system

Abstract

We provide the system. [Solution] Image acquisition method, An analysis means that analyzes the acquired image and recognizes the dimensions of the space and the position of objects, A generation means for generating object placement based on analysis results and user preference information, A visualization means for visualizing the generated arrangement in a virtual reality environment, A means of adjusting the layout by obtaining user feedback, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When changing patterns, in order to consider an optimal arrangement based on the dimensions of the space and the size of the furniture, a lot of time and labor are required, and there is also a problem that an ideal interior design cannot be specifically imagined. In addition, there may be a burden due to manual trial and error of the arrangement and a shortage of effective utilization of the space due to an inappropriate arrangement. There is a need for a technology that solves these problems and supports efficient and intuitive pattern change.

Means for Solving the Problems

[0005] This invention involves acquiring an image of a room using an image acquisition means, and recognizing the spatial dimensions and the positions of objects such as furniture from the image using an analysis means. Furthermore, a generation means automatically generates the optimal arrangement of objects based on the user's preference information. This generated arrangement is visualized in a virtual reality environment by a visualization means, allowing the user to experience a concrete redecoration plan. Additionally, an adjustment means receives feedback from the user and dynamically adjusts the arrangement to achieve an optimized interior design. This provides a system that offers an efficient and intuitive redecoration experience.

[0006] "Image acquisition means" refers to a device or software used to take a photograph of a room and import that image data into the system.

[0007] "Analysis means" refers to a device or software that has the function of processing acquired images to recognize spatial dimensions and the positions of objects.

[0008] "Generation means" refers to a device or software that automatically calculates and generates the optimal arrangement of objects based on analysis results and user preference information.

[0009] "Visualization means" refers to a device or software for visually displaying the generated arrangement within a virtual reality environment.

[0010] "Adjustment means" refers to a device or software for re-evaluating the configuration generated based on user feedback and making adjustments as necessary.

[0011] "User preference information" refers to information about the interior style, color scheme, functionality, and other preferences of the user.

[0012] A "virtual reality environment" is a virtual three-dimensional space created using digital technology, in which users can interact as if they were actually present. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention provides a system for efficiently and intuitively rearranging furniture, and its embodiments are described in detail below. This system mainly consists of a user's terminal, a server, and a communication network that connects them.

[0035] First, the user takes a photo of the entire room they want to redecorate using a smartphone or tablet. The user uploads this image to the server through a dedicated application. The uploaded image is processed by an image analysis module on the server, which automatically analyzes the dimensions of the space and the positions of objects such as furniture.

[0036] Next, the server uses the analyzed information to generate a virtual three-dimensional space and proposes the optimal layout for the interior design. The proposal is customized based on the user's preferences entered in the application, such as desired style and color scheme.

[0037] The generated layout plan is sent from the server to the user's terminal and visualized as a virtual reality (VR) environment. In this process, a VR application installed on the user's terminal generates a virtual space, providing the user with an immersive experience. The user can explore this virtual space using a VR headset or smartphone and visually check the proposed furniture layout from various angles.

[0038] Furthermore, after experiencing the virtual space, users provide feedback to the system, including their opinions and impressions. The server receives this feedback, and the AI ​​model adjusts and updates the layout plan based on it. Changes made in response to the user's new requests can then be viewed again in the virtual space.

[0039] As a concrete example, consider a scenario where a user wants to rearrange their living room. The user takes a photo of the living room with their smartphone and uploads it to the server. The server analyzes the photo, recognizes the positions of the sofa, table, and TV stand, and the amount of available space, and then proposes a new arrangement in a modern style. Based on the style chosen by the user, the server provides a plan that shifts the sofa slightly away from the center of the room and places the TV stand along the wall. The user can then review this in a VR environment, and if they wish to make further adjustments, the system will provide a new proposal that reflects those changes.

[0040] As described above, the present invention supports the optimization of interiors in a visually appealing way while saving users time and effort.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user takes a photo of the room using their device. The user activates the camera on their device and takes a photo to confirm the size of the room they want to redecorate, and after taking the photo, they review it through the application.

[0044] Step 2:

[0045] The application uploads photos taken by the user to the server. Pressing the upload button sends the photo data to the server via the network.

[0046] Step 3:

[0047] The server analyzes the received image data. Using an image analysis algorithm, the server extracts the necessary information to determine the room dimensions and the location of each piece of furniture. At this stage, deep learning techniques are applied to perform object recognition.

[0048] Step 4:

[0049] The server generates a 3D model based on the analysis results. Using the extracted dimensional information, it constructs a virtual three-dimensional model of the room and, based on that, generates an optimal furniture arrangement that takes into account the user's preferences.

[0050] Step 5:

[0051] The server sends data to the terminal to provide the user with a layout plan it has generated. The layout plan is encoded in a data format suitable for visualization in a VR application.

[0052] Step 6:

[0053] The system launches a VR application based on data received by the device and generates a virtual space. This creates an environment where users can experience the suggested furniture arrangement using a VR headset or the device's screen.

[0054] Step 7:

[0055] Users explore the virtual space and check the placement of furniture. Users can examine the placement and size of each piece of furniture from various angles and provide feedback on any points of concern or areas they would like to change.

[0056] Step 8:

[0057] The user sends feedback from the device to the server. The server receives the feedback and uses it to adjust the layout or suggest improvements.

[0058] Step 9:

[0059] The server analyzes the feedback it receives and readjusts the furniture placement using a generated AI model. The newly generated placement plan is then customized to the user's preferences.

[0060] Step 10:

[0061] The server sends the updated layout plan to the terminal, allowing users to review it again in the virtual space. This process allows users to experience the "optimal" interior layout that reflects their preferences.

[0062] (Example 1)

[0063] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0064] Traditional methods for redecorating and planning interior designs heavily rely on subjective judgments based on visual intuition and aesthetic sense, resulting in time-consuming and labor-intensive processes. Furthermore, optimizing object placement requires specialized knowledge, making it difficult for users to easily create high-quality spatial designs. There is a need to improve this situation and develop more efficient and intuitive methods for designing spaces.

[0065] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0066] In this invention, the server includes an image acquisition means that acquires visual information of the entire space using the user's observation device, an analysis means that analyzes the acquired visual information and recognizes the dimensions of the structure and the position of the object, and a generation means that generates the object arrangement based on the analysis results and the user's selection criteria. This allows the user to be effectively and quickly presented with the optimal arrangement based on their desired interior style, and furthermore, to visually confirm that arrangement in a realistic virtual environment.

[0067] "Image acquisition means" refers to elements used to acquire visual information of the entire space using the user's observation device.

[0068] "Analysis means" refers to an element that has the function of analyzing acquired visual information and recognizing the dimensions of a structure and the position of an object.

[0069] "Generation means" refers to means for concretizing the arrangement of objects based on analysis results and user selection criteria.

[0070] "Visualization means" refers to a device or process for converting a generated arrangement into a realistic virtual environment and displaying it visually.

[0071] "Adjustment means" are elements used to improve the arrangement of objects based on feedback received from users.

[0072] A "virtual environment" is a visual space that digitally reproduces a real-world space, and is an environment that users can visually confirm.

[0073] An "observation device" is a device operated by the user that has the function of capturing or recording the overall image of a space.

[0074] "Selection criteria" refer to information based on the user's preferred interior style and color scheme, and are the criteria considered when generating the layout.

[0075] This invention is a system for streamlining spatial design, utilizing a user's terminal, a server, and a communication network connecting the two.

[0076] The user uses a device such as a smartphone or tablet to take a photograph of the entire space they wish to redecorate. This device has an interface for sending the captured image to a server via an application.

[0077] The server performs image analysis on the received images. This analysis utilizes an image analysis module employing deep learning algorithms, enabling accurate recognition of spatial dimensions and the positions of objects such as furniture. Based on these analysis results and the user's selection criteria, such as preferred style and color scheme, the server automatically generates an optimal layout plan using a generative AI model. This generative AI model operates based on pre-collected and trained design patterns.

[0078] Next, the server sends the generated layout plan to the user's device. The VR application on the device uses this data to construct a virtual reality environment. The VR application visually presents the generated layout plan to the user through the VR headset or smartphone display. As a result, the user can confirm the spatial design based on the proposal with a sense of reality.

[0079] For example, if a user wants to redecorate their living room, they upload a photo of the living room taken with their smartphone to the server. The server analyzes the photo, recognizes the positions of the sofa and TV stand, and can then suggest a modern-style layout. This suggestion might include specific arrangements such as changing the sofa's position or placing the TV stand against the wall. This suggestion may also be generated by the following prompts.

[0080] Example prompt: "Please suggest the best furniture arrangement when changing the living room to a modern style. The current arrangement has a sofa in the center and a TV stand in the corner."

[0081] Through this system, users can quickly and efficiently visually review space designs and receive personalized interior plans tailored to their preferences.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The user uses a device to take a photograph of the entire space they wish to redecorate. The device then activates an interface via a dedicated application to send this image to a server. The image of the space taken by the user is used as input. The output is the image sent to the server.

[0085] Step 2:

[0086] The server retrieves the received image and passes it to the image analysis module. The analysis module processes the image using a deep learning algorithm and generates data to recognize spatial dimensions and furniture positions. The input is an image sent by the user, and the output is the spatial analysis result.

[0087] Step 3:

[0088] The server receives analysis results and user selection criteria (preferred style and color scheme) as input, and uses a generative AI model to generate optimal placement plans for the objects based on this information. As part of the data processing, the generative AI model refers to past design patterns and outputs a specific placement plan that matches the user's preferences.

[0089] Step 4:

[0090] The server sends the generated layout plan to the user's terminal. The terminal uses the received data to construct a virtual reality environment using a VR application. In this step, an environment is created on the terminal where the user can visually confirm the proposed layout through a VR headset or display. The layout data from the server is used as input, and the virtual reality environment is presented to the user as output.

[0091] Step 5:

[0092] The user explores the VR environment and checks the suggested furniture arrangement. After checking, the user provides feedback to the system. The input is the user's visual evaluation and comments, and the output is specific opinion information fed back to the system.

[0093] Step 6:

[0094] The server analyzes the feedback received from the user and passes it as input to the generating AI model. The AI ​​model re-evaluates the placement proposal based on the feedback and generates a new, more suitable placement proposal. The updated placement proposal is then output and sent back to the user's terminal.

[0095] (Application Example 1)

[0096] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0097] Rearranging and changing the layout of furniture is a time-consuming and laborious task for users, especially when dealing with large objects that are difficult to move physically. Therefore, there is a need for a method that allows for efficient and intuitive rearrangement. Furthermore, there is a need to automate not only the virtual layout planning but also the actual reflection of the layout in the physical space.

[0098] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0099] In this invention, the server includes an image acquisition means, an analysis means for analyzing the acquired image and recognizing the dimensions of the space and the position of objects, a generation means for generating object arrangements based on the analysis results and user preference information, a visualization means for visualizing the generated arrangements in a virtual reality environment, an adjustment means for obtaining feedback from the user and adjusting the arrangements, and a control means for reflecting the generated arrangements in physical space. This enables the user to efficiently rearrange their space and confirm and realize the proposed arrangements in both virtual and real space.

[0100] "Image acquisition means" refers to a device or software that acquires photographs or videos through user operation and provides them to the system.

[0101] "Analysis means" refers to a device or program that processes acquired image data and has the function of identifying spatial dimensions and object position information.

[0102] The "generation means" is a software module for proposing the optimal object placement based on the analysis results and the user's style preference information.

[0103] "Visualization means" refers to a device or program that displays the generated proposal in a virtual reality environment, allowing the user to visually confirm it.

[0104] A "modification mechanism" is a system component that has the function of receiving feedback from the user and optimizing the proposed arrangement based on that information.

[0105] "Control means" refers to devices or software used to operate machines or equipment in order to reflect the arrangement generated in a virtual space onto the actual physical space.

[0106] To implement this invention, a system is needed in which a user's terminal, a server responsible for controlling in-home devices, and a home robot work together. First, the user takes a photograph of the entire room they wish to redecorate using a smartphone or tablet. A dedicated application is installed on the terminal, and this application has the function of uploading the captured image to the server.

[0107] The server uses image processing libraries such as OpenCV for image analysis. Uploaded photos are analyzed by a generative AI model running on the server to determine the spatial dimensions and furniture positions. This allows the current layout to be obtained as digital data. The server also uses machine learning frameworks such as TENSORFLOW® to suggest the optimal furniture arrangement based on the user's specified preferences. This generated arrangement is then sent back to the user's terminal and visualized as a VR (virtual reality) environment.

[0108] Users can view the virtual space through a VR headset or smartphone and visually check the proposed layout from various angles. The feedback obtained here is sent back to the server, and the AI ​​model uses that feedback to propose even more rational layout options.

[0109] Furthermore, this system reflects a virtually proposed arrangement in physical space by having a household robot actually move the objects that are placed in the system. The robot can use a microcontroller such as Arduino or Raspberry Pi, and can move real furniture to a specified position using its built-in camera and sensors.

[0110] For example, if a user wants to change the position of a sofa and table in their living room, they can specify the arrangement using a prompt message on their device such as "Suggest a modern-style arrangement." This allows them to see the furniture arrangement in a virtual space, which is then ultimately implemented by a robot. In this way, users can intuitively and easily reconfigure their space while utilizing advanced technology.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The user takes a photo of the entire room they wish to redecorate using their smartphone. The input is a photo of the room, and the output is the image data after the photo is taken. This image data is uploaded to the server via a dedicated application.

[0114] Step 2:

[0115] The server receives the uploaded image data. Next, it performs image analysis using an image processing library such as OpenCV. The input is image data, and the output is room dimension data and furniture position data. The server saves this data in a digital format.

