upload Photo" button, the device selects a photo file and sends it to the server via an HTTP request. A success message is displayed to the user.

[0344] Step 2:

[0345] Emotion data collection and analysis

[0346] The device uses a built-in camera and microphone to collect facial and voice data from the user.

[0347] The data collected by the device is analyzed by an emotion analysis engine to recognize the user's emotional state (e.g., stress, joy).

[0348] Input: User's facial expression data, voice data.

[0349] Output: Recognized emotion data (JSON format).

[0350] Specific operation: The device activates the camera and instructs the user to make facial expressions in front of the camera. The user also speaks according to the voice instructions. This data is processed locally and analyzed by the emotion analysis engine. The resulting emotion data is sent to the server in JSON format.

[0351] Step 3:

[0352] Data Receipt and Validation

[0353] The server receives the photo and emotion data sent from the user's terminal.

[0354] The server validates the photo's resolution and file format, returning an error message if it's not in JPEG format.

[0355] Input: Photo data (JPEG format), emotion data (JSON format).

[0356] Output: The validated data or an error message.

[0357] What happens: The server processes the incoming HTTP request, checks the photo's resolution (for example, 1280x720 pixels or more) and file format, and returns an error message if there is a problem.

[0358] Step 4:

[0359] Image analysis

[0360] The server calls the AI ​​model and provides the received photos as input data.

[0361] The AI ​​model uses object recognition and semantic segmentation to identify furniture and items within a room.

[0362] Input: Photo data.

[0363] Output: Object recognition results (location and type of object).

[0364] How it works: The server processes the photos using an AI model to generate bounding boxes and labels for each object, using TensorFlow and PyTorch.

[0365] Step 5:

[0366] Identifying the issue

[0367] The server receives the analysis results from the AI ​​model and lists current issues.

[0368] Input: Object recognition results.

[0369] Output: A list of identified issues.

[0370] Specific operation: The server automatically extracts issues based on the analysis results, such as a sofa blocking traffic flow or a cluttered bookshelf, and lists specific problems.

[0371] Step 6:

[0372] Proposal Generation

[0373] The server generates solutions based on the identified problems.

[0374] The server adjusts the suggestions based on the user's emotional data.

[0375] Input: Identified issues, emotion data.

[0376] Output: Specific proposals.

[0377] Specific behavior: The system generates suggestions based on the list of issues and optimizes them using emotional data. For example, it recommends relaxing layouts for users who are feeling stressed. Specific suggestions include how to organize bookshelves and recommending new furniture to purchase.

[0378] Step 7:

[0379] Generation of simulation images

[0380] The server generates a simulation image that visually shows the proposal.

[0381] Input: Specific proposal details.

[0382] Output: Simulation image.

[0383] Specific operation: The server generates a simulation image using 3D modeling software (e.g. Blender), which is temporarily stored on the server.

[0384] Step 8:

[0385] Send and view proposals and simulation images

[0386] The server sends the proposal and the simulation image to the user's terminal.

[0387] The terminal displays the received proposal and a simulation image.

[0388] Input: Proposal content, simulation image.

[0389] Output: Proposal and simulation images displayed on the user interface.

[0390] Specific operation: The server sends the generated proposal and a simulation image to the terminal as an HTTP response, and the terminal displays it on the user interface. The user can then review it and decide whether to redecorate the room as proposed.

[0391] This system allows users to receive specific suggestions for redecorating and tidying up their rooms, and provides appropriate advice based on their emotional state, making it easier to maintain a comfortable living environment.

[0392] (Application example 2)

[0393] 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."

[0394] When optimizing store layout and product displays, manual rearrangement and trial and error require time and effort. Furthermore, the manager's emotional state can affect the best proposal, making it difficult to efficiently determine the optimal layout. Furthermore, since the proposals are not visually displayed, it is difficult for managers to visualize them.

[0395] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0396] In this invention, the server includes: [means for a user to take and upload photos of a room or store; [means for the server to input the photos received by the server into an AI model for image analysis and analyze them to identify the location and type of furniture and items in the room or store; [means for the server to list current issues based on the analysis results; [means for the server to generate solutions to the issues and provide suggestions to the user; [means for the server to generate simulation images to visually show the solutions; [means for the server to recognize the user's emotional state and reflect that in the suggestions; and [means for the server to send the suggestions and simulation images from the server to the user's terminal and display them on the terminal.] This allows for efficient optimal suggestions regarding store layout and product display, enables more appropriate suggestions based on the manager's emotional state, and allows the manager to visually confirm the suggestions.

[0397] A "user" is someone who uses the application to take and upload photos of rooms or stores.

[0398] "Photos of rooms and stores" refer to photos of the interior of rooms and stores taken by users using their own smartphones or digital cameras.

[0399] A "server" is a device or system that receives photo data, stores AI models for image analysis, and generates and provides suggestions to users.

[0400] An "AI model for image analysis" is an artificial intelligence model that analyzes photographic data to identify the location and type of furniture and items within a room or store.

[0401] "Furniture and items" refers to sofas, tables, bookshelves, books, decorations, shelves, products, etc. that exist in rooms or stores.

[0402] "Current Issues" are layout problems and inefficiencies identified based on analyzed photos of rooms and stores.

[0403] "Solutions" are proposed ways to rearrange or organize furniture or items to address identified issues.

[0404] A "simulated image" is a virtual image generated to visually represent a proposed solution.

[0405] "User's emotional state" refers to the user's state of mind (e.g., stress or joy) recognized based on the user's facial expressions and voice.

[0406] "Proposal content" refers to specific proposals for changes to the layout of a room or store or product displays, which are generated based on the analysis results and the user's emotional state.

[0407] "Terminal" refers to a device (such as a smartphone or tablet) on which a user receives and displays proposals and simulation images.

[0408] The system for implementing this invention starts when a user takes a photo of a room or store and uploads it to a server. Details of the hardware and software used and an overview of the process are described below.

[0409] 1. System Configuration

[0410] Hardware:

[0411] User device: A smartphone or digital camera, specifically a smart device with a built-in high-resolution camera and microphone.

[0412] Server: A cloud server or dedicated server with high-performance computing power.

[0413] software:

[0414] Image processing library: Uses Python and OpenCV.

[0415] AI model: Uses TensorFlow and Keras for object recognition and semantic segmentation.

[0416] Emotion Recognition: Facial expression recognition is performed using OpenCV and DeepFace, and voice analysis is performed using Python.

[0417] Backend framework: Django is used for server data management and processing.

[0418] Database: PostgreSQL or SQLite.

[0419] Front-end: Developing mobile applications using React Native.

[0420] 2. Processing Flow

[0421] Photo upload and emotion recognition

[0422] 1. A user takes a photo of the interior of a store using their smartphone, which is saved in high-resolution JPEG format.

[0423] 2. The user uploads a photo to the application by tapping the "Upload Photo" button within the app.

[0424] 3. The device's camera and microphone are used to capture the user's facial expressions and voice. The captured data is then used to recognize facial expressions using OpenCV and DeepFace, and the voice data is analyzed using Python.

[0425] Server analysis and suggestions

[0426] 1. The server receives the photo data sent by the user and starts image analysis. It uses OpenCV to preprocess the photo, adjust the resolution, and verify the file format.

[0427] 2. The server invokes the AI ​​model (TensorFlow and Keras) and starts analyzing the photo, performing object recognition and semantic segmentation to identify the location and type of furniture and items in the room or store.

[0428] 3. The server lists current issues based on the analysis results, such as inefficient product display methods or blocked flow paths.

[0429] 4. The server adjusts the suggestions based on the emotional data. If the user is feeling stressed, the suggestions will be changed to include placements and items that will help them relax.

[0430] 5. The server generates proposals based on the analysis results and emotion data. These proposals include product rearrangement, new display methods, and improvements to traffic flow. Additionally, it generates simulation images to provide a visual representation.

[0431] View Suggestions

[0432] 1. The server sends the generated proposal and simulation image to the user terminal.

[0433] 2. The terminal displays the proposal and a simulation image to the user, who then checks the proposal and decides whether to implement it.

[0434] Specific examples

[0435] Specific processing examples:

[0436] Users take photos of the store and upload them to the app.

[0437] The server receives the photo and analyzes it using an AI model, which identifies the product display as messy and generates suggestions for tidying up.

[0438] The server refers to the emotional data and suggests layouts that can be easily changed based on the stress the user is feeling.

[0439] The proposal recommends rearranging products to ensure new flow of traffic, and displays a simulated image of the resulting situation.

[0440] Example prompt for a generative AI model:

[0441] Generate optimal store layout and product placement suggestions based on store photos and the store manager's sentiment data below.

[0442] Emotion data: {"emotion": "stress", "score": 0.7}

[0443] Store photo: (binary photo data)

[0444] Please include specific layout changes and simulation images based on photos of the store in your proposal.

[0445] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0446] Step 1:

[0447] Users take photos of rooms or stores with their smartphones and upload them to the application. At this time, the input data of the photo is saved in JPEG format and sent to the server by tapping the "Upload Photo" button in the app. The output data is the uploaded high-resolution photo file.

[0448] Step 2:

[0449] The device uses a camera and microphone to capture the user's facial expressions and voice. The input data is the user's facial image and voice data, which are then analyzed by the emotion engine. The output data is the user's emotional state (e.g., stress, joy).

[0450] Step 3:

[0451] The server receives the received photos and emotion data and performs preprocessing on the photos. The input data is a JPEG photo and emotion data, and OpenCV is used to adjust the resolution and verify the file format. The processed photo is obtained as output data.

[0452] Step 4:

[0453] The server inputs the processed photos into an AI model (TensorFlow, Keras) for image analysis. The input data is the preprocessed photos, and the AI ​​model performs object recognition and semantic segmentation. The output data identifies the location and type of furniture and items within a room or store.

[0454] Step 5:

[0455] The server lists current issues based on the analysis results of the AI ​​model. The input data is the analysis results, which identify inefficient furniture placement, product display methods, etc. The output data is the list of issues.

[0456] Step 6:

[0457] The server generates solutions to the listed problems and generates a simulation image to visually demonstrate them. The input data is the problem and the user's emotional data, and the solutions include how to rearrange furniture and products, how to organize, etc. The output data is a simulation image and specific proposals.

[0458] Step 7:

[0459] The server sends the generated proposal and simulation image to the user's terminal. The input data is the proposal content and simulation image, and this is sent to the user's terminal. The output data is the proposal content and simulation image that the user can view.

[0460] Step 8:

[0461] The terminal displays the proposal and a simulation image to the user, who then confirms the proposal. The input data are the proposal and simulation image sent from the server, and the user decides whether to change the layout of the room or store based on this. The proposal to be adopted by the user is confirmed as output data.

[0462] 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.

[0463] 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.

[0464] 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.

[0465] [Second embodiment]

[0466] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0467] 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.

[0468] 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).

[0469] 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.

[0470] 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.

[0471] 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).

[0472] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0473] 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.

[0474] 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.

[0475] 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.

[0476] 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.

[0477] 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."

[0478] This invention relates to a system that provides suggestions for redecorating and tidying up a room. A user takes a photo of the room, and AI analyzes the photo to identify current issues and generate specific suggestions for improvement. The processing content of the system's program and specific operation examples are explained below.

[0479] Overall system overview

[0480] 1. User operations

[0481] Users take photos of their rooms with their smartphones or digital cameras and upload them to the application by tapping the "Upload Photos" button within the app.

[0482] 2. Server Processing

[0483] The server receives the photo sent from the user's device, verifies the resolution and file format of the photo, and if there are no problems, proceeds to the next processing step.

[0484] The server calls the AI ​​model, providing it with the received photo as input, which then analyzes the image to identify the location and type of furniture (sofa, table, bookshelf, etc.) and other items (books, decorations, etc.) in the room.

[0485] 3. Identifying the issue

[0486] Based on the results of image analysis, the server lists current issues, such as cramped furniture arrangement, cluttered bookshelves, blocked windows, etc.

[0487] 4. Proposal Generation

[0488] Based on the analysis, the server generates specific suggestions for solving the problem, such as how to organize bookshelves, rearrange furniture, or recommend new furniture or decorative items to purchase.

[0489] Based on the proposal, the server generates a simulation image that visually shows the proposal.

[0490] 5. Displaying the results

[0491] The terminal displays the proposals and simulation images received from the server on the user interface, and the user can view them and decide whether to redecorate the room as proposed.

[0492] Specific examples

[0493] User Action:

[0494] A user takes a photo of their living room with their smartphone and taps the "Upload Photo" button in the application to send it to the server, where it is verified that the photo has the correct resolution and is in JPEG format.

[0495] Server image analysis:

[0496] The server inputs the received photos into the AI ​​model and begins analysis. The AI ​​model recognizes objects such as sofas, tables, bookshelves, books, and decorations, and identifies their locations and types.

[0497] Identifying the issue:

[0498] Based on the analysis results, the server lists problems such as the sofa in the living room being located in the center of the room and obstructing traffic flow, or the bookshelf being disorganized.

[0499] Proposal generation:

[0500] The server moves the sofa to a window wall, organizes bookshelves by color, generates suggestions for purchasing additional storage boxes, and even recommends new curtains.

[0501] The server generates a visual simulation of the proposal, allowing the user to visually see how the room will change.

[0502] Viewing Results:

[0503] The device displays the proposal and a simulation image to the user, who then reviews the proposal and decides whether to redecorate the room.

[0504] This system allows users to easily receive specific suggestions for redecorating and tidying up their rooms, and gives them creative ideas that they would not have thought of on their own, making it easier to maintain a comfortable living environment.

[0505] The processing flow will be explained below.

[0506] Step 1:

[0507] The user takes a photo of the room with a smartphone or digital camera, opens the application, selects the photo, and taps the "Upload Photo" button.

[0508] Step 2:

[0509] The device uploads the selected photo to the server, verifies the resolution and file format of the photo, and sends it to the server if there are no problems.

[0510] Step 3:

[0511] The server receives the received photos and prepares them for input into the AI ​​model, checking that the photo resolution and file format are suitable for analysis.

[0512] Step 4:

[0513] The server calls the AI ​​model and provides the photo as input data, which then initiates object recognition and semantic segmentation to identify the location and type of furniture (e.g., sofa, table, bookshelf) and items (e.g., books, decorations) in the room.

[0514] Step 5:

[0515] The server receives the analysis results from the AI ​​model and lists current issues, identifying specific problems such as inefficient furniture placement or cluttered bookshelves.

[0516] Step 6:

[0517] The server generates solutions based on the identified problems, such as how to organize bookshelves, rearrange furniture, or recommend new furniture or decorative items to purchase.

[0518] Step 7:

[0519] The server generates a simulation image to visually represent the generated solution, allowing the user to visually confirm the proposed changes.

[0520] Step 8:

[0521] The server sends the proposal and the simulated image to the user's terminal. The proposal content and the visualized simulated image are sent together.

[0522] Step 9:

[0523] The device displays the received suggestions and simulation images on the user interface, allowing the user to decide whether to carry out the suggested rearrangements and tidying up.

[0524] This allows users to come up with creative ideas that they would not have thought of on their own, making it easier to improve their rooms.

[0525] Example 1

[0526] 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."

[0527] In modern homes, rearranging and tidying rooms requires a lot of time and effort. It can be difficult for individual users to come up with an efficient and aesthetically pleasing arrangement and tidying method. Furthermore, the lack of a way to visually confirm the results of rearranging furniture and items in a room before rearranging them can easily leave users feeling anxious and dissatisfied. The present invention aims to solve these problems and provide a system that allows users to rearrange and tidy their rooms easily and effectively.

[0528] 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.

[0529] In this invention, the server includes: [means for a user to take and upload photos of the room; [means for the server to input the photos received by the server into a generative AI model for image analysis and analyze them to identify the locations and types of furniture and items in the room; [means for the server to list current issues based on the analysis results; [means for the server to generate solutions to the issues and provide suggestions to the user; [means for the server to generate simulation images to visually show the solutions; and [means for the server to send the suggestions and simulation images from the server to the user's terminal and display them on the terminal.] This enables users to efficiently and effectively rearrange and tidy up their rooms, and provides an environment in which specific changes to the placement of furniture and items can be visually confirmed.

[0530] A "user" refers to a person who uses the system to receive suggestions for redecorating and tidying up their room.

[0531] "Server" refers to the computer system that receives photos uploaded by users, analyzes the images using a generative AI model, identifies issues, and generates solutions.

[0532] "Device" means the device used by the User to take and upload photos and to view proposals and simulated images received from the Server, such as a smartphone or tablet.

[0533] "Generative AI model" refers to the artificial intelligence algorithm used by the server, a machine learning model used to analyze photos of a room and identify the location and type of furniture and items.

[0534] "Image analysis" refers to the process by which a generative AI model takes a photo as input and identifies the furniture and items in the room, determining their location and type.

[0535] "Issues" refer to problems with the current state of the room that were identified as a result of image analysis. Examples include obstructions to traffic flow and disorganized storage.

[0536] "Solutions" refer to specific methods the server suggests to resolve the issues it has identified, such as rearranging furniture, organizing storage, or suggesting the purchase of additional items.

[0537] A "simulation image" is an image created to visually show the content of a proposal generated by the server, and is used by the user to check the effect of the proposal in advance.

[0538] "Uploading" refers to the act of sending a photo of a room taken by a user to a server.

[0539] "Photo" refers to an image file taken by the user of the current state of the room.

[0540] This invention relates to a system that provides suggestions for redecorating and tidying up a room. A user takes a photo of the room, and a generative AI model on a server analyzes the photo to identify current issues and generate specific suggestions for improvement. The processing content of the system's program and specific operation examples are explained below.

