System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving as input spatial and lifestyle information from a user; means for generating interior design and organization suggestions based on the input information using a generative AI; means for transmitting the generated suggestions to a device for display; means for receiving user feedback on the suggestions and modifying the suggestions based on the feedback; and means for providing the modified suggestions back to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] The present invention aims to solve the problem of the difficulty of easily realizing proposals for home interior design and organization that meet the individual lifestyles and spatial preferences of each user. In particular, there is a problem that uniform designs and solutions that address the different needs of each household are not satisfactory and do not create a harmonious living environment. As a result, many households spend a lot of time and effort to realize the space that is best suited to them, and as a result, there is a lack of design solutions that can provide a satisfying living environment. [Means for solving the problem]

[0005] The present invention solves these problems by providing the following means.

[0006] A means of receiving spatial information and lifestyle information from users as input

[0007] A means for generating interior design and organization proposals using a generative AI based on the input information.

[0008] means for transmitting the generated proposal to a terminal for display;

[0009] means for receiving user feedback on the suggestions and modifying the suggestions based on the feedback;

[0010] A means to resubmit revised suggestions to the user

[0011] Furthermore, the system includes a means for providing purchasing assistance for furniture and interior items based on the proposed interior design, and a means for completing the purchasing process in collaboration with affiliated retailers. The system also includes a means for repeatedly receiving feedback from users and updating the generating AI to improve the accuracy of subsequent suggestions. In this way, it is possible to provide personalized interior design and organization solutions that meet the individual needs of each user, creating a harmonious living environment.

[0012] "User" refers to any individual or entity that utilizes the System to provide interior design and organization solutions.

[0013] "Spatial information" refers to physical information necessary for interior design, such as the layout of the residence, room size, furniture arrangement, and wall color, provided by the user.

[0014] "Lifestyle information" refers to information about an individual's lifestyle that influences design proposals, such as the user's lifestyle habits and preferences, family composition, hobbies, activities at home, and frequency of use.

[0015] "Generative AI" refers to artificial intelligence techniques for generating interior design and organization suggestions based on input data.

[0016] "Proposal" refers to the interior design and organization solutions created by the generative AI, including the specific design plans and layouts provided to the user.

[0017] A "terminal" is a hardware device that a user uses to access the system, and includes smartphones, tablets, personal computers, etc.

[0018] "Feedback" refers to the act of a user responding to a provided suggestion with opinions, requests, or corrections.

[0019] "Retailers" refer to companies and stores that sell furniture and interior items, and work in conjunction with this system to provide products to users.

[0020] "Purchase assistance" refers to a function that provides links and coupon codes and assists with the purchase process so that users can smoothly purchase suggested interior items.

[0021] "Continuous support" refers to the system regularly collecting user feedback and usage status, updating the generative AI model, and providing new suggestions to maintain user support over the long term. [Brief explanation of the drawings]

[0022] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0023] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0024] First, the terms used in the following description will be explained.

[0025] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0026] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0027] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0028] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0029] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0030] [First embodiment]

[0031] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0032] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0033] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0034] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0035] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0036] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0037] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0038] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0040] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0041] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0042] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0043] The present invention is a system for providing interior design and organization solutions based on a user's individual lifestyle and spatial preferences, specifically a platform that utilizes generative AI to provide optimal suggestions to users. The system is implemented in the following steps:

[0044] Program processing

[0045] 1. User Information Collection:

[0046] Users first launch the application and create a new account, then enter information such as family composition, home layout, room size, current furniture arrangement, wall color, lifestyle habits and preferences, and budget range.

[0047] The terminal collects this input information and transmits it to the server as necessary data.

[0048] 2. Data analysis and interior proposal generation:

[0049] The server stores the information submitted by the user in a database, and a generative AI module analyzes the data, creating a profile based on the user's lifestyle and spatial preferences.

[0050] Based on this profile, the generative AI generates optimal interior design and organization suggestions, such as furniture placement and color choices for a living room, as well as suggested accessories for each room.

[0051] 3. Viewing and Feedback on Suggestions:

[0052] The server then sends the generated interior design proposal to the device and displays it to the user, including a 3D model of the room and a detailed layout diagram.

[0053] The user reviews the suggestions and provides feedback if necessary, for example, to specify specific requests such as changing the color of a particular piece of furniture or rearranging it.

[0054] 4. Feedback and suggested updates:

[0055] The terminal transmits the user's feedback to the server.

[0056] The server analyzes this feedback and uses the generative AI to modify the proposal, which is then sent back to the user for confirmation.

[0057] This process is repeated until the user is satisfied.

[0058] 5. Purchasing Assistance and Partnerships:

[0059] If the user wishes to purchase the suggested interior item, the terminal sends a purchase request to the server.

[0060] The server connects with the systems of affiliated retailers to obtain product availability and links to checkout.

[0061] The terminal displays a link to the purchase procedure and a coupon code to the user to support the purchase procedure.

[0062] 6. Ongoing Support and Updates:

[0063] The server continuously collects user feedback and usage data and periodically updates the generative AI model.

[0064] The server periodically provides users with new interior design suggestions and organization solutions.

[0065] The terminal will notify and display these new suggestions and solutions to the user.

[0066] Specific examples

[0067] Case Study: Proposals for Users with Different Family Structures

[0068] User A (single person living in a one-room apartment in an urban area):

[0069] Device: User A launches the app and uploads their name, age, address, and a photo of their room.

[0070] Server: Generative AI analyzes this data and proposes Scandinavian-inspired interior designs that maximize the use of small urban spaces, including compact storage units, multifunctional furniture, and simple color palettes.

[0071] User A: Review the proposal and submit feedback saying they would like to add cafe-style tables.

[0072] Server: Sends a regenerated proposal based on the feedback to User A, and repeats the process of adjusting it until User A is satisfied.

[0073] User B (family of four, suburban detached house):

[0074] Terminal: User B inputs the family composition (husband, wife, two children), room size, layout of existing furniture, budget, etc.

[0075] Server: Generative AI generates a design for a family with a spacious living room and multiple bedrooms. The proposal includes furniture arranged to accommodate the entire family, a play space for children, and storage solutions.

[0076] User B: Review the proposal and submit feedback that they would like to change the theme color for the children's room.

[0077] Server: Sends a regenerated proposal based on the feedback to User B, and repeats the process of adjustment until User B is satisfied.

[0078] In this way, the present invention provides interior design and organization solutions tailored to the user's individual needs, resulting in a harmonious living environment.

[0079] The processing flow will be explained below.

[0080] Step 1:

[0081] The user launches the application and creates a new account.

[0082] Users enter basic information such as their name, age, address, family composition, lifestyle habits, interior design preferences, and budget range.

[0083] The terminal transmits the input information to the server.

[0084] Step 2:

[0085] The server stores the information sent by the user in a database.

[0086] The server uses a generative AI module to analyze the data it receives.

[0087] The server creates a profile based on the user's lifestyle and spatial preferences.

[0088] Step 3:

[0089] The server generates optimal interior design and organization solutions based on the profile.

[0090] This includes furniture placement, color selection, and accessory suggestions.

[0091] The server transmits the generated design proposal to the terminal.

[0092] Step 4:

[0093] The terminal displays the interior design proposal to the user.

[0094] The user reviews the proposal and provides feedback if necessary.

[0095] For example, you can enter specific opinions such as "I want to change the color of the sofa" or "I want to change the position of the table."

[0096] Step 5:

[0097] The terminal transmits the user's feedback to the server.

[0098] The server receives the feedback and modifies the design proposal using a generative AI module.

[0099] The server sends the revised proposal back to the terminal and presents it to the user.

[0100] Step 6:

[0101] The user reviews the revised proposal and repeats the same procedure if they wish to provide feedback again.

[0102] This cycle is repeated until the user is satisfied with the proposal.

[0103] Step 7:

[0104] If the user wishes to purchase the suggested interior item, the terminal sends a purchase request to the server.

[0105] The server connects to the systems of partner retailers to obtain inventory information and checkout links.

[0106] The server transmits the acquired information to the terminal.

[0107] Step 8:

[0108] The terminal displays a link to the purchase procedure and a coupon code to the user to support the purchase procedure.

[0109] The user clicks on the link and completes the purchase on the affiliated retailer's website.

[0110] Step 9:

[0111] The server continuously collects user feedback and usage data to update the generative AI model.

[0112] The server periodically generates new interior design proposals and organization solutions based on the updated model and sends them to the terminal.

[0113] The terminal notifies and displays new suggestions and solutions to the user.

[0114] Example 1

[0115] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0116] Addressing today's diverse lifestyles and spatial preferences and providing optimal interior design and organization solutions to users requires advanced data analysis and personalized response. However, conventional systems are unable to fully meet individual user needs and have difficulty efficiently incorporating feedback. Furthermore, the process of purchasing interior items is cumbersome, creating a need for an integrated solution to improve the user experience.

[0117] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0118] In this invention, the server includes means for receiving spatial information and lifestyle information from a user as input, means for generating interior design and organization proposals using a generative AI based on the input information, means for transmitting the generated proposals to a user interface for displaying them, means for receiving user feedback on the proposals and revising the proposals based on the feedback, means for re-presenting the revised proposals to the user, means for recording user information in a database in real time and formatting it into a required data format, and means for converting the proposal content into visual data and visually displaying it. This allows for optimal interior design proposals tailored to individual needs, efficiently incorporating feedback, and achieving a smooth purchasing process.

[0119] "User" refers to an individual or group that uses this system and is the entity that inputs spatial information and lifestyle information.

[0120] "Spatial information" refers to information about the physical space in which a user lives or uses, and includes the layout, size of the room, furniture arrangement, wall color, and the like.

[0121] "Lifestyle information" refers to information related to a user's individual lifestyle, such as their lifestyle habits, preferences, and budget range.

[0122] The "means for receiving input" refers to the interface through which the user provides spatial and lifestyle information to the system, and typically includes an application or web form.

[0123] "Generative AI" refers to artificial intelligence technology that analyzes input data and generates optimal interior design and organization solutions.

[0124] "Interior design" refers to the planning of a space to make it beautiful and functional, including the arrangement of furniture and decorations, color selection, and style.

[0125] "Organization suggestions" include ideas and methods for efficiently organizing the user's living space and making it easier to use.

[0126] "User interface" refers to the screens and methods by which a user interacts with a system to input information, provide feedback, and view suggestions.

[0127] "Feedback" refers to opinions and requests provided by users regarding proposals, and is information that the system uses to revise the proposals.

[0128] The "database" refers to an electronic storehouse that organizes and stores user input information, generated design proposals, feedback, etc.

[0129] "Visual Data" includes graphic and diagrammatic data for visually representing proposed interior design and organization solutions.

[0130] "Retailer" refers to a commercial entity that interacts with the system to provide suggested interior items to users.

[0131] This invention is a system for providing interior design and organization solutions based on users' lifestyles and spatial preferences, and a platform that utilizes generative AI models to provide optimal suggestions to users. The system consists of the following steps: collecting user information, data analysis, suggestion generation, feedback processing, purchasing assistance, and ongoing support.

[0132] First, the user launches the application and enters information such as family composition, home layout, room size, current furniture arrangement, wall color, lifestyle habits and preferences, and budget range. The information entered by the user is recorded in a database in real time by the device and organized into the required data format. The device then sends the entered information to the server.

[0133] The server stores the user-submitted information in a database, standardizing and cleaning it. It then uses a generative AI model (e.g., OpenAI's GPT-3 or other custom models) to create a profile based on the user's lifestyle and spatial preferences. Based on this profile, the server generates optimal interior design and organization suggestions, including furniture placement and color choices for the living room and suggested accessories for each room.

[0134] The generated proposals are converted into a data format by the server and sent to the device. The device receives this information and visually displays it to the user via a user interface. This display includes visual data such as a 3D model of the room and a detailed layout diagram. The user can review the proposals and provide feedback if necessary. For example, they can express specific preferences such as changing the color of a particular piece of furniture or rearranging it.

[0135] The user's feedback is sent to the server via the device, which analyzes it. The generative AI model takes the feedback into account and regenerates a revised proposal. The revised proposal is then sent back to the device and displayed to the user. This process is repeated until the user is satisfied.

[0136] Furthermore, if the user wishes to purchase a suggested interior item, the device sends a purchase request to the server, which then connects with the retailer's system to obtain product availability and a link to the checkout process. The device then displays these links and coupon codes to the user, helping them through the checkout process.

[0137] The server continuously collects user feedback and usage data and periodically updates the generative AI model. This update process involves ingesting new datasets and training the model. Periodically, new interior design suggestions and organization solutions are generated and provided to the user through the server. The device notifies the user of these new suggestions and solutions and allows them to be reviewed through the interface.

[0138] Specific examples

[0139] For example, if a single person named User A lives in a studio apartment in an urban area, User A launches the app and uploads their name, age, address, and a photo of their room. The device sends this information to the server, which then uses generative AI to suggest a Scandinavian-style interior design that makes the most of the small urban space. The suggestions include compact storage units, multifunctional furniture, and simple colors. If User A provides feedback that they would like to add a cafe-style table, the server will again revise the suggestions and make repeated adjustments until User A is satisfied.

[0140] For example, if User B is a family of four living in a suburban detached house, User B inputs information such as the family composition, room size, existing furniture layout, and budget. The device sends this information to the server, which then uses generative AI to generate a design for the family with a spacious living room and multiple bedrooms. The proposal includes furniture arranged to ensure the whole family is comfortable, a play space for children, and storage solutions. If User B provides feedback that they would like to change the theme color of the children's room, the server again modifies the proposal and makes repeated adjustments until User B is satisfied.

[0141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0142] Step 1:

[0143] Collection of User Information

[0144] First, users launch the application and create a new account, inputting information such as family composition, layout of the home, room size, current furniture arrangement, wall color, lifestyle habits and preferences, and budget range.

[0145] The device records this input information in a database in real time, formats it, and sends it to the server, prompting the user to check and correct the input as necessary.

[0146] Input: Personal and spatial information entered by the user

[0147] Output: Formatted data sent to the server

[0148] Step 2:

[0149] Data analysis

[0150] The server stores the information submitted by users in a database, standardizes and cleans it, checks for missing values ​​and inconsistencies, and performs any necessary data imputation.

[0151] Input: User information received from the device

[0152] Output: Standardized and cleaned dataset

[0153] Step 3:

[0154] Generate interior proposals

[0155] The server passes the standardized dataset to a generative AI model (such as OpenAI's GPT-3), which creates a profile based on the user's lifestyle and spatial preferences.

[0156] Based on that profile, the generative AI model generates optimal interior design and organization suggestions, including specific furniture placement, color choices, storage solutions, and more.

[0157] Input: Standardized and cleaned dataset

[0158] Output: Interior design and organization proposals

[0159] Step 4:

[0160] View Suggestions

[0161] The server converts the generated interior design proposal into a data format and sends it to the terminal, along with creating visual data such as a 3D model and detailed layout drawings.

[0162] The terminal receives this data and visually displays it to the user through a user interface.

[0163] Input: Interior design and organization suggestions

[0164] Output: Visual data that is displayed to the user

[0165] Step 5:

[0166] Gathering feedback

[0167] The user reviews the proposal and provides feedback if necessary. For example, they can input their preferences, such as changing the color of a particular piece of furniture or rearranging it.

[0168] The terminal transmits the user's feedback to the server.

[0169] Input: User feedback

[0170] Output: Feedback data sent to the server

[0171] Step 6:

[0172] Updates incorporating feedback

[0173] The server analyzes the received feedback and inputs it back into the generative AI model, which then takes the feedback into account to generate new suggestions.

[0174] The revised proposal is sent back to the terminal and displayed to the user, and this process is repeated until the user is satisfied.

[0175] Input: Parsed feedback

[0176] Output: Revised interior design proposal

[0177] Step 7:

[0178] Purchase assistance

[0179] If the user wishes to purchase the suggested interior item, the terminal sends a purchase request to the server.

[0180] The server connects with the systems of affiliated retailers to obtain product availability and links to checkout.

[0181] The terminal displays these links and coupon codes to the user and assists in the purchase process.

[0182] Input: User purchase request

[0183] Output: Stock information and purchase links from retailers

[0184] Step 8:

[0185] Ongoing support and updates

[0186] The server continuously collects user feedback and usage data and periodically updates the generative AI model, a process that involves ingesting new datasets and training the model.

[0187] Periodically, new interior design proposals and organization solutions are generated and provided to the user through the server. The terminal notifies the user of these new proposals and solutions and allows them to be viewed through the interface.

[0188] Input: Feedback and usage data collected continuously

[0189] Output: Updated generative AI model and new proposals

[0190] (Application example 1)

[0191] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0192] Conventional factory layout and workflow optimization relies heavily on experience, making it difficult to design an efficient and productive layout in a short period of time. It is also difficult to effectively incorporate feedback into the proposed layout, resulting in suboptimal productivity.

[0193] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0194] In this invention, the server includes: means for receiving spatial information and lifestyle information from a user as input; means for generating interior design and organization proposals using a generative AI based on the input information; means for transmitting the generated proposals to a terminal for displaying them; means for receiving factory layout information and workflow information from a user as input; means for generating an optimal layout based on work efficiency and productivity based on the factory layout information and workflow information; means for displaying the generated layout and performing a virtual simulation; means for receiving user feedback on the proposals and revising the proposals based on the feedback; and means for providing the revised proposals to the user again, thereby enabling efficient and productive design of factory layouts and workflows.

[0195] "Spatial information" is data about the physical location and layout of factories and work areas.

[0196] "Lifestyle information" is data on the behavioral patterns and work habits of factory workers.

[0197] "Generative AI" is an algorithm that uses artificial intelligence technology to generate optimal suggestions.

[0198] "Interior design" is a proposal for layout and decoration to improve the aesthetics and functionality of a space.

[0199] "Organizational proposals" are specific ideas and plans for achieving efficient work flows and layouts.

[0200] A "terminal" is an electronic device that allows a user to view suggestions and enter feedback.

[0201] "Layout information" is data regarding the layout of machines and equipment within a factory.

[0202] "Work flow information" is data related to the order and processes of work within a factory.

[0203] "Virtual simulation" is a technology that allows a proposed layout to be reproduced in a virtual space and visually confirmed.

[0204] "Feedback" is information regarding the user's opinions and requests for corrections regarding the proposal.

[0205] "Productivity" is the ability to achieve maximum results with limited resources.

[0206] Efficiency is the ability to get more out of less.

[0207] "Procurement support" is a function that provides assistance in purchasing necessary equipment and facilities.

[0208] "Partner Retailers" are partner companies that provide products and services.

[0209] A "checkout" is a series of actions taken to purchase a product or service.

[0210] "Updating generative AI" means improving the artificial intelligence algorithms to make more accurate suggestions based on new data and feedback.

[0211] The present invention provides a system that can make proposals based on a user's lifestyle and spatial preferences, and is particularly concerned with optimizing the layout and work flow within a factory. This system is composed of a user, a terminal, and a server, all of which work in conjunction with each other.

[0212] System configuration

[0213] 1. Collection of User Information

[0214] A user first logs into the system and launches a corresponding application.

[0215] The terminal accepts input from users and collects data such as factory layout information, work flow information, budgets, and efficiency and productivity priorities.

[0216] As an example of input, a prompt such as "We would like to optimize the layout of our factory. Please generate the optimal proposal based on the following information: Current layout diagram: 'current_layout.json', Workflow diagram: 'workflows.json', Constraints: 'Budget: 100,000, Time: 200', Priorities: 'Efficiency: High, Safety: Medium'" is used.

[0217] 2. Data analysis and layout proposal generation

[0218] The server receives the collected data and stores it in a database.

[0219] Inside the server, a Generative AI model is used to analyze the data, generating optimal layout and workflow proposals that maximize efficiency and productivity within the factory.

[0220] 3. Proposal presentation and virtual simulation

[0221] The server transmits the generated layout proposal to the terminal.

[0222] The device uses software (e.g., Unity) to display this as a 3D model, allowing the user to visually confirm it.

[0223] Virtual simulations can be performed to validate the proposed layout.

[0224] 4. User feedback and suggested fixes

[0225] The user can then input feedback on the proposals from the terminal, for example, by entering a specific request such as "I would like to change the placement of a specific machine."

[0226] The server receives the feedback and again uses the Generative AI model to modify the proposal, which generates a new proposal that reflects the feedback.

[0227] 5. Submitting a revision proposal again

[0228] The revised proposal is sent back to the terminal and provided to the user.

[0229] This process is repeated until the user is satisfied.

[0230] Program processing description

[0231] Hardware and software used

[0232] Terminal: PC or tablet for user operation

[0233] Server: A server that runs data analysis and generative AI models

[0234] Software: Databases, generative AI models (e.g., TensorFlow), 3D model display software (e.g., Unity)

[0235] Process Overview

[0236] The server stores the received user information in a database and uses a generative AI model to generate optimal factory layout proposals.

[0237] The generated proposal is sent to the device and displayed to the user as a 3D model.

[0238] The user inputs feedback on the suggestions from the terminal, and the feedback is sent to the server.

[0239] The server receives the feedback and runs the generative AI model again to generate revised suggestions.

[0240] The suggested revisions are sent back to the terminal and the process is repeated until the user is satisfied.

[0241] Specific examples

[0242] For example, in a metal processing factory, users would input data such as the current layout, workflow information, budget, and safety standards into the application. The server receives this data, and the generative AI model proposes optimal machine placement and work flow. This is then displayed as a 3D model on the device, and users can provide feedback on their specific requests, which will result in further improved proposals.

[0243] This enables efficient and productive design of factory layouts and work flows.

[0244] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0245] Step 1:

[0246] The user launches the application and logs in. After logging in, they input the information needed to optimize the factory layout, including the current factory layout diagram, workflow information, budget, and priorities related to efficiency and productivity.

[0247] Input: User login information, factory layout diagram (e.g. 'current_layout.json'), workflow information (e.g. 'workflows.json'), budget, efficiency and productivity priorities

[0248] Output: The information entered

[0249] Step 2:

[0250] The terminal receives the user's input information and sends it to a database, where it is stored on a server.

[0251] Inputs: User-entered factory layout diagrams, work flow information, budgets, efficiency and productivity priorities

[0252] Output: Data sent to the server

[0253] Step 3:

[0254] The server analyzes the received data and generates factory layout optimization proposals using a Generative AI model. In this process, a data analysis module interprets the data, and Generative AI calculates the optimal layout and work flow.

