System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting information about a user's preferences and lifestyle; means for collecting, encrypting, and transmitting the user's inputted information; means for receiving and storing the inputted information in a database; means for generating an optimal house design using a generative AI that analyzes the inputted information; means for presenting the generated house design to a user and collecting user feedback; and means for modifying the house design based on the user feedback.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] Conventional home design services have had issues with low satisfaction, as they do not adequately reflect the individual preferences and lifestyles of users. They also face issues such as a lack of concrete ideas and proposals for homebuyers to realize their ideal living environment. Furthermore, it is difficult for users to participate in the home design process, making it difficult for them to put their own ideas into practice. [Means for solving the problem]

[0005] The above problem is solved by providing a system including: a means for inputting information about a user's preferences and lifestyle; a means for collecting, encrypting, and transmitting the user's input information; a means for receiving the input information and storing it in a database; a means for generating an optimal home design using a generation AI that analyzes the input information; a means for presenting the generated home design to the user and collecting user feedback; and a means for modifying the home design based on the user feedback. Specifically, the system allows users to actively participate in the home design process and realize an original home design that suits their preferences and lifestyle. This system provides a unique and comfortable living environment that highly satisfies users.

[0006] A "user" is an individual or corporation who uses the system to input information about a house design and receive design proposals.

[0007] "Preferences and lifestyle" is information including the lifestyle habits, hobbies, values, and desired characteristics of a home that a user places importance on on a daily basis.

[0008] "Means of input" refers to any device or software that allows a user to input information via a terminal or interface.

[0009] The "means for collecting, encrypting and transmitting" refers to a system that has the function of converting information entered by the user into a data format and transmitting it securely to a server using an encryption protocol.

[0010] "Means for receiving and storing in a database" refers to a system that has the function for the server to receive information sent from the terminal and store it in an appropriate database.

[0011] "Generative AI" refers to artificial intelligence technology and machine learning algorithms used to analyze user information and generate optimal home design proposals.

[0012] The "means for collecting feedback" is a system that provides a function for users to input and collect feedback such as comments, evaluations, and requests regarding presented design proposals.

[0013] The "means of correction" is a system that has the ability to improve and update design proposals by having the generating AI reanalyze them based on feedback collected from users.

[0014] "Presentation means" refers to an interface or software that visually displays the generated housing design proposal to the user and allows the user to check the details. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

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

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

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

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

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

[0030] 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.

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

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] The system of the present invention proposes optimal home designs based on the user's preferences and lifestyle. A specific embodiment of the system is described below.

[0037] System Overview

[0038] 1. Enter user information

[0039] Users launch a dedicated application on their device (smartphone, tablet, PC, etc.) and enter detailed profile information (age, family composition, hobbies, etc.) and specific requirements for the home (spacious living room, natural light, eco-friendly design, etc.).

[0040] 2. Data collection and transmission

[0041] The terminal collects the data entered by the user and converts it into a data format (JSON, XML, etc.).

[0042] The collected data is encrypted on the device and securely sent to the server.

[0043] 3. Saving data and preparing for analysis

[0044] The server decrypts the received user data and stores it in a database.

[0045] The received data is passed to the generation AI and prepared for detailed analysis.

[0046] 4. Analysis by generative AI

[0047] The AI ​​on the server generates the optimal home design based on the information entered by the user. This AI analyzes data from past home designs and takes into account the user's lifestyle information.

[0048] The generated home design proposals may have multiple variations, and are designed to expand the user's options.

[0049] 5. Presenting design proposals and collecting feedback

[0050] The server transmits the generated multiple housing design plans to the terminal.

[0051] The device visually displays these design proposals to the user and also provides detailed information about each proposal (floor plan, design, materials used, etc.).

[0052] Users can review the proposed design and provide their ratings and feedback, including specific requests such as "I'd like more natural light in the living room."

[0053] 6. Incorporating feedback

[0054] The terminal collects feedback from the user and transmits it back to the server.

[0055] The server runs the generative AI again based on the collected feedback and improves the design proposal.

[0056] The improved design proposal is sent to the terminal as the final design proposal and provided to the user.

[0057] Specific examples

[0058] Example 1: User A's case

[0059] 1. User A enters their preferences for a "large living room" and a "garden with a natural feel" in a dedicated app.

[0060] 2. The device collects this information, encrypts it, and sends it to the server.

[0061] 3. The server analyzes the data using generative AI and generates multiple home design proposals with spacious living rooms and gardens.

[0062] 4. The server sends the design proposal to the terminal, and User A checks the displayed proposal.

[0063] 5. When User A sends feedback such as "I would like more windows in the living room," the server reflects that request and provides the final design proposal.

[0064] Example 2: User B's case

[0065] 1. User B inputs that they need an "eco-friendly design" and a "large home office."

[0066] 2. The device collects the information, encrypts it, and sends it to the server.

[0067] 3. The server uses generative AI to generate multiple design options, including eco-friendly materials and a large home office.

[0068] 4. User B inputs detailed requests based on the presented design proposal and sends feedback to the server.

[0069] 5. The server refines the design based on the feedback and provides the final design.

[0070] In this way, this system can efficiently realize optimal home designs that meet the individual needs of users. By utilizing generative AI, it is possible to promote a user-centered design process and provide a living environment that will satisfy users.

[0071] The processing flow will be explained below.

[0072] Step 1:

[0073] Users launch a dedicated application on their device and enter detailed information about themselves (age, family composition, hobbies, etc.) and the requirements they have for their home (spacious living room, natural light, eco-friendly design, etc.).

[0074] Step 2:

[0075] The terminal collects the data entered by the user and converts it into an appropriate data format, such as JSON. The terminal provides an interface for the user to review and correct the input data as needed.

[0076] Step 3:

[0077] The terminal encrypts the collected data using an encryption protocol such as AES, ensuring security before sending it to the server.

[0078] Step 4:

[0079] The server decrypts the received encrypted data and stores it in a database, after which it performs processing to check the timestamp and data consistency.

[0080] Step 5:

[0081] The server converts the stored user data into a format that can be interpreted by the generation AI and prepares it for delivery to the generation AI, where data cleaning and normalization are performed as necessary.

[0082] Step 6:

[0083] The AI ​​on the server analyzes the user's input in detail and generates the optimal home design. This analysis reflects parameters based on the user's lifestyle and preferences. It also takes into account past design data and trend information.

[0084] Step 7:

[0085] The server organizes the generated multiple home design proposals and converts them into a presentation format, which allows them to be sent to devices in a visually easy-to-understand format.

[0086] Step 8:

[0087] The terminal visually displays the received housing design proposals to the user, providing an interface that includes detailed information about each proposal (floor plan, design, materials used, etc.) to enable the user to easily understand and evaluate them.

[0088] Step 9:

[0089] Users review the proposed design proposals and enter their ratings and feedback (e.g., "I'd like more windows in the living room.") Feedback is provided using simple forms and options on the UI.

[0090] Step 10:

[0091] The device collects feedback from the user, re-encrypts it, and sends it to the server.

[0092] Step 11:

[0093] The server analyzes the collected feedback and runs the generative AI again, generating improvements based on the feedback and updating the design proposal.

[0094] Step 12:

[0095] The server generates the final design proposal and sends it to the terminal, where it is presented to the user, who can approve and confirm the proposal.

[0096] In this way, the system executes a series of processes that generate multiple home design proposals based on information based on the user's preferences and lifestyle, and provides a final design proposal that reflects the user's feedback.

[0097] Example 1

[0098] 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."

[0099] Conventional home design proposal systems have had difficulty efficiently generating optimal designs that correspond to a user's individual lifestyle and preferences. Furthermore, incorporating user feedback is cumbersome, making it difficult to quickly improve the design. The present invention aims to solve these problems and provide a system that efficiently provides optimal home designs that meet the user's needs.

[0100] 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.

[0101] In this invention, the server includes: means for inputting information about a user's preferences and lifestyle; means for collecting the user's input information, converting it into JSON format, encrypting it, and transmitting it; means for receiving the input information, decrypting it, and storing it in a database; means for generating multiple home design proposals using a generation AI based on the user's input information; means for visually presenting the generated multiple home design proposals to the user and collecting feedback according to the user's preferences and lifestyle; and means for collecting the user's feedback and running the generation AI again to modify the home design proposals. This makes it possible to quickly and efficiently generate and improve home design proposals based on the user's input information and feedback.

[0102] "Information about the user's preferences and lifestyle" refers to specific factors that the user places importance on in the living environment, such as the user's age, family structure, hobbies, conditions of the place of residence, and requirements for the home.

[0103] "Input means" refers to devices or software that provide an interface for users to input information. Specifically, this refers to electronic devices such as smartphones, tablets, and PCs, as well as the applications that run on them.

[0104] "Means of converting to a data format, encrypting, and transmitting" refers to the series of processes in which information entered by the user is converted into a standard data format such as JSON or XML, encrypted using an encryption algorithm such as AES-256, and transmitted to a server via a communication line.

[0105] "Means of receiving, decrypting, and storing in a database" refers to the process by which the server receives the encrypted data sent from the terminal, decrypts it using the private key, and then stores the data in a database (e.g., MySQL).

[0106] "Means for generating multiple home design proposals using generative AI" refers to the process of using generative AI (artificial intelligence) to create multiple home design proposals that meet the user's needs based on past home design data and the user's lifestyle information.

[0107] "Means for visually presenting to the user and collecting feedback based on the user's preferences and lifestyle" refers to a process that displays multiple generated design proposals on the user's device and provides an interactive interface that allows the user to review the proposals and input their ratings and suggestions for improvement.

[0108] "Means for re-running the generating AI to modify the housing design proposal" refers to the process of re-running the generating AI and improving the design proposal based on feedback collected from the user, resulting in a final design proposal that more accurately reflects the user's wishes.

[0109] The present invention is a system that proposes optimal home design based on a user's preferences and lifestyle. The system of the present invention mainly operates in cooperation with the user, a terminal, and a server. The processing at each step and the hardware and software used are described in detail below.

[0110] 1. Enter user information

[0111] Users launch a dedicated application using a device such as a smartphone, tablet, or PC. Through this application, users input information such as their age, family composition, hobbies, location, and specific requirements for the home (e.g., a spacious living room, natural light, eco-friendly design, etc.). This input interface is often implemented using front-end frameworks such as React or Angular.

[0112] 2. Data collection and transmission

[0113] The terminal collects the information entered by the user and converts it into JSON format. It then encrypts the data using the AES-256 encryption algorithm. The encrypted data is then sent to the server using a secure communication protocol (e.g., HTTPS). This process typically uses a backend framework such as Node.js or Python.

[0114] 3. Saving data and preparing for analysis

[0115] The server receives the encrypted data sent from the device and decrypts it using the private key. This decrypted data is then returned to its original format using JSON decoding. The data is then stored in a database such as MySQL or PostgreSQL. The server retrieves the user data from the database and prepares it for analysis before passing it to the generation AI.

[0116] 4. Analysis by generative AI

[0117] A generative AI (e.g., OpenAI GPT-4 model) located on the server generates optimal home design proposals based on user input. This generative AI performs analysis while taking into account past home design data and the user's lifestyle information. The generated home design proposals have multiple variations, designed to expand the user's options.

[0118] 5. Presenting design proposals and collecting feedback

[0119] The server sends the generated multiple home design proposals to the terminal. The terminal visually displays these design proposals to the user. 2D or 3D graphics are often used as the display method. Specifically, the interface can be built using libraries such as Three.js or Unity. The user reviews the proposed design proposals and enters their ratings and feedback. This feedback can include specific requests such as "I want more natural light in the living room."

[0120] 6. Incorporating feedback

[0121] The device collects feedback from the user and sends it back to the server. The server then operates the generation AI again based on the collected feedback to improve the design proposal. The improved design proposal is sent to the device as the final design proposal and provided to the user. The final design proposal reflects the user's requests as much as possible, making it possible to achieve a higher level of satisfaction.

[0122] Example prompt sentences

[0123] 1. For User A

[0124] The user wants a "large living room" and a "natural garden." Please propose the optimal house design based on this.

[0125] 2. For User B

[0126] The user wants an "eco-friendly design" and a "large home office." Please generate the optimal home design proposal taking this into consideration.

[0127] The system of this invention can efficiently realize optimal home designs that meet the individual needs of users. Furthermore, by utilizing generative AI, it is possible to promote a user-centered design process and provide a living environment that is highly satisfying.

[0128] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0129] Step 1:

[0130] The user enters information

[0131] Specific operation: The user launches a dedicated application on a device such as a smartphone, tablet, or PC and enters their profile information (age, family composition, hobbies, etc.) and their specific requirements for the home (spacious living room, natural light, eco-friendly design, etc.).

[0132] Input: Manual input of information by the user.

[0133] Output: Data entered by the user that is temporarily stored on the device.

[0134] Step 2:

[0135] The device collects data, converts and encrypts it, and then transmits it.

[0136] What it does: The device collects the information entered by the user, converts it into JSON format, encrypts the data using the AES-256 encryption algorithm, and sends it to the server via a secure communication protocol (HTTPS).

[0137] Input: Information entered by the user (e.g., "We want a large living room with natural light for a 30-year-old couple and their 5-year-old child").

[0138] Output: The encrypted user information is sent to the server.

[0139] Step 3:

[0140] The server receives the data, decrypts it, and stores it in a database

[0141] How it works: The server receives the encrypted data sent from the device and decrypts it using the private key. The decrypted data is then converted back to its original format using JSON decoding. This data is then stored in a database such as MySQL or PostgreSQL.

[0142] Input: Encrypted user information.

[0143] Output: The decrypted user information is stored in the database.

[0144] Step 4:

[0145] The server generates a house design plan using generation AI

[0146] Specific operation: The server retrieves user information stored in the database and passes it to the generation AI. The generation AI (e.g., OpenAI GPT-4 model) generates multiple home design proposals based on past home design data and the user's lifestyle information, tailored to the user's needs.

[0147] Input: User information decrypted and stored in a database.

[0148] Output: Multiple house design proposals generated.

[0149] Step 5:

[0150] The server sends the design proposal to the terminal, which then presents it to the user.

[0151] How it works: The server sends multiple home design proposals generated by the AI ​​to the device. The device then displays these proposals to the user in a visually easy-to-read format (e.g., 2D or 3D graphics). Libraries such as Three.js and Unity are often used.

[0152] Input: Generated multiple house design proposals.

[0153] Output: A visual representation of the design to the user.

[0154] Step 6:

[0155] User enters feedback

[0156] Specific operation: The user reviews the presented design proposals and enters their evaluation and feedback on improvements. An example of feedback might be, "I'd like more windows in the living room."

[0157] Input: Manual input of feedback by the user.

[0158] Output: Feedback data temporarily stored in the device.

[0159] Step 7:

[0160] The device collects feedback and sends it to the server

[0161] Specific operation: The device collects the feedback entered by the user, converts it back into JSON format, encrypts it, and sends it to the server using a secure communication protocol (HTTPS).

[0162] Input: Feedback entered by the user.

[0163] Output: The encrypted feedback data is sent to the server.

[0164] Step 8:

[0165] The server improves the design based on the feedback.

[0166] How it works: The server receives the encrypted feedback, decrypts it, and decodes the JSON. It then passes the feedback to the generation AI, which analyzes it again to generate the optimal design. The improved design is then sent back to the device and provided to the user.

[0167] Input: The decrypted and JSON decoded feedback data.

[0168] Output: An improved final house design.

[0169] (Application example 1)

[0170] 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."

[0171] While conventional systems generally propose home designs based on the user's preferences and lifestyle, they lack support for store layouts. In particular, there are no systems that propose optimal layouts based on the store owner's business requirements. This results in the problem of store owners spending a great deal of time and money designing their ideal store layout.