[0541] Hardware and software used

[0542] To implement this system, the following hardware and software is required:

[0543] User devices: smartphones, tablets, digital cameras, etc. Used to upload photos taken to the server.

[0544] Server: A powerful computer system used to receive photos from users, perform analysis, generate suggestions, and create simulated images.

[0545] Generative AI models: For example, AI models built using machine learning libraries such as TensorFlow or PyTorch, which are used to analyze photos of a room and identify the location and type of furniture and items.

[0546] Data processing and calculation details

[0547] The system operates as follows.

[0548] 1. Take and upload a photo:

[0549] The user takes a photo of the room using a smartphone or digital camera and uploads the photo to the server through the application, for example, by tapping the "Upload Photo" button.

[0550] 2. Photo Receipt and Verification:

[0551] The server receives the photo sent by the user. After receiving it, it verifies the photo's resolution (for example, 300 dpi or higher) and file format (JPEG format), and if there are no problems, it proceeds to the next step.

[0552] 3. Image Analysis:

[0553] The server calls a built-in generative AI model (for example, a TensorFlow object detection model) and provides the received photo as input. The AI ​​model analyzes the image and identifies the location and type of furniture, such as sofas, tables, bookshelves, and other items, such as books and decorations. This process uses image processing technology to recognize objects.

[0554] 4. Identify the problem:

[0555] Based on the results of the image analysis, the server lists the current issues in the room, pointing out specific problems such as "the sofa is placed in the center of the room and obstructs traffic flow," "the bookshelf is cluttered," and "the window is blocked by furniture."

[0556] 5. Proposal generation:

[0557] Based on the analysis results, the server generates specific suggestions for solving the problem, such as "move the sofa to the window," "organize the bookshelf by color," or "purchase additional storage boxes." It also generates a simulated image that visually shows the suggestions. Image synthesis technology is used to simulate what the proposed layout will look like.

[0558] 6. Displaying the results:

[0559] The terminal displays the proposals and simulation images received from the server on the user interface. The user checks the proposals and decides whether to redecorate or tidy up the room.

[0560] Examples and prompts

[0561] User Action:

[0562] A user takes a photo of their living room with their smartphone and taps the "Upload Photo" button in the application, which validates the photo's resolution and file format.

[0563] Server image analysis:

[0564] The server inputs the received photos into the AI ​​model and begins analysis. The AI ​​model recognizes objects such as sofas, tables, bookshelves, books, and decorations, and identifies their locations and types.

[0565] Identifying the issue:

[0566] The server lists problems based on the analysis results, such as the sofa in the living room being in the middle, which makes it difficult to move around, or the bookshelf being cluttered and in need of organization.

[0567] Proposal generation:

[0568] The server generates suggestions such as moving the sofa to the window, arranging the bookshelf by color, purchasing additional storage boxes, purchasing new curtains, etc. It also generates simulated images that reflect these suggestions so that the user can visually confirm them.

[0569] Viewing Results:

[0570] The device displays the proposal and a simulation image to the user, who can then review the proposal and decide whether or not to actually redecorate the room.

[0571] Prompt Sentence Examples

[0572] "You take a photo of your living room and upload it to the server through the app. The server analyzes the photo and provides specific suggestions for furniture arrangement and organization. For example, you might move the sofa to a window, organize your bookshelves by color, or buy additional storage boxes. You can visually review the suggestions and then decide whether to implement them."

[0573] This system allows users to receive specific suggestions for easily rearranging and tidying their rooms, and gives them creative ideas that they would not have thought of on their own, making it easier to maintain a comfortable living environment.

[0574] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0575] Step 1:

[0576] The user takes a photo of the room with a smartphone or digital camera and then taps the "Upload Photo" button in the application to send the photo to the server.

[0577] Input: Room photo (JPEG format)

[0578] Output: Uploaded photo

[0579] Specific behavior: Check the resolution of the photo taken by the user and upload it to the server through the application.

[0580] Step 2:

[0581] The server receives the photo sent by the user. The server verifies the resolution and file format of the received photo, and if there are no problems, it proceeds to the next step.

[0582] Input: A photo submitted by the user

[0583] Output: Verification result (whether the photo is appropriate or not)

[0584] Specific operation: Check whether the resolution of the received photo is 300 dpi or higher and whether the file format is JPEG.

[0585] Step 3:

[0586] The server invokes a generative AI model (for example, a TensorFlow object detection model) and provides the received photo as input. The AI ​​model analyzes the image and identifies the furniture and items in the room.

[0587] Input: A photo that passes validation

[0588] Output: Image analysis results (furniture and item locations and types)

[0589] What it does: It uses AI models to analyze images and identify objects such as sofas, tables, bookshelves, books, and decorative items, and their contents.

[0590] Step 4:

[0591] Based on the results of image analysis, the server lists the current issues in the room.

[0592] Input: Image analysis results

[0593] Output: Issue list (current issues)

[0594] Specific operation: Based on the analysis results, it makes a list of problems such as cramped furniture layout, poor traffic flow, and cluttered bookshelves.

[0595] Step 5:

[0596] The server generates specific proposals to address the identified issues, such as rearranging the sofa, reorganizing the bookshelf, or adding new items, and generates a visual simulation image.

[0597] Input: Issues list

[0598] Output: Proposal and simulation images

[0599] Specific actions: Using machine learning models, the system generates specific rearrangement and suggestions, such as moving the sofa to a window, organizing bookshelves by color, or purchasing additional storage boxes, and then uses image synthesis technology to create simulated images.

[0600] Step 6:

[0601] The generated proposal and simulation image are sent from the server to the user, who then displays them on the terminal.

[0602] Input: Proposal content and simulation image

[0603] Output: Proposal and simulation images displayed on the user's device

[0604] Specific operation: The proposals and simulation images are sent to the user's device and displayed in an application on the device. The user reviews the proposals and decides whether to redecorate and organize the room.

[0605] (Application example 1)

[0606] 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."

[0607] In modern factory environments, work efficiency and space optimization are important challenges. However, achieving these goals requires specialized knowledge and time, and there are limitations to how much manual work can be done by on-site personnel. Furthermore, it is difficult to accurately identify current problems and propose specific improvement measures. This can result in unnecessary traffic flow and inappropriate machine placement, leading to reduced productivity. Therefore, a system that can automatically and efficiently solve these issues is needed.

[0608] 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.

[0609] In this invention, the server includes: [means for a user to take a photo of the environment and upload it; [means for the server to input the received photo into an AI model for image analysis and analyze it to identify the location and type of objects and items in the environment; [means for the server to list current issues based on the analysis results; [means for the server to generate solutions to the issues and provide proposals to the user; [means for the server to generate simulation images to visually show the solutions; [means for the server to send the proposals and simulation images from the server to the user's terminal and display them on the terminal; and [means for the server to generate proposals to optimize the factory's work area and machine layout.] This makes it possible to automatically identify current problems in the factory environment and visually show specific improvement measures, thereby improving work efficiency and optimizing space.

[0610] "Environment" refers to the place where machines and equipment are installed, such as a factory or work site.

[0611] "Placement" refers to objects in the environment, such as machines, equipment, workbenches, storage facilities, etc.

[0612] "Items" refer to small objects present in the environment, such as tools, materials, products, and decorations.

[0613] "Location" refers to the specific location that a placement or item occupies within the environment.

[0614] "Type" refers to an attribute that indicates the characteristics and classification of a placement or item.

[0615] "Challenges" refer to problems within the environment that hinder work efficiency or space optimization.

[0616] "Solution" refers to specific measures or proposals to resolve the identified issues.

[0617] "Simulation image" refers to a virtual image that visually represents a solution.

[0618] "Work area" refers to the specific location or space where work is performed.

[0619] "Machine layout" refers to the way machines and equipment are arranged within a work area.

[0620] "Device" refers to the device (e.g., smartphone, tablet, head-mounted display, etc.) that a user uses to upload photos and view suggestions.

[0621] "Receiving" refers to the server acquiring the photo data sent from the user's terminal.

[0622] "Analysis" refers to the process of using AI models to process photo data and identify the location and type of objects and items.

[0623] This invention relates to a system for optimizing work efficiency and space in a factory environment. Users take photos of the factory interior using a smartphone or head-mounted display (HMD) and upload them to a dedicated application. Based on the photos, a server uses an AI model to perform image analysis, identify current problems, and generate specific improvement proposals. The processing content of the system's program and specific operation examples are explained below.

[0624] Overall system overview

[0625] User operations

[0626] Users use their smartphones or HMDs to take photos of the current state of the factory, and then tap the "Upload Photos" button in the dedicated application to send the photo data to the server.

[0627] Server Processing

[0628] The server verifies the received photo data and, if there are no problems, inputs it into an AI model for image analysis. This AI model uses TensorFlow and PyTorch. The AI ​​identifies the location and type of objects (machines, equipment, workbenches, storage facilities) and items (tools, materials, products, decorations) in the image.

[0629] Identifying the issue

[0630] Based on the analysis results, the server lists current problems such as narrowing of the work area and obstructions to traffic flow.

[0631] Proposal Generation

[0632] The server generates specific solutions to the identified problems, such as rearranging work areas or organizing storage facilities, and also generates simulation images that visually show these proposals.

[0633] Displaying the results

[0634] The proposals and simulation images generated by the server are sent to the user's device (smartphone or HMD) and displayed on the user interface. The user can review these and decide whether to make layout changes or improvements as proposed.

[0635] Hardware and Software Used

[0636] Hardware:

[0637] Smartphones (e.g. iPhone 12, Samsung Galaxy S21)

[0638] Head-mounted displays (e.g. Microsoft HoloLens 2, Oculus Quest 2)

[0639] Server (e.g. Google Cloud Platform, Amazon Web Services)

[0640] software:

[0641] Dedicated app for smartphones or HMDs (development environment: Android Studio, Xcode)

[0642] AI models for image analysis (e.g., TensorFlow, PyTorch)

[0643] Server-side processing (e.g., Node.js, Python)

[0644] Specific examples

[0645] A user puts on a HoloLens 2 and scans the layout of machines and equipment in a factory, uploading the data to an application. The server analyzes the received images using TensorFlow to detect problems such as overly narrow work areas and blocked traffic lines. Specific proposals for rearranging machines, installing new storage facilities, and streamlining traffic lines are then generated, and simulated images of these are displayed on the HoloLens for the user to check on the spot.

[0646] Example of input prompt for generative AI model:

[0647] Upload a photo of your current factory environment and we'll generate suggestions to optimize work areas and machine locations.

[0648] This will enable factory managers to improve work efficiency and make layout changes without waste based on expert advice.

[0649] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0650] Step 1:

[0651] Users use a smartphone or head-mounted display to take photos of the factory and upload them into the application.

[0652] Input: Photographic data of the physical environment

[0653] Output: Photo data sent to the server

[0654] Specific behavior: When the user taps the "Upload Photo" button in the application, the photo they took is sent to the server.

[0655] Step 2:

[0656] The server validates the received photo data and checks whether the resolution and file format are appropriate.

[0657] Input: Photo data sent by the user

[0658] Output: Analysis request to the configured AI model

[0659] Specific operation: The server checks the resolution and file format (e.g., JPEG, PNG) of the photo data, and if there are no problems, it proceeds to the next processing step. If not, it returns an error message.

[0660] Step 3:

[0661] The photo data received by the server is input into the AI ​​model and image analysis is performed.

[0662] Input: Photo data

[0663] Output: A list of placements and items, with their locations and types.

[0664] Specific operation: The server uses TensorFlow and PyTorch to call AI models, analyze the photo data, and identify the location and type of objects (machines, equipment) and items (tools, materials).

[0665] Step 4:

[0666] Based on the analysis results, the server lists current problems.

[0667] Input: List of positions and types

[0668] Output: A list of issues

[0669] Specific operation: The server extracts problems such as obstructions to traffic flow and narrowing of work areas from the analysis results and creates a list of problems.

[0670] Step 5:

[0671] The server generates solutions and visual simulation images for the listed problems.

[0672] Input: List of issues

[0673] Output: Solution proposal and simulation images

[0674] Specific operation: The server generates specific solutions to the problem, such as proposing machine relocation or the introduction of new storage facilities. It also generates simulation images so that the user can visually confirm the proposed solutions.

[0675] Step 6:

[0676] The generated proposal and simulation image are sent from the server to the user's terminal, which displays them.

[0677] Input: Solution proposal and simulated images

[0678] Output: Proposal and simulation images displayed

[0679] Specific operation: The server sends the generated proposal and simulation images to the user's smartphone or HMD, where they are displayed on the application. The user then checks the proposal and decides whether to make layout changes or carry out improvements.

[0680] This is the flow of the system program's processing to optimize the factory environment. By performing appropriate data processing and calculations at each step, the system provides specific proposals and visual simulations.

[0681] 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.

[0682] This invention relates to a system that provides suggestions for redecorating and tidying up a room. Users take photos of the room, and AI analyzes the photos to identify current issues and generate specific suggestions for improvement. Furthermore, the system incorporates an emotion engine that can recognize the user's emotional state and reflect it in the suggestions.

[0683] Overall system overview

[0684] 1. User operations

[0685] Users take photos of their rooms with their smartphones or digital cameras and upload them to the application by tapping the "Upload Photos" button within the app.

[0686] 2. User Emotion Recognition

[0687] The device uses an emotion engine to recognize emotions from the user's facial expressions and voice, using the smartphone's camera and microphone.

[0688] The device transmits the recognized emotion data to the server.

[0689] 3. Server Processing

[0690] The server receives the photo and emotion data sent from the user's device, verifies the photo's resolution and file format, and if there are no problems, proceeds to the next processing step.

[0691] 4. Image Analysis

[0692] The server calls the AI ​​model and provides the photo as input data, which then initiates object recognition and semantic segmentation to identify the location and type of furniture (e.g., sofa, table, bookshelf) and items (e.g., books, decorations) in the room.

[0693] 5. Identifying the Issues

[0694] The server receives the analysis results from the AI ​​model and lists current issues, identifying specific problems such as inefficient furniture placement or cluttered bookshelves.

[0695] 6. Proposal Generation

[0696] The server generates solutions based on the identified problems, such as how to organize bookshelves, rearrange furniture, or recommend new furniture or decorative items to purchase.

[0697] The server adjusts the suggestions based on the user's emotional data. For example, if the user is feeling stressed, it will recommend placements and items that will help them relax.

[0698] Based on the proposal, the server generates a simulation image that visually shows the proposal.

[0699] 7. Displaying the results

[0700] The terminal displays the proposals and simulation images received from the server on the user interface, and the user can view them and decide whether to redecorate the room as proposed.

[0701] Specific examples

[0702] User Action:

[0703] A user takes a photo of their living room with their smartphone and taps the "Upload Photo" button in the application to send it to the server, where it is verified that the photo has the correct resolution and is in JPEG format.

[0704] User Emotion Recognition:

[0705] The device uses the smartphone camera to analyze the user's facial expressions, and the emotion engine recognizes the user's emotional state (e.g., stress, joy).

[0706] The device transmits the recognized emotion data to the server.

[0707] Server image analysis:

[0708] The server inputs the received photos into the AI ​​model and begins analysis. The AI ​​model recognizes objects such as sofas, tables, bookshelves, books, and decorations, and identifies their locations and types.

[0709] Identifying the issue:

[0710] Based on the analysis results, the server lists problems such as the sofa in the living room being located in the center of the room and obstructing traffic flow, or the bookshelf being disorganized.

[0711] Proposal generation:

[0712] The server moves the sofa to a window wall, organizes bookshelves by color, generates suggestions for purchasing additional storage boxes, and even recommends new curtains.

[0713] The server references the user's emotional data and suggests placements and items that will help users who are feeling stressed to relax.

[0714] The server generates a visual simulation of the proposal, allowing the user to visually see how the room will change.

[0715] Viewing Results:

[0716] The device displays the proposal and a simulation image to the user, who then reviews the proposal and decides whether to redecorate the room.

[0717] This system allows users to easily receive specific suggestions for redecorating and tidying up their rooms, and can provide creative ideas that they would not have thought of on their own. In addition, by receiving appropriate suggestions based on the user's emotional state, it becomes easier to maintain a more comfortable living environment.

[0718] The processing flow will be explained below.

[0719] Step 1:

[0720] The user takes a photo of the room with a smartphone or digital camera, opens the application, selects the photo, and taps the "Upload Photo" button.

[0721] Step 2:

[0722] The device uploads the selected photo to the server, verifies the resolution and file format of the photo, and sends it to the server if there are no problems.

[0723] Step 3:

[0724] The device captures the user's facial expressions using the smartphone's camera and captures their voice using the microphone. The emotion engine analyzes this data to identify the user's emotional state.

[0725] Step 4:

[0726] The device transmits the recognized emotion data to the server.

[0727] Step 5:

[0728] The server receives the photo and emotion data sent from the user's device. It verifies the photo's resolution and file format again, and if there are no problems, it proceeds to the next step.

[0729] Step 6:

[0730] The server calls the AI ​​model and provides the photo as input data, which then initiates object recognition and semantic segmentation to identify the location and type of furniture (sofa, table, bookshelf, etc.) and items (books, decorations, etc.) in the room.

[0731] Step 7:

[0732] The server receives the analysis results from the AI ​​model and lists current issues, identifying specific problems such as inefficient furniture placement or cluttered bookshelves.