[0255] Inputs: Factory layout diagrams stored on a server, work flow information, budgets, efficiency and productivity priorities

[0256] Data processing: Analysis of input data and generation of profiles

[0257] Data calculation: Generative AI generates optimal layout proposals

[0258] Output: Optimized factory layout proposal

[0259] Step 4:

[0260] The server transmits the generated layout proposal to the terminal.

[0261] Input: Optimized factory layout proposal

[0262] Output: Layout proposal sent to device

[0263] Step 5:

[0264] The device displays the received layout proposal as a 3D model, allowing the user to visually confirm the proposal. Specifically, 3D model display software (e.g., Unity) is used.

[0265] Input: Layout proposal received from the server

[0266] Output: Display of 3D model

[0267] Step 6:

[0268] The user reviews the proposed layout and enters any necessary feedback, such as specific requests such as "I would like to change the placement of a particular machine."

[0269] Input: User feedback

[0270] Output: Feedback data

[0271] Step 7:

[0272] The terminal transmits the user's feedback to the server.

[0273] Input: Feedback data

[0274] Output: Feedback sent to the server

[0275] Step 8:

[0276] The server analyzes the received feedback and re-runs the GenerativeAI model to refine the suggestions. New suggestions are generated and stored on the server.

[0277] Input: Feedback data from users

[0278] Data processing: Analyzing feedback and updating profiles

[0279] Data Computing: Regenerating revised layout proposals with generative AI

[0280] Output: Revised layout proposal

[0281] Step 9:

[0282] The server sends the revised proposal back to the terminal.

[0283] Input: revised layout proposal

[0284] Output: Suggested fixes sent to terminal

[0285] Step 10:

[0286] The device then displays the revised proposal again as a 3D model, and the user reviews the new proposal and provides feedback again if necessary. This process is repeated until the user is satisfied.

[0287] Input: revised layout proposal

[0288] Output: 3D model display and user feedback

[0289] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0290] This invention is a system for providing interior design and organization solutions based on a user's individual lifestyle and spatial preferences, and further utilizes an emotion engine to recognize the user's emotions and make suggestions that reflect those emotions. Specifically, this system is configured as a platform that combines generative AI and an emotion engine.

[0291] Program processing

[0292] 1. User Information Collection:

[0293] A user launches the application and creates a new account, entering basic information such as name, age, address, family composition, lifestyle habits, interior design preferences, and budget range.

[0294] The terminal transmits this input information to the server in real time.

[0295] 2. Data analysis and interior proposal generation:

[0296] The server stores the information submitted by the user in a database, and a generative AI module analyzes the data, creating a profile based on the user's lifestyle and spatial preferences.

[0297] Based on this profile, the generative AI generates optimal interior design and organization solutions, such as furniture placement and color choices for a living room, as well as suggested accessories for each room.

[0298] 3. Use of Emotion Engine:

[0299] The server sends the generated interior design proposal to the device and uses an emotion engine to analyze the user's facial expressions, tone of voice, and text input to recognize the user's emotions before displaying it to the user.

[0300] The server further customizes the suggestions based on the emotional data obtained from the emotion engine, so that the suggestions are more in line with the user's current mental state and emotions.

[0301] 4. Viewing and Feedback on Suggestions:

[0302] The device displays customized interior design proposals to the user, including a 3D model of the room and a detailed floor plan.

[0303] The user reviews the suggestions and provides feedback if necessary, for example, to specify specific requests such as changing the color of a particular piece of furniture or rearranging it.

[0304] 5. Incorporating feedback and suggested updates:

[0305] The terminal transmits the user's feedback to the server.

[0306] The server analyzes the feedback, utilizes a generative AI module to modify the proposal, again using the emotion engine to reflect the user's emotions, and presents the modified proposal to the user again. This process is repeated until the user is satisfied.

[0307] 6. Purchasing Assistance and Partnerships:

[0308] If the user wishes to purchase the suggested interior item, the terminal sends a purchase request to the server.

[0309] The server connects to the systems of partner retailers to obtain product availability and checkout links.

[0310] The server sends the acquired information to the device, which then displays a checkout link and a coupon code to the user, helping the user complete the checkout process.

[0311] 7. Ongoing Support and Updates:

[0312] The server continuously collects user feedback and usage data and periodically updates the generative AI model and emotion engine.

[0313] The server periodically generates new interior design proposals and organization solutions based on the updated model and sends them to the terminal.

[0314] The terminal will notify and display these new suggestions and solutions to the user.

[0315] Specific examples

[0316] Case Study: Personalized Recommendations Using Emotion Recognition

[0317] User A (single person living in a one-room apartment in an urban area):

[0318] Device: User A launches the app and uploads their name, age, address, and a photo of their room.

[0319] Server: Generative AI analyzes this data and proposes Scandinavian-inspired interior designs that maximize the use of small urban spaces, selecting compact storage units, multifunctional furniture, and simple color palettes.

[0320] Emotion engine: Before making a suggestion, it analyzes User A's facial expressions and tone of voice and evaluates whether the suggestion matches User A's current emotions. For example, if it recognizes that User A is feeling stressed, it will add interior elements that have a relaxing effect.

[0321] User A: Review the proposal and submit feedback saying they would like to add cafe-style tables.

[0322] Server: The regenerated proposal based on the feedback is rechecked by the emotion engine and sent to User A. Adjustments are repeated until User A is satisfied.

[0323] User B (family of four, suburban detached house):

[0324] Terminal: User B inputs the family composition (husband, wife, two children), room size, layout of existing furniture, budget, etc.

[0325] Server: Generative AI generates a design for a family with a spacious living room and multiple bedrooms. The proposal includes furniture arranged to accommodate the entire family, a play space for children, and storage solutions.

[0326] Emotion engine: Before displaying suggestions, analyze User B's emotional data and make suggestions that will help the whole family relax.

[0327] User B: Review the proposal and submit feedback that they would like to change the theme color for the children's room.

[0328] Server: The server evaluates the regenerated proposal based on the feedback using the emotion engine and sends it to User B. Adjustments are repeated until User B is satisfied.

[0329] In this way, the present invention takes into account the user's emotions to provide more personalized interior design and organization solutions, resulting in a harmonious living environment.

[0330] The processing flow will be explained below.

[0331] Step 1:

[0332] A user launches the application and creates a new account. They enter basic information such as their name, age, address, family composition, lifestyle habits, interior design preferences, and budget range. The device then transmits this information to the server in real time.

[0333] Step 2:

[0334] The server stores the information submitted by the user in a database, and then uses a generative AI module to analyze the data it receives, creating a profile based on the user's lifestyle and spatial preferences.

[0335] Step 3:

[0336] The server generates optimal interior design and organization solutions based on the profile, including furniture placement, color selection, and accessory suggestions, and then sends the generated design proposals to the device.

[0337] Step 4:

[0338] Before displaying interior design suggestions to the user, the device activates an emotion engine that analyzes the user's facial expressions, tone of voice, and text input to recognize the user's emotions.

[0339] Step 5:

[0340] The server customizes the suggestions based on the emotional data obtained from the emotion engine. For example, if the server detects that the user is feeling stressed, it adds relaxing interior elements to the suggestions.

[0341] Step 6:

[0342] The device displays customized interior design proposals to the user, including a 3D model of the room and a detailed floor plan, and the user can review the proposals and provide feedback if needed.

[0343] Step 7:

[0344] The device sends the user's feedback to the server, which receives the feedback and modifies the proposal using the generative AI module. It then uses the emotion engine again to confirm the user's emotions and presents the modified proposal to the user again. This process is repeated until the user is satisfied.

[0345] Step 8:

[0346] If the user wishes to purchase a suggested interior item, the device sends a purchase request to the server. The server connects to the affiliated retailer's system to obtain product availability and a checkout link. The server then sends the obtained information to the device, which then displays the checkout link and coupon code to the user. The device then assists the user in clicking the link and completing the purchase on the affiliated retailer's website.

[0347] Step 9:

[0348] The server continuously collects user feedback and usage data and periodically updates the generative AI model and emotion engine. The server periodically generates new interior design proposals and organization solutions based on the updated model and sends them to the device. The device notifies and displays these new proposals and solutions to the user.

[0349] Example 2

[0350] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0351] Providing optimal interior design and organization solutions for each individual user to meet today's diverse lifestyles is challenging. There is also a need to provide customized suggestions based on the user's emotional state and increase user satisfaction. Furthermore, there is a lack of systems that incorporate user feedback and provide continuously improved suggestions.

[0352] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving spatial information and lifestyle information from a user as input, means for generating interior design and organization proposals using a generative AI based on the input information, means for analyzing the user's facial expressions, tone of voice, and text input to collect emotional data, means for customizing the generated proposals based on the emotional data, means for receiving user feedback on the proposals and revising the proposals based on the feedback, means for re-presenting the revised proposals to the user, and means for continuously collecting user usage status and updating the generative AI model and emotion engine. This makes it possible to provide interior design and organization solutions that match the user's lifestyle and take into account their emotional state.

[0353] "User" refers to an individual or household that receives interior design and organization solutions from the system.

[0354] "Spatial information" is detailed information about the user's living space, including data such as room dimensions, layout, and existing furniture arrangement.

[0355] "Lifestyle information" refers to information about the user's individual lifestyle, such as the user's daily habits, family structure, preferred interior style, and budget.

[0356] "Generative AI" refers to an artificial intelligence model that generates optimal interior design and organization solutions based on user-provided information.

[0357] A "proposal" is an interior design or organization plan created by the generative AI by analyzing the user's information, including specific furniture placement and color selection.

[0358] "Emotion data" is data that indicates the user's emotional state, analyzed from the user's facial expression, tone of voice, text input, and the like.

[0359] An "emotion engine" refers to an engine that analyzes a user's emotional data and customizes the suggestions provided by the generative AI based on that information.

[0360] "Feedback" refers to a user returning to the system their opinions and requests regarding suggestions provided by the system.

[0361] "Customization" refers to the process by which the generative AI modifies its suggestions to best suit the user based on the user's emotional data and feedback.

[0362] "Update" refers to the generative AI model and emotion engine refining its algorithms and data to improve accuracy and effectiveness based on user feedback and usage data.

[0363] "Terminal" refers to a device, such as a computer or smartphone, through which a user inputs information or receives suggestions.

[0364] This invention is a system for providing interior design and organization solutions based on users' lifestyles and spatial preferences. The system is configured as a platform that combines generative AI and an emotion engine.

[0365] System Overview

[0366] The user first launches the application and enters basic information such as name, age, address, family composition, lifestyle habits, interior design preferences, and budget range. This information is sent to the server via the device. The server stores the received information in a database, and a generative AI module analyzes the data and creates a profile based on the user's lifestyle and spatial preferences. The generative AI (e.g., a large-scale language model such as GPT-3) generates optimal interior design and organization solutions.

[0367] Use of emotion engine

[0368] The generated interior design proposals are customized by an emotion engine before being displayed to the user. The emotion engine (e.g., Affectiva SDK) is used to recognize the user's emotions by analyzing the user's facial expressions, tone of voice, and text input. The emotion data obtained by the emotion engine is analyzed by the server, and the generative AI further customizes the proposals. As a result, the proposals are more in line with the user's current mental state and emotions.

[0369] View suggestions and give feedback

[0370] The customized interior design proposal is sent to the device and displayed to the user. This includes a 3D model of the room and a detailed layout. The user reviews the proposal and provides feedback on specific preferences (e.g., changing the color or positioning of certain furniture). This feedback is sent via the device to the server, which analyzes the feedback and modifies the proposal using a generative AI module. This process is repeated until the user is satisfied.

[0371] Buying Assistance and Partnerships

[0372] If the user wishes to purchase a suggested interior item, the device sends a purchase request to the server, which connects to the retailer's system to obtain product availability and a link to checkout. The information is then sent to the device, where the user is presented with a link to checkout and a coupon code.

[0373] Ongoing support and updates

[0374] The server continuously collects user feedback and usage data and periodically updates the generative AI model and emotion engine. New interior design proposals and organization solutions are periodically generated based on the updated model and sent to the device. The device notifies and displays these new proposals and solutions to the user.

[0375] Specific examples

[0376] Case Study 1: Personalized Recommendations Using Emotion Recognition

[0377] User A (single person living in a studio apartment in an urban area):

[0378] Device: User A launches the app and uploads their name, age, address, and a photo of their room.

[0379] Server: Generative AI analyzes this data and proposes Scandinavian-inspired interior designs that maximize the use of small urban spaces, selecting compact storage units, multifunctional furniture, and simple color palettes.

[0380] Emotion engine: Before making a suggestion, it analyzes User A's facial expressions and tone of voice and evaluates whether the suggestion matches User A's current emotions. For example, if it recognizes that User A is feeling stressed, it will add interior elements that have a relaxing effect.

[0381] User A: Review the proposal and submit feedback saying they would like to add cafe-style tables.

[0382] Server: The regenerated proposal based on the feedback is rechecked by the emotion engine and sent to User A. Adjustments are repeated until User A is satisfied.

[0383] Prompt Sentence Examples

[0384] Prompt 1: "Generate the best interior design suggestions based on the user's basic information, including age, address, preferred style, and budget range."

[0385] Prompt 2: "Customize the generated interior design suggestions based on the recorded user emotional data. Be sure to add elements that have a relaxing effect."

[0386] In this way, the present invention takes into account the user's emotions to provide more personalized interior design and organization solutions, resulting in a harmonious living environment.

[0387] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0388] Step 1: Collect user information

[0389] User Action:

[0390] Users launch the application and tap the "Create a new account" button, then enter basic information such as their name, age, address, family composition, lifestyle habits, interior design preferences, and budget range.

[0391] input:

[0392] Information such as name, age, address, family composition, lifestyle habits, interior design preferences, budget range, etc.

[0393] Terminal behavior:

[0394] The device collects input in real time, encrypts the data using HTTPS, and sends it securely to the server.

[0395] output:

[0396] The user information is sent to the server and account creation is complete.

[0397] Step 2: Data analysis and interior design proposal generation

[0398] Server behavior:

[0399] The server stores the received user information in a database (e.g., MySQL) and then invokes a generative AI module to analyze the information and create a user profile.

[0400] input:

[0401] User information stored in a database.

[0402] Data processing:

[0403] Based on the stored information, a generative AI analyzes the data and creates a profile based on your lifestyle and spatial preferences.

[0404] output:

[0405] Proposing the best interior design and organization solutions for users.

[0406] Step 3: Use the Emotion Engine

[0407] Terminal behavior:

[0408] It records facial expressions and tone of voice through the user's webcam and microphone, and collects text input.

[0409] input:

[0410] User facial expression data, tone of voice, and text input.

[0411] Terminal behavior:

[0412] The collected data is sent to the server in real time.

[0413] Server behavior:

[0414] An emotion engine is used to analyze emotions from collected data.

[0415] Data processing:

[0416] Emotional data is quantified and the suggestions provided by the generative AI are customized based on that data.

[0417] output:

[0418] Customized interior suggestions based on user emotions.

[0419] Step 4: View and give feedback on suggestions

[0420] Terminal behavior:

[0421] It presents users with customized interior design proposals, including 3D models of rooms and detailed floor plans.

[0422] input:

[0423] Customized interior design proposals.

[0424] User Action:

[0425] Review the suggestions and enter specific feedback into the device, such as changing the color of certain furniture or rearranging it.

[0426] Terminal behavior:

[0427] Collect user feedback and send it to the server.

[0428] output:

[0429] User feedback on the proposal.

[0430] Step 5: Incorporating feedback and proposing updates

[0431] Server behavior:

[0432] Analyze user feedback and refine suggestions using a generative AI module.

[0433] input:

[0434] User feedback.

[0435] Data processing:

[0436] Update suggestions based on feedback and use the sentiment engine again to reflect user sentiment.

[0437] Server behavior:

[0438] The revised proposal is resubmitted to the user.

[0439] output:

[0440] Revised interior design proposal.

[0441] Step 6: Assist with purchasing and execute partnerships

[0442] User Action:

[0443] If the customer wishes to purchase the suggested interior item, the customer inputs a purchase request on the terminal.

[0444] Terminal behavior:

[0445] Send the purchase request to the server.

[0446] Server behavior:

[0447] Connect to partner retailer systems to retrieve product availability and checkout links.

[0448] input:

[0449] Purchase requests and interior item information.

[0450] Data processing:

[0451] Link with retailer systems to obtain necessary information.

[0452] output:

[0453] A link to checkout and a coupon code will be sent to your device.

[0454] Step 7: Ongoing support and updates

[0455] Server behavior:

[0456] We continuously collect user feedback and usage data to regularly update our generative AI models and emotion engine.

[0457] input:

[0458] User feedback and usage data.

[0459] Data processing:

[0460] Analyze the collected data and improve the model.

[0461] Server behavior:

[0462] New interior design proposals and organisational solutions are generated based on the updated model and sent to the device.

[0463] output:

[0464] New interior design proposals and organisation solutions.

[0465] (Application example 2)

[0466] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0467] Conventional interior design systems make suggestions based on the user's lifestyle and spatial preferences, but they are unable to consider the user's emotional state, making it difficult to provide suggestions that truly satisfy the user. While systems exist that receive feedback on suggestions, they lack the ability to respond quickly to feedback or regenerate customized suggestions based on emotions, limiting their ability to improve the user experience.

[0468] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for utilizing an emotion engine that recognizes the user's emotions and further customizes proposals based on those emotions, means for displaying proposals generated on the terminal using 3D models or augmented reality, and means for continuously customizing proposals using emotion data generated by the emotion engine. This makes it possible to provide personalized interior design proposals that take the user's emotional state into consideration. In addition, by receiving feedback in real time and quickly generating and displaying appropriate proposals, it becomes possible to provide proposals that highly satisfy the user.

[0469] "User" refers to the end user who uses the proposal system and is the person who receives the proposals for interior design and organization.

[0470] "Spatial information" refers to data such as the layout, size, shape, and current furniture arrangement of a user's living space or office.

[0471] "Lifestyle information" refers to individual information such as the user's lifestyle, family structure, hobbies and preferences, activity patterns, budget range, and the like.

[0472] "Generative AI" is a system that uses artificial intelligence algorithms to generate optimal interior design proposals from given input data.

[0473] "Interior design" is a general term for design that includes the arrangement of furniture and decorations, color selection, etc. to make a user's space beautiful and functional.

[0474] "Organization suggestions" refer to suggestions for storage methods and layout changes to make efficient use of the user's space.

[0475] "Suggestions" refer to interior design and organization ideas presented to the user by the generative AI.

[0476] A "terminal" is a device that allows a user to view interior design proposals and enter feedback, and includes smartphones, tablets, PCs, smart glasses, etc.

[0477] "Feedback" refers to the act of a user inputting their opinions or requests regarding the content of a proposal after checking it.

[0478] An "emotion engine" refers to an algorithm that recognizes emotions from a user's facial expressions, voice, etc., and customizes suggestions based on those emotions.

[0479] A "3D model" refers to an interior design model that is expressed in three dimensions and is used to make proposals visually easier to understand.

[0480] "Augmented reality" is a technology that overlays computer-generated information onto real-world visual information, and is used to display interior design proposals in real spaces.

[0481] The present invention provides an interior design proposing system that takes into account the user's emotions, and is specifically implemented as follows.

[0482] First, the user inputs spatial and lifestyle information using a device, such as a smartphone, tablet, PC, or smart glasses. This input information includes the user's name, age, family composition, lifestyle habits, interior design preferences, and budget range. This information is sent to a central server in real time.

[0483] The server stores the received user information in a database, which is then analyzed by a generative AI module. The generative AI module generates the optimal interior design based on the user's lifestyle and spatial preferences. The specific system uses machine learning libraries such as Python and TensorFlow to build the AI ​​model.

[0484] The generated interior design proposals are first customized by an emotion engine. The emotion engine uses the camera and microphone of the smart glasses or smartphone to analyze the user's facial expressions and tone of voice to recognize their emotions. The proposals are then adjusted based on this emotion data. Specific implementations of the emotion engine utilize OpenCV and the Google Cloud Speech-to-Text API.

[0485] The device then displays the tailored interior design proposal to the user, including 3D models and augmented reality (AR) visualizations, allowing the user to see the proposal in the real space. The AR rendering uses Unity or ARKit / ARCore.

[0486] The user reviews the suggestions and provides feedback, which is sent from the device to the server, which then updates the suggestions using the generative AI module and emotion engine. This process is repeated until the user is satisfied.

[0487] If the user wishes to purchase a suggested interior item, the server connects to the retailer's system to retrieve product availability and a link to the purchase process, which is then displayed on the user's device. This allows the user to easily purchase the suggested item.

[0488] For example, the following prompt will prompt the user for information:

[0489] Example prompt:

[0490] Name: Taro Yamada

[0491] Age: 35

[0492] Family status: Single

[0493] Interior preferences: Modern style, muted colors

[0494] Budget range: 50,000 yen

[0495] Feedback example:

[0496] "I would like a more relaxing atmosphere. I would like the lighting to be changed to warmer tones."

[0497] In this way, by taking into account the user's emotions and feedback, a more satisfying interior design can be provided.

[0498] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0499] Step 1:

[0500] Collection of User Information

[0501] The user starts up the device and enters basic information such as name, age, address, family composition, lifestyle habits, interior design preferences, and budget range. This input data is sent from the device to the server in real time. The input information includes the user's lifestyle and spatial preferences, and subsequent data analysis is based on this information.

[0502] Step 2:

[0503] Data analysis and interior design proposal generation

[0504] The server stores the information submitted by the user in a database. The generative AI module analyzes the stored user data and creates a user profile based on lifestyle and spatial preferences. Based on this profile, the generative AI generates optimal interior design and organization solutions, including suggestions for living room furniture placement, color selection, and accessories for each room. The output design proposals are sent to the next step.

[0505] Step 3:

[0506] Use of emotion engine

[0507] The emotion engine recognizes the user's emotions before displaying suggestions to the user. The device uses a camera and microphone to analyze emotions from the user's facial expressions and tone of voice. The emotion data is sent to the server, which then uses the data to customize the generated interior design suggestions. For example, if the user is feeling stressed, it can add elements that have a relaxing effect. The revised suggestions are then sent to the next step.

[0508] Step 4:

[0509] View suggestions and give feedback

[0510] The device displays interior design proposals customized by the emotion engine to the user, including a 3D model of the room and a detailed layout. The user can review the proposals and provide feedback as needed. For example, specific requests can be made, such as changing the color or placement of certain furniture. Feedback is sent to the server in real time.