[0116] Step 3:

[0117] The server uses a generative AI model to generate the optimal furniture arrangement based on user preferences. The input consists of dimensional data obtained from analysis and user preferences, while the output is the proposed arrangement. The AI ​​model uses this data to calculate the recommended arrangement and stores the generated arrangement as digital data.

[0118] Step 4:

[0119] The server sends the generated layout plan to the user's terminal. The input is the proposed layout plan, and the output is the display of the virtual reality environment on the user's terminal. The user can visually confirm this layout plan in the virtual space through the terminal's VR application.

[0120] Step 5:

[0121] The user sends feedback obtained through the virtual space from their terminal to the server. The input is the user's feedback data, and the output is the storage of that feedback data on the server side. Based on this, the server adjusts the placement plan using an AI model and generates a more optimal proposal again.

[0122] Step 6:

[0123] Based on the generated layout plan, once the user approves the placement in the real world, a home robot begins moving the physical furniture through its control system. The input is the finally approved layout plan, and the output is the actual change in the furniture's placement in the physical space. The robot uses cameras and sensors, along with an Arduino or Raspberry Pi, to precisely implement the layout changes.

[0124] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0125] This invention is a system that utilizes an emotion engine to analyze user emotions and provide more precise and personalized room makeover suggestions. The system consists of a user's terminal, a server, and a communication network that connects them.

[0126] First, the user takes a picture of the room using their device and uploads the image data. At the same time, the user can record their facial expressions and voice through the application and send this data to the server. This data is analyzed in real time by an emotion engine to recognize the user's emotional state.

[0127] The server performs image analysis to determine the dimensions of the space and the placement of furniture. Then, the emotion engine interprets the user's emotions. For example, if it determines that the user is seeking relaxation, it can recommend an interior style with calming colors.

[0128] Based on the analysis results, the AI ​​model on the server optimizes the interior layout. This optimization process also takes into account information obtained from the emotion engine, automatically generating the optimal layout that matches the user's emotional state.

[0129] The generated interior design is visualized in a virtual reality environment, and the data is sent from the server to the terminal. A VR application on the terminal displays the proposed plan in three-dimensional space, allowing the user to experience it.

[0130] Users provide feedback on placement and design through their VR experience. Emotions may also be recorded again to gain a deeper understanding of the user's intentions. This feedback is sent to a server, where an emotion engine performs additional analysis, contributing to further improvements in optimal placement.

[0131] As a concrete example, let's assume a user desires a space that reduces stress. If tension is detected from the user's voice recorded on the device, the emotion engine transmits this to the server. The server then simulates a layout that contributes to relaxation (for example, arranging furniture to let in natural light or suggesting warm color schemes) in a VR environment and provides it to the user.

[0132] This invention offers a more comfortable and satisfying redecorating experience by proposing interior plans that dynamically reflect the user's emotions.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The user takes a picture of the room using their device's camera. The user then views and saves the captured image through the application.

[0136] Step 2:

[0137] The user uploads photos from the application to the server. At the same time, the user's facial expressions and voice are recorded by sensors on the device, and emotional data is also sent to the server.

[0138] Step 3:

[0139] The server analyzes the received image data. Using an image analysis module, the server extracts data to determine the dimensions of the space and the location of furniture.

[0140] Step 4:

[0141] The server uses an emotion engine to analyze the user's uploaded facial expressions and voice. The emotion engine determines the user's emotional state, such as relaxed or stressed.

[0142] Step 5:

[0143] The server uses a generative AI model to generate the optimal furniture arrangement based on the analysis results and emotional state. The design is customized based on the user's preferences and emotional information.

[0144] Step 6:

[0145] The server sends the generated layout plan to the terminal, which then launches the VR application. The VR application visualizes the proposed layout in a virtual reality environment.

[0146] Step 7:

[0147] Users experience a virtual space through a VR headset or device screen. Users then verify the proposed interior design by actually walking around in it.

[0148] Step 8:

[0149] The user inputs their feedback about the layout they experienced into the device. The device then sends this feedback to the server.

[0150] Step 9:

[0151] The server improves placement based on user feedback and newly recorded emotional data. The emotion engine analyzes the user's latest emotional state and incorporates it into the optimization process.

[0152] Step 10:

[0153] The server resends the updated layout plan to the terminal, which then provides the user with another chance to review it in a VR environment. This iterative process ensures that the optimal interior layout is achieved, thereby increasing user satisfaction.

[0154] (Example 2)

[0155] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0156] In modern living spaces, there is a demand for interior design that matches individual emotions and preferences. However, conventional systems have struggled to accurately reflect users' emotions in their proposals. Many design proposal systems are based solely on visual preferences, and designs that take into account the emotional state of the user are limited.

[0157] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0158] In this invention, the server includes an image acquisition means, an analysis means for analyzing the acquired image and recognizing the dimensions of the space and the position of objects, an emotion analysis means for analyzing the user's emotions, and a generation means for generating object arrangements based on the analysis results and emotion information. This makes it possible to propose personalized interior designs that reflect the user's emotion information.

[0159] "Image acquisition means" refers to a method or device for capturing images of a user's living space and importing that data into the system.

[0160] "Analysis means" refers to a function or device that performs processing to determine the dimensions of a space and the positions of objects placed in it from an acquired image.

[0161] "Emotion analysis means" refers to a function that analyzes and recognizes a user's emotional state based on their facial expressions and voice data.

[0162] "Generation means" refers to methods and technologies for creating optimal object placement and interior design proposals based on analysis results and user emotional information.

[0163] "Visualization means" refers to a method for reproducing the generated interior design proposals within a virtual reality environment and presenting them in a way that users can visually experience.

[0164] "Adjustment mechanisms" refer to functions that take user feedback into consideration, re-evaluate proposed designs and layouts, and make modifications as necessary.

[0165] This invention is a system that proposes interior design that takes into account the user's emotional state. This system uses the user's terminal, server, and communication network to assist in redecorating a room.

[0166] First, users can take photos of their room using their own devices and save the image data to their devices. Furthermore, a dedicated application is installed on the devices, which allows them to record facial expressions and voices. This data is then sent from the devices to the server.

[0167] The server uses image analysis algorithms to analyze the received image data and recognize the room dimensions and furniture placement. Simultaneously, an emotion analysis engine analyzes the user's emotional state from their facial expressions and voice data, and uses the results to infer the user's needs.

[0168] By utilizing a generative AI model, the server automatically generates the optimal interior layout based on the user's emotions. For example, if emotion analysis determines that the user desires a calming space, the server can suggest an interior style that promotes relaxation. This suggestion is then sent from the server to the user's device.

[0169] On the device, the proposed design is visualized in three-dimensional space using VR technology, allowing the user to experience it from various angles. The user inputs feedback obtained through the VR experience into the device, and this data is then sent back to the server.

[0170] The server takes user feedback into account and performs sentiment analysis again. This process further improves the proposed interior design, adjusting it to better meet user expectations.

[0171] For example, if a user desires a relaxing space, the system might detect tension in their facial expressions and voice data recorded on their device. The server then receives this information and suggests furniture arrangements that utilize natural light and warm color tones. This system helps users create a comfortable space that matches their own emotional state.

[0172] Example prompt: "I want to make the layout of this room relaxing. Please perform an emotion analysis based on my voice."

[0173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0174] Step 1:

[0175] The user takes pictures of the room using their device. The input is image data of the room acquired through the camera. The user also records their facial expressions and voice using an application on their device. This data is then sent from the device to the server. The output is image data and voice / facial expression data.

[0176] Step 2:

[0177] The server receives image data and uses image analysis algorithms to analyze the dimensions of the space and the positions of the furniture. The input is image data, and the processing involves various filtering and feature extraction. The output provides information on the dimensions of the space and the placement of the furniture.

[0178] Step 3:

[0179] The server feeds voice and facial expression data into an emotion analysis engine to analyze the user's emotional state. The input consists of facial expression data and voice data, and emotions are recognized using facial recognition and voice tone analysis. The output is data indicating the user's emotional state.

[0180] Step 4:

[0181] The server inputs the results of image analysis and emotion analysis into an AI model to generate the optimal interior layout. Inputs include spatial dimensions, furniture placement information, and the user's emotional state. The AI ​​model generates design proposals based on this information. The output is a personalized interior design proposal for the user.

[0182] Step 5:

[0183] The server sends the generated interior design to the terminal, which then visualizes it in a three-dimensional space using a VR application. The input is design proposal data, which is converted into a format viewable in a VR environment. The output is a three-dimensional interior view that the user can experience.

[0184] Step 6:

[0185] The user reviews the design proposal in a VR space and enters feedback into their device. This feedback includes preferred changes and suggestions for improvement. The device then sends this feedback back to the server.

[0186] Step 7:

[0187] The server analyzes user feedback and new sentiment data, and, if necessary, uses a generative AI model to propose a revised layout. Inputs include feedback and the latest sentiment data, and processing generates a new layout. The output is an improved design proposal.

[0188] (Application Example 2)

[0189] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0190] In modern times, there is a growing demand for spatial design that caters to individual preferences and emotional states. However, achieving this requires specialized knowledge and skills, making it difficult for the average user. In particular, dynamically optimizing interiors in response to emotional changes is not easy. Furthermore, the technology for machines to understand human emotions and reflect them in interior design is still immature, meaning that personalized design based on emotions has not been fully realized.

[0191] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0192] In this invention, the server includes an image acquisition device, an analysis device that analyzes the collected images and recognizes the dimensions of the space and the positions of objects, an emotion analysis device that detects the analysis results and the emotional state of the user, a generation device that generates object arrangements based on the emotional state, a visualization device that visualizes the generated arrangements within the visual environment, and an adjustment device that obtains evaluations from the user and adjusts the arrangements. This enables the provision of an optimal interior arrangement based on the emotional state of the user and the automation of emotionally responsive spatial design.

[0193] An "image acquisition device" is a device used to acquire images within a space, and that image data is used for analysis.

[0194] An "analysis device" is a device that analyzes acquired image data to understand spatial dimensions and the arrangement of objects.

[0195] An "emotion analysis device" is a device that detects the emotional state of a user and provides important information for generating interior design layouts based on that state.

[0196] A "generation device" is a device for designing an optimized object arrangement, taking into account the analysis results and the user's emotional state.

[0197] A "visualization device" is a device that displays the generated interior layout within a visual environment, allowing users to confirm it.

[0198] A "adjustment device" is a device that receives feedback from users and re-evaluates and adjusts the interior layout based on that feedback.

[0199] The system implementing the present invention includes a user interface device and a server device. First, an image acquisition device collects image data of the space. When a user is active in the smart home, a robot acquires image data by taking pictures of the room with a camera. This device is incorporated into the user's control device, and the collected data is transmitted to a server in the cloud.

[0200] The server uses an analysis device to analyze the transmitted images and determine the dimensions of the space and the positions of objects such as furniture. This analysis helps understand what kind of interior arrangement is possible and serves as foundational data. Furthermore, an emotion analysis device analyzes the user's real-time emotional state. For example, emotional needs such as whether the user is seeking relaxation or energy can be identified from facial expressions and voice data.

[0201] The generation device uses an AI model based on analysis results and data from the emotion analysis device to design an interior layout that suits the user's emotional state. This automatically generates an optimal, customized interior design for each individual user.

[0202] The generated interior layout is reproduced in a visual environment using a visualization device. For example, a robot can project a virtual interior plan into the user's room using a projector, allowing the user to experience it visually. The user can then review the visualized layout and provide feedback as needed.

[0203] The adjustment system uses user feedback to make necessary adjustments to the interior layout. Through this process, an interior that better meets the user's desired emotional needs is provided.

[0204] For example, if a user states, "I want to create a space where I can truly relax when I come home," the emotion analysis device will read the user's intention to relax from their voice and transmit that information to the generative AI model. The server may then suggest a layout that incorporates plenty of natural light. This layout can be projected into the room by a projector, allowing the user to visually confirm and evaluate it.

[0205] An example of a prompt message is: "User's mood: Desires relaxation. Room characteristics: Maximize natural light. Please propose the optimal interior design plan."

[0206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0207] Step 1:

[0208] The terminal activates the image acquisition device and acquires images of the room. It uses camera footage as input and obtains image data of the room as output. This is intended to capture the overall view of the room specified by the user.

[0209] Step 2:

[0210] The device uses an emotion analysis device to analyze the user's facial expressions and voice. The input is the user's facial expression data and voice data, and the output is the analysis result indicating the user's emotional state. In this step, the device identifies what emotional state the user is in, such as relaxed or tense.

[0211] Step 3:

[0212] The server analyzes image data acquired through the analysis device to determine the dimensions of the space and the location of furniture. The input is image data of the room, and the output is spatial mapping information. This clarifies the actual layout of the room.

[0213] Step 4:

[0214] The server generates interior layouts using an AI model based on emotion analysis results and spatial mapping information. The input is the user's emotional state and spatial information, and the output is an optimized interior layout plan. At this stage, emotion-based design proposals are materialized.

[0215] Step 5:

[0216] The server uses a visualization device to visually reproduce the interior layout generated on the terminal. The input is an optimized layout plan, and the output is a visually reproduced interior design. The user can review and visually experience this.

[0217] Step 6:

[0218] The user provides feedback on the visualized interior layout. The input is the user's evaluation of the visual design, and the output is feedback data. The user's feedback helps determine whether further layout adjustments are needed.

[0219] Step 7:

[0220] The server uses an adjustment device to readjust the interior layout, incorporating user feedback. The input is user feedback data, and the output is the adjusted interior layout. This provides an interior that better suits the user's needs.

