system

Abstract

Provide a system. 【Solution means】 Input means for the user to input an image, Analysis means for analyzing the image to identify an external area, Generation means for generating a painting candidate based on the identified external area, Visualization means for expressing and outputting the generated painting candidate, Modification means for the user to modify the painting candidate, Association means for associating candidates using a product identifier, Display means for displaying candidates based on the product identifier, A system including.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 considering painting the outer wall of a house, users have a need to visually confirm various painting designs. However, conventional methods often require advice from experts, and there is a problem that it is difficult for users to try design plans by themselves. In addition, since there is a lack of means to easily compare and consider multiple painting options, it is difficult to make an optimal choice.

Means for Solving the Problems

[0005] This invention provides an information processing system that analyzes user-inputted images to identify exterior wall areas and generates painting options for these identified areas. Users can visually simulate the generated painting options and adjust them in real time. This allows users to easily experiment with various design proposals from their own perspective and make the optimal choice without the need for expert assistance.

[0006] A "user" is an individual or group that uses the system to experiment with exterior wall painting designs for their home.

[0007] "Image" refers to a visual data file of the building's exterior that the user has photographed or acquired.

[0008] "Input means" refers to a device or process for a user to upload an image to the system.

[0009] "Analysis means" refers to a technical method for processing an input image to identify a specific area, particularly the exterior wall portion.

[0010] "Exterior wall area" refers to a specific portion of an image that has been identified as the exterior wall of a building.

[0011] "Generation means" refers to a function or algorithm that creates painting options based on the analyzed exterior wall area.

[0012] "Painting options" refer to the choices of paint colors, textures, patterns, and other elements that can be applied to the exterior walls.

[0013] "Visualization means" refers to a technology or tool for visually representing and presenting the generated painting options to the user.

[0014] "Adjustment means" refers to an interface or function used by the user to change or adjust the generated paint options. [Brief explanation of the drawing]

[0015] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] Shows an emotion map to which multiple emotions are mapped. [Figure 10] Shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0018] In the following embodiments, a 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.

[0019] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a 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, and the like.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

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

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0030] 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.

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

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention provides an information processing system that enables users to realize their ideas for exterior wall painting. This system allows users to visually simulate various painting options based on photographs of their own buildings. The program's processing and specific examples are shown below.

[0037] The system begins with the user inputting an image of the house's exterior through an interface. The image uploaded to the terminal is sent to a server, which performs image analysis to identify the exterior wall areas. This analysis uses computer vision technology to identify the exterior wall sections.

[0038] Based on the identified exterior wall area, the server utilizes generative AI to generate a variety of painting options. These include design proposals with variations in color, texture, and pattern. These generated options are then presented to the user as choices.

[0039] Next, visualization technology is used to reflect the generated painting options onto the image. This allows the user to see a realistic simulation image. The user can interactively adjust the colors and textures through their device and see the simulation image change in real time.

[0040] For example, if a user wants to choose a green color scheme, the generating AI will provide a simulation with multiple shades of green applied to the exterior wall. The user can then select the optimal shade from this simulation and add a matching texture.

[0041] This system allows users to easily try out numerous paint designs without the need for expert assistance, streamlining the process of discovering their ideal design.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user takes a photo of the exterior of their home and uploads the image to their device. The device then prepares to submit this image to the server via the system's dedicated interface.

[0045] Step 2:

[0046] The server receives the image sent from the terminal and prepares to analyze it. Using analysis tools, the server identifies the exterior wall region within the image. Here, edge detection algorithms and segmentation techniques are utilized to efficiently extract the exterior wall portion.

[0047] Step 3:

[0048] The server generates painting options based on information about the exterior wall area. In this generation process, the generating AI suggests multiple color palettes, textures, and patterns. This broadens the range of design options available to the user.

[0049] Step 4:

[0050] The server passes the generated painting options to a visualization tool, which then applies them to the exterior wall image. This process uses rendering techniques to create realistic simulation images, which are then prepared for user review.

[0051] Step 5:

[0052] Users select and adjust their preferred colors and textures while viewing a simulated image displayed on their device. During this process, the user's selection information is transmitted from the device to the server in real time.

[0053] Step 6:

[0054] The server receives user adjustment requests and updates the visualization. It then presents the user with a real-time simulation image reflecting the new design proposal. This allows the user to try out multiple variations and decide on the optimal paint design.

[0055] (Example 1)

[0056] 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."

[0057] When choosing exterior wall decorations and paint designs for a house, it is often difficult for users to concretely visualize the many options available, and they frequently have to rely on experts. Furthermore, there is a lack of environments that allow for realistic visualization and rapid trial and error.

[0058] 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.

[0059] In this invention, the server includes acquisition means for the user to input visual information, analysis means for analyzing the visual information and identifying boundary regions, and generation means for generating decorative options based on the identified boundary regions. This allows the user to instantly visualize a variety of designs, freely modify them, and quickly select the ideal decoration.

[0060] "Acquisition means" refers to a function that allows users to select or input visual information through their terminal and incorporate it into the system.

[0061] "Analysis means" refers to a function that uses image processing technology based on acquired visual information to recognize specific boundary regions and identify necessary areas.

[0062] The "generation method" refers to a function that utilizes a generative AI model to create various decorative options for the analyzed boundary region, devising designs that include diverse colors, textures, and patterns.

[0063] "Display means" refers to a function that uses advanced visualization technology to visualize the generated decorative options and present them to the user, providing immediate feedback to the user.

[0064] The "adjustment method" is a function that allows users to interactively change the color tone and texture based on the displayed decorative options, and instantly reflects the changes in the simulated image.

[0065] This invention provides an information processing system that allows users to easily experiment with decorative designs for the exterior walls of their homes. This system operates via an internet-connected terminal and server, and utilizes a generation AI model to propose and visualize a variety of designs.

[0066] The user first uses a device to acquire an image of the exterior of their home and upload it to the system. This device can be a smart device with a camera or a computer. A highly secure data transfer protocol is used when the image is sent from the device to the server. The server uses image analysis software and computer vision technology to identify the exterior wall areas of the building. Deep learning algorithms are used for this analysis.

[0067] Next, the server uses a generation AI model based on the identified exterior wall area to create decorative options. This generation process devises multiple design proposals with different tones, textures, and patterns. A prompt such as "Suggest a modern-looking exterior paint job" is used.

[0068] Users review the generated decorative options through simulated images visualized on their devices. The system uses advanced visualization technology to reproduce realistic textures and colors, presenting the designs to the user instantly.

[0069] Furthermore, users can adjust design details through their devices and interactively change colors and textures. This allows them to instantly see simulation results based on their desired design.

[0070] By utilizing the system of this invention, users can quickly discover the optimal solution for the exterior wall design of their house without requiring specialized technical skills, and enjoy rare creative freedom.

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] Users use their devices to acquire images of the exterior of their homes and upload them to the system. In this process, users send images taken with their smartphones or digital cameras to the system via a dedicated application. Input includes image data in JPEG or PNG format, as selected by the user. Output is the image data sent from the device to the server.

[0074] Step 2:

[0075] The terminal sends image data to the server. The server receives the image data using a secure data transfer protocol. This process ensures data integrity and security. The input is the image data sent from the terminal, and the output is the storage of the complete image data on the server.

[0076] Step 3:

[0077] The server performs image analysis on the received image data. The server uses computer vision algorithms to identify the exterior wall areas of buildings from the images. The input is image data, and data processing includes boundary detection and shape recognition to identify the exterior walls. The output is data indicating the exterior wall areas.

[0078] Step 4:

[0079] The server generates decorative options using a generative AI model based on identified exterior wall area data. This process devises multiple paint design proposals with different colors, textures, and patterns. The input consists of exterior wall area data and a prompt statement (e.g., "Suggest a modern-looking exterior paint job"). The data calculation includes the generative AI generating and applying design parameters. The output is a dataset of the generated decorative options.

[0080] Step 5:

[0081] The server visualizes the generated decorative options using visualization technology and sends them to the user's terminal. This allows the user to view the virtual design of the exterior wall in real time. The input is a dataset of decorative options, and the output is a visualized simulation image.

[0082] Step 6:

[0083] The user reviews the design presented through the terminal and makes interactive adjustments to colors and textures as needed. The adjusted data is immediately sent to the server. Input consists of adjustment instructions from the user, and data processing includes updating selected parameters. Output is a simulated image reflecting the updated design.

[0084] Step 7:

[0085] The user finalizes the design and saves or shares it via their device. The server records the design and retains it for the user. The input is the finalized design data, and the output is the saved design data and a record of data that can be used for subsequent applications.

[0086] (Application Example 1)

[0087] 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."

[0088] When purchasing painted products or decorative items in a store, it is difficult for customers to visually confirm how they will actually look in their homes. This increases the risk of disappointment after purchase, making it essential to provide appropriate simulations before purchase.

[0089] 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.

[0090] In this invention, the server includes an input means for the user to input an image, an analysis means for analyzing the image to identify an external region, and a generation means for generating paint candidates based on the identified external region. This allows customers to perform a real-time visual simulation of the product using a smart device before purchasing.

[0091] "Input means" refers to a device or method for a user to supply visual information to a system.

[0092] "Analysis means" refers to an apparatus or method for performing processing to identify a specific region from supplied visual information.

[0093] "Generating means" refers to an apparatus or method for creating new visual options based on an identified specific region.

[0094] "Visualization means" refers to a device or method for displaying generated visual options to a customer.

[0095] "Modification means" refers to a device or method for a user to adjust visual options.

[0096] "Association means" refers to a device or method for linking visual options from identification information.

[0097] "Display means" refers to a device or method for presenting associated options to the user.

[0098] This invention relates to an information processing system for assisting users in selecting products in a store. Specifically, it provides a method for users to simulate painting and decorating items for the exterior of their home using a smart device.

[0099] The system begins with the user inputting an image of their home via their device and sending it to the server. The server uses image analysis software such as OpenCV to identify the external area from the received image. Next, a generative AI model utilizing machine learning frameworks such as PyTorch and TENSORFLOW® generates multiple paint options based on the identified external area. These paint options offer a variety of choices, including color, texture, and pattern.

[0100] The generated options are displayed on smart devices using visualization technologies such as Unity and Unreal Engine. This allows users to visualize various design options for the exterior of their home in real time. Furthermore, users can make adjustments to the simulated images according to their preferences, and the results are immediately reflected on the device.

[0101] For example, if a user selects a specific green product in a store, the terminal scans the product's barcode and inputs a prompt message into the AI ​​model: "Apply the specified 'forest green' tone to the input image and generate a simulated image of the exterior wall." This allows the user to see how the color will actually appear in their home.

[0102] This allows users to visualize in advance how the actual product will fit into their living environment, enabling them to make more appropriate purchasing decisions.

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The user takes a photo of the exterior of their home with their device and inputs the image into an application on the device. The input image is temporarily saved as an external file.

[0106] Step 2:

[0107] The terminal sends the saved image to the server. The server receives the image and starts image analysis using OpenCV. This analysis identifies the external region by examining the information of each pixel in the input image. As a result, information about the position and shape of the exterior wall is obtained.