[0733] Step 8:

[0734] The server generates solutions based on the identified problems, such as how to organize bookshelves, rearrange furniture, or recommend new furniture or decorative items to purchase.

[0735] Step 9:

[0736] The server adjusts the suggestions based on the user's emotional data. For example, if the user is feeling stressed, it will recommend placements and items that will help them relax.

[0737] Step 10:

[0738] The server generates a simulated image to visually represent the proposed changes, allowing the user to visually confirm the proposed changes.

[0739] Step 11:

[0740] The server sends the proposal and the simulated image to the user's terminal. The proposal content and the visualized simulated image are sent together.

[0741] Step 12:

[0742] The device displays the received proposal and the simulation image on the user interface, and the user can view it and decide whether to redecorate the room according to the proposal.

[0743] This processing flow allows users to come up with creative ideas that they would not have thought of on their own, and by receiving appropriate suggestions based on their emotional state, it becomes easier to maintain a more comfortable living environment.

[0744] Example 2

[0745] 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."

[0746] Redecorating and tidying a room is generally time-consuming, and it is often difficult for users to find the optimal arrangement. Furthermore, if the suggestions are not appropriate for the user's emotional state, they are unlikely to accept them. Therefore, there is a need for a system that can provide specific suggestions for efficiently redecorating and tidying a room and give appropriate advice based on the user's emotional state.

[0747] 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.

[0748] In this invention, the server includes a means for a user to take a photo of their room and upload it, a means for the terminal to collect the user's facial expression and voice data and recognize their emotional state using an emotion analysis engine, and a means for the terminal to transmit the recognized emotional data to the server. This allows the user to receive optimal suggestions for redecorating and tidying up their room based on the photo of their room and their emotional state at the time. Furthermore, because the suggestions are suited to the user's emotional state, they are more likely to be accepted, ensuring a more comfortable living environment.

[0749] A "user" refers to an individual who uses the system to take photos of their room and receive suggestions for redecorating and tidying up.

[0750] A "terminal" is an information processing device such as a smartphone or tablet that is operated by a user.

[0751] A "server" is a remote computer that performs the analysis processing that is the core of the system.

[0752] An "emotion analysis engine" refers to a program or algorithm that analyzes a user's facial expressions and voice data to recognize their emotional state.

[0753] An "AI model" is a trained program that uses artificial intelligence to analyze photos and identify the location and type of furniture and items in a room.

[0754] "Image analysis" refers to the process of using AI models to recognize objects in a photo and identify their location and type.

[0755] "Issues" refer to areas or situations within a room that need improvement and are identified from the analysis results.

[0756] "Solution" refers to a specific suggestion for improvement generated by the server for an identified issue.

[0757] A "simulation image" is a virtual image created to visually show the proposal content generated by the server.

[0758] "Suggestion" refers to specific instructions or advice provided by the server on how to rearrange or tidy up the user's room.

[0759] This invention relates to a system that provides suggestions for redecorating and tidying up a room. The system allows users to take and upload photos of their room, analyzes the photos using an AI model, identifies current issues, and proposes specific solutions. Furthermore, the system incorporates an emotion analysis engine that can recognize the user's emotional state and reflect it in the suggestions.

[0760] This system consists of a photographic device such as a smartphone or digital camera, a terminal for user operation (such as a smartphone or tablet), a server that performs the analysis processing, and an AI model and emotion analysis engine.

[0761] Hardware and Software Configuration

[0762] 1. Photo device: The user uses a smartphone or digital camera to take photos of the room. The photos are saved on the device and uploaded to the server via the application.

[0763] 2. Device: A smartphone or tablet on which users upload photos and run an emotion analysis engine. The device collects facial and voice data from users and analyzes their emotional state. The emotion analysis engine is implemented using, for example, Google's Face API or Amazon's Rekognition.

[0764] 3. Server: A remote computer that analyzes the received photos and emotion data. The server uses AI models (such as TensorFlow or PyTorch) to analyze the photos and perform object recognition and semantic segmentation. It then identifies issues based on the analysis results and generates specific suggestions and visual simulation images.

[0765] Data Processing and Computation

[0766] The server receives the photo and emotion data sent from the user's device. It verifies the photo's resolution and file format, and if there are no problems, it proceeds to the next analysis step. The server then calls the AI ​​model and provides the photo as input data. The AI ​​model then performs object recognition and semantic segmentation to identify the location and type of furniture and items in the room. Based on the analysis results, it lists current issues and generates solutions. These include how to organize bookshelves, rearrange furniture, and recommend new furniture or decorative items to purchase. The suggestions are adjusted based on the user's emotion data. For example, if the user is feeling stressed, it will recommend placements and items that will help them relax. Finally, a visual simulation image is generated based on the suggestions and sent to the user's device.

[0767] Examples of concrete examples and prompts

[0768] For example, a user takes a photo of their living room with their smartphone and taps the "Upload Photo" button in the application to send it to the server. The device uses the smartphone's camera to analyze the user's facial expressions, and the emotion engine recognizes the user's emotional state (e.g., stress, joy). The device then sends the recognized emotion data to the server. The server then inputs the received photo into an AI model and begins analysis. The AI ​​model recognizes objects such as sofas, tables, bookshelves, books, and decorative items, and identifies their respective positions and types. Based on the analysis results, the server lists problems such as the sofa being in the middle of the room and blocking traffic flow, or the bookshelf being cluttered. The server then generates suggestions to move the sofa to the window, organize the bookshelf by color, and purchase additional storage boxes. It also recommends the purchase of new curtains. The suggestions are generated as simulated images and ultimately sent to the user's device.

[0769] An example prompt might be, "I'd like some suggestions for redecorating my room. Please generate suggestions based on the photos and emotion data below."

[0770] Photo: [livingroom.jpg]

[0771] Emotional data: Feeling stressed

[0772] Please provide specific suggestions and visual simulation images."

[0773] According to the present invention, a user can easily receive specific suggestions for rearranging and tidying a room, and by receiving appropriate suggestions according to the user's emotional state, the user can maintain a comfortable living environment.

[0774] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0775] Step 1:

[0776] Taking and uploading photos

[0777] A user takes a photo of a room using a smartphone or digital camera.

[0778] The user takes a photo and taps the "Upload Photo" button in the application to send it to the server.

[0779] Input: A photo of the room (JPEG format).

[0780] Output: Photo data uploaded to the server.

[0781] Specific behavior: When the user taps the "Upload Photo" button, the device selects a photo file and sends it to the server via an HTTP request. A success message is displayed to the user.

[0782] Step 2:

[0783] Emotion data collection and analysis

[0784] The device uses a built-in camera and microphone to collect facial and voice data from the user.

[0785] The data collected by the device is analyzed by an emotion analysis engine to recognize the user's emotional state (e.g., stress, joy).

[0786] Input: User's facial expression data, voice data.

[0787] Output: Recognized emotion data (JSON format).

[0788] Specific operation: The device activates the camera and instructs the user to make facial expressions in front of the camera. The user also speaks according to the voice instructions. This data is processed locally and analyzed by the emotion analysis engine. The resulting emotion data is sent to the server in JSON format.

[0789] Step 3:

[0790] Data Receipt and Validation

[0791] The server receives the photo and emotion data sent from the user's terminal.

[0792] The server validates the photo's resolution and file format, returning an error message if it's not in JPEG format.

[0793] Input: Photo data (JPEG format), emotion data (JSON format).

[0794] Output: The validated data or an error message.

[0795] What happens: The server processes the incoming HTTP request, checks the photo's resolution (for example, 1280x720 pixels or more) and file format, and returns an error message if there is a problem.

[0796] Step 4:

[0797] Image analysis

[0798] The server calls the AI ​​model and provides the received photos as input data.

[0799] The AI ​​model uses object recognition and semantic segmentation to identify furniture and items within a room.

[0800] Input: Photo data.

[0801] Output: Object recognition results (location and type of object).

[0802] How it works: The server processes the photos using an AI model to generate bounding boxes and labels for each object, using TensorFlow and PyTorch.

[0803] Step 5:

[0804] Identifying the issue

[0805] The server receives the analysis results from the AI ​​model and lists current issues.

[0806] Input: Object recognition results.

[0807] Output: A list of identified issues.

[0808] Specific operation: The server automatically extracts issues based on the analysis results, such as a sofa blocking traffic flow or a cluttered bookshelf, and lists specific problems.

[0809] Step 6:

[0810] Proposal Generation

[0811] The server generates solutions based on the identified problems.

[0812] The server adjusts the suggestions based on the user's emotional data.

[0813] Input: Identified issues, emotion data.

[0814] Output: Specific proposals.

[0815] Specific behavior: The system generates suggestions based on the list of issues and optimizes them using emotional data. For example, it recommends relaxing layouts for users who are feeling stressed. Specific suggestions include how to organize bookshelves and recommending new furniture to purchase.

[0816] Step 7:

[0817] Generation of simulation images

[0818] The server generates a simulation image that visually shows the proposal.

[0819] Input: Specific proposal details.

[0820] Output: Simulation image.

[0821] Specific operation: The server generates a simulation image using 3D modeling software (e.g. Blender), which is temporarily stored on the server.

[0822] Step 8:

[0823] Send and view proposals and simulation images

[0824] The server sends the proposal and the simulation image to the user's terminal.

[0825] The terminal displays the received proposal and a simulation image.

[0826] Input: Proposal content, simulation image.

[0827] Output: Proposal and simulation images displayed on the user interface.

[0828] Specific operation: The server sends the generated proposal and a simulation image to the terminal as an HTTP response, and the terminal displays it on the user interface. The user can then review it and decide whether to redecorate the room as proposed.

[0829] This system allows users to receive specific suggestions for redecorating and tidying up their rooms, and provides appropriate advice based on their emotional state, making it easier to maintain a comfortable living environment.

[0830] (Application example 2)

[0831] 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."

[0832] When optimizing store layout and product displays, manual rearrangement and trial and error require time and effort. Furthermore, the manager's emotional state can affect the best proposal, making it difficult to efficiently determine the optimal layout. Furthermore, since the proposals are not visually displayed, it is difficult for managers to visualize them.

[0833] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0834] In this invention, the server includes: [means for a user to take and upload photos of a room or store; [means for the server to input the photos received by the server into an AI model for image analysis and analyze them to identify the location and type of furniture and items in the room or store; [means for the server to list current issues based on the analysis results; [means for the server to generate solutions to the issues and provide suggestions to the user; [means for the server to generate simulation images to visually show the solutions; [means for the server to recognize the user's emotional state and reflect that in the suggestions; and [means for the server to send the suggestions and simulation images from the server to the user's terminal and display them on the terminal.] This allows for efficient optimal suggestions regarding store layout and product display, enables more appropriate suggestions based on the manager's emotional state, and allows the manager to visually confirm the suggestions.

[0835] A "user" is someone who uses the application to take and upload photos of rooms or stores.

[0836] "Photos of rooms and stores" refer to photos of the interior of rooms and stores taken by users using their own smartphones or digital cameras.

[0837] A "server" is a device or system that receives photo data, stores AI models for image analysis, and generates and provides suggestions to users.

[0838] An "AI model for image analysis" is an artificial intelligence model that analyzes photographic data to identify the location and type of furniture and items within a room or store.

[0839] "Furniture and items" refers to sofas, tables, bookshelves, books, decorations, shelves, products, etc. that exist in rooms or stores.

[0840] "Current Issues" are layout problems and inefficiencies identified based on analyzed photos of rooms and stores.

[0841] "Solutions" are proposed ways to rearrange or organize furniture or items to address identified issues.

[0842] A "simulated image" is a virtual image generated to visually represent a proposed solution.

[0843] "User's emotional state" refers to the user's state of mind (e.g., stress or joy) recognized based on the user's facial expressions and voice.

[0844] "Proposal content" refers to specific proposals for changes to the layout of a room or store or product displays, which are generated based on the analysis results and the user's emotional state.

[0845] "Terminal" refers to a device (such as a smartphone or tablet) on which a user receives and displays proposals and simulation images.

[0846] The system for implementing this invention starts when a user takes a photo of a room or store and uploads it to a server. Details of the hardware and software used and an overview of the process are described below.

[0847] 1. System Configuration

[0848] Hardware:

[0849] User device: A smartphone or digital camera, specifically a smart device with a built-in high-resolution camera and microphone.

[0850] Server: A cloud server or dedicated server with high-performance computing power.

[0851] software:

[0852] Image processing library: Uses Python and OpenCV.

[0853] AI model: Uses TensorFlow and Keras for object recognition and semantic segmentation.

[0854] Emotion Recognition: Facial expression recognition is performed using OpenCV and DeepFace, and voice analysis is performed using Python.

[0855] Backend framework: Django is used for server data management and processing.

[0856] Database: PostgreSQL or SQLite.

[0857] Front-end: Developing mobile applications using React Native.

[0858] 2. Processing Flow

[0859] Photo upload and emotion recognition

[0860] 1. A user takes a photo of the interior of a store using their smartphone, which is saved in high-resolution JPEG format.

[0861] 2. The user uploads a photo to the application by tapping the "Upload Photo" button within the app.

[0862] 3. The device's camera and microphone are used to capture the user's facial expressions and voice. The captured data is then used to recognize facial expressions using OpenCV and DeepFace, and the voice data is analyzed using Python.

[0863] Server analysis and suggestions

[0864] 1. The server receives the photo data sent by the user and starts image analysis. It uses OpenCV to preprocess the photo, adjust the resolution, and verify the file format.

[0865] 2. The server invokes the AI ​​model (TensorFlow and Keras) and starts analyzing the photo, performing object recognition and semantic segmentation to identify the location and type of furniture and items in the room or store.

[0866] 3. The server lists current issues based on the analysis results, such as inefficient product display methods or blocked flow paths.

[0867] 4. The server adjusts the suggestions based on the emotional data. If the user is feeling stressed, the suggestions will be changed to include placements and items that will help them relax.

[0868] 5. The server generates proposals based on the analysis results and emotion data. These proposals include product rearrangement, new display methods, and improvements to traffic flow. Additionally, it generates simulation images to provide a visual representation.

[0869] View Suggestions

[0870] 1. The server sends the generated proposal and simulation image to the user terminal.

[0871] 2. The terminal displays the proposal and a simulation image to the user, who then checks the proposal and decides whether to implement it.

[0872] Specific examples

[0873] Specific processing examples:

[0874] Users take photos of the store and upload them to the app.

[0875] The server receives the photo and analyzes it using an AI model, which identifies the product display as messy and generates suggestions for tidying up.

[0876] The server refers to the emotional data and suggests layouts that can be easily changed based on the stress the user is feeling.

[0877] The proposal recommends rearranging products to ensure new flow of traffic, and displays a simulated image of the resulting situation.

[0878] Example prompt for a generative AI model:

[0879] Generate optimal store layout and product placement suggestions based on store photos and the store manager's sentiment data below.

[0880] Emotion data: {"emotion": "stress", "score": 0.7}

[0881] Store photo: (binary photo data)

[0882] Please include specific layout changes and simulation images based on photos of the store in your proposal.

[0883] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0884] Step 1:

[0885] Users take photos of rooms or stores with their smartphones and upload them to the application. At this time, the input data of the photo is saved in JPEG format and sent to the server by tapping the "Upload Photo" button in the app. The output data is the uploaded high-resolution photo file.

[0886] Step 2:

[0887] The device uses a camera and microphone to capture the user's facial expressions and voice. The input data is the user's facial image and voice data, which are then analyzed by the emotion engine. The output data is the user's emotional state (e.g., stress, joy).

[0888] Step 3:

[0889] The server receives the received photos and emotion data and performs preprocessing on the photos. The input data is a JPEG photo and emotion data, and OpenCV is used to adjust the resolution and verify the file format. The processed photo is obtained as output data.

[0890] Step 4:

[0891] The server inputs the processed photos into an AI model (TensorFlow, Keras) for image analysis. The input data is the preprocessed photos, and the AI ​​model performs object recognition and semantic segmentation. The output data identifies the location and type of furniture and items within a room or store.

[0892] Step 5:

[0893] The server lists current issues based on the analysis results of the AI ​​model. The input data is the analysis results, which identify inefficient furniture placement, product display methods, etc. The output data is the list of issues.

[0894] Step 6:

[0895] The server generates solutions to the listed problems and generates a simulation image to visually demonstrate them. The input data is the problem and the user's emotional data, and the solutions include how to rearrange furniture and products, how to organize, etc. The output data is a simulation image and specific proposals.

[0896] Step 7:

[0897] The server sends the generated proposal and simulation image to the user's terminal. The input data is the proposal content and simulation image, and this is sent to the user's terminal. The output data is the proposal content and simulation image that the user can view.

[0898] Step 8:

[0899] The terminal displays the proposal and a simulation image to the user, who then confirms the proposal. The input data are the proposal and simulation image sent from the server, and the user decides whether to change the layout of the room or store based on this. The proposal to be adopted by the user is confirmed as output data.

[0900] 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.

[0901] 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.

[0902] 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.

[0903] [Third embodiment]

[0904] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0905] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0906] 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).

[0907] 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.

[0908] 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.

[0909] 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).

[0910] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0911] 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.

[0912] 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.

[0913] 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.

[0914] 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.

[0915] 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."

[0916] This invention relates to a system that provides suggestions for redecorating and tidying up a room. A user takes a photo of the room, and AI analyzes the photo to identify current issues and generate specific suggestions for improvement. The processing content of the system's program and specific operation examples are explained below.

[0917] Overall system overview

[0918] 1. User operations

[0919] Users take photos of their rooms with their smartphones or digital cameras and upload them to the application by tapping the "Upload Photos" button within the app.