[0221] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0222] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0223] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0227] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0228] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0229] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0230] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0231] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0232] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0233] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0234] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0235] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0236] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0237] This invention provides a system for efficiently and intuitively rearranging furniture, and its embodiments are described in detail below. This system mainly consists of a user's terminal, a server, and a communication network that connects them.

[0238] First, the user takes a photo of the entire room they want to redecorate using a smartphone or tablet. The user uploads this image to the server through a dedicated application. The uploaded image is processed by an image analysis module on the server, which automatically analyzes the dimensions of the space and the positions of objects such as furniture.

[0239] Next, the server uses the analyzed information to generate a virtual three-dimensional space and proposes the optimal layout for the interior design. The proposal is customized based on the user's preferences entered in the application, such as desired style and color scheme.

[0240] The generated layout plan is sent from the server to the user's terminal and visualized as a virtual reality (VR) environment. In this process, a VR application installed on the user's terminal generates a virtual space, providing the user with an immersive experience. The user can explore this virtual space using a VR headset or smartphone and visually check the proposed furniture layout from various angles.

[0241] Furthermore, after experiencing the virtual space, users provide feedback to the system, including their opinions and impressions. The server receives this feedback, and the AI ​​model adjusts and updates the layout plan based on it. Changes made in response to the user's new requests can then be viewed again in the virtual space.

[0242] As a concrete example, consider a scenario where a user wants to rearrange their living room. The user takes a photo of the living room with their smartphone and uploads it to the server. The server analyzes the photo, recognizes the positions of the sofa, table, and TV stand, and the amount of available space, and then proposes a new arrangement in a modern style. Based on the style chosen by the user, the server provides a plan that shifts the sofa slightly away from the center of the room and places the TV stand along the wall. The user can then review this in a VR environment, and if they wish to make further adjustments, the system will provide a new proposal that reflects those changes.

[0243] As described above, the present invention supports the optimization of interiors in a visually appealing way while saving users time and effort.

[0244] The following describes the processing flow.

[0245] Step 1:

[0246] The user takes a photo of the room using their device. The user activates the camera on their device and takes a photo to confirm the size of the room they want to redecorate, and after taking the photo, they review it through the application.

[0247] Step 2:

[0248] The application uploads photos taken by the user to the server. Pressing the upload button sends the photo data to the server via the network.

[0249] Step 3:

[0250] The server analyzes the received image data. Using an image analysis algorithm, the server extracts the necessary information to determine the room dimensions and the location of each piece of furniture. At this stage, deep learning techniques are applied to perform object recognition.

[0251] Step 4:

[0252] The server generates a 3D model based on the analysis results. Using the extracted dimensional information, it constructs a virtual three-dimensional model of the room and, based on that, generates an optimal furniture arrangement that takes into account the user's preferences.

[0253] Step 5:

[0254] The server sends data to the terminal to provide the user with a layout plan it has generated. The layout plan is encoded in a data format suitable for visualization in a VR application.

[0255] Step 6:

[0256] The system launches a VR application based on data received by the device and generates a virtual space. This creates an environment where users can experience the suggested furniture arrangement using a VR headset or the device's screen.

[0257] Step 7:

[0258] Users explore the virtual space and check the placement of furniture. Users can examine the placement and size of each piece of furniture from various angles and provide feedback on any points of concern or areas they would like to change.

[0259] Step 8:

[0260] The user sends feedback from the device to the server. The server receives the feedback and uses it to adjust the layout or suggest improvements.

[0261] Step 9:

[0262] The server analyzes the feedback it receives and readjusts the furniture placement using a generated AI model. The newly generated placement plan is then customized to the user's preferences.

[0263] Step 10:

[0264] The server sends the updated layout plan to the terminal, allowing users to review it again in the virtual space. This process allows users to experience the "optimal" interior layout that reflects their preferences.

[0265] (Example 1)

[0266] Next, we will describe Example 1. 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."

[0267] Traditional methods for redecorating and planning interior designs heavily rely on subjective judgments based on visual intuition and aesthetic sense, resulting in time-consuming and labor-intensive processes. Furthermore, optimizing object placement requires specialized knowledge, making it difficult for users to easily create high-quality spatial designs. There is a need to improve this situation and develop more efficient and intuitive methods for designing spaces.

[0268] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0269] In this invention, the server includes an image acquisition means that acquires visual information of the entire space using the user's observation device, an analysis means that analyzes the acquired visual information and recognizes the dimensions of the structure and the position of the object, and a generation means that generates the object arrangement based on the analysis results and the user's selection criteria. This allows the user to be effectively and quickly presented with the optimal arrangement based on their desired interior style, and furthermore, to visually confirm that arrangement in a realistic virtual environment.

[0270] "Image acquisition means" refers to elements used to acquire visual information of the entire space using the user's observation device.

[0271] "Analysis means" refers to an element that has the function of analyzing acquired visual information and recognizing the dimensions of a structure and the position of an object.

[0272] "Generation means" refers to means for concretizing the arrangement of objects based on analysis results and user selection criteria.

[0273] "Visualization means" refers to a device or process for converting a generated arrangement into a realistic virtual environment and displaying it visually.

[0274] "Adjustment means" are elements used to improve the arrangement of objects based on feedback received from users.

[0275] A "virtual environment" is a visual space that digitally reproduces a real-world space, and is an environment that users can visually confirm.

[0276] An "observation device" is a device operated by the user that has the function of capturing or recording the overall image of a space.

[0277] "Selection criteria" refer to information based on the user's preferred interior style and color scheme, and are the criteria considered when generating the layout.

[0278] This invention is a system for streamlining spatial design, utilizing a user's terminal, a server, and a communication network connecting the two.

[0279] The user uses a device such as a smartphone or tablet to take a photograph of the entire space they wish to redecorate. This device has an interface for sending the captured image to a server via an application.

[0280] The server performs image analysis on the received images. This analysis utilizes an image analysis module employing deep learning algorithms, enabling accurate recognition of spatial dimensions and the positions of objects such as furniture. Based on these analysis results and the user's selection criteria, such as preferred style and color scheme, the server automatically generates an optimal layout plan using a generative AI model. This generative AI model operates based on pre-collected and trained design patterns.

[0281] Next, the server sends the generated layout plan to the user's device. The VR application on the device uses this data to construct a virtual reality environment. The VR application visually presents the generated layout plan to the user through the VR headset or smartphone display. As a result, the user can confirm the spatial design based on the proposal with a sense of reality.

[0282] For example, if a user wants to redecorate their living room, they upload a photo of the living room taken with their smartphone to the server. The server analyzes the photo, recognizes the positions of the sofa and TV stand, and can then suggest a modern-style layout. This suggestion might include specific arrangements such as changing the sofa's position or placing the TV stand against the wall. This suggestion may also be generated by the following prompts.

[0283] Prompt example: "Please propose the optimal furniture arrangement when changing the living room to a modern style. The current arrangement has a sofa in the center and a TV stand in the corner."

[0284] Through this system, users can efficiently visually confirm the design of the space in a short time and receive an individual interior plan that suits their preferences.

[0285] The flow of the specific process in Example 1 will be described using FIG. 11.

[0286] Step 1:

[0287] The user uses the terminal to take a photo of the panorama of the space where they want to change the pattern. The terminal activates an interface to send this image to the server through a dedicated application. As input, the image of the space taken by the user is used. As output, the image is sent to the server.

[0288] Step 2:

[0289] The server acquires the received image and passes it to the image analysis module. The analysis module processes the image using a deep learning algorithm and generates data for recognizing the dimensions of the space and the positions of the furniture. As input, the image sent from the user is used, and as output, the space analysis result is obtained.

[0290] Step 3:

[0291] The server receives the analysis result and the user's selection criteria (preferred style and color tone) as input, and based on this, uses the generated AI model to generate an optimal arrangement plan for the object. As data processing, the generated AI model refers to past design patterns and outputs a specific arrangement plan that matches the user's preferences.

[0292] Step 4:

[0293] The server sends the generated layout plan to the user's terminal. The terminal uses the received data to construct a virtual reality environment using a VR application. In this step, an environment is created on the terminal where the user can visually confirm the proposed layout through a VR headset or display. The layout data from the server is used as input, and the virtual reality environment is presented to the user as output.

[0294] Step 5:

[0295] The user explores the VR environment and checks the suggested furniture arrangement. After checking, the user provides feedback to the system. The input is the user's visual evaluation and comments, and the output is specific opinion information fed back to the system.

[0296] Step 6:

[0297] The server analyzes the feedback received from the user and passes it as input to the generating AI model. The AI ​​model re-evaluates the placement proposal based on the feedback and generates a new, more suitable placement proposal. The updated placement proposal is then output and sent back to the user's terminal.

[0298] (Application Example 1)

[0299] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0300] Rearranging and changing the layout of furniture is a time-consuming and laborious task for users, especially when dealing with large objects that are difficult to move physically. Therefore, there is a need for a method that allows for efficient and intuitive rearrangement. Furthermore, there is a need to automate not only the virtual layout planning but also the actual reflection of the layout in the physical space.

[0301] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following respective means.

[0302] In this invention, the server includes an image acquisition means, an analysis means for analyzing the acquired image to recognize the dimensions of the space and the positions of the objects, a generation means for generating an object arrangement based on the analysis result and the preference information of the user, a visualization means for visualizing the generated arrangement in a virtual reality environment, an adjustment means for acquiring feedback from the user and adjusting the arrangement, and a control means for reflecting the generated arrangement in the physical space. Thereby, the user can efficiently perform pattern replacement and confirm and realize the proposed arrangement in the virtual space and the real space.

[0303] The "image acquisition means" is a device or software for acquiring photos and videos by the operation of the user and providing them to the system.

[0304] The "analysis means" is a device or program having a function for processing the acquired image data and specifying the dimensions of the space and the position information of the objects.

[0305] The "generation means" is a software module for proposing an optimal object arrangement based on the analysis result and the user's style preference information.

[0306] The "visualization means" is a device or program for displaying the generated proposal in a virtual reality environment so that the user can visually confirm it.

[0307] The "adjustment means" is a system component having a function for receiving feedback from the user and optimizing the proposed arrangement based on the information.

[0308] The "control means" is a device or software for operating a machine or device in order to reflect the arrangement generated in the virtual space in the actual physical space.

[0309] To implement this invention, a system is needed in which a user's terminal, a server responsible for controlling in-home devices, and a home robot work together. First, the user takes a photograph of the entire room they wish to redecorate using a smartphone or tablet. A dedicated application is installed on the terminal, and this application has the function of uploading the captured image to the server.

[0310] The server uses image processing libraries such as OpenCV for image analysis. Uploaded photos are analyzed by a generative AI model running on the server to determine the spatial dimensions and furniture positions. This allows the current layout to be obtained as digital data. The server also uses machine learning frameworks such as TensorFlow to suggest the optimal furniture arrangement based on the user's specified preferences. This generated arrangement is then sent back to the user's terminal and visualized as a VR (virtual reality) environment.

[0311] Users can view the virtual space through a VR headset or smartphone and visually check the proposed layout from various angles. The feedback obtained here is sent back to the server, and the AI ​​model uses that feedback to propose even more rational layout options.

[0312] Furthermore, this system reflects a virtually proposed arrangement in physical space by having a household robot actually move the objects that are placed in the system. The robot can use a microcontroller such as Arduino or Raspberry Pi, and can move real furniture to a specified position using its built-in camera and sensors.

[0313] For example, if a user wants to change the position of a sofa and table in their living room, they can specify the arrangement using a prompt message on their device such as "Suggest a modern-style arrangement." This allows them to see the furniture arrangement in a virtual space, which is then ultimately implemented by a robot. In this way, users can intuitively and easily reconfigure their space while utilizing advanced technology.

[0314] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0315] Step 1:

[0316] The user takes a photo of the entire room they wish to redecorate using their smartphone. The input is a photo of the room, and the output is the image data after the photo is taken. This image data is uploaded to the server via a dedicated application.

[0317] Step 2:

[0318] The server receives the uploaded image data. Next, it performs image analysis using an image processing library such as OpenCV. The input is image data, and the output is room dimension data and furniture position data. The server saves this data in a digital format.

[0319] Step 3:

[0320] The server uses a generative AI model to generate the optimal furniture arrangement based on user preferences. The input consists of dimensional data obtained from analysis and user preferences, while the output is the proposed arrangement. The AI ​​model uses this data to calculate the recommended arrangement and stores the generated arrangement as digital data.

[0321] Step 4:

[0322] The server sends the generated layout plan to the user's terminal. The input is the proposed layout plan, and the output is the display of the virtual reality environment on the user's terminal. The user can visually confirm this layout plan in the virtual space through the terminal's VR application.

[0323] Step 5:

[0324] The user sends feedback obtained through the virtual space from their terminal to the server. The input is the user's feedback data, and the output is the storage of that feedback data on the server side. Based on this, the server adjusts the placement plan using an AI model and generates a more optimal proposal again.

[0325] Step 6:

[0326] Based on the generated layout plan, once the user approves the placement in the real world, a home robot begins moving the physical furniture through its control system. The input is the finally approved layout plan, and the output is the actual change in the furniture's placement in the physical space. The robot uses cameras and sensors, along with an Arduino or Raspberry Pi, to precisely implement the layout changes.

[0327] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0328] This invention is a system that utilizes an emotion engine to analyze user emotions and provide more precise and personalized room makeover suggestions. The system consists of a user's terminal, a server, and a communication network that connects them.

[0329] First, the user takes a picture of the room using their device and uploads the image data. At the same time, the user can record their facial expressions and voice through the application and send this data to the server. This data is analyzed in real time by an emotion engine to recognize the user's emotional state.