[0108] Step 3:

[0109] Based on the analysis results, the server generates paint options using a generative AI model. This process creates a variety of color and texture options based on the specified prompt: "Apply the specified color tone to the input image to generate a simulated image of the exterior wall." The output includes multiple simulated design options.

[0110] Step 4:

[0111] The generated paint options are sent from the server to the terminal. The terminal uses Unity or Unreal Engine to display the visualized designs to the user. In this step, the generated designs are overlaid on the input image, allowing the user to check the results.

[0112] Step 5:

[0113] Users can make their preferred adjustments to the generated paint options via the device's interface. These adjustments are processed in real time on the device, and the results are displayed immediately. This allows users to repeatedly review and experiment with designs.

[0114] 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.

[0115] This invention provides an information processing system that takes into account the user's emotions when selecting a building exterior paint design, and offers more personalized design suggestions. The following shows the program processing and specific examples of this system.

[0116] The system begins with the user taking a photo of the exterior of their home and uploading the image to their device. This image is sent to a server, which analyzes the image to identify the exterior wall area. Based on the analyzed exterior wall area, a generating AI creates various painting options.

[0117] Next, the emotion recognition system identifies the user's emotions. This is done by analyzing the user's facial expression and voice data. The server retrieves the emotion data and understands the user's current emotional state.

[0118] The server recommends paint options that are appropriate for the user's emotions based on the acquired emotional data. This recommendation process can, for example, prioritize calming color options if the user is relaxed. This makes it easy for users to select a design that matches their emotional state.

[0119] Users can view simulation images displayed on their devices and compare each design to choose their preferred one. Since the selected design is based on emotional data, a highly satisfying choice can be expected.

[0120] For example, if a user displays a cheerful expression while using the system, bright colors and playful patterns will be automatically recommended as painting options. This allows users to easily try out interesting options and find a design that matches their mood.

[0121] Thus, the present invention enables design selection that takes user emotions into consideration, providing an experience that better meets individual user needs than conventional systems.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] The user takes a photo of the exterior of their home and uploads the image to their device. The device then prepares to send this image to the server.

[0125] Step 2:

[0126] The server analyzes the images received from the terminal. Using image analysis technology, it identifies the exterior wall portion and extracts region data.

[0127] Step 3:

[0128] The server uses a generative AI to generate a variety of painting options based on exterior wall area data. These options include various colors, textures, and design patterns.

[0129] Step 4:

[0130] Users provide emotional data via their device using a facial recognition camera and microphone. The emotion engine analyzes this data to determine the user's current emotional state.

[0131] Step 5:

[0132] The server uses emotion data to recommend painting options that match the user's emotional state. It prioritizes designs that are appropriate for the emotion and presents them to the user.

[0133] Step 6:

[0134] Users can view paint simulations presented through their device and compare designs that align with their emotions. They can then select the optimal design. The selection is sent from the device to a server and saved.

[0135] (Example 2)

[0136] 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".

[0137] Conventional information processing systems have a problem in that they do not offer personalized design suggestions that take user emotions into consideration, resulting in users being unable to easily select the design they want. To solve this problem, there was a need for a system that could suggest appropriate design options that reflect user emotions and enable highly satisfying choices.

[0138] 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.

[0139] In this invention, the server includes an input means for the user to input an image, an analysis means for analyzing the image and identifying an object region, a generation means for generating design options based on the identified object region, an emotion recognition means for analyzing the user's emotions and acquiring analysis data, a recommendation means for recommending design options suitable for the user based on the analysis data, a visualization means for simulating and displaying the generated design options, and an operation means for the user to adjust the design options. This enables personalized design suggestions based on the user's emotions.

[0140] An "input method" is an interface that allows a user to provide images or information to the system.

[0141] "Analysis means" refers to algorithms and processes for processing input images and identifying specific object regions.

[0142] A "generation mechanism" is a mechanism for creating new design options based on analyzed information.

[0143] An "emotion recognition system" is a system that analyzes a user's facial expressions and voice data to determine the user's emotional state.

[0144] A "recommendation mechanism" is a system that has the function of suggesting the most appropriate design option based on the user's emotional state.

[0145] A "visualization method" is a system that presents the generated design options in a way that allows the user to visually confirm them.

[0146] "Operational means" refers to the interface that allows the user to adjust and select the displayed design options.

[0147] The embodiments for carrying out the present invention are described in detail below.

[0148] This system provides a process that offers suggestions that reflect the user's emotions when customizing the exterior design of buildings such as their homes.

[0149] Users take photos of the exterior of their homes with their smartphones or digital cameras and upload the images to the system via their devices. The devices then send the images to the server via the internet. This communication generally uses HTTPS, which is a secure communication protocol.

[0150] The server analyzes images using image analysis libraries (e.g., OpenCV or TensorFlow) to identify the exterior wall areas of buildings. This analysis provides the foundational data needed to generate customized options suitable for the user's building.

[0151] Generative AI models (e.g., DALL-E and Stable Diffusion) run on a server and generate multiple design options based on the analysis results. For example, the model is driven by prompts such as "Suggest colorful patterns for exterior walls."

[0152] The device records the user's facial expressions and voice through its built-in camera and microphone to acquire data for emotion recognition. This data is sent to a server, which analyzes the user's emotions using an emotion recognition algorithm (e.g., an emotion recognition API).

[0153] The server uses analytical data based on the user's emotional state to recommend personalized design options. For example, if the user has a cheerful expression, the server may recommend a bright and playful design.

[0154] As a concrete example, when a user uses the prompt phrase "a bright and playful exterior design," the generating AI model provides the corresponding design options, which the user can then visually confirm and select on their device.

[0155] In this way, the present invention realizes an information processing system that assists in design selection while taking user emotions into consideration and provides optimized customization suggestions.

[0156] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0157] Step 1:

[0158] The user takes a picture of the exterior of their home with a smartphone or digital camera and uploads the image to the terminal. The terminal receives the user's input image through an interface and sends this image data to the server. As a result, the server receives the image data for analysis.

[0159] Step 2:

[0160] The server performs image processing using the received image data. Here, it uses tools such as OpenCV and TensorFlow to identify the exterior wall areas of buildings from the images. This process is achieved using algorithms such as contour detection and region segmentation, and the output generates data indicating the exterior wall areas.

[0161] Step 3:

[0162] The server generates design options using a generative AI model based on the analyzed exterior wall area data. By providing the model with information on the shape and size of the exterior wall as input, and prompts (e.g., "Please suggest colorful patterns for the exterior wall"), several design options are generated as output.

[0163] Step 4:

[0164] The device uses its camera and microphone to record the user's facial expressions and voice data. This data is used to estimate the user's emotional state, and the device sends the collected data to a server. This allows the server to obtain input data for emotion analysis.

[0165] Step 5:

[0166] The server analyzes facial expression and voice data transmitted from the terminal using an emotion recognition algorithm. It uses a service such as Microsoft® Azure® Emotion Recognition API to obtain the user's instantaneous emotional state as output.

[0167] Step 6:

[0168] The server uses the emotion recognition results to recommend the most suitable design option from the generated options. This process involves an algorithm selecting the optimal design based on the obtained emotion data. The appropriate design option is then sent to the terminal.

[0169] Step 7:

[0170] The user reviews design options displayed on their device and visualizes them on a simulation screen. The user then compares and adjusts these designs to make a selection. The chosen design is based on suggestions derived from the user's emotions.

[0171] This allows users to easily choose an exterior wall design that is customized to their own feelings, resulting in a high level of satisfaction.

[0172] (Application Example 2)

[0173] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0174] Traditionally, there has been a lack of personalized suggestions that take into account user emotions in interior design and wallpaper selection, resulting in low user satisfaction. Furthermore, users faced the burden of choosing from a vast number of options.

[0175] 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.

[0176] In this invention, the server includes an input means for the user to input an image, an analysis means for analyzing the image and identifying a display area, and a recommendation means for recognizing the user's emotions and recommending design options based on those emotions. This makes it possible to propose designs that reflect the user's emotions.

[0177] "Input means" refers to methods or devices that users use to input images or data into a system.

[0178] "Analysis means" refers to a method or apparatus that extracts necessary information from input images or data and performs processing to distinguish specific regions.

[0179] "Generative means" refers to the mechanisms and processes for creating various designs and options based on the analyzed information.

[0180] A "recommendation method" is a system that uses acquired user sentiment information to suggest design options that match the user's needs and circumstances.

[0181] A "visualization method" is a method or device that has the function of visually simulating and presenting generated design options to the user.

[0182] "Adjustment means" refers to methods or devices that allow users to change or edit a proposed design to suit their own preferences.

[0183] "Emotional data" refers to information indicating a user's psychological state, obtained from their facial expressions and voice, and is used to personalize designs.

[0184] The system that realizes this invention begins with user input. At this stage, the user uses a device such as a smartphone or smart glasses to take images of the target facility and upload them to a cloud server. This image data is analyzed on the server using OpenCV, an image analysis software, to identify the areas to be designed for the interior.

[0185] Next, the server processes facial and voice data using the Azure Cognitive Services Emotion API to recognize the user's emotions. Based on the emotion data obtained during this process, a generative AI model (e.g., Stable Diffusion) is used to generate multiple design options that match the user's emotions.

[0186] The generated design is then displayed on the device using Vue.js as a visualization tool, allowing the user to visually review it and adjust it to their liking using the adjustment tools. This design suggestion, based on the user's emotions, can improve user satisfaction.

[0187] For example, if emotional data reveals that users are relaxed in the store, a calming design with natural colors is recommended. This makes it easier to select interior design elements that match the store's atmosphere.

[0188] An example of a prompt message is, "Please suggest wallpaper designs that would suit the calm atmosphere of a cafe," which can be used to instruct the generation AI model. By using this prompt, the system can present specific design options that match the user's request.

[0189] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0190] Step 1:

[0191] The user takes pictures of the facility with their device and uploads them to the server. The input is the captured image data, and the output is the image file saved on the server.

[0192] Step 2:

[0193] The server uses image analysis software (OpenCV) to identify the design target area within the uploaded image. The input is the image data obtained in step 1, and the output is information about the identified target area. This information is used for design generation in the next step.

[0194] Step 3:

[0195] The server uses the Emotion API to analyze facial and voice data obtained from the user and recognize the user's emotions. The input is real-time facial and voice data from the user, and the output is data on the user's current emotional state. This output data is used in the next step.

[0196] Step 4:

[0197] The server inputs information about the identified display area and user emotion data into a generating AI model (e.g., Stable Diffusion) to generate design options that are appropriate for the user's emotions. As output, multiple design options that match the user's emotions are obtained.

[0198] Step 5:

[0199] The server displays the generated design options using the device's visualization tool (Vue.js) and provides the user with the simulation results. The input is the design options obtained in step 4, and the output is a visually reproduced simulation image displayed on the device.