[0920] 2. Server Processing

[0921] The server receives the photo sent from the user's device, verifies the resolution and file format of the photo, and if there are no problems, proceeds to the next processing step.

[0922] The server calls the AI ​​model, providing it with the received photo as input, which then analyzes the image to identify the location and type of furniture (sofa, table, bookshelf, etc.) and other items (books, decorations, etc.) in the room.

[0923] 3. Identifying the issue

[0924] Based on the results of image analysis, the server lists current issues, such as cramped furniture arrangement, cluttered bookshelves, blocked windows, etc.

[0925] 4. Proposal Generation

[0926] Based on the analysis, the server generates specific suggestions for solving the problem, such as how to organize bookshelves, rearrange furniture, or recommend new furniture or decorative items to purchase.

[0927] Based on the proposal, the server generates a simulation image that visually shows the proposal.

[0928] 5. Displaying the results

[0929] The terminal displays the proposals and simulation images received from the server on the user interface, and the user can view them and decide whether to redecorate the room as proposed.

[0930] Specific examples

[0931] User Action:

[0932] A user takes a photo of their living room with their smartphone and taps the "Upload Photo" button in the application to send it to the server, where it is verified that the photo has the correct resolution and is in JPEG format.

[0933] Server image analysis:

[0934] The server inputs the received photos into the AI ​​model and begins analysis. The AI ​​model recognizes objects such as sofas, tables, bookshelves, books, and decorations, and identifies their locations and types.

[0935] Identifying the issue:

[0936] Based on the analysis results, the server lists problems such as the sofa in the living room being located in the center of the room and obstructing traffic flow, or the bookshelf being disorganized.

[0937] Proposal generation:

[0938] The server moves the sofa to a window wall, organizes bookshelves by color, generates suggestions for purchasing additional storage boxes, and even recommends new curtains.

[0939] The server generates a visual simulation of the proposal, allowing the user to visually see how the room will change.

[0940] Viewing Results:

[0941] The device displays the proposal and a simulation image to the user, who then reviews the proposal and decides whether to redecorate the room.

[0942] This system allows users to easily receive specific suggestions for redecorating and tidying up their rooms, and gives them creative ideas that they would not have thought of on their own, making it easier to maintain a comfortable living environment.

[0943] The processing flow will be explained below.

[0944] Step 1:

[0945] The user takes a photo of the room with a smartphone or digital camera, opens the application, selects the photo, and taps the "Upload Photo" button.

[0946] Step 2:

[0947] The device uploads the selected photo to the server, verifies the resolution and file format of the photo, and sends it to the server if there are no problems.

[0948] Step 3:

[0949] The server receives the received photos and prepares them for input into the AI ​​model, checking that the photo resolution and file format are suitable for analysis.

[0950] Step 4:

[0951] The server calls the AI ​​model and provides the photo as input data, which then initiates object recognition and semantic segmentation to identify the location and type of furniture (e.g., sofa, table, bookshelf) and items (e.g., books, decorations) in the room.

[0952] Step 5:

[0953] The server receives the analysis results from the AI ​​model and lists current issues, identifying specific problems such as inefficient furniture placement or cluttered bookshelves.

[0954] Step 6:

[0955] The server generates solutions based on the identified problems, such as how to organize bookshelves, rearrange furniture, or recommend new furniture or decorative items to purchase.

[0956] Step 7:

[0957] The server generates a simulation image to visually represent the generated solution, allowing the user to visually confirm the proposed changes.

[0958] Step 8:

[0959] The server sends the proposal and the simulated image to the user's terminal. The proposal content and the visualized simulated image are sent together.

[0960] Step 9:

[0961] The device displays the received suggestions and simulation images on the user interface, allowing the user to decide whether to carry out the suggested rearrangements and tidying up.

[0962] This allows users to come up with creative ideas that they would not have thought of on their own, making it easier to improve their rooms.

[0963] Example 1

[0964] 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."

[0965] In modern homes, rearranging and tidying rooms requires a lot of time and effort. It can be difficult for individual users to come up with an efficient and aesthetically pleasing arrangement and tidying method. Furthermore, the lack of a way to visually confirm the results of rearranging furniture and items in a room before rearranging them can easily leave users feeling anxious and dissatisfied. The present invention aims to solve these problems and provide a system that allows users to rearrange and tidy their rooms easily and effectively.

[0966] 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.

[0967] In this invention, the server includes: [means for a user to take and upload photos of the room; [means for the server to input the photos received by the server into a generative AI model for image analysis and analyze them to identify the locations and types of furniture and items in the room; [means for the server to list current issues based on the analysis results; [means for the server to generate solutions to the issues and provide suggestions to the user; [means for the server to generate simulation images to visually show the solutions; and [means for the server to send the suggestions and simulation images from the server to the user's terminal and display them on the terminal.] This enables users to efficiently and effectively rearrange and tidy up their rooms, and provides an environment in which specific changes to the placement of furniture and items can be visually confirmed.

[0968] A "user" refers to a person who uses the system to receive suggestions for redecorating and tidying up their room.

[0969] "Server" refers to the computer system that receives photos uploaded by users, analyzes the images using a generative AI model, identifies issues, and generates solutions.

[0970] "Device" means the device used by the User to take and upload photos and to view proposals and simulated images received from the Server, such as a smartphone or tablet.

[0971] "Generative AI model" refers to the artificial intelligence algorithm used by the server, a machine learning model used to analyze photos of a room and identify the location and type of furniture and items.

[0972] "Image analysis" refers to the process by which a generative AI model takes a photo as input and identifies the furniture and items in the room, determining their location and type.

[0973] "Issues" refer to problems with the current state of the room that were identified as a result of image analysis. Examples include obstructions to traffic flow and disorganized storage.

[0974] "Solutions" refer to specific methods the server suggests to resolve the issues it has identified, such as rearranging furniture, organizing storage, or suggesting the purchase of additional items.

[0975] A "simulation image" is an image created to visually show the content of a proposal generated by the server, and is used by the user to check the effect of the proposal in advance.

[0976] "Uploading" refers to the act of sending a photo of a room taken by a user to a server.

[0977] "Photo" refers to an image file taken by the user of the current state of the room.

[0978] This invention relates to a system that provides suggestions for redecorating and tidying up a room. A user takes a photo of the room, and a generative AI model on a server analyzes the photo to identify current issues and generate specific suggestions for improvement. The processing content of the system's program and specific operation examples are explained below.

[0979] Hardware and software used

[0980] To implement this system, the following hardware and software is required:

[0981] User devices: smartphones, tablets, digital cameras, etc. Used to upload photos taken to the server.

[0982] Server: A powerful computer system used to receive photos from users, perform analysis, generate suggestions, and create simulated images.

[0983] Generative AI models: For example, AI models built using machine learning libraries such as TensorFlow or PyTorch, which are used to analyze photos of a room and identify the location and type of furniture and items.

[0984] Data processing and calculation details

[0985] The system operates as follows.

[0986] 1. Take and upload a photo:

[0987] The user takes a photo of the room using a smartphone or digital camera and uploads the photo to the server through the application, for example, by tapping the "Upload Photo" button.

[0988] 2. Photo Receipt and Verification:

[0989] The server receives the photo sent by the user. After receiving it, it verifies the photo's resolution (for example, 300 dpi or higher) and file format (JPEG format), and if there are no problems, it proceeds to the next step.

[0990] 3. Image Analysis:

[0991] The server calls a built-in generative AI model (for example, a TensorFlow object detection model) and provides the received photo as input. The AI ​​model analyzes the image and identifies the location and type of furniture, such as sofas, tables, bookshelves, and other items, such as books and decorations. This process uses image processing technology to recognize objects.

[0992] 4. Identify the problem:

[0993] Based on the results of the image analysis, the server lists the current issues in the room, pointing out specific problems such as "the sofa is placed in the center of the room and obstructs traffic flow," "the bookshelf is cluttered," and "the window is blocked by furniture."

[0994] 5. Proposal generation:

[0995] Based on the analysis results, the server generates specific suggestions for solving the problem, such as "move the sofa to the window," "organize the bookshelf by color," or "purchase additional storage boxes." It also generates a simulated image that visually shows the suggestions. Image synthesis technology is used to simulate what the proposed layout will look like.

[0996] 6. Displaying the results:

[0997] The terminal displays the proposals and simulation images received from the server on the user interface. The user checks the proposals and decides whether to redecorate or tidy up the room.

[0998] Examples and prompts

[0999] User Action:

[1000] A user takes a photo of their living room with their smartphone and taps the "Upload Photo" button in the application, which validates the photo's resolution and file format.

[1001] Server image analysis:

[1002] The server inputs the received photos into the AI ​​model and begins analysis. The AI ​​model recognizes objects such as sofas, tables, bookshelves, books, and decorations, and identifies their locations and types.

[1003] Identifying the issue:

[1004] The server lists problems based on the analysis results, such as the sofa in the living room being in the middle, which makes it difficult to move around, or the bookshelf being cluttered and in need of organization.

[1005] Proposal generation:

[1006] The server generates suggestions such as moving the sofa to the window, arranging the bookshelf by color, purchasing additional storage boxes, purchasing new curtains, etc. It also generates simulated images that reflect these suggestions so that the user can visually confirm them.

[1007] Viewing Results:

[1008] The device displays the proposal and a simulation image to the user, who can then review the proposal and decide whether or not to actually redecorate the room.

[1009] Prompt Sentence Examples

[1010] "You take a photo of your living room and upload it to the server through the app. The server analyzes the photo and provides specific suggestions for furniture arrangement and organization. For example, you might move the sofa to a window, organize your bookshelves by color, or buy additional storage boxes. You can visually review the suggestions and then decide whether to implement them."

[1011] This system allows users to receive specific suggestions for easily rearranging and tidying their rooms, and gives them creative ideas that they would not have thought of on their own, making it easier to maintain a comfortable living environment.

[1012] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1013] Step 1:

[1014] The user takes a photo of the room with a smartphone or digital camera and then taps the "Upload Photo" button in the application to send the photo to the server.

[1015] Input: Room photo (JPEG format)

[1016] Output: Uploaded photo

[1017] Specific behavior: Check the resolution of the photo taken by the user and upload it to the server through the application.

[1018] Step 2:

[1019] The server receives the photo sent by the user. The server verifies the resolution and file format of the received photo, and if there are no problems, it proceeds to the next step.

[1020] Input: A photo submitted by the user

[1021] Output: Verification result (whether the photo is appropriate or not)

[1022] Specific operation: Check whether the resolution of the received photo is 300 dpi or higher and whether the file format is JPEG.

[1023] Step 3:

[1024] The server invokes a generative AI model (for example, a TensorFlow object detection model) and provides the received photo as input. The AI ​​model analyzes the image and identifies the furniture and items in the room.

[1025] Input: A photo that passes validation

[1026] Output: Image analysis results (furniture and item locations and types)

[1027] What it does: It uses AI models to analyze images and identify objects such as sofas, tables, bookshelves, books, and decorative items, and their contents.

[1028] Step 4:

[1029] Based on the results of image analysis, the server lists the current issues in the room.

[1030] Input: Image analysis results

[1031] Output: Issue list (current issues)

[1032] Specific operation: Based on the analysis results, it makes a list of problems such as cramped furniture layout, poor traffic flow, and cluttered bookshelves.

[1033] Step 5:

[1034] The server generates specific proposals to address the identified issues, such as rearranging the sofa, reorganizing the bookshelf, or adding new items, and generates a visual simulation image.

[1035] Input: Issues list

[1036] Output: Proposal and simulation images

[1037] Specific actions: Using machine learning models, the system generates specific rearrangement and suggestions, such as moving the sofa to a window, organizing bookshelves by color, or purchasing additional storage boxes, and then uses image synthesis technology to create simulated images.

[1038] Step 6:

[1039] The generated proposal and simulation image are sent from the server to the user, who then displays them on the terminal.

[1040] Input: Proposal content and simulation image

[1041] Output: Proposal and simulation images displayed on the user's device

[1042] Specific operation: The proposals and simulation images are sent to the user's device and displayed in an application on the device. The user reviews the proposals and decides whether to redecorate and organize the room.

[1043] (Application example 1)

[1044] 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."

[1045] In modern factory environments, work efficiency and space optimization are important challenges. However, achieving these goals requires specialized knowledge and time, and there are limitations to how much manual work can be done by on-site personnel. Furthermore, it is difficult to accurately identify current problems and propose specific improvement measures. This can result in unnecessary traffic flow and inappropriate machine placement, leading to reduced productivity. Therefore, a system that can automatically and efficiently solve these issues is needed.

[1046] 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.

[1047] In this invention, the server includes: [means for a user to take a photo of the environment and upload it; [means for the server to input the received photo into an AI model for image analysis and analyze it to identify the location and type of objects and items in the environment; [means for the server to list current issues based on the analysis results; [means for the server to generate solutions to the issues and provide proposals to the user; [means for the server to generate simulation images to visually show the solutions; [means for the server to send the proposals and simulation images from the server to the user's terminal and display them on the terminal; and [means for the server to generate proposals to optimize the factory's work area and machine layout.] This makes it possible to automatically identify current problems in the factory environment and visually show specific improvement measures, thereby improving work efficiency and optimizing space.

[1048] "Environment" refers to the place where machines and equipment are installed, such as a factory or work site.

[1049] "Placement" refers to objects in the environment, such as machines, equipment, workbenches, storage facilities, etc.

[1050] "Items" refer to small objects present in the environment, such as tools, materials, products, and decorations.

[1051] "Location" refers to the specific location that a placement or item occupies within the environment.

[1052] "Type" refers to an attribute that indicates the characteristics and classification of a placement or item.

[1053] "Challenges" refer to problems within the environment that hinder work efficiency or space optimization.

[1054] "Solution" refers to specific measures or proposals to resolve the identified issues.

[1055] "Simulation image" refers to a virtual image that visually represents a solution.

[1056] "Work area" refers to the specific location or space where work is performed.

[1057] "Machine layout" refers to the way machines and equipment are arranged within a work area.

[1058] "Device" refers to the device (e.g., smartphone, tablet, head-mounted display, etc.) that a user uses to upload photos and view suggestions.

[1059] "Receiving" refers to the server acquiring the photo data sent from the user's terminal.

[1060] "Analysis" refers to the process of using AI models to process photo data and identify the location and type of objects and items.

[1061] This invention relates to a system for optimizing work efficiency and space in a factory environment. Users take photos of the factory interior using a smartphone or head-mounted display (HMD) and upload them to a dedicated application. Based on the photos, a server uses an AI model to perform image analysis, identify current problems, and generate specific improvement proposals. The processing content of the system's program and specific operation examples are explained below.

[1062] Overall system overview

[1063] User operations

[1064] Users use their smartphones or HMDs to take photos of the current state of the factory, and then tap the "Upload Photos" button in the dedicated application to send the photo data to the server.

[1065] Server Processing

[1066] The server verifies the received photo data and, if there are no problems, inputs it into an AI model for image analysis. This AI model uses TensorFlow and PyTorch. The AI ​​identifies the location and type of objects (machines, equipment, workbenches, storage facilities) and items (tools, materials, products, decorations) in the image.

[1067] Identifying the issue

[1068] Based on the analysis results, the server lists current problems such as narrowing of the work area and obstructions to traffic flow.

[1069] Proposal Generation

[1070] The server generates specific solutions to the identified problems, such as rearranging work areas or organizing storage facilities, and also generates simulation images that visually show these proposals.

[1071] Displaying the results

[1072] The proposals and simulation images generated by the server are sent to the user's device (smartphone or HMD) and displayed on the user interface. The user can review these and decide whether to make layout changes or improvements as proposed.

[1073] Hardware and Software Used

[1074] Hardware:

[1075] Smartphones (e.g. iPhone 12, Samsung Galaxy S21)

[1076] Head-mounted displays (e.g. Microsoft HoloLens 2, Oculus Quest 2)

[1077] Server (e.g. Google Cloud Platform, Amazon Web Services)

[1078] software:

[1079] Dedicated app for smartphones or HMDs (development environment: Android Studio, Xcode)

[1080] AI models for image analysis (e.g., TensorFlow, PyTorch)

[1081] Server-side processing (e.g., Node.js, Python)

[1082] Specific examples

[1083] A user puts on a HoloLens 2 and scans the layout of machines and equipment in a factory, uploading the data to an application. The server analyzes the received images using TensorFlow to detect problems such as overly narrow work areas and blocked traffic lines. Specific proposals for rearranging machines, installing new storage facilities, and streamlining traffic lines are then generated, and simulated images of these are displayed on the HoloLens for the user to check on the spot.

[1084] Example of input prompt for generative AI model:

[1085] Upload a photo of your current factory environment and we'll generate suggestions to optimize work areas and machine locations.

[1086] This will enable factory managers to improve work efficiency and make layout changes without waste based on expert advice.

[1087] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1088] Step 1:

[1089] Users use a smartphone or head-mounted display to take photos of the factory and upload them into the application.

[1090] Input: Photographic data of the physical environment

[1091] Output: Photo data sent to the server

[1092] Specific behavior: When the user taps the "Upload Photo" button in the application, the photo they took is sent to the server.

[1093] Step 2:

[1094] The server validates the received photo data and checks whether the resolution and file format are appropriate.

[1095] Input: Photo data sent by the user

[1096] Output: Analysis request to the configured AI model

[1097] Specific operation: The server checks the resolution and file format (e.g., JPEG, PNG) of the photo data, and if there are no problems, it proceeds to the next processing step. If not, it returns an error message.