[0511] Step 5:

[0512] Incorporating feedback and suggested updates

[0513] The server analyzes the feedback received from the user and modifies the proposal using the generative AI module. The modified proposal is then adjusted again through the emotion engine to reflect the user's emotional data. The new, adjusted proposal is then sent back to the user. This process is repeated until the user is satisfied.

[0514] Step 6:

[0515] Buying Assistance and Partnerships

[0516] If the user wishes to purchase a suggested interior item, the device sends a purchase request to the server. The server connects to the partner retailer's system to obtain product availability and a link to checkout. The obtained information is sent to the device, which then displays a link to checkout and a coupon code to the user, allowing the user to easily purchase the suggested item.

[0517] Step 7:

[0518] Ongoing support and updates

[0519] The server continuously collects user feedback and usage data and periodically updates the generative AI model and emotion engine. Based on the updated model, new interior design proposals and organization solutions are periodically generated and sent to the device. The device then notifies and displays the new proposals and solutions to the user, ensuring that the user always receives the latest proposals.

[0520] These are the specific processing steps of the system that realizes this application example. The detailed operations at each step, such as data input and output, utilization of emotional data, and reflection of feedback, support high user satisfaction throughout the system.

[0521] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0522] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0523] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0524] [Second embodiment]

[0525] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0526] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0527] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0528] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0529] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0530] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0531] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0532] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0533] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0534] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0535] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0536] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0537] The present invention is a system for providing interior design and organization solutions based on a user's individual lifestyle and spatial preferences, specifically a platform that utilizes generative AI to provide optimal suggestions to users. The system is implemented in the following steps:

[0538] Program processing

[0539] 1. User Information Collection:

[0540] Users first launch the application and create a new account, then enter information such as family composition, home layout, room size, current furniture arrangement, wall color, lifestyle habits and preferences, and budget range.

[0541] The terminal collects this input information and transmits it to the server as necessary data.

[0542] 2. Data analysis and interior proposal generation:

[0543] The server stores the information submitted by the user in a database, and a generative AI module analyzes the data, creating a profile based on the user's lifestyle and spatial preferences.

[0544] Based on this profile, the generative AI generates optimal interior design and organization suggestions, such as furniture placement and color choices for a living room, as well as suggested accessories for each room.

[0545] 3. Viewing and Feedback on Suggestions:

[0546] The server then sends the generated interior design proposal to the device and displays it to the user, including a 3D model of the room and a detailed layout diagram.

[0547] The user reviews the suggestions and provides feedback if necessary, for example, to specify specific requests such as changing the color of a particular piece of furniture or rearranging it.

[0548] 4. Feedback and suggested updates:

[0549] The terminal transmits the user's feedback to the server.

[0550] The server analyzes this feedback and uses the generative AI to modify the proposal, which is then sent back to the user for confirmation.

[0551] This process is repeated until the user is satisfied.

[0552] 5. Purchasing Assistance and Partnerships:

[0553] If the user wishes to purchase the suggested interior item, the terminal sends a purchase request to the server.

[0554] The server connects with the systems of affiliated retailers to obtain product availability and links to checkout.

[0555] The terminal displays a link to the purchase procedure and a coupon code to the user to support the purchase procedure.

[0556] 6. Ongoing Support and Updates:

[0557] The server continuously collects user feedback and usage data and periodically updates the generative AI model.

[0558] The server periodically provides users with new interior design suggestions and organization solutions.

[0559] The terminal will notify and display these new suggestions and solutions to the user.

[0560] Specific examples

[0561] Case Study: Proposals for Users with Different Family Structures

[0562] User A (single person living in a one-room apartment in an urban area):

[0563] Device: User A launches the app and uploads their name, age, address, and a photo of their room.

[0564] Server: Generative AI analyzes this data and proposes Scandinavian-inspired interior designs that maximize the use of small urban spaces, including compact storage units, multifunctional furniture, and simple color palettes.

[0565] User A: Review the proposal and submit feedback saying they would like to add cafe-style tables.

[0566] Server: Sends a regenerated proposal based on the feedback to User A, and repeats the process of adjusting it until User A is satisfied.

[0567] User B (family of four, suburban detached house):

[0568] Terminal: User B inputs the family composition (husband, wife, two children), room size, layout of existing furniture, budget, etc.

[0569] Server: Generative AI generates a design for a family with a spacious living room and multiple bedrooms. The proposal includes furniture arranged to accommodate the entire family, a play space for children, and storage solutions.

[0570] User B: Review the proposal and submit feedback that they would like to change the theme color for the children's room.

[0571] Server: Sends a regenerated proposal based on the feedback to User B, and repeats the process of adjustment until User B is satisfied.

[0572] In this way, the present invention provides interior design and organization solutions tailored to the user's individual needs, resulting in a harmonious living environment.

[0573] The processing flow will be explained below.

[0574] Step 1:

[0575] The user launches the application and creates a new account.

[0576] Users enter basic information such as their name, age, address, family composition, lifestyle habits, interior design preferences, and budget range.

[0577] The terminal transmits the input information to the server.

[0578] Step 2:

[0579] The server stores the information sent by the user in a database.

[0580] The server uses a generative AI module to analyze the data it receives.

[0581] The server creates a profile based on the user's lifestyle and spatial preferences.

[0582] Step 3:

[0583] The server generates optimal interior design and organization solutions based on the profile.

[0584] This includes furniture placement, color selection, and accessory suggestions.

[0585] The server transmits the generated design proposal to the terminal.

[0586] Step 4:

[0587] The terminal displays the interior design proposal to the user.

[0588] The user reviews the proposal and provides feedback if necessary.

[0589] For example, you can enter specific opinions such as "I want to change the color of the sofa" or "I want to change the position of the table."

[0590] Step 5:

[0591] The terminal transmits the user's feedback to the server.

[0592] The server receives the feedback and modifies the design proposal using a generative AI module.

[0593] The server sends the revised proposal back to the terminal and presents it to the user.

[0594] Step 6:

[0595] The user reviews the revised proposal and repeats the same procedure if they wish to provide feedback again.

[0596] This cycle is repeated until the user is satisfied with the proposal.

[0597] Step 7:

[0598] If the user wishes to purchase the suggested interior item, the terminal sends a purchase request to the server.

[0599] The server connects to the systems of partner retailers to obtain inventory information and checkout links.

[0600] The server transmits the acquired information to the terminal.

[0601] Step 8:

[0602] The terminal displays a link to the purchase procedure and a coupon code to the user to support the purchase procedure.

[0603] The user clicks on the link and completes the purchase on the affiliated retailer's website.

[0604] Step 9:

[0605] The server continuously collects user feedback and usage data to update the generative AI model.

[0606] The server periodically generates new interior design proposals and organization solutions based on the updated model and sends them to the terminal.

[0607] The terminal notifies and displays new suggestions and solutions to the user.

[0608] Example 1

[0609] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0610] Addressing today's diverse lifestyles and spatial preferences and providing optimal interior design and organization solutions to users requires advanced data analysis and personalized response. However, conventional systems are unable to fully meet individual user needs and have difficulty efficiently incorporating feedback. Furthermore, the process of purchasing interior items is cumbersome, creating a need for an integrated solution to improve the user experience.

[0611] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0612] In this invention, the server includes means for receiving spatial information and lifestyle information from a user as input, means for generating interior design and organization proposals using a generative AI based on the input information, means for transmitting the generated proposals to a user interface for displaying them, means for receiving user feedback on the proposals and revising the proposals based on the feedback, means for re-presenting the revised proposals to the user, means for recording user information in a database in real time and formatting it into a required data format, and means for converting the proposal content into visual data and visually displaying it. This allows for optimal interior design proposals tailored to individual needs, efficiently incorporating feedback, and achieving a smooth purchasing process.

[0613] "User" refers to an individual or group that uses this system and is the entity that inputs spatial information and lifestyle information.

[0614] "Spatial information" refers to information about the physical space in which a user lives or uses, and includes the layout, size of the room, furniture arrangement, wall color, and the like.

[0615] "Lifestyle information" refers to information related to a user's individual lifestyle, such as their lifestyle habits, preferences, and budget range.

[0616] The "means for receiving input" refers to the interface through which the user provides spatial and lifestyle information to the system, and typically includes an application or web form.

[0617] "Generative AI" refers to artificial intelligence technology that analyzes input data and generates optimal interior design and organization solutions.

[0618] "Interior design" refers to the planning of a space to make it beautiful and functional, including the arrangement of furniture and decorations, color selection, and style.

[0619] "Organization suggestions" include ideas and methods for efficiently organizing the user's living space and making it easier to use.

[0620] "User interface" refers to the screens and methods by which a user interacts with a system to input information, provide feedback, and view suggestions.

[0621] "Feedback" refers to opinions and requests provided by users regarding proposals, and is information that the system uses to revise the proposals.

[0622] The "database" refers to an electronic storehouse that organizes and stores user input information, generated design proposals, feedback, etc.

[0623] "Visual Data" includes graphic and diagrammatic data for visually representing proposed interior design and organization solutions.

[0624] "Retailer" refers to a commercial entity that interacts with the system to provide suggested interior items to users.

[0625] This invention is a system for providing interior design and organization solutions based on users' lifestyles and spatial preferences, and a platform that utilizes generative AI models to provide optimal suggestions to users. The system consists of the following steps: collecting user information, data analysis, suggestion generation, feedback processing, purchasing assistance, and ongoing support.

[0626] First, the user launches the application and enters information such as family composition, home layout, room size, current furniture arrangement, wall color, lifestyle habits and preferences, and budget range. The information entered by the user is recorded in a database in real time by the device and organized into the required data format. The device then sends the entered information to the server.

[0627] The server stores the user-submitted information in a database, standardizing and cleaning it. It then uses a generative AI model (e.g., OpenAI's GPT-3 or other custom models) to create a profile based on the user's lifestyle and spatial preferences. Based on this profile, the server generates optimal interior design and organization suggestions, including furniture placement and color choices for the living room and suggested accessories for each room.

[0628] The generated proposals are converted into a data format by the server and sent to the device. The device receives this information and visually displays it to the user via a user interface. This display includes visual data such as a 3D model of the room and a detailed layout diagram. The user can review the proposals and provide feedback if necessary. For example, they can express specific preferences such as changing the color of a particular piece of furniture or rearranging it.

[0629] The user's feedback is sent to the server via the device, which analyzes it. The generative AI model takes the feedback into account and regenerates a revised proposal. The revised proposal is then sent back to the device and displayed to the user. This process is repeated until the user is satisfied.

[0630] Furthermore, if the user wishes to purchase a suggested interior item, the device sends a purchase request to the server, which then connects with the retailer's system to obtain product availability and a link to the checkout process. The device then displays these links and coupon codes to the user, helping them through the checkout process.

[0631] The server continuously collects user feedback and usage data and periodically updates the generative AI model. This update process involves ingesting new datasets and training the model. Periodically, new interior design suggestions and organization solutions are generated and provided to the user through the server. The device notifies the user of these new suggestions and solutions and allows them to be reviewed through the interface.

[0632] Specific examples

[0633] For example, if a single person named User A lives in a studio apartment in an urban area, User A launches the app and uploads their name, age, address, and a photo of their room. The device sends this information to the server, which then uses generative AI to suggest a Scandinavian-style interior design that makes the most of the small urban space. The suggestions include compact storage units, multifunctional furniture, and simple colors. If User A provides feedback that they would like to add a cafe-style table, the server will again revise the suggestions and make repeated adjustments until User A is satisfied.

[0634] For example, if User B is a family of four living in a suburban detached house, User B inputs information such as the family composition, room size, existing furniture layout, and budget. The device sends this information to the server, which then uses generative AI to generate a design for the family with a spacious living room and multiple bedrooms. The proposal includes furniture arranged to ensure the whole family is comfortable, a play space for children, and storage solutions. If User B provides feedback that they would like to change the theme color of the children's room, the server again modifies the proposal and makes repeated adjustments until User B is satisfied.

[0635] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0636] Step 1:

[0637] Collection of User Information

[0638] First, users launch the application and create a new account, inputting information such as family composition, layout of the home, room size, current furniture arrangement, wall color, lifestyle habits and preferences, and budget range.

[0639] The device records this input information in a database in real time, formats it, and sends it to the server, prompting the user to check and correct the input as necessary.

[0640] Input: Personal and spatial information entered by the user

[0641] Output: Formatted data sent to the server

[0642] Step 2:

[0643] Data analysis

[0644] The server stores the information submitted by users in a database, standardizes and cleans it, checks for missing values ​​and inconsistencies, and performs any necessary data imputation.

[0645] Input: User information received from the device

[0646] Output: Standardized and cleaned dataset

[0647] Step 3:

[0648] Generate interior proposals

[0649] The server passes the standardized dataset to a generative AI model (such as OpenAI's GPT-3), which creates a profile based on the user's lifestyle and spatial preferences.

[0650] Based on that profile, the generative AI model generates optimal interior design and organization suggestions, including specific furniture placement, color choices, storage solutions, and more.

[0651] Input: Standardized and cleaned dataset

[0652] Output: Interior design and organization proposals

[0653] Step 4:

[0654] View Suggestions

[0655] The server converts the generated interior design proposal into a data format and sends it to the terminal, along with creating visual data such as a 3D model and detailed layout drawings.

[0656] The terminal receives this data and visually displays it to the user through a user interface.

[0657] Input: Interior design and organization suggestions

[0658] Output: Visual data that is displayed to the user

[0659] Step 5:

[0660] Gathering feedback

[0661] The user reviews the proposal and provides feedback if necessary. For example, they can input their preferences, such as changing the color of a particular piece of furniture or rearranging it.

[0662] The terminal transmits the user's feedback to the server.

[0663] Input: User feedback

[0664] Output: Feedback data sent to the server

[0665] Step 6:

[0666] Updates incorporating feedback

[0667] The server analyzes the received feedback and inputs it back into the generative AI model, which then takes the feedback into account to generate new suggestions.

[0668] The revised proposal is sent back to the terminal and displayed to the user, and this process is repeated until the user is satisfied.

[0669] Input: Parsed feedback

[0670] Output: Revised interior design proposal

[0671] Step 7:

[0672] Purchase assistance

[0673] If the user wishes to purchase the suggested interior item, the terminal sends a purchase request to the server.

[0674] The server connects with the systems of affiliated retailers to obtain product availability and links to checkout.

[0675] The terminal displays these links and coupon codes to the user and assists in the purchase process.

[0676] Input: User purchase request

[0677] Output: Stock information and purchase links from retailers

[0678] Step 8:

[0679] Ongoing support and updates

[0680] The server continuously collects user feedback and usage data and periodically updates the generative AI model, a process that involves ingesting new datasets and training the model.

[0681] Periodically, new interior design proposals and organization solutions are generated and provided to the user through the server. The terminal notifies the user of these new proposals and solutions and allows them to be viewed through the interface.

[0682] Input: Feedback and usage data collected continuously

[0683] Output: Updated generative AI model and new proposals

[0684] (Application example 1)

[0685] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0686] Conventional factory layout and workflow optimization relies heavily on experience, making it difficult to design an efficient and productive layout in a short period of time. It is also difficult to effectively incorporate feedback into the proposed layout, resulting in suboptimal productivity.

[0687] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0688] In this invention, the server includes: means for receiving spatial information and lifestyle information from a user as input; means for generating interior design and organization proposals using a generative AI based on the input information; means for transmitting the generated proposals to a terminal for displaying them; means for receiving factory layout information and workflow information from a user as input; means for generating an optimal layout based on work efficiency and productivity based on the factory layout information and workflow information; means for displaying the generated layout and performing a virtual simulation; means for receiving user feedback on the proposals and revising the proposals based on the feedback; and means for providing the revised proposals to the user again, thereby enabling efficient and productive design of factory layouts and workflows.

[0689] "Spatial information" is data about the physical location and layout of factories and work areas.

[0690] "Lifestyle information" is data on the behavioral patterns and work habits of factory workers.

[0691] "Generative AI" is an algorithm that uses artificial intelligence technology to generate optimal suggestions.

[0692] "Interior design" is a proposal for layout and decoration to improve the aesthetics and functionality of a space.

[0693] "Organizational proposals" are specific ideas and plans for achieving efficient work flows and layouts.

[0694] A "terminal" is an electronic device that allows a user to view suggestions and enter feedback.

[0695] "Layout information" is data regarding the layout of machines and equipment within a factory.

[0696] "Work flow information" is data related to the order and processes of work within a factory.

[0697] "Virtual simulation" is a technology that allows a proposed layout to be reproduced in a virtual space and visually confirmed.

[0698] "Feedback" is information regarding the user's opinions and requests for corrections regarding the proposal.

[0699] "Productivity" is the ability to achieve maximum results with limited resources.

[0700] Efficiency is the ability to get more out of less.

[0701] "Procurement support" is a function that provides assistance in purchasing necessary equipment and facilities.

[0702] "Partner Retailers" are partner companies that provide products and services.

[0703] A "checkout" is a series of actions taken to purchase a product or service.

[0704] "Updating generative AI" means improving the artificial intelligence algorithms to make more accurate suggestions based on new data and feedback.

[0705] The present invention provides a system that can make proposals based on a user's lifestyle and spatial preferences, and is particularly concerned with optimizing the layout and work flow within a factory. This system is composed of a user, a terminal, and a server, all of which work in conjunction with each other.

[0706] System configuration

[0707] 1. Collection of User Information

[0708] A user first logs into the system and launches a corresponding application.

[0709] The terminal accepts input from users and collects data such as factory layout information, work flow information, budgets, and efficiency and productivity priorities.

[0710] As an example of input, a prompt such as "We would like to optimize the layout of our factory. Please generate the optimal proposal based on the following information: Current layout diagram: 'current_layout.json', Workflow diagram: 'workflows.json', Constraints: 'Budget: 100,000, Time: 200', Priorities: 'Efficiency: High, Safety: Medium'" is used.

[0711] 2. Data analysis and layout proposal generation

[0712] The server receives the collected data and stores it in a database.

[0713] Inside the server, a Generative AI model is used to analyze the data, generating optimal layout and workflow proposals that maximize efficiency and productivity within the factory.

[0714] 3. Proposal presentation and virtual simulation

[0715] The server transmits the generated layout proposal to the terminal.

[0716] The device uses software (e.g., Unity) to display this as a 3D model, allowing the user to visually confirm it.

[0717] Virtual simulations can be performed to validate the proposed layout.

[0718] 4. User feedback and suggested fixes

[0719] The user can then input feedback on the proposals from the terminal, for example, by entering a specific request such as "I would like to change the placement of a specific machine."

[0720] The server receives the feedback and again uses the Generative AI model to modify the proposal, which generates a new proposal that reflects the feedback.

[0721] 5. Submitting a revision proposal again

[0722] The revised proposal is sent back to the terminal and provided to the user.

[0723] This process is repeated until the user is satisfied.

[0724] Program processing description

[0725] Hardware and software used

[0726] Terminal: PC or tablet for user operation

[0727] Server: A server that runs data analysis and generative AI models

[0728] Software: Databases, generative AI models (e.g., TensorFlow), 3D model display software (e.g., Unity)

[0729] Process Overview

[0730] The server stores the received user information in a database and uses a generative AI model to generate optimal factory layout proposals.

[0731] The generated proposal is sent to the device and displayed to the user as a 3D model.

[0732] The user inputs feedback on the suggestions from the terminal, and the feedback is sent to the server.

[0733] The server receives the feedback and runs the generative AI model again to generate revised suggestions.

[0734] The suggested revisions are sent back to the terminal and the process is repeated until the user is satisfied.

[0735] Specific examples

[0736] For example, in a metal processing factory, users would input data such as the current layout, workflow information, budget, and safety standards into the application. The server receives this data, and the generative AI model proposes optimal machine placement and work flow. This is then displayed as a 3D model on the device, and users can provide feedback on their specific requests, which will result in further improved proposals.

[0737] This enables efficient and productive design of factory layouts and work flows.

[0738] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0739] Step 1:

[0740] The user launches the application and logs in. After logging in, they input the information needed to optimize the factory layout, including the current factory layout diagram, workflow information, budget, and priorities related to efficiency and productivity.

[0741] Input: User login information, factory layout diagram (e.g. 'current_layout.json'), workflow information (e.g. 'workflows.json'), budget, efficiency and productivity priorities

[0742] Output: The information entered

[0743] Step 2:

[0744] The terminal receives the user's input information and sends it to a database, where it is stored on a server.

[0745] Inputs: User-entered factory layout diagrams, work flow information, budgets, efficiency and productivity priorities

[0746] Output: Data sent to the server

[0747] Step 3:

[0748] The server analyzes the received data and generates factory layout optimization proposals using a Generative AI model. In this process, a data analysis module interprets the data, and Generative AI calculates the optimal layout and work flow.

[0749] Inputs: Factory layout diagrams stored on a server, work flow information, budgets, efficiency and productivity priorities

[0750] Data processing: Analysis of input data and generation of profiles

[0751] Data calculation: Generative AI generates optimal layout proposals

[0752] Output: Optimized factory layout proposal

[0753] Step 4:

[0754] The server transmits the generated layout proposal to the terminal.

[0755] Input: Optimized factory layout proposal

[0756] Output: Layout proposal sent to device

[0757] Step 5:

[0758] The device displays the received layout proposal as a 3D model, allowing the user to visually confirm the proposal. Specifically, 3D model display software (e.g., Unity) is used.

[0759] Input: Layout proposal received from the server

[0760] Output: Display of 3D model

[0761] Step 6:

[0762] The user reviews the proposed layout and enters any necessary feedback, such as specific requests such as "I would like to change the placement of a particular machine."

[0763] Input: User feedback

[0764] Output: Feedback data

[0765] Step 7:

[0766] The terminal transmits the user's feedback to the server.

[0767] Input: Feedback data

[0768] Output: Feedback sent to the server

[0769] Step 8:

[0770] The server analyzes the received feedback and re-runs the GenerativeAI model to refine the suggestions. New suggestions are generated and stored on the server.

[0771] Input: Feedback data from users

[0772] Data processing: Analyzing feedback and updating profiles

[0773] Data Computing: Regenerating revised layout proposals with generative AI

[0774] Output: Revised layout proposal

[0775] Step 9:

[0776] The server sends the revised proposal back to the terminal.

[0777] Input: revised layout proposal

[0778] Output: Suggested fixes sent to terminal

[0779] Step 10:

[0780] The device then displays the revised proposal again as a 3D model, and the user reviews the new proposal and provides feedback again if necessary. This process is repeated until the user is satisfied.