[0200] Step 6:

[0201] The user adjusts design options using the device's interface. The input is the user's adjustments, and the output is the final design adjusted by the user. This design is customized to the user's needs.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] [Second Embodiment]

[0206] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0207] 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.

[0208] 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).

[0209] 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.

[0210] 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.

[0211] 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).

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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".

[0218] This invention provides an information processing system that enables users to realize their ideas for exterior wall painting. This system allows users to visually simulate various painting options based on photographs of their own buildings. The program's processing and specific examples are shown below.

[0219] The system begins with the user inputting an image of the house's exterior through an interface. The image uploaded to the terminal is sent to a server, which performs image analysis to identify the exterior wall areas. This analysis uses computer vision technology to identify the exterior wall sections.

[0220] Based on the identified exterior wall area, the server utilizes generative AI to generate a variety of painting options. These include design proposals with variations in color, texture, and pattern. These generated options are then presented to the user as choices.

[0221] Next, visualization technology is used to reflect the generated painting options onto the image. This allows the user to see a realistic simulation image. The user can interactively adjust the colors and textures through their device and see the simulation image change in real time.

[0222] For example, if a user wants to choose a green color scheme, the generating AI will provide a simulation with multiple shades of green applied to the exterior wall. The user can then select the optimal shade from this simulation and add a matching texture.

[0223] This system allows users to easily try out numerous paint designs without the need for expert assistance, streamlining the process of discovering their ideal design.

[0224] The following describes the processing flow.

[0225] Step 1:

[0226] The user takes a photo of the exterior of their home and uploads the image to their device. The device then prepares to submit this image to the server via the system's dedicated interface.

[0227] Step 2:

[0228] The server receives the image sent from the terminal and prepares to analyze it. Using analysis tools, the server identifies the exterior wall region within the image. Here, edge detection algorithms and segmentation techniques are utilized to efficiently extract the exterior wall portion.

[0229] Step 3:

[0230] The server generates painting options based on information about the exterior wall area. In this generation process, the generating AI suggests multiple color palettes, textures, and patterns. This broadens the range of design options available to the user.

[0231] Step 4:

[0232] The server passes the generated painting options to a visualization tool, which then applies them to the exterior wall image. This process uses rendering techniques to create realistic simulation images, which are then prepared for user review.

[0233] Step 5:

[0234] Users select and adjust their preferred colors and textures while viewing a simulated image displayed on their device. During this process, the user's selection information is transmitted from the device to the server in real time.

[0235] Step 6:

[0236] The server receives user adjustment requests and updates the visualization. It then presents the user with a real-time simulation image reflecting the new design proposal. This allows the user to try out multiple variations and decide on the optimal paint design.

[0237] (Example 1)

[0238] 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."

[0239] When choosing exterior wall decorations and paint designs for a house, it is often difficult for users to concretely visualize the many options available, and they frequently have to rely on experts. Furthermore, there is a lack of environments that allow for realistic visualization and rapid trial and error.

[0240] 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.

[0241] In this invention, the server includes acquisition means for the user to input visual information, analysis means for analyzing the visual information and identifying boundary regions, and generation means for generating decorative options based on the identified boundary regions. This allows the user to instantly visualize a variety of designs, freely modify them, and quickly select the ideal decoration.

[0242] "Acquisition means" refers to a function that allows users to select or input visual information through their terminal and incorporate it into the system.

[0243] "Analysis means" refers to a function that uses image processing technology based on acquired visual information to recognize specific boundary regions and identify necessary areas.

[0244] The "generation method" refers to a function that utilizes a generative AI model to create various decorative options for the analyzed boundary region, devising designs that include diverse colors, textures, and patterns.

[0245] "Display means" refers to a function that uses advanced visualization technology to visualize the generated decorative options and present them to the user, providing immediate feedback to the user.

[0246] The "adjustment method" is a function that allows users to interactively change the color tone and texture based on the displayed decorative options, and instantly reflects the changes in the simulated image.

[0247] This invention provides an information processing system that allows users to easily experiment with decorative designs for the exterior walls of their homes. This system operates via an internet-connected terminal and server, and utilizes a generation AI model to propose and visualize a variety of designs.

[0248] The user first uses a device to acquire an image of the exterior of their home and upload it to the system. This device can be a smart device with a camera or a computer. A highly secure data transfer protocol is used when the image is sent from the device to the server. The server uses image analysis software and computer vision technology to identify the exterior wall areas of the building. Deep learning algorithms are used for this analysis.

[0249] Next, the server uses a generation AI model based on the identified exterior wall area to create decorative options. This generation process devises multiple design proposals with different tones, textures, and patterns. A prompt such as "Suggest a modern-looking exterior paint job" is used.

[0250] Users review the generated decorative options through simulated images visualized on their devices. The system uses advanced visualization technology to reproduce realistic textures and colors, presenting the designs to the user instantly.

[0251] Furthermore, users can adjust design details through their devices and interactively change colors and textures. This allows them to instantly see simulation results based on their desired design.

[0252] By utilizing the system of this invention, users can quickly discover the optimal solution for the exterior wall design of their house without requiring specialized technical skills, and enjoy rare creative freedom.

[0253] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0254] Step 1:

[0255] Users use their devices to acquire images of the exterior of their homes and upload them to the system. In this process, users send images taken with their smartphones or digital cameras to the system via a dedicated application. Input includes image data in JPEG or PNG format, as selected by the user. Output is the image data sent from the device to the server.

[0256] Step 2:

[0257] The terminal sends image data to the server. The server receives the image data using a secure data transfer protocol. This process ensures data integrity and security. The input is the image data sent from the terminal, and the output is the storage of the complete image data on the server.

[0258] Step 3:

[0259] The server performs image analysis on the received image data. The server uses computer vision algorithms to identify the exterior wall areas of buildings from the images. The input is image data, and data processing includes boundary detection and shape recognition to identify the exterior walls. The output is data indicating the exterior wall areas.

[0260] Step 4:

[0261] The server generates decorative options using a generative AI model based on identified exterior wall area data. This process devises multiple paint design proposals with different colors, textures, and patterns. The input consists of exterior wall area data and a prompt statement (e.g., "Suggest a modern-looking exterior paint job"). The data calculation includes the generative AI generating and applying design parameters. The output is a dataset of the generated decorative options.

[0262] Step 5:

[0263] The server visualizes the generated decorative options using visualization technology and sends them to the user's terminal. This allows the user to view the virtual design of the exterior wall in real time. The input is a dataset of decorative options, and the output is a visualized simulation image.

[0264] Step 6:

[0265] The user reviews the design presented through the terminal and makes interactive adjustments to colors and textures as needed. The adjusted data is immediately sent to the server. Input consists of adjustment instructions from the user, and data processing includes updating selected parameters. Output is a simulated image reflecting the updated design.

[0266] Step 7:

[0267] The user finalizes the design and saves or shares it via their device. The server records the design and retains it for the user. The input is the finalized design data, and the output is the saved design data and a record of data that can be used for subsequent applications.

[0268] (Application Example 1)

[0269] 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."

[0270] When purchasing painted products or decorative items in a store, it is difficult for customers to visually confirm how they will actually look in their homes. This increases the risk of disappointment after purchase, making it essential to provide appropriate simulations before purchase.

[0271] 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.

[0272] In this invention, the server includes an input means for the user to input an image, an analysis means for analyzing the image to identify an external region, and a generation means for generating paint candidates based on the identified external region. This allows customers to perform a real-time visual simulation of the product using a smart device before purchasing.

[0273] "Input means" refers to a device or method for a user to supply visual information to a system.

[0274] "Analysis means" refers to an apparatus or method for performing processing to identify a specific region from supplied visual information.

[0275] "Generating means" refers to an apparatus or method for creating new visual options based on an identified specific region.

[0276] "Visualization means" refers to a device or method for displaying generated visual options to a customer.

[0277] "Modification means" refers to a device or method for a user to adjust visual options.

[0278] "Association means" refers to a device or method for linking visual options from identification information.

[0279] "Display means" refers to a device or method for presenting associated options to the user.

[0280] This invention relates to an information processing system for assisting users in selecting products in a store. Specifically, it provides a method for users to simulate painting and decorating items for the exterior of their home using a smart device.

[0281] The system starts by first inputting an image of the user's home through their terminal and sending it to the server. The server uses image analysis software such as OpenCV to identify the external area from the received image. Next, a generative AI model leveraging machine learning frameworks like PyTorch or TensorFlow generates multiple painting candidates based on the identified external area. These painting candidates offer a variety of options including color shades, textures, and patterns.

[0282] The generated candidates are displayed on the smart device using visualization technologies such as Unity or Unreal Engine. This enables the user to visualize various design options for their home's exterior in real time. Furthermore, the user can make adjustments to the simulated image according to their preferences, and the results are immediately reflected on the device.

[0283] For example, if the user selects a specific green product in the store, they scan the product's barcode with the terminal and input a prompt sentence like "Please generate a simulation image of the outer wall by applying the specified 'forest green' tone to the input image." into the generative AI model. This allows them to confirm how the corresponding color would actually look on their home.

[0284] This enables the user to pre-visualize how the actual product would fit into their living environment and make a more appropriate purchasing decision.

[0285] The flow of the specific process in Application Example 1 will be described using Figure 12.

[0286] Step 1:

[0287] The user takes a photo of their home's exterior with the terminal and inputs the image into an application within the terminal. The input image is temporarily saved in an external file format.

[0288] Step 2:

[0289] The terminal sends the saved image to the server. The server receives the image and starts image analysis using OpenCV. This analysis identifies the external region by examining the information of each pixel in the input image. As a result, information about the position and shape of the exterior wall is obtained.

[0290] Step 3:

[0291] Based on the analysis results, the server generates paint options using a generative AI model. This process creates a variety of color and texture options based on the specified prompt: "Apply the specified color tone to the input image to generate a simulated image of the exterior wall." The output includes multiple simulated design options.

[0292] Step 4:

[0293] The generated paint options are sent from the server to the terminal. The terminal uses Unity or Unreal Engine to display the visualized designs to the user. In this step, the generated designs are overlaid on the input image, allowing the user to check the results.

[0294] Step 5:

[0295] Users can make their preferred adjustments to the generated paint options via the device's interface. These adjustments are processed in real time on the device, and the results are displayed immediately. This allows users to repeatedly review and experiment with designs.

[0296] 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.

[0297] This invention provides an information processing system that takes into account the user's emotions when selecting a building exterior paint design, and offers more personalized design suggestions. The following shows the program processing and specific examples of this system.

[0298] The system begins with the user taking a photo of the exterior of their home and uploading the image to their device. This image is sent to a server, which analyzes the image to identify the exterior wall area. Based on the analyzed exterior wall area, a generating AI creates various painting options.

[0299] Next, the emotion recognition system identifies the user's emotions. This is done by analyzing the user's facial expression and voice data. The server retrieves the emotion data and understands the user's current emotional state.

[0300] The server recommends paint options that are appropriate for the user's emotions based on the acquired emotional data. This recommendation process can, for example, prioritize calming color options if the user is relaxed. This makes it easy for users to select a design that matches their emotional state.