[0330] The server performs image analysis to determine the dimensions of the space and the placement of furniture. Then, the emotion engine interprets the user's emotions. For example, if it determines that the user is seeking relaxation, it can recommend an interior style with calming colors.

[0331] Based on the analysis results, the AI ​​model on the server optimizes the interior layout. This optimization process also takes into account information obtained from the emotion engine, automatically generating the optimal layout that matches the user's emotional state.

[0332] The generated interior design is visualized in a virtual reality environment, and the data is sent from the server to the terminal. A VR application on the terminal displays the proposed plan in three-dimensional space, allowing the user to experience it.

[0333] Users provide feedback on placement and design through their VR experience. Emotions may also be recorded again to gain a deeper understanding of the user's intentions. This feedback is sent to a server, where an emotion engine performs additional analysis, contributing to further improvements in optimal placement.

[0334] As a concrete example, let's assume a user desires a space that reduces stress. If tension is detected from the user's voice recorded on the device, the emotion engine transmits this to the server. The server then simulates a layout that contributes to relaxation (for example, arranging furniture to let in natural light or suggesting warm color schemes) in a VR environment and provides it to the user.

[0335] This invention offers a more comfortable and satisfying redecorating experience by proposing interior plans that dynamically reflect the user's emotions.

[0336] The following describes the processing flow.

[0337] Step 1:

[0338] The user takes a picture of the room using their device's camera. The user then views and saves the captured image through the application.

[0339] Step 2:

[0340] The user uploads photos from the application to the server. At the same time, the user's facial expressions and voice are recorded by sensors on the device, and emotional data is also sent to the server.

[0341] Step 3:

[0342] The server analyzes the received image data. Using an image analysis module, the server extracts data to determine the dimensions of the space and the location of furniture.

[0343] Step 4:

[0344] The server uses an emotion engine to analyze the user's uploaded facial expressions and voice. The emotion engine determines the user's emotional state, such as relaxed or stressed.

[0345] Step 5:

[0346] The server uses a generative AI model to generate the optimal furniture arrangement based on the analysis results and emotional state. The design is customized based on the user's preferences and emotional information.

[0347] Step 6:

[0348] The server sends the generated layout plan to the terminal, which then launches the VR application. The VR application visualizes the proposed layout in a virtual reality environment.

[0349] Step 7:

[0350] Users experience a virtual space through a VR headset or device screen. Users then verify the proposed interior design by actually walking around in it.

[0351] Step 8:

[0352] The user inputs their feedback about the layout they experienced into the device. The device then sends this feedback to the server.

[0353] Step 9:

[0354] The server improves placement based on user feedback and newly recorded emotional data. The emotion engine analyzes the user's latest emotional state and incorporates it into the optimization process.

[0355] Step 10:

[0356] The server resends the updated layout plan to the terminal, which then provides the user with another chance to review it in a VR environment. This iterative process ensures that the optimal interior layout is achieved, thereby increasing user satisfaction.

[0357] (Example 2)

[0358] Next, we will describe Example 2. 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".

[0359] In modern living spaces, there is a demand for interior design that matches individual emotions and preferences. However, conventional systems have struggled to accurately reflect users' emotions in their proposals. Many design proposal systems are based solely on visual preferences, and designs that take into account the emotional state of the user are limited.

[0360] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0361] In this invention, the server includes an image acquisition means, an analysis means for analyzing the acquired image and recognizing the dimensions of the space and the position of objects, an emotion analysis means for analyzing the user's emotions, and a generation means for generating object arrangements based on the analysis results and emotion information. This makes it possible to propose personalized interior designs that reflect the user's emotion information.

[0362] "Image acquisition means" refers to a method or device for capturing images of a user's living space and importing that data into the system.

[0363] "Analysis means" refers to a function or device that performs processing to determine the dimensions of a space and the positions of objects placed in it from an acquired image.

[0364] "Emotion analysis means" refers to a function that analyzes and recognizes a user's emotional state based on their facial expressions and voice data.

[0365] "Generation means" refers to methods and technologies for creating optimal object placement and interior design proposals based on analysis results and user emotional information.

[0366] "Visualization means" refers to a method for reproducing the generated interior design proposals within a virtual reality environment and presenting them in a way that users can visually experience.

[0367] "Adjustment mechanisms" refer to functions that take user feedback into consideration, re-evaluate proposed designs and layouts, and make modifications as necessary.

[0368] This invention is a system that proposes interior design that takes into account the user's emotional state. This system uses the user's terminal, server, and communication network to assist in redecorating a room.

[0369] First, users can take photos of their room using their own devices and save the image data to their devices. Furthermore, a dedicated application is installed on the devices, which allows them to record facial expressions and voices. This data is then sent from the devices to the server.

[0370] The server uses image analysis algorithms to analyze the received image data and recognize the room dimensions and furniture placement. Simultaneously, an emotion analysis engine analyzes the user's emotional state from their facial expressions and voice data, and uses the results to infer the user's needs.

[0371] By utilizing a generative AI model, the server automatically generates the optimal interior layout based on the user's emotions. For example, if emotion analysis determines that the user desires a calming space, the server can suggest an interior style that promotes relaxation. This suggestion is then sent from the server to the user's device.

[0372] On the device, the proposed design is visualized in three-dimensional space using VR technology, allowing the user to experience it from various angles. The user inputs feedback obtained through the VR experience into the device, and this data is then sent back to the server.

[0373] The server takes user feedback into account and performs sentiment analysis again. This process further improves the proposed interior design, adjusting it to better meet user expectations.

[0374] For example, if a user desires a relaxing space, the system might detect tension in their facial expressions and voice data recorded on their device. The server then receives this information and suggests furniture arrangements that utilize natural light and warm color tones. This system helps users create a comfortable space that matches their own emotional state.

[0375] Example prompt: "I want to make the layout of this room relaxing. Please perform an emotion analysis based on my voice."

[0376] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0377] Step 1:

[0378] The user takes pictures of the room using their device. The input is image data of the room acquired through the camera. The user also records their facial expressions and voice using an application on their device. This data is then sent from the device to the server. The output is image data and voice / facial expression data.

[0379] Step 2:

[0380] The server receives image data and uses image analysis algorithms to analyze the dimensions of the space and the positions of the furniture. The input is image data, and the processing involves various filtering and feature extraction. The output provides information on the dimensions of the space and the placement of the furniture.

[0381] Step 3:

[0382] The server feeds voice and facial expression data into an emotion analysis engine to analyze the user's emotional state. The input consists of facial expression data and voice data, and emotions are recognized using facial recognition and voice tone analysis. The output is data indicating the user's emotional state.

[0383] Step 4:

[0384] The server inputs the results of image analysis and emotion analysis into an AI model to generate the optimal interior layout. Inputs include spatial dimensions, furniture placement information, and the user's emotional state. The AI ​​model generates design proposals based on this information. The output is a personalized interior design proposal for the user.

[0385] Step 5:

[0386] The server sends the generated interior design to the terminal, which then visualizes it in a three-dimensional space using a VR application. The input is design proposal data, which is converted into a format viewable in a VR environment. The output is a three-dimensional interior view that the user can experience.

[0387] Step 6:

[0388] The user reviews the design proposal in a VR space and enters feedback into their device. This feedback includes preferred changes and suggestions for improvement. The device then sends this feedback back to the server.

[0389] Step 7:

[0390] The server analyzes user feedback and new sentiment data, and, if necessary, uses a generative AI model to propose a revised layout. Inputs include feedback and the latest sentiment data, and processing generates a new layout. The output is an improved design proposal.

[0391] (Application Example 2)

[0392] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0393] In modern times, there is a growing demand for spatial design that caters to individual preferences and emotional states. However, achieving this requires specialized knowledge and skills, making it difficult for the average user. In particular, dynamically optimizing interiors in response to emotional changes is not easy. Furthermore, the technology for machines to understand human emotions and reflect them in interior design is still immature, meaning that personalized design based on emotions has not been fully realized.

[0394] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0395] In this invention, the server includes an image acquisition device, an analysis device that analyzes the collected images and recognizes the dimensions of the space and the positions of objects, an emotion analysis device that detects the analysis results and the emotional state of the user, a generation device that generates object arrangements based on the emotional state, a visualization device that visualizes the generated arrangements within the visual environment, and an adjustment device that obtains evaluations from the user and adjusts the arrangements. This enables the provision of an optimal interior arrangement based on the emotional state of the user and the automation of emotionally responsive spatial design.

[0396] An "image acquisition device" is a device used to acquire images within a space, and that image data is used for analysis.

[0397] An "analysis device" is a device that analyzes acquired image data to understand spatial dimensions and the arrangement of objects.

[0398] An "emotion analysis device" is a device that detects the emotional state of a user and provides important information for generating interior design layouts based on that state.

[0399] A "generation device" is a device for designing an optimized object arrangement, taking into account the analysis results and the user's emotional state.

[0400] A "visualization device" is a device that displays the generated interior layout within a visual environment, allowing users to confirm it.

[0401] A "adjustment device" is a device that receives feedback from users and re-evaluates and adjusts the interior layout based on that feedback.

[0402] The system implementing the present invention includes a user interface device and a server device. First, an image acquisition device collects image data of the space. When a user is active in the smart home, a robot acquires image data by taking pictures of the room with a camera. This device is incorporated into the user's control device, and the collected data is transmitted to a server in the cloud.

[0403] The server uses an analysis device to analyze the transmitted images and determine the dimensions of the space and the positions of objects such as furniture. This analysis helps understand what kind of interior arrangement is possible and serves as foundational data. Furthermore, an emotion analysis device analyzes the user's real-time emotional state. For example, emotional needs such as whether the user is seeking relaxation or energy can be identified from facial expressions and voice data.

[0404] The generation device uses an AI model based on analysis results and data from the emotion analysis device to design an interior layout that suits the user's emotional state. This automatically generates an optimal, customized interior design for each individual user.

[0405] The generated interior layout is reproduced in a visual environment using a visualization device. For example, a robot can project a virtual interior plan into the user's room using a projector, allowing the user to experience it visually. The user can then review the visualized layout and provide feedback as needed.

[0406] The adjustment system uses user feedback to make necessary adjustments to the interior layout. Through this process, an interior that better meets the user's desired emotional needs is provided.

[0407] For example, if a user states, "I want to create a space where I can truly relax when I come home," the emotion analysis device will read the user's intention to relax from their voice and transmit that information to the generative AI model. The server may then suggest a layout that incorporates plenty of natural light. This layout can be projected into the room by a projector, allowing the user to visually confirm and evaluate it.

[0408] An example of a prompt message is: "User's mood: Desires relaxation. Room characteristics: Maximize natural light. Please propose the optimal interior design plan."

[0409] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0410] Step 1:

[0411] The terminal activates the image acquisition device and acquires images of the room. It uses camera footage as input and obtains image data of the room as output. This is intended to capture the overall view of the room specified by the user.

[0412] Step 2:

[0413] The device uses an emotion analysis device to analyze the user's facial expressions and voice. The input is the user's facial expression data and voice data, and the output is the analysis result indicating the user's emotional state. In this step, the device identifies what emotional state the user is in, such as relaxed or tense.

[0414] Step 3:

[0415] The server analyzes image data acquired through the analysis device to determine the dimensions of the space and the location of furniture. The input is image data of the room, and the output is spatial mapping information. This clarifies the actual layout of the room.

[0416] Step 4:

[0417] The server generates interior layouts using an AI model based on emotion analysis results and spatial mapping information. The input is the user's emotional state and spatial information, and the output is an optimized interior layout plan. At this stage, emotion-based design proposals are materialized.

[0418] Step 5:

[0419] The server uses a visualization device to visually reproduce the interior layout generated on the terminal. The input is an optimized layout plan, and the output is a visually reproduced interior design. The user can review and visually experience this.

[0420] Step 6:

[0421] The user provides feedback on the visualized interior layout. The input is the user's evaluation of the visual design, and the output is feedback data. The user's feedback helps determine whether further layout adjustments are needed.

[0422] Step 7:

[0423] The server uses an adjustment device to readjust the interior layout, incorporating user feedback. The input is user feedback data, and the output is the adjusted interior layout. This provides an interior that better suits the user's needs.

[0424] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0425] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0426] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0427] [Third Embodiment]

[0428] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0429] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0430] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0431] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0432] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0433] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0434] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0435] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0436] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0437] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0438] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0439] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0440] This invention provides a system for efficiently and intuitively rearranging furniture, and its embodiments are described in detail below. This system mainly consists of a user's terminal, a server, and a communication network that connects them.

[0441] First, the user takes a photo of the entire room they want to redecorate using a smartphone or tablet. The user uploads this image to the server through a dedicated application. The uploaded image is processed by an image analysis module on the server, which automatically analyzes the dimensions of the space and the positions of objects such as furniture.

[0442] Next, the server uses the analyzed information to generate a virtual three-dimensional space and proposes the optimal layout for the interior design. The proposal is customized based on the user's preferences entered in the application, such as desired style and color scheme.

[0443] The generated layout plan is sent from the server to the user's terminal and visualized as a virtual reality (VR) environment. In this process, a VR application installed on the user's terminal generates a virtual space, providing the user with an immersive experience. The user can explore this virtual space using a VR headset or smartphone and visually check the proposed furniture layout from various angles.

[0444] Furthermore, after experiencing the virtual space, users provide feedback to the system, including their opinions and impressions. The server receives this feedback, and the AI ​​model adjusts and updates the layout plan based on it. Changes made in response to the user's new requests can then be viewed again in the virtual space.