[0781] Input: revised layout proposal

[0782] Output: 3D model display and user feedback

[0783] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0784] This invention is a system for providing interior design and organization solutions based on a user's individual lifestyle and spatial preferences, and further utilizes an emotion engine to recognize the user's emotions and make suggestions that reflect those emotions. Specifically, this system is configured as a platform that combines generative AI and an emotion engine.

[0785] Program processing

[0786] 1. User Information Collection:

[0787] A user launches the application and creates a new account, entering basic information such as name, age, address, family composition, lifestyle habits, interior design preferences, and budget range.

[0788] The terminal transmits this input information to the server in real time.

[0789] 2. Data analysis and interior proposal generation:

[0790] The server stores the information submitted by the user in a database, and a generative AI module analyzes the data, creating a profile based on the user's lifestyle and spatial preferences.

[0791] Based on this profile, the generative AI generates optimal interior design and organization solutions, such as furniture placement and color choices for a living room, as well as suggested accessories for each room.

[0792] 3. Use of Emotion Engine:

[0793] The server sends the generated interior design proposal to the device and uses an emotion engine to analyze the user's facial expressions, tone of voice, and text input to recognize the user's emotions before displaying it to the user.

[0794] The server further customizes the suggestions based on the emotional data obtained from the emotion engine, so that the suggestions are more in line with the user's current mental state and emotions.

[0795] 4. Viewing and Feedback on Suggestions:

[0796] The device displays customized interior design proposals to the user, including a 3D model of the room and a detailed floor plan.

[0797] The user reviews the suggestions and provides feedback if necessary, for example, to specify specific requests such as changing the color of a particular piece of furniture or rearranging it.

[0798] 5. Incorporating feedback and suggested updates:

[0799] The terminal transmits the user's feedback to the server.

[0800] The server analyzes the feedback, utilizes a generative AI module to modify the proposal, again using the emotion engine to reflect the user's emotions, and presents the modified proposal to the user again. This process is repeated until the user is satisfied.

[0801] 6. Purchasing Assistance and Partnerships:

[0802] If the user wishes to purchase the suggested interior item, the terminal sends a purchase request to the server.

[0803] The server connects to the systems of partner retailers to obtain product availability and checkout links.

[0804] The server sends the acquired information to the device, which then displays a checkout link and a coupon code to the user, helping the user complete the checkout process.

[0805] 7. Ongoing Support and Updates:

[0806] The server continuously collects user feedback and usage data and periodically updates the generative AI model and emotion engine.

[0807] The server periodically generates new interior design proposals and organization solutions based on the updated model and sends them to the terminal.

[0808] The terminal will notify and display these new suggestions and solutions to the user.

[0809] Specific examples

[0810] Case Study: Personalized Recommendations Using Emotion Recognition

[0811] User A (single person living in a one-room apartment in an urban area):

[0812] Device: User A launches the app and uploads their name, age, address, and a photo of their room.

[0813] Server: Generative AI analyzes this data and proposes Scandinavian-inspired interior designs that maximize the use of small urban spaces, selecting compact storage units, multifunctional furniture, and simple color palettes.

[0814] Emotion engine: Before making a suggestion, it analyzes User A's facial expressions and tone of voice and evaluates whether the suggestion matches User A's current emotions. For example, if it recognizes that User A is feeling stressed, it will add interior elements that have a relaxing effect.

[0815] User A: Review the proposal and submit feedback saying they would like to add cafe-style tables.

[0816] Server: The regenerated proposal based on the feedback is rechecked by the emotion engine and sent to User A. Adjustments are repeated until User A is satisfied.

[0817] User B (family of four, suburban detached house):

[0818] Terminal: User B inputs the family composition (husband, wife, two children), room size, layout of existing furniture, budget, etc.

[0819] Server: Generative AI generates a design for a family with a spacious living room and multiple bedrooms. The proposal includes furniture arranged to accommodate the entire family, a play space for children, and storage solutions.

[0820] Emotion engine: Before displaying suggestions, analyze User B's emotional data and make suggestions that will help the whole family relax.

[0821] User B: Review the proposal and submit feedback that they would like to change the theme color for the children's room.

[0822] Server: The server evaluates the regenerated proposal based on the feedback using the emotion engine and sends it to User B. Adjustments are repeated until User B is satisfied.

[0823] In this way, the present invention takes into account the user's emotions to provide more personalized interior design and organization solutions, resulting in a harmonious living environment.

[0824] The processing flow will be explained below.

[0825] Step 1:

[0826] A user launches the application and creates a new account. They enter basic information such as their name, age, address, family composition, lifestyle habits, interior design preferences, and budget range. The device then transmits this information to the server in real time.

[0827] Step 2:

[0828] The server stores the information submitted by the user in a database, and then uses a generative AI module to analyze the data it receives, creating a profile based on the user's lifestyle and spatial preferences.

[0829] Step 3:

[0830] The server generates optimal interior design and organization solutions based on the profile, including furniture placement, color selection, and accessory suggestions, and then sends the generated design proposals to the device.

[0831] Step 4:

[0832] Before displaying interior design suggestions to the user, the device activates an emotion engine that analyzes the user's facial expressions, tone of voice, and text input to recognize the user's emotions.

[0833] Step 5:

[0834] The server customizes the suggestions based on the emotional data obtained from the emotion engine. For example, if the server detects that the user is feeling stressed, it adds relaxing interior elements to the suggestions.

[0835] Step 6:

[0836] The device displays customized interior design proposals to the user, including a 3D model of the room and a detailed floor plan, and the user can review the proposals and provide feedback if needed.

[0837] Step 7:

[0838] The device sends the user's feedback to the server, which receives the feedback and modifies the proposal using the generative AI module. It then uses the emotion engine again to confirm the user's emotions and presents the modified proposal to the user again. This process is repeated until the user is satisfied.

[0839] Step 8:

[0840] If the user wishes to purchase a suggested interior item, the device sends a purchase request to the server. The server connects to the affiliated retailer's system to obtain product availability and a checkout link. The server then sends the obtained information to the device, which then displays the checkout link and coupon code to the user. The device then assists the user in clicking the link and completing the purchase on the affiliated retailer's website.

[0841] Step 9:

[0842] The server continuously collects user feedback and usage data and periodically updates the generative AI model and emotion engine. The server periodically generates new interior design proposals and organization solutions based on the updated model and sends them to the device. The device notifies and displays these new proposals and solutions to the user.

[0843] Example 2

[0844] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0845] Providing optimal interior design and organization solutions for each individual user to meet today's diverse lifestyles is challenging. There is also a need to provide customized suggestions based on the user's emotional state and increase user satisfaction. Furthermore, there is a lack of systems that incorporate user feedback and provide continuously improved suggestions.

[0846] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving spatial information and lifestyle information from a user as input, means for generating interior design and organization proposals using a generative AI based on the input information, means for analyzing the user's facial expressions, tone of voice, and text input to collect emotional data, means for customizing the generated proposals based on the emotional data, means for receiving user feedback on the proposals and revising the proposals based on the feedback, means for re-presenting the revised proposals to the user, and means for continuously collecting user usage status and updating the generative AI model and emotion engine. This makes it possible to provide interior design and organization solutions that match the user's lifestyle and take into account their emotional state.

[0847] "User" refers to an individual or household that receives interior design and organization solutions from the system.

[0848] "Spatial information" is detailed information about the user's living space, including data such as room dimensions, layout, and existing furniture arrangement.

[0849] "Lifestyle information" refers to information about the user's individual lifestyle, such as the user's daily habits, family structure, preferred interior style, and budget.

[0850] "Generative AI" refers to an artificial intelligence model that generates optimal interior design and organization solutions based on user-provided information.

[0851] A "proposal" is an interior design or organization plan created by the generative AI by analyzing the user's information, including specific furniture placement and color selection.

[0852] "Emotion data" is data that indicates the user's emotional state, analyzed from the user's facial expression, tone of voice, text input, and the like.

[0853] An "emotion engine" refers to an engine that analyzes a user's emotional data and customizes the suggestions provided by the generative AI based on that information.

[0854] "Feedback" refers to a user returning to the system their opinions and requests regarding suggestions provided by the system.

[0855] "Customization" refers to the process by which the generative AI modifies its suggestions to best suit the user based on the user's emotional data and feedback.

[0856] "Update" refers to the generative AI model and emotion engine refining its algorithms and data to improve accuracy and effectiveness based on user feedback and usage data.

[0857] "Terminal" refers to a device, such as a computer or smartphone, through which a user inputs information or receives suggestions.

[0858] This invention is a system for providing interior design and organization solutions based on users' lifestyles and spatial preferences. The system is configured as a platform that combines generative AI and an emotion engine.

[0859] System Overview

[0860] The user first launches the application and enters basic information such as name, age, address, family composition, lifestyle habits, interior design preferences, and budget range. This information is sent to the server via the device. The server stores the received information in a database, and a generative AI module analyzes the data and creates a profile based on the user's lifestyle and spatial preferences. The generative AI (e.g., a large-scale language model such as GPT-3) generates optimal interior design and organization solutions.

[0861] Use of emotion engine

[0862] The generated interior design proposals are customized by an emotion engine before being displayed to the user. The emotion engine (e.g., Affectiva SDK) is used to recognize the user's emotions by analyzing the user's facial expressions, tone of voice, and text input. The emotion data obtained by the emotion engine is analyzed by the server, and the generative AI further customizes the proposals. As a result, the proposals are more in line with the user's current mental state and emotions.

[0863] View suggestions and give feedback

[0864] The customized interior design proposal is sent to the device and displayed to the user. This includes a 3D model of the room and a detailed layout. The user reviews the proposal and provides feedback on specific preferences (e.g., changing the color or positioning of certain furniture). This feedback is sent via the device to the server, which analyzes the feedback and modifies the proposal using a generative AI module. This process is repeated until the user is satisfied.

[0865] Buying Assistance and Partnerships

[0866] If the user wishes to purchase a suggested interior item, the device sends a purchase request to the server, which connects to the retailer's system to obtain product availability and a link to checkout. The information is then sent to the device, where the user is presented with a link to checkout and a coupon code.

[0867] Ongoing support and updates

[0868] The server continuously collects user feedback and usage data and periodically updates the generative AI model and emotion engine. New interior design proposals and organization solutions are periodically generated based on the updated model and sent to the device. The device notifies and displays these new proposals and solutions to the user.

[0869] Specific examples

[0870] Case Study 1: Personalized Recommendations Using Emotion Recognition

[0871] User A (single person living in a studio apartment in an urban area):

[0872] Device: User A launches the app and uploads their name, age, address, and a photo of their room.

[0873] Server: Generative AI analyzes this data and proposes Scandinavian-inspired interior designs that maximize the use of small urban spaces, selecting compact storage units, multifunctional furniture, and simple color palettes.

[0874] Emotion engine: Before making a suggestion, it analyzes User A's facial expressions and tone of voice and evaluates whether the suggestion matches User A's current emotions. For example, if it recognizes that User A is feeling stressed, it will add interior elements that have a relaxing effect.

[0875] User A: Review the proposal and submit feedback saying they would like to add cafe-style tables.

[0876] Server: The regenerated proposal based on the feedback is rechecked by the emotion engine and sent to User A. Adjustments are repeated until User A is satisfied.

[0877] Prompt Sentence Examples

[0878] Prompt 1: "Generate the best interior design suggestions based on the user's basic information, including age, address, preferred style, and budget range."

[0879] Prompt 2: "Customize the generated interior design suggestions based on the recorded user emotional data. Be sure to add elements that have a relaxing effect."

[0880] In this way, the present invention takes into account the user's emotions to provide more personalized interior design and organization solutions, resulting in a harmonious living environment.

[0881] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0882] Step 1: Collect user information

[0883] User Action:

[0884] Users launch the application and tap the "Create a new account" button, then enter basic information such as their name, age, address, family composition, lifestyle habits, interior design preferences, and budget range.

[0885] input:

[0886] Information such as name, age, address, family composition, lifestyle habits, interior design preferences, budget range, etc.

[0887] Terminal behavior:

[0888] The device collects input in real time, encrypts the data using HTTPS, and sends it securely to the server.

[0889] output:

[0890] The user information is sent to the server and account creation is complete.

[0891] Step 2: Data analysis and interior design proposal generation

[0892] Server behavior:

[0893] The server stores the received user information in a database (e.g., MySQL) and then invokes a generative AI module to analyze the information and create a user profile.

[0894] input:

[0895] User information stored in a database.

[0896] Data processing:

[0897] Based on the stored information, a generative AI analyzes the data and creates a profile based on your lifestyle and spatial preferences.

[0898] output:

[0899] Proposing the best interior design and organization solutions for users.

[0900] Step 3: Use the Emotion Engine

[0901] Terminal behavior:

[0902] It records facial expressions and tone of voice through the user's webcam and microphone, and collects text input.

[0903] input:

[0904] User facial expression data, tone of voice, and text input.

[0905] Terminal behavior:

[0906] The collected data is sent to the server in real time.

[0907] Server behavior:

[0908] An emotion engine is used to analyze emotions from collected data.

[0909] Data processing:

[0910] Emotional data is quantified and the suggestions provided by the generative AI are customized based on that data.

[0911] output:

[0912] Customized interior suggestions based on user emotions.

[0913] Step 4: View and give feedback on suggestions

[0914] Terminal behavior:

[0915] It presents users with customized interior design proposals, including 3D models of rooms and detailed floor plans.

[0916] input:

[0917] Customized interior design proposals.

[0918] User Action:

[0919] Review the suggestions and enter specific feedback into the device, such as changing the color of certain furniture or rearranging it.

[0920] Terminal behavior:

[0921] Collect user feedback and send it to the server.

[0922] output:

[0923] User feedback on the proposal.

[0924] Step 5: Incorporating feedback and proposing updates

[0925] Server behavior:

[0926] Analyze user feedback and refine suggestions using a generative AI module.

[0927] input:

[0928] User feedback.

[0929] Data processing:

[0930] Update suggestions based on feedback and use the sentiment engine again to reflect user sentiment.

[0931] Server behavior:

[0932] The revised proposal is resubmitted to the user.

[0933] output:

[0934] Revised interior design proposal.

[0935] Step 6: Assist with purchasing and execute partnerships

[0936] User Action:

[0937] If the customer wishes to purchase the suggested interior item, the customer inputs a purchase request on the terminal.

[0938] Terminal behavior:

[0939] Send the purchase request to the server.

[0940] Server behavior:

[0941] Connect to partner retailer systems to retrieve product availability and checkout links.

[0942] input:

[0943] Purchase requests and interior item information.

[0944] Data processing:

[0945] Link with retailer systems to obtain necessary information.

[0946] output:

[0947] A link to checkout and a coupon code will be sent to your device.

[0948] Step 7: Ongoing support and updates

[0949] Server behavior:

[0950] We continuously collect user feedback and usage data to regularly update our generative AI models and emotion engine.

[0951] input:

[0952] User feedback and usage data.

[0953] Data processing:

[0954] Analyze the collected data and improve the model.

[0955] Server behavior:

[0956] New interior design proposals and organisational solutions are generated based on the updated model and sent to the device.

[0957] output:

[0958] New interior design proposals and organisation solutions.

[0959] (Application example 2)

[0960] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0961] Conventional interior design systems make suggestions based on the user's lifestyle and spatial preferences, but they are unable to consider the user's emotional state, making it difficult to provide suggestions that truly satisfy the user. While systems exist that receive feedback on suggestions, they lack the ability to respond quickly to feedback or regenerate customized suggestions based on emotions, limiting their ability to improve the user experience.

[0962] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for utilizing an emotion engine that recognizes the user's emotions and further customizes proposals based on those emotions, means for displaying proposals generated on the terminal using 3D models or augmented reality, and means for continuously customizing proposals using emotion data generated by the emotion engine. This makes it possible to provide personalized interior design proposals that take the user's emotional state into consideration. In addition, by receiving feedback in real time and quickly generating and displaying appropriate proposals, it becomes possible to provide proposals that highly satisfy the user.

[0963] "User" refers to the end user who uses the proposal system and is the person who receives the proposals for interior design and organization.

[0964] "Spatial information" refers to data such as the layout, size, shape, and current furniture arrangement of a user's living space or office.

[0965] "Lifestyle information" refers to individual information such as the user's lifestyle, family structure, hobbies and preferences, activity patterns, budget range, and the like.

[0966] "Generative AI" is a system that uses artificial intelligence algorithms to generate optimal interior design proposals from given input data.

[0967] "Interior design" is a general term for design that includes the arrangement of furniture and decorations, color selection, etc. to make a user's space beautiful and functional.

[0968] "Organization suggestions" refer to suggestions for storage methods and layout changes to make efficient use of the user's space.

[0969] "Suggestions" refer to interior design and organization ideas presented to the user by the generative AI.

[0970] A "terminal" is a device that allows a user to view interior design proposals and enter feedback, and includes smartphones, tablets, PCs, smart glasses, etc.

[0971] "Feedback" refers to the act of a user inputting their opinions or requests regarding the content of a proposal after checking it.

[0972] An "emotion engine" refers to an algorithm that recognizes emotions from a user's facial expressions, voice, etc., and customizes suggestions based on those emotions.

[0973] A "3D model" refers to an interior design model that is expressed in three dimensions and is used to make proposals visually easier to understand.

[0974] "Augmented reality" is a technology that overlays computer-generated information onto real-world visual information, and is used to display interior design proposals in real spaces.

[0975] The present invention provides an interior design proposing system that takes into account the user's emotions, and is specifically implemented as follows.

[0976] First, the user inputs spatial and lifestyle information using a device, such as a smartphone, tablet, PC, or smart glasses. This input information includes the user's name, age, family composition, lifestyle habits, interior design preferences, and budget range. This information is sent to a central server in real time.

[0977] The server stores the received user information in a database, which is then analyzed by a generative AI module. The generative AI module generates the optimal interior design based on the user's lifestyle and spatial preferences. The specific system uses machine learning libraries such as Python and TensorFlow to build the AI ​​model.

[0978] The generated interior design proposals are first customized by an emotion engine. The emotion engine uses the camera and microphone of the smart glasses or smartphone to analyze the user's facial expressions and tone of voice to recognize their emotions. The proposals are then adjusted based on this emotion data. Specific implementations of the emotion engine utilize OpenCV and the Google Cloud Speech-to-Text API.

[0979] The device then displays the tailored interior design proposal to the user, including 3D models and augmented reality (AR) visualizations, allowing the user to see the proposal in the real space. The AR rendering uses Unity or ARKit / ARCore.

[0980] The user reviews the suggestions and provides feedback, which is sent from the device to the server, which then updates the suggestions using the generative AI module and emotion engine. This process is repeated until the user is satisfied.

[0981] If the user wishes to purchase a suggested interior item, the server connects to the retailer's system to retrieve product availability and a link to the purchase process, which is then displayed on the user's device. This allows the user to easily purchase the suggested item.

[0982] For example, the following prompt will prompt the user for information:

[0983] Example prompt:

[0984] Name: Taro Yamada

[0985] Age: 35

[0986] Family status: Single

[0987] Interior preferences: Modern style, muted colors

[0988] Budget range: 50,000 yen

[0989] Feedback example:

[0990] "I would like a more relaxing atmosphere. I would like the lighting to be changed to warmer tones."

[0991] In this way, by taking into account the user's emotions and feedback, a more satisfying interior design can be provided.

[0992] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0993] Step 1:

[0994] Collection of User Information

[0995] The user starts up the device and enters basic information such as name, age, address, family composition, lifestyle habits, interior design preferences, and budget range. This input data is sent from the device to the server in real time. The input information includes the user's lifestyle and spatial preferences, and subsequent data analysis is based on this information.

[0996] Step 2:

[0997] Data analysis and interior design proposal generation

[0998] The server stores the information submitted by the user in a database. The generative AI module analyzes the stored user data and creates a user profile based on lifestyle and spatial preferences. Based on this profile, the generative AI generates optimal interior design and organization solutions, including suggestions for living room furniture placement, color selection, and accessories for each room. The output design proposals are sent to the next step.

[0999] Step 3:

[1000] Use of emotion engine

[1001] The emotion engine recognizes the user's emotions before displaying suggestions to the user. The device uses a camera and microphone to analyze emotions from the user's facial expressions and tone of voice. The emotion data is sent to the server, which then uses the data to customize the generated interior design suggestions. For example, if the user is feeling stressed, it can add elements that have a relaxing effect. The revised suggestions are then sent to the next step.

[1002] Step 4:

[1003] View suggestions and give feedback

[1004] The device displays interior design proposals customized by the emotion engine to the user, including a 3D model of the room and a detailed layout. The user can review the proposals and provide feedback as needed. For example, specific requests can be made, such as changing the color or placement of certain furniture. Feedback is sent to the server in real time.

[1005] Step 5:

[1006] Incorporating feedback and suggested updates

[1007] The server analyzes the feedback received from the user and modifies the proposal using the generative AI module. The modified proposal is then adjusted again through the emotion engine to reflect the user's emotional data. The new, adjusted proposal is then sent back to the user. This process is repeated until the user is satisfied.

[1008] Step 6:

[1009] Buying Assistance and Partnerships

[1010] If the user wishes to purchase a suggested interior item, the device sends a purchase request to the server. The server connects to the partner retailer's system to obtain product availability and a link to checkout. The obtained information is sent to the device, which then displays a link to checkout and a coupon code to the user, allowing the user to easily purchase the suggested item.

[1011] Step 7:

[1012] Ongoing support and updates

[1013] The server continuously collects user feedback and usage data and periodically updates the generative AI model and emotion engine. Based on the updated model, new interior design proposals and organization solutions are periodically generated and sent to the device. The device then notifies and displays the new proposals and solutions to the user, ensuring that the user always receives the latest proposals.

[1014] These are the specific processing steps of the system that realizes this application example. The detailed operations at each step, such as data input and output, utilization of emotional data, and reflection of feedback, support high user satisfaction throughout the system.

[1015] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1016] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1017] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1018] [Third embodiment]

[1019] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1020] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1021] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1022] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1023] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1024] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1025] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1026] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1027] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1028] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1029] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1030] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1031] The present invention is a system for providing interior design and organization solutions based on a user's individual lifestyle and spatial preferences, specifically a platform that utilizes generative AI to provide optimal suggestions to users. The system is implemented in the following steps:

[1032] Program processing

[1033] 1. User Information Collection:

[1034] Users first launch the application and create a new account, then enter information such as family composition, home layout, room size, current furniture arrangement, wall color, lifestyle habits and preferences, and budget range.

[1035] The terminal collects this input information and transmits it to the server as necessary data.