[0301] Users can view simulation images displayed on their devices and compare each design to choose their preferred one. Since the selected design is based on emotional data, a highly satisfying choice can be expected.

[0302] For example, if a user displays a cheerful expression while using the system, bright colors and playful patterns will be automatically recommended as painting options. This allows users to easily try out interesting options and find a design that matches their mood.

[0303] Thus, the present invention enables design selection that takes user emotions into consideration, providing an experience that better meets individual user needs than conventional systems.

[0304] The following describes the process flow.

[0305] Step 1:

[0306] The user takes a photo of the exterior of their home and uploads the image to the terminal. The terminal prepares to send this image to the server.

[0307] Step 2:

[0308] The server analyzes the image received from the terminal. Using image analysis technology, it identifies the exterior wall portion and extracts region data.

[0309] Step 3:

[0310] Based on the exterior wall region data, the server uses a generative AI to generate various painting options. These include options for various colors, textures, and design patterns.

[0311] Step 4:

[0312] The user provides emotion data via the terminal using a face recognition camera or microphone. The emotion engine analyzes this data to determine the user's current emotional state.

[0313] Step 5:

[0314] Based on the emotion data, the server recommends painting options according to the user's emotional state. It sets the priority order of designs suitable for the emotion and presents it to the user.

[0315] Step 6:

[0316] The user checks the painting simulation presented via the terminal and considers comparing the designs according to the emotion. The user can select the optimal design. The result of the selection is sent from the terminal to the server and saved.

[0317] (Example 2)

[0318] 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".

[0319] Conventional information processing systems have a problem in that they do not offer personalized design suggestions that take user emotions into consideration, resulting in users being unable to easily select the design they want. To solve this problem, there was a need for a system that could suggest appropriate design options that reflect user emotions and enable highly satisfying choices.

[0320] 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.

[0321] In this invention, the server includes an input means for the user to input an image, an analysis means for analyzing the image and identifying an object region, a generation means for generating design options based on the identified object region, an emotion recognition means for analyzing the user's emotions and acquiring analysis data, a recommendation means for recommending design options suitable for the user based on the analysis data, a visualization means for simulating and displaying the generated design options, and an operation means for the user to adjust the design options. This enables personalized design suggestions based on the user's emotions.

[0322] An "input method" is an interface that allows a user to provide images or information to the system.

[0323] "Analysis means" refers to algorithms and processes for processing input images and identifying specific object regions.

[0324] A "generation mechanism" is a mechanism for creating new design options based on analyzed information.

[0325] An "emotion recognition system" is a system that analyzes a user's facial expressions and voice data to determine the user's emotional state.

[0326] A "recommendation mechanism" is a system that has the function of suggesting the most appropriate design option based on the user's emotional state.

[0327] A "visualization method" is a system that presents the generated design options in a way that allows the user to visually confirm them.

[0328] "Operational means" refers to the interface that allows the user to adjust and select the displayed design options.

[0329] The embodiments for carrying out the present invention are described in detail below.

[0330] This system provides a process that offers suggestions that reflect the user's emotions when customizing the exterior design of buildings such as their homes.

[0331] Users take photos of the exterior of their homes with their smartphones or digital cameras and upload the images to the system via their devices. The devices then send the images to the server via the internet. This communication generally uses HTTPS, which is a secure communication protocol.

[0332] The server analyzes images using image analysis libraries (e.g., OpenCV or TensorFlow) to identify the exterior wall areas of buildings. This analysis provides the foundational data needed to generate customized options suitable for the user's building.

[0333] Generative AI models (e.g., DALL-E and Stable Diffusion) run on a server and generate multiple design options based on the analysis results. For example, the model is driven by prompts such as "Suggest colorful patterns for exterior walls."

[0334] The device records the user's facial expressions and voice through its built-in camera and microphone to acquire data for emotion recognition. This data is sent to a server, which analyzes the user's emotions using an emotion recognition algorithm (e.g., an emotion recognition API).

[0335] The server uses analytical data based on the user's emotional state to recommend personalized design options. For example, if the user has a cheerful expression, the server may recommend a bright and playful design.

[0336] As a concrete example, when a user uses the prompt phrase "a bright and playful exterior design," the generating AI model provides the corresponding design options, which the user can then visually confirm and select on their device.

[0337] In this way, the present invention realizes an information processing system that assists in design selection while taking user emotions into consideration and provides optimized customization suggestions.

[0338] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0339] Step 1:

[0340] The user takes a picture of the exterior of their home with a smartphone or digital camera and uploads the image to the terminal. The terminal receives the user's input image through an interface and sends this image data to the server. As a result, the server receives the image data for analysis.

[0341] Step 2:

[0342] The server performs image processing using the received image data. Here, it uses tools such as OpenCV and TensorFlow to identify the exterior wall areas of buildings from the images. This process is achieved using algorithms such as contour detection and region segmentation, and the output generates data indicating the exterior wall areas.

[0343] Step 3:

[0344] The server generates design options using a generative AI model based on the analyzed exterior wall area data. By providing the model with information on the shape and size of the exterior wall as input, and prompts (e.g., "Please suggest colorful patterns for the exterior wall"), several design options are generated as output.

[0345] Step 4:

[0346] The device uses its camera and microphone to record the user's facial expressions and voice data. This data is used to estimate the user's emotional state, and the device sends the collected data to a server. This allows the server to obtain input data for emotion analysis.

[0347] Step 5:

[0348] The server analyzes facial expression and voice data sent from the terminal using an emotion recognition algorithm. It uses a service like Microsoft Azure's emotion recognition API to obtain the user's instantaneous emotional state as output.

[0349] Step 6:

[0350] The server uses the emotion recognition results to recommend the most suitable design option from the generated options. This process involves an algorithm selecting the optimal design based on the obtained emotion data. The appropriate design option is then sent to the terminal.

[0351] Step 7:

[0352] The user reviews design options displayed on their device and visualizes them on a simulation screen. The user then compares and adjusts these designs to make a selection. The chosen design is based on suggestions derived from the user's emotions.

[0353] This allows users to easily choose an exterior wall design that is customized to their own feelings, resulting in a high level of satisfaction.

[0354] (Application Example 2)

[0355] 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 as the "terminal".

[0356] Traditionally, there has been a lack of personalized suggestions that take into account user emotions in interior design and wallpaper selection, resulting in low user satisfaction. Furthermore, users faced the burden of choosing from a vast number of options.

[0357] 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.

[0358] In this invention, the server includes an input means for the user to input an image, an analysis means for analyzing the image and identifying a display area, and a recommendation means for recognizing the user's emotions and recommending design options based on those emotions. This makes it possible to propose designs that reflect the user's emotions.

[0359] "Input means" refers to methods or devices that users use to input images or data into a system.

[0360] "Analysis means" refers to a method or apparatus that extracts necessary information from input images or data and performs processing to distinguish specific regions.

[0361] "Generative means" refers to the mechanisms and processes for creating various designs and options based on the analyzed information.

[0362] A "recommendation method" is a system that uses acquired user sentiment information to suggest design options that match the user's needs and circumstances.

[0363] A "visualization method" is a method or device that has the function of visually simulating and presenting generated design options to the user.

[0364] "Adjustment means" refers to methods or devices that allow users to change or edit a proposed design to suit their own preferences.

[0365] "Emotional data" refers to information indicating a user's psychological state, obtained from their facial expressions and voice, and is used to personalize designs.

[0366] The system that realizes this invention begins with user input. At this stage, the user uses a device such as a smartphone or smart glasses to take images of the target facility and upload them to a cloud server. This image data is analyzed on the server using OpenCV, an image analysis software, to identify the areas to be designed for the interior.

[0367] Next, the server processes facial and voice data using the Azure Cognitive Services Emotion API to recognize the user's emotions. Based on the emotion data obtained during this process, a generative AI model (e.g., Stable Diffusion) is used to generate multiple design options that match the user's emotions.

[0368] The generated design is then displayed on the device using Vue.js as a visualization tool, allowing the user to visually review it and adjust it to their liking using the adjustment tools. This design suggestion, based on the user's emotions, can improve user satisfaction.

[0369] For example, if emotional data reveals that users are relaxed in the store, a calming design with natural colors is recommended. This makes it easier to select interior design elements that match the store's atmosphere.

[0370] An example of a prompt message is, "Please suggest wallpaper designs that would suit the calm atmosphere of a cafe," which can be used to instruct the generation AI model. By using this prompt, the system can present specific design options that match the user's request.

[0371] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0372] Step 1:

[0373] The user takes pictures of the facility with their device and uploads them to the server. The input is the captured image data, and the output is the image file saved on the server.

[0374] Step 2:

[0375] The server uses image analysis software (OpenCV) to identify the design target area within the uploaded image. The input is the image data obtained in step 1, and the output is information about the identified target area. This information is used for design generation in the next step.

[0376] Step 3:

[0377] The server uses the Emotion API to analyze facial and voice data obtained from the user and recognize the user's emotions. The input is real-time facial and voice data from the user, and the output is data on the user's current emotional state. This output data is used in the next step.

[0378] Step 4:

[0379] The server inputs information about the identified display area and user emotion data into a generating AI model (e.g., Stable Diffusion) to generate design options that are appropriate for the user's emotions. As output, multiple design options that match the user's emotions are obtained.

[0380] Step 5:

[0381] The server displays the generated design options using the device's visualization tool (Vue.js) and provides the user with the simulation results. The input is the design options obtained in step 4, and the output is a visually reproduced simulation image displayed on the device.

[0382] Step 6:

[0383] The user adjusts design options using the device's interface. The input is the user's adjustments, and the output is the final design adjusted by the user. This design is customized to the user's needs.

[0384] 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.

[0385] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One 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.

[0386] 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.

[0387] [Third Embodiment]

[0388] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0389] 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.

[0390] 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).

[0391] 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.

[0392] 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.

[0393] 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).

[0394] 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.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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".

[0400] This invention provides an information processing system that enables users to realize their ideas for exterior wall painting. This system allows users to visually simulate various painting options based on photographs of their own buildings. The program's processing and specific examples are shown below.

[0401] The system begins with the user inputting an image of the house's exterior through an interface. The image uploaded to the terminal is sent to a server, which performs image analysis to identify the exterior wall areas. This analysis uses computer vision technology to identify the exterior wall sections.

[0402] Based on the identified exterior wall area, the server utilizes generative AI to generate a variety of painting options. These include design proposals with variations in color, texture, and pattern. These generated options are then presented to the user as choices.

[0403] Next, visualization technology is used to reflect the generated painting options onto the image. This allows the user to see a realistic simulation image. The user can interactively adjust the colors and textures through their device and see the simulation image change in real time.

[0404] For example, if a user wants to choose a green color scheme, the generating AI will provide a simulation with multiple shades of green applied to the exterior wall. The user can then select the optimal shade from this simulation and add a matching texture.

[0405] This system allows users to easily try out numerous paint designs without the need for expert assistance, streamlining the process of discovering their ideal design.