[0172] 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.

[0173] In this invention, the server includes means for inputting information about a user's preferences and lifestyle, means for collecting the user's input information, encrypting and transmitting it, means for receiving the input information and storing it in a database, means for generating an optimal home design using a generation AI that analyzes the input information, means for presenting the generated home design to the user and collecting user feedback, means for modifying the home design based on the user feedback, means for inputting the user's business requirements, and means for generating a store layout plan based on the business requirements and presenting it to the user. This makes it possible to propose and improve an ideal store layout based on the store owner's business requirements.

[0174] "User" refers to any individual or corporation that requires proposals for home design or store layout.

[0175] "Preferences" refer to specific attributes such as design, layout, and functionality desired by the user.

[0176] "Lifestyle" refers to the habits and behavioral patterns of a user's daily life.

[0177] "Means for inputting information" refers to the interface for collecting information such as user preferences, lifestyle, and business requirements.

[0178] "Means of collection, encryption and transmission" refers to devices or software that electronically collect information entered by users, encrypt it to keep the data secure, and transmit it to a server.

[0179] "Means for receiving and storing in a database" refers to devices or software that receive encrypted user information, decrypt it, and store it in a database.

[0180] "Generative AI" refers to artificial intelligence that automatically generates optimal design proposals based on information provided by the user.

[0181] "Home design" refers to the layout and design of a home that suits the user's preferences and lifestyle.

[0182] The "means for presenting and collecting feedback" refers to a device or software that visually displays the generated design proposal to the user and receives evaluations and additional requests from the user.

[0183] "Modification tools" refers to devices or software that refine designs based on feedback collected from users.

[0184] "Business requirements" refer to the specific conditions or goals that a store owner wants to achieve in the design and layout of their store.

[0185] "Store layout proposal" refers to the layout and design of a store generated based on the user's business requirements.

[0186] The present invention includes a system for proposing a home design based on a user's preferences and lifestyle, and a system for proposing a store layout. Specific embodiments for carrying out the invention are described below.

[0187] System Overview

[0188] Enter user information

[0189] Users launch a dedicated application on their device (smartphone, tablet, PC, etc.) and enter their own profile information (age, family composition, hobbies, etc.) and detailed requirements for their specific home (spacious living room, natural light, eco-friendly design, etc.). In the case of store layouts, users also enter business requirements (customer flow, placement of specific product sections, eco-friendly elements, etc.).

[0190] Data collection and transmission

[0191] The terminal collects the data entered by the user and converts it into a data format (JSON, XML, etc.), after which the data is encrypted using a library such as the Fernet encryption library and securely sent to the server.

[0192] Saving data and preparing for analysis

[0193] The server receives the encrypted user data, decrypts it, and stores it in a database, preparing it for analysis using the generative AI.

[0194] Analysis by generative AI

[0195] The AI ​​on the server generates optimal design proposals based on the information entered by the user. This AI analyzes past design data and takes into account the user's lifestyle information. Multiple variations of home designs and store layouts are often generated.

[0196] Presenting design proposals and collecting feedback

[0197] The server sends the generated multiple design proposals to the terminal. The terminal visually displays these proposals to the user and also provides detailed information about each proposal (floor plan, design, materials used, etc.). The user reviews the proposed design proposals and enters their evaluation and feedback. For example, the user can enter specific requests such as "I want more natural light in the living room" or "I want the store's cash register to be located closer to the entrance."

[0198] Reflecting feedback

[0199] The device collects feedback from the user and sends it back to the server. The server then runs the generative AI again based on the collected feedback to improve the design proposal. The improved design proposal is sent to the device as the final design proposal and provided to the user.

[0200] Specific examples

[0201] Home design examples

[0202] User A uses a dedicated app to input their preferences for a "large living room" and a "garden with a natural feel." The device collects this information, encrypts it, and sends it to the server. The server analyzes it using a generative AI and generates multiple home design proposals with large living rooms and gardens. The server sends the design proposals to the device, and User A checks the displayed proposals. When User A sends feedback such as "I would like more windows in the living room," the server reflects this request and provides the final design proposal.

[0203] Store layout example

[0204] Store owner B inputs that he needs an "eco-friendly design" and a "large home office." The device collects the information, encrypts it, and sends it to the server. The server uses generative AI to generate multiple design proposals that include eco-friendly materials and a large home office. User B inputs detailed requests from the presented design proposals and sends feedback to the server. The server uses the feedback to improve the design proposals and provides the final design proposal.

[0205] Prompt Sentence Examples

[0206] Prompts are used to provide user input to the generative AI model and generate optimal design proposals. Examples of prompts are shown below.

[0207] "User profile: Age 35, Business type: Bookstore, Desired wide aisles, Desired eco-friendly design, Considered customer flow. Generate the optimal store layout proposal based on these requirements."

[0208] As described above, the present invention is a system that provides optimal design proposals based on the user's preferences, lifestyle, and business requirements, and realizes a user-centered design process by utilizing generative AI.

[0209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0210] Step 1:

[0211] Users enter information using a dedicated application, including profile information (age, family composition, hobbies, etc.), housing requirements (spacious living room, natural light, eco-friendly design, etc.), and business requirements for the store layout (customer flow, product placement, eco-friendly design, etc.). This allows users to input their preferences, lifestyle, and business requirements.

[0212] Input: User profile information, housing requirements, business requirements

[0213] Output: User information entered

[0214] Step 2:

[0215] The device collects the input information and converts it into a data format (JSON, XML, etc.), then uses the Fernet encryption library to encrypt the collected data and send it securely to the server.

[0216] Input: User information entered

[0217] Output: Encrypted user data

[0218] Step 3:

[0219] The server receives the encrypted user data, decrypts it and stores useful information in a database.

[0220] Input: Encrypted user data

[0221] Output: User information stored in the database

[0222] Step 4:

[0223] The server passes the information stored in the database to a generative AI model, which analyzes the data to generate home design proposals and store layout proposals. The generative AI used for analysis generates optimal design proposals based on past data and prompts.

[0224] Input: User information stored in the database

[0225] Output: Generated house design proposal or store layout proposal

[0226] Step 5:

[0227] The server sends the generated multiple design proposals to the device, which visually displays the proposals to the user and provides detailed information about each proposal (floor plan, design, materials used, etc.).

[0228] Input: Generated residential design or store layout proposal

[0229] Output: Design proposals presented to the user

[0230] Step 6:

[0231] Users can review the proposed design proposals and enter their ratings and feedback. They can also enter specific requests (e.g., "I want more natural light in the living room" or "I want the cashier counter in the store to be closer to the entrance").

[0232] Input: User feedback

[0233] Output: Feedback information

[0234] Step 7:

[0235] The device collects user feedback information and sends it back to the server, which then runs the generative AI model again and refines the design proposal based on the feedback.

[0236] Input: User feedback information

[0237] Output: Improved design

[0238] Step 8:

[0239] The server transmits the improved design proposal as a final design proposal to the terminal, which then provides the final design proposal to the user.

[0240] Input: Improved design proposal

[0241] Output: Presentation of final design proposal

[0242] This series of processes enables the system to efficiently propose optimal home designs and store layouts based on the user's preferences, lifestyle, and business requirements, thereby increasing user satisfaction.

[0243] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0244] The system of the present invention combines a user's preferences and lifestyle information with an emotion engine that recognizes the user's emotions to propose optimal home designs. A specific embodiment of this system is described below.

[0245] System Overview

[0246] 1. Enter user information

[0247] Users launch a dedicated application on their device (smartphone, tablet, PC, etc.) and enter detailed profile information (age, family composition, hobbies, etc.) and specific requirements for the home (spacious living room, natural light, eco-friendly design, etc.).

[0248] At this time, the emotion engine analyzes the user's facial expressions, voice, and input data in real time to detect the user's emotional state.

[0249] 2. Data collection and transmission

[0250] The device collects the user's input data and emotion data, converts them into an appropriate data format, and transmits them to the server while ensuring the data security using encryption protocols.

[0251] 3. Saving data and preparing for analysis

[0252] The server decrypts the received encrypted data and stores it in a database. Emotional data in particular is specially tagged and managed to provide useful information for analysis.

[0253] After storage, the data is passed to a generative AI, ready for further analysis.

[0254] 4. Analysis by generative AI

[0255] The AI ​​on the server generates optimal home designs based on the user's preferences, lifestyle information, and emotional data. Emotional data is evaluated as a particularly important factor, generating multiple design proposals that will satisfy the user.

[0256] 5. Presenting design proposals and collecting feedback

[0257] The server transmits the generated multiple house design plans to the terminal.

[0258] The device visually displays these design proposals to the user and provides detailed information about each proposal (floor plan, design, materials used, etc.). In addition, an emotion engine analyzes users' reactions and comments in real time.

[0259] The user reviews the proposed design proposals and enters their evaluation and feedback. The emotion engine also records the user's emotional state when providing feedback, and sends it to the server.

[0260] 6. Incorporating feedback

[0261] The device collects feedback and emotional data from the user, encrypts it, and sends it to the server.

[0262] The server analyzes the collected feedback and emotional data and runs the generative AI again, generating improvement proposals that place particular emphasis on emotional data and updating the design proposals.

[0263] The final design is sent to the terminal and presented to the user.

[0264] Specific examples

[0265] Example 1: For user A

[0266] 1. User A enters their preferences for a "large living room" and a "natural garden" in the dedicated app. At this time, the emotion engine detects User A's relaxed facial expression.

[0267] 2. The device collects input information and emotional data, encrypts it, and sends it to the server.

[0268] 3. The server uses a generative AI to generate multiple home design proposals with spacious living rooms and gardens. By referencing the emotional data, design proposals that emphasize spacious, relaxing spaces are prioritized.

[0269] 4. The server sends the design proposal to the device, where User A confirms the displayed proposal. The emotion engine analyzes the user's facial expressions while the proposal is displayed and collects further feedback.

[0270] 5. When User A sends feedback such as "I want more windows in the living room," the emotion engine detects a satisfied facial expression, and the server reflects that request and provides a final design proposal.

[0271] Example 2: For user B

[0272] 1. User B inputs that he needs "eco-friendly design" and "large home office." The emotion engine detects User B's interesting facial expression.

[0273] 2. The device collects the information, encrypts it, and sends it to the server.

[0274] 3. The server uses generative AI to generate multiple design proposals, including eco-friendly materials and a large home office. Based on interesting facial expressions, design proposals with particularly innovative ideas are presented.

[0275] 4. User B inputs feedback based on the presented design proposal, and the emotion engine analyzes the user's emotions in real time and sends them to the server.

[0276] 5. The server refines the design proposal based on the feedback and emotion data and provides the final design proposal.

[0277] In this way, by incorporating user emotional data, the system of the present invention can propose more precise and satisfying home design proposals than ever before. The combination of the emotion engine and generative AI realizes a more advanced, user-centered design process.

[0278] The processing flow will be explained below.

[0279] Step 1:

[0280] Users launch a dedicated application on their device and enter their profile information (age, family composition, hobbies, etc.) and detailed requirements for their home (spacious living room, natural light, eco-friendly design, etc.). As they enter their information, the emotion engine analyzes the user's facial expressions and voice in real time and records emotional data.

[0281] Step 2:

[0282] The device combines the data entered by the user and the emotional data collected by the emotion engine, converting it into an appropriate data format, such as JSON, and then labeling the converted data for easy reading.

[0283] Step 3:

[0284] The terminal encrypts the collected data using an encryption protocol such as AES, ensuring the security of the data before sending it to the server.

[0285] Step 4:

[0286] The server decrypts the received encrypted data and stores it in a database. At this time, the emotion data is given a special tag and managed so that it can be used for analysis.

[0287] Step 5:

[0288] The server passes the stored user data and emotion data to the generative AI and prepares it for analysis. Data cleaning and normalization are also performed at this stage.

[0289] Step 6:

[0290] The AI ​​on the server generates optimal home designs based on user input and emotional data. Emotional data is particularly important, as it serves as an indicator for evaluating which design the user will be most satisfied with.

[0291] Step 7:

[0292] The server organizes the generated multiple housing design proposals, converts them into a presentation format, and sends them to the terminal, where they are prepared to be displayed in an easy-to-understand manner for the user.

[0293] Step 8:

[0294] The terminal visually displays the received housing design proposal to the user, and the interface includes detailed information about the design proposal (floor plan, design, materials used, etc.) to make it easy for the user to understand and evaluate.

[0295] Step 9:

[0296] The user reviews the proposed design and provides their evaluation and feedback. At this time, the emotion engine again analyzes the user's facial expressions and voice and records the emotional data at the time of feedback.

[0297] Step 10:

[0298] The device collects the feedback from the user and the recollected emotional data, re-encrypts it, and sends it to the server.

[0299] Step 11:

[0300] The server analyzes the collected feedback and additional emotional data and runs the generative AI again, generating an improved proposal that emphasizes elements that the user expressed a high sensitivity to, taking into account the emotional data in particular.

[0301] Step 12:

[0302] The server generates a final design proposal and sends it to the device, which is individually adjusted based on the emotion data and feedback. The final design proposal is presented to the user, who can approve and confirm it.

[0303] This process utilizes emotional data in addition to user preferences and lifestyle information to produce highly accurate home design proposals. The combination of the emotion engine and generative AI provides a living environment that is more satisfying to users than conventional systems.

[0304] Example 2

[0305] 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."

[0306] Conventional home design proposal systems make proposals based on the user's preferences and lifestyle, but are unable to incorporate data on the user's emotions. This makes it difficult to propose designs that reflect the user's true satisfaction. Furthermore, when modifying home designs based solely on feedback, the user's emotional state cannot be taken into account, resulting in a high likelihood of the design proposals not meeting the user's expectations. There was a need to solve these problems and realize more precise home design proposals that would provide greater user satisfaction.

[0307] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting, encrypting, and transmitting user input information and emotional data, means for receiving the input information and emotional data and storing them in a database, and means for generating an optimal home design using a generative AI model that analyzes the input information and emotional data. This makes it possible to propose a precise and satisfying home design that reflects the user's emotions.

[0308] A "user" is an individual or group that utilizes the system to input their preferences, lifestyle information, and emotional data.

[0309] "Information about preferences and lifestyle" is data about personal preferences and lifestyle habits such as the user's age, family structure, hobbies, and housing requirements.

[0310] "Emotion data" refers to data relating to the emotional state of a user that is acquired in real time from the user's facial expressions, voice, and behavior.

[0311] A "terminal" is a device used by a user to input information and communicate collected data, including smartphones, tablets, and personal computers.

[0312] "Encryption" is the process of transforming data according to a specific algorithm to ensure security during transmission.

[0313] A "server" is a computer system that decrypts the data it receives, stores it, and analyzes it using a generative AI model.

[0314] A "database" is a system for systematically storing and managing user input information and emotional data.

[0315] A "generative AI model" is an artificial intelligence model that analyzes user input information and emotional data to generate optimal home designs.

[0316] "Feedback" refers to opinions and evaluations provided by users regarding proposed home design plans.

[0317] "Revising" is the process of improving the proposed home design based on user feedback and sentiment data.

[0318] A "design proposal" is a specific design plan for a house created by a generative AI model that responds to the user's requests and emotions.

[0319] The system of the present invention combines a user's preferences and lifestyle information with an emotion engine that recognizes the user's emotions to propose optimal home designs. A specific embodiment of this system is described below.