[1036] 2. Data analysis and interior proposal generation:

[1037] The server stores the information submitted by the user in a database, and a generative AI module analyzes the data, creating a profile based on the user's lifestyle and spatial preferences.

[1038] Based on this profile, the generative AI generates optimal interior design and organization suggestions, such as furniture placement and color choices for a living room, as well as suggested accessories for each room.

[1039] 3. Viewing and Feedback on Suggestions:

[1040] The server then sends the generated interior design proposal to the device and displays it to the user, including a 3D model of the room and a detailed layout diagram.

[1041] The user reviews the suggestions and provides feedback if necessary, for example, to specify specific requests such as changing the color of a particular piece of furniture or rearranging it.

[1042] 4. Feedback and suggested updates:

[1043] The terminal transmits the user's feedback to the server.

[1044] The server analyzes this feedback and uses the generative AI to modify the proposal, which is then sent back to the user for confirmation.

[1045] This process is repeated until the user is satisfied.

[1046] 5. Purchasing Assistance and Partnerships:

[1047] If the user wishes to purchase the suggested interior item, the terminal sends a purchase request to the server.

[1048] The server connects with the systems of affiliated retailers to obtain product availability and links to checkout.

[1049] The terminal displays a link to the purchase procedure and a coupon code to the user to support the purchase procedure.

[1050] 6. Ongoing Support and Updates:

[1051] The server continuously collects user feedback and usage data and periodically updates the generative AI model.

[1052] The server periodically provides users with new interior design suggestions and organization solutions.

[1053] The terminal will notify and display these new suggestions and solutions to the user.

[1054] Specific examples

[1055] Case Study: Proposals for Users with Different Family Structures

[1056] User A (single person living in a one-room apartment in an urban area):

[1057] Device: User A launches the app and uploads their name, age, address, and a photo of their room.

[1058] Server: Generative AI analyzes this data and proposes Scandinavian-inspired interior designs that maximize the use of small urban spaces, including compact storage units, multifunctional furniture, and simple color palettes.

[1059] User A: Review the proposal and submit feedback saying they would like to add cafe-style tables.

[1060] Server: Sends a regenerated proposal based on the feedback to User A, and repeats the process of adjusting it until User A is satisfied.

[1061] User B (family of four, suburban detached house):

[1062] Terminal: User B inputs the family composition (husband, wife, two children), room size, layout of existing furniture, budget, etc.

[1063] Server: Generative AI generates a design for a family with a spacious living room and multiple bedrooms. The proposal includes furniture arranged to accommodate the entire family, a play space for children, and storage solutions.

[1064] User B: Review the proposal and submit feedback that they would like to change the theme color for the children's room.

[1065] Server: Sends a regenerated proposal based on the feedback to User B, and repeats the process of adjustment until User B is satisfied.

[1066] In this way, the present invention provides interior design and organization solutions tailored to the user's individual needs, resulting in a harmonious living environment.

[1067] The processing flow will be explained below.

[1068] Step 1:

[1069] The user launches the application and creates a new account.

[1070] Users enter basic information such as their name, age, address, family composition, lifestyle habits, interior design preferences, and budget range.

[1071] The terminal transmits the input information to the server.

[1072] Step 2:

[1073] The server stores the information sent by the user in a database.

[1074] The server uses a generative AI module to analyze the data it receives.

[1075] The server creates a profile based on the user's lifestyle and spatial preferences.

[1076] Step 3:

[1077] The server generates optimal interior design and organization solutions based on the profile.

[1078] This includes furniture placement, color selection, and accessory suggestions.

[1079] The server transmits the generated design proposal to the terminal.

[1080] Step 4:

[1081] The terminal displays the interior design proposal to the user.

[1082] The user reviews the proposal and provides feedback if necessary.

[1083] For example, you can enter specific opinions such as "I want to change the color of the sofa" or "I want to change the position of the table."

[1084] Step 5:

[1085] The terminal transmits the user's feedback to the server.

[1086] The server receives the feedback and modifies the design proposal using a generative AI module.

[1087] The server sends the revised proposal back to the terminal and presents it to the user.

[1088] Step 6:

[1089] The user reviews the revised proposal and repeats the same procedure if they wish to provide feedback again.

[1090] This cycle is repeated until the user is satisfied with the proposal.

[1091] Step 7:

[1092] If the user wishes to purchase the suggested interior item, the terminal sends a purchase request to the server.

[1093] The server connects to the systems of partner retailers to obtain inventory information and checkout links.

[1094] The server transmits the acquired information to the terminal.

[1095] Step 8:

[1096] The terminal displays a link to the purchase procedure and a coupon code to the user to support the purchase procedure.

[1097] The user clicks on the link and completes the purchase on the affiliated retailer's website.

[1098] Step 9:

[1099] The server continuously collects user feedback and usage data to update the generative AI model.

[1100] The server periodically generates new interior design proposals and organization solutions based on the updated model and sends them to the terminal.

[1101] The terminal notifies and displays new suggestions and solutions to the user.

[1102] Example 1

[1103] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1104] Addressing today's diverse lifestyles and spatial preferences and providing optimal interior design and organization solutions to users requires advanced data analysis and personalized response. However, conventional systems are unable to fully meet individual user needs and have difficulty efficiently incorporating feedback. Furthermore, the process of purchasing interior items is cumbersome, creating a need for an integrated solution to improve the user experience.

[1105] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1106] In this invention, the server includes means for receiving spatial information and lifestyle information from a user as input, means for generating interior design and organization proposals using a generative AI based on the input information, means for transmitting the generated proposals to a user interface for displaying them, means for receiving user feedback on the proposals and revising the proposals based on the feedback, means for re-presenting the revised proposals to the user, means for recording user information in a database in real time and formatting it into a required data format, and means for converting the proposal content into visual data and visually displaying it. This allows for optimal interior design proposals tailored to individual needs, efficiently incorporating feedback, and achieving a smooth purchasing process.

[1107] "User" refers to an individual or group that uses this system and is the entity that inputs spatial information and lifestyle information.

[1108] "Spatial information" refers to information about the physical space in which a user lives or uses, and includes the layout, size of the room, furniture arrangement, wall color, and the like.

[1109] "Lifestyle information" refers to information related to a user's individual lifestyle, such as their lifestyle habits, preferences, and budget range.

[1110] The "means for receiving input" refers to the interface through which the user provides spatial and lifestyle information to the system, and typically includes an application or web form.

[1111] "Generative AI" refers to artificial intelligence technology that analyzes input data and generates optimal interior design and organization solutions.

[1112] "Interior design" refers to the planning of a space to make it beautiful and functional, including the arrangement of furniture and decorations, color selection, and style.

[1113] "Organization suggestions" include ideas and methods for efficiently organizing the user's living space and making it easier to use.

[1114] "User interface" refers to the screens and methods by which a user interacts with a system to input information, provide feedback, and view suggestions.

[1115] "Feedback" refers to opinions and requests provided by users regarding proposals, and is information that the system uses to revise the proposals.

[1116] The "database" refers to an electronic storehouse that organizes and stores user input information, generated design proposals, feedback, etc.

[1117] "Visual Data" includes graphic and diagrammatic data for visually representing proposed interior design and organization solutions.

[1118] "Retailer" refers to a commercial entity that interacts with the system to provide suggested interior items to users.

[1119] This invention is a system for providing interior design and organization solutions based on users' lifestyles and spatial preferences, and a platform that utilizes generative AI models to provide optimal suggestions to users. The system consists of the following steps: collecting user information, data analysis, suggestion generation, feedback processing, purchasing assistance, and ongoing support.

[1120] First, the user launches the application and enters information such as family composition, home layout, room size, current furniture arrangement, wall color, lifestyle habits and preferences, and budget range. The information entered by the user is recorded in a database in real time by the device and organized into the required data format. The device then sends the entered information to the server.

[1121] The server stores the user-submitted information in a database, standardizing and cleaning it. It then uses a generative AI model (e.g., OpenAI's GPT-3 or other custom models) to create a profile based on the user's lifestyle and spatial preferences. Based on this profile, the server generates optimal interior design and organization suggestions, including furniture placement and color choices for the living room and suggested accessories for each room.

[1122] The generated proposals are converted into a data format by the server and sent to the device. The device receives this information and visually displays it to the user via a user interface. This display includes visual data such as a 3D model of the room and a detailed layout diagram. The user can review the proposals and provide feedback if necessary. For example, they can express specific preferences such as changing the color of a particular piece of furniture or rearranging it.

[1123] The user's feedback is sent to the server via the device, which analyzes it. The generative AI model takes the feedback into account and regenerates a revised proposal. The revised proposal is then sent back to the device and displayed to the user. This process is repeated until the user is satisfied.

[1124] Furthermore, if the user wishes to purchase a suggested interior item, the device sends a purchase request to the server, which then connects with the retailer's system to obtain product availability and a link to the checkout process. The device then displays these links and coupon codes to the user, helping them through the checkout process.

[1125] The server continuously collects user feedback and usage data and periodically updates the generative AI model. This update process involves ingesting new datasets and training the model. Periodically, new interior design suggestions and organization solutions are generated and provided to the user through the server. The device notifies the user of these new suggestions and solutions and allows them to be reviewed through the interface.

[1126] Specific examples

[1127] For example, if a single person named User A lives in a studio apartment in an urban area, User A launches the app and uploads their name, age, address, and a photo of their room. The device sends this information to the server, which then uses generative AI to suggest a Scandinavian-style interior design that makes the most of the small urban space. The suggestions include compact storage units, multifunctional furniture, and simple colors. If User A provides feedback that they would like to add a cafe-style table, the server will again revise the suggestions and make repeated adjustments until User A is satisfied.

[1128] For example, if User B is a family of four living in a suburban detached house, User B inputs information such as the family composition, room size, existing furniture layout, and budget. The device sends this information to the server, which then uses generative AI to generate a design for the family with a spacious living room and multiple bedrooms. The proposal includes furniture arranged to ensure the whole family is comfortable, a play space for children, and storage solutions. If User B provides feedback that they would like to change the theme color of the children's room, the server again modifies the proposal and makes repeated adjustments until User B is satisfied.

[1129] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1130] Step 1:

[1131] Collection of User Information

[1132] First, users launch the application and create a new account, inputting information such as family composition, layout of the home, room size, current furniture arrangement, wall color, lifestyle habits and preferences, and budget range.

[1133] The device records this input information in a database in real time, formats it, and sends it to the server, prompting the user to check and correct the input as necessary.

[1134] Input: Personal and spatial information entered by the user

[1135] Output: Formatted data sent to the server

[1136] Step 2:

[1137] Data analysis

[1138] The server stores the information submitted by users in a database, standardizes and cleans it, checks for missing values ​​and inconsistencies, and performs any necessary data imputation.

[1139] Input: User information received from the device

[1140] Output: Standardized and cleaned dataset

[1141] Step 3:

[1142] Generate interior proposals

[1143] The server passes the standardized dataset to a generative AI model (such as OpenAI's GPT-3), which creates a profile based on the user's lifestyle and spatial preferences.

[1144] Based on that profile, the generative AI model generates optimal interior design and organization suggestions, including specific furniture placement, color choices, storage solutions, and more.

[1145] Input: Standardized and cleaned dataset

[1146] Output: Interior design and organization proposals

[1147] Step 4:

[1148] View Suggestions

[1149] The server converts the generated interior design proposal into a data format and sends it to the terminal, along with creating visual data such as a 3D model and detailed layout drawings.

[1150] The terminal receives this data and visually displays it to the user through a user interface.

[1151] Input: Interior design and organization suggestions

[1152] Output: Visual data that is displayed to the user

[1153] Step 5:

[1154] Gathering feedback

[1155] The user reviews the proposal and provides feedback if necessary. For example, they can input their preferences, such as changing the color of a particular piece of furniture or rearranging it.

[1156] The terminal transmits the user's feedback to the server.

[1157] Input: User feedback

[1158] Output: Feedback data sent to the server

[1159] Step 6:

[1160] Updates incorporating feedback

[1161] The server analyzes the received feedback and inputs it back into the generative AI model, which then takes the feedback into account to generate new suggestions.

[1162] The revised proposal is sent back to the terminal and displayed to the user, and this process is repeated until the user is satisfied.

[1163] Input: Parsed feedback

[1164] Output: Revised interior design proposal

[1165] Step 7:

[1166] Purchase assistance

[1167] If the user wishes to purchase the suggested interior item, the terminal sends a purchase request to the server.

[1168] The server connects with the systems of affiliated retailers to obtain product availability and links to checkout.

[1169] The terminal displays these links and coupon codes to the user and assists in the purchase process.

[1170] Input: User purchase request

[1171] Output: Stock information and purchase links from retailers

[1172] Step 8:

[1173] Ongoing support and updates

[1174] The server continuously collects user feedback and usage data and periodically updates the generative AI model, a process that involves ingesting new datasets and training the model.

[1175] Periodically, new interior design proposals and organization solutions are generated and provided to the user through the server. The terminal notifies the user of these new proposals and solutions and allows them to be viewed through the interface.

[1176] Input: Feedback and usage data collected continuously

[1177] Output: Updated generative AI model and new proposals

[1178] (Application example 1)

[1179] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1180] Conventional factory layout and workflow optimization relies heavily on experience, making it difficult to design an efficient and productive layout in a short period of time. It is also difficult to effectively incorporate feedback into the proposed layout, resulting in suboptimal productivity.

[1181] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1182] In this invention, the server includes: means for receiving spatial information and lifestyle information from a user as input; means for generating interior design and organization proposals using a generative AI based on the input information; means for transmitting the generated proposals to a terminal for displaying them; means for receiving factory layout information and workflow information from a user as input; means for generating an optimal layout based on work efficiency and productivity based on the factory layout information and workflow information; means for displaying the generated layout and performing a virtual simulation; means for receiving user feedback on the proposals and revising the proposals based on the feedback; and means for providing the revised proposals to the user again, thereby enabling efficient and productive design of factory layouts and workflows.

[1183] "Spatial information" is data about the physical location and layout of factories and work areas.

[1184] "Lifestyle information" is data on the behavioral patterns and work habits of factory workers.

[1185] "Generative AI" is an algorithm that uses artificial intelligence technology to generate optimal suggestions.

[1186] "Interior design" is a proposal for layout and decoration to improve the aesthetics and functionality of a space.

[1187] "Organizational proposals" are specific ideas and plans for achieving efficient work flows and layouts.

[1188] A "terminal" is an electronic device that allows a user to view suggestions and enter feedback.

[1189] "Layout information" is data regarding the layout of machines and equipment within a factory.

[1190] "Work flow information" is data related to the order and processes of work within a factory.

[1191] "Virtual simulation" is a technology that allows a proposed layout to be reproduced in a virtual space and visually confirmed.

[1192] "Feedback" is information regarding the user's opinions and requests for corrections regarding the proposal.

[1193] "Productivity" is the ability to achieve maximum results with limited resources.

[1194] Efficiency is the ability to get more out of less.

[1195] "Procurement support" is a function that provides assistance in purchasing necessary equipment and facilities.

[1196] "Partner Retailers" are partner companies that provide products and services.

[1197] A "checkout" is a series of actions taken to purchase a product or service.

[1198] "Updating generative AI" means improving the artificial intelligence algorithms to make more accurate suggestions based on new data and feedback.

[1199] The present invention provides a system that can make proposals based on a user's lifestyle and spatial preferences, and is particularly concerned with optimizing the layout and work flow within a factory. This system is composed of a user, a terminal, and a server, all of which work in conjunction with each other.

[1200] System configuration

[1201] 1. Collection of User Information

[1202] A user first logs into the system and launches a corresponding application.

[1203] The terminal accepts input from users and collects data such as factory layout information, work flow information, budgets, and efficiency and productivity priorities.

[1204] As an example of input, a prompt such as "We would like to optimize the layout of our factory. Please generate the optimal proposal based on the following information: Current layout diagram: 'current_layout.json', Workflow diagram: 'workflows.json', Constraints: 'Budget: 100,000, Time: 200', Priorities: 'Efficiency: High, Safety: Medium'" is used.

[1205] 2. Data analysis and layout proposal generation

[1206] The server receives the collected data and stores it in a database.

[1207] Inside the server, a Generative AI model is used to analyze the data, generating optimal layout and workflow proposals that maximize efficiency and productivity within the factory.

[1208] 3. Proposal presentation and virtual simulation

[1209] The server transmits the generated layout proposal to the terminal.

[1210] The device uses software (e.g., Unity) to display this as a 3D model, allowing the user to visually confirm it.

[1211] Virtual simulations can be performed to validate the proposed layout.

[1212] 4. User feedback and suggested fixes

[1213] The user can then input feedback on the proposals from the terminal, for example, by entering a specific request such as "I would like to change the placement of a specific machine."

[1214] The server receives the feedback and again uses the Generative AI model to modify the proposal, which generates a new proposal that reflects the feedback.

[1215] 5. Submitting a revision proposal again

[1216] The revised proposal is sent back to the terminal and provided to the user.

[1217] This process is repeated until the user is satisfied.

[1218] Program processing description

[1219] Hardware and software used

[1220] Terminal: PC or tablet for user operation

[1221] Server: A server that runs data analysis and generative AI models

[1222] Software: Databases, generative AI models (e.g., TensorFlow), 3D model display software (e.g., Unity)

[1223] Process Overview

[1224] The server stores the received user information in a database and uses a generative AI model to generate optimal factory layout proposals.

[1225] The generated proposal is sent to the device and displayed to the user as a 3D model.

[1226] The user inputs feedback on the suggestions from the terminal, and the feedback is sent to the server.

[1227] The server receives the feedback and runs the generative AI model again to generate revised suggestions.

[1228] The suggested revisions are sent back to the terminal and the process is repeated until the user is satisfied.

[1229] Specific examples

[1230] For example, in a metal processing factory, users would input data such as the current layout, workflow information, budget, and safety standards into the application. The server receives this data, and the generative AI model proposes optimal machine placement and work flow. This is then displayed as a 3D model on the device, and users can provide feedback on their specific requests, which will result in further improved proposals.

[1231] This enables efficient and productive design of factory layouts and work flows.

[1232] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1233] Step 1:

[1234] The user launches the application and logs in. After logging in, they input the information needed to optimize the factory layout, including the current factory layout diagram, workflow information, budget, and priorities related to efficiency and productivity.

[1235] Input: User login information, factory layout diagram (e.g. 'current_layout.json'), workflow information (e.g. 'workflows.json'), budget, efficiency and productivity priorities

[1236] Output: The information entered

[1237] Step 2:

[1238] The terminal receives the user's input information and sends it to a database, where it is stored on a server.

[1239] Inputs: User-entered factory layout diagrams, work flow information, budgets, efficiency and productivity priorities

[1240] Output: Data sent to the server

[1241] Step 3:

[1242] The server analyzes the received data and generates factory layout optimization proposals using a Generative AI model. In this process, a data analysis module interprets the data, and Generative AI calculates the optimal layout and work flow.

[1243] Inputs: Factory layout diagrams stored on a server, work flow information, budgets, efficiency and productivity priorities

[1244] Data processing: Analysis of input data and generation of profiles

[1245] Data calculation: Generative AI generates optimal layout proposals

[1246] Output: Optimized factory layout proposal

[1247] Step 4:

[1248] The server transmits the generated layout proposal to the terminal.

[1249] Input: Optimized factory layout proposal

[1250] Output: Layout proposal sent to device

[1251] Step 5:

[1252] The device displays the received layout proposal as a 3D model, allowing the user to visually confirm the proposal. Specifically, 3D model display software (e.g., Unity) is used.

[1253] Input: Layout proposal received from the server

[1254] Output: Display of 3D model

[1255] Step 6:

[1256] The user reviews the proposed layout and enters any necessary feedback, such as specific requests such as "I would like to change the placement of a particular machine."

[1257] Input: User feedback

[1258] Output: Feedback data

[1259] Step 7:

[1260] The terminal transmits the user's feedback to the server.

[1261] Input: Feedback data

[1262] Output: Feedback sent to the server

[1263] Step 8:

[1264] The server analyzes the received feedback and re-runs the GenerativeAI model to refine the suggestions. New suggestions are generated and stored on the server.

[1265] Input: Feedback data from users

[1266] Data processing: Analyzing feedback and updating profiles

[1267] Data Computing: Regenerating revised layout proposals with generative AI

[1268] Output: Revised layout proposal

[1269] Step 9:

[1270] The server sends the revised proposal back to the terminal.

[1271] Input: revised layout proposal

[1272] Output: Suggested fixes sent to terminal

[1273] Step 10:

[1274] The device then displays the revised proposal again as a 3D model, and the user reviews the new proposal and provides feedback again if necessary. This process is repeated until the user is satisfied.

[1275] Input: revised layout proposal

[1276] Output: 3D model display and user feedback

[1277] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1278] This invention is a system for providing interior design and organization solutions based on a user's individual lifestyle and spatial preferences, and further utilizes an emotion engine to recognize the user's emotions and make suggestions that reflect those emotions. Specifically, this system is configured as a platform that combines generative AI and an emotion engine.

[1279] Program processing

[1280] 1. User Information Collection:

[1281] A user launches the application and creates a new account, entering basic information such as name, age, address, family composition, lifestyle habits, interior design preferences, and budget range.

[1282] The terminal transmits this input information to the server in real time.

[1283] 2. Data analysis and interior proposal generation:

[1284] The server stores the information submitted by the user in a database, and a generative AI module analyzes the data, creating a profile based on the user's lifestyle and spatial preferences.

[1285] Based on this profile, the generative AI generates optimal interior design and organization solutions, such as furniture placement and color choices for a living room, as well as suggested accessories for each room.

[1286] 3. Use of Emotion Engine:

[1287] The server sends the generated interior design proposal to the device and uses an emotion engine to analyze the user's facial expressions, tone of voice, and text input to recognize the user's emotions before displaying it to the user.

[1288] The server further customizes the suggestions based on the emotional data obtained from the emotion engine, so that the suggestions are more in line with the user's current mental state and emotions.

[1289] 4. Viewing and Feedback on Suggestions:

[1290] The device displays customized interior design proposals to the user, including a 3D model of the room and a detailed floor plan.

[1291] The user reviews the suggestions and provides feedback if necessary, for example, to specify specific requests such as changing the color of a particular piece of furniture or rearranging it.

[1292] 5. Incorporating feedback and suggested updates:

[1293] The terminal transmits the user's feedback to the server.

[1294] The server analyzes the feedback, utilizes a generative AI module to modify the proposal, again using the emotion engine to reflect the user's emotions, and presents the modified proposal to the user again. This process is repeated until the user is satisfied.