[0445] As a concrete example, consider a scenario where a user wants to rearrange their living room. The user takes a photo of the living room with their smartphone and uploads it to the server. The server analyzes the photo, recognizes the positions of the sofa, table, and TV stand, and the amount of available space, and then proposes a new arrangement in a modern style. Based on the style chosen by the user, the server provides a plan that shifts the sofa slightly away from the center of the room and places the TV stand along the wall. The user can then review this in a VR environment, and if they wish to make further adjustments, the system will provide a new proposal that reflects those changes.

[0446] As described above, the present invention supports the optimization of interiors in a visually appealing way while saving users time and effort.

[0447] The following describes the processing flow.

[0448] Step 1:

[0449] The user takes a photo of the room using their device. The user activates the camera on their device and takes a photo to confirm the size of the room they want to redecorate, and after taking the photo, they review it through the application.

[0450] Step 2:

[0451] The application uploads photos taken by the user to the server. Pressing the upload button sends the photo data to the server via the network.

[0452] Step 3:

[0453] The server analyzes the received image data. Using an image analysis algorithm, the server extracts the necessary information to determine the room dimensions and the location of each piece of furniture. At this stage, deep learning techniques are applied to perform object recognition.

[0454] Step 4:

[0455] The server generates a 3D model based on the analysis results. Using the extracted dimensional information, it constructs a virtual three-dimensional model of the room and, based on that, generates an optimal furniture arrangement that takes into account the user's preferences.

[0456] Step 5:

[0457] The server sends data to the terminal to provide the user with a layout plan it has generated. The layout plan is encoded in a data format suitable for visualization in a VR application.

[0458] Step 6:

[0459] The system launches a VR application based on data received by the device and generates a virtual space. This creates an environment where users can experience the suggested furniture arrangement using a VR headset or the device's screen.

[0460] Step 7:

[0461] Users explore the virtual space and check the placement of furniture. Users can examine the placement and size of each piece of furniture from various angles and provide feedback on any points of concern or areas they would like to change.

[0462] Step 8:

[0463] The user sends feedback from the device to the server. The server receives the feedback and uses it to adjust the layout or suggest improvements.

[0464] Step 9:

[0465] The server analyzes the feedback it receives and readjusts the furniture placement using a generated AI model. The newly generated placement plan is then customized to the user's preferences.

[0466] Step 10:

[0467] The server sends the updated layout plan to the terminal, allowing users to review it again in the virtual space. This process allows users to experience the "optimal" interior layout that reflects their preferences.

[0468] (Example 1)

[0469] Next, we will describe Example 1. 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."

[0470] Traditional methods for redecorating and planning interior designs heavily rely on subjective judgments based on visual intuition and aesthetic sense, resulting in time-consuming and labor-intensive processes. Furthermore, optimizing object placement requires specialized knowledge, making it difficult for users to easily create high-quality spatial designs. There is a need to improve this situation and develop more efficient and intuitive methods for designing spaces.

[0471] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0472] In this invention, the server includes an image acquisition means that acquires visual information of the entire space using the user's observation device, an analysis means that analyzes the acquired visual information and recognizes the dimensions of the structure and the position of the object, and a generation means that generates the object arrangement based on the analysis results and the user's selection criteria. This allows the user to be effectively and quickly presented with the optimal arrangement based on their desired interior style, and furthermore, to visually confirm that arrangement in a realistic virtual environment.

[0473] "Image acquisition means" refers to elements used to acquire visual information of the entire space using the user's observation device.

[0474] "Analysis means" refers to an element that has the function of analyzing acquired visual information and recognizing the dimensions of a structure and the position of an object.

[0475] "Generation means" refers to means for concretizing the arrangement of objects based on analysis results and user selection criteria.

[0476] "Visualization means" refers to a device or process for converting a generated arrangement into a realistic virtual environment and displaying it visually.

[0477] "Adjustment means" are elements used to improve the arrangement of objects based on feedback received from users.

[0478] A "virtual environment" is a visual space that digitally reproduces a real-world space, and is an environment that users can visually confirm.

[0479] An "observation device" is a device operated by the user that has the function of capturing or recording the overall image of a space.

[0480] "Selection criteria" refer to information based on the user's preferred interior style and color scheme, and are the criteria considered when generating the layout.

[0481] This invention is a system for streamlining spatial design, utilizing a user's terminal, a server, and a communication network connecting the two.

[0482] The user uses a device such as a smartphone or tablet to take a photograph of the entire space they wish to redecorate. This device has an interface for sending the captured image to a server via an application.

[0483] The server performs image analysis on the received images. This analysis utilizes an image analysis module employing deep learning algorithms, enabling accurate recognition of spatial dimensions and the positions of objects such as furniture. Based on these analysis results and the user's selection criteria, such as preferred style and color scheme, the server automatically generates an optimal layout plan using a generative AI model. This generative AI model operates based on pre-collected and trained design patterns.

[0484] Next, the server sends the generated layout plan to the user's device. The VR application on the device uses this data to construct a virtual reality environment. The VR application visually presents the generated layout plan to the user through the VR headset or smartphone display. As a result, the user can confirm the spatial design based on the proposal with a sense of reality.

[0485] For example, if a user wants to redecorate their living room, they upload a photo of the living room taken with their smartphone to the server. The server analyzes the photo, recognizes the positions of the sofa and TV stand, and can then suggest a modern-style layout. This suggestion might include specific arrangements such as changing the sofa's position or placing the TV stand against the wall. This suggestion may also be generated by the following prompts.

[0486] Example prompt: "Please suggest the best furniture arrangement when changing the living room to a modern style. The current arrangement has a sofa in the center and a TV stand in the corner."

[0487] Through this system, users can quickly and efficiently visually review space designs and receive personalized interior plans tailored to their preferences.

[0488] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0489] Step 1:

[0490] The user uses a device to take a photograph of the entire space they wish to redecorate. The device then activates an interface via a dedicated application to send this image to a server. The image of the space taken by the user is used as input. The output is the image sent to the server.

[0491] Step 2:

[0492] The server retrieves the received image and passes it to the image analysis module. The analysis module processes the image using a deep learning algorithm and generates data to recognize spatial dimensions and furniture positions. The input is an image sent by the user, and the output is the spatial analysis result.

[0493] Step 3:

[0494] The server receives analysis results and user selection criteria (preferred style and color scheme) as input, and uses a generative AI model to generate optimal placement plans for the objects based on this information. As part of the data processing, the generative AI model refers to past design patterns and outputs a specific placement plan that matches the user's preferences.

[0495] Step 4:

[0496] The server sends the generated layout plan to the user's terminal. The terminal uses the received data to construct a virtual reality environment using a VR application. In this step, an environment is created on the terminal where the user can visually confirm the proposed layout through a VR headset or display. The layout data from the server is used as input, and the virtual reality environment is presented to the user as output.

[0497] Step 5:

[0498] The user explores the VR environment and checks the suggested furniture arrangement. After checking, the user provides feedback to the system. The input is the user's visual evaluation and comments, and the output is specific opinion information fed back to the system.

[0499] Step 6:

[0500] The server analyzes the feedback received from the user and passes it as input to the generating AI model. The AI ​​model re-evaluates the placement proposal based on the feedback and generates a new, more suitable placement proposal. The updated placement proposal is then output and sent back to the user's terminal.

[0501] (Application Example 1)

[0502] Next, we will explain Application Example 1. In the following explanation, 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."

[0503] Rearranging and changing the layout of furniture is a time-consuming and laborious task for users, especially when dealing with large objects that are difficult to move physically. Therefore, there is a need for a method that allows for efficient and intuitive rearrangement. Furthermore, there is a need to automate not only the virtual layout planning but also the actual reflection of the layout in the physical space.

[0504] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0505] In this invention, the server includes an image acquisition means, an analysis means for analyzing the acquired image and recognizing the dimensions of the space and the position of objects, a generation means for generating object arrangements based on the analysis results and user preference information, a visualization means for visualizing the generated arrangements in a virtual reality environment, an adjustment means for obtaining feedback from the user and adjusting the arrangements, and a control means for reflecting the generated arrangements in physical space. This enables the user to efficiently rearrange their space and confirm and realize the proposed arrangements in both virtual and real space.

[0506] "Image acquisition means" refers to a device or software that acquires photographs or videos through user operation and provides them to the system.

[0507] "Analysis means" refers to a device or program that processes acquired image data and has the function of identifying spatial dimensions and object position information.

[0508] The "generation means" is a software module for proposing the optimal object placement based on the analysis results and the user's style preference information.

[0509] "Visualization means" refers to a device or program that displays the generated proposal in a virtual reality environment, allowing the user to visually confirm it.

[0510] A "modification mechanism" is a system component that has the function of receiving feedback from the user and optimizing the proposed arrangement based on that information.

[0511] "Control means" refers to devices or software used to operate machines or equipment in order to reflect the arrangement generated in a virtual space onto the actual physical space.

[0512] To implement this invention, a system is needed in which a user's terminal, a server responsible for controlling in-home devices, and a home robot work together. First, the user takes a photograph of the entire room they wish to redecorate using a smartphone or tablet. A dedicated application is installed on the terminal, and this application has the function of uploading the captured image to the server.

[0513] The server uses image processing libraries such as OpenCV for image analysis. Uploaded photos are analyzed by a generative AI model running on the server to determine the spatial dimensions and furniture positions. This allows the current layout to be obtained as digital data. The server also uses machine learning frameworks such as TensorFlow to suggest the optimal furniture arrangement based on the user's specified preferences. This generated arrangement is then sent back to the user's terminal and visualized as a VR (virtual reality) environment.

[0514] Users can view the virtual space through a VR headset or smartphone and visually check the proposed layout from various angles. The feedback obtained here is sent back to the server, and the AI ​​model uses that feedback to propose even more rational layout options.

[0515] Furthermore, this system reflects a virtually proposed arrangement in physical space by having a household robot actually move the objects that are placed in the system. The robot can use a microcontroller such as Arduino or Raspberry Pi, and can move real furniture to a specified position using its built-in camera and sensors.

[0516] For example, if a user wants to change the position of a sofa and table in their living room, they can specify the arrangement using a prompt message on their device such as "Suggest a modern-style arrangement." This allows them to see the furniture arrangement in a virtual space, which is then ultimately implemented by a robot. In this way, users can intuitively and easily reconfigure their space while utilizing advanced technology.

[0517] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0518] Step 1:

[0519] The user takes a photo of the entire room they wish to redecorate using their smartphone. The input is a photo of the room, and the output is the image data after the photo is taken. This image data is uploaded to the server via a dedicated application.

[0520] Step 2:

[0521] The server receives the uploaded image data. Next, it performs image analysis using an image processing library such as OpenCV. The input is image data, and the output is room dimension data and furniture position data. The server saves this data in a digital format.

[0522] Step 3:

[0523] The server uses a generative AI model to generate the optimal furniture arrangement based on user preferences. The input consists of dimensional data obtained from analysis and user preferences, while the output is the proposed arrangement. The AI ​​model uses this data to calculate the recommended arrangement and stores the generated arrangement as digital data.

[0524] Step 4:

[0525] The server sends the generated layout plan to the user's terminal. The input is the proposed layout plan, and the output is the display of the virtual reality environment on the user's terminal. The user can visually confirm this layout plan in the virtual space through the terminal's VR application.

[0526] Step 5:

[0527] The user sends feedback obtained through the virtual space from their terminal to the server. The input is the user's feedback data, and the output is the storage of that feedback data on the server side. Based on this, the server adjusts the placement plan using an AI model and generates a more optimal proposal again.

[0528] Step 6:

[0529] Based on the generated layout plan, once the user approves the placement in the real world, a home robot begins moving the physical furniture through its control system. The input is the finally approved layout plan, and the output is the actual change in the furniture's placement in the physical space. The robot uses cameras and sensors, along with an Arduino or Raspberry Pi, to precisely implement the layout changes.

[0530] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0531] This invention is a system that utilizes an emotion engine to analyze user emotions and provide more precise and personalized room makeover suggestions. The system consists of a user's terminal, a server, and a communication network that connects them.

[0532] First, the user takes a picture of the room using their device and uploads the image data. At the same time, the user can record their facial expressions and voice through the application and send this data to the server. This data is analyzed in real time by an emotion engine to recognize the user's emotional state.

[0533] The server performs image analysis to determine the dimensions of the space and the placement of furniture. Then, the emotion engine interprets the user's emotions. For example, if it determines that the user is seeking relaxation, it can recommend an interior style with calming colors.

[0534] Based on the analysis results, the AI ​​model on the server optimizes the interior layout. This optimization process also takes into account information obtained from the emotion engine, automatically generating the optimal layout that matches the user's emotional state.

[0535] The generated interior design is visualized in a virtual reality environment, and the data is sent from the server to the terminal. A VR application on the terminal displays the proposed plan in three-dimensional space, allowing the user to experience it.

[0536] Users provide feedback on placement and design through their VR experience. Emotions may also be recorded again to gain a deeper understanding of the user's intentions. This feedback is sent to a server, where an emotion engine performs additional analysis, contributing to further improvements in optimal placement.

[0537] As a concrete example, let's assume a user desires a space that reduces stress. If tension is detected from the user's voice recorded on the device, the emotion engine transmits this to the server. The server then simulates a layout that contributes to relaxation (for example, arranging furniture to let in natural light or suggesting warm color schemes) in a VR environment and provides it to the user.

[0538] This invention offers a more comfortable and satisfying redecorating experience by proposing interior plans that dynamically reflect the user's emotions.

[0539] The following describes the processing flow.

[0540] Step 1:

[0541] The user takes a picture of the room using their device's camera. The user then views and saves the captured image through the application.