[0320] Enter user information

[0321] Users launch a dedicated application on their device (smartphone, tablet, personal computer, etc.). They then enter their own profile information (e.g., age, family composition, hobbies, etc.) and detailed information about their specific home requirements (e.g., spacious living room, natural light, eco-friendly design, etc.). The emotion engine analyzes the user's facial expressions, voice, and input data in real time to detect their emotional state. This emotional data is an important element in the system's design proposals.

[0322] Data collection and transmission

[0323] The device collects the information and emotion data entered by the user and converts it into an appropriate data format, such as JSON. The converted data is then sent to the server using an encryption protocol (e.g., TLS). This encryption ensures the security of the data.

[0324] Saving data and preparing for analysis

[0325] After receiving the encrypted data, the server decrypts it and extracts it as JSON data. The extracted data is then stored in a database. Emotional data in particular is tagged and stored in a format that is easy to use during analysis. After all the collected data has been saved, it is passed to the generation AI and prepared for analysis.

[0326] Analysis by generative AI

[0327] The server-based AI generates multiple optimal home design proposals based on the user's saved preferences, lifestyle information, and emotional data. Emotional data is evaluated as a particularly important factor in this analysis process. Design proposals are generated using prompt sentences.

[0328] Prompt Sentence Examples

[0329] "The user has a relaxed expression and wants a spacious living room and a garden that allows for a natural feel. Please propose the optimal home design based on this."

[0330] "The user is looking for an eco-friendly design and a large home office, which is an interesting look. Please suggest the best home design based on this."

[0331] Presenting design proposals and collecting feedback

[0332] The server sends the generated house design proposal to the terminal. The terminal visually displays the design proposal to the user and provides detailed information (e.g., floor plan, design, materials used, etc.). The emotion engine analyzes the user's reactions (facial expressions and voice) again and collects feedback from the user in real time. The user's emotional state at the time of feedback is also recorded.

[0333] Reflecting feedback

[0334] The device collects feedback and emotional data from the user, re-encrypts it, and sends it to the server. The server analyzes the collected data and uses generative AI to improve the design proposal, placing particular emphasis on emotional data to consider modifications to the design proposal. After the final design proposal is generated, it is sent back to the device and presented to the user.

[0335] Specific examples

[0336] Example 1: For user A

[0337] 1. User A enters their preferences for a "large living room" and a "natural garden" into the app. The emotion engine detects a relaxed facial expression.

[0338] 2. The device encrypts this information and emotion data and sends it to the server.

[0339] 3. The server uses a generative AI to generate multiple home design proposals with spacious living rooms and gardens. By referencing the emotional data, design proposals that emphasize spacious, relaxing spaces are prioritized.

[0340] 4. The server sends the design proposal to the device, where User A confirms it. The emotion engine analyzes the facial expressions displayed and collects feedback.

[0341] 5. When User A provides feedback such as "I want more windows in the living room," the server reflects that request and provides a final design proposal.

[0342] Example 2: For user B

[0343] 1. User B inputs that they want an "eco-friendly design" and a "large home office," and the emotion engine detects an interesting facial expression.

[0344] 2. The device collects this information, encrypts it, and sends it to the server.

[0345] 3. The server uses generative AI to generate multiple eco-friendly design proposals, and proposals including innovative ideas are presented based on interesting facial expressions.

[0346] 4. User B enters feedback on the design proposal, and the emotion engine analyzes the emotions in real time and sends them to the server.

[0347] 5. Finally, the server refines the design based on feedback and emotional data and provides the final design.

[0348] By incorporating user emotional data, the system of this invention can provide more accurate and satisfying home design proposals than ever before. The collaboration between the emotion engine and generative AI realizes an advanced, user-centered design process.

[0349] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0350] Step 1: Enter your information

[0351] The user launches a dedicated application on their device. Here, the user enters their profile information (age, family composition, hobbies, etc.) and specific requirements for the home (spacious living room, natural light, eco-friendly design, etc.). The emotion engine then analyzes the user's facial expressions and voice in real time to obtain data on their emotional state (relaxed, interested, etc.). The input data is saved on the device as profile information, requirement data, and emotion data.

[0352] Input: User profile information, housing requirements, user emotion data

[0353] Output: User information and emotion data stored on the device

[0354] Step 2: Collect and send data

[0355] The device collects the user-entered data and emotion data together, converts them into an appropriate data format, such as JSON, and then securely transmits the converted data to the server using an encryption protocol (e.g., TLS), while maintaining the confidentiality and integrity of the data.

[0356] Input: User information and emotional data stored on the device

[0357] Output: Data sent to the server using an encryption protocol

[0358] Step 3: Save the data and prepare it for analysis

[0359] The server receives the encrypted data and decrypts it. The decrypted data is extracted as JSON data and stored in a database as user information and emotional data. In particular, the emotional data is tagged and managed as useful information for analysis. After all the data is stored, it is passed to the generation AI and prepared for analysis.

[0360] Input: Encrypted data sent to the server

[0361] Output: User information and emotion data stored in a database

[0362] Step 4: Analysis by generative AI

[0363] The generation AI on the server receives the saved user information and emotional data as input and analyzes it using natural language processing and machine learning algorithms. Based on this analysis, it generates multiple optimal home design proposals that reflect the user's preferences and emotional data. Specific design proposals are generated using prompt sentences.

[0364] Input: User information and emotion data in a database

[0365] Output: Multiple house design proposals

[0366] Step 5: Present your design and gather feedback

[0367] The server sends the generated house design proposals to the terminal. The terminal visually displays the proposed multiple design proposals to the user and provides detailed information (floor plan, design, materials used, etc.). The emotion engine analyzes the user's reactions again and collects feedback from the user in real time. The user's feedback and emotional state data are recorded on the terminal.

[0368] Input: Generated house design plan

[0369] Output: User feedback and emotion data

[0370] Step 6: Incorporating feedback

[0371] The device re-encrypts the feedback and emotional data collected from the user and sends it to the server. The server analyzes this data and uses generative AI to improve the design proposal. It places particular emphasis on emotional data to generate a final design proposal that reflects the user's feedback. The final design proposal is then sent to the device and presented to the user.

[0372] Input: User feedback and emotional data

[0373] Output: Revised final house design

[0374] (Application example 2)

[0375] 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."

[0376] There is a demand for a system that can provide optimal suggestions to individual users by taking into account not only the user's preferences and lifestyle information but also real-time emotional data. However, conventional systems lack the means to analyze the user's emotions, making it difficult to further improve user satisfaction. The present invention aims to provide a system that can provide suggestions with higher satisfaction by acquiring real-time emotional data via a device worn by the user and adjusting the content of the suggestions based on that data.

[0377] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting information about a user's preferences and lifestyle; means for collecting, encrypting, and transmitting the user's input information; means for receiving the input information and storing it in a database; means for generating optimal suggestions using a generation AI that analyzes the input information; means for presenting the generated suggestions to the user and collecting user feedback; means for analyzing the user's facial expressions and voice in real time and acquiring emotional data; means for adjusting the suggestions based on the emotional data; means for analyzing the user's interests and satisfaction via a device worn by the user; and means for modifying the suggestions based on the user's feedback. This enables suggestions that reflect the user's emotions in real time.

[0378] "User" refers to the consumer or customer who uses the system.

[0379] "Preferences" is information that indicates a user's personal tastes and preferences.

[0380] "Lifestyle" is information about the user's daily habits and behavior patterns.

[0381] "Means for inputting information" refers to a function that allows a user to provide their own profile information through a terminal.

[0382] "Means for encryption and transmission" refers to the function of encrypting collected data to protect it from unauthorized access from outside and transmitting it safely to a server.

[0383] A "database" is a system for storing and managing information obtained from users.

[0384] "Generative AI" refers to artificial intelligence algorithms used to analyze collected data and generate optimal recommendations.

[0385] "Suggestions" refer to recommended options or plans that generative AI creates based on user information and emotional data.

[0386] "Means for collecting feedback" refers to the functionality for incorporating user-provided opinions and reactions into the system.

[0387] "Emotion data" refers to data that indicates the emotional state of a user, obtained by analyzing the user's facial expressions, voice, etc. in real time.

[0388] A "wearable device" is a device that is worn by the user, such as smart glasses or a head-mounted display.

[0389] "Means of adjustment" refers to the ability to change and optimize proposal content in real time based on emotional data and feedback.

[0390] "Means for analyzing interest and satisfaction" refers to a function that measures user interest and satisfaction based on their reactions.

[0391] "Means to modify" refers to the ability to improve existing suggestions based on user feedback.

[0392] An embodiment of the present invention will be described. The system of the present invention combines a user's preference and lifestyle information with an emotion engine that recognizes the user's emotions to provide optimal suggestions. The system includes: means for inputting information about a user's preferences and lifestyle; means for collecting, encrypting, and transmitting the input information; means for receiving the input information and storing it in a database; means for generating optimal suggestions using a generation AI that analyzes the input information; means for presenting the generated suggestions to the user and collecting user feedback; means for analyzing the user's facial expressions and voice in real time to obtain emotion data; means for adjusting the suggestions based on the emotion data; means for analyzing the user's interests and satisfaction via a device worn by the user; and means for modifying the suggestions based on user feedback.

[0393] System Structure

[0394] 1. User information input: Users input their preferences and lifestyle information into a dedicated application via devices such as smart glasses or head-mounted displays. The input includes preferred categories and specific conditions. This allows the user's preferences and lifestyle information to be collected.

[0395] 2. Data collection and transmission: User input data is collected in real time by the device and transmitted to the server using a secure encryption protocol. During this process, the emotion engine analyzes the user's facial expressions and voice, and simultaneously collects emotional data.

[0396] 3. Data storage and analysis preparation: The server decrypts the received encrypted data and stores it in a database. The stored data is labeled with emotion data and used for later analysis.

[0397] 4. Analysis by Generative AI: The Generative AI on the server generates optimal suggestions based on the user's preferences, lifestyle information, and emotional data. The Generative AI analyzes the data and generates suggestions to increase user satisfaction. This analysis includes data analysis using the Google Cloud Vision API and Flask.

[0398] 5. Proposal presentation and feedback collection: The server sends the generated proposals to the device and presents them visually to the user. The user reviews the proposals and provides feedback on their opinions and reactions to each proposal. During this process, the emotion engine continuously analyzes the user's facial expressions and voice to collect emotional data in real time.

[0399] 6. Reflecting Emotional Data and Modifying Proposals: The server analyzes the collected feedback and emotional data and revises the proposals as necessary. It places particular emphasis on emotional data and adjusts the proposals to reflect the user's interests and satisfaction, thereby meeting the user's sophisticated requirements.

[0400] Hardware and software used

[0401] Hardware: smart glasses, head-mounted displays, smartphones

[0402] Software: Google Cloud Vision API, Flask, Emotion Engine

[0403] Specific examples

[0404] Example 1: When the user is shopping

[0405] Suppose User A is wearing smart glasses in a physical store. When he or she looks at a specific product from the gallery, the application detects the gaze and uses an emotion engine to analyze whether the product is interesting to the user. As a result, the application suggests, "Are you interested in this product?" and also presents other related and recommended products.

[0406] Example 2: User is looking for a new product

[0407] Let's say User B is wearing a head-mounted display. While searching for new fashion items, he stops at a specific zone. The emotion engine analyzes the user's facial expression of joy. The application then recommends, "Are you still interested in products in this zone?" and provides related product information.

[0408] Prompt Sentence Examples

[0409] "How can we make optimal shopping recommendations based on user profile information and real-time sentiment data? For example, this could include using recommendation technology to target products that users are interested in, thereby increasing interest and satisfaction."

[0410] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0411] Step 1:

[0412] Through a device worn by the user (smart glasses or a head-mounted display), the user logs into the application and inputs information about their preferences and lifestyle. The input information includes age, hobbies, and categories of purchase interest. This information is collected by the user's device. As an output, the collected user data is encrypted.

[0413] Step 2:

[0414] The device sends the collected user data to the server using an encryption protocol. This process ensures the security of the user information and prevents unauthorized access. The input is the encrypted user data, and the output is the data securely transmitted to the server.

[0415] Step 3:

[0416] The server decrypts the received encrypted data and stores it in a user database, which often contains user profile and lifestyle information for later analysis. The input is the decrypted user data, and the output is the data stored in the database.

[0417] Step 4:

[0418] The server uses a generation AI to analyze the user's input information and generate optimal suggestions, taking into account the user's emotional data. The generation AI uses the Google Cloud Vision API to deeply analyze the user's interests and satisfaction. The input is the user data and emotional data in the database, and the output is the generated suggestion data.

[0419] Step 5:

[0420] The server sends the generated suggestions to the user's device, where the user visually confirms the suggestions through smart glasses or a head-mounted display. The user provides feedback on each suggestion, and the emotion engine analyzes the user's facial expressions and voice to collect emotion data. The input is the generated suggestion data, and the output is the user's feedback and emotion data.

[0421] Step 6:

[0422] The device retransmits the collected feedback and emotion data to the server. This feedback includes the user's specific opinions and reactions to the suggestions. The input is the user's feedback and emotion data, and the output is the state sent to the server.

[0423] Step 7:

[0424] The server analyzes the collected feedback and sentiment data and adjusts the suggestions as needed. It then uses generative AI to generate new suggestions, further improving user satisfaction. This process again uses the Google Cloud Vision API. The input is the collected feedback and sentiment data, and the output is improved suggestions.

[0425] Step 8:

[0426] The server sends the improved proposal data back to the user terminal and provides the final proposal to the user, who can then review it and make a final decision. The input is the improved proposal data, and the output is the improved proposal presented to the user.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] [Second embodiment]

[0431] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0432] 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.

[0433] 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).

[0434] 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.

[0435] 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.

[0436] 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).

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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."

[0443] The system of the present invention proposes optimal home designs based on the user's preferences and lifestyle. A specific embodiment of the system is described below.

[0444] System Overview

[0445] 1. Enter user information

[0446] Users launch a dedicated application on their device (smartphone, tablet, PC, etc.) and enter detailed profile information (age, family composition, hobbies, etc.) and specific requirements for the home (spacious living room, natural light, eco-friendly design, etc.).

[0447] 2. Data collection and transmission

[0448] The terminal collects the data entered by the user and converts it into a data format (JSON, XML, etc.).

[0449] The collected data is encrypted on the device and securely sent to the server.

[0450] 3. Saving data and preparing for analysis

[0451] The server decrypts the received user data and stores it in a database.

[0452] The received data is passed to the generation AI and prepared for detailed analysis.

[0453] 4. Analysis by generative AI

[0454] The AI ​​on the server generates the optimal home design based on the information entered by the user. This AI analyzes data from past home designs and takes into account the user's lifestyle information.

[0455] The generated home design proposals may have multiple variations, and are designed to expand the user's options.

[0456] 5. Presenting design proposals and collecting feedback

[0457] The server transmits the generated multiple housing design plans to the terminal.

[0458] The device visually displays these design proposals to the user and also provides detailed information about each proposal (floor plan, design, materials used, etc.).

[0459] Users can review the proposed design and provide their ratings and feedback, including specific requests such as "I'd like more natural light in the living room."

[0460] 6. Incorporating feedback

[0461] The terminal collects feedback from the user and transmits it back to the server.

[0462] The server runs the generative AI again based on the collected feedback and improves the design proposal.

[0463] The improved design proposal is sent to the terminal as the final design proposal and provided to the user.

[0464] Specific examples

[0465] Example 1: User A's case

[0466] 1. User A enters their preferences for a "large living room" and a "garden with a natural feel" in a dedicated app.

[0467] 2. The device collects this information, encrypts it, and sends it to the server.

[0468] 3. The server analyzes the data using generative AI and generates multiple home design proposals with spacious living rooms and gardens.