[1295] 6. Purchasing Assistance and Partnerships:

[1296] If the user wishes to purchase the suggested interior item, the terminal sends a purchase request to the server.

[1297] The server connects to the systems of partner retailers to obtain product availability and checkout links.

[1298] The server sends the acquired information to the device, which then displays a checkout link and a coupon code to the user, helping the user complete the checkout process.

[1299] 7. Ongoing Support and Updates:

[1300] The server continuously collects user feedback and usage data and periodically updates the generative AI model and emotion engine.

[1301] The server periodically generates new interior design proposals and organization solutions based on the updated model and sends them to the terminal.

[1302] The terminal will notify and display these new suggestions and solutions to the user.

[1303] Specific examples

[1304] Case Study: Personalized Recommendations Using Emotion Recognition

[1305] User A (single person living in a one-room apartment in an urban area):

[1306] Device: User A launches the app and uploads their name, age, address, and a photo of their room.

[1307] Server: Generative AI analyzes this data and proposes Scandinavian-inspired interior designs that maximize the use of small urban spaces, selecting compact storage units, multifunctional furniture, and simple color palettes.

[1308] Emotion engine: Before making a suggestion, it analyzes User A's facial expressions and tone of voice and evaluates whether the suggestion matches User A's current emotions. For example, if it recognizes that User A is feeling stressed, it will add interior elements that have a relaxing effect.

[1309] User A: Review the proposal and submit feedback saying they would like to add cafe-style tables.

[1310] Server: The regenerated proposal based on the feedback is rechecked by the emotion engine and sent to User A. Adjustments are repeated until User A is satisfied.

[1311] User B (family of four, suburban detached house):

[1312] Terminal: User B inputs the family composition (husband, wife, two children), room size, layout of existing furniture, budget, etc.

[1313] Server: Generative AI generates a design for a family with a spacious living room and multiple bedrooms. The proposal includes furniture arranged to accommodate the entire family, a play space for children, and storage solutions.

[1314] Emotion engine: Before displaying suggestions, analyze User B's emotional data and make suggestions that will help the whole family relax.

[1315] User B: Review the proposal and submit feedback that they would like to change the theme color for the children's room.

[1316] Server: The server evaluates the regenerated proposal based on the feedback using the emotion engine and sends it to User B. Adjustments are repeated until User B is satisfied.

[1317] In this way, the present invention takes into account the user's emotions to provide more personalized interior design and organization solutions, resulting in a harmonious living environment.

[1318] The processing flow will be explained below.

[1319] Step 1:

[1320] A user launches the application and creates a new account. They enter basic information such as their name, age, address, family composition, lifestyle habits, interior design preferences, and budget range. The device then transmits this information to the server in real time.

[1321] Step 2:

[1322] The server stores the information submitted by the user in a database, and then uses a generative AI module to analyze the data it receives, creating a profile based on the user's lifestyle and spatial preferences.

[1323] Step 3:

[1324] The server generates optimal interior design and organization solutions based on the profile, including furniture placement, color selection, and accessory suggestions, and then sends the generated design proposals to the device.

[1325] Step 4:

[1326] Before displaying interior design suggestions to the user, the device activates an emotion engine that analyzes the user's facial expressions, tone of voice, and text input to recognize the user's emotions.

[1327] Step 5:

[1328] The server customizes the suggestions based on the emotional data obtained from the emotion engine. For example, if the server detects that the user is feeling stressed, it adds relaxing interior elements to the suggestions.

[1329] Step 6:

[1330] The device displays customized interior design proposals to the user, including a 3D model of the room and a detailed floor plan, and the user can review the proposals and provide feedback if needed.

[1331] Step 7:

[1332] The device sends the user's feedback to the server, which receives the feedback and modifies the proposal using the generative AI module. It then uses the emotion engine again to confirm the user's emotions and presents the modified proposal to the user again. This process is repeated until the user is satisfied.

[1333] Step 8:

[1334] If the user wishes to purchase a suggested interior item, the device sends a purchase request to the server. The server connects to the affiliated retailer's system to obtain product availability and a checkout link. The server then sends the obtained information to the device, which then displays the checkout link and coupon code to the user. The device then assists the user in clicking the link and completing the purchase on the affiliated retailer's website.

[1335] Step 9:

[1336] The server continuously collects user feedback and usage data and periodically updates the generative AI model and emotion engine. The server periodically generates new interior design proposals and organization solutions based on the updated model and sends them to the device. The device notifies and displays these new proposals and solutions to the user.

[1337] Example 2

[1338] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1339] Providing optimal interior design and organization solutions for each individual user to meet today's diverse lifestyles is challenging. There is also a need to provide customized suggestions based on the user's emotional state and increase user satisfaction. Furthermore, there is a lack of systems that incorporate user feedback and provide continuously improved suggestions.

[1340] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving spatial information and lifestyle information from a user as input, means for generating interior design and organization proposals using a generative AI based on the input information, means for analyzing the user's facial expressions, tone of voice, and text input to collect emotional data, means for customizing the generated proposals based on the emotional data, means for receiving user feedback on the proposals and revising the proposals based on the feedback, means for re-presenting the revised proposals to the user, and means for continuously collecting user usage status and updating the generative AI model and emotion engine. This makes it possible to provide interior design and organization solutions that match the user's lifestyle and take into account their emotional state.

[1341] "User" refers to an individual or household that receives interior design and organization solutions from the system.

[1342] "Spatial information" is detailed information about the user's living space, including data such as room dimensions, layout, and existing furniture arrangement.

[1343] "Lifestyle information" refers to information about the user's individual lifestyle, such as the user's daily habits, family structure, preferred interior style, and budget.

[1344] "Generative AI" refers to an artificial intelligence model that generates optimal interior design and organization solutions based on user-provided information.

[1345] A "proposal" is an interior design or organization plan created by the generative AI by analyzing the user's information, including specific furniture placement and color selection.

[1346] "Emotion data" is data that indicates the user's emotional state, analyzed from the user's facial expression, tone of voice, text input, and the like.

[1347] An "emotion engine" refers to an engine that analyzes a user's emotional data and customizes the suggestions provided by the generative AI based on that information.

[1348] "Feedback" refers to a user returning to the system their opinions and requests regarding suggestions provided by the system.

[1349] "Customization" refers to the process by which the generative AI modifies its suggestions to best suit the user based on the user's emotional data and feedback.

[1350] "Update" refers to the generative AI model and emotion engine refining its algorithms and data to improve accuracy and effectiveness based on user feedback and usage data.

[1351] "Terminal" refers to a device, such as a computer or smartphone, through which a user inputs information or receives suggestions.

[1352] This invention is a system for providing interior design and organization solutions based on users' lifestyles and spatial preferences. The system is configured as a platform that combines generative AI and an emotion engine.

[1353] System Overview

[1354] The user first launches the application and enters basic information such as name, age, address, family composition, lifestyle habits, interior design preferences, and budget range. This information is sent to the server via the device. The server stores the received information in a database, and a generative AI module analyzes the data and creates a profile based on the user's lifestyle and spatial preferences. The generative AI (e.g., a large-scale language model such as GPT-3) generates optimal interior design and organization solutions.

[1355] Use of emotion engine

[1356] The generated interior design proposals are customized by an emotion engine before being displayed to the user. The emotion engine (e.g., Affectiva SDK) is used to recognize the user's emotions by analyzing the user's facial expressions, tone of voice, and text input. The emotion data obtained by the emotion engine is analyzed by the server, and the generative AI further customizes the proposals. As a result, the proposals are more in line with the user's current mental state and emotions.

[1357] View suggestions and give feedback

[1358] The customized interior design proposal is sent to the device and displayed to the user. This includes a 3D model of the room and a detailed layout. The user reviews the proposal and provides feedback on specific preferences (e.g., changing the color or positioning of certain furniture). This feedback is sent via the device to the server, which analyzes the feedback and modifies the proposal using a generative AI module. This process is repeated until the user is satisfied.

[1359] Buying Assistance and Partnerships

[1360] If the user wishes to purchase a suggested interior item, the device sends a purchase request to the server, which connects to the retailer's system to obtain product availability and a link to checkout. The information is then sent to the device, where the user is presented with a link to checkout and a coupon code.

[1361] Ongoing support and updates

[1362] The server continuously collects user feedback and usage data and periodically updates the generative AI model and emotion engine. New interior design proposals and organization solutions are periodically generated based on the updated model and sent to the device. The device notifies and displays these new proposals and solutions to the user.

[1363] Specific examples

[1364] Case Study 1: Personalized Recommendations Using Emotion Recognition

[1365] User A (single person living in a studio apartment in an urban area):

[1366] Device: User A launches the app and uploads their name, age, address, and a photo of their room.

[1367] Server: Generative AI analyzes this data and proposes Scandinavian-inspired interior designs that maximize the use of small urban spaces, selecting compact storage units, multifunctional furniture, and simple color palettes.

[1368] Emotion engine: Before making a suggestion, it analyzes User A's facial expressions and tone of voice and evaluates whether the suggestion matches User A's current emotions. For example, if it recognizes that User A is feeling stressed, it will add interior elements that have a relaxing effect.

[1369] User A: Review the proposal and submit feedback saying they would like to add cafe-style tables.

[1370] Server: The regenerated proposal based on the feedback is rechecked by the emotion engine and sent to User A. Adjustments are repeated until User A is satisfied.

[1371] Prompt Sentence Examples

[1372] Prompt 1: "Generate the best interior design suggestions based on the user's basic information, including age, address, preferred style, and budget range."

[1373] Prompt 2: "Customize the generated interior design suggestions based on the recorded user emotional data. Be sure to add elements that have a relaxing effect."

[1374] In this way, the present invention takes into account the user's emotions to provide more personalized interior design and organization solutions, resulting in a harmonious living environment.

[1375] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1376] Step 1: Collect user information

[1377] User Action:

[1378] Users launch the application and tap the "Create a new account" button, then enter basic information such as their name, age, address, family composition, lifestyle habits, interior design preferences, and budget range.

[1379] input:

[1380] Information such as name, age, address, family composition, lifestyle habits, interior design preferences, budget range, etc.

[1381] Terminal behavior:

[1382] The device collects input in real time, encrypts the data using HTTPS, and sends it securely to the server.

[1383] output:

[1384] The user information is sent to the server and account creation is complete.

[1385] Step 2: Data analysis and interior design proposal generation

[1386] Server behavior:

[1387] The server stores the received user information in a database (e.g., MySQL) and then invokes a generative AI module to analyze the information and create a user profile.

[1388] input:

[1389] User information stored in a database.

[1390] Data processing:

[1391] Based on the stored information, a generative AI analyzes the data and creates a profile based on your lifestyle and spatial preferences.

[1392] output:

[1393] Proposing the best interior design and organization solutions for users.

[1394] Step 3: Use the Emotion Engine

[1395] Terminal behavior:

[1396] It records facial expressions and tone of voice through the user's webcam and microphone, and collects text input.

[1397] input:

[1398] User facial expression data, tone of voice, and text input.

[1399] Terminal behavior:

[1400] The collected data is sent to the server in real time.

[1401] Server behavior:

[1402] An emotion engine is used to analyze emotions from collected data.

[1403] Data processing:

[1404] Emotional data is quantified and the suggestions provided by the generative AI are customized based on that data.

[1405] output:

[1406] Customized interior suggestions based on user emotions.

[1407] Step 4: View and give feedback on suggestions

[1408] Terminal behavior:

[1409] It presents users with customized interior design proposals, including 3D models of rooms and detailed floor plans.

[1410] input:

[1411] Customized interior design proposals.

[1412] User Action:

[1413] Review the suggestions and enter specific feedback into the device, such as changing the color of certain furniture or rearranging it.

[1414] Terminal behavior:

[1415] Collect user feedback and send it to the server.

[1416] output:

[1417] User feedback on the proposal.

[1418] Step 5: Incorporating feedback and proposing updates

[1419] Server behavior:

[1420] Analyze user feedback and refine suggestions using a generative AI module.

[1421] input:

[1422] User feedback.

[1423] Data processing:

[1424] Update suggestions based on feedback and use the sentiment engine again to reflect user sentiment.

[1425] Server behavior:

[1426] The revised proposal is resubmitted to the user.

[1427] output:

[1428] Revised interior design proposal.

[1429] Step 6: Assist with purchasing and execute partnerships

[1430] User Action:

[1431] If the customer wishes to purchase the suggested interior item, the customer inputs a purchase request on the terminal.

[1432] Terminal behavior:

[1433] Send the purchase request to the server.

[1434] Server behavior:

[1435] Connect to partner retailer systems to retrieve product availability and checkout links.

[1436] input:

[1437] Purchase requests and interior item information.

[1438] Data processing:

[1439] Link with retailer systems to obtain necessary information.

[1440] output:

[1441] A link to checkout and a coupon code will be sent to your device.

[1442] Step 7: Ongoing support and updates

[1443] Server behavior:

[1444] We continuously collect user feedback and usage data to regularly update our generative AI models and emotion engine.

[1445] input:

[1446] User feedback and usage data.

[1447] Data processing:

[1448] Analyze the collected data and improve the model.

[1449] Server behavior:

[1450] New interior design proposals and organisational solutions are generated based on the updated model and sent to the device.

[1451] output:

[1452] New interior design proposals and organisation solutions.

[1453] (Application example 2)

[1454] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1455] Conventional interior design systems make suggestions based on the user's lifestyle and spatial preferences, but they are unable to consider the user's emotional state, making it difficult to provide suggestions that truly satisfy the user. While systems exist that receive feedback on suggestions, they lack the ability to respond quickly to feedback or regenerate customized suggestions based on emotions, limiting their ability to improve the user experience.

[1456] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for utilizing an emotion engine that recognizes the user's emotions and further customizes proposals based on those emotions, means for displaying proposals generated on the terminal using 3D models or augmented reality, and means for continuously customizing proposals using emotion data generated by the emotion engine. This makes it possible to provide personalized interior design proposals that take the user's emotional state into consideration. In addition, by receiving feedback in real time and quickly generating and displaying appropriate proposals, it becomes possible to provide proposals that highly satisfy the user.

[1457] "User" refers to the end user who uses the proposal system and is the person who receives the proposals for interior design and organization.

[1458] "Spatial information" refers to data such as the layout, size, shape, and current furniture arrangement of a user's living space or office.

[1459] "Lifestyle information" refers to individual information such as the user's lifestyle, family structure, hobbies and preferences, activity patterns, budget range, and the like.

[1460] "Generative AI" is a system that uses artificial intelligence algorithms to generate optimal interior design proposals from given input data.

[1461] "Interior design" is a general term for design that includes the arrangement of furniture and decorations, color selection, etc. to make a user's space beautiful and functional.

[1462] "Organization suggestions" refer to suggestions for storage methods and layout changes to make efficient use of the user's space.

[1463] "Suggestions" refer to interior design and organization ideas presented to the user by the generative AI.

[1464] A "terminal" is a device that allows a user to view interior design proposals and enter feedback, and includes smartphones, tablets, PCs, smart glasses, etc.

[1465] "Feedback" refers to the act of a user inputting their opinions or requests regarding the content of a proposal after checking it.

[1466] An "emotion engine" refers to an algorithm that recognizes emotions from a user's facial expressions, voice, etc., and customizes suggestions based on those emotions.

[1467] A "3D model" refers to an interior design model that is expressed in three dimensions and is used to make proposals visually easier to understand.

[1468] "Augmented reality" is a technology that overlays computer-generated information onto real-world visual information, and is used to display interior design proposals in real spaces.

[1469] The present invention provides an interior design proposing system that takes into account the user's emotions, and is specifically implemented as follows.

[1470] First, the user inputs spatial and lifestyle information using a device, such as a smartphone, tablet, PC, or smart glasses. This input information includes the user's name, age, family composition, lifestyle habits, interior design preferences, and budget range. This information is sent to a central server in real time.

[1471] The server stores the received user information in a database, which is then analyzed by a generative AI module. The generative AI module generates the optimal interior design based on the user's lifestyle and spatial preferences. The specific system uses machine learning libraries such as Python and TensorFlow to build the AI ​​model.

[1472] The generated interior design proposals are first customized by an emotion engine. The emotion engine uses the camera and microphone of the smart glasses or smartphone to analyze the user's facial expressions and tone of voice to recognize their emotions. The proposals are then adjusted based on this emotion data. Specific implementations of the emotion engine utilize OpenCV and the Google Cloud Speech-to-Text API.

[1473] The device then displays the tailored interior design proposal to the user, including 3D models and augmented reality (AR) visualizations, allowing the user to see the proposal in the real space. The AR rendering uses Unity or ARKit / ARCore.

[1474] The user reviews the suggestions and provides feedback, which is sent from the device to the server, which then updates the suggestions using the generative AI module and emotion engine. This process is repeated until the user is satisfied.

[1475] If the user wishes to purchase a suggested interior item, the server connects to the retailer's system to retrieve product availability and a link to the purchase process, which is then displayed on the user's device. This allows the user to easily purchase the suggested item.

[1476] For example, the following prompt will prompt the user for information:

[1477] Example prompt:

[1478] Name: Taro Yamada

[1479] Age: 35

[1480] Family status: Single

[1481] Interior preferences: Modern style, muted colors

[1482] Budget range: 50,000 yen

[1483] Feedback example:

[1484] "I would like a more relaxing atmosphere. I would like the lighting to be changed to warmer tones."

[1485] In this way, by taking into account the user's emotions and feedback, a more satisfying interior design can be provided.

[1486] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1487] Step 1:

[1488] Collection of User Information

[1489] The user starts up the device and enters basic information such as name, age, address, family composition, lifestyle habits, interior design preferences, and budget range. This input data is sent from the device to the server in real time. The input information includes the user's lifestyle and spatial preferences, and subsequent data analysis is based on this information.

[1490] Step 2:

[1491] Data analysis and interior design proposal generation

[1492] The server stores the information submitted by the user in a database. The generative AI module analyzes the stored user data and creates a user profile based on lifestyle and spatial preferences. Based on this profile, the generative AI generates optimal interior design and organization solutions, including suggestions for living room furniture placement, color selection, and accessories for each room. The output design proposals are sent to the next step.

[1493] Step 3:

[1494] Use of emotion engine

[1495] The emotion engine recognizes the user's emotions before displaying suggestions to the user. The device uses a camera and microphone to analyze emotions from the user's facial expressions and tone of voice. The emotion data is sent to the server, which then uses the data to customize the generated interior design suggestions. For example, if the user is feeling stressed, it can add elements that have a relaxing effect. The revised suggestions are then sent to the next step.

[1496] Step 4:

[1497] View suggestions and give feedback

[1498] The device displays interior design proposals customized by the emotion engine to the user, including a 3D model of the room and a detailed layout. The user can review the proposals and provide feedback as needed. For example, specific requests can be made, such as changing the color or placement of certain furniture. Feedback is sent to the server in real time.

[1499] Step 5:

[1500] Incorporating feedback and suggested updates

[1501] The server analyzes the feedback received from the user and modifies the proposal using the generative AI module. The modified proposal is then adjusted again through the emotion engine to reflect the user's emotional data. The new, adjusted proposal is then sent back to the user. This process is repeated until the user is satisfied.

[1502] Step 6:

[1503] Buying Assistance and Partnerships

[1504] If the user wishes to purchase a suggested interior item, the device sends a purchase request to the server. The server connects to the partner retailer's system to obtain product availability and a link to checkout. The obtained information is sent to the device, which then displays a link to checkout and a coupon code to the user, allowing the user to easily purchase the suggested item.

[1505] Step 7:

[1506] Ongoing support and updates

[1507] The server continuously collects user feedback and usage data and periodically updates the generative AI model and emotion engine. Based on the updated model, new interior design proposals and organization solutions are periodically generated and sent to the device. The device then notifies and displays the new proposals and solutions to the user, ensuring that the user always receives the latest proposals.

[1508] These are the specific processing steps of the system that realizes this application example. The detailed operations at each step, such as data input and output, utilization of emotional data, and reflection of feedback, support high user satisfaction throughout the system.

[1509] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1510] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1511] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1512] [Fourth embodiment]

[1513] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1514] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1515] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1516] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1517] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1518] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1519] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1520] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1521] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1522] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1523] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1524] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1525] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1526] The present invention is a system for providing interior design and organization solutions based on a user's individual lifestyle and spatial preferences, specifically a platform that utilizes generative AI to provide optimal suggestions to users. The system is implemented in the following steps:

[1527] Program processing

[1528] 1. User Information Collection:

[1529] Users first launch the application and create a new account, then enter information such as family composition, home layout, room size, current furniture arrangement, wall color, lifestyle habits and preferences, and budget range.

[1530] The terminal collects this input information and transmits it to the server as necessary data.

[1531] 2. Data analysis and interior proposal generation:

[1532] The server stores the information submitted by the user in a database, and a generative AI module analyzes the data, creating a profile based on the user's lifestyle and spatial preferences.

[1533] Based on this profile, the generative AI generates optimal interior design and organization suggestions, such as furniture placement and color choices for a living room, as well as suggested accessories for each room.

[1534] 3. Viewing and Feedback on Suggestions:

[1535] The server then sends the generated interior design proposal to the device and displays it to the user, including a 3D model of the room and a detailed layout diagram.

[1536] The user reviews the suggestions and provides feedback if necessary, for example, to specify specific requests such as changing the color of a particular piece of furniture or rearranging it.

[1537] 4. Feedback and suggested updates:

[1538] The terminal transmits the user's feedback to the server.

[1539] The server analyzes this feedback and uses the generative AI to modify the proposal, which is then sent back to the user for confirmation.

[1540] This process is repeated until the user is satisfied.

[1541] 5. Purchasing Assistance and Partnerships:

[1542] If the user wishes to purchase the suggested interior item, the terminal sends a purchase request to the server.

[1543] The server connects with the systems of affiliated retailers to obtain product availability and links to checkout.

[1544] The terminal displays a link to the purchase procedure and a coupon code to the user to support the purchase procedure.

[1545] 6. Ongoing Support and Updates:

[1546] The server continuously collects user feedback and usage data and periodically updates the generative AI model.

[1547] The server periodically provides users with new interior design suggestions and organization solutions.

[1548] The terminal will notify and display these new suggestions and solutions to the user.

[1549] Specific examples

[1550] Case Study: Proposals for Users with Different Family Structures

[1551] User A (single person living in a one-room apartment in an urban area):

[1552] Device: User A launches the app and uploads their name, age, address, and a photo of their room.

[1553] Server: Generative AI analyzes this data and proposes Scandinavian-inspired interior designs that maximize the use of small urban spaces, including compact storage units, multifunctional furniture, and simple color palettes.