[0542] Step 2:

[0543] The user uploads photos from the application to the server. At the same time, the user's facial expressions and voice are recorded by sensors on the device, and emotional data is also sent to the server.

[0544] Step 3:

[0545] The server analyzes the received image data. Using an image analysis module, the server extracts data to determine the dimensions of the space and the location of furniture.

[0546] Step 4:

[0547] The server uses an emotion engine to analyze the user's uploaded facial expressions and voice. The emotion engine determines the user's emotional state, such as relaxed or stressed.

[0548] Step 5:

[0549] The server uses a generative AI model to generate the optimal furniture arrangement based on the analysis results and emotional state. The design is customized based on the user's preferences and emotional information.

[0550] Step 6:

[0551] The server sends the generated layout plan to the terminal, which then launches the VR application. The VR application visualizes the proposed layout in a virtual reality environment.

[0552] Step 7:

[0553] Users experience a virtual space through a VR headset or device screen. Users then verify the proposed interior design by actually walking around in it.

[0554] Step 8:

[0555] The user inputs their feedback about the layout they experienced into the device. The device then sends this feedback to the server.

[0556] Step 9:

[0557] The server improves placement based on user feedback and newly recorded emotional data. The emotion engine analyzes the user's latest emotional state and incorporates it into the optimization process.

[0558] Step 10:

[0559] The server resends the updated layout plan to the terminal, which then provides the user with another chance to review it in a VR environment. This iterative process ensures that the optimal interior layout is achieved, thereby increasing user satisfaction.

[0560] (Example 2)

[0561] Next, we will describe Example 2. 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."

[0562] In modern living spaces, there is a demand for interior design that matches individual emotions and preferences. However, conventional systems have struggled to accurately reflect users' emotions in their proposals. Many design proposal systems are based solely on visual preferences, and designs that take into account the emotional state of the user are limited.

[0563] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0564] In this invention, the server includes an image acquisition means, an analysis means for analyzing the acquired image and recognizing the dimensions of the space and the position of objects, an emotion analysis means for analyzing the user's emotions, and a generation means for generating object arrangements based on the analysis results and emotion information. This makes it possible to propose personalized interior designs that reflect the user's emotion information.

[0565] "Image acquisition means" refers to a method or device for capturing images of a user's living space and importing that data into the system.

[0566] "Analysis means" refers to a function or device that performs processing to determine the dimensions of a space and the positions of objects placed in it from an acquired image.

[0567] "Emotion analysis means" refers to a function that analyzes and recognizes a user's emotional state based on their facial expressions and voice data.

[0568] "Generation means" refers to methods and technologies for creating optimal object placement and interior design proposals based on analysis results and user emotional information.

[0569] "Visualization means" refers to a method for reproducing the generated interior design proposals within a virtual reality environment and presenting them in a way that users can visually experience.

[0570] "Adjustment mechanisms" refer to functions that take user feedback into consideration, re-evaluate proposed designs and layouts, and make modifications as necessary.

[0571] This invention is a system that proposes interior design that takes into account the user's emotional state. This system uses the user's terminal, server, and communication network to assist in redecorating a room.

[0572] First, users can take photos of their room using their own devices and save the image data to their devices. Furthermore, a dedicated application is installed on the devices, which allows them to record facial expressions and voices. This data is then sent from the devices to the server.

[0573] The server uses image analysis algorithms to analyze the received image data and recognize the room dimensions and furniture placement. Simultaneously, an emotion analysis engine analyzes the user's emotional state from their facial expressions and voice data, and uses the results to infer the user's needs.

[0574] By utilizing a generative AI model, the server automatically generates the optimal interior layout based on the user's emotions. For example, if emotion analysis determines that the user desires a calming space, the server can suggest an interior style that promotes relaxation. This suggestion is then sent from the server to the user's device.

[0575] On the device, the proposed design is visualized in three-dimensional space using VR technology, allowing the user to experience it from various angles. The user inputs feedback obtained through the VR experience into the device, and this data is then sent back to the server.

[0576] The server takes user feedback into account and performs sentiment analysis again. This process further improves the proposed interior design, adjusting it to better meet user expectations.

[0577] For example, if a user desires a relaxing space, the system might detect tension in their facial expressions and voice data recorded on their device. The server then receives this information and suggests furniture arrangements that utilize natural light and warm color tones. This system helps users create a comfortable space that matches their own emotional state.

[0578] Example prompt: "I want to make the layout of this room relaxing. Please perform an emotion analysis based on my voice."

[0579] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0580] Step 1:

[0581] The user takes pictures of the room using their device. The input is image data of the room acquired through the camera. The user also records their facial expressions and voice using an application on their device. This data is then sent from the device to the server. The output is image data and voice / facial expression data.

[0582] Step 2:

[0583] The server receives image data and uses image analysis algorithms to analyze the dimensions of the space and the positions of the furniture. The input is image data, and the processing involves various filtering and feature extraction. The output provides information on the dimensions of the space and the placement of the furniture.

[0584] Step 3:

[0585] The server feeds voice and facial expression data into an emotion analysis engine to analyze the user's emotional state. The input consists of facial expression data and voice data, and emotions are recognized using facial recognition and voice tone analysis. The output is data indicating the user's emotional state.

[0586] Step 4:

[0587] The server inputs the results of image analysis and emotion analysis into an AI model to generate the optimal interior layout. Inputs include spatial dimensions, furniture placement information, and the user's emotional state. The AI ​​model generates design proposals based on this information. The output is a personalized interior design proposal for the user.

[0588] Step 5:

[0589] The server sends the generated interior design to the terminal, which then visualizes it in a three-dimensional space using a VR application. The input is design proposal data, which is converted into a format viewable in a VR environment. The output is a three-dimensional interior view that the user can experience.

[0590] Step 6:

[0591] The user reviews the design proposal in a VR space and enters feedback into their device. This feedback includes preferred changes and suggestions for improvement. The device then sends this feedback back to the server.

[0592] Step 7:

[0593] The server analyzes user feedback and new sentiment data, and, if necessary, uses a generative AI model to propose a revised layout. Inputs include feedback and the latest sentiment data, and processing generates a new layout. The output is an improved design proposal.

[0594] (Application Example 2)

[0595] Next, we will explain application example 2. In the following explanation, 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."

[0596] In modern times, there is a growing demand for spatial design that caters to individual preferences and emotional states. However, achieving this requires specialized knowledge and skills, making it difficult for the average user. In particular, dynamically optimizing interiors in response to emotional changes is not easy. Furthermore, the technology for machines to understand human emotions and reflect them in interior design is still immature, meaning that personalized design based on emotions has not been fully realized.

[0597] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0598] In this invention, the server includes an image acquisition device, an analysis device that analyzes the collected images and recognizes the dimensions of the space and the positions of objects, an emotion analysis device that detects the analysis results and the emotional state of the user, a generation device that generates object arrangements based on the emotional state, a visualization device that visualizes the generated arrangements within the visual environment, and an adjustment device that obtains evaluations from the user and adjusts the arrangements. This enables the provision of an optimal interior arrangement based on the emotional state of the user and the automation of emotionally responsive spatial design.

[0599] An "image acquisition device" is a device used to acquire images within a space, and that image data is used for analysis.

[0600] An "analysis device" is a device that analyzes acquired image data to understand spatial dimensions and the arrangement of objects.

[0601] An "emotion analysis device" is a device that detects the emotional state of a user and provides important information for generating interior design layouts based on that state.

[0602] A "generation device" is a device for designing an optimized object arrangement, taking into account the analysis results and the user's emotional state.

[0603] A "visualization device" is a device that displays the generated interior layout within a visual environment, allowing users to confirm it.

[0604] A "adjustment device" is a device that receives feedback from users and re-evaluates and adjusts the interior layout based on that feedback.

[0605] The system implementing the present invention includes a user interface device and a server device. First, an image acquisition device collects image data of the space. When a user is active in the smart home, a robot acquires image data by taking pictures of the room with a camera. This device is incorporated into the user's control device, and the collected data is transmitted to a server in the cloud.

[0606] The server uses an analysis device to analyze the transmitted images and determine the dimensions of the space and the positions of objects such as furniture. This analysis helps understand what kind of interior arrangement is possible and serves as foundational data. Furthermore, an emotion analysis device analyzes the user's real-time emotional state. For example, emotional needs such as whether the user is seeking relaxation or energy can be identified from facial expressions and voice data.

[0607] The generation device uses an AI model based on analysis results and data from the emotion analysis device to design an interior layout that suits the user's emotional state. This automatically generates an optimal, customized interior design for each individual user.

[0608] The generated interior layout is reproduced in a visual environment using a visualization device. For example, a robot can project a virtual interior plan into the user's room using a projector, allowing the user to experience it visually. The user can then review the visualized layout and provide feedback as needed.

[0609] The adjustment system uses user feedback to make necessary adjustments to the interior layout. Through this process, an interior that better meets the user's desired emotional needs is provided.

[0610] For example, if a user states, "I want to create a space where I can truly relax when I come home," the emotion analysis device will read the user's intention to relax from their voice and transmit that information to the generative AI model. The server may then suggest a layout that incorporates plenty of natural light. This layout can be projected into the room by a projector, allowing the user to visually confirm and evaluate it.

[0611] An example of a prompt message is: "User's mood: Desires relaxation. Room characteristics: Maximize natural light. Please propose the optimal interior design plan."

[0612] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0613] Step 1:

[0614] The terminal activates the image acquisition device and acquires images of the room. It uses camera footage as input and obtains image data of the room as output. This is intended to capture the overall view of the room specified by the user.

[0615] Step 2:

[0616] The device uses an emotion analysis device to analyze the user's facial expressions and voice. The input is the user's facial expression data and voice data, and the output is the analysis result indicating the user's emotional state. In this step, the device identifies what emotional state the user is in, such as relaxed or tense.

[0617] Step 3:

[0618] The server analyzes image data acquired through the analysis device to determine the dimensions of the space and the location of furniture. The input is image data of the room, and the output is spatial mapping information. This clarifies the actual layout of the room.

[0619] Step 4:

[0620] The server generates interior layouts using an AI model based on emotion analysis results and spatial mapping information. The input is the user's emotional state and spatial information, and the output is an optimized interior layout plan. At this stage, emotion-based design proposals are materialized.

[0621] Step 5:

[0622] The server uses a visualization device to visually reproduce the interior layout generated on the terminal. The input is an optimized layout plan, and the output is a visually reproduced interior design. The user can review and visually experience this.

[0623] Step 6:

[0624] The user provides feedback on the visualized interior layout. The input is the user's evaluation of the visual design, and the output is feedback data. The user's feedback helps determine whether further layout adjustments are needed.

[0625] Step 7:

[0626] The server uses an adjustment device to readjust the interior layout, incorporating user feedback. The input is user feedback data, and the output is the adjusted interior layout. This provides an interior that better suits the user's needs.

[0627] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0628] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0629] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0630] [Fourth Embodiment]

[0631] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0632] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0633] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0634] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0635] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0636] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0637] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0638] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0639] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0640] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0641] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0642] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0643] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0644] This invention provides a system for efficiently and intuitively rearranging furniture, and its embodiments are described in detail below. This system mainly consists of a user's terminal, a server, and a communication network that connects them.

[0645] First, the user takes a photo of the entire room they want to redecorate using a smartphone or tablet. The user uploads this image to the server through a dedicated application. The uploaded image is processed by an image analysis module on the server, which automatically analyzes the dimensions of the space and the positions of objects such as furniture.

[0646] Next, the server uses the analyzed information to generate a virtual three-dimensional space and proposes the optimal layout for the interior design. The proposal is customized based on the user's preferences entered in the application, such as desired style and color scheme.

[0647] The generated layout plan is sent from the server to the user's terminal and visualized as a virtual reality (VR) environment. In this process, a VR application installed on the user's terminal generates a virtual space, providing the user with an immersive experience. The user can explore this virtual space using a VR headset or smartphone and visually check the proposed furniture layout from various angles.

[0648] Furthermore, after experiencing the virtual space, users provide feedback to the system, including their opinions and impressions. The server receives this feedback, and the AI ​​model adjusts and updates the layout plan based on it. Changes made in response to the user's new requests can then be viewed again in the virtual space.

[0649] As a concrete example, consider a scenario where a user wants to rearrange their living room. The user takes a photo of the living room with their smartphone and uploads it to the server. The server analyzes the photo, recognizes the positions of the sofa, table, and TV stand, and the amount of available space, and then proposes a new arrangement in a modern style. Based on the style chosen by the user, the server provides a plan that shifts the sofa slightly away from the center of the room and places the TV stand along the wall. The user can then review this in a VR environment, and if they wish to make further adjustments, the system will provide a new proposal that reflects those changes.

[0650] As described above, the present invention supports the optimization of interiors in a visually appealing way while saving users time and effort.

[0651] The following describes the processing flow.

[0652] Step 1:

[0653] The user takes a photo of the room using their device. The user activates the camera on their device and takes a photo to confirm the size of the room they want to redecorate, and after taking the photo, they review it through the application.

[0654] Step 2:

[0655] The application uploads photos taken by the user to the server. Pressing the upload button sends the photo data to the server via the network.

[0656] Step 3:

[0657] The server analyzes the received image data. Using an image analysis algorithm, the server extracts the necessary information to determine the room dimensions and the location of each piece of furniture. At this stage, deep learning techniques are applied to perform object recognition.

[0658] Step 4:

[0659] The server generates a 3D model based on the analysis results. Using the extracted dimensional information, it constructs a virtual three-dimensional model of the room and, based on that, generates an optimal furniture arrangement that takes into account the user's preferences.