[0469] 4. The server sends the design proposal to the terminal, and User A checks the displayed proposal.

[0470] 5. When User A sends feedback such as "I would like more windows in the living room," the server reflects that request and provides the final design proposal.

[0471] Example 2: User B's case

[0472] 1. User B inputs that they need an "eco-friendly design" and a "large home office."

[0473] 2. The device collects the information, encrypts it, and sends it to the server.

[0474] 3. The server uses generative AI to generate multiple design options, including eco-friendly materials and a large home office.

[0475] 4. User B inputs detailed requests based on the presented design proposal and sends feedback to the server.

[0476] 5. The server refines the design based on the feedback and provides the final design.

[0477] In this way, this system can efficiently realize optimal home designs that meet the individual needs of users. By utilizing generative AI, it is possible to promote a user-centered design process and provide a living environment that will satisfy users.

[0478] The processing flow will be explained below.

[0479] Step 1:

[0480] Users launch a dedicated application on their device and enter detailed information about themselves (age, family composition, hobbies, etc.) and the requirements they have for their home (spacious living room, natural light, eco-friendly design, etc.).

[0481] Step 2:

[0482] The terminal collects the data entered by the user and converts it into an appropriate data format, such as JSON. The terminal provides an interface for the user to review and correct the input data as needed.

[0483] Step 3:

[0484] The terminal encrypts the collected data using an encryption protocol such as AES, ensuring security before sending it to the server.

[0485] Step 4:

[0486] The server decrypts the received encrypted data and stores it in a database, after which it performs processing to check the timestamp and data consistency.

[0487] Step 5:

[0488] The server converts the stored user data into a format that can be interpreted by the generation AI and prepares it for delivery to the generation AI, where data cleaning and normalization are performed as necessary.

[0489] Step 6:

[0490] The AI ​​on the server analyzes the user's input in detail and generates the optimal home design. This analysis reflects parameters based on the user's lifestyle and preferences. It also takes into account past design data and trend information.

[0491] Step 7:

[0492] The server organizes the generated multiple home design proposals and converts them into a presentation format, which allows them to be sent to devices in a visually easy-to-understand format.

[0493] Step 8:

[0494] The terminal visually displays the received housing design proposals to the user, providing an interface that includes detailed information about each proposal (floor plan, design, materials used, etc.) to enable the user to easily understand and evaluate them.

[0495] Step 9:

[0496] Users review the proposed design proposals and enter their ratings and feedback (e.g., "I'd like more windows in the living room.") Feedback is provided using simple forms and options on the UI.

[0497] Step 10:

[0498] The device collects feedback from the user, re-encrypts it, and sends it to the server.

[0499] Step 11:

[0500] The server analyzes the collected feedback and runs the generative AI again, generating improvements based on the feedback and updating the design proposal.

[0501] Step 12:

[0502] The server generates the final design proposal and sends it to the terminal, where it is presented to the user, who can approve and confirm the proposal.

[0503] In this way, the system executes a series of processes that generate multiple home design proposals based on information based on the user's preferences and lifestyle, and provides a final design proposal that reflects the user's feedback.

[0504] Example 1

[0505] 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."

[0506] Conventional home design proposal systems have had difficulty efficiently generating optimal designs that correspond to a user's individual lifestyle and preferences. Furthermore, incorporating user feedback is cumbersome, making it difficult to quickly improve the design. The present invention aims to solve these problems and provide a system that efficiently provides optimal home designs that meet the user's needs.

[0507] 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.

[0508] In this invention, the server includes: means for inputting information about a user's preferences and lifestyle; means for collecting the user's input information, converting it into JSON format, encrypting it, and transmitting it; means for receiving the input information, decrypting it, and storing it in a database; means for generating multiple home design proposals using a generation AI based on the user's input information; means for visually presenting the generated multiple home design proposals to the user and collecting feedback according to the user's preferences and lifestyle; and means for collecting the user's feedback and running the generation AI again to modify the home design proposals. This makes it possible to quickly and efficiently generate and improve home design proposals based on the user's input information and feedback.

[0509] "Information about the user's preferences and lifestyle" refers to specific factors that the user places importance on in the living environment, such as the user's age, family structure, hobbies, conditions of the place of residence, and requirements for the home.

[0510] "Input means" refers to devices or software that provide an interface for users to input information. Specifically, this refers to electronic devices such as smartphones, tablets, and PCs, as well as the applications that run on them.

[0511] "Means of converting to a data format, encrypting, and transmitting" refers to the series of processes in which information entered by the user is converted into a standard data format such as JSON or XML, encrypted using an encryption algorithm such as AES-256, and transmitted to a server via a communication line.

[0512] "Means of receiving, decrypting, and storing in a database" refers to the process by which the server receives the encrypted data sent from the terminal, decrypts it using the private key, and then stores the data in a database (e.g., MySQL).

[0513] "Means for generating multiple home design proposals using generative AI" refers to the process of using generative AI (artificial intelligence) to create multiple home design proposals that meet the user's needs based on past home design data and the user's lifestyle information.

[0514] "Means for visually presenting to the user and collecting feedback based on the user's preferences and lifestyle" refers to a process that displays multiple generated design proposals on the user's device and provides an interactive interface that allows the user to review the proposals and input their ratings and suggestions for improvement.

[0515] "Means for re-running the generating AI to modify the housing design proposal" refers to the process of re-running the generating AI and improving the design proposal based on feedback collected from the user, resulting in a final design proposal that more accurately reflects the user's wishes.

[0516] The present invention is a system that proposes optimal home design based on a user's preferences and lifestyle. The system of the present invention mainly operates in cooperation with the user, a terminal, and a server. The processing at each step and the hardware and software used are described in detail below.

[0517] 1. Enter user information

[0518] Users launch a dedicated application using a device such as a smartphone, tablet, or PC. Through this application, users input information such as their age, family composition, hobbies, location, and specific requirements for the home (e.g., a spacious living room, natural light, eco-friendly design, etc.). This input interface is often implemented using front-end frameworks such as React or Angular.

[0519] 2. Data collection and transmission

[0520] The terminal collects the information entered by the user and converts it into JSON format. It then encrypts the data using the AES-256 encryption algorithm. The encrypted data is then sent to the server using a secure communication protocol (e.g., HTTPS). This process typically uses a backend framework such as Node.js or Python.

[0521] 3. Saving data and preparing for analysis

[0522] The server receives the encrypted data sent from the device and decrypts it using the private key. This decrypted data is then returned to its original format using JSON decoding. The data is then stored in a database such as MySQL or PostgreSQL. The server retrieves the user data from the database and prepares it for analysis before passing it to the generation AI.

[0523] 4. Analysis by generative AI

[0524] A generative AI (e.g., OpenAI GPT-4 model) located on the server generates optimal home design proposals based on user input. This generative AI performs analysis while taking into account past home design data and the user's lifestyle information. The generated home design proposals have multiple variations, designed to expand the user's options.

[0525] 5. Presenting design proposals and collecting feedback

[0526] The server sends the generated multiple home design proposals to the terminal. The terminal visually displays these design proposals to the user. 2D or 3D graphics are often used as the display method. Specifically, the interface can be built using libraries such as Three.js or Unity. The user reviews the proposed design proposals and enters their ratings and feedback. This feedback can include specific requests such as "I want more natural light in the living room."

[0527] 6. Incorporating feedback

[0528] The device collects feedback from the user and sends it back to the server. The server then operates the generation AI again based on the collected feedback to improve the design proposal. The improved design proposal is sent to the device as the final design proposal and provided to the user. The final design proposal reflects the user's requests as much as possible, making it possible to achieve a higher level of satisfaction.

[0529] Example prompt sentences

[0530] 1. For User A

[0531] The user wants a "large living room" and a "natural garden." Please propose the optimal house design based on this.

[0532] 2. For User B

[0533] The user wants an "eco-friendly design" and a "large home office." Please generate the optimal home design proposal taking this into consideration.

[0534] The system of this invention can efficiently realize optimal home designs that meet the individual needs of users. Furthermore, by utilizing generative AI, it is possible to promote a user-centered design process and provide a living environment that is highly satisfying.

[0535] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0536] Step 1:

[0537] The user enters information

[0538] Specific operation: The user launches a dedicated application on a device such as a smartphone, tablet, or PC and enters their profile information (age, family composition, hobbies, etc.) and their specific requirements for the home (spacious living room, natural light, eco-friendly design, etc.).

[0539] Input: Manual input of information by the user.

[0540] Output: Data entered by the user that is temporarily stored on the device.

[0541] Step 2:

[0542] The device collects data, converts and encrypts it, and then transmits it.

[0543] What it does: The device collects the information entered by the user, converts it into JSON format, encrypts the data using the AES-256 encryption algorithm, and sends it to the server via a secure communication protocol (HTTPS).

[0544] Input: Information entered by the user (e.g., "We want a large living room with natural light for a 30-year-old couple and their 5-year-old child").

[0545] Output: The encrypted user information is sent to the server.

[0546] Step 3:

[0547] The server receives the data, decrypts it, and stores it in a database

[0548] How it works: The server receives the encrypted data sent from the device and decrypts it using the private key. The decrypted data is then converted back to its original format using JSON decoding. This data is then stored in a database such as MySQL or PostgreSQL.

[0549] Input: Encrypted user information.

[0550] Output: The decrypted user information is stored in the database.

[0551] Step 4:

[0552] The server generates a house design plan using generation AI

[0553] Specific operation: The server retrieves user information stored in the database and passes it to the generation AI. The generation AI (e.g., OpenAI GPT-4 model) generates multiple home design proposals based on past home design data and the user's lifestyle information, tailored to the user's needs.

[0554] Input: User information decrypted and stored in a database.

[0555] Output: Multiple house design proposals generated.

[0556] Step 5:

[0557] The server sends the design proposal to the terminal, which then presents it to the user.

[0558] How it works: The server sends multiple home design proposals generated by the AI ​​to the device. The device then displays these proposals to the user in a visually easy-to-read format (e.g., 2D or 3D graphics). Libraries such as Three.js and Unity are often used.

[0559] Input: Generated multiple house design proposals.

[0560] Output: A visual representation of the design to the user.

[0561] Step 6:

[0562] User enters feedback

[0563] Specific operation: The user reviews the presented design proposals and enters their evaluation and feedback on improvements. An example of feedback might be, "I'd like more windows in the living room."

[0564] Input: Manual input of feedback by the user.

[0565] Output: Feedback data temporarily stored in the device.

[0566] Step 7:

[0567] The device collects feedback and sends it to the server

[0568] Specific operation: The device collects the feedback entered by the user, converts it back into JSON format, encrypts it, and sends it to the server using a secure communication protocol (HTTPS).

[0569] Input: Feedback entered by the user.

[0570] Output: The encrypted feedback data is sent to the server.

[0571] Step 8:

[0572] The server improves the design based on the feedback.

[0573] How it works: The server receives the encrypted feedback, decrypts it, and decodes the JSON. It then passes the feedback to the generation AI, which analyzes it again to generate the optimal design. The improved design is then sent back to the device and provided to the user.

[0574] Input: The decrypted and JSON decoded feedback data.

[0575] Output: An improved final house design.

[0576] (Application example 1)

[0577] 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."

[0578] While conventional systems generally propose home designs based on the user's preferences and lifestyle, they lack support for store layouts. In particular, there are no systems that propose optimal layouts based on the store owner's business requirements. This results in the problem of store owners spending a great deal of time and money designing their ideal store layout.

[0579] 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.

[0580] In this invention, the server includes means for inputting information about a user's preferences and lifestyle, means for collecting the user's input information, encrypting and transmitting it, means for receiving the input information and storing it in a database, means for generating an optimal home design using a generation AI that analyzes the input information, means for presenting the generated home design to the user and collecting user feedback, means for modifying the home design based on the user feedback, means for inputting the user's business requirements, and means for generating a store layout plan based on the business requirements and presenting it to the user. This makes it possible to propose and improve an ideal store layout based on the store owner's business requirements.

[0581] "User" refers to any individual or corporation that requires proposals for home design or store layout.

[0582] "Preferences" refer to specific attributes such as design, layout, and functionality desired by the user.

[0583] "Lifestyle" refers to the habits and behavioral patterns of a user's daily life.

[0584] "Means for inputting information" refers to the interface for collecting information such as user preferences, lifestyle, and business requirements.

[0585] "Means of collection, encryption and transmission" refers to devices or software that electronically collect information entered by users, encrypt it to keep the data secure, and transmit it to a server.

[0586] "Means for receiving and storing in a database" refers to devices or software that receive encrypted user information, decrypt it, and store it in a database.

[0587] "Generative AI" refers to artificial intelligence that automatically generates optimal design proposals based on information provided by the user.

[0588] "Home design" refers to the layout and design of a home that suits the user's preferences and lifestyle.

[0589] The "means for presenting and collecting feedback" refers to a device or software that visually displays the generated design proposal to the user and receives evaluations and additional requests from the user.

[0590] "Modification tools" refers to devices or software that refine designs based on feedback collected from users.

[0591] "Business requirements" refer to the specific conditions or goals that a store owner wants to achieve in the design and layout of their store.

[0592] "Store layout proposal" refers to the layout and design of a store generated based on the user's business requirements.

[0593] The present invention includes a system for proposing a home design based on a user's preferences and lifestyle, and a system for proposing a store layout. Specific embodiments for carrying out the invention are described below.

[0594] System Overview

[0595] Enter user information

[0596] Users launch a dedicated application on their device (smartphone, tablet, PC, etc.) and enter their own profile information (age, family composition, hobbies, etc.) and detailed requirements for their specific home (spacious living room, natural light, eco-friendly design, etc.). In the case of store layouts, users also enter business requirements (customer flow, placement of specific product sections, eco-friendly elements, etc.).

[0597] Data collection and transmission

[0598] The terminal collects the data entered by the user and converts it into a data format (JSON, XML, etc.), after which the data is encrypted using a library such as the Fernet encryption library and securely sent to the server.

[0599] Saving data and preparing for analysis

[0600] The server receives the encrypted user data, decrypts it, and stores it in a database, preparing it for analysis using the generative AI.

[0601] Analysis by generative AI

[0602] The AI ​​on the server generates optimal design proposals based on the information entered by the user. This AI analyzes past design data and takes into account the user's lifestyle information. Multiple variations of home designs and store layouts are often generated.

[0603] Presenting design proposals and collecting feedback

[0604] The server sends the generated multiple design proposals to the terminal. The terminal visually displays these proposals to the user and also provides detailed information about each proposal (floor plan, design, materials used, etc.). The user reviews the proposed design proposals and enters their evaluation and feedback. For example, the user can enter specific requests such as "I want more natural light in the living room" or "I want the store's cash register to be located closer to the entrance."

[0605] Reflecting feedback

[0606] The device collects feedback from the user and sends it back to the server. The server then runs the generative AI again based on the collected feedback to improve the design proposal. The improved design proposal is sent to the device as the final design proposal and provided to the user.

[0607] Specific examples

[0608] Home design examples

[0609] User A uses a dedicated app to input their preferences for a "large living room" and a "garden with a natural feel." The device collects this information, encrypts it, and sends it to the server. The server analyzes it using a generative AI and generates multiple home design proposals with large living rooms and gardens. The server sends the design proposals to the device, and User A checks the displayed proposals. When User A sends feedback such as "I would like more windows in the living room," the server reflects this request and provides the final design proposal.

[0610] Store layout example

[0611] Store owner B inputs that he needs an "eco-friendly design" and a "large home office." The device collects the information, encrypts it, and sends it to the server. The server uses generative AI to generate multiple design proposals that include eco-friendly materials and a large home office. User B inputs detailed requests from the presented design proposals and sends feedback to the server. The server uses the feedback to improve the design proposals and provides the final design proposal.