[1098] Step 3:

[1099] The photo data received by the server is input into the AI ​​model and image analysis is performed.

[1100] Input: Photo data

[1101] Output: A list of placements and items, with their locations and types.

[1102] Specific operation: The server uses TensorFlow and PyTorch to call AI models, analyze the photo data, and identify the location and type of objects (machines, equipment) and items (tools, materials).

[1103] Step 4:

[1104] Based on the analysis results, the server lists current problems.

[1105] Input: List of positions and types

[1106] Output: A list of issues

[1107] Specific operation: The server extracts problems such as obstructions to traffic flow and narrowing of work areas from the analysis results and creates a list of problems.

[1108] Step 5:

[1109] The server generates solutions and visual simulation images for the listed problems.

[1110] Input: List of issues

[1111] Output: Solution proposal and simulation images

[1112] Specific operation: The server generates specific solutions to the problem, such as proposing machine relocation or the introduction of new storage facilities. It also generates simulation images so that the user can visually confirm the proposed solutions.

[1113] Step 6:

[1114] The generated proposal and simulation image are sent from the server to the user's terminal, which displays them.

[1115] Input: Solution proposal and simulated images

[1116] Output: Proposal and simulation images displayed

[1117] Specific operation: The server sends the generated proposal and simulation images to the user's smartphone or HMD, where they are displayed on the application. The user then checks the proposal and decides whether to make layout changes or carry out improvements.

[1118] This is the flow of the system program's processing to optimize the factory environment. By performing appropriate data processing and calculations at each step, the system provides specific proposals and visual simulations.

[1119] 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.

[1120] This invention relates to a system that provides suggestions for redecorating and tidying up a room. Users take photos of the room, and AI analyzes the photos to identify current issues and generate specific suggestions for improvement. Furthermore, the system incorporates an emotion engine that can recognize the user's emotional state and reflect it in the suggestions.

[1121] Overall system overview

[1122] 1. User operations

[1123] Users take photos of their rooms with their smartphones or digital cameras and upload them to the application by tapping the "Upload Photos" button within the app.

[1124] 2. User Emotion Recognition

[1125] The device uses an emotion engine to recognize emotions from the user's facial expressions and voice, using the smartphone's camera and microphone.

[1126] The device transmits the recognized emotion data to the server.

[1127] 3. Server Processing

[1128] The server receives the photo and emotion data sent from the user's device, verifies the photo's resolution and file format, and if there are no problems, proceeds to the next processing step.

[1129] 4. Image Analysis

[1130] The server calls the AI ​​model and provides the photo as input data, which then initiates object recognition and semantic segmentation to identify the location and type of furniture (e.g., sofa, table, bookshelf) and items (e.g., books, decorations) in the room.

[1131] 5. Identifying the Issues

[1132] The server receives the analysis results from the AI ​​model and lists current issues, identifying specific problems such as inefficient furniture placement or cluttered bookshelves.

[1133] 6. Proposal Generation

[1134] The server generates solutions based on the identified problems, such as how to organize bookshelves, rearrange furniture, or recommend new furniture or decorative items to purchase.

[1135] The server adjusts the suggestions based on the user's emotional data. For example, if the user is feeling stressed, it will recommend placements and items that will help them relax.

[1136] Based on the proposal, the server generates a simulation image that visually shows the proposal.

[1137] 7. Displaying the results

[1138] The terminal displays the proposals and simulation images received from the server on the user interface, and the user can view them and decide whether to redecorate the room as proposed.

[1139] Specific examples

[1140] User Action:

[1141] A user takes a photo of their living room with their smartphone and taps the "Upload Photo" button in the application to send it to the server, where it is verified that the photo has the correct resolution and is in JPEG format.

[1142] User Emotion Recognition:

[1143] The device uses the smartphone camera to analyze the user's facial expressions, and the emotion engine recognizes the user's emotional state (e.g., stress, joy).

[1144] The device transmits the recognized emotion data to the server.

[1145] Server image analysis:

[1146] The server inputs the received photos into the AI ​​model and begins analysis. The AI ​​model recognizes objects such as sofas, tables, bookshelves, books, and decorations, and identifies their locations and types.

[1147] Identifying the issue:

[1148] Based on the analysis results, the server lists problems such as the sofa in the living room being located in the center of the room and obstructing traffic flow, or the bookshelf being disorganized.

[1149] Proposal generation:

[1150] The server moves the sofa to a window wall, organizes bookshelves by color, generates suggestions for purchasing additional storage boxes, and even recommends new curtains.

[1151] The server references the user's emotional data and suggests placements and items that will help users who are feeling stressed to relax.

[1152] The server generates a visual simulation of the proposal, allowing the user to visually see how the room will change.

[1153] Viewing Results:

[1154] The device displays the proposal and a simulation image to the user, who then reviews the proposal and decides whether to redecorate the room.

[1155] This system allows users to easily receive specific suggestions for redecorating and tidying up their rooms, and can provide creative ideas that they would not have thought of on their own. In addition, by receiving appropriate suggestions based on the user's emotional state, it becomes easier to maintain a more comfortable living environment.

[1156] The processing flow will be explained below.

[1157] Step 1:

[1158] The user takes a photo of the room with a smartphone or digital camera, opens the application, selects the photo, and taps the "Upload Photo" button.

[1159] Step 2:

[1160] The device uploads the selected photo to the server, verifies the resolution and file format of the photo, and sends it to the server if there are no problems.

[1161] Step 3:

[1162] The device captures the user's facial expressions using the smartphone's camera and captures their voice using the microphone. The emotion engine analyzes this data to identify the user's emotional state.

[1163] Step 4:

[1164] The device transmits the recognized emotion data to the server.

[1165] Step 5:

[1166] The server receives the photo and emotion data sent from the user's device. It verifies the photo's resolution and file format again, and if there are no problems, it proceeds to the next step.

[1167] Step 6:

[1168] The server calls the AI ​​model and provides the photo as input data, which then initiates object recognition and semantic segmentation to identify the location and type of furniture (sofa, table, bookshelf, etc.) and items (books, decorations, etc.) in the room.

[1169] Step 7:

[1170] The server receives the analysis results from the AI ​​model and lists current issues, identifying specific problems such as inefficient furniture placement or cluttered bookshelves.

[1171] Step 8:

[1172] The server generates solutions based on the identified problems, such as how to organize bookshelves, rearrange furniture, or recommend new furniture or decorative items to purchase.

[1173] Step 9:

[1174] The server adjusts the suggestions based on the user's emotional data. For example, if the user is feeling stressed, it will recommend placements and items that will help them relax.

[1175] Step 10:

[1176] The server generates a simulated image to visually represent the proposed changes, allowing the user to visually confirm the proposed changes.

[1177] Step 11:

[1178] The server sends the proposal and the simulated image to the user's terminal. The proposal content and the visualized simulated image are sent together.

[1179] Step 12:

[1180] The device displays the received proposal and the simulation image on the user interface, and the user can view it and decide whether to redecorate the room according to the proposal.

[1181] This processing flow allows users to come up with creative ideas that they would not have thought of on their own, and by receiving appropriate suggestions based on their emotional state, it becomes easier to maintain a more comfortable living environment.

[1182] Example 2

[1183] 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."

[1184] Redecorating and tidying a room is generally time-consuming, and it is often difficult for users to find the optimal arrangement. Furthermore, if the suggestions are not appropriate for the user's emotional state, they are unlikely to accept them. Therefore, there is a need for a system that can provide specific suggestions for efficiently redecorating and tidying a room and give appropriate advice based on the user's emotional state.

[1185] 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.

[1186] In this invention, the server includes a means for a user to take a photo of their room and upload it, a means for the terminal to collect the user's facial expression and voice data and recognize their emotional state using an emotion analysis engine, and a means for the terminal to transmit the recognized emotional data to the server. This allows the user to receive optimal suggestions for redecorating and tidying up their room based on the photo of their room and their emotional state at the time. Furthermore, because the suggestions are suited to the user's emotional state, they are more likely to be accepted, ensuring a more comfortable living environment.

[1187] A "user" refers to an individual who uses the system to take photos of their room and receive suggestions for redecorating and tidying up.

[1188] A "terminal" is an information processing device such as a smartphone or tablet that is operated by a user.

[1189] A "server" is a remote computer that performs the analysis processing that is the core of the system.

[1190] An "emotion analysis engine" refers to a program or algorithm that analyzes a user's facial expressions and voice data to recognize their emotional state.

[1191] An "AI model" is a trained program that uses artificial intelligence to analyze photos and identify the location and type of furniture and items in a room.

[1192] "Image analysis" refers to the process of using AI models to recognize objects in a photo and identify their location and type.

[1193] "Issues" refer to areas or situations within a room that need improvement and are identified from the analysis results.

[1194] "Solution" refers to a specific suggestion for improvement generated by the server for an identified issue.

[1195] A "simulation image" is a virtual image created to visually show the proposal content generated by the server.

[1196] "Suggestion" refers to specific instructions or advice provided by the server on how to rearrange or tidy up the user's room.

[1197] This invention relates to a system that provides suggestions for redecorating and tidying up a room. The system allows users to take and upload photos of their room, analyzes the photos using an AI model, identifies current issues, and proposes specific solutions. Furthermore, the system incorporates an emotion analysis engine that can recognize the user's emotional state and reflect it in the suggestions.

[1198] This system consists of a photographic device such as a smartphone or digital camera, a terminal for user operation (such as a smartphone or tablet), a server that performs the analysis processing, and an AI model and emotion analysis engine.

[1199] Hardware and Software Configuration

[1200] 1. Photo device: The user uses a smartphone or digital camera to take photos of the room. The photos are saved on the device and uploaded to the server via the application.

[1201] 2. Device: A smartphone or tablet on which users upload photos and run an emotion analysis engine. The device collects facial and voice data from users and analyzes their emotional state. The emotion analysis engine is implemented using, for example, Google's Face API or Amazon's Rekognition.

[1202] 3. Server: A remote computer that analyzes the received photos and emotion data. The server uses AI models (such as TensorFlow or PyTorch) to analyze the photos and perform object recognition and semantic segmentation. It then identifies issues based on the analysis results and generates specific suggestions and visual simulation images.

[1203] Data Processing and Computation

[1204] The server receives the photo and emotion data sent from the user's device. It verifies the photo's resolution and file format, and if there are no problems, it proceeds to the next analysis step. The server then calls the AI ​​model and provides the photo as input data. The AI ​​model then performs object recognition and semantic segmentation to identify the location and type of furniture and items in the room. Based on the analysis results, it lists current issues and generates solutions. These include how to organize bookshelves, rearrange furniture, and recommend new furniture or decorative items to purchase. The suggestions are adjusted based on the user's emotion data. For example, if the user is feeling stressed, it will recommend placements and items that will help them relax. Finally, a visual simulation image is generated based on the suggestions and sent to the user's device.

[1205] Examples of concrete examples and prompts

[1206] For example, a user takes a photo of their living room with their smartphone and taps the "Upload Photo" button in the application to send it to the server. The device uses the smartphone's camera to analyze the user's facial expressions, and the emotion engine recognizes the user's emotional state (e.g., stress, joy). The device then sends the recognized emotion data to the server. The server then inputs the received photo into an AI model and begins analysis. The AI ​​model recognizes objects such as sofas, tables, bookshelves, books, and decorative items, and identifies their respective positions and types. Based on the analysis results, the server lists problems such as the sofa being in the middle of the room and blocking traffic flow, or the bookshelf being cluttered. The server then generates suggestions to move the sofa to the window wall, organize the bookshelves by color, and purchase additional storage boxes. It also recommends purchasing new curtains. The suggestions are generated as simulated images and ultimately sent to the user's device.

[1207] An example prompt might be, "I'd like some suggestions for redecorating my room. Please generate suggestions based on the photos and emotion data below."

[1208] Photo: [livingroom.jpg]

[1209] Emotional data: Feeling stressed

[1210] Please provide specific suggestions and visual simulation images."

[1211] According to the present invention, a user can easily receive specific suggestions for rearranging and tidying a room, and by receiving appropriate suggestions according to the user's emotional state, the user can maintain a comfortable living environment.

[1212] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1213] Step 1:

[1214] Taking and uploading photos

[1215] A user takes a photo of a room using a smartphone or digital camera.

[1216] The user takes a photo and taps the "Upload Photo" button in the application to send it to the server.

[1217] Input: A photo of the room (JPEG format).

[1218] Output: Photo data uploaded to the server.

[1219] Specific behavior: When the user taps the "Upload Photo" button, the device selects a photo file and sends it to the server via an HTTP request. A success message is displayed to the user.

[1220] Step 2:

[1221] Emotion data collection and analysis

[1222] The device uses a built-in camera and microphone to collect facial and voice data from the user.

[1223] The data collected by the device is analyzed by an emotion analysis engine to recognize the user's emotional state (e.g., stress, joy).

[1224] Input: User's facial expression data, voice data.

[1225] Output: Recognized emotion data (JSON format).

[1226] Specific operation: The device activates the camera and instructs the user to make facial expressions in front of the camera. The user also speaks according to the voice instructions. This data is processed locally and analyzed by the emotion analysis engine. The resulting emotion data is sent to the server in JSON format.

[1227] Step 3:

[1228] Data Receipt and Validation

[1229] The server receives the photo and emotion data sent from the user's terminal.

[1230] The server validates the photo's resolution and file format, returning an error message if it's not in JPEG format.

[1231] Input: Photo data (JPEG format), emotion data (JSON format).

[1232] Output: The validated data or an error message.

[1233] What happens: The server processes the incoming HTTP request, checks the photo's resolution (for example, 1280x720 pixels or more) and file format, and returns an error message if there is a problem.

[1234] Step 4:

[1235] Image analysis

[1236] The server calls the AI ​​model and provides the received photos as input data.

[1237] The AI ​​model uses object recognition and semantic segmentation to identify furniture and items within a room.

[1238] Input: Photo data.

[1239] Output: Object recognition results (location and type of object).

[1240] How it works: The server processes the photos using an AI model to generate bounding boxes and labels for each object, using TensorFlow and PyTorch.

[1241] Step 5:

[1242] Identifying the issue

[1243] The server receives the analysis results from the AI ​​model and lists current issues.

[1244] Input: Object recognition results.

[1245] Output: A list of identified issues.

[1246] Specific operation: The server automatically extracts issues based on the analysis results, such as a sofa blocking traffic flow or a cluttered bookshelf, and lists specific problems.

[1247] Step 6:

[1248] Proposal Generation

[1249] The server generates solutions based on the identified problems.

[1250] The server adjusts the suggestions based on the user's emotional data.

[1251] Input: Identified issues, emotion data.

[1252] Output: Specific proposals.

[1253] Specific behavior: The system generates suggestions based on the list of issues and optimizes them using emotional data. For example, it recommends relaxing layouts for users who are feeling stressed. Specific suggestions include how to organize bookshelves and recommending new furniture to purchase.

[1254] Step 7:

[1255] Generation of simulation images

[1256] The server generates a simulation image that visually shows the proposal.

[1257] Input: Specific proposal details.

[1258] Output: Simulation image.

[1259] Specific operation: The server generates a simulation image using 3D modeling software (e.g. Blender), which is temporarily stored on the server.

[1260] Step 8:

[1261] Send and view proposals and simulation images

[1262] The server sends the proposal and the simulation image to the user's terminal.

[1263] The terminal displays the received proposal and a simulation image.

[1264] Input: Proposal content, simulation image.

[1265] Output: Proposal and simulation images displayed on the user interface.

[1266] Specific operation: The server sends the generated proposal and a simulation image to the terminal as an HTTP response, and the terminal displays it on the user interface. The user can then review it and decide whether to redecorate the room as proposed.

[1267] This system allows users to receive specific suggestions for redecorating and tidying up their rooms, and provides appropriate advice based on their emotional state, making it easier to maintain a comfortable living environment.

[1268] (Application example 2)

[1269] 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."

[1270] When optimizing store layout and product displays, manual rearrangement and trial and error require time and effort. Furthermore, the manager's emotional state can affect the best proposal, making it difficult to efficiently determine the optimal layout. Furthermore, since the proposals are not visually displayed, it is difficult for managers to visualize them.

[1271] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1272] In this invention, the server includes: [means for a user to take and upload photos of a room or store; [means for the server to input the photos received by the server into an AI model for image analysis and analyze them to identify the location and type of furniture and items in the room or store; [means for the server to list current issues based on the analysis results; [means for the server to generate solutions to the issues and provide suggestions to the user; [means for the server to generate simulation images to visually show the solutions; [means for the server to recognize the user's emotional state and reflect that in the suggestions; and [means for the server to send the suggestions and simulation images from the server to the user's terminal and display them on the terminal.] This allows for efficient optimal suggestions regarding store layout and product display, enables more appropriate suggestions based on the manager's emotional state, and allows the manager to visually confirm the suggestions.

[1273] A "user" is someone who uses the application to take and upload photos of rooms or stores.

[1274] "Photos of rooms and stores" refer to photos of the interior of rooms and stores taken by users using their own smartphones or digital cameras.

[1275] A "server" is a device or system that receives photo data, stores AI models for image analysis, and generates and provides suggestions to users.

[1276] An "AI model for image analysis" is an artificial intelligence model that analyzes photographic data to identify the location and type of furniture and items within a room or store.

[1277] "Furniture and items" refers to sofas, tables, bookshelves, books, decorations, shelves, products, etc. that exist in rooms or stores.

[1278] "Current Issues" are layout problems and inefficiencies identified based on analyzed photos of rooms and stores.