[0406] The following describes the processing flow.

[0407] Step 1:

[0408] The user takes a photo of the exterior of their home and uploads the image to their device. The device then prepares to submit this image to the server via the system's dedicated interface.

[0409] Step 2:

[0410] The server receives the image sent from the terminal and prepares to analyze it. Using analysis tools, the server identifies the exterior wall region within the image. Here, edge detection algorithms and segmentation techniques are utilized to efficiently extract the exterior wall portion.

[0411] Step 3:

[0412] The server generates painting options based on information about the exterior wall area. In this generation process, the generating AI suggests multiple color palettes, textures, and patterns. This broadens the range of design options available to the user.

[0413] Step 4:

[0414] The server passes the generated painting options to a visualization tool, which then applies them to the exterior wall image. This process uses rendering techniques to create realistic simulation images, which are then prepared for user review.

[0415] Step 5:

[0416] Users select and adjust their preferred colors and textures while viewing a simulated image displayed on their device. During this process, the user's selection information is transmitted from the device to the server in real time.

[0417] Step 6:

[0418] The server receives user adjustment requests and updates the visualization. It then presents the user with a real-time simulation image reflecting the new design proposal. This allows the user to try out multiple variations and decide on the optimal paint design.

[0419] (Example 1)

[0420] 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."

[0421] When choosing exterior wall decorations and paint designs for a house, it is often difficult for users to concretely visualize the many options available, and they frequently have to rely on experts. Furthermore, there is a lack of environments that allow for realistic visualization and rapid trial and error.

[0422] 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.

[0423] In this invention, the server includes acquisition means for the user to input visual information, analysis means for analyzing the visual information and identifying boundary regions, and generation means for generating decorative options based on the identified boundary regions. This allows the user to instantly visualize a variety of designs, freely modify them, and quickly select the ideal decoration.

[0424] "Acquisition means" refers to a function that allows users to select or input visual information through their terminal and incorporate it into the system.

[0425] "Analysis means" refers to a function that uses image processing technology based on acquired visual information to recognize specific boundary regions and identify necessary areas.

[0426] The "generation method" refers to a function that utilizes a generative AI model to create various decorative options for the analyzed boundary region, devising designs that include diverse colors, textures, and patterns.

[0427] "Display means" refers to a function that uses advanced visualization technology to visualize the generated decorative options and present them to the user, providing immediate feedback to the user.

[0428] The "adjustment method" is a function that allows users to interactively change the color tone and texture based on the displayed decorative options, and instantly reflects the changes in the simulated image.

[0429] This invention provides an information processing system that allows users to easily experiment with decorative designs for the exterior walls of their homes. This system operates via an internet-connected terminal and server, and utilizes a generation AI model to propose and visualize a variety of designs.

[0430] The user first uses a device to acquire an image of the exterior of their home and upload it to the system. This device can be a smart device with a camera or a computer. A highly secure data transfer protocol is used when the image is sent from the device to the server. The server uses image analysis software and computer vision technology to identify the exterior wall areas of the building. Deep learning algorithms are used for this analysis.

[0431] Next, the server uses a generation AI model based on the identified exterior wall area to create decorative options. This generation process devises multiple design proposals with different tones, textures, and patterns. A prompt such as "Suggest a modern-looking exterior paint job" is used.

[0432] Users review the generated decorative options through simulated images visualized on their devices. The system uses advanced visualization technology to reproduce realistic textures and colors, presenting the designs to the user instantly.

[0433] Furthermore, users can adjust design details through their devices and interactively change colors and textures. This allows them to instantly see simulation results based on their desired design.

[0434] By utilizing the system of this invention, users can quickly discover the optimal solution for the exterior wall design of their house without requiring specialized technical skills, and enjoy rare creative freedom.

[0435] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0436] Step 1:

[0437] Users use their devices to acquire images of the exterior of their homes and upload them to the system. In this process, users send images taken with their smartphones or digital cameras to the system via a dedicated application. Input includes image data in JPEG or PNG format, as selected by the user. Output is the image data sent from the device to the server.

[0438] Step 2:

[0439] The terminal sends image data to the server. The server receives the image data using a secure data transfer protocol. This process ensures data integrity and security. The input is the image data sent from the terminal, and the output is the storage of the complete image data on the server.

[0440] Step 3:

[0441] The server performs image analysis on the received image data. The server uses computer vision algorithms to identify the exterior wall areas of buildings from the images. The input is image data, and data processing includes boundary detection and shape recognition to identify the exterior walls. The output is data indicating the exterior wall areas.

[0442] Step 4:

[0443] The server generates decorative options using a generative AI model based on identified exterior wall area data. This process devises multiple paint design proposals with different colors, textures, and patterns. The input consists of exterior wall area data and a prompt statement (e.g., "Suggest a modern-looking exterior paint job"). The data calculation includes the generative AI generating and applying design parameters. The output is a dataset of the generated decorative options.

[0444] Step 5:

[0445] The server visualizes the generated decorative options using visualization technology and sends them to the user's terminal. This allows the user to view the virtual design of the exterior wall in real time. The input is a dataset of decorative options, and the output is a visualized simulation image.

[0446] Step 6:

[0447] The user reviews the design presented through the terminal and makes interactive adjustments to colors and textures as needed. The adjusted data is immediately sent to the server. Input consists of adjustment instructions from the user, and data processing includes updating selected parameters. Output is a simulated image reflecting the updated design.

[0448] Step 7:

[0449] The user finalizes the design and saves or shares it via their device. The server records the design and retains it for the user. The input is the finalized design data, and the output is the saved design data and a record of data that can be used for subsequent applications.

[0450] (Application Example 1)

[0451] 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."

[0452] When purchasing painted products or decorative items in a store, it is difficult for customers to visually confirm how they will actually look in their homes. This increases the risk of disappointment after purchase, making it essential to provide appropriate simulations before purchase.

[0453] 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.

[0454] In this invention, the server includes an input means for the user to input an image, an analysis means for analyzing the image to identify an external region, and a generation means for generating paint candidates based on the identified external region. This allows customers to perform a real-time visual simulation of the product using a smart device before purchasing.

[0455] "Input means" refers to a device or method for a user to supply visual information to a system.

[0456] "Analysis means" refers to an apparatus or method for performing processing to identify a specific region from supplied visual information.

[0457] "Generating means" refers to an apparatus or method for creating new visual options based on an identified specific region.

[0458] "Visualization means" refers to a device or method for displaying generated visual options to a customer.

[0459] "Modification means" refers to a device or method for a user to adjust visual options.

[0460] "Association means" refers to a device or method for linking visual options from identification information.

[0461] "Display means" refers to a device or method for presenting associated options to the user.

[0462] This invention relates to an information processing system for assisting users in selecting products in a store. Specifically, it provides a method for users to simulate painting and decorating items for the exterior of their home using a smart device.

[0463] The system begins with the user inputting an image of their home via their device and sending it to the server. The server uses image analysis software such as OpenCV to identify the external region from the received image. Next, a generative AI model utilizing machine learning frameworks such as PyTorch and TensorFlow generates multiple paint options based on the identified external region. These paint options offer a variety of choices, including color, texture, and pattern.

[0464] The generated options are displayed on smart devices using visualization technologies such as Unity and Unreal Engine. This allows users to visualize various design options for the exterior of their home in real time. Furthermore, users can make adjustments to the simulated images according to their preferences, and the results are immediately reflected on the device.

[0465] For example, if a user selects a specific green product in a store, the terminal scans the product's barcode and inputs a prompt message into the AI ​​model: "Apply the specified 'forest green' tone to the input image and generate a simulated image of the exterior wall." This allows the user to see how the color will actually appear in their home.

[0466] This allows users to visualize in advance how the actual product will fit into their living environment, enabling them to make more appropriate purchasing decisions.

[0467] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0468] Step 1:

[0469] The user takes a photo of the exterior of their home with their device and inputs the image into an application on the device. The input image is temporarily saved as an external file.

[0470] Step 2:

[0471] The terminal sends the saved image to the server. The server receives the image and starts image analysis using OpenCV. This analysis identifies the external region by examining the information of each pixel in the input image. As a result, information about the position and shape of the exterior wall is obtained.

[0472] Step 3:

[0473] Based on the analysis results, the server generates paint options using a generative AI model. This process creates a variety of color and texture options based on the specified prompt: "Apply the specified color tone to the input image to generate a simulated image of the exterior wall." The output includes multiple simulated design options.

[0474] Step 4:

[0475] The generated paint options are sent from the server to the terminal. The terminal uses Unity or Unreal Engine to display the visualized designs to the user. In this step, the generated designs are overlaid on the input image, allowing the user to check the results.

[0476] Step 5:

[0477] Users can make their preferred adjustments to the generated paint options via the device's interface. These adjustments are processed in real time on the device, and the results are displayed immediately. This allows users to repeatedly review and experiment with designs.

[0478] 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.

[0479] This invention provides an information processing system that takes into account the user's emotions when selecting a building exterior paint design, and offers more personalized design suggestions. The following shows the program processing and specific examples of this system.

[0480] The system begins with the user taking a photo of the exterior of their home and uploading the image to their device. This image is sent to a server, which analyzes the image to identify the exterior wall area. Based on the analyzed exterior wall area, a generating AI creates various painting options.

[0481] Next, the emotion recognition system identifies the user's emotions. This is done by analyzing the user's facial expression and voice data. The server retrieves the emotion data and understands the user's current emotional state.

[0482] The server recommends paint options that are appropriate for the user's emotions based on the acquired emotional data. This recommendation process can, for example, prioritize calming color options if the user is relaxed. This makes it easy for users to select a design that matches their emotional state.

[0483] Users can view simulation images displayed on their devices and compare each design to choose their preferred one. Since the selected design is based on emotional data, a highly satisfying choice can be expected.

[0484] For example, if a user displays a cheerful expression while using the system, bright colors and playful patterns will be automatically recommended as painting options. This allows users to easily try out interesting options and find a design that matches their mood.

[0485] Thus, the present invention enables design selection that takes user emotions into consideration, providing an experience that better meets individual user needs than conventional systems.

[0486] The following describes the processing flow.

[0487] Step 1:

[0488] The user takes a photo of the exterior of their home and uploads the image to their device. The device then prepares to send this image to the server.

[0489] Step 2:

[0490] The server analyzes the images received from the terminal. Using image analysis technology, it identifies the exterior wall portion and extracts region data.

[0491] Step 3:

[0492] The server uses a generative AI to generate a variety of painting options based on exterior wall area data. These options include various colors, textures, and design patterns.

[0493] Step 4:

[0494] Users provide emotional data via their device using a facial recognition camera and microphone. The emotion engine analyzes this data to determine the user's current emotional state.

[0495] Step 5:

[0496] The server uses emotion data to recommend painting options that match the user's emotional state. It prioritizes designs that are appropriate for the emotion and presents them to the user.

[0497] Step 6:

[0498] Users can view paint simulations presented through their device and compare designs that align with their emotions. They can then select the optimal design. The selection is sent from the device to a server and saved.