[0612] Prompt Sentence Examples

[0613] Prompts are used to provide user input to the generative AI model and generate optimal design proposals. Examples of prompts are shown below.

[0614] "User profile: Age 35, Business type: Bookstore, Desired wide aisles, Desired eco-friendly design, Considered customer flow. Generate the optimal store layout proposal based on these requirements."

[0615] As described above, the present invention is a system that provides optimal design proposals based on the user's preferences, lifestyle, and business requirements, and realizes a user-centered design process by utilizing generative AI.

[0616] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0617] Step 1:

[0618] Users enter information using a dedicated application, including profile information (age, family composition, hobbies, etc.), housing requirements (spacious living room, natural light, eco-friendly design, etc.), and business requirements for the store layout (customer flow, product placement, eco-friendly design, etc.). This allows users to input their preferences, lifestyle, and business requirements.

[0619] Input: User profile information, housing requirements, business requirements

[0620] Output: User information entered

[0621] Step 2:

[0622] The device collects the input information and converts it into a data format (JSON, XML, etc.), then uses the Fernet encryption library to encrypt the collected data and send it securely to the server.

[0623] Input: User information entered

[0624] Output: Encrypted user data

[0625] Step 3:

[0626] The server receives the encrypted user data, decrypts it and stores useful information in a database.

[0627] Input: Encrypted user data

[0628] Output: User information stored in the database

[0629] Step 4:

[0630] The server passes the information stored in the database to a generative AI model, which analyzes the data to generate home design proposals and store layout proposals. The generative AI used for analysis generates optimal design proposals based on past data and prompts.

[0631] Input: User information stored in the database

[0632] Output: Generated house design proposal or store layout proposal

[0633] Step 5:

[0634] The server sends the generated multiple design proposals to the device, which visually displays the proposals to the user and provides detailed information about each proposal (floor plan, design, materials used, etc.).

[0635] Input: Generated residential design or store layout proposal

[0636] Output: Design proposals presented to the user

[0637] Step 6:

[0638] Users can review the proposed design proposals and enter their ratings and feedback. They can also enter specific requests (e.g., "I want more natural light in the living room" or "I want the cashier counter in the store to be closer to the entrance").

[0639] Input: User feedback

[0640] Output: Feedback information

[0641] Step 7:

[0642] The device collects user feedback information and sends it back to the server, which then runs the generative AI model again and refines the design proposal based on the feedback.

[0643] Input: User feedback information

[0644] Output: Improved design

[0645] Step 8:

[0646] The server transmits the improved design proposal as a final design proposal to the terminal, which then provides the final design proposal to the user.

[0647] Input: Improved design proposal

[0648] Output: Presentation of final design proposal

[0649] This series of processes enables the system to efficiently propose optimal home designs and store layouts based on the user's preferences, lifestyle, and business requirements, thereby increasing user satisfaction.

[0650] 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.

[0651] The system of the present invention combines a user's preferences and lifestyle information with an emotion engine that recognizes the user's emotions to propose optimal home designs. A specific embodiment of this system is described below.

[0652] System Overview

[0653] 1. Enter user information

[0654] Users launch a dedicated application on their device (smartphone, tablet, PC, etc.) and enter detailed profile information (age, family composition, hobbies, etc.) and specific requirements for the home (spacious living room, natural light, eco-friendly design, etc.).

[0655] At this time, the emotion engine analyzes the user's facial expressions, voice, and input data in real time to detect the user's emotional state.

[0656] 2. Data collection and transmission

[0657] The device collects the user's input data and emotion data, converts them into an appropriate data format, and transmits them to the server while ensuring the data security using encryption protocols.

[0658] 3. Saving data and preparing for analysis

[0659] The server decrypts the received encrypted data and stores it in a database. Emotional data in particular is specially tagged and managed to provide useful information for analysis.

[0660] After storage, the data is passed to a generative AI, ready for further analysis.

[0661] 4. Analysis by generative AI

[0662] The AI ​​on the server generates optimal home designs based on the user's preferences, lifestyle information, and emotional data. Emotional data is evaluated as a particularly important factor, generating multiple design proposals that will satisfy the user.

[0663] 5. Presenting design proposals and collecting feedback

[0664] The server transmits the generated multiple house design plans to the terminal.

[0665] The device visually displays these design proposals to the user and provides detailed information about each proposal (floor plan, design, materials used, etc.). In addition, an emotion engine analyzes users' reactions and comments in real time.

[0666] The user reviews the proposed design proposals and enters their evaluation and feedback. The emotion engine also records the user's emotional state when providing feedback, and sends it to the server.

[0667] 6. Incorporating feedback

[0668] The device collects feedback and emotional data from the user, encrypts it, and sends it to the server.

[0669] The server analyzes the collected feedback and emotional data and runs the generative AI again, generating improvement proposals that place particular emphasis on emotional data and updating the design proposals.

[0670] The final design is sent to the terminal and presented to the user.

[0671] Specific examples

[0672] Example 1: For user A

[0673] 1. User A enters their preferences for a "large living room" and a "natural garden" in the dedicated app. At this time, the emotion engine detects User A's relaxed facial expression.

[0674] 2. The device collects input information and emotional data, encrypts it, and sends it to the server.

[0675] 3. The server uses a generative AI to generate multiple home design proposals with spacious living rooms and gardens. By referencing the emotional data, design proposals that emphasize spacious, relaxing spaces are prioritized.

[0676] 4. The server sends the design proposal to the device, where User A confirms the displayed proposal. The emotion engine analyzes the user's facial expressions while the proposal is displayed and collects further feedback.

[0677] 5. When User A sends feedback such as "I want more windows in the living room," the emotion engine detects a satisfied facial expression, and the server reflects that request and provides a final design proposal.

[0678] Example 2: For user B

[0679] 1. User B inputs that he needs "eco-friendly design" and "large home office." The emotion engine detects User B's interesting facial expression.

[0680] 2. The device collects the information, encrypts it, and sends it to the server.

[0681] 3. The server uses generative AI to generate multiple design proposals, including eco-friendly materials and a large home office. Based on interesting facial expressions, design proposals with particularly innovative ideas are presented.

[0682] 4. User B inputs feedback based on the presented design proposal, and the emotion engine analyzes the user's emotions in real time and sends them to the server.

[0683] 5. The server refines the design proposal based on the feedback and emotion data and provides the final design proposal.

[0684] In this way, by incorporating user emotional data, the system of the present invention can propose more precise and satisfying home design proposals than ever before. The combination of the emotion engine and generative AI realizes a more advanced, user-centered design process.

[0685] The processing flow will be explained below.

[0686] Step 1:

[0687] Users launch a dedicated application on their device and enter their profile information (age, family composition, hobbies, etc.) and detailed requirements for their home (spacious living room, natural light, eco-friendly design, etc.). As they enter their information, the emotion engine analyzes the user's facial expressions and voice in real time and records emotional data.

[0688] Step 2:

[0689] The device combines the data entered by the user and the emotional data collected by the emotion engine, converting it into an appropriate data format, such as JSON, and then labeling the converted data for easy reading.

[0690] Step 3:

[0691] The terminal encrypts the collected data using an encryption protocol such as AES, ensuring the security of the data before sending it to the server.

[0692] Step 4:

[0693] The server decrypts the received encrypted data and stores it in a database. At this time, the emotion data is given a special tag and managed so that it can be used for analysis.

[0694] Step 5:

[0695] The server passes the stored user data and emotion data to the generative AI and prepares it for analysis. Data cleaning and normalization are also performed at this stage.

[0696] Step 6:

[0697] The AI ​​on the server generates optimal home designs based on user input and emotional data. Emotional data is particularly important, as it serves as an indicator for evaluating which design the user will be most satisfied with.

[0698] Step 7:

[0699] The server organizes the generated multiple housing design proposals, converts them into a presentation format, and sends them to the terminal, where they are prepared to be displayed in an easy-to-understand manner for the user.

[0700] Step 8:

[0701] The terminal visually displays the received housing design proposal to the user, and the interface includes detailed information about the design proposal (floor plan, design, materials used, etc.) to make it easy for the user to understand and evaluate.

[0702] Step 9:

[0703] The user reviews the proposed design and provides their evaluation and feedback. At this time, the emotion engine again analyzes the user's facial expressions and voice and records the emotional data at the time of feedback.

[0704] Step 10:

[0705] The device collects the feedback from the user and the recollected emotional data, re-encrypts it, and sends it to the server.

[0706] Step 11:

[0707] The server analyzes the collected feedback and additional emotional data and runs the generative AI again, generating an improved proposal that emphasizes elements that the user expressed a high sensitivity to, taking into account the emotional data in particular.

[0708] Step 12:

[0709] The server generates a final design proposal and sends it to the device, which is individually adjusted based on the emotion data and feedback. The final design proposal is presented to the user, who can approve and confirm it.

[0710] This process utilizes emotional data in addition to user preferences and lifestyle information to produce highly accurate home design proposals. The combination of the emotion engine and generative AI provides a living environment that is more satisfying to users than conventional systems.

[0711] Example 2

[0712] 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."

[0713] Conventional home design proposal systems make proposals based on the user's preferences and lifestyle, but are unable to incorporate data on the user's emotions. This makes it difficult to propose designs that reflect the user's true satisfaction. Furthermore, when modifying home designs based solely on feedback, the user's emotional state cannot be taken into account, resulting in a high likelihood of the design proposals not meeting the user's expectations. There was a need to solve these problems and realize more precise home design proposals that would provide greater user satisfaction.

[0714] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting, encrypting, and transmitting user input information and emotional data, means for receiving the input information and emotional data and storing them in a database, and means for generating an optimal home design using a generative AI model that analyzes the input information and emotional data. This makes it possible to propose a precise and satisfying home design that reflects the user's emotions.

[0715] A "user" is an individual or group that utilizes the system to input their preferences, lifestyle information, and emotional data.

[0716] "Information about preferences and lifestyle" is data about personal preferences and lifestyle habits such as the user's age, family structure, hobbies, and housing requirements.

[0717] "Emotion data" refers to data relating to the emotional state of a user that is acquired in real time from the user's facial expressions, voice, and behavior.

[0718] A "terminal" is a device used by a user to input information and communicate collected data, including smartphones, tablets, and personal computers.

[0719] "Encryption" is the process of transforming data according to a specific algorithm to ensure security during transmission.

[0720] A "server" is a computer system that decrypts the data it receives, stores it, and analyzes it using a generative AI model.

[0721] A "database" is a system for systematically storing and managing user input information and emotional data.

[0722] A "generative AI model" is an artificial intelligence model that analyzes user input information and emotional data to generate optimal home designs.

[0723] "Feedback" refers to opinions and evaluations provided by users regarding proposed home design plans.

[0724] "Revising" is the process of improving the proposed home design based on user feedback and sentiment data.

[0725] A "design proposal" is a specific design plan for a house created by a generative AI model that responds to the user's requests and emotions.

[0726] The system of the present invention combines a user's preferences and lifestyle information with an emotion engine that recognizes the user's emotions to propose optimal home designs. A specific embodiment of this system is described below.

[0727] Enter user information

[0728] Users launch a dedicated application on their device (smartphone, tablet, personal computer, etc.). They then enter their own profile information (e.g., age, family composition, hobbies, etc.) and detailed information about their specific home requirements (e.g., spacious living room, natural light, eco-friendly design, etc.). The emotion engine analyzes the user's facial expressions, voice, and input data in real time to detect their emotional state. This emotional data is an important element in the system's design proposals.

[0729] Data collection and transmission

[0730] The device collects the information and emotion data entered by the user and converts it into an appropriate data format, such as JSON. The converted data is then sent to the server using an encryption protocol (e.g., TLS). This encryption ensures the security of the data.

[0731] Saving data and preparing for analysis

[0732] After receiving the encrypted data, the server decrypts it and extracts it as JSON data. The extracted data is then stored in a database. Emotional data in particular is tagged and stored in a format that is easy to use during analysis. After all the collected data has been saved, it is passed to the generation AI and prepared for analysis.

[0733] Analysis by generative AI

[0734] The server-based AI generates multiple optimal home design proposals based on the user's saved preferences, lifestyle information, and emotional data. Emotional data is evaluated as a particularly important factor in this analysis process. Design proposals are generated using prompt sentences.

[0735] Prompt Sentence Examples

[0736] "The user has a relaxed expression and wants a spacious living room and a garden that allows for a natural feel. Please propose the optimal home design based on this."

[0737] "The user is looking for an eco-friendly design and a large home office, which is an interesting look. Please suggest the best home design based on this."

[0738] Presenting design proposals and collecting feedback

[0739] The server sends the generated house design proposal to the terminal. The terminal visually displays the design proposal to the user and provides detailed information (e.g., floor plan, design, materials used, etc.). The emotion engine analyzes the user's reactions (facial expressions and voice) again and collects feedback from the user in real time. The user's emotional state at the time of feedback is also recorded.

[0740] Reflecting feedback

[0741] The device collects feedback and emotional data from the user, re-encrypts it, and sends it to the server. The server analyzes the collected data and uses generative AI to improve the design proposal, placing particular emphasis on emotional data to consider modifications to the design proposal. After the final design proposal is generated, it is sent back to the device and presented to the user.

[0742] Specific examples

[0743] Example 1: For user A

[0744] 1. User A enters their preferences for a "large living room" and a "natural garden" into the app. The emotion engine detects a relaxed facial expression.

[0745] 2. The device encrypts this information and emotion data and sends it to the server.

[0746] 3. The server uses a generative AI to generate multiple home design proposals with spacious living rooms and gardens. By referencing the emotional data, design proposals that emphasize spacious, relaxing spaces are prioritized.

[0747] 4. The server sends the design proposal to the device, where User A confirms it. The emotion engine analyzes the facial expressions displayed and collects feedback.

[0748] 5. When User A provides feedback such as "I want more windows in the living room," the server reflects that request and provides a final design proposal.

[0749] Example 2: For user B

[0750] 1. User B inputs that they want an "eco-friendly design" and a "large home office," and the emotion engine detects an interesting facial expression.

[0751] 2. The device collects this information, encrypts it, and sends it to the server.

[0752] 3. The server uses generative AI to generate multiple eco-friendly design proposals, and proposals including innovative ideas are presented based on interesting facial expressions.

[0753] 4. User B enters feedback on the design proposal, and the emotion engine analyzes the emotions in real time and sends them to the server.

[0754] 5. Finally, the server refines the design based on feedback and emotional data and provides the final design.

[0755] By incorporating user emotional data, the system of this invention can provide more accurate and satisfying home design proposals than ever before. The collaboration between the emotion engine and generative AI realizes an advanced, user-centered design process.

[0756] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0757] Step 1: Enter your information

[0758] The user launches a dedicated application on their device. Here, the user enters their profile information (age, family composition, hobbies, etc.) and specific requirements for the home (spacious living room, natural light, eco-friendly design, etc.). The emotion engine then analyzes the user's facial expressions and voice in real time to obtain data on their emotional state (relaxed, interested, etc.). The input data is saved on the device as profile information, requirement data, and emotion data.

[0759] Input: User profile information, housing requirements, user emotion data

[0760] Output: User information and emotion data stored on the device

[0761] Step 2: Collect and send data

[0762] The device collects the user-entered data and emotion data together, converts them into an appropriate data format, such as JSON, and then securely transmits the converted data to the server using an encryption protocol (e.g., TLS), while maintaining the confidentiality and integrity of the data.