[1279] "Solutions" are proposed ways to rearrange or organize furniture or items to address identified issues.

[1280] A "simulated image" is a virtual image generated to visually represent a proposed solution.

[1281] "User's emotional state" refers to the user's state of mind (e.g., stress or joy) recognized based on the user's facial expressions and voice.

[1282] "Proposal content" refers to specific proposals for changes to the layout of a room or store or product displays, which are generated based on the analysis results and the user's emotional state.

[1283] "Terminal" refers to a device (such as a smartphone or tablet) on which a user receives and displays proposals and simulation images.

[1284] The system for implementing this invention starts when a user takes a photo of a room or store and uploads it to a server. Details of the hardware and software used and an overview of the process are described below.

[1285] 1. System Configuration

[1286] Hardware:

[1287] User device: A smartphone or digital camera, specifically a smart device with a built-in high-resolution camera and microphone.

[1288] Server: A cloud server or dedicated server with high-performance computing power.

[1289] software:

[1290] Image processing library: Uses Python and OpenCV.

[1291] AI model: Uses TensorFlow and Keras for object recognition and semantic segmentation.

[1292] Emotion Recognition: Facial expression recognition is performed using OpenCV and DeepFace, and voice analysis is performed using Python.

[1293] Backend framework: Django is used for server data management and processing.

[1294] Database: PostgreSQL or SQLite.

[1295] Front-end: Developing mobile applications using React Native.

[1296] 2. Processing Flow

[1297] Photo upload and emotion recognition

[1298] 1. A user takes a photo of the interior of a store using their smartphone, which is saved in high-resolution JPEG format.

[1299] 2. The user uploads a photo to the application by tapping the "Upload Photo" button within the app.

[1300] 3. The device's camera and microphone are used to capture the user's facial expressions and voice. The captured data is then used to recognize facial expressions using OpenCV and DeepFace, and the voice data is analyzed using Python.

[1301] Server analysis and suggestions

[1302] 1. The server receives the photo data sent by the user and starts image analysis. It uses OpenCV to preprocess the photo, adjust the resolution, and verify the file format.

[1303] 2. The server invokes the AI ​​model (TensorFlow and Keras) and starts analyzing the photo, performing object recognition and semantic segmentation to identify the location and type of furniture and items in the room or store.

[1304] 3. The server lists current issues based on the analysis results, such as inefficient product display methods or blocked flow paths.

[1305] 4. The server adjusts the suggestions based on the emotional data. If the user is feeling stressed, the suggestions will be changed to include placements and items that will help them relax.

[1306] 5. The server generates proposals based on the analysis results and emotion data. These proposals include product rearrangement, new display methods, and improvements to traffic flow. Additionally, it generates simulation images to provide a visual representation.

[1307] View Suggestions

[1308] 1. The server sends the generated proposal and simulation image to the user terminal.

[1309] 2. The terminal displays the proposal and a simulation image to the user, who then checks the proposal and decides whether to implement it.

[1310] Specific examples

[1311] Specific processing examples:

[1312] Users take photos of the store and upload them to the app.

[1313] The server receives the photo and analyzes it using an AI model, which identifies the product display as messy and generates suggestions for tidying up.

[1314] The server refers to the emotional data and suggests layouts that can be easily changed based on the stress the user is feeling.

[1315] The proposal recommends rearranging products to ensure new flow of traffic, and displays a simulated image of the resulting situation.

[1316] Example prompt for a generative AI model:

[1317] Generate optimal store layout and product placement suggestions based on store photos and the store manager's sentiment data below.

[1318] Emotion data: {"emotion": "stress", "score": 0.7}

[1319] Store photo: (binary photo data)

[1320] Please include specific layout changes and simulation images based on photos of the store in your proposal.

[1321] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1322] Step 1:

[1323] Users take photos of rooms or stores with their smartphones and upload them to the application. At this time, the input data of the photo is saved in JPEG format and sent to the server by tapping the "Upload Photo" button in the app. The output data is the uploaded high-resolution photo file.

[1324] Step 2:

[1325] The device uses a camera and microphone to capture the user's facial expressions and voice. The input data is the user's facial image and voice data, which are then analyzed by the emotion engine. The output data is the user's emotional state (e.g., stress, joy).

[1326] Step 3:

[1327] The server receives the received photos and emotion data and performs preprocessing on the photos. The input data is a JPEG photo and emotion data, and OpenCV is used to adjust the resolution and verify the file format. The processed photo is obtained as output data.

[1328] Step 4:

[1329] The server inputs the processed photos into an AI model (TensorFlow, Keras) for image analysis. The input data is the preprocessed photos, and the AI ​​model performs object recognition and semantic segmentation. The output data identifies the location and type of furniture and items within a room or store.

[1330] Step 5:

[1331] The server lists current issues based on the analysis results of the AI ​​model. The input data is the analysis results, which identify inefficient furniture placement, product display methods, etc. The output data is the list of issues.

[1332] Step 6:

[1333] The server generates solutions to the listed problems and generates a simulation image to visually demonstrate them. The input data is the problem and the user's emotional data, and the solutions include how to rearrange furniture and products, how to organize, etc. The output data is a simulation image and specific proposals.

[1334] Step 7:

[1335] The server sends the generated proposal and simulation image to the user's terminal. The input data is the proposal content and simulation image, and this is sent to the user's terminal. The output data is the proposal content and simulation image that the user can view.

[1336] Step 8:

[1337] The terminal displays the proposal and a simulation image to the user, who then confirms the proposal. The input data are the proposal and simulation image sent from the server, and the user decides whether to change the layout of the room or store based on this. The proposal to be adopted by the user is confirmed as output data.

[1338] 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.

[1339] 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.

[1340] 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.

[1341] [Fourth embodiment]

[1342] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1343] 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.

[1344] 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).

[1345] 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.

[1346] 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.

[1347] 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).

[1348] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1349] 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.

[1350] 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.

[1351] 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.

[1352] 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.

[1353] 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.

[1354] 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."

[1355] This invention relates to a system that provides suggestions for redecorating and tidying up a room. A user takes a photo of the room, and AI analyzes the photo to identify current issues and generate specific suggestions for improvement. The processing content of the system's program and specific operation examples are explained below.

[1356] Overall system overview

[1357] 1. User operations

[1358] Users take photos of their rooms with their smartphones or digital cameras and upload them to the application by tapping the "Upload Photos" button within the app.

[1359] 2. Server Processing

[1360] The server receives the photo sent from the user's device, verifies the resolution and file format of the photo, and if there are no problems, proceeds to the next processing step.

[1361] The server calls the AI ​​model, providing it with the received photo as input, which then analyzes the image to identify the location and type of furniture (sofa, table, bookshelf, etc.) and other items (books, decorations, etc.) in the room.

[1362] 3. Identifying the issue

[1363] Based on the results of image analysis, the server lists current issues, such as cramped furniture arrangement, cluttered bookshelves, blocked windows, etc.

[1364] 4. Proposal Generation

[1365] Based on the analysis, the server generates specific suggestions for solving the problem, such as how to organize bookshelves, rearrange furniture, or recommend new furniture or decorative items to purchase.

[1366] Based on the proposal, the server generates a simulation image that visually shows the proposal.

[1367] 5. Displaying the results

[1368] The terminal displays the proposals and simulation images received from the server on the user interface, and the user can view them and decide whether to redecorate the room as proposed.

[1369] Specific examples

[1370] User Action:

[1371] A user takes a photo of their living room with their smartphone and taps the "Upload Photo" button in the application to send it to the server, where it is verified that the photo has the correct resolution and is in JPEG format.

[1372] Server image analysis:

[1373] The server inputs the received photos into the AI ​​model and begins analysis. The AI ​​model recognizes objects such as sofas, tables, bookshelves, books, and decorations, and identifies their locations and types.

[1374] Identifying the issue:

[1375] Based on the analysis results, the server lists problems such as the sofa in the living room being located in the center of the room and obstructing traffic flow, or the bookshelf being disorganized.

[1376] Proposal generation:

[1377] The server moves the sofa to a window wall, organizes bookshelves by color, generates suggestions for purchasing additional storage boxes, and even recommends new curtains.

[1378] The server generates a visual simulation of the proposal, allowing the user to visually see how the room will change.

[1379] Viewing Results:

[1380] The device displays the proposal and a simulation image to the user, who then reviews the proposal and decides whether to redecorate the room.

[1381] This system allows users to easily receive specific suggestions for redecorating and tidying up their rooms, and gives them creative ideas that they would not have thought of on their own, making it easier to maintain a comfortable living environment.

[1382] The processing flow will be explained below.

[1383] Step 1:

[1384] The user takes a photo of the room with a smartphone or digital camera, opens the application, selects the photo, and taps the "Upload Photo" button.

[1385] Step 2:

[1386] The device uploads the selected photo to the server, verifies the resolution and file format of the photo, and sends it to the server if there are no problems.

[1387] Step 3:

[1388] The server receives the received photos and prepares them for input into the AI ​​model, checking that the photo resolution and file format are suitable for analysis.

[1389] Step 4:

[1390] The server calls the AI ​​model and provides the photo as input data, which then initiates object recognition and semantic segmentation to identify the location and type of furniture (e.g., sofa, table, bookshelf) and items (e.g., books, decorations) in the room.

[1391] Step 5:

[1392] The server receives the analysis results from the AI ​​model and lists current issues, identifying specific problems such as inefficient furniture placement or cluttered bookshelves.

[1393] Step 6:

[1394] The server generates solutions based on the identified problems, such as how to organize bookshelves, rearrange furniture, or recommend new furniture or decorative items to purchase.

[1395] Step 7:

[1396] The server generates a simulation image to visually represent the generated solution, allowing the user to visually confirm the proposed changes.

[1397] Step 8:

[1398] The server sends the proposal and the simulated image to the user's terminal. The proposal content and the visualized simulated image are sent together.

[1399] Step 9:

[1400] The device displays the received suggestions and simulation images on the user interface, allowing the user to decide whether to carry out the suggested rearrangements and tidying up.

[1401] This allows users to come up with creative ideas that they would not have thought of on their own, making it easier to improve their rooms.

[1402] Example 1

[1403] 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."

[1404] In modern homes, rearranging and tidying rooms requires a lot of time and effort. It can be difficult for individual users to come up with an efficient and aesthetically pleasing arrangement and tidying method. Furthermore, the lack of a way to visually confirm the results of rearranging furniture and items in a room before rearranging them can easily leave users feeling anxious and dissatisfied. The present invention aims to solve these problems and provide a system that allows users to rearrange and tidy their rooms easily and effectively.

[1405] 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.

[1406] In this invention, the server includes: [means for a user to take and upload photos of the room; [means for the server to input the photos received by the server into a generative AI model for image analysis and analyze them to identify the locations and types of furniture and items in the room; [means for the server to list current issues based on the analysis results; [means for the server to generate solutions to the issues and provide suggestions to the user; [means for the server to generate simulation images to visually show the solutions; and [means for the server to send the suggestions and simulation images from the server to the user's terminal and display them on the terminal.] This enables users to efficiently and effectively rearrange and tidy up their rooms, and provides an environment in which specific changes to the placement of furniture and items can be visually confirmed.

[1407] A "user" refers to a person who uses the system to receive suggestions for redecorating and tidying up their room.

[1408] "Server" refers to the computer system that receives photos uploaded by users, analyzes the images using a generative AI model, identifies issues, and generates solutions.

[1409] "Device" means the device used by the User to take and upload photos and to view proposals and simulated images received from the Server, such as a smartphone or tablet.

[1410] "Generative AI model" refers to the artificial intelligence algorithm used by the server, a machine learning model used to analyze photos of a room and identify the location and type of furniture and items.

[1411] "Image analysis" refers to the process by which a generative AI model takes a photo as input and identifies the furniture and items in the room, determining their location and type.

[1412] "Issues" refer to problems with the current state of the room that were identified as a result of image analysis. Examples include obstructions to traffic flow and disorganized storage.

[1413] "Solutions" refer to specific methods the server suggests to resolve the issues it has identified, such as rearranging furniture, organizing storage, or suggesting the purchase of additional items.

[1414] A "simulation image" is an image created to visually show the content of a proposal generated by the server, and is used by the user to check the effect of the proposal in advance.

[1415] "Uploading" refers to the act of sending a photo of a room taken by a user to a server.

[1416] "Photo" refers to an image file taken by the user of the current state of the room.

[1417] This invention relates to a system that provides suggestions for redecorating and tidying up a room. A user takes a photo of the room, and a generative AI model on a server analyzes the photo to identify current issues and generate specific suggestions for improvement. The processing content of the system's program and specific operation examples are explained below.

[1418] Hardware and software used

[1419] To implement this system, the following hardware and software is required:

[1420] User devices: smartphones, tablets, digital cameras, etc. Used to upload photos taken to the server.

[1421] Server: A powerful computer system used to receive photos from users, perform analysis, generate suggestions, and create simulated images.

[1422] Generative AI models: For example, AI models built using machine learning libraries such as TensorFlow or PyTorch, which are used to analyze photos of a room and identify the location and type of furniture and items.

[1423] Data processing and calculation details

[1424] The system operates as follows.

[1425] 1. Take and upload a photo:

[1426] The user takes a photo of the room using a smartphone or digital camera and uploads the photo to the server through the application, for example, by tapping the "Upload Photo" button.

[1427] 2. Photo Receipt and Verification:

[1428] The server receives the photo sent by the user. After receiving it, it verifies the photo's resolution (for example, 300 dpi or higher) and file format (JPEG format), and if there are no problems, it proceeds to the next step.

[1429] 3. Image Analysis:

[1430] The server calls a built-in generative AI model (for example, a TensorFlow object detection model) and provides the received photo as input. The AI ​​model analyzes the image and identifies the location and type of furniture, such as sofas, tables, bookshelves, and other items, such as books and decorations. This process uses image processing technology to recognize objects.

[1431] 4. Identify the problem:

[1432] Based on the results of the image analysis, the server lists the current issues in the room, pointing out specific problems such as "the sofa is placed in the center of the room and obstructs traffic flow," "the bookshelf is cluttered," and "the window is blocked by furniture."

[1433] 5. Proposal generation:

[1434] Based on the analysis results, the server generates specific suggestions for solving the problem, such as "move the sofa to the window," "organize the bookshelf by color," or "purchase additional storage boxes." It also generates a simulated image that visually shows the suggestions. Image synthesis technology is used to simulate what the proposed layout will look like.

[1435] 6. Displaying the results:

[1436] The terminal displays the proposals and simulation images received from the server on the user interface. The user checks the proposals and decides whether to redecorate or tidy up the room.

[1437] Examples and prompts

[1438] User Action:

[1439] A user takes a photo of their living room with their smartphone and taps the "Upload Photo" button in the application, which validates the photo's resolution and file format.

[1440] Server image analysis:

[1441] The server inputs the received photos into the AI ​​model and begins analysis. The AI ​​model recognizes objects such as sofas, tables, bookshelves, books, and decorations, and identifies their locations and types.

[1442] Identifying the issue:

[1443] The server lists problems based on the analysis results, such as the sofa in the living room being in the middle, which makes it difficult to move around, or the bookshelf being cluttered and in need of organization.

[1444] Proposal generation:

[1445] The server generates suggestions such as moving the sofa to the window, arranging the bookshelf by color, purchasing additional storage boxes, purchasing new curtains, etc. It also generates simulated images that reflect these suggestions so that the user can visually confirm them.

[1446] Viewing Results:

[1447] The device displays the proposal and a simulation image to the user, who can then review the proposal and decide whether or not to actually redecorate the room.

[1448] Prompt Sentence Examples

[1449] "You take a photo of your living room and upload it to the server through the app. The server analyzes the photo and provides specific suggestions for furniture arrangement and organization. For example, you might move the sofa to a window, organize your bookshelves by color, or buy additional storage boxes. You can visually review the suggestions and then decide whether to implement them."

[1450] This system allows users to receive specific suggestions for easily rearranging and tidying their rooms, and gives them creative ideas that they would not have thought of on their own, making it easier to maintain a comfortable living environment.

[1451] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1452] Step 1:

[1453] The user takes a photo of the room with a smartphone or digital camera and then taps the "Upload Photo" button in the application to send the photo to the server.

[1454] Input: Room photo (JPEG format)

[1455] Output: Uploaded photo

[1456] Specific behavior: Check the resolution of the photo taken by the user and upload it to the server through the application.

[1457] Step 2:

[1458] The server receives the photo sent by the user. The server verifies the resolution and file format of the received photo, and if there are no problems, it proceeds to the next step.

[1459] Input: A photo submitted by the user

[1460] Output: Verification result (whether the photo is appropriate or not)

[1461] Specific operation: Check whether the resolution of the received photo is 300 dpi or higher and whether the file format is JPEG.

[1462] Step 3:

[1463] The server invokes a generative AI model (for example, a TensorFlow object detection model) and provides the received photo as input. The AI ​​model analyzes the image and identifies the furniture and items in the room.

[1464] Input: A photo that passes validation

[1465] Output: Image analysis results (furniture and item locations and types)

[1466] What it does: It uses AI models to analyze images and identify objects such as sofas, tables, bookshelves, books, and decorative items, and their contents.

[1467] Step 4:

[1468] Based on the results of image analysis, the server lists the current issues in the room.

[1469] Input: Image analysis results

[1470] Output: Issue list (current issues)

[1471] Specific operation: Based on the analysis results, it makes a list of problems such as cramped furniture layout, poor traffic flow, and cluttered bookshelves.