[1554] User A: Review the proposal and submit feedback saying they would like to add cafe-style tables.

[1555] Server: Sends a regenerated proposal based on the feedback to User A, and repeats the process of adjusting it until User A is satisfied.

[1556] User B (family of four, suburban detached house):

[1557] Terminal: User B inputs the family composition (husband, wife, two children), room size, layout of existing furniture, budget, etc.

[1558] Server: Generative AI generates a design for a family with a spacious living room and multiple bedrooms. The proposal includes furniture arranged to accommodate the entire family, a play space for children, and storage solutions.

[1559] User B: Review the proposal and submit feedback that they would like to change the theme color for the children's room.

[1560] Server: Sends a regenerated proposal based on the feedback to User B, and repeats the process of adjustment until User B is satisfied.

[1561] In this way, the present invention provides interior design and organization solutions tailored to the user's individual needs, resulting in a harmonious living environment.

[1562] The processing flow will be explained below.

[1563] Step 1:

[1564] The user launches the application and creates a new account.

[1565] Users enter basic information such as their name, age, address, family composition, lifestyle habits, interior design preferences, and budget range.

[1566] The terminal transmits the input information to the server.

[1567] Step 2:

[1568] The server stores the information sent by the user in a database.

[1569] The server uses a generative AI module to analyze the data it receives.

[1570] The server creates a profile based on the user's lifestyle and spatial preferences.

[1571] Step 3:

[1572] The server generates optimal interior design and organization solutions based on the profile.

[1573] This includes furniture placement, color selection, and accessory suggestions.

[1574] The server transmits the generated design proposal to the terminal.

[1575] Step 4:

[1576] The terminal displays the interior design proposal to the user.

[1577] The user reviews the proposal and provides feedback if necessary.

[1578] For example, you can enter specific opinions such as "I want to change the color of the sofa" or "I want to change the position of the table."

[1579] Step 5:

[1580] The terminal transmits the user's feedback to the server.

[1581] The server receives the feedback and modifies the design proposal using a generative AI module.

[1582] The server sends the revised proposal back to the terminal and presents it to the user.

[1583] Step 6:

[1584] The user reviews the revised proposal and repeats the same procedure if they wish to provide feedback again.

[1585] This cycle is repeated until the user is satisfied with the proposal.

[1586] Step 7:

[1587] If the user wishes to purchase the suggested interior item, the terminal sends a purchase request to the server.

[1588] The server connects to the systems of partner retailers to obtain inventory information and checkout links.

[1589] The server transmits the acquired information to the terminal.

[1590] Step 8:

[1591] The terminal displays a link to the purchase procedure and a coupon code to the user to support the purchase procedure.

[1592] The user clicks on the link and completes the purchase on the affiliated retailer's website.

[1593] Step 9:

[1594] The server continuously collects user feedback and usage data to update the generative AI model.

[1595] The server periodically generates new interior design proposals and organization solutions based on the updated model and sends them to the terminal.

[1596] The terminal notifies and displays new suggestions and solutions to the user.

[1597] Example 1

[1598] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1599] Addressing today's diverse lifestyles and spatial preferences and providing optimal interior design and organization solutions to users requires advanced data analysis and personalized response. However, conventional systems are unable to fully meet individual user needs and have difficulty efficiently incorporating feedback. Furthermore, the process of purchasing interior items is cumbersome, creating a need for an integrated solution to improve the user experience.

[1600] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1601] In this invention, the server includes means for receiving spatial information and lifestyle information from a user as input, means for generating interior design and organization proposals using a generative AI based on the input information, means for transmitting the generated proposals to a user interface for displaying them, means for receiving user feedback on the proposals and revising the proposals based on the feedback, means for re-presenting the revised proposals to the user, means for recording user information in a database in real time and formatting it into a required data format, and means for converting the proposal content into visual data and visually displaying it. This allows for optimal interior design proposals tailored to individual needs, efficiently incorporating feedback, and achieving a smooth purchasing process.

[1602] "User" refers to an individual or group that uses this system and is the entity that inputs spatial information and lifestyle information.

[1603] "Spatial information" refers to information about the physical space in which a user lives or uses, and includes the layout, size of the room, furniture arrangement, wall color, and the like.

[1604] "Lifestyle information" refers to information related to a user's individual lifestyle, such as their lifestyle habits, preferences, and budget range.

[1605] The "means for receiving input" refers to the interface through which the user provides spatial and lifestyle information to the system, and typically includes an application or web form.

[1606] "Generative AI" refers to artificial intelligence technology that analyzes input data and generates optimal interior design and organization solutions.

[1607] "Interior design" refers to the planning of a space to make it beautiful and functional, including the arrangement of furniture and decorations, color selection, and style.

[1608] "Organization suggestions" include ideas and methods for efficiently organizing the user's living space and making it easier to use.

[1609] "User interface" refers to the screens and methods by which a user interacts with a system to input information, provide feedback, and view suggestions.

[1610] "Feedback" refers to opinions and requests provided by users regarding proposals, and is information that the system uses to revise the proposals.

[1611] The "database" refers to an electronic storehouse that organizes and stores user input information, generated design proposals, feedback, etc.

[1612] "Visual Data" includes graphic and diagrammatic data for visually representing proposed interior design and organization solutions.

[1613] "Retailer" refers to a commercial entity that interacts with the system to provide suggested interior items to users.

[1614] This invention is a system for providing interior design and organization solutions based on users' lifestyles and spatial preferences, and a platform that utilizes generative AI models to provide optimal suggestions to users. The system consists of the following steps: collecting user information, data analysis, suggestion generation, feedback processing, purchasing assistance, and ongoing support.

[1615] First, the user launches the application and enters information such as family composition, home layout, room size, current furniture arrangement, wall color, lifestyle habits and preferences, and budget range. The information entered by the user is recorded in a database in real time by the device and organized into the required data format. The device then sends the entered information to the server.

[1616] The server stores the user-submitted information in a database, standardizing and cleaning it. It then uses a generative AI model (e.g., OpenAI's GPT-3 or other custom models) to create a profile based on the user's lifestyle and spatial preferences. Based on this profile, the server generates optimal interior design and organization suggestions, including furniture placement and color choices for the living room and suggested accessories for each room.

[1617] The generated proposals are converted into a data format by the server and sent to the device. The device receives this information and visually displays it to the user via a user interface. This display includes visual data such as a 3D model of the room and a detailed layout diagram. The user can review the proposals and provide feedback if necessary. For example, they can express specific preferences such as changing the color of a particular piece of furniture or rearranging it.

[1618] The user's feedback is sent to the server via the device, which analyzes it. The generative AI model takes the feedback into account and regenerates a revised proposal. The revised proposal is then sent back to the device and displayed to the user. This process is repeated until the user is satisfied.

[1619] Furthermore, if the user wishes to purchase a suggested interior item, the device sends a purchase request to the server, which then connects with the retailer's system to obtain product availability and a link to the checkout process. The device then displays these links and coupon codes to the user, helping them through the checkout process.

[1620] The server continuously collects user feedback and usage data and periodically updates the generative AI model. This update process involves ingesting new datasets and training the model. Periodically, new interior design suggestions and organization solutions are generated and provided to the user through the server. The device notifies the user of these new suggestions and solutions and allows them to be reviewed through the interface.

[1621] Specific examples

[1622] For example, if a single person named User A lives in a studio apartment in an urban area, User A launches the app and uploads their name, age, address, and a photo of their room. The device sends this information to the server, which then uses generative AI to suggest a Scandinavian-style interior design that makes the most of the small urban space. The suggestions include compact storage units, multifunctional furniture, and simple colors. If User A provides feedback that they would like to add a cafe-style table, the server will again revise the suggestions and make repeated adjustments until User A is satisfied.

[1623] For example, if User B is a family of four living in a suburban detached house, User B inputs information such as the family composition, room size, existing furniture layout, and budget. The device sends this information to the server, which then uses generative AI to generate a design for the family with a spacious living room and multiple bedrooms. The proposal includes furniture arranged to ensure the whole family is comfortable, a play space for children, and storage solutions. If User B provides feedback that they would like to change the theme color of the children's room, the server again modifies the proposal and makes repeated adjustments until User B is satisfied.

[1624] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1625] Step 1:

[1626] Collection of User Information

[1627] First, users launch the application and create a new account, inputting information such as family composition, layout of the home, room size, current furniture arrangement, wall color, lifestyle habits and preferences, and budget range.

[1628] The device records this input information in a database in real time, formats it, and sends it to the server, prompting the user to check and correct the input as necessary.

[1629] Input: Personal and spatial information entered by the user

[1630] Output: Formatted data sent to the server

[1631] Step 2:

[1632] Data analysis

[1633] The server stores the information submitted by users in a database, standardizes and cleans it, checks for missing values ​​and inconsistencies, and performs any necessary data imputation.

[1634] Input: User information received from the device

[1635] Output: Standardized and cleaned dataset

[1636] Step 3:

[1637] Generate interior proposals

[1638] The server passes the standardized dataset to a generative AI model (such as OpenAI's GPT-3), which creates a profile based on the user's lifestyle and spatial preferences.

[1639] Based on that profile, the generative AI model generates optimal interior design and organization suggestions, including specific furniture placement, color choices, storage solutions, and more.

[1640] Input: Standardized and cleaned dataset

[1641] Output: Interior design and organization proposals

[1642] Step 4:

[1643] View Suggestions

[1644] The server converts the generated interior design proposal into a data format and sends it to the terminal, along with creating visual data such as a 3D model and detailed layout drawings.

[1645] The terminal receives this data and visually displays it to the user through a user interface.

[1646] Input: Interior design and organization suggestions

[1647] Output: Visual data that is displayed to the user

[1648] Step 5:

[1649] Gathering feedback

[1650] The user reviews the proposal and provides feedback if necessary. For example, they can input their preferences, such as changing the color of a particular piece of furniture or rearranging it.

[1651] The terminal transmits the user's feedback to the server.

[1652] Input: User feedback

[1653] Output: Feedback data sent to the server

[1654] Step 6:

[1655] Updates incorporating feedback

[1656] The server analyzes the received feedback and inputs it back into the generative AI model, which then takes the feedback into account to generate new suggestions.

[1657] The revised proposal is sent back to the terminal and displayed to the user, and this process is repeated until the user is satisfied.

[1658] Input: Parsed feedback

[1659] Output: Revised interior design proposal

[1660] Step 7:

[1661] Purchase assistance

[1662] If the user wishes to purchase the suggested interior item, the terminal sends a purchase request to the server.

[1663] The server connects with the systems of affiliated retailers to obtain product availability and links to checkout.

[1664] The terminal displays these links and coupon codes to the user and assists in the purchase process.

[1665] Input: User purchase request

[1666] Output: Stock information and purchase links from retailers

[1667] Step 8:

[1668] Ongoing support and updates

[1669] The server continuously collects user feedback and usage data and periodically updates the generative AI model, a process that involves ingesting new datasets and training the model.

[1670] Periodically, new interior design proposals and organization solutions are generated and provided to the user through the server. The terminal notifies the user of these new proposals and solutions and allows them to be viewed through the interface.

[1671] Input: Feedback and usage data collected continuously

[1672] Output: Updated generative AI model and new proposals

[1673] (Application example 1)

[1674] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1675] Conventional factory layout and workflow optimization relies heavily on experience, making it difficult to design an efficient and productive layout in a short period of time. It is also difficult to effectively incorporate feedback into the proposed layout, resulting in suboptimal productivity.

[1676] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1677] In this invention, the server includes: means for receiving spatial information and lifestyle information from a user as input; means for generating interior design and organization proposals using a generative AI based on the input information; means for transmitting the generated proposals to a terminal for displaying them; means for receiving factory layout information and workflow information from a user as input; means for generating an optimal layout based on work efficiency and productivity based on the factory layout information and workflow information; means for displaying the generated layout and performing a virtual simulation; means for receiving user feedback on the proposals and revising the proposals based on the feedback; and means for providing the revised proposals to the user again, thereby enabling efficient and productive design of factory layouts and workflows.

[1678] "Spatial information" is data about the physical location and layout of factories and work areas.

[1679] "Lifestyle information" is data on the behavioral patterns and work habits of factory workers.

[1680] "Generative AI" is an algorithm that uses artificial intelligence technology to generate optimal suggestions.

[1681] "Interior design" is a proposal for layout and decoration to improve the aesthetics and functionality of a space.

[1682] "Organizational proposals" are specific ideas and plans for achieving efficient work flows and layouts.

[1683] A "terminal" is an electronic device that allows a user to view suggestions and enter feedback.

[1684] "Layout information" is data regarding the layout of machines and equipment within a factory.

[1685] "Work flow information" is data related to the order and processes of work within a factory.

[1686] "Virtual simulation" is a technology that allows a proposed layout to be reproduced in a virtual space and visually confirmed.

[1687] "Feedback" is information regarding the user's opinions and requests for corrections regarding the proposal.

[1688] "Productivity" is the ability to achieve maximum results with limited resources.

[1689] Efficiency is the ability to get more out of less.

[1690] "Procurement support" is a function that provides assistance in purchasing necessary equipment and facilities.

[1691] "Partner Retailers" are partner companies that provide products and services.

[1692] A "checkout" is a series of actions taken to purchase a product or service.

[1693] "Updating generative AI" means improving the artificial intelligence algorithms to make more accurate suggestions based on new data and feedback.

[1694] The present invention provides a system that can make proposals based on a user's lifestyle and spatial preferences, and is particularly concerned with optimizing the layout and work flow within a factory. This system is composed of a user, a terminal, and a server, all of which work in conjunction with each other.

[1695] System configuration

[1696] 1. Collection of User Information

[1697] A user first logs into the system and launches a corresponding application.

[1698] The terminal accepts input from users and collects data such as factory layout information, work flow information, budgets, and efficiency and productivity priorities.

[1699] As an example of input, a prompt such as "We would like to optimize the layout of our factory. Please generate the optimal proposal based on the following information: Current layout diagram: 'current_layout.json', Workflow diagram: 'workflows.json', Constraints: 'Budget: 100,000, Time: 200', Priorities: 'Efficiency: High, Safety: Medium'" is used.

[1700] 2. Data analysis and layout proposal generation

[1701] The server receives the collected data and stores it in a database.

[1702] Inside the server, a Generative AI model is used to analyze the data, generating optimal layout and workflow proposals that maximize efficiency and productivity within the factory.

[1703] 3. Proposal presentation and virtual simulation

[1704] The server transmits the generated layout proposal to the terminal.

[1705] The device uses software (e.g., Unity) to display this as a 3D model, allowing the user to visually confirm it.

[1706] Virtual simulations can be performed to validate the proposed layout.

[1707] 4. User feedback and suggested fixes

[1708] The user can then input feedback on the proposals from the terminal, for example, by entering a specific request such as "I would like to change the placement of a specific machine."

[1709] The server receives the feedback and again uses the Generative AI model to modify the proposal, which generates a new proposal that reflects the feedback.

[1710] 5. Submitting a revision proposal again

[1711] The revised proposal is sent back to the terminal and provided to the user.

[1712] This process is repeated until the user is satisfied.

[1713] Program processing description

[1714] Hardware and software used

[1715] Terminal: PC or tablet for user operation

[1716] Server: A server that runs data analysis and generative AI models

[1717] Software: Databases, generative AI models (e.g., TensorFlow), 3D model display software (e.g., Unity)

[1718] Process Overview

[1719] The server stores the received user information in a database and uses a generative AI model to generate optimal factory layout proposals.

[1720] The generated proposal is sent to the device and displayed to the user as a 3D model.

[1721] The user inputs feedback on the suggestions from the terminal, and the feedback is sent to the server.

[1722] The server receives the feedback and runs the generative AI model again to generate revised suggestions.

[1723] The suggested revisions are sent back to the terminal and the process is repeated until the user is satisfied.

[1724] Specific examples

[1725] For example, in a metal processing factory, users would input data such as the current layout, workflow information, budget, and safety standards into the application. The server receives this data, and the generative AI model proposes optimal machine placement and work flow. This is then displayed as a 3D model on the device, and users can provide feedback on their specific requests, which will result in further improved proposals.

[1726] This enables efficient and productive design of factory layouts and work flows.

[1727] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1728] Step 1:

[1729] The user launches the application and logs in. After logging in, they input the information needed to optimize the factory layout, including the current factory layout diagram, workflow information, budget, and priorities related to efficiency and productivity.

[1730] Input: User login information, factory layout diagram (e.g. 'current_layout.json'), workflow information (e.g. 'workflows.json'), budget, efficiency and productivity priorities

[1731] Output: The information entered

[1732] Step 2:

[1733] The terminal receives the user's input information and sends it to a database, where it is stored on a server.

[1734] Inputs: User-entered factory layout diagrams, work flow information, budgets, efficiency and productivity priorities

[1735] Output: Data sent to the server

[1736] Step 3:

[1737] The server analyzes the received data and generates factory layout optimization proposals using a Generative AI model. In this process, a data analysis module interprets the data, and Generative AI calculates the optimal layout and work flow.

[1738] Inputs: Factory layout diagrams stored on a server, work flow information, budgets, efficiency and productivity priorities

[1739] Data processing: Analysis of input data and generation of profiles

[1740] Data calculation: Generative AI generates optimal layout proposals

[1741] Output: Optimized factory layout proposal

[1742] Step 4:

[1743] The server transmits the generated layout proposal to the terminal.

[1744] Input: Optimized factory layout proposal

[1745] Output: Layout proposal sent to device

[1746] Step 5:

[1747] The device displays the received layout proposal as a 3D model, allowing the user to visually confirm the proposal. Specifically, 3D model display software (e.g., Unity) is used.

[1748] Input: Layout proposal received from the server

[1749] Output: Display of 3D model

[1750] Step 6:

[1751] The user reviews the proposed layout and enters any necessary feedback, such as specific requests such as "I would like to change the placement of a particular machine."

[1752] Input: User feedback

[1753] Output: Feedback data

[1754] Step 7:

[1755] The terminal transmits the user's feedback to the server.

[1756] Input: Feedback data

[1757] Output: Feedback sent to the server

[1758] Step 8:

[1759] The server analyzes the received feedback and re-runs the GenerativeAI model to refine the suggestions. New suggestions are generated and stored on the server.

[1760] Input: Feedback data from users

[1761] Data processing: Analyzing feedback and updating profiles

[1762] Data Computing: Regenerating revised layout proposals with generative AI

[1763] Output: Revised layout proposal

[1764] Step 9:

[1765] The server sends the revised proposal back to the terminal.

[1766] Input: revised layout proposal

[1767] Output: Suggested fixes sent to terminal

[1768] Step 10:

[1769] The device then displays the revised proposal again as a 3D model, and the user reviews the new proposal and provides feedback again if necessary. This process is repeated until the user is satisfied.

[1770] Input: revised layout proposal

[1771] Output: 3D model display and user feedback

[1772] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1773] This invention is a system for providing interior design and organization solutions based on a user's individual lifestyle and spatial preferences, and further utilizes an emotion engine to recognize the user's emotions and make suggestions that reflect those emotions. Specifically, this system is configured as a platform that combines generative AI and an emotion engine.

[1774] Program processing

[1775] 1. User Information Collection:

[1776] A user launches the application and creates a new account, entering basic information such as name, age, address, family composition, lifestyle habits, interior design preferences, and budget range.

[1777] The terminal transmits this input information to the server in real time.

[1778] 2. Data analysis and interior proposal generation:

[1779] The server stores the information submitted by the user in a database, and a generative AI module analyzes the data, creating a profile based on the user's lifestyle and spatial preferences.

[1780] Based on this profile, the generative AI generates optimal interior design and organization solutions, such as furniture placement and color choices for a living room, as well as suggested accessories for each room.

[1781] 3. Use of Emotion Engine:

[1782] The server sends the generated interior design proposal to the device and uses an emotion engine to analyze the user's facial expressions, tone of voice, and text input to recognize the user's emotions before displaying it to the user.

[1783] The server further customizes the suggestions based on the emotional data obtained from the emotion engine, so that the suggestions are more in line with the user's current mental state and emotions.

[1784] 4. Viewing and Feedback on Suggestions:

[1785] The device displays customized interior design proposals to the user, including a 3D model of the room and a detailed floor plan.

[1786] The user reviews the suggestions and provides feedback if necessary, for example, to specify specific requests such as changing the color of a particular piece of furniture or rearranging it.

[1787] 5. Incorporating feedback and suggested updates:

[1788] The terminal transmits the user's feedback to the server.

[1789] The server analyzes the feedback, utilizes a generative AI module to modify the proposal, again using the emotion engine to reflect the user's emotions, and presents the modified proposal to the user again. This process is repeated until the user is satisfied.

[1790] 6. Purchasing Assistance and Partnerships:

[1791] If the user wishes to purchase the suggested interior item, the terminal sends a purchase request to the server.

[1792] The server connects to the systems of partner retailers to obtain product availability and checkout links.

[1793] The server sends the acquired information to the device, which then displays a checkout link and a coupon code to the user, helping the user complete the checkout process.

[1794] 7. Ongoing Support and Updates:

[1795] The server continuously collects user feedback and usage data and periodically updates the generative AI model and emotion engine.

[1796] The server periodically generates new interior design proposals and organization solutions based on the updated model and sends them to the terminal.

[1797] The terminal will notify and display these new suggestions and solutions to the user.

[1798] Specific examples

[1799] Case Study: Personalized Recommendations Using Emotion Recognition

[1800] User A (single person living in a one-room apartment in an urban area):

[1801] Device: User A launches the app and uploads their name, age, address, and a photo of their room.

[1802] Server: Generative AI analyzes this data and proposes Scandinavian-inspired interior designs that maximize the use of small urban spaces, selecting compact storage units, multifunctional furniture, and simple color palettes.

[1803] Emotion engine: Before making a suggestion, it analyzes User A's facial expressions and tone of voice and evaluates whether the suggestion matches User A's current emotions. For example, if it recognizes that User A is feeling stressed, it will add interior elements that have a relaxing effect.

[1804] User A: Review the proposal and submit feedback saying they would like to add cafe-style tables.

[1805] Server: The regenerated proposal based on the feedback is rechecked by the emotion engine and sent to User A. Adjustments are repeated until User A is satisfied.

[1806] User B (family of four, suburban detached house):

[1807] Terminal: User B inputs the family composition (husband, wife, two children), room size, layout of existing furniture, budget, etc.

[1808] Server: Generative AI generates a design for a family with a spacious living room and multiple bedrooms. The proposal includes furniture arranged to accommodate the entire family, a play space for children, and storage solutions.