[0763] Input: User information and emotional data stored on the device

[0764] Output: Data sent to the server using an encryption protocol

[0765] Step 3: Save the data and prepare it for analysis

[0766] The server receives the encrypted data and decrypts it. The decrypted data is extracted as JSON data and stored in a database as user information and emotional data. In particular, the emotional data is tagged and managed as useful information for analysis. After all the data is stored, it is passed to the generation AI and prepared for analysis.

[0767] Input: Encrypted data sent to the server

[0768] Output: User information and emotion data stored in a database

[0769] Step 4: Analysis by generative AI

[0770] The generation AI on the server receives the saved user information and emotional data as input and analyzes it using natural language processing and machine learning algorithms. Based on this analysis, it generates multiple optimal home design proposals that reflect the user's preferences and emotional data. Specific design proposals are generated using prompt sentences.

[0771] Input: User information and emotion data in a database

[0772] Output: Multiple house design proposals

[0773] Step 5: Present your design and gather feedback

[0774] The server sends the generated house design proposals to the terminal. The terminal visually displays the proposed multiple design proposals to the user and provides detailed information (floor plan, design, materials used, etc.). The emotion engine analyzes the user's reactions again and collects feedback from the user in real time. The user's feedback and emotional state data are recorded on the terminal.

[0775] Input: Generated house design plan

[0776] Output: User feedback and emotion data

[0777] Step 6: Incorporating feedback

[0778] The device re-encrypts the feedback and emotional data collected from the user and sends it to the server. The server analyzes this data and uses generative AI to improve the design proposal. It places particular emphasis on emotional data to generate a final design proposal that reflects the user's feedback. The final design proposal is then sent to the device and presented to the user.

[0779] Input: User feedback and emotional data

[0780] Output: Revised final house design

[0781] (Application example 2)

[0782] 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."

[0783] There is a demand for a system that can provide optimal suggestions to individual users by taking into account not only the user's preferences and lifestyle information but also real-time emotional data. However, conventional systems lack the means to analyze the user's emotions, making it difficult to further improve user satisfaction. The present invention aims to provide a system that can provide suggestions with higher satisfaction by acquiring real-time emotional data via a device worn by the user and adjusting the content of the suggestions based on that data.

[0784] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting information about a user's preferences and lifestyle; means for collecting, encrypting, and transmitting the user's input information; means for receiving the input information and storing it in a database; means for generating optimal suggestions using a generation AI that analyzes the input information; means for presenting the generated suggestions to the user and collecting user feedback; means for analyzing the user's facial expressions and voice in real time and acquiring emotional data; means for adjusting the suggestions based on the emotional data; means for analyzing the user's interests and satisfaction via a device worn by the user; and means for modifying the suggestions based on the user's feedback. This enables suggestions that reflect the user's emotions in real time.

[0785] "User" refers to the consumer or customer who uses the system.

[0786] "Preferences" is information that indicates a user's personal tastes and preferences.

[0787] "Lifestyle" is information about the user's daily habits and behavior patterns.

[0788] "Means for inputting information" refers to a function that allows a user to provide their own profile information through a terminal.

[0789] "Means for encryption and transmission" refers to the function of encrypting collected data to protect it from unauthorized access from outside and transmitting it safely to a server.

[0790] A "database" is a system for storing and managing information obtained from users.

[0791] "Generative AI" refers to artificial intelligence algorithms used to analyze collected data and generate optimal recommendations.

[0792] "Suggestions" refer to recommended options or plans that generative AI creates based on user information and emotional data.

[0793] "Means for collecting feedback" refers to the functionality for incorporating user-provided opinions and reactions into the system.

[0794] "Emotion data" refers to data that indicates the emotional state of a user, obtained by analyzing the user's facial expressions, voice, etc. in real time.

[0795] A "wearable device" is a device that is worn by the user, such as smart glasses or a head-mounted display.

[0796] "Means of adjustment" refers to the ability to change and optimize proposal content in real time based on emotional data and feedback.

[0797] "Means for analyzing interest and satisfaction" refers to a function that measures user interest and satisfaction based on their reactions.

[0798] "Means to modify" refers to the ability to improve existing suggestions based on user feedback.

[0799] An embodiment of the present invention will be described. The system of the present invention combines a user's preference and lifestyle information with an emotion engine that recognizes the user's emotions to provide optimal suggestions. The system includes: means for inputting information about a user's preferences and lifestyle; means for collecting, encrypting, and transmitting the input information; means for receiving the input information and storing it in a database; means for generating optimal suggestions using a generation AI that analyzes the input information; means for presenting the generated suggestions to the user and collecting user feedback; means for analyzing the user's facial expressions and voice in real time to obtain emotion data; means for adjusting the suggestions based on the emotion data; means for analyzing the user's interests and satisfaction via a device worn by the user; and means for modifying the suggestions based on user feedback.

[0800] System Structure

[0801] 1. User information input: Users input their preferences and lifestyle information into a dedicated application via devices such as smart glasses or head-mounted displays. The input includes preferred categories and specific conditions. This allows the user's preferences and lifestyle information to be collected.

[0802] 2. Data collection and transmission: User input data is collected in real time by the device and transmitted to the server using a secure encryption protocol. During this process, the emotion engine analyzes the user's facial expressions and voice, and simultaneously collects emotional data.

[0803] 3. Data storage and analysis preparation: The server decrypts the received encrypted data and stores it in a database. The stored data is labeled with emotion data and used for later analysis.

[0804] 4. Analysis by Generative AI: The Generative AI on the server generates optimal suggestions based on the user's preferences, lifestyle information, and emotional data. The Generative AI analyzes the data and generates suggestions to increase user satisfaction. This analysis includes data analysis using the Google Cloud Vision API and Flask.

[0805] 5. Proposal presentation and feedback collection: The server sends the generated proposals to the device and presents them visually to the user. The user reviews the proposals and provides feedback on their opinions and reactions to each proposal. During this process, the emotion engine continuously analyzes the user's facial expressions and voice to collect emotional data in real time.

[0806] 6. Reflecting Emotional Data and Modifying Proposals: The server analyzes the collected feedback and emotional data and revises the proposals as necessary. It places particular emphasis on emotional data and adjusts the proposals to reflect the user's interests and satisfaction, thereby meeting the user's sophisticated requirements.

[0807] Hardware and software used

[0808] Hardware: smart glasses, head-mounted displays, smartphones

[0809] Software: Google Cloud Vision API, Flask, Emotion Engine

[0810] Specific examples

[0811] Example 1: When the user is shopping

[0812] Suppose User A is wearing smart glasses in a physical store. When he or she looks at a specific product from the gallery, the application detects the gaze and uses an emotion engine to analyze whether the product is interesting to the user. As a result, the application suggests, "Are you interested in this product?" and also presents other related and recommended products.

[0813] Example 2: User is looking for a new product

[0814] Let's say User B is wearing a head-mounted display. While searching for new fashion items, he stops at a specific zone. The emotion engine analyzes the user's facial expression of joy. The application then recommends, "Are you still interested in products in this zone?" and provides related product information.

[0815] Prompt Sentence Examples

[0816] "How can we make optimal shopping recommendations based on user profile information and real-time sentiment data? For example, this could include using recommendation technology to target products that users are interested in, thereby increasing interest and satisfaction."

[0817] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0818] Step 1:

[0819] Through a device worn by the user (smart glasses or a head-mounted display), the user logs into the application and inputs information about their preferences and lifestyle. The input information includes age, hobbies, and categories of purchase interest. This information is collected by the user's device. As an output, the collected user data is encrypted.

[0820] Step 2:

[0821] The device sends the collected user data to the server using an encryption protocol. This process ensures the security of the user information and prevents unauthorized access. The input is the encrypted user data, and the output is the data securely transmitted to the server.

[0822] Step 3:

[0823] The server decrypts the received encrypted data and stores it in a user database, which often contains user profile and lifestyle information for later analysis. The input is the decrypted user data, and the output is the data stored in the database.

[0824] Step 4:

[0825] The server uses a generation AI to analyze the user's input information and generate optimal suggestions, taking into account the user's emotional data. The generation AI uses the Google Cloud Vision API to deeply analyze the user's interests and satisfaction. The input is the user data and emotional data in the database, and the output is the generated suggestion data.

[0826] Step 5:

[0827] The server sends the generated suggestions to the user's device, where the user visually confirms the suggestions through smart glasses or a head-mounted display. The user provides feedback on each suggestion, and the emotion engine analyzes the user's facial expressions and voice to collect emotion data. The input is the generated suggestion data, and the output is the user's feedback and emotion data.

[0828] Step 6:

[0829] The device retransmits the collected feedback and emotion data to the server. This feedback includes the user's specific opinions and reactions to the suggestions. The input is the user's feedback and emotion data, and the output is the state sent to the server.

[0830] Step 7:

[0831] The server analyzes the collected feedback and sentiment data and adjusts the suggestions as needed. It then uses generative AI to generate new suggestions, further improving user satisfaction. This process again uses the Google Cloud Vision API. The input is the collected feedback and sentiment data, and the output is improved suggestions.

[0832] Step 8:

[0833] The server sends the improved proposal data back to the user terminal and provides the final proposal to the user, who can then review it and make a final decision. The input is the improved proposal data, and the output is the improved proposal presented to the user.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] [Third embodiment]

[0838] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0839] 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.

[0840] 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).

[0841] 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.

[0842] 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.

[0843] 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).

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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."

[0850] The system of the present invention proposes optimal home designs based on the user's preferences and lifestyle. A specific embodiment of the system is described below.

[0851] System Overview

[0852] 1. Enter user information

[0853] Users launch a dedicated application on their device (smartphone, tablet, PC, etc.) and enter detailed profile information (age, family composition, hobbies, etc.) and specific requirements for the home (spacious living room, natural light, eco-friendly design, etc.).

[0854] 2. Data collection and transmission

[0855] The terminal collects the data entered by the user and converts it into a data format (JSON, XML, etc.).

[0856] The collected data is encrypted on the device and securely sent to the server.

[0857] 3. Saving data and preparing for analysis

[0858] The server decrypts the received user data and stores it in a database.

[0859] The received data is passed to the generation AI and prepared for detailed analysis.

[0860] 4. Analysis by generative AI

[0861] The AI ​​on the server generates the optimal home design based on the information entered by the user. This AI analyzes data from past home designs and takes into account the user's lifestyle information.

[0862] The generated home design proposals may have multiple variations, and are designed to expand the user's options.

[0863] 5. Presenting design proposals and collecting feedback

[0864] The server transmits the generated multiple housing design plans to the terminal.

[0865] The device visually displays these design proposals to the user and also provides detailed information about each proposal (floor plan, design, materials used, etc.).

[0866] Users can review the proposed design and provide their ratings and feedback, including specific requests such as "I'd like more natural light in the living room."

[0867] 6. Incorporating feedback

[0868] The terminal collects feedback from the user and transmits it back to the server.

[0869] The server runs the generative AI again based on the collected feedback and improves the design proposal.

[0870] The improved design proposal is sent to the terminal as the final design proposal and provided to the user.

[0871] Specific examples

[0872] Example 1: User A's case

[0873] 1. User A enters their preferences for a "large living room" and a "garden with a natural feel" in a dedicated app.

[0874] 2. The device collects this information, encrypts it, and sends it to the server.

[0875] 3. The server analyzes the data using generative AI and generates multiple home design proposals with spacious living rooms and gardens.

[0876] 4. The server sends the design proposal to the terminal, and User A checks the displayed proposal.

[0877] 5. When User A sends feedback such as "I would like more windows in the living room," the server reflects that request and provides the final design proposal.

[0878] Example 2: User B's case

[0879] 1. User B inputs that they need an "eco-friendly design" and a "large home office."

[0880] 2. The device collects the information, encrypts it, and sends it to the server.

[0881] 3. The server uses generative AI to generate multiple design options, including eco-friendly materials and a large home office.

[0882] 4. User B inputs detailed requests based on the presented design proposal and sends feedback to the server.

[0883] 5. The server refines the design based on the feedback and provides the final design.

[0884] In this way, this system can efficiently realize optimal home designs that meet the individual needs of users. By utilizing generative AI, it is possible to promote a user-centered design process and provide a living environment that will satisfy users.

[0885] The processing flow will be explained below.

[0886] Step 1:

[0887] Users launch a dedicated application on their device and enter detailed information about themselves (age, family composition, hobbies, etc.) and the requirements they have for their home (spacious living room, natural light, eco-friendly design, etc.).

[0888] Step 2:

[0889] The terminal collects the data entered by the user and converts it into an appropriate data format, such as JSON. The terminal provides an interface for the user to review and correct the input data as needed.

[0890] Step 3:

[0891] The terminal encrypts the collected data using an encryption protocol such as AES, ensuring security before sending it to the server.

[0892] Step 4:

[0893] The server decrypts the received encrypted data and stores it in a database, after which it performs processing to check the timestamp and data consistency.

[0894] Step 5:

[0895] The server converts the stored user data into a format that can be interpreted by the generation AI and prepares it for delivery to the generation AI, where data cleaning and normalization are performed as necessary.

[0896] Step 6:

[0897] The AI ​​on the server analyzes the user's input in detail and generates the optimal home design. This analysis reflects parameters based on the user's lifestyle and preferences. It also takes into account past design data and trend information.

[0898] Step 7:

[0899] The server organizes the generated multiple home design proposals and converts them into a presentation format, which allows them to be sent to devices in a visually easy-to-understand format.

[0900] Step 8:

[0901] The terminal visually displays the received housing design proposals to the user, providing an interface that includes detailed information about each proposal (floor plan, design, materials used, etc.) to enable the user to easily understand and evaluate them.

[0902] Step 9:

[0903] Users review the proposed design proposals and enter their ratings and feedback (e.g., "I'd like more windows in the living room.") Feedback is provided using simple forms and options on the UI.

[0904] Step 10:

[0905] The device collects feedback from the user, re-encrypts it, and sends it to the server.

[0906] Step 11:

[0907] The server analyzes the collected feedback and runs the generative AI again, generating improvements based on the feedback and updating the design proposal.

[0908] Step 12:

[0909] The server generates the final design proposal and sends it to the terminal, where it is presented to the user, who can approve and confirm the proposal.

[0910] In this way, the system executes a series of processes that generate multiple home design proposals based on information based on the user's preferences and lifestyle, and provides a final design proposal that reflects the user's feedback.

[0911] Example 1

[0912] 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."

[0913] Conventional home design proposal systems have had difficulty efficiently generating optimal designs that correspond to a user's individual lifestyle and preferences. Furthermore, incorporating user feedback is cumbersome, making it difficult to quickly improve the design. The present invention aims to solve these problems and provide a system that efficiently provides optimal home designs that meet the user's needs.

[0914] 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.

[0915] In this invention, the server includes: means for inputting information about a user's preferences and lifestyle; means for collecting the user's input information, converting it into JSON format, encrypting it, and transmitting it; means for receiving the input information, decrypting it, and storing it in a database; means for generating multiple home design proposals using a generation AI based on the user's input information; means for visually presenting the generated multiple home design proposals to the user and collecting feedback according to the user's preferences and lifestyle; and means for collecting the user's feedback and running the generation AI again to modify the home design proposals. This makes it possible to quickly and efficiently generate and improve home design proposals based on the user's input information and feedback.

[0916] "Information about the user's preferences and lifestyle" refers to specific factors that the user places importance on in the living environment, such as the user's age, family structure, hobbies, conditions of the place of residence, and requirements for the home.

[0917] "Input means" refers to devices or software that provide an interface for users to input information. Specifically, this refers to electronic devices such as smartphones, tablets, and PCs, as well as the applications that run on them.