[1472] Step 5:

[1473] The server generates specific proposals to address the identified issues, such as rearranging the sofa, reorganizing the bookshelf, or adding new items, and generates a visual simulation image.

[1474] Input: Issues list

[1475] Output: Proposal and simulation images

[1476] Specific actions: Using machine learning models, the system generates specific rearrangement and suggestions, such as moving the sofa to a window, organizing bookshelves by color, or purchasing additional storage boxes, and then uses image synthesis technology to create simulated images.

[1477] Step 6:

[1478] The generated proposal and simulation image are sent from the server to the user, who then displays them on the terminal.

[1479] Input: Proposal content and simulation image

[1480] Output: Proposal and simulation images displayed on the user's device

[1481] Specific operation: The proposals and simulation images are sent to the user's device and displayed in an application on the device. The user reviews the proposals and decides whether to redecorate and organize the room.

[1482] (Application example 1)

[1483] 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."

[1484] In modern factory environments, work efficiency and space optimization are important challenges. However, achieving these goals requires specialized knowledge and time, and there are limitations to how much manual work can be done by on-site personnel. Furthermore, it is difficult to accurately identify current problems and propose specific improvement measures. This can result in unnecessary traffic flow and inappropriate machine placement, leading to reduced productivity. Therefore, a system that can automatically and efficiently solve these issues is needed.

[1485] 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.

[1486] In this invention, the server includes: [means for a user to take a photo of the environment and upload it; [means for the server to input the received photo into an AI model for image analysis and analyze it to identify the location and type of objects and items in the environment; [means for the server to list current issues based on the analysis results; [means for the server to generate solutions to the issues and provide proposals to the user; [means for the server to generate simulation images to visually show the solutions; [means for the server to send the proposals and simulation images from the server to the user's terminal and display them on the terminal; and [means for the server to generate proposals to optimize the factory's work area and machine layout.] This makes it possible to automatically identify current problems in the factory environment and visually show specific improvement measures, thereby improving work efficiency and optimizing space.

[1487] "Environment" refers to the place where machines and equipment are installed, such as a factory or work site.

[1488] "Placement" refers to objects in the environment, such as machines, equipment, workbenches, storage facilities, etc.

[1489] "Items" refer to small objects present in the environment, such as tools, materials, products, and decorations.

[1490] "Location" refers to the specific location that a placement or item occupies within the environment.

[1491] "Type" refers to an attribute that indicates the characteristics and classification of a placement or item.

[1492] "Challenges" refer to problems within the environment that hinder work efficiency or space optimization.

[1493] "Solution" refers to specific measures or proposals to resolve the identified issues.

[1494] "Simulation image" refers to a virtual image that visually represents a solution.

[1495] "Work area" refers to the specific location or space where work is performed.

[1496] "Machine layout" refers to the way machines and equipment are arranged within a work area.

[1497] "Device" refers to the device (e.g., smartphone, tablet, head-mounted display, etc.) that a user uses to upload photos and view suggestions.

[1498] "Receiving" refers to the server acquiring the photo data sent from the user's terminal.

[1499] "Analysis" refers to the process of using AI models to process photo data and identify the location and type of objects and items.

[1500] This invention relates to a system for optimizing work efficiency and space in a factory environment. Users take photos of the factory interior using a smartphone or head-mounted display (HMD) and upload them to a dedicated application. Based on the photos, a server uses an AI model to perform image analysis, identify current problems, and generate specific improvement proposals. The processing content of the system's program and specific operation examples are explained below.

[1501] Overall system overview

[1502] User operations

[1503] Users use their smartphones or HMDs to take photos of the current state of the factory, and then tap the "Upload Photos" button in the dedicated application to send the photo data to the server.

[1504] Server Processing

[1505] The server verifies the received photo data and, if there are no problems, inputs it into an AI model for image analysis. This AI model uses TensorFlow and PyTorch. The AI ​​identifies the location and type of objects (machines, equipment, workbenches, storage facilities) and items (tools, materials, products, decorations) in the image.

[1506] Identifying the issue

[1507] Based on the analysis results, the server lists current problems such as narrowing of the work area and obstructions to traffic flow.

[1508] Proposal Generation

[1509] The server generates specific solutions to the identified problems, such as rearranging work areas or organizing storage facilities, and also generates simulation images that visually show these proposals.

[1510] Displaying the results

[1511] The proposals and simulation images generated by the server are sent to the user's device (smartphone or HMD) and displayed on the user interface. The user can review these and decide whether to make layout changes or improvements as proposed.

[1512] Hardware and Software Used

[1513] Hardware:

[1514] Smartphones (e.g. iPhone 12, Samsung Galaxy S21)

[1515] Head-mounted displays (e.g. Microsoft HoloLens 2, Oculus Quest 2)

[1516] Server (e.g. Google Cloud Platform, Amazon Web Services)

[1517] software:

[1518] Dedicated app for smartphones or HMDs (development environment: Android Studio, Xcode)

[1519] AI models for image analysis (e.g., TensorFlow, PyTorch)

[1520] Server-side processing (e.g., Node.js, Python)

[1521] Specific examples

[1522] A user puts on a HoloLens 2 and scans the layout of machines and equipment in a factory, uploading the data to an application. The server analyzes the received images using TensorFlow to detect problems such as overly narrow work areas and blocked traffic lines. Specific proposals for rearranging machines, installing new storage facilities, and streamlining traffic lines are then generated, and simulated images of these are displayed on the HoloLens for the user to check on the spot.

[1523] Example of input prompt for generative AI model:

[1524] Upload a photo of your current factory environment and we'll generate suggestions to optimize work areas and machine locations.

[1525] This will enable factory managers to improve work efficiency and make layout changes without waste based on expert advice.

[1526] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1527] Step 1:

[1528] Users use a smartphone or head-mounted display to take photos of the factory and upload them into the application.

[1529] Input: Photographic data of the physical environment

[1530] Output: Photo data sent to the server

[1531] Specific behavior: When the user taps the "Upload Photo" button in the application, the photo they took is sent to the server.

[1532] Step 2:

[1533] The server validates the received photo data and checks whether the resolution and file format are appropriate.

[1534] Input: Photo data sent by the user

[1535] Output: Analysis request to the configured AI model

[1536] Specific operation: The server checks the resolution and file format (e.g., JPEG, PNG) of the photo data, and if there are no problems, it proceeds to the next processing step. If not, it returns an error message.

[1537] Step 3:

[1538] The photo data received by the server is input into the AI ​​model and image analysis is performed.

[1539] Input: Photo data

[1540] Output: A list of placements and items, with their locations and types.

[1541] Specific operation: The server uses TensorFlow and PyTorch to call AI models, analyze the photo data, and identify the location and type of objects (machines, equipment) and items (tools, materials).

[1542] Step 4:

[1543] Based on the analysis results, the server lists current problems.

[1544] Input: List of positions and types

[1545] Output: A list of issues

[1546] Specific operation: The server extracts problems such as obstructions to traffic flow and narrowing of work areas from the analysis results and creates a list of problems.

[1547] Step 5:

[1548] The server generates solutions and visual simulation images for the listed problems.

[1549] Input: List of issues

[1550] Output: Solution proposal and simulation images

[1551] Specific operation: The server generates specific solutions to the problem, such as proposing machine relocation or the introduction of new storage facilities. It also generates simulation images so that the user can visually confirm the proposed solutions.

[1552] Step 6:

[1553] The generated proposal and simulation image are sent from the server to the user's terminal, which displays them.

[1554] Input: Solution proposal and simulated images

[1555] Output: Proposal and simulation images displayed

[1556] Specific operation: The server sends the generated proposal and simulation images to the user's smartphone or HMD, where they are displayed on the application. The user then checks the proposal and decides whether to make layout changes or carry out improvements.

[1557] This is the flow of the system program's processing to optimize the factory environment. By performing appropriate data processing and calculations at each step, the system provides specific proposals and visual simulations.

[1558] 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.

[1559] This invention relates to a system that provides suggestions for redecorating and tidying up a room. Users take photos of the room, and AI analyzes the photos to identify current issues and generate specific suggestions for improvement. Furthermore, the system incorporates an emotion engine that can recognize the user's emotional state and reflect it in the suggestions.

[1560] Overall system overview

[1561] 1. User operations

[1562] Users take photos of their rooms with their smartphones or digital cameras and upload them to the application by tapping the "Upload Photos" button within the app.

[1563] 2. User Emotion Recognition

[1564] The device uses an emotion engine to recognize emotions from the user's facial expressions and voice, using the smartphone's camera and microphone.

[1565] The device transmits the recognized emotion data to the server.

[1566] 3. Server Processing

[1567] The server receives the photo and emotion data sent from the user's device, verifies the photo's resolution and file format, and if there are no problems, proceeds to the next processing step.

[1568] 4. Image Analysis

[1569] The server calls the AI ​​model and provides the photo as input data, which then initiates object recognition and semantic segmentation to identify the location and type of furniture (e.g., sofa, table, bookshelf) and items (e.g., books, decorations) in the room.

[1570] 5. Identifying the Issues

[1571] The server receives the analysis results from the AI ​​model and lists current issues, identifying specific problems such as inefficient furniture placement or cluttered bookshelves.

[1572] 6. Proposal Generation

[1573] The server generates solutions based on the identified problems, such as how to organize bookshelves, rearrange furniture, or recommend new furniture or decorative items to purchase.

[1574] The server adjusts the suggestions based on the user's emotional data. For example, if the user is feeling stressed, it will recommend placements and items that will help them relax.

[1575] Based on the proposal, the server generates a simulation image that visually shows the proposal.

[1576] 7. Displaying the results

[1577] The terminal displays the proposals and simulation images received from the server on the user interface, and the user can view them and decide whether to redecorate the room as proposed.

[1578] Specific examples

[1579] User Action:

[1580] A user takes a photo of their living room with their smartphone and taps the "Upload Photo" button in the application to send it to the server, where it is verified that the photo has the correct resolution and is in JPEG format.

[1581] User Emotion Recognition:

[1582] The device uses the smartphone camera to analyze the user's facial expressions, and the emotion engine recognizes the user's emotional state (e.g., stress, joy).

[1583] The device transmits the recognized emotion data to the server.

[1584] Server image analysis:

[1585] The server inputs the received photos into the AI ​​model and begins analysis. The AI ​​model recognizes objects such as sofas, tables, bookshelves, books, and decorations, and identifies their locations and types.

[1586] Identifying the issue:

[1587] Based on the analysis results, the server lists problems such as the sofa in the living room being located in the center of the room and obstructing traffic flow, or the bookshelf being disorganized.

[1588] Proposal generation:

[1589] The server moves the sofa to a window wall, organizes bookshelves by color, generates suggestions for purchasing additional storage boxes, and even recommends new curtains.

[1590] The server references the user's emotional data and suggests placements and items that will help users who are feeling stressed to relax.

[1591] The server generates a visual simulation of the proposal, allowing the user to visually see how the room will change.

[1592] Viewing Results:

[1593] The device displays the proposal and a simulation image to the user, who then reviews the proposal and decides whether to redecorate the room.

[1594] This system allows users to easily receive specific suggestions for redecorating and tidying up their rooms, and can provide creative ideas that they would not have thought of on their own. In addition, by receiving appropriate suggestions based on the user's emotional state, it becomes easier to maintain a more comfortable living environment.

[1595] The processing flow will be explained below.

[1596] Step 1:

[1597] The user takes a photo of the room with a smartphone or digital camera, opens the application, selects the photo, and taps the "Upload Photo" button.

[1598] Step 2:

[1599] The device uploads the selected photo to the server, verifies the resolution and file format of the photo, and sends it to the server if there are no problems.

[1600] Step 3:

[1601] The device captures the user's facial expressions using the smartphone's camera and captures their voice using the microphone. The emotion engine analyzes this data to identify the user's emotional state.

[1602] Step 4:

[1603] The device transmits the recognized emotion data to the server.

[1604] Step 5:

[1605] The server receives the photo and emotion data sent from the user's device. It verifies the photo's resolution and file format again, and if there are no problems, it proceeds to the next step.

[1606] Step 6:

[1607] The server calls the AI ​​model and provides the photo as input data, which then initiates object recognition and semantic segmentation to identify the location and type of furniture (sofa, table, bookshelf, etc.) and items (books, decorations, etc.) in the room.

[1608] Step 7:

[1609] The server receives the analysis results from the AI ​​model and lists current issues, identifying specific problems such as inefficient furniture placement or cluttered bookshelves.

[1610] Step 8:

[1611] The server generates solutions based on the identified problems, such as how to organize bookshelves, rearrange furniture, or recommend new furniture or decorative items to purchase.

[1612] Step 9:

[1613] The server adjusts the suggestions based on the user's emotional data. For example, if the user is feeling stressed, it will recommend placements and items that will help them relax.

[1614] Step 10:

[1615] The server generates a simulated image to visually represent the proposed changes, allowing the user to visually confirm the proposed changes.

[1616] Step 11:

[1617] The server sends the proposal and the simulated image to the user's terminal. The proposal content and the visualized simulated image are sent together.

[1618] Step 12:

[1619] The device displays the received proposal and the simulation image on the user interface, and the user can view it and decide whether to redecorate the room according to the proposal.

[1620] This processing flow allows users to come up with creative ideas that they would not have thought of on their own, and by receiving appropriate suggestions based on their emotional state, it becomes easier to maintain a more comfortable living environment.

[1621] Example 2

[1622] 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."

[1623] Redecorating and tidying a room is generally time-consuming, and it is often difficult for users to find the optimal arrangement. Furthermore, if the suggestions are not appropriate for the user's emotional state, they are unlikely to accept them. Therefore, there is a need for a system that can provide specific suggestions for efficiently redecorating and tidying a room and give appropriate advice based on the user's emotional state.

[1624] 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.

[1625] In this invention, the server includes a means for a user to take a photo of their room and upload it, a means for the terminal to collect the user's facial expression and voice data and recognize their emotional state using an emotion analysis engine, and a means for the terminal to transmit the recognized emotional data to the server. This allows the user to receive optimal suggestions for redecorating and tidying up their room based on the photo of their room and their emotional state at the time. Furthermore, because the suggestions are suited to the user's emotional state, they are more likely to be accepted, ensuring a more comfortable living environment.

[1626] A "user" refers to an individual who uses the system to take photos of their room and receive suggestions for redecorating and tidying up.

[1627] A "terminal" is an information processing device such as a smartphone or tablet that is operated by a user.

[1628] A "server" is a remote computer that performs the analysis processing that is the core of the system.

[1629] An "emotion analysis engine" refers to a program or algorithm that analyzes a user's facial expressions and voice data to recognize their emotional state.

[1630] An "AI model" is a trained program that uses artificial intelligence to analyze photos and identify the location and type of furniture and items in a room.

[1631] "Image analysis" refers to the process of using AI models to recognize objects in a photo and identify their location and type.

[1632] "Issues" refer to areas or situations within a room that need improvement and are identified from the analysis results.

[1633] "Solution" refers to a specific suggestion for improvement generated by the server for an identified issue.

[1634] A "simulation image" is a virtual image created to visually show the proposal content generated by the server.

[1635] "Suggestion" refers to specific instructions or advice provided by the server on how to rearrange or tidy up the user's room.

[1636] This invention relates to a system that provides suggestions for redecorating and tidying up a room. The system allows users to take and upload photos of their room, analyzes the photos using an AI model, identifies current issues, and proposes specific solutions. Furthermore, the system incorporates an emotion analysis engine that can recognize the user's emotional state and reflect it in the suggestions.

[1637] This system consists of a photographic device such as a smartphone or digital camera, a terminal for user operation (such as a smartphone or tablet), a server that performs the analysis processing, and an AI model and emotion analysis engine.

[1638] Hardware and Software Configuration

[1639] 1. Photo device: The user uses a smartphone or digital camera to take photos of the room. The photos are saved on the device and uploaded to the server via the application.

[1640] 2. Device: A smartphone or tablet on which users upload photos and run an emotion analysis engine. The device collects facial and voice data from users and analyzes their emotional state. The emotion analysis engine is implemented using, for example, Google's Face API or Amazon's Rekognition.

[1641] 3. Server: A remote computer that analyzes the received photos and emotion data. The server uses AI models (such as TensorFlow or PyTorch) to analyze the photos and perform object recognition and semantic segmentation. It then identifies issues based on the analysis results and generates specific suggestions and visual simulation images.

[1642] Data Processing and Computation

[1643] The server receives the photo and emotion data sent from the user's device. It verifies the photo's resolution and file format, and if there are no problems, it proceeds to the next analysis step. The server then calls the AI ​​model and provides the photo as input data. The AI ​​model then performs object recognition and semantic segmentation to identify the location and type of furniture and items in the room. Based on the analysis results, it lists current issues and generates solutions. These include how to organize bookshelves, rearrange furniture, and recommend new furniture or decorative items to purchase. The suggestions are adjusted based on the user's emotion data. For example, if the user is feeling stressed, it will recommend placements and items that will help them relax. Finally, a visual simulation image is generated based on the suggestions and sent to the user's device.

[1644] Examples of concrete examples and prompts

[1645] For example, a user takes a photo of their living room with their smartphone and taps the "Upload Photo" button in the application to send it to the server. The device uses the smartphone's camera to analyze the user's facial expressions, and the emotion engine recognizes the user's emotional state (e.g., stress, joy). The device then sends the recognized emotion data to the server. The server then inputs the received photo into an AI model and begins analysis. The AI ​​model recognizes objects such as sofas, tables, bookshelves, books, and decorative items, and identifies their respective positions and types. Based on the analysis results, the server lists problems such as the sofa being in the middle of the room and blocking traffic flow, or the bookshelf being cluttered. The server then generates suggestions to move the sofa to the window, organize the bookshelf by color, and purchase additional storage boxes. It also recommends the purchase of new curtains. The suggestions are generated as simulated images and ultimately sent to the user's device.