[0660] Step 5:

[0661] The server sends data to the terminal to provide the user with a layout plan it has generated. The layout plan is encoded in a data format suitable for visualization in a VR application.

[0662] Step 6:

[0663] The system launches a VR application based on data received by the device and generates a virtual space. This creates an environment where users can experience the suggested furniture arrangement using a VR headset or the device's screen.

[0664] Step 7:

[0665] Users explore the virtual space and check the placement of furniture. Users can examine the placement and size of each piece of furniture from various angles and provide feedback on any points of concern or areas they would like to change.

[0666] Step 8:

[0667] The user sends feedback from the device to the server. The server receives the feedback and uses it to adjust the layout or suggest improvements.

[0668] Step 9:

[0669] The server analyzes the feedback it receives and readjusts the furniture placement using a generated AI model. The newly generated placement plan is then customized to the user's preferences.

[0670] Step 10:

[0671] The server sends the updated layout plan to the terminal, allowing users to review it again in the virtual space. This process allows users to experience the "optimal" interior layout that reflects their preferences.

[0672] (Example 1)

[0673] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0674] Traditional methods for redecorating and planning interior designs heavily rely on subjective judgments based on visual intuition and aesthetic sense, resulting in time-consuming and labor-intensive processes. Furthermore, optimizing object placement requires specialized knowledge, making it difficult for users to easily create high-quality spatial designs. There is a need to improve this situation and develop more efficient and intuitive methods for designing spaces.

[0675] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0676] In this invention, the server includes an image acquisition means that acquires visual information of the entire space using the user's observation device, an analysis means that analyzes the acquired visual information and recognizes the dimensions of the structure and the position of the object, and a generation means that generates the object arrangement based on the analysis results and the user's selection criteria. This allows the user to be effectively and quickly presented with the optimal arrangement based on their desired interior style, and furthermore, to visually confirm that arrangement in a realistic virtual environment.

[0677] "Image acquisition means" refers to elements used to acquire visual information of the entire space using the user's observation device.

[0678] "Analysis means" refers to an element that has the function of analyzing acquired visual information and recognizing the dimensions of a structure and the position of an object.

[0679] "Generation means" refers to means for concretizing the arrangement of objects based on analysis results and user selection criteria.

[0680] "Visualization means" refers to a device or process for converting a generated arrangement into a realistic virtual environment and displaying it visually.

[0681] "Adjustment means" are elements used to improve the arrangement of objects based on feedback received from users.

[0682] A "virtual environment" is a visual space that digitally reproduces a real-world space, and is an environment that users can visually confirm.

[0683] An "observation device" is a device operated by the user that has the function of capturing or recording the overall image of a space.

[0684] "Selection criteria" refer to information based on the user's preferred interior style and color scheme, and are the criteria considered when generating the layout.

[0685] This invention is a system for streamlining spatial design, utilizing a user's terminal, a server, and a communication network connecting the two.

[0686] The user uses a device such as a smartphone or tablet to take a photograph of the entire space they wish to redecorate. This device has an interface for sending the captured image to a server via an application.

[0687] The server performs image analysis on the received images. This analysis utilizes an image analysis module employing deep learning algorithms, enabling accurate recognition of spatial dimensions and the positions of objects such as furniture. Based on these analysis results and the user's selection criteria, such as preferred style and color scheme, the server automatically generates an optimal layout plan using a generative AI model. This generative AI model operates based on pre-collected and trained design patterns.

[0688] Next, the server sends the generated layout plan to the user's device. The VR application on the device uses this data to construct a virtual reality environment. The VR application visually presents the generated layout plan to the user through the VR headset or smartphone display. As a result, the user can confirm the spatial design based on the proposal with a sense of reality.

[0689] For example, if a user wants to redecorate their living room, they upload a photo of the living room taken with their smartphone to the server. The server analyzes the photo, recognizes the positions of the sofa and TV stand, and can then suggest a modern-style layout. This suggestion might include specific arrangements such as changing the sofa's position or placing the TV stand against the wall. This suggestion may also be generated by the following prompts.

[0690] Example prompt: "Please suggest the best furniture arrangement when changing the living room to a modern style. The current arrangement has a sofa in the center and a TV stand in the corner."

[0691] Through this system, users can quickly and efficiently visually review space designs and receive personalized interior plans tailored to their preferences.

[0692] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0693] Step 1:

[0694] The user uses a device to take a photograph of the entire space they wish to redecorate. The device then activates an interface via a dedicated application to send this image to a server. The image of the space taken by the user is used as input. The output is the image sent to the server.

[0695] Step 2:

[0696] The server retrieves the received image and passes it to the image analysis module. The analysis module processes the image using a deep learning algorithm and generates data to recognize spatial dimensions and furniture positions. The input is an image sent by the user, and the output is the spatial analysis result.

[0697] Step 3:

[0698] The server receives analysis results and user selection criteria (preferred style and color scheme) as input, and uses a generative AI model to generate optimal placement plans for the objects based on this information. As part of the data processing, the generative AI model refers to past design patterns and outputs a specific placement plan that matches the user's preferences.

[0699] Step 4:

[0700] The server sends the generated layout plan to the user's terminal. The terminal uses the received data to construct a virtual reality environment using a VR application. In this step, an environment is created on the terminal where the user can visually confirm the proposed layout through a VR headset or display. The layout data from the server is used as input, and the virtual reality environment is presented to the user as output.

[0701] Step 5:

[0702] The user explores the VR environment and checks the suggested furniture arrangement. After checking, the user provides feedback to the system. The input is the user's visual evaluation and comments, and the output is specific opinion information fed back to the system.

[0703] Step 6:

[0704] The server analyzes the feedback received from the user and passes it as input to the generating AI model. The AI ​​model re-evaluates the placement proposal based on the feedback and generates a new, more suitable placement proposal. The updated placement proposal is then output and sent back to the user's terminal.

[0705] (Application Example 1)

[0706] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0707] Rearranging and changing the layout of furniture is a time-consuming and laborious task for users, especially when dealing with large objects that are difficult to move physically. Therefore, there is a need for a method that allows for efficient and intuitive rearrangement. Furthermore, there is a need to automate not only the virtual layout planning but also the actual reflection of the layout in the physical space.

[0708] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0709] In this invention, the server includes an image acquisition means, an analysis means for analyzing the acquired image and recognizing the dimensions of the space and the position of objects, a generation means for generating object arrangements based on the analysis results and user preference information, a visualization means for visualizing the generated arrangements in a virtual reality environment, an adjustment means for obtaining feedback from the user and adjusting the arrangements, and a control means for reflecting the generated arrangements in physical space. This enables the user to efficiently rearrange their space and confirm and realize the proposed arrangements in both virtual and real space.

[0710] "Image acquisition means" refers to a device or software that acquires photographs or videos through user operation and provides them to the system.

[0711] "Analysis means" refers to a device or program that processes acquired image data and has the function of identifying spatial dimensions and object position information.

[0712] The "generation means" is a software module for proposing the optimal object placement based on the analysis results and the user's style preference information.

[0713] "Visualization means" refers to a device or program that displays the generated proposal in a virtual reality environment, allowing the user to visually confirm it.

[0714] A "modification mechanism" is a system component that has the function of receiving feedback from the user and optimizing the proposed arrangement based on that information.

[0715] "Control means" refers to devices or software used to operate machines or equipment in order to reflect the arrangement generated in a virtual space onto the actual physical space.

[0716] To implement this invention, a system is needed in which a user's terminal, a server responsible for controlling in-home devices, and a home robot work together. First, the user takes a photograph of the entire room they wish to redecorate using a smartphone or tablet. A dedicated application is installed on the terminal, and this application has the function of uploading the captured image to the server.

[0717] The server uses image processing libraries such as OpenCV for image analysis. Uploaded photos are analyzed by a generative AI model running on the server to determine the spatial dimensions and furniture positions. This allows the current layout to be obtained as digital data. The server also uses machine learning frameworks such as TensorFlow to suggest the optimal furniture arrangement based on the user's specified preferences. This generated arrangement is then sent back to the user's terminal and visualized as a VR (virtual reality) environment.

[0718] Users can view the virtual space through a VR headset or smartphone and visually check the proposed layout from various angles. The feedback obtained here is sent back to the server, and the AI ​​model uses that feedback to propose even more rational layout options.

[0719] Furthermore, this system reflects a virtually proposed arrangement in physical space by having a household robot actually move the objects that are placed in the system. The robot can use a microcontroller such as Arduino or Raspberry Pi, and can move real furniture to a specified position using its built-in camera and sensors.

[0720] For example, if a user wants to change the position of a sofa and table in their living room, they can specify the arrangement using a prompt message on their device such as "Suggest a modern-style arrangement." This allows them to see the furniture arrangement in a virtual space, which is then ultimately implemented by a robot. In this way, users can intuitively and easily reconfigure their space while utilizing advanced technology.

[0721] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0722] Step 1:

[0723] The user takes a photo of the entire room they wish to redecorate using their smartphone. The input is a photo of the room, and the output is the image data after the photo is taken. This image data is uploaded to the server via a dedicated application.

[0724] Step 2:

[0725] The server receives the uploaded image data. Next, it performs image analysis using an image processing library such as OpenCV. The input is image data, and the output is room dimension data and furniture position data. The server saves this data in a digital format.

[0726] Step 3:

[0727] The server uses a generative AI model to generate the optimal furniture arrangement based on user preferences. The input consists of dimensional data obtained from analysis and user preferences, while the output is the proposed arrangement. The AI ​​model uses this data to calculate the recommended arrangement and stores the generated arrangement as digital data.

[0728] Step 4:

[0729] The server sends the generated layout plan to the user's terminal. The input is the proposed layout plan, and the output is the display of the virtual reality environment on the user's terminal. The user can visually confirm this layout plan in the virtual space through the terminal's VR application.

[0730] Step 5:

[0731] The user sends feedback obtained through the virtual space from their terminal to the server. The input is the user's feedback data, and the output is the storage of that feedback data on the server side. Based on this, the server adjusts the placement plan using an AI model and generates a more optimal proposal again.

[0732] Step 6:

[0733] Based on the generated layout plan, once the user approves the placement in the real world, a home robot begins moving the physical furniture through its control system. The input is the finally approved layout plan, and the output is the actual change in the furniture's placement in the physical space. The robot uses cameras and sensors, along with an Arduino or Raspberry Pi, to precisely implement the layout changes.

[0734] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0735] This invention is a system that utilizes an emotion engine to analyze user emotions and provide more precise and personalized room makeover suggestions. The system consists of a user's terminal, a server, and a communication network that connects them.

[0736] First, the user takes a picture of the room using their device and uploads the image data. At the same time, the user can record their facial expressions and voice through the application and send this data to the server. This data is analyzed in real time by an emotion engine to recognize the user's emotional state.

[0737] The server performs image analysis to determine the dimensions of the space and the placement of furniture. Then, the emotion engine interprets the user's emotions. For example, if it determines that the user is seeking relaxation, it can recommend an interior style with calming colors.

[0738] Based on the analysis results, the AI ​​model on the server optimizes the interior layout. This optimization process also takes into account information obtained from the emotion engine, automatically generating the optimal layout that matches the user's emotional state.

[0739] The generated interior design is visualized in a virtual reality environment, and the data is sent from the server to the terminal. A VR application on the terminal displays the proposed plan in three-dimensional space, allowing the user to experience it.

[0740] Users provide feedback on placement and design through their VR experience. Emotions may also be recorded again to gain a deeper understanding of the user's intentions. This feedback is sent to a server, where an emotion engine performs additional analysis, contributing to further improvements in optimal placement.

[0741] As a concrete example, let's assume a user desires a space that reduces stress. If tension is detected from the user's voice recorded on the device, the emotion engine transmits this to the server. The server then simulates a layout that contributes to relaxation (for example, arranging furniture to let in natural light or suggesting warm color schemes) in a VR environment and provides it to the user.

[0742] This invention offers a more comfortable and satisfying redecorating experience by proposing interior plans that dynamically reflect the user's emotions.

[0743] The following describes the processing flow.

[0744] Step 1:

[0745] The user takes a picture of the room using their device's camera. The user then views and saves the captured image through the application.

[0746] Step 2:

[0747] The user uploads photos from the application to the server. At the same time, the user's facial expressions and voice are recorded by sensors on the device, and emotional data is also sent to the server.

[0748] Step 3:

[0749] The server analyzes the received image data. Using an image analysis module, the server extracts data to determine the dimensions of the space and the location of furniture.

[0750] Step 4:

[0751] The server uses an emotion engine to analyze the user's uploaded facial expressions and voice. The emotion engine determines the user's emotional state, such as relaxed or stressed.

[0752] Step 5:

[0753] The server uses a generative AI model to generate the optimal furniture arrangement based on the analysis results and emotional state. The design is customized based on the user's preferences and emotional information.

[0754] Step 6:

[0755] The server sends the generated layout plan to the terminal, which then launches the VR application. The VR application visualizes the proposed layout in a virtual reality environment.

[0756] Step 7:

[0757] Users experience a virtual space through a VR headset or device screen. Users then verify the proposed interior design by actually walking around in it.

[0758] Step 8:

[0759] The user inputs their feedback about the layout they experienced into the device. The device then sends this feedback to the server.

[0760] Step 9:

[0761] The server improves placement based on user feedback and newly recorded emotional data. The emotion engine analyzes the user's latest emotional state and incorporates it into the optimization process.

[0762] Step 10:

[0763] The server resends the updated layout plan to the terminal, which then provides the user with another chance to review it in a VR environment. This iterative process ensures that the optimal interior layout is achieved, thereby increasing user satisfaction.