[0918] "Means of converting to a data format, encrypting, and transmitting" refers to the series of processes in which information entered by the user is converted into a standard data format such as JSON or XML, encrypted using an encryption algorithm such as AES-256, and transmitted to a server via a communication line.

[0919] "Means of receiving, decrypting, and storing in a database" refers to the process by which the server receives the encrypted data sent from the terminal, decrypts it using the private key, and then stores the data in a database (e.g., MySQL).

[0920] "Means for generating multiple home design proposals using generative AI" refers to the process of using generative AI (artificial intelligence) to create multiple home design proposals that meet the user's needs based on past home design data and the user's lifestyle information.

[0921] "Means for visually presenting to the user and collecting feedback based on the user's preferences and lifestyle" refers to a process that displays multiple generated design proposals on the user's device and provides an interactive interface that allows the user to review the proposals and input their ratings and suggestions for improvement.

[0922] "Means for re-running the generating AI to modify the housing design proposal" refers to the process of re-running the generating AI and improving the design proposal based on feedback collected from the user, resulting in a final design proposal that more accurately reflects the user's wishes.

[0923] The present invention is a system that proposes optimal home design based on a user's preferences and lifestyle. The system of the present invention mainly operates in cooperation with the user, a terminal, and a server. The processing at each step and the hardware and software used are described in detail below.

[0924] 1. Enter user information

[0925] Users launch a dedicated application using a device such as a smartphone, tablet, or PC. Through this application, users input information such as their age, family composition, hobbies, location, and specific requirements for the home (e.g., a spacious living room, natural light, eco-friendly design, etc.). This input interface is often implemented using front-end frameworks such as React or Angular.

[0926] 2. Data collection and transmission

[0927] The terminal collects the information entered by the user and converts it into JSON format. It then encrypts the data using the AES-256 encryption algorithm. The encrypted data is then sent to the server using a secure communication protocol (e.g., HTTPS). This process typically uses a backend framework such as Node.js or Python.

[0928] 3. Saving data and preparing for analysis

[0929] The server receives the encrypted data sent from the device and decrypts it using the private key. This decrypted data is then returned to its original format using JSON decoding. The data is then stored in a database such as MySQL or PostgreSQL. The server retrieves the user data from the database and prepares it for analysis before passing it to the generation AI.

[0930] 4. Analysis by generative AI

[0931] A generative AI (e.g., OpenAI GPT-4 model) located on the server generates optimal home design proposals based on user input. This generative AI performs analysis while taking into account past home design data and the user's lifestyle information. The generated home design proposals have multiple variations, designed to expand the user's options.

[0932] 5. Presenting design proposals and collecting feedback

[0933] The server sends the generated multiple home design proposals to the terminal. The terminal visually displays these design proposals to the user. 2D or 3D graphics are often used as the display method. Specifically, the interface can be built using libraries such as Three.js or Unity. The user reviews the proposed design proposals and enters their ratings and feedback. This feedback can include specific requests such as "I want more natural light in the living room."

[0934] 6. Incorporating feedback

[0935] The device collects feedback from the user and sends it back to the server. The server then operates the generation AI again based on the collected feedback to improve the design proposal. The improved design proposal is sent to the device as the final design proposal and provided to the user. The final design proposal reflects the user's requests as much as possible, making it possible to achieve a higher level of satisfaction.

[0936] Example prompt sentences

[0937] 1. For User A

[0938] The user wants a "large living room" and a "natural garden." Please propose the optimal house design based on this.

[0939] 2. For User B

[0940] The user wants an "eco-friendly design" and a "large home office." Please generate the optimal home design proposal taking this into consideration.

[0941] The system of this invention can efficiently realize optimal home designs that meet the individual needs of users. Furthermore, by utilizing generative AI, it is possible to promote a user-centered design process and provide a living environment that is highly satisfying.

[0942] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0943] Step 1:

[0944] The user enters information

[0945] Specific operation: The user launches a dedicated application on a device such as a smartphone, tablet, or PC and enters their profile information (age, family composition, hobbies, etc.) and their specific requirements for the home (spacious living room, natural light, eco-friendly design, etc.).

[0946] Input: Manual input of information by the user.

[0947] Output: Data entered by the user that is temporarily stored on the device.

[0948] Step 2:

[0949] The device collects data, converts and encrypts it, and then transmits it.

[0950] What it does: The device collects the information entered by the user, converts it into JSON format, encrypts the data using the AES-256 encryption algorithm, and sends it to the server via a secure communication protocol (HTTPS).

[0951] Input: Information entered by the user (e.g., "We want a large living room with natural light for a 30-year-old couple and their 5-year-old child").

[0952] Output: The encrypted user information is sent to the server.

[0953] Step 3:

[0954] The server receives the data, decrypts it, and stores it in a database

[0955] How it works: The server receives the encrypted data sent from the device and decrypts it using the private key. The decrypted data is then converted back to its original format using JSON decoding. This data is then stored in a database such as MySQL or PostgreSQL.

[0956] Input: Encrypted user information.

[0957] Output: The decrypted user information is stored in the database.

[0958] Step 4:

[0959] The server generates a house design plan using generation AI

[0960] Specific operation: The server retrieves user information stored in the database and passes it to the generation AI. The generation AI (e.g., OpenAI GPT-4 model) generates multiple home design proposals based on past home design data and the user's lifestyle information, tailored to the user's needs.

[0961] Input: User information decrypted and stored in a database.

[0962] Output: Multiple house design proposals generated.

[0963] Step 5:

[0964] The server sends the design proposal to the terminal, which then presents it to the user.

[0965] How it works: The server sends multiple home design proposals generated by the AI ​​to the device. The device then displays these proposals to the user in a visually easy-to-read format (e.g., 2D or 3D graphics). Libraries such as Three.js and Unity are often used.

[0966] Input: Generated multiple house design proposals.

[0967] Output: A visual representation of the design to the user.

[0968] Step 6:

[0969] User enters feedback

[0970] Specific operation: The user reviews the presented design proposals and enters their evaluation and feedback on improvements. An example of feedback might be, "I'd like more windows in the living room."

[0971] Input: Manual input of feedback by the user.

[0972] Output: Feedback data temporarily stored in the device.

[0973] Step 7:

[0974] The device collects feedback and sends it to the server

[0975] Specific operation: The device collects the feedback entered by the user, converts it back into JSON format, encrypts it, and sends it to the server using a secure communication protocol (HTTPS).

[0976] Input: Feedback entered by the user.

[0977] Output: The encrypted feedback data is sent to the server.

[0978] Step 8:

[0979] The server improves the design based on the feedback.

[0980] How it works: The server receives the encrypted feedback, decrypts it, and decodes the JSON. It then passes the feedback to the generation AI, which analyzes it again to generate the optimal design. The improved design is then sent back to the device and provided to the user.

[0981] Input: The decrypted and JSON decoded feedback data.

[0982] Output: An improved final house design.

[0983] (Application example 1)

[0984] 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."

[0985] While conventional systems generally propose home designs based on the user's preferences and lifestyle, they lack support for store layouts. In particular, there are no systems that propose optimal layouts based on the store owner's business requirements. This results in the problem of store owners spending a great deal of time and money designing their ideal store layout.

[0986] 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.

[0987] In this invention, the server includes means for inputting information about a user's preferences and lifestyle, means for collecting the user's input information, encrypting and transmitting it, means for receiving the input information and storing it in a database, means for generating an optimal home design using a generation AI that analyzes the input information, means for presenting the generated home design to the user and collecting user feedback, means for modifying the home design based on the user feedback, means for inputting the user's business requirements, and means for generating a store layout plan based on the business requirements and presenting it to the user. This makes it possible to propose and improve an ideal store layout based on the store owner's business requirements.

[0988] "User" refers to any individual or corporation that requires proposals for home design or store layout.

[0989] "Preferences" refer to specific attributes such as design, layout, and functionality desired by the user.

[0990] "Lifestyle" refers to the habits and behavioral patterns of a user's daily life.

[0991] "Means for inputting information" refers to the interface for collecting information such as user preferences, lifestyle, and business requirements.

[0992] "Means of collection, encryption and transmission" refers to devices or software that electronically collect information entered by users, encrypt it to keep the data secure, and transmit it to a server.

[0993] "Means for receiving and storing in a database" refers to devices or software that receive encrypted user information, decrypt it, and store it in a database.

[0994] "Generative AI" refers to artificial intelligence that automatically generates optimal design proposals based on information provided by the user.

[0995] "Home design" refers to the layout and design of a home that suits the user's preferences and lifestyle.

[0996] The "means for presenting and collecting feedback" refers to a device or software that visually displays the generated design proposal to the user and receives evaluations and additional requests from the user.

[0997] "Modification tools" refers to devices or software that refine designs based on feedback collected from users.

[0998] "Business requirements" refer to the specific conditions or goals that a store owner wants to achieve in the design and layout of their store.

[0999] "Store layout proposal" refers to the layout and design of a store generated based on the user's business requirements.

[1000] The present invention includes a system for proposing a home design based on a user's preferences and lifestyle, and a system for proposing a store layout. Specific embodiments for carrying out the invention are described below.

[1001] System Overview

[1002] Enter user information

[1003] Users launch a dedicated application on their device (smartphone, tablet, PC, etc.) and enter their own profile information (age, family composition, hobbies, etc.) and detailed requirements for their specific home (spacious living room, natural light, eco-friendly design, etc.). In the case of store layouts, users also enter business requirements (customer flow, placement of specific product sections, eco-friendly elements, etc.).

[1004] Data collection and transmission

[1005] The terminal collects the data entered by the user and converts it into a data format (JSON, XML, etc.), after which the data is encrypted using a library such as the Fernet encryption library and securely sent to the server.

[1006] Saving data and preparing for analysis

[1007] The server receives the encrypted user data, decrypts it, and stores it in a database, preparing it for analysis using the generative AI.

[1008] Analysis by generative AI

[1009] The AI ​​on the server generates optimal design proposals based on the information entered by the user. This AI analyzes past design data and takes into account the user's lifestyle information. Multiple variations of home designs and store layouts are often generated.

[1010] Presenting design proposals and collecting feedback

[1011] The server sends the generated multiple design proposals to the terminal. The terminal visually displays these proposals to the user and also provides detailed information about each proposal (floor plan, design, materials used, etc.). The user reviews the proposed design proposals and enters their evaluation and feedback. For example, the user can enter specific requests such as "I want more natural light in the living room" or "I want the store's cash register to be located closer to the entrance."

[1012] Reflecting feedback

[1013] The device collects feedback from the user and sends it back to the server. The server then runs the generative AI again based on the collected feedback to improve the design proposal. The improved design proposal is sent to the device as the final design proposal and provided to the user.

[1014] Specific examples

[1015] Home design examples

[1016] User A uses a dedicated app to input their preferences for a "large living room" and a "garden with a natural feel." The device collects this information, encrypts it, and sends it to the server. The server analyzes it using a generative AI and generates multiple home design proposals with large living rooms and gardens. The server sends the design proposals to the device, and User A checks the displayed proposals. When User A sends feedback such as "I would like more windows in the living room," the server reflects this request and provides the final design proposal.

[1017] Store layout example

[1018] Store owner B inputs that he needs an "eco-friendly design" and a "large home office." The device collects the information, encrypts it, and sends it to the server. The server uses generative AI to generate multiple design proposals that include eco-friendly materials and a large home office. User B inputs detailed requests from the presented design proposals and sends feedback to the server. The server uses the feedback to improve the design proposals and provides the final design proposal.

[1019] Prompt Sentence Examples

[1020] Prompts are used to provide user input to the generative AI model and generate optimal design proposals. Examples of prompts are shown below.

[1021] "User profile: Age 35, Business type: Bookstore, Desired wide aisles, Desired eco-friendly design, Considered customer flow. Generate the optimal store layout proposal based on these requirements."

[1022] As described above, the present invention is a system that provides optimal design proposals based on the user's preferences, lifestyle, and business requirements, and realizes a user-centered design process by utilizing generative AI.

[1023] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1024] Step 1:

[1025] Users enter information using a dedicated application, including profile information (age, family composition, hobbies, etc.), housing requirements (spacious living room, natural light, eco-friendly design, etc.), and business requirements for the store layout (customer flow, product placement, eco-friendly design, etc.). This allows users to input their preferences, lifestyle, and business requirements.

[1026] Input: User profile information, housing requirements, business requirements

[1027] Output: User information entered

[1028] Step 2:

[1029] The device collects the input information and converts it into a data format (JSON, XML, etc.), then uses the Fernet encryption library to encrypt the collected data and send it securely to the server.

[1030] Input: User information entered

[1031] Output: Encrypted user data

[1032] Step 3:

[1033] The server receives the encrypted user data, decrypts it and stores useful information in a database.

[1034] Input: Encrypted user data

[1035] Output: User information stored in the database

[1036] Step 4:

[1037] The server passes the information stored in the database to a generative AI model, which analyzes the data to generate home design proposals and store layout proposals. The generative AI used for analysis generates optimal design proposals based on past data and prompts.

[1038] Input: User information stored in the database

[1039] Output: Generated house design proposal or store layout proposal

[1040] Step 5:

[1041] The server sends the generated multiple design proposals to the device, which visually displays the proposals to the user and provides detailed information about each proposal (floor plan, design, materials used, etc.).

[1042] Input: Generated residential design or store layout proposal

[1043] Output: Design proposals presented to the user

[1044] Step 6:

[1045] Users can review the proposed design proposals and enter their ratings and feedback. They can also enter specific requests (e.g., "I want more natural light in the living room" or "I want the cashier counter in the store to be closer to the entrance").

[1046] Input: User feedback

[1047] Output: Feedback information

[1048] Step 7:

[1049] The device collects user feedback information and sends it back to the server, which then runs the generative AI model again and refines the design proposal based on the feedback.

[1050] Input: User feedback information

[1051] Output: Improved design

[1052] Step 8:

[1053] The server transmits the improved design proposal as a final design proposal to the terminal, which then provides the final design proposal to the user.

[1054] Input: Improved design proposal

[1055] Output: Presentation of final design proposal

[1056] This series of processes enables the system to efficiently propose optimal home designs and store layouts based on the user's preferences, lifestyle, and business requirements, thereby increasing user satisfaction.

[1057] 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.

[1058] The system of the present invention combines a user's preferences and lifestyle information with an emotion engine that recognizes the user's emotions to propose optimal home designs. A specific embodiment of this system is described below.

[1059] System Overview

[1060] 1. Enter user information

[1061] Users launch a dedicated application on their device (smartphone, tablet, PC, etc.) and enter detailed profile information (age, family composition, hobbies, etc.) and specific requirements for the home (spacious living room, natural light, eco-friendly design, etc.).

[1062] At this time, the emotion engine analyzes the user's facial expressions, voice, and input data in real time to detect the user's emotional state.

[1063] 2. Data collection and transmission

[1064] The device collects the user's input data and emotion data, converts them into an appropriate data format, and transmits them to the server while ensuring the data security using encryption protocols.

[1065] 3. Saving data and preparing for analysis

[1066] The server decrypts the received encrypted data and stores it in a database. Emotional data in particular is specially tagged and managed to provide useful information for analysis.

[1067] After storage, the data is passed to a generative AI, ready for further analysis.

[1068] 4. Analysis by generative AI

[1069] The AI ​​on the server generates optimal home designs based on the user's preferences, lifestyle information, and emotional data. Emotional data is evaluated as a particularly important factor, generating multiple design proposals that will satisfy the user.

[1070] 5. Presenting design proposals and collecting feedback

[1071] The server transmits the generated multiple house design plans to the terminal.

[1072] The device visually displays these design proposals to the user and provides detailed information about each proposal (floor plan, design, materials used, etc.). In addition, an emotion engine analyzes users' reactions and comments in real time.