[1646] An example prompt might be, "I'd like some suggestions for redecorating my room. Please generate suggestions based on the photos and emotion data below."

[1647] Photo: [livingroom.jpg]

[1648] Emotional data: Feeling stressed

[1649] Please provide specific suggestions and visual simulation images."

[1650] According to the present invention, a user can easily receive specific suggestions for rearranging and tidying a room, and by receiving appropriate suggestions according to the user's emotional state, the user can maintain a comfortable living environment.

[1651] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1652] Step 1:

[1653] Taking and uploading photos

[1654] A user takes a photo of a room using a smartphone or digital camera.

[1655] The user takes a photo and taps the "Upload Photo" button in the application to send it to the server.

[1656] Input: A photo of the room (JPEG format).

[1657] Output: Photo data uploaded to the server.

[1658] Specific behavior: When the user taps the "Upload Photo" button, the device selects a photo file and sends it to the server via an HTTP request. A success message is displayed to the user.

[1659] Step 2:

[1660] Emotion data collection and analysis

[1661] The device uses a built-in camera and microphone to collect facial and voice data from the user.

[1662] The data collected by the device is analyzed by an emotion analysis engine to recognize the user's emotional state (e.g., stress, joy).

[1663] Input: User's facial expression data, voice data.

[1664] Output: Recognized emotion data (JSON format).

[1665] Specific operation: The device activates the camera and instructs the user to make facial expressions in front of the camera. The user also speaks according to the voice instructions. This data is processed locally and analyzed by the emotion analysis engine. The resulting emotion data is sent to the server in JSON format.

[1666] Step 3:

[1667] Data Receipt and Validation

[1668] The server receives the photo and emotion data sent from the user's terminal.

[1669] The server validates the photo's resolution and file format, returning an error message if it's not in JPEG format.

[1670] Input: Photo data (JPEG format), emotion data (JSON format).

[1671] Output: The validated data or an error message.

[1672] What happens: The server processes the incoming HTTP request, checks the photo's resolution (for example, 1280x720 pixels or more) and file format, and returns an error message if there is a problem.

[1673] Step 4:

[1674] Image analysis

[1675] The server calls the AI ​​model and provides the received photos as input data.

[1676] The AI ​​model uses object recognition and semantic segmentation to identify furniture and items within a room.

[1677] Input: Photo data.

[1678] Output: Object recognition results (location and type of object).

[1679] How it works: The server processes the photos using an AI model to generate bounding boxes and labels for each object, using TensorFlow and PyTorch.

[1680] Step 5:

[1681] Identifying the issue

[1682] The server receives the analysis results from the AI ​​model and lists current issues.

[1683] Input: Object recognition results.

[1684] Output: A list of identified issues.

[1685] Specific operation: The server automatically extracts issues based on the analysis results, such as a sofa blocking traffic flow or a cluttered bookshelf, and lists specific problems.

[1686] Step 6:

[1687] Proposal Generation

[1688] The server generates solutions based on the identified problems.

[1689] The server adjusts the suggestions based on the user's emotional data.

[1690] Input: Identified issues, emotion data.

[1691] Output: Specific proposals.

[1692] Specific behavior: The system generates suggestions based on the list of issues and optimizes them using emotional data. For example, it recommends relaxing layouts for users who are feeling stressed. Specific suggestions include how to organize bookshelves and recommending new furniture to purchase.

[1693] Step 7:

[1694] Generation of simulation images

[1695] The server generates a simulation image that visually shows the proposal.

[1696] Input: Specific proposal details.

[1697] Output: Simulation image.

[1698] Specific operation: The server generates a simulation image using 3D modeling software (e.g. Blender), which is temporarily stored on the server.

[1699] Step 8:

[1700] Send and view proposals and simulation images

[1701] The server sends the proposal and the simulation image to the user's terminal.

[1702] The terminal displays the received proposal and a simulation image.

[1703] Input: Proposal content, simulation image.

[1704] Output: Proposal and simulation images displayed on the user interface.

[1705] Specific operation: The server sends the generated proposal and a simulation image to the terminal as an HTTP response, and the terminal displays it on the user interface. The user can then review it and decide whether to redecorate the room as proposed.

[1706] This system allows users to receive specific suggestions for redecorating and tidying up their rooms, and provides appropriate advice based on their emotional state, making it easier to maintain a comfortable living environment.

[1707] (Application example 2)

[1708] 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."

[1709] When optimizing store layout and product displays, manual rearrangement and trial and error require time and effort. Furthermore, the manager's emotional state can affect the best proposal, making it difficult to efficiently determine the optimal layout. Furthermore, since the proposals are not visually displayed, it is difficult for managers to visualize them.

[1710] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1711] In this invention, the server includes: [means for a user to take and upload photos of a room or store; [means for the server to input the photos received by the server into an AI model for image analysis and analyze them to identify the location and type of furniture and items in the room or store; [means for the server to list current issues based on the analysis results; [means for the server to generate solutions to the issues and provide suggestions to the user; [means for the server to generate simulation images to visually show the solutions; [means for the server to recognize the user's emotional state and reflect that in the suggestions; and [means for the server to send the suggestions and simulation images from the server to the user's terminal and display them on the terminal.] This allows for efficient optimal suggestions regarding store layout and product display, enables more appropriate suggestions based on the manager's emotional state, and allows the manager to visually confirm the suggestions.

[1712] A "user" is someone who uses the application to take and upload photos of rooms or stores.

[1713] "Photos of rooms and stores" refer to photos of the interior of rooms and stores taken by users using their own smartphones or digital cameras.

[1714] A "server" is a device or system that receives photo data, stores AI models for image analysis, and generates and provides suggestions to users.

[1715] An "AI model for image analysis" is an artificial intelligence model that analyzes photographic data to identify the location and type of furniture and items within a room or store.

[1716] "Furniture and items" refers to sofas, tables, bookshelves, books, decorations, shelves, products, etc. that exist in rooms or stores.

[1717] "Current Issues" are layout problems and inefficiencies identified based on analyzed photos of rooms and stores.

[1718] "Solutions" are proposed ways to rearrange or organize furniture or items to address identified issues.

[1719] A "simulated image" is a virtual image generated to visually represent a proposed solution.

[1720] "User's emotional state" refers to the user's state of mind (e.g., stress or joy) recognized based on the user's facial expressions and voice.

[1721] "Proposal content" refers to specific proposals for changes to the layout of a room or store or product displays, which are generated based on the analysis results and the user's emotional state.

[1722] "Terminal" refers to a device (such as a smartphone or tablet) on which a user receives and displays proposals and simulation images.

[1723] The system for implementing this invention starts when a user takes a photo of a room or store and uploads it to a server. Details of the hardware and software used and an overview of the process are described below.

[1724] 1. System Configuration

[1725] Hardware:

[1726] User device: A smartphone or digital camera, specifically a smart device with a built-in high-resolution camera and microphone.

[1727] Server: A cloud server or dedicated server with high-performance computing power.

[1728] software:

[1729] Image processing library: Uses Python and OpenCV.

[1730] AI model: Uses TensorFlow and Keras for object recognition and semantic segmentation.

[1731] Emotion Recognition: Facial expression recognition is performed using OpenCV and DeepFace, and voice analysis is performed using Python.

[1732] Backend framework: Django is used for server data management and processing.

[1733] Database: PostgreSQL or SQLite.

[1734] Front-end: Developing mobile applications using React Native.

[1735] 2. Processing Flow

[1736] Photo upload and emotion recognition

[1737] 1. A user takes a photo of the interior of a store using their smartphone, which is saved in high-resolution JPEG format.

[1738] 2. The user uploads a photo to the application by tapping the "Upload Photo" button within the app.

[1739] 3. The device's camera and microphone are used to capture the user's facial expressions and voice. The captured data is then used to recognize facial expressions using OpenCV and DeepFace, and the voice data is analyzed using Python.

[1740] Server analysis and suggestions

[1741] 1. The server receives the photo data sent by the user and starts image analysis. It uses OpenCV to preprocess the photo, adjust the resolution, and verify the file format.

[1742] 2. The server invokes the AI ​​model (TensorFlow and Keras) and starts analyzing the photo, performing object recognition and semantic segmentation to identify the location and type of furniture and items in the room or store.

[1743] 3. The server lists current issues based on the analysis results, such as inefficient product display methods or blocked flow paths.

[1744] 4. The server adjusts the suggestions based on the emotional data. If the user is feeling stressed, the suggestions will be changed to include placements and items that will help them relax.

[1745] 5. The server generates proposals based on the analysis results and emotion data. These proposals include product rearrangement, new display methods, and improvements to traffic flow. Additionally, it generates simulation images to provide a visual representation.

[1746] View Suggestions

[1747] 1. The server sends the generated proposal and simulation image to the user terminal.

[1748] 2. The terminal displays the proposal and a simulation image to the user, who then checks the proposal and decides whether to implement it.

[1749] Specific examples

[1750] Specific processing examples:

[1751] Users take photos of the store and upload them to the app.

[1752] The server receives the photo and analyzes it using an AI model, which identifies the product display as messy and generates suggestions for tidying up.

[1753] The server refers to the emotional data and suggests layouts that can be easily changed based on the stress the user is feeling.

[1754] The proposal recommends rearranging products to ensure new flow of traffic, and displays a simulated image of the resulting situation.

[1755] Example prompt for a generative AI model:

[1756] Generate optimal store layout and product placement suggestions based on store photos and the store manager's sentiment data below.

[1757] Emotion data: {"emotion": "stress", "score": 0.7}

[1758] Store photo: (binary photo data)

[1759] Please include specific layout changes and simulation images based on photos of the store in your proposal.

[1760] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1761] Step 1:

[1762] Users take photos of rooms or stores with their smartphones and upload them to the application. At this time, the input data of the photo is saved in JPEG format and sent to the server by tapping the "Upload Photo" button in the app. The output data is the uploaded high-resolution photo file.

[1763] Step 2:

[1764] The device uses a camera and microphone to capture the user's facial expressions and voice. The input data is the user's facial image and voice data, which are then analyzed by the emotion engine. The output data is the user's emotional state (e.g., stress, joy).

[1765] Step 3:

[1766] The server receives the received photos and emotion data and performs preprocessing on the photos. The input data is a JPEG photo and emotion data, and OpenCV is used to adjust the resolution and verify the file format. The processed photo is obtained as output data.

[1767] Step 4:

[1768] The server inputs the processed photos into an AI model (TensorFlow, Keras) for image analysis. The input data is the preprocessed photos, and the AI ​​model performs object recognition and semantic segmentation. The output data identifies the location and type of furniture and items within a room or store.

[1769] Step 5:

[1770] The server lists current issues based on the analysis results of the AI ​​model. The input data is the analysis results, which identify inefficient furniture placement, product display methods, etc. The output data is the list of issues.

[1771] Step 6:

[1772] The server generates solutions to the listed problems and generates a simulation image to visually demonstrate them. The input data is the problem and the user's emotional data, and the solutions include how to rearrange furniture and products, how to organize, etc. The output data is a simulation image and specific proposals.

[1773] Step 7:

[1774] The server sends the generated proposal and simulation image to the user's terminal. The input data is the proposal content and simulation image, and this is sent to the user's terminal. The output data is the proposal content and simulation image that the user can view.

[1775] Step 8:

[1776] The terminal displays the proposal and a simulation image to the user, who then confirms the proposal. The input data are the proposal and simulation image sent from the server, and the user decides whether to change the layout of the room or store based on this. The proposal to be adopted by the user is confirmed as output data.

[1777] 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.

[1778] 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.

[1779] 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.

[1780] 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.

[1781] 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.

[1782] 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.

[1783] 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).

[1784] 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.

[1785] 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."

[1786] 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.

[1787] 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).

[1788] 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.

[1789] 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.

[1790] 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.

[1791] 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.

[1792] 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.

[1793] 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.

[1794] 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.

[1795] 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.

[1796] 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.

[1797] 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.

[1798] The following is further disclosed regarding the above embodiment.

[1799] (Claim 1)

[1800] [A means for users to take and upload photos of their rooms;

[1801] [Means for inputting the photos received by the server into an AI model for image analysis and analyzing them to identify the location and type of furniture and items in the room;

[1802] [Means for the server to list current issues based on the analysis results,

[1803] [Means for the server to generate solutions corresponding to the problem and provide suggestions to the user;

[1804] [Means for the server to generate a simulation image to visually show the solution;

[1805] [Means for transmitting the proposal and simulation image from the server to the user's terminal and displaying them on the terminal;

[1806] A system including:

[1807] (Claim 2)

[1808] [The system of claim 1, wherein the server-generated suggestions include how to organize a bookshelf.

[1809] (Claim 3)

[1810] [The system of claim 1, wherein the server-generated suggestions include furniture rearrangement.

[1811] "Example 1"

[1812] (Claim 1)

[1813] [A means for users to take and upload photos of their rooms;

[1814] [Means for inputting the photos received by the server into a generative AI model for image analysis and analyzing them to identify the location and type of furniture and items in the room;

[1815] [Means for the server to list current issues based on the analysis results,

[1816] [Means for the server to generate solutions corresponding to the problem and provide suggestions to the user;

[1817] [Means for the server to generate a simulation image to visually show the solution;

[1818] [Means for transmitting the proposal and simulation image from the server to the user's terminal and displaying them on the terminal;

[1819] A system including:

[1820] (Claim 2)

[1821] [The system of claim 1, wherein the suggestions generated by the server include methods for organizing storage furniture.

[1822] (Claim 3)

[1823] [The system of claim 1, wherein the server-generated suggestions include rearranging interior furnishings.]

[1824] "Application Example 1"

[1825] (Claim 1)

[1826] [A means for users to take and upload photos of the environment;

[1827] [Means for inputting the photos received by the server into an AI model for image analysis and performing analysis to identify the location and type of objects and items within the environment;

[1828] [Means for the server to list current issues based on the analysis results,

[1829] [Means for the server to generate solutions corresponding to the problem and provide suggestions to the user;

[1830] [Means for the server to generate a simulation image to visually show the solution;

[1831] [Means for transmitting the proposal and simulation image from the server to the user's terminal and displaying them on the terminal;

[1832] [A system including a means for a server to generate proposals for optimizing work areas and machine layouts in a factory.

[1833] (Claim 2)

[1834] [The system of claim 1, wherein the suggestions generated by the server include methods for organizing storage facilities.]

[1835] (Claim 3)

[1836] [The system of claim 1, wherein the server-generated suggestions include rearrangement of the work area.

[1837] "Example 2: Combining Emotion Engines"

[1838] (Claim 1)

[1839] [A way for users to take and upload photos of their rooms],

[1840] [Means for the device to collect the user's facial expression and voice data and recognize the user's emotional state using an emotion analysis engine];

[1841] [Means for the device to transmit the recognized emotion data to the server];

[1842] [Means for inputting the photos and emotion data received by the server into an AI model for image analysis and performing analysis to identify the location and type of items in the room]

[1843] [Means for the server to list current issues based on the analysis results obtained from the AI ​​model],

[1844] [Means for the server to generate solutions to the identified problems and provide suggestions to the user];

[1845] [Means for the server to adjust the content of suggestions based on the user's emotional data],

[1846] [Means for the server to generate simulation images to visually show the solution],

[1847] [Means for the server to send the proposal and simulation images to the user's terminal and for the terminal to display them];

[1848] A system including:

[1849] (Claim 2)

[1850] [The system of claim 1, wherein the server-generated suggestions include how to organize a bookshelf.

[1851] (Claim 3)

[1852] [The system of claim 1, wherein the server-generated suggestions include furniture rearrangement.

[1853] "Application example 2 when combining emotion engines"

[1854] (Claim 1)

[1855] [A means for users to take and upload photos of rooms and stores,

[1856] [Means for inputting the photos received by the server into an AI model for image analysis and performing analysis to identify the location and type of furniture and items in a room or store;

[1857] [Means for the server to list current issues based on the analysis results,

[1858] [Means for the server to generate solutions corresponding to the problem and provide suggestions to the user;

[1859] [Means for the server to generate a simulation image to visually show the solution;

[1860] [Means for the server to recognize the user's emotional state and reflect it in the content of the suggestions;

[1861] [Means for transmitting the proposal and simulation image from the server to the user's terminal and displaying them on the terminal;

[1862] A system including:

[1863] (Claim 2)

[1864] [The system of claim 1, wherein the server-generated suggestions include how to organize a bookshelf.

[1865] (Claim 3)

[1866] [The system of claim 1, wherein the server-generated suggestions include rearrangement of furniture and merchandise within the store. [Explanation of symbols]

[1867] 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 take and upload a photo of the room; The server inputs the received photos into an AI model for image analysis, and performs analysis to identify the location and type of furniture and items in the room. The server will list current issues based on the analysis results, a means for the server to generate solutions corresponding to the challenges and provide the suggestions to the user; a means for the server to generate a simulation image for visually showing the solution; means for transmitting the proposal and the simulation image from the server to the user's terminal and displaying them on the terminal; A system including:

2. 2. The system of claim 1, wherein the server-generated suggestions include how to organize a bookshelf.

3. The system of claim 1 , wherein the server-generated suggestions include furniture rearrangement.

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