[0764] (Example 2)

[0765] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0766] In modern living spaces, there is a demand for interior design that matches individual emotions and preferences. However, conventional systems have struggled to accurately reflect users' emotions in their proposals. Many design proposal systems are based solely on visual preferences, and designs that take into account the emotional state of the user are limited.

[0767] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0768] In this invention, the server includes an image acquisition means, an analysis means for analyzing the acquired image and recognizing the dimensions of the space and the position of objects, an emotion analysis means for analyzing the user's emotions, and a generation means for generating object arrangements based on the analysis results and emotion information. This makes it possible to propose personalized interior designs that reflect the user's emotion information.

[0769] "Image acquisition means" refers to a method or device for capturing images of a user's living space and importing that data into the system.

[0770] "Analysis means" refers to a function or device that performs processing to determine the dimensions of a space and the positions of objects placed in it from an acquired image.

[0771] "Emotion analysis means" refers to a function that analyzes and recognizes a user's emotional state based on their facial expressions and voice data.

[0772] "Generation means" refers to methods and technologies for creating optimal object placement and interior design proposals based on analysis results and user emotional information.

[0773] "Visualization means" refers to a method for reproducing the generated interior design proposals within a virtual reality environment and presenting them in a way that users can visually experience.

[0774] "Adjustment mechanisms" refer to functions that take user feedback into consideration, re-evaluate proposed designs and layouts, and make modifications as necessary.

[0775] This invention is a system that proposes interior design that takes into account the user's emotional state. This system uses the user's terminal, server, and communication network to assist in redecorating a room.

[0776] First, users can take photos of their room using their own devices and save the image data to their devices. Furthermore, a dedicated application is installed on the devices, which allows them to record facial expressions and voices. This data is then sent from the devices to the server.

[0777] The server uses image analysis algorithms to analyze the received image data and recognize the room dimensions and furniture placement. Simultaneously, an emotion analysis engine analyzes the user's emotional state from their facial expressions and voice data, and uses the results to infer the user's needs.

[0778] By utilizing a generative AI model, the server automatically generates the optimal interior layout based on the user's emotions. For example, if emotion analysis determines that the user desires a calming space, the server can suggest an interior style that promotes relaxation. This suggestion is then sent from the server to the user's device.

[0779] On the device, the proposed design is visualized in three-dimensional space using VR technology, allowing the user to experience it from various angles. The user inputs feedback obtained through the VR experience into the device, and this data is then sent back to the server.

[0780] The server takes user feedback into account and performs sentiment analysis again. This process further improves the proposed interior design, adjusting it to better meet user expectations.

[0781] For example, if a user desires a relaxing space, the system might detect tension in their facial expressions and voice data recorded on their device. The server then receives this information and suggests furniture arrangements that utilize natural light and warm color tones. This system helps users create a comfortable space that matches their own emotional state.

[0782] Example prompt: "I want to make the layout of this room relaxing. Please perform an emotion analysis based on my voice."

[0783] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0784] Step 1:

[0785] The user takes pictures of the room using their device. The input is image data of the room acquired through the camera. The user also records their facial expressions and voice using an application on their device. This data is then sent from the device to the server. The output is image data and voice / facial expression data.

[0786] Step 2:

[0787] The server receives image data and uses image analysis algorithms to analyze the dimensions of the space and the positions of the furniture. The input is image data, and the processing involves various filtering and feature extraction. The output provides information on the dimensions of the space and the placement of the furniture.

[0788] Step 3:

[0789] The server feeds voice and facial expression data into an emotion analysis engine to analyze the user's emotional state. The input consists of facial expression data and voice data, and emotions are recognized using facial recognition and voice tone analysis. The output is data indicating the user's emotional state.

[0790] Step 4:

[0791] The server inputs the results of image analysis and emotion analysis into an AI model to generate the optimal interior layout. Inputs include spatial dimensions, furniture placement information, and the user's emotional state. The AI ​​model generates design proposals based on this information. The output is a personalized interior design proposal for the user.

[0792] Step 5:

[0793] The server sends the generated interior design to the terminal, which then visualizes it in a three-dimensional space using a VR application. The input is design proposal data, which is converted into a format viewable in a VR environment. The output is a three-dimensional interior view that the user can experience.

[0794] Step 6:

[0795] The user reviews the design proposal in a VR space and enters feedback into their device. This feedback includes preferred changes and suggestions for improvement. The device then sends this feedback back to the server.

[0796] Step 7:

[0797] The server analyzes user feedback and new sentiment data, and, if necessary, uses a generative AI model to propose a revised layout. Inputs include feedback and the latest sentiment data, and processing generates a new layout. The output is an improved design proposal.

[0798] (Application Example 2)

[0799] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0800] In modern times, there is a growing demand for spatial design that caters to individual preferences and emotional states. However, achieving this requires specialized knowledge and skills, making it difficult for the average user. In particular, dynamically optimizing interiors in response to emotional changes is not easy. Furthermore, the technology for machines to understand human emotions and reflect them in interior design is still immature, meaning that personalized design based on emotions has not been fully realized.

[0801] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0802] In this invention, the server includes an image acquisition device, an analysis device that analyzes the collected images and recognizes the dimensions of the space and the positions of objects, an emotion analysis device that detects the analysis results and the emotional state of the user, a generation device that generates object arrangements based on the emotional state, a visualization device that visualizes the generated arrangements within the visual environment, and an adjustment device that obtains evaluations from the user and adjusts the arrangements. This enables the provision of an optimal interior arrangement based on the emotional state of the user and the automation of emotionally responsive spatial design.

[0803] An "image acquisition device" is a device used to acquire images within a space, and that image data is used for analysis.

[0804] An "analysis device" is a device that analyzes acquired image data to understand spatial dimensions and the arrangement of objects.

[0805] An "emotion analysis device" is a device that detects the emotional state of a user and provides important information for generating interior design layouts based on that state.

[0806] A "generation device" is a device for designing an optimized object arrangement, taking into account the analysis results and the user's emotional state.

[0807] A "visualization device" is a device that displays the generated interior layout within a visual environment, allowing users to confirm it.

[0808] A "adjustment device" is a device that receives feedback from users and re-evaluates and adjusts the interior layout based on that feedback.

[0809] The system implementing the present invention includes a user interface device and a server device. First, an image acquisition device collects image data of the space. When a user is active in the smart home, a robot acquires image data by taking pictures of the room with a camera. This device is incorporated into the user's control device, and the collected data is transmitted to a server in the cloud.

[0810] The server uses an analysis device to analyze the transmitted images and determine the dimensions of the space and the positions of objects such as furniture. This analysis helps understand what kind of interior arrangement is possible and serves as foundational data. Furthermore, an emotion analysis device analyzes the user's real-time emotional state. For example, emotional needs such as whether the user is seeking relaxation or energy can be identified from facial expressions and voice data.

[0811] The generation device uses an AI model based on analysis results and data from the emotion analysis device to design an interior layout that suits the user's emotional state. This automatically generates an optimal, customized interior design for each individual user.

[0812] The generated interior layout is reproduced in a visual environment using a visualization device. For example, a robot can project a virtual interior plan into the user's room using a projector, allowing the user to experience it visually. The user can then review the visualized layout and provide feedback as needed.

[0813] The adjustment system uses user feedback to make necessary adjustments to the interior layout. Through this process, an interior that better meets the user's desired emotional needs is provided.

[0814] For example, if a user states, "I want to create a space where I can truly relax when I come home," the emotion analysis device will read the user's intention to relax from their voice and transmit that information to the generative AI model. The server may then suggest a layout that incorporates plenty of natural light. This layout can be projected into the room by a projector, allowing the user to visually confirm and evaluate it.

[0815] An example of a prompt message is: "User's mood: Desires relaxation. Room characteristics: Maximize natural light. Please propose the optimal interior design plan."

[0816] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0817] Step 1:

[0818] The terminal activates the image acquisition device and acquires images of the room. It uses camera footage as input and obtains image data of the room as output. This is intended to capture the overall view of the room specified by the user.

[0819] Step 2:

[0820] The device uses an emotion analysis device to analyze the user's facial expressions and voice. The input is the user's facial expression data and voice data, and the output is the analysis result indicating the user's emotional state. In this step, the device identifies what emotional state the user is in, such as relaxed or tense.

[0821] Step 3:

[0822] The server analyzes image data acquired through the analysis device to determine the dimensions of the space and the location of furniture. The input is image data of the room, and the output is spatial mapping information. This clarifies the actual layout of the room.

[0823] Step 4:

[0824] The server generates interior layouts using an AI model based on emotion analysis results and spatial mapping information. The input is the user's emotional state and spatial information, and the output is an optimized interior layout plan. At this stage, emotion-based design proposals are materialized.

[0825] Step 5:

[0826] The server uses a visualization device to visually reproduce the interior layout generated on the terminal. The input is an optimized layout plan, and the output is a visually reproduced interior design. The user can review and visually experience this.

[0827] Step 6:

[0828] The user provides feedback on the visualized interior layout. The input is the user's evaluation of the visual design, and the output is feedback data. The user's feedback helps determine whether further layout adjustments are needed.

[0829] Step 7:

[0830] The server uses an adjustment device to readjust the interior layout, incorporating user feedback. The input is user feedback data, and the output is the adjusted interior layout. This provides an interior that better suits the user's needs.

[0831] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0832] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0833] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0834] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0835] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0836] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0837] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0838] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0839] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0840] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0841] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0842] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0843] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0844] 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.

[0845] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0846] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0847] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0848] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0849] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0850] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0851] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0852] The following is further disclosed regarding the embodiments described above.

[0853] (Claim 1)

[0854] Image acquisition method,

[0855] An analysis means that analyzes the acquired image and recognizes the dimensions of the space and the position of objects,

[0856] A generation means for generating object placement based on analysis results and user preference information,

[0857] A visualization means for visualizing the generated arrangement in a virtual reality environment,

[0858] A means of adjusting the layout by obtaining user feedback,

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, wherein the generation means optimizes the arrangement based on preference information selected by the user.

[0862] (Claim 3)

[0863] The system according to claim 1, which performs visualization of placement in a virtual reality environment on the user's terminal.

[0864] "Example 1"

[0865] (Claim 1)

[0866] An image acquisition means that acquires visual information of the entire space using the user's observation device,

[0867] An analysis means that analyzes acquired visual information to recognize the dimensions of the structure and the position of the object,

[0868] A generation means for generating object placement based on analysis results and user selection criteria,

[0869] A visualization means that converts the generated arrangement into a realistic virtual environment and visualizes it,

[0870] A means of adjusting the layout by acquiring opinion information received from users,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, wherein the generating means arranges more appropriately based on selection criteria specified by the user.

[0874] (Claim 3)

[0875] The system according to claim 1, which performs visualization of arrangement in a realistic virtual environment on the user's observation device.

[0876] "Application Example 1"

[0877] (Claim 1)

[0878] Image acquisition method,

[0879] An analysis means that analyzes the acquired image and recognizes the dimensions of the space and the position of objects,

[0880] A generation means for generating object placement based on analysis results and user preference information,

[0881] A visualization means for visualizing the generated arrangement in a virtual reality environment,

[0882] A means of adjusting the layout by obtaining user feedback,

[0883] A control means that reflects the generated arrangement in physical space,

[0884] A system that includes this.

[0885] (Claim 2)

[0886] The system according to claim 1, wherein a generation means optimizes the arrangement based on preference information selected by the user, and moves the object in real space through a control means.

[0887] (Claim 3)

[0888] The system according to claim 1, wherein the visualization and control of placement in a virtual reality environment is performed on the user's terminal.

[0889] "Example 2 of combining an emotion engine"

[0890] (Claim 1)

[0891] Image acquisition method,

[0892] An analysis means that analyzes the acquired image and recognizes the dimensions of the space and the position of objects,

[0893] A means of analyzing user emotions,

[0894] A generation means for generating object placement based on analysis results and emotional information,

[0895] A visualization means for visualizing the generated arrangement in a virtual reality environment,

[0896] A means of obtaining user feedback and adjusting the layout,

[0897] A system that includes this.

[0898] (Claim 2)

[0899] The system according to claim 1, wherein the generation means optimizes the arrangement based on user emotion information and a generation AI model.

[0900] (Claim 3)

[0901] The system according to claim 1, which performs visualization of placement in a virtual reality environment on the user's terminal.

[0902] "Application example 2 when combining with an emotional engine"

[0903] (Claim 1)

[0904] Image acquisition device,

[0905] An analysis device that analyzes collected images and recognizes spatial dimensions and object positions,

[0906] An emotion analysis device that detects the analysis results and the emotional state of the user,

[0907] A generation device that generates object arrangements based on emotional states,

[0908] A visualization device that visualizes the generated arrangement within a visual environment,

[0909] An adjustment device that obtains user feedback and adjusts the layout,

[0910] A system that includes this.

[0911] (Claim 2)

[0912] The system according to claim 1, wherein the generating device optimizes the arrangement based on the user's emotions.

[0913] (Claim 3)

[0914] The system according to claim 1, which performs visualization of arrangement in a visual environment on a user's control device. [Explanation of Symbols]

[0915] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Image acquisition method, An analysis means that analyzes the acquired image and recognizes the dimensions of the space and the position of objects, A generation means for generating object placement based on analysis results and user preference information, A visualization means for visualizing the generated arrangement in a virtual reality environment, A means of adjusting the layout by obtaining user feedback, A system that includes this.

2. The system according to claim 1, wherein the generation means optimizes the arrangement based on preference information selected by the user.

3. The system according to claim 1, which performs visualization of placement in a virtual reality environment on the user's terminal.

Images