[0499] (Example 2)

[0500] 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."

[0501] Conventional information processing systems have a problem in that they do not offer personalized design suggestions that take user emotions into consideration, resulting in users being unable to easily select the design they want. To solve this problem, there was a need for a system that could suggest appropriate design options that reflect user emotions and enable highly satisfying choices.

[0502] 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.

[0503] In this invention, the server includes an input means for the user to input an image, an analysis means for analyzing the image and identifying an object region, a generation means for generating design options based on the identified object region, an emotion recognition means for analyzing the user's emotions and acquiring analysis data, a recommendation means for recommending design options suitable for the user based on the analysis data, a visualization means for simulating and displaying the generated design options, and an operation means for the user to adjust the design options. This enables personalized design suggestions based on the user's emotions.

[0504] An "input method" is an interface that allows a user to provide images or information to the system.

[0505] "Analysis means" refers to algorithms and processes for processing input images and identifying specific object regions.

[0506] A "generation mechanism" is a mechanism for creating new design options based on analyzed information.

[0507] An "emotion recognition system" is a system that analyzes a user's facial expressions and voice data to determine the user's emotional state.

[0508] A "recommendation mechanism" is a system that has the function of suggesting the most appropriate design option based on the user's emotional state.

[0509] A "visualization method" is a system that presents the generated design options in a way that allows the user to visually confirm them.

[0510] "Operational means" refers to the interface that allows the user to adjust and select the displayed design options.

[0511] The embodiments for carrying out the present invention are described in detail below.

[0512] This system provides a process that offers suggestions that reflect the user's emotions when customizing the exterior design of buildings such as their homes.

[0513] Users take photos of the exterior of their homes with their smartphones or digital cameras and upload the images to the system via their devices. The devices then send the images to the server via the internet. This communication generally uses HTTPS, which is a secure communication protocol.

[0514] The server analyzes images using image analysis libraries (e.g., OpenCV or TensorFlow) to identify the exterior wall areas of buildings. This analysis provides the foundational data needed to generate customized options suitable for the user's building.

[0515] Generative AI models (e.g., DALL-E and Stable Diffusion) run on a server and generate multiple design options based on the analysis results. For example, the model is driven by prompts such as "Suggest colorful patterns for exterior walls."

[0516] The device records the user's facial expressions and voice through its built-in camera and microphone to acquire data for emotion recognition. This data is sent to a server, which analyzes the user's emotions using an emotion recognition algorithm (e.g., an emotion recognition API).

[0517] The server uses analytical data based on the user's emotional state to recommend personalized design options. For example, if the user has a cheerful expression, the server may recommend a bright and playful design.

[0518] As a concrete example, when a user uses the prompt phrase "a bright and playful exterior design," the generating AI model provides the corresponding design options, which the user can then visually confirm and select on their device.

[0519] In this way, the present invention realizes an information processing system that assists in design selection while taking user emotions into consideration and provides optimized customization suggestions.

[0520] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0521] Step 1:

[0522] The user takes a picture of the exterior of their home with a smartphone or digital camera and uploads the image to the terminal. The terminal receives the user's input image through an interface and sends this image data to the server. As a result, the server receives the image data for analysis.

[0523] Step 2:

[0524] The server performs image processing using the received image data. Here, it uses tools such as OpenCV and TensorFlow to identify the exterior wall areas of buildings from the images. This process is achieved using algorithms such as contour detection and region segmentation, and the output generates data indicating the exterior wall areas.

[0525] Step 3:

[0526] The server generates design options using a generative AI model based on the analyzed exterior wall area data. By providing the model with information on the shape and size of the exterior wall as input, and prompts (e.g., "Please suggest colorful patterns for the exterior wall"), several design options are generated as output.

[0527] Step 4:

[0528] The device uses its camera and microphone to record the user's facial expressions and voice data. This data is used to estimate the user's emotional state, and the device sends the collected data to a server. This allows the server to obtain input data for emotion analysis.

[0529] Step 5:

[0530] The server analyzes facial expression and voice data sent from the terminal using an emotion recognition algorithm. It uses a service like Microsoft Azure's emotion recognition API to obtain the user's instantaneous emotional state as output.

[0531] Step 6:

[0532] The server uses the emotion recognition results to recommend the most suitable design option from the generated options. This process involves an algorithm selecting the optimal design based on the obtained emotion data. The appropriate design option is then sent to the terminal.

[0533] Step 7:

[0534] The user reviews design options displayed on their device and visualizes them on a simulation screen. The user then compares and adjusts these designs to make a selection. The chosen design is based on suggestions derived from the user's emotions.

[0535] This allows users to easily choose an exterior wall design that is customized to their own feelings, resulting in a high level of satisfaction.

[0536] (Application Example 2)

[0537] 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."

[0538] Traditionally, there has been a lack of personalized suggestions that take into account user emotions in interior design and wallpaper selection, resulting in low user satisfaction. Furthermore, users faced the burden of choosing from a vast number of options.

[0539] 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.

[0540] In this invention, the server includes an input means for the user to input an image, an analysis means for analyzing the image and identifying a display area, and a recommendation means for recognizing the user's emotions and recommending design options based on those emotions. This makes it possible to propose designs that reflect the user's emotions.

[0541] "Input means" refers to methods or devices that users use to input images or data into a system.

[0542] "Analysis means" refers to a method or apparatus that extracts necessary information from input images or data and performs processing to distinguish specific regions.

[0543] "Generative means" refers to the mechanisms and processes for creating various designs and options based on the analyzed information.

[0544] A "recommendation method" is a system that uses acquired user sentiment information to suggest design options that match the user's needs and circumstances.

[0545] A "visualization method" is a method or device that has the function of visually simulating and presenting generated design options to the user.

[0546] "Adjustment means" refers to methods or devices that allow users to change or edit a proposed design to suit their own preferences.

[0547] "Emotional data" refers to information indicating a user's psychological state, obtained from their facial expressions and voice, and is used to personalize designs.

[0548] The system that realizes this invention begins with user input. At this stage, the user uses a device such as a smartphone or smart glasses to take images of the target facility and upload them to a cloud server. This image data is analyzed on the server using OpenCV, an image analysis software, to identify the areas to be designed for the interior.

[0549] Next, the server processes facial and voice data using the Azure Cognitive Services Emotion API to recognize the user's emotions. Based on the emotion data obtained during this process, a generative AI model (e.g., Stable Diffusion) is used to generate multiple design options that match the user's emotions.

[0550] The generated design is then displayed on the device using Vue.js as a visualization tool, allowing the user to visually review it and adjust it to their liking using the adjustment tools. This design suggestion, based on the user's emotions, can improve user satisfaction.

[0551] For example, if emotional data reveals that users are relaxed in the store, a calming design with natural colors is recommended. This makes it easier to select interior design elements that match the store's atmosphere.

[0552] An example of a prompt message is, "Please suggest wallpaper designs that would suit the calm atmosphere of a cafe," which can be used to instruct the generation AI model. By using this prompt, the system can present specific design options that match the user's request.

[0553] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0554] Step 1:

[0555] The user takes pictures of the facility with their device and uploads them to the server. The input is the captured image data, and the output is the image file saved on the server.

[0556] Step 2:

[0557] The server uses image analysis software (OpenCV) to identify the design target area within the uploaded image. The input is the image data obtained in step 1, and the output is information about the identified target area. This information is used for design generation in the next step.

[0558] Step 3:

[0559] The server uses the Emotion API to analyze facial and voice data obtained from the user and recognize the user's emotions. The input is real-time facial and voice data from the user, and the output is data on the user's current emotional state. This output data is used in the next step.

[0560] Step 4:

[0561] The server inputs information about the identified display area and user emotion data into a generating AI model (e.g., Stable Diffusion) to generate design options that are appropriate for the user's emotions. As output, multiple design options that match the user's emotions are obtained.

[0562] Step 5:

[0563] The server displays the generated design options using the device's visualization tool (Vue.js) and provides the user with the simulation results. The input is the design options obtained in step 4, and the output is a visually reproduced simulation image displayed on the device.

[0564] Step 6:

[0565] The user adjusts design options using the device's interface. The input is the user's adjustments, and the output is the final design adjusted by the user. This design is customized to the user's needs.

[0566] 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.

[0567] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One 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.

[0568] 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.

[0569] [Fourth Embodiment]

[0570] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0571] 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.

[0572] 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).

[0573] 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.

[0574] 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.

[0575] 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).

[0576] 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.

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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".

[0583] This invention provides an information processing system that enables users to realize their ideas for exterior wall painting. This system allows users to visually simulate various painting options based on photographs of their own buildings. The program's processing and specific examples are shown below.

[0584] The system begins with the user inputting an image of the house's exterior through an interface. The image uploaded to the terminal is sent to a server, which performs image analysis to identify the exterior wall areas. This analysis uses computer vision technology to identify the exterior wall sections.

[0585] Based on the identified exterior wall area, the server utilizes generative AI to generate a variety of painting options. These include design proposals with variations in color, texture, and pattern. These generated options are then presented to the user as choices.

[0586] Next, visualization technology is used to reflect the generated painting options onto the image. This allows the user to see a realistic simulation image. The user can interactively adjust the colors and textures through their device and see the simulation image change in real time.

[0587] For example, if a user wants to choose a green color scheme, the generating AI will provide a simulation with multiple shades of green applied to the exterior wall. The user can then select the optimal shade from this simulation and add a matching texture.

[0588] This system allows users to easily try out numerous paint designs without the need for expert assistance, streamlining the process of discovering their ideal design.

[0589] The following describes the processing flow.

[0590] Step 1:

[0591] The user takes a photo of the exterior of their home and uploads the image to their device. The device then prepares to submit this image to the server via the system's dedicated interface.

[0592] Step 2:

[0593] The server receives the image sent from the terminal and prepares to analyze it. Using analysis tools, the server identifies the exterior wall region within the image. Here, edge detection algorithms and segmentation techniques are utilized to efficiently extract the exterior wall portion.

[0594] Step 3:

[0595] The server generates painting options based on information about the exterior wall area. In this generation process, the generating AI suggests multiple color palettes, textures, and patterns. This broadens the range of design options available to the user.

[0596] Step 4:

[0597] The server passes the generated painting options to a visualization tool, which then applies them to the exterior wall image. This process uses rendering techniques to create realistic simulation images, which are then prepared for user review.

[0598] Step 5:

[0599] Users select and adjust their preferred colors and textures while viewing a simulated image displayed on their device. During this process, the user's selection information is transmitted from the device to the server in real time.

[0600] Step 6:

[0601] The server receives user adjustment requests and updates the visualization. It then presents the user with a real-time simulation image reflecting the new design proposal. This allows the user to try out multiple variations and decide on the optimal paint design.

[0602] (Example 1)

[0603] 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".

[0604] When choosing exterior wall decorations and paint designs for a house, it is often difficult for users to concretely visualize the many options available, and they frequently have to rely on experts. Furthermore, there is a lack of environments that allow for realistic visualization and rapid trial and error.