[1809] Emotion engine: Before displaying suggestions, analyze User B's emotional data and make suggestions that will help the whole family relax.

[1810] User B: Review the proposal and submit feedback that they would like to change the theme color for the children's room.

[1811] Server: The server evaluates the regenerated proposal based on the feedback using the emotion engine and sends it to User B. Adjustments are repeated until User B is satisfied.

[1812] In this way, the present invention takes into account the user's emotions to provide more personalized interior design and organization solutions, resulting in a harmonious living environment.

[1813] The processing flow will be explained below.

[1814] Step 1:

[1815] A user launches the application and creates a new account. They enter basic information such as their name, age, address, family composition, lifestyle habits, interior design preferences, and budget range. The device then transmits this information to the server in real time.

[1816] Step 2:

[1817] The server stores the information submitted by the user in a database, and then uses a generative AI module to analyze the data it receives, creating a profile based on the user's lifestyle and spatial preferences.

[1818] Step 3:

[1819] The server generates optimal interior design and organization solutions based on the profile, including furniture placement, color selection, and accessory suggestions, and then sends the generated design proposals to the device.

[1820] Step 4:

[1821] Before displaying interior design suggestions to the user, the device activates an emotion engine that analyzes the user's facial expressions, tone of voice, and text input to recognize the user's emotions.

[1822] Step 5:

[1823] The server customizes the suggestions based on the emotional data obtained from the emotion engine. For example, if the server detects that the user is feeling stressed, it adds relaxing interior elements to the suggestions.

[1824] Step 6:

[1825] The device displays customized interior design proposals to the user, including a 3D model of the room and a detailed floor plan, and the user can review the proposals and provide feedback if needed.

[1826] Step 7:

[1827] The device sends the user's feedback to the server, which receives the feedback and modifies the proposal using the generative AI module. It then uses the emotion engine again to confirm the user's emotions and presents the modified proposal to the user again. This process is repeated until the user is satisfied.

[1828] Step 8:

[1829] If the user wishes to purchase a suggested interior item, the device sends a purchase request to the server. The server connects to the affiliated retailer's system to obtain product availability and a checkout link. The server then sends the obtained information to the device, which then displays the checkout link and coupon code to the user. The device then assists the user in clicking the link and completing the purchase on the affiliated retailer's website.

[1830] Step 9:

[1831] The server continuously collects user feedback and usage data and periodically updates the generative AI model and emotion engine. The server periodically generates new interior design proposals and organization solutions based on the updated model and sends them to the device. The device notifies and displays these new proposals and solutions to the user.

[1832] Example 2

[1833] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1834] Providing optimal interior design and organization solutions for each individual user to meet today's diverse lifestyles is challenging. There is also a need to provide customized suggestions based on the user's emotional state and increase user satisfaction. Furthermore, there is a lack of systems that incorporate user feedback and provide continuously improved suggestions.

[1835] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving spatial information and lifestyle information from a user as input, means for generating interior design and organization proposals using a generative AI based on the input information, means for analyzing the user's facial expressions, tone of voice, and text input to collect emotional data, means for customizing the generated proposals based on the emotional data, means for receiving user feedback on the proposals and revising the proposals based on the feedback, means for re-presenting the revised proposals to the user, and means for continuously collecting user usage status and updating the generative AI model and emotion engine. This makes it possible to provide interior design and organization solutions that match the user's lifestyle and take into account their emotional state.

[1836] "User" refers to an individual or household that receives interior design and organization solutions from the system.

[1837] "Spatial information" is detailed information about the user's living space, including data such as room dimensions, layout, and existing furniture arrangement.

[1838] "Lifestyle information" refers to information about the user's individual lifestyle, such as the user's daily habits, family structure, preferred interior style, and budget.

[1839] "Generative AI" refers to an artificial intelligence model that generates optimal interior design and organization solutions based on user-provided information.

[1840] A "proposal" is an interior design or organization plan created by the generative AI by analyzing the user's information, including specific furniture placement and color selection.

[1841] "Emotion data" is data that indicates the user's emotional state, analyzed from the user's facial expression, tone of voice, text input, and the like.

[1842] An "emotion engine" refers to an engine that analyzes a user's emotional data and customizes the suggestions provided by the generative AI based on that information.

[1843] "Feedback" refers to a user returning to the system their opinions and requests regarding suggestions provided by the system.

[1844] "Customization" refers to the process by which the generative AI modifies its suggestions to best suit the user based on the user's emotional data and feedback.

[1845] "Update" refers to the generative AI model and emotion engine refining its algorithms and data to improve accuracy and effectiveness based on user feedback and usage data.

[1846] "Terminal" refers to a device, such as a computer or smartphone, through which a user inputs information or receives suggestions.

[1847] This invention is a system for providing interior design and organization solutions based on users' lifestyles and spatial preferences. The system is configured as a platform that combines generative AI and an emotion engine.

[1848] System Overview

[1849] The user first launches the application and enters basic information such as name, age, address, family composition, lifestyle habits, interior design preferences, and budget range. This information is sent to the server via the device. The server stores the received information in a database, and a generative AI module analyzes the data and creates a profile based on the user's lifestyle and spatial preferences. The generative AI (e.g., a large-scale language model such as GPT-3) generates optimal interior design and organization solutions.

[1850] Use of emotion engine

[1851] The generated interior design proposals are customized by an emotion engine before being displayed to the user. The emotion engine (e.g., Affectiva SDK) is used to recognize the user's emotions by analyzing the user's facial expressions, tone of voice, and text input. The emotion data obtained by the emotion engine is analyzed by the server, and the generative AI further customizes the proposals. As a result, the proposals are more in line with the user's current mental state and emotions.

[1852] View suggestions and give feedback

[1853] The customized interior design proposal is sent to the device and displayed to the user. This includes a 3D model of the room and a detailed layout. The user reviews the proposal and provides feedback on specific preferences (e.g., changing the color or positioning of certain furniture). This feedback is sent via the device to the server, which analyzes the feedback and modifies the proposal using a generative AI module. This process is repeated until the user is satisfied.

[1854] Buying Assistance and Partnerships

[1855] If the user wishes to purchase a suggested interior item, the device sends a purchase request to the server, which connects to the retailer's system to obtain product availability and a link to checkout. The information is then sent to the device, where the user is presented with a link to checkout and a coupon code.

[1856] Ongoing support and updates

[1857] The server continuously collects user feedback and usage data and periodically updates the generative AI model and emotion engine. New interior design proposals and organization solutions are periodically generated based on the updated model and sent to the device. The device notifies and displays these new proposals and solutions to the user.

[1858] Specific examples

[1859] Case Study 1: Personalized Recommendations Using Emotion Recognition

[1860] User A (single person living in a studio apartment in an urban area):

[1861] Device: User A launches the app and uploads their name, age, address, and a photo of their room.

[1862] Server: Generative AI analyzes this data and proposes Scandinavian-inspired interior designs that maximize the use of small urban spaces, selecting compact storage units, multifunctional furniture, and simple color palettes.

[1863] Emotion engine: Before making a suggestion, it analyzes User A's facial expressions and tone of voice and evaluates whether the suggestion matches User A's current emotions. For example, if it recognizes that User A is feeling stressed, it will add interior elements that have a relaxing effect.

[1864] User A: Review the proposal and submit feedback saying they would like to add cafe-style tables.

[1865] Server: The regenerated proposal based on the feedback is rechecked by the emotion engine and sent to User A. Adjustments are repeated until User A is satisfied.

[1866] Prompt Sentence Examples

[1867] Prompt 1: "Generate the best interior design suggestions based on the user's basic information, including age, address, preferred style, and budget range."

[1868] Prompt 2: "Customize the generated interior design suggestions based on the recorded user emotional data. Be sure to add elements that have a relaxing effect."

[1869] In this way, the present invention takes into account the user's emotions to provide more personalized interior design and organization solutions, resulting in a harmonious living environment.

[1870] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1871] Step 1: Collect user information

[1872] User Action:

[1873] Users launch the application and tap the "Create a new account" button, then enter basic information such as their name, age, address, family composition, lifestyle habits, interior design preferences, and budget range.

[1874] input:

[1875] Information such as name, age, address, family composition, lifestyle habits, interior design preferences, budget range, etc.

[1876] Terminal behavior:

[1877] The device collects input in real time, encrypts the data using HTTPS, and sends it securely to the server.

[1878] output:

[1879] The user information is sent to the server and account creation is complete.

[1880] Step 2: Data analysis and interior design proposal generation

[1881] Server behavior:

[1882] The server stores the received user information in a database (e.g., MySQL) and then invokes a generative AI module to analyze the information and create a user profile.

[1883] input:

[1884] User information stored in a database.

[1885] Data processing:

[1886] Based on the stored information, a generative AI analyzes the data and creates a profile based on your lifestyle and spatial preferences.

[1887] output:

[1888] Proposing the best interior design and organization solutions for users.

[1889] Step 3: Use the Emotion Engine

[1890] Terminal behavior:

[1891] It records facial expressions and tone of voice through the user's webcam and microphone, and collects text input.

[1892] input:

[1893] User facial expression data, tone of voice, and text input.

[1894] Terminal behavior:

[1895] The collected data is sent to the server in real time.

[1896] Server behavior:

[1897] An emotion engine is used to analyze emotions from collected data.

[1898] Data processing:

[1899] Emotional data is quantified and the suggestions provided by the generative AI are customized based on that data.

[1900] output:

[1901] Customized interior suggestions based on user emotions.

[1902] Step 4: View and give feedback on suggestions

[1903] Terminal behavior:

[1904] It presents users with customized interior design proposals, including 3D models of rooms and detailed floor plans.

[1905] input:

[1906] Customized interior design proposals.

[1907] User Action:

[1908] Review the suggestions and enter specific feedback into the device, such as changing the color of certain furniture or rearranging it.

[1909] Terminal behavior:

[1910] Collect user feedback and send it to the server.

[1911] output:

[1912] User feedback on the proposal.

[1913] Step 5: Incorporating feedback and proposing updates

[1914] Server behavior:

[1915] Analyze user feedback and refine suggestions using a generative AI module.

[1916] input:

[1917] User feedback.

[1918] Data processing:

[1919] Update suggestions based on feedback and use the sentiment engine again to reflect user sentiment.

[1920] Server behavior:

[1921] The revised proposal is resubmitted to the user.

[1922] output:

[1923] Revised interior design proposal.

[1924] Step 6: Assist with purchasing and execute partnerships

[1925] User Action:

[1926] If the customer wishes to purchase the suggested interior item, the customer inputs a purchase request on the terminal.

[1927] Terminal behavior:

[1928] Send the purchase request to the server.

[1929] Server behavior:

[1930] Connect to partner retailer systems to retrieve product availability and checkout links.

[1931] input:

[1932] Purchase requests and interior item information.

[1933] Data processing:

[1934] Link with retailer systems to obtain necessary information.

[1935] output:

[1936] A link to checkout and a coupon code will be sent to your device.

[1937] Step 7: Ongoing support and updates

[1938] Server behavior:

[1939] We continuously collect user feedback and usage data to regularly update our generative AI models and emotion engine.

[1940] input:

[1941] User feedback and usage data.

[1942] Data processing:

[1943] Analyze the collected data and improve the model.

[1944] Server behavior:

[1945] New interior design proposals and organisational solutions are generated based on the updated model and sent to the device.

[1946] output:

[1947] New interior design proposals and organisation solutions.

[1948] (Application example 2)

[1949] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1950] Conventional interior design systems make suggestions based on the user's lifestyle and spatial preferences, but they are unable to consider the user's emotional state, making it difficult to provide suggestions that truly satisfy the user. While systems exist that receive feedback on suggestions, they lack the ability to respond quickly to feedback or regenerate customized suggestions based on emotions, limiting their ability to improve the user experience.

[1951] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for utilizing an emotion engine that recognizes the user's emotions and further customizes proposals based on those emotions, means for displaying proposals generated on the terminal using 3D models or augmented reality, and means for continuously customizing proposals using emotion data generated by the emotion engine. This makes it possible to provide personalized interior design proposals that take the user's emotional state into consideration. In addition, by receiving feedback in real time and quickly generating and displaying appropriate proposals, it becomes possible to provide proposals that highly satisfy the user.

[1952] "User" refers to the end user who uses the proposal system and is the person who receives the proposals for interior design and organization.

[1953] "Spatial information" refers to data such as the layout, size, shape, and current furniture arrangement of a user's living space or office.

[1954] "Lifestyle information" refers to individual information such as the user's lifestyle, family structure, hobbies and preferences, activity patterns, budget range, and the like.

[1955] "Generative AI" is a system that uses artificial intelligence algorithms to generate optimal interior design proposals from given input data.

[1956] "Interior design" is a general term for design that includes the arrangement of furniture and decorations, color selection, etc. to make a user's space beautiful and functional.

[1957] "Organization suggestions" refer to suggestions for storage methods and layout changes to make efficient use of the user's space.

[1958] "Suggestions" refer to interior design and organization ideas presented to the user by the generative AI.

[1959] A "terminal" is a device that allows a user to view interior design proposals and enter feedback, and includes smartphones, tablets, PCs, smart glasses, etc.

[1960] "Feedback" refers to the act of a user inputting their opinions or requests regarding the content of a proposal after checking it.

[1961] An "emotion engine" refers to an algorithm that recognizes emotions from a user's facial expressions, voice, etc., and customizes suggestions based on those emotions.

[1962] A "3D model" refers to an interior design model that is expressed in three dimensions and is used to make proposals visually easier to understand.

[1963] "Augmented reality" is a technology that overlays computer-generated information onto real-world visual information, and is used to display interior design proposals in real spaces.

[1964] The present invention provides an interior design proposing system that takes into account the user's emotions, and is specifically implemented as follows.

[1965] First, the user inputs spatial and lifestyle information using a device, such as a smartphone, tablet, PC, or smart glasses. This input information includes the user's name, age, family composition, lifestyle habits, interior design preferences, and budget range. This information is sent to a central server in real time.

[1966] The server stores the received user information in a database, which is then analyzed by a generative AI module. The generative AI module generates the optimal interior design based on the user's lifestyle and spatial preferences. The specific system uses machine learning libraries such as Python and TensorFlow to build the AI ​​model.

[1967] The generated interior design proposals are first customized by an emotion engine. The emotion engine uses the camera and microphone of the smart glasses or smartphone to analyze the user's facial expressions and tone of voice to recognize their emotions. The proposals are then adjusted based on this emotion data. Specific implementations of the emotion engine utilize OpenCV and the Google Cloud Speech-to-Text API.

[1968] The device then displays the tailored interior design proposal to the user, including 3D models and augmented reality (AR) visualizations, allowing the user to see the proposal in the real space. The AR rendering uses Unity or ARKit / ARCore.

[1969] The user reviews the suggestions and provides feedback, which is sent from the device to the server, which then updates the suggestions using the generative AI module and emotion engine. This process is repeated until the user is satisfied.

[1970] If the user wishes to purchase a suggested interior item, the server connects to the retailer's system to retrieve product availability and a link to the purchase process, which is then displayed on the user's device. This allows the user to easily purchase the suggested item.

[1971] For example, the following prompt will prompt the user for information:

[1972] Example prompt:

[1973] Name: Taro Yamada

[1974] Age: 35

[1975] Family status: Single

[1976] Interior preferences: Modern style, muted colors

[1977] Budget range: 50,000 yen

[1978] Feedback example:

[1979] "I would like a more relaxing atmosphere. I would like the lighting to be changed to warmer tones."

[1980] In this way, by taking into account the user's emotions and feedback, a more satisfying interior design can be provided.

[1981] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1982] Step 1:

[1983] Collection of User Information

[1984] The user starts up the device and enters basic information such as name, age, address, family composition, lifestyle habits, interior design preferences, and budget range. This input data is sent from the device to the server in real time. The input information includes the user's lifestyle and spatial preferences, and subsequent data analysis is based on this information.

[1985] Step 2:

[1986] Data analysis and interior design proposal generation

[1987] The server stores the information submitted by the user in a database. The generative AI module analyzes the stored user data and creates a user profile based on lifestyle and spatial preferences. Based on this profile, the generative AI generates optimal interior design and organization solutions, including suggestions for living room furniture placement, color selection, and accessories for each room. The output design proposals are sent to the next step.

[1988] Step 3:

[1989] Use of emotion engine

[1990] The emotion engine recognizes the user's emotions before displaying suggestions to the user. The device uses a camera and microphone to analyze emotions from the user's facial expressions and tone of voice. The emotion data is sent to the server, which then uses the data to customize the generated interior design suggestions. For example, if the user is feeling stressed, it can add elements that have a relaxing effect. The revised suggestions are then sent to the next step.

[1991] Step 4:

[1992] View suggestions and give feedback

[1993] The device displays interior design proposals customized by the emotion engine to the user, including a 3D model of the room and a detailed layout. The user can review the proposals and provide feedback as needed. For example, specific requests can be made, such as changing the color or placement of certain furniture. Feedback is sent to the server in real time.

[1994] Step 5:

[1995] Incorporating feedback and suggested updates

[1996] The server analyzes the feedback received from the user and modifies the proposal using the generative AI module. The modified proposal is then adjusted again through the emotion engine to reflect the user's emotional data. The new, adjusted proposal is then sent back to the user. This process is repeated until the user is satisfied.

[1997] Step 6:

[1998] Buying Assistance and Partnerships

[1999] If the user wishes to purchase a suggested interior item, the device sends a purchase request to the server. The server connects to the partner retailer's system to obtain product availability and a link to checkout. The obtained information is sent to the device, which then displays a link to checkout and a coupon code to the user, allowing the user to easily purchase the suggested item.

[2000] Step 7:

[2001] Ongoing support and updates

[2002] The server continuously collects user feedback and usage data and periodically updates the generative AI model and emotion engine. Based on the updated model, new interior design proposals and organization solutions are periodically generated and sent to the device. The device then notifies and displays the new proposals and solutions to the user, ensuring that the user always receives the latest proposals.

[2003] These are the specific processing steps of the system that realizes this application example. The detailed operations at each step, such as data input and output, utilization of emotional data, and reflection of feedback, support high user satisfaction throughout the system.

[2004] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2005] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2006] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2007] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2008] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2009] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2010] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2011] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2012] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2013] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2014] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2015] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2016] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2017] 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.

[2018] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2019] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2020] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2021] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2022] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2023] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2024] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2025] The following is further disclosed regarding the above embodiment.

[2026] (Claim 1)

[2027] means for receiving spatial information and lifestyle information from a user as input;

[2028] A means for generating interior design and organization proposals using a generative AI based on the input information;

[2029] means for transmitting the generated proposal to a terminal for display;

[2030] means for receiving user feedback on the suggestions and modifying the suggestions based on the feedback;

[2031] A means to provide the revised proposal to the user again.

[2032] A system including:

[2033] (Claim 2)

[2034] a means for providing assistance in purchasing furniture and interior items based on the proposed interior design;

[2035] Includes means to complete purchase procedures in collaboration with affiliated retailers

[2036] 10. The system of claim 1.

[2037] (Claim 3)

[2038] Includes a means to repeatedly receive user feedback and update the generative AI to improve the accuracy of subsequent suggestions.

[2039] 10. The system of claim 1.

[2040] "Example 1"

[2041] (Claim 1)

[2042] means for receiving spatial information and lifestyle information as input from a user;

[2043] A means for generating interior design and organization proposals using a generative AI based on the input information;

[2044] means for transmitting the generated suggestions to a user interface for display;

[2045] means for receiving user feedback on the suggestions and modifying the suggestions based on the feedback;

[2046] means for providing the revised suggestions to the user again;

[2047] A means of recording user information in a database in real time and formatting it into the required data format;

[2048] A means for converting the proposal content into visual data and visually displaying it;

[2049] A system including:

[2050] (Claim 2)

[2051] a means for providing assistance in purchasing furniture and interior items based on the proposed interior design;

[2052] Includes means to complete purchase procedures in collaboration with affiliated retailers

[2053] 10. The system of claim 1.

[2054] (Claim 3)

[2055] Includes a means to repeatedly receive user feedback and update the generative AI to improve the accuracy of subsequent suggestions.

[2056] 10. The system of claim 1.

[2057] "Application Example 1"

[2058] (Claim 1)

[2059] means for receiving spatial information and lifestyle information from a user as input;

[2060] A means for generating interior design and organization proposals using a generative AI based on the input information;

[2061] means for transmitting the generated proposal to a terminal for display;

[2062] means for receiving factory layout information and work flow information as input from a user;

[2063] a means for generating an optimal layout based on the factory layout information and work flow information, and the optimal layout is based on work efficiency and productivity;

[2064] a means for displaying the generated layout and performing a virtual simulation;

[2065] means for receiving user feedback on the suggestions and modifying the suggestions based on the feedback;

[2066] means for providing the revised suggestions to the user again;

[2067] A system including:

[2068] (Claim 2)

[2069] A means of providing assistance in procuring machinery and factory supplies based on the proposed factory layout;

[2070] 10. The system of claim 1, further comprising means for carrying out purchase procedures in cooperation with affiliated retailers.

[2071] (Claim 3)

[2072] 10. The system of claim 1, further comprising means for repeatedly receiving feedback from the user and updating the generating AI to improve the accuracy of subsequent suggestions.

[2073] "Example 2: Combining Emotion Engines"

[2074] (Claim 1)

[2075] means for receiving spatial information and lifestyle information as input from a user;

[2076] A means for generating interior design and organization proposals using a generative AI based on the input information;

[2077] means for transmitting the generated proposal to a terminal;

[2078] a means for collecting emotional data by analyzing a user's facial expressions, tone of voice, and text input;

[2079] means for customizing the generated suggestions based on emotion data;

[2080] means for transmitting the customized suggestions to a terminal for display;

[2081] means for receiving user feedback on the suggestions and modifying the suggestions based on the feedback;

[2082] means f...

Claims

1. means for receiving spatial information and lifestyle information from a user as input; A means for generating interior design and organization proposals using a generative AI based on the input information; means for transmitting the generated proposal to a terminal for display; means for receiving user feedback on the suggestions and modifying the suggestions based on the feedback; A means to provide the revised proposal to the user again. A system including:

2. a means for providing assistance in purchasing furniture and interior items based on the proposed interior design; Includes means to complete purchase procedures in collaboration with affiliated retailers The system of claim 1 .

3. Includes a means to repeatedly receive user feedback and update the generative AI to improve the accuracy of subsequent suggestions. The system of claim 1 .

Images