[0605] 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.

[0606] In this invention, the server includes acquisition means for the user to input visual information, analysis means for analyzing the visual information and identifying boundary regions, and generation means for generating decorative options based on the identified boundary regions. This allows the user to instantly visualize a variety of designs, freely modify them, and quickly select the ideal decoration.

[0607] "Acquisition means" refers to a function that allows users to select or input visual information through their terminal and incorporate it into the system.

[0608] "Analysis means" refers to a function that uses image processing technology based on acquired visual information to recognize specific boundary regions and identify necessary areas.

[0609] The "generation method" refers to a function that utilizes a generative AI model to create various decorative options for the analyzed boundary region, devising designs that include diverse colors, textures, and patterns.

[0610] "Display means" refers to a function that uses advanced visualization technology to visualize the generated decorative options and present them to the user, providing immediate feedback to the user.

[0611] The "adjustment method" is a function that allows users to interactively change the color tone and texture based on the displayed decorative options, and instantly reflects the changes in the simulated image.

[0612] This invention provides an information processing system that allows users to easily experiment with decorative designs for the exterior walls of their homes. This system operates via an internet-connected terminal and server, and utilizes a generation AI model to propose and visualize a variety of designs.

[0613] The user first uses a device to acquire an image of the exterior of their home and upload it to the system. This device can be a smart device with a camera or a computer. A highly secure data transfer protocol is used when the image is sent from the device to the server. The server uses image analysis software and computer vision technology to identify the exterior wall areas of the building. Deep learning algorithms are used for this analysis.

[0614] Next, the server uses a generation AI model based on the identified exterior wall area to create decorative options. This generation process devises multiple design proposals with different tones, textures, and patterns. A prompt such as "Suggest a modern-looking exterior paint job" is used.

[0615] Users review the generated decorative options through simulated images visualized on their devices. The system uses advanced visualization technology to reproduce realistic textures and colors, presenting the designs to the user instantly.

[0616] Furthermore, users can adjust design details through their devices and interactively change colors and textures. This allows them to instantly see simulation results based on their desired design.

[0617] By utilizing the system of this invention, users can quickly discover the optimal solution for the exterior wall design of their house without requiring specialized technical skills, and enjoy rare creative freedom.

[0618] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0619] Step 1:

[0620] Users use their devices to acquire images of the exterior of their homes and upload them to the system. In this process, users send images taken with their smartphones or digital cameras to the system via a dedicated application. Input includes image data in JPEG or PNG format, as selected by the user. Output is the image data sent from the device to the server.

[0621] Step 2:

[0622] The terminal sends image data to the server. The server receives the image data using a secure data transfer protocol. This process ensures data integrity and security. The input is the image data sent from the terminal, and the output is the storage of the complete image data on the server.

[0623] Step 3:

[0624] The server performs image analysis on the received image data. The server uses computer vision algorithms to identify the exterior wall areas of buildings from the images. The input is image data, and data processing includes boundary detection and shape recognition to identify the exterior walls. The output is data indicating the exterior wall areas.

[0625] Step 4:

[0626] The server generates decorative options using a generative AI model based on identified exterior wall area data. This process devises multiple paint design proposals with different colors, textures, and patterns. The input consists of exterior wall area data and a prompt statement (e.g., "Suggest a modern-looking exterior paint job"). The data calculation includes the generative AI generating and applying design parameters. The output is a dataset of the generated decorative options.

[0627] Step 5:

[0628] The server visualizes the generated decorative options using visualization technology and sends them to the user's terminal. This allows the user to view the virtual design of the exterior wall in real time. The input is a dataset of decorative options, and the output is a visualized simulation image.

[0629] Step 6:

[0630] The user reviews the design presented through the terminal and makes interactive adjustments to colors and textures as needed. The adjusted data is immediately sent to the server. Input consists of adjustment instructions from the user, and data processing includes updating selected parameters. Output is a simulated image reflecting the updated design.

[0631] Step 7:

[0632] The user finalizes the design and saves or shares it via their device. The server records the design and retains it for the user. The input is the finalized design data, and the output is the saved design data and a record of data that can be used for subsequent applications.

[0633] (Application Example 1)

[0634] 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".

[0635] When purchasing painted products or decorative items in a store, it is difficult for customers to visually confirm how they will actually look in their homes. This increases the risk of disappointment after purchase, making it essential to provide appropriate simulations before purchase.

[0636] 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.

[0637] In this invention, the server includes an input means for the user to input an image, an analysis means for analyzing the image to identify an external region, and a generation means for generating paint candidates based on the identified external region. This allows customers to perform a real-time visual simulation of the product using a smart device before purchasing.

[0638] "Input means" refers to a device or method for a user to supply visual information to a system.

[0639] "Analysis means" refers to an apparatus or method for performing processing to identify a specific region from supplied visual information.

[0640] "Generating means" refers to an apparatus or method for creating new visual options based on an identified specific region.

[0641] "Visualization means" refers to a device or method for displaying generated visual options to a customer.

[0642] "Modification means" refers to a device or method for a user to adjust visual options.

[0643] "Association means" refers to a device or method for linking visual options from identification information.

[0644] "Display means" refers to a device or method for presenting associated options to the user.

[0645] This invention relates to an information processing system for assisting users in selecting products in a store. Specifically, it provides a method for users to simulate painting and decorating items for the exterior of their home using a smart device.

[0646] The system begins with the user inputting an image of their home via their device and sending it to the server. The server uses image analysis software such as OpenCV to identify the external region from the received image. Next, a generative AI model utilizing machine learning frameworks such as PyTorch and TensorFlow generates multiple paint options based on the identified external region. These paint options offer a variety of choices, including color, texture, and pattern.

[0647] The generated options are displayed on smart devices using visualization technologies such as Unity and Unreal Engine. This allows users to visualize various design options for the exterior of their home in real time. Furthermore, users can make adjustments to the simulated images according to their preferences, and the results are immediately reflected on the device.

[0648] For example, if a user selects a specific green product in a store, the terminal scans the product's barcode and inputs a prompt message into the AI ​​model: "Apply the specified 'forest green' tone to the input image and generate a simulated image of the exterior wall." This allows the user to see how the color will actually appear in their home.

[0649] This allows users to visualize in advance how the actual product will fit into their living environment, enabling them to make more appropriate purchasing decisions.

[0650] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0651] Step 1:

[0652] The user takes a photo of the exterior of their home with their device and inputs the image into an application on the device. The input image is temporarily saved as an external file.

[0653] Step 2:

[0654] The terminal sends the saved image to the server. The server receives the image and starts image analysis using OpenCV. This analysis identifies the external region by examining the information of each pixel in the input image. As a result, information about the position and shape of the exterior wall is obtained.

[0655] Step 3:

[0656] Based on the analysis results, the server generates paint options using a generative AI model. This process creates a variety of color and texture options based on the specified prompt: "Apply the specified color tone to the input image to generate a simulated image of the exterior wall." The output includes multiple simulated design options.

[0657] Step 4:

[0658] The generated paint options are sent from the server to the terminal. The terminal uses Unity or Unreal Engine to display the visualized designs to the user. In this step, the generated designs are overlaid on the input image, allowing the user to check the results.

[0659] Step 5:

[0660] Users can make their preferred adjustments to the generated paint options via the device's interface. These adjustments are processed in real time on the device, and the results are displayed immediately. This allows users to repeatedly review and experiment with designs.

[0661] 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.

[0662] This invention provides an information processing system that takes into account the user's emotions when selecting a building exterior paint design, and offers more personalized design suggestions. The following shows the program processing and specific examples of this system.

[0663] The system begins with the user taking a photo of the exterior of their home and uploading the image to their device. This image is sent to a server, which analyzes the image to identify the exterior wall area. Based on the analyzed exterior wall area, a generating AI creates various painting options.

[0664] Next, the emotion recognition system identifies the user's emotions. This is done by analyzing the user's facial expression and voice data. The server retrieves the emotion data and understands the user's current emotional state.

[0665] The server recommends paint options that are appropriate for the user's emotions based on the acquired emotional data. This recommendation process can, for example, prioritize calming color options if the user is relaxed. This makes it easy for users to select a design that matches their emotional state.

[0666] Users can view simulation images displayed on their devices and compare each design to choose their preferred one. Since the selected design is based on emotional data, a highly satisfying choice can be expected.

[0667] For example, if a user displays a cheerful expression while using the system, bright colors and playful patterns will be automatically recommended as painting options. This allows users to easily try out interesting options and find a design that matches their mood.

[0668] Thus, the present invention enables design selection that takes user emotions into consideration, providing an experience that better meets individual user needs than conventional systems.

[0669] The following describes the processing flow.

[0670] Step 1:

[0671] The user takes a photo of the exterior of their home and uploads the image to their device. The device then prepares to send this image to the server.

[0672] Step 2:

[0673] The server analyzes the images received from the terminal. Using image analysis technology, it identifies the exterior wall portion and extracts region data.

[0674] Step 3:

[0675] The server uses a generative AI to generate a variety of painting options based on exterior wall area data. These options include various colors, textures, and design patterns.

[0676] Step 4:

[0677] Users provide emotional data via their device using a facial recognition camera and microphone. The emotion engine analyzes this data to determine the user's current emotional state.

[0678] Step 5:

[0679] The server uses emotion data to recommend painting options that match the user's emotional state. It prioritizes designs that are appropriate for the emotion and presents them to the user.

[0680] Step 6:

[0681] Users can view paint simulations presented through their device and compare designs that align with their emotions. They can then select the optimal design. The selection is sent from the device to a server and saved.

[0682] (Example 2)

[0683] 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".

[0684] Conventional information processing systems have a problem in that they do not offer personalized design suggestions that take user emotions into consideration, resulting in users being unable to easily select the design they want. To solve this problem, there was a need for a system that could suggest appropriate design options that reflect user emotions and enable highly satisfying choices.

[0685] 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.

[0686] In this invention, the server includes an input means for the user to input an image, an analysis means for analyzing the image and identifying an object region, a generation means for generating design options based on the identified object region, an emotion recognition means for analyzing the user's emotions and acquiring analysis data, a recommendation means for recommending design options suitable for the user based on the analysis data, a visualization means for simulating and displaying the generated design options, and an operation means for the user to adjust the design options. This enables personalized design suggestions based on the user's emotions.

[0687] An "input method" is an interface that allows a user to provide images or information to the system.

[0688] "Analysis means" refers to algorithms and processes for processing input images and identifying specific object regions.

[0689] A "generation mechanism" is a mechanism for creating new design options based on analyzed information.

[0690] An "emotion recognition system" is a system that analyzes a user's facial expressions and voice data to determine the user's emotional state.

[0691] A "recommendation mechanism" is a system that has the function of suggesting the most appropriate design option based on the user's emotional state.

[0692] A "visualization method" is a system that presents the generated design options in a way that allows the user to visually confirm them.

[0693] "Operational means" refers to the interface that allows the user to adjust and select the displayed design options.

[0694] The embodiments for carrying out the present invention are described in detail below.

[0695] This system provides a process that offers suggestions that reflect the user's emotions when customizing the exterior design of buildings such as their homes.

[0696] Users take photos of the exterior of their homes with their smartphones or digital cameras and upload the images to the system via their devices. The devices then send the images to the server via the internet. This communication generally uses HTTPS, which is a secure communication protocol.

[0697] The server analyzes images using image analysis libraries (e.g., OpenCV or TensorFlow) to identify the exterior wall areas of buildings. This analysis provides the foundational data needed to generate customized options suitable for the user's building.

[0698] Generative AI models (e.g., DALL-E and Stable Diffusion) run on a server and generate multiple design options based on the analysis results. For example, the model is driven by prompts such as "Suggest colorful patterns for exterior walls."

[0699] The device records the user's facial expressions and voice through its built-in camera and microphone to acquire data for emotion recognition. This data is sent to a server, which analyzes the user's emotions using an emotion recognition algorithm (e.g., an emotion recognition API).

[0700] The server uses analytical data based on the user's emotional state to recommend personalized design options. For example, if the user has a cheerful expression, the server may recommend a bright and playful design.

[0701] As a concrete example, when a user uses the prompt phrase "a bright and playful exterior design," the generating AI model provides the corresponding design options, which the user can then visually confirm and select on their device.

[0702] In this way, the present invention realizes an information processing system that assists in design selection while taking user emotions into consideration and provides optimized customization suggestions.

[0703] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0704] Step 1:

[0705] The user takes a picture of the exterior of their home with a smartphone or digital camera and uploads the image to the terminal. The terminal receives the user's input image through an interface and sends this image data to the server. As a result, the server receives the image data for analysis.

[0706] Step 2:

[0707] The server performs image processing using the received image data. Here, it uses tools such as OpenCV and TensorFlow to identify the exterior wall areas of buildings from the images. This process is achieved using algorithms such as contour detection and region segmentation, and the output generates data indicating the exterior wall areas.

[0708] Step 3:

[0709] The server generates design options using a generative AI model based on the analyzed exterior wall area data. By providing the model with information on the shape and size of the exterior wall as input, and prompts (e.g., "Please suggest colorful patterns for the exterior wall"), several design options are generated as output.

[0710] Step 4:

[0711] The device uses its camera and microphone to record the user's facial expressions and voice data. This data is used to estimate the user's emotional state, and the device sends the collected data to a server. This allows the server to obtain input data for emotion analysis.

[0712] Step 5:

[0713] The server analyzes facial expression and voice data sent from the terminal using an emotion recognition algorithm. It uses a service like Microsoft Azure's emotion recognition API to obtain the user's instantaneous emotional state as output.

[0714] Step 6:

[0715] The server uses the emotion recognition results to recommend the most suitable design option from the generated options. This process involves an algorithm selecting the optimal design based on the obtained emotion data. The appropriate design option is then sent to the terminal.

[0716] Step 7:

[0717] The user reviews design options displayed on their device and visualizes them on a simulation screen. The user then compares and adjusts these designs to make a selection. The chosen design is based on suggestions derived from the user's emotions.

[0718] This allows users to easily choose an exterior wall design that is customized to their own feelings, resulting in a high level of satisfaction.

[0719] (Application Example 2)

[0720] 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".

[0721] Traditionally, there has been a lack of personalized suggestions that take into account user emotions in interior design and wallpaper selection, resulting in low user satisfaction. Furthermore, users faced the burden of choosing from a vast number of options.

[0722] 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.

[0723] In this invention, the server includes an input means for the user to input an image, an analysis means for analyzing the image and identifying a display area, and a recommendation means for recognizing the user's emotions and recommending design options based on those emotions. This makes it possible to propose designs that reflect the user's emotions.

[0724] "Input means" refers to methods or devices that users use to input images or data into a system.

[0725] "Analysis means" refers to a method or apparatus that extracts necessary information from input images or data and performs processing to distinguish specific regions.

[0726] "Generative means" refers to the mechanisms and processes for creating various designs and options based on the analyzed information.

[0727] A "recommendation method" is a system that uses acquired user sentiment information to suggest design options that match the user's needs and circumstances.

[0728] A "visualization method" is a method or device that has the function of visually simulating and presenting generated design options to the user.

[0729] "Adjustment means" refers to methods or devices that allow users to change or edit a proposed design to suit their own preferences.

[0730] "Emotional data" refers to information indicating a user's psychological state, obtained from their facial expressions and voice, and is used to personalize designs.

[0731] The system that realizes this invention begins with user input. At this stage, the user uses a device such as a smartphone or smart glasses to take images of the target facility and upload them to a cloud server. This image data is analyzed on the server using OpenCV, an image analysis software, to identify the areas to be designed for the interior.

[0732] Next, the server processes facial and voice data using the Azure Cognitive Services Emotion API to recognize the user's emotions. Based on the emotion data obtained during this process, a generative AI model (e.g., Stable Diffusion) is used to generate multiple design options that match the user's emotions.

[0733] The generated design is then displayed on the device using Vue.js as a visualization tool, allowing the user to visually review it and adjust it to their liking using the adjustment tools. This design suggestion, based on the user's emotions, can improve user satisfaction.

[0734] For example, if emotional data reveals that users are relaxed in the store, a calming design with natural colors is recommended. This makes it easier to select interior design elements that match the store's atmosphere.

[0735] An example of a prompt message is, "Please suggest wallpaper designs that would suit the calm atmosphere of a cafe," which can be used to instruct the generation AI model. By using this prompt, the system can present specific design options that match the user's request.

[0736] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0737] Step 1:

[0738] The user takes pictures of the facility with their device and uploads them to the server. The input is the captured image data, and the output is the image file saved on the server.

[0739] Step 2:

[0740] The server uses image analysis software (OpenCV) to identify the design target area within the uploaded image. The input is the image data obtained in step 1, and the output is information about the identified target area. This information is used for design generation in the next step.

[0741] Step 3:

[0742] The server uses the Emotion API to analyze facial and voice data obtained from the user and recognize the user's emotions. The input is real-time facial and voice data from the user, and the output is data on the user's current emotional state. This output data is used in the next step.

[0743] Step 4:

[0744] The server inputs information about the identified display area and user emotion data into a generating AI model (e.g., Stable Diffusion) to generate design options that are appropriate for the user's emotions. As output, multiple design options that match the user's emotions are obtained.

[0745] Step 5:

[0746] The server displays the generated design options using the device's visualization tool (Vue.js) and provides the user with the simulation results. The input is the design options obtained in step 4, and the output is a visually reproduced simulation image displayed on the device.

[0747] Step 6:

[0748] The user adjusts design options using the device's interface. The input is the user's adjustments, and the output is the final design adjusted by the user. This design is customized to the user's needs.

[0749] 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.

[0750] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One 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.

[0751] 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.

[0752] 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.

[0753] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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."

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] The following is further disclosed regarding the embodiments described above.

[0771] (Claim 1)

[0772] An input method for the user to input an image,

[0773] An analysis means for analyzing the aforementioned image to identify the exterior wall region,

[0774] A generation means for generating painting options based on the identified exterior wall region,

[0775] A visualization means for simulating and displaying the generated painting options,

[0776] An information processing system including adjustment means for a user to adjust the aforementioned painting options.

[0777] (Claim 2)

[0778] The information processing system according to claim 1, wherein the generated painting options include color, texture, and pattern.

[0779] (Claim 3)

[0780] The information processing system according to claim 1, wherein the visualization means can be updated in real time according to user adjustments.

[0781] "Example 1"

[0782] (Claim 1)

[0783] A means of acquiring visual information that the user inputs,

[0784] An analysis means for analyzing the aforementioned visual information to identify the boundary region,

[0785] A generation means for generating decorative options based on the identified boundary region,

[0786] A display means for visualizing and presenting the generated decorative options,

[0787] A system including adjustment means for a user to modify the aforementioned decorative options.

[0788] (Claim 2)

[0789] The system according to claim 1, wherein the generated decorative options include color, texture, and pattern.

[0790] (Claim 3)

[0791] The system according to claim 1, wherein the display means is immediately updateable in response to user modifications.

[0792] "Application Example 1"

[0793] (Claim 1)

[0794] An input method for the user to input an image,

[0795] An analysis means for analyzing the aforementioned image to identify an external region,

[0796] A generation means for generating paint candidates based on the identified external region,

[0797] A visualization means for representing and outputting the generated paint candidates,

[0798] A means for the user to modify the aforementioned paint candidate,

[0799] A means for associating candidates using product identifiers,

[0800] A display means for displaying candidates based on the aforementioned product identifier,

[0801] A system that includes this.

[0802] (Claim 2)

[0803] The system according to claim 1, wherein the generated paint candidates include color, texture, and pattern.

[0804] (Claim 3)

[0805] The system according to claim 1, wherein the visualization means is capable of being updated in real time in response to user modifications.

[0806] "Example 2 of combining an emotion engine"

[0807] (Claim 1)

[0808] An input method for the user to input an image,

[0809] An analysis means for analyzing the aforementioned image to identify the object region,

[0810] A generation means for generating design options based on the identified object region,

[0811] An emotion recognition method that analyzes user emotions and obtains analytical data,

[0812] A recommendation means that recommends design options suitable for the user based on the aforementioned analysis data,

[0813] A visualization means for simulating and displaying the generated design options,

[0814] Operating means for the user to adjust the design options,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, wherein the generated design options include color, texture, and pattern.

[0818] (Claim 3)

[0819] The system according to claim 1, wherein the visualization means can be updated in real time in response to user operation.

[0820] "Application example 2 when combining with an emotional engine"

[0821] (Claim 1)

[0822] An input method for the user to input an image,

[0823] An analysis means for analyzing the aforementioned image to identify the display target area,

[0824] A generation means for generating design options based on the identified display target area,

[0825] A recommendation system that recognizes user emotions and recommends design options based on those emotions,

[0826] A visualization means for simulating and displaying the aforementioned recommended design options,

[0827] An adjustment means for the user to adjust the aforementioned design options,

[0828] A means of acquiring emotional data and implementing a process to personalize design suggestions to the user's emotions,

[0829] ...

[0830] A system that includes this.

[0831] (Claim 2)

[0832] The system according to claim 1, wherein the generated design options include color, texture, and pattern.

[0833] (Claim 3)

[0834] The system according to claim 1, wherein the visualization means can be updated in real time according to user adjustments. [Explanation of Symbols]

[0835] 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. An input method for the user to input an image, An analysis means for analyzing the aforementioned image to identify an external region, A generation means for generating paint candidates based on the identified external region, A visualization means for representing and outputting the generated paint candidates, A means for the user to modify the aforementioned paint candidate, A means for associating candidates using product identifiers, A display means for displaying candidates based on the aforementioned product identifier, A system that includes this.

2. The system according to claim 1, wherein the generated paint candidates include color, texture, and pattern.

3. The system according to claim 1, wherein the visualization means can be updated in real time in response to user modifications.

Images