[1073] The user reviews the proposed design proposals and enters their evaluation and feedback. The emotion engine also records the user's emotional state when providing feedback, and sends it to the server.

[1074] 6. Incorporating feedback

[1075] The device collects feedback and emotional data from the user, encrypts it, and sends it to the server.

[1076] The server analyzes the collected feedback and emotional data and runs the generative AI again, generating improvement proposals that place particular emphasis on emotional data and updating the design proposals.

[1077] The final design is sent to the terminal and presented to the user.

[1078] Specific examples

[1079] Example 1: For user A

[1080] 1. User A enters their preferences for a "large living room" and a "natural garden" in the dedicated app. At this time, the emotion engine detects User A's relaxed facial expression.

[1081] 2. The device collects input information and emotional data, encrypts it, and sends it to the server.

[1082] 3. The server uses a generative AI to generate multiple home design proposals with spacious living rooms and gardens. By referencing the emotional data, design proposals that emphasize spacious, relaxing spaces are prioritized.

[1083] 4. The server sends the design proposal to the device, where User A confirms the displayed proposal. The emotion engine analyzes the user's facial expressions while the proposal is displayed and collects further feedback.

[1084] 5. When User A sends feedback such as "I want more windows in the living room," the emotion engine detects a satisfied facial expression, and the server reflects that request and provides a final design proposal.

[1085] Example 2: For user B

[1086] 1. User B inputs that he needs "eco-friendly design" and "large home office." The emotion engine detects User B's interesting facial expression.

[1087] 2. The device collects the information, encrypts it, and sends it to the server.

[1088] 3. The server uses generative AI to generate multiple design proposals, including eco-friendly materials and a large home office. Based on interesting facial expressions, design proposals with particularly innovative ideas are presented.

[1089] 4. User B inputs feedback based on the presented design proposal, and the emotion engine analyzes the user's emotions in real time and sends them to the server.

[1090] 5. The server refines the design proposal based on the feedback and emotion data and provides the final design proposal.

[1091] In this way, by incorporating user emotional data, the system of the present invention can propose more precise and satisfying home design proposals than ever before. The combination of the emotion engine and generative AI realizes a more advanced, user-centered design process.

[1092] The processing flow will be explained below.

[1093] Step 1:

[1094] Users launch a dedicated application on their device and enter their profile information (age, family composition, hobbies, etc.) and detailed requirements for their home (spacious living room, natural light, eco-friendly design, etc.). As they enter their information, the emotion engine analyzes the user's facial expressions and voice in real time and records emotional data.

[1095] Step 2:

[1096] The device combines the data entered by the user and the emotional data collected by the emotion engine, converting it into an appropriate data format, such as JSON, and then labeling the converted data for easy reading.

[1097] Step 3:

[1098] The terminal encrypts the collected data using an encryption protocol such as AES, ensuring the security of the data before sending it to the server.

[1099] Step 4:

[1100] The server decrypts the received encrypted data and stores it in a database. At this time, the emotion data is given a special tag and managed so that it can be used for analysis.

[1101] Step 5:

[1102] The server passes the stored user data and emotion data to the generative AI and prepares it for analysis. Data cleaning and normalization are also performed at this stage.

[1103] Step 6:

[1104] The AI ​​on the server generates optimal home designs based on user input and emotional data. Emotional data is particularly important, as it serves as an indicator for evaluating which design the user will be most satisfied with.

[1105] Step 7:

[1106] The server organizes the generated multiple housing design proposals, converts them into a presentation format, and sends them to the terminal, where they are prepared to be displayed in an easy-to-understand manner for the user.

[1107] Step 8:

[1108] The terminal visually displays the received housing design proposal to the user, and the interface includes detailed information about the design proposal (floor plan, design, materials used, etc.) to make it easy for the user to understand and evaluate.

[1109] Step 9:

[1110] The user reviews the proposed design and provides their evaluation and feedback. At this time, the emotion engine again analyzes the user's facial expressions and voice and records the emotional data at the time of feedback.

[1111] Step 10:

[1112] The device collects the feedback from the user and the recollected emotional data, re-encrypts it, and sends it to the server.

[1113] Step 11:

[1114] The server analyzes the collected feedback and additional emotional data and runs the generative AI again, generating an improved proposal that emphasizes elements that the user expressed a high sensitivity to, taking into account the emotional data in particular.

[1115] Step 12:

[1116] The server generates a final design proposal and sends it to the device, which is individually adjusted based on the emotion data and feedback. The final design proposal is presented to the user, who can approve and confirm it.

[1117] This process utilizes emotional data in addition to user preferences and lifestyle information to produce highly accurate home design proposals. The combination of the emotion engine and generative AI provides a living environment that is more satisfying to users than conventional systems.

[1118] Example 2

[1119] 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."

[1120] Conventional home design proposal systems make proposals based on the user's preferences and lifestyle, but are unable to incorporate data on the user's emotions. This makes it difficult to propose designs that reflect the user's true satisfaction. Furthermore, when modifying home designs based solely on feedback, the user's emotional state cannot be taken into account, resulting in a high likelihood of the design proposals not meeting the user's expectations. There was a need to solve these problems and realize more precise home design proposals that would provide greater user satisfaction.

[1121] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting, encrypting, and transmitting user input information and emotional data, means for receiving the input information and emotional data and storing them in a database, and means for generating an optimal home design using a generative AI model that analyzes the input information and emotional data. This makes it possible to propose a precise and satisfying home design that reflects the user's emotions.

[1122] A "user" is an individual or group that utilizes the system to input their preferences, lifestyle information, and emotional data.

[1123] "Information about preferences and lifestyle" is data about personal preferences and lifestyle habits such as the user's age, family structure, hobbies, and housing requirements.

[1124] "Emotion data" refers to data relating to the emotional state of a user that is acquired in real time from the user's facial expressions, voice, and behavior.

[1125] A "terminal" is a device used by a user to input information and communicate collected data, including smartphones, tablets, and personal computers.

[1126] "Encryption" is the process of transforming data according to a specific algorithm to ensure security during transmission.

[1127] A "server" is a computer system that decrypts the data it receives, stores it, and analyzes it using a generative AI model.

[1128] A "database" is a system for systematically storing and managing user input information and emotional data.

[1129] A "generative AI model" is an artificial intelligence model that analyzes user input information and emotional data to generate optimal home designs.

[1130] "Feedback" refers to opinions and evaluations provided by users regarding proposed home design plans.

[1131] "Revising" is the process of improving the proposed home design based on user feedback and sentiment data.

[1132] A "design proposal" is a specific design plan for a house created by a generative AI model that responds to the user's requests and emotions.

[1133] The system of the present invention combines a user's preferences and lifestyle information with an emotion engine that recognizes the user's emotions to propose optimal home designs. A specific embodiment of this system is described below.

[1134] Enter user information

[1135] Users launch a dedicated application on their device (smartphone, tablet, personal computer, etc.). They then enter their own profile information (e.g., age, family composition, hobbies, etc.) and detailed information about their specific home requirements (e.g., spacious living room, natural light, eco-friendly design, etc.). The emotion engine analyzes the user's facial expressions, voice, and input data in real time to detect their emotional state. This emotional data is an important element in the system's design proposals.

[1136] Data collection and transmission

[1137] The device collects the information and emotion data entered by the user and converts it into an appropriate data format, such as JSON. The converted data is then sent to the server using an encryption protocol (e.g., TLS). This encryption ensures the security of the data.

[1138] Saving data and preparing for analysis

[1139] After receiving the encrypted data, the server decrypts it and extracts it as JSON data. The extracted data is then stored in a database. Emotional data in particular is tagged and stored in a format that is easy to use during analysis. After all the collected data has been saved, it is passed to the generation AI and prepared for analysis.

[1140] Analysis by generative AI

[1141] The server-based AI generates multiple optimal home design proposals based on the user's saved preferences, lifestyle information, and emotional data. Emotional data is evaluated as a particularly important factor in this analysis process. Design proposals are generated using prompt sentences.

[1142] Prompt Sentence Examples

[1143] "The user has a relaxed expression and wants a spacious living room and a garden that allows for a natural feel. Please propose the optimal home design based on this."

[1144] "The user is looking for an eco-friendly design and a large home office, which is an interesting look. Please suggest the best home design based on this."

[1145] Presenting design proposals and collecting feedback

[1146] The server sends the generated house design proposal to the terminal. The terminal visually displays the design proposal to the user and provides detailed information (e.g., floor plan, design, materials used, etc.). The emotion engine analyzes the user's reactions (facial expressions and voice) again and collects feedback from the user in real time. The user's emotional state at the time of feedback is also recorded.

[1147] Reflecting feedback

[1148] The device collects feedback and emotional data from the user, re-encrypts it, and sends it to the server. The server analyzes the collected data and uses generative AI to improve the design proposal, placing particular emphasis on emotional data to consider modifications to the design proposal. After the final design proposal is generated, it is sent back to the device and presented to the user.

[1149] Specific examples

[1150] Example 1: For user A

[1151] 1. User A enters their preferences for a "large living room" and a "natural garden" into the app. The emotion engine detects a relaxed facial expression.

[1152] 2. The device encrypts this information and emotion data and sends it to the server.

[1153] 3. The server uses a generative AI to generate multiple home design proposals with spacious living rooms and gardens. By referencing the emotional data, design proposals that emphasize spacious, relaxing spaces are prioritized.

[1154] 4. The server sends the design proposal to the device, where User A confirms it. The emotion engine analyzes the facial expressions displayed and collects feedback.

[1155] 5. When User A provides feedback such as "I want more windows in the living room," the server reflects that request and provides a final design proposal.

[1156] Example 2: For user B

[1157] 1. User B inputs that they want an "eco-friendly design" and a "large home office," and the emotion engine detects an interesting facial expression.

[1158] 2. The device collects this information, encrypts it, and sends it to the server.

[1159] 3. The server uses generative AI to generate multiple eco-friendly design proposals, and proposals including innovative ideas are presented based on interesting facial expressions.

[1160] 4. User B enters feedback on the design proposal, and the emotion engine analyzes the emotions in real time and sends them to the server.

[1161] 5. Finally, the server refines the design based on feedback and emotional data and provides the final design.

[1162] By incorporating user emotional data, the system of this invention can provide more accurate and satisfying home design proposals than ever before. The collaboration between the emotion engine and generative AI realizes an advanced, user-centered design process.

[1163] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1164] Step 1: Enter your information

[1165] The user launches a dedicated application on their device. Here, the user enters their profile information (age, family composition, hobbies, etc.) and specific requirements for the home (spacious living room, natural light, eco-friendly design, etc.). The emotion engine then analyzes the user's facial expressions and voice in real time to obtain data on their emotional state (relaxed, interested, etc.). The input data is saved on the device as profile information, requirement data, and emotion data.

[1166] Input: User profile information, housing requirements, user emotion data

[1167] Output: User information and emotion data stored on the device

[1168] Step 2: Collect and send data

[1169] The device collects the user-entered data and emotion data together, converts them into an appropriate data format, such as JSON, and then securely transmits the converted data to the server using an encryption protocol (e.g., TLS), while maintaining the confidentiality and integrity of the data.

[1170] Input: User information and emotional data stored on the device

[1171] Output: Data sent to the server using an encryption protocol

[1172] Step 3: Save the data and prepare it for analysis

[1173] The server receives the encrypted data and decrypts it. The decrypted data is extracted as JSON data and stored in a database as user information and emotional data. In particular, the emotional data is tagged and managed as useful information for analysis. After all the data is stored, it is passed to the generation AI and prepared for analysis.

[1174] Input: Encrypted data sent to the server

[1175] Output: User information and emotion data stored in a database

[1176] Step 4: Analysis by generative AI

[1177] The generation AI on the server receives the saved user information and emotional data as input and analyzes it using natural language processing and machine learning algorithms. Based on this analysis, it generates multiple optimal home design proposals that reflect the user's preferences and emotional data. Specific design proposals are generated using prompt sentences.

[1178] Input: User information and emotion data in a database

[1179] Output: Multiple house design proposals

[1180] Step 5: Present your design and gather feedback

[1181] The server sends the generated house design proposals to the terminal. The terminal visually displays the proposed multiple design proposals to the user and provides detailed information (floor plan, design, materials used, etc.). The emotion engine analyzes the user's reactions again and collects feedback from the user in real time. The user's feedback and emotional state data are recorded on the terminal.

[1182] Input: Generated house design plan

[1183] Output: User feedback and emotion data

[1184] Step 6: Incorporating feedback

[1185] The device re-encrypts the feedback and emotional data collected from the user and sends it to the server. The server analyzes this data and uses generative AI to improve the design proposal. It places particular emphasis on emotional data to generate a final design proposal that reflects the user's feedback. The final design proposal is then sent to the device and presented to the user.

[1186] Input: User feedback and emotional data

[1187] Output: Revised final house design

[1188] (Application example 2)

[1189] 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."

[1190] There is a demand for a system that can provide optimal suggestions to individual users by taking into account not only the user's preferences and lifestyle information but also real-time emotional data. However, conventional systems lack the means to analyze the user's emotions, making it difficult to further improve user satisfaction. The present invention aims to provide a system that can provide suggestions with higher satisfaction by acquiring real-time emotional data via a device worn by the user and adjusting the content of the suggestions based on that data.

[1191] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting information about a user's preferences and lifestyle; means for collecting, encrypting, and transmitting the user's input information; means for receiving the input information and storing it in a database; means for generating optimal suggestions using a generation AI that analyzes the input information; means for presenting the generated suggestions to the user and collecting user feedback; means for analyzing the user's facial expressions and voice in real time and acquiring emotional data; means for adjusting the suggestions based on the emotional data; means for analyzing the user's interests and satisfaction via a device worn by the user; and means for modifying the suggestions based on the user's feedback. This enables suggestions that reflect the user's emotions in real time.

[1192] "User" refers to the consumer or customer who uses the system.

[1193] "Preferences" is information that indicates a user's personal tastes and preferences.

[1194] "Lifestyle" is information about the user's daily habits and behavior patterns.

[1195] "Means for inputting information" refers to a function that allows a user to provide their own profile information through a terminal.

[1196] "Means for encryption and transmission" refers to the function of encrypting collected data to protect it from unauthorized access from outside and transmitting it safely to a server.

[1197] A "database" is a system for storing and managing information obtained from users.

[1198] "Generative AI" refers to artificial intelligence algorithms used to analyze collected data and generate optimal recommendations.

[1199] "Suggestions" refer to recommended options or plans that generative AI creates based on user information and emotional data.

[1200] "Means for collecting feedback" refers to the functionality for incorporating user-provided opinions and reactions into the system.

[1201] "Emotion data" refers to data that indicates the emotional state of a user, obtained by analyzing the user's facial expressions, voice, etc. in real time.

[1202] A "wearable device" is a device that is worn by the user, such as smart glasses or a head-mounted display.

[1203] "Means of adjustment" refers to the ability to change and optimize proposal content in real time based on emotional data and feedback.

[1204] "Means for analyzing interest and satisfaction" refers to a function that measures user interest and satisfaction based on their reactions.

[1205] "Means to modify" refers to the ability to improve existing suggestions based on user feedback.

[1206] An embodiment of the present invention will be described. The system of the present invention combines a user's preference and lifestyle information with an emotion engine that recognizes the user's emotions to provide optimal suggestions. The system includes: means for inputting information about a user's preferences and lifestyle; means for collecting, encrypting, and transmitting the input information; means for receiving the input information and storing it in a database; means for generating optimal suggestions using a generation AI that analyzes the input information; means for presenting the generated suggestions to the user and collecting user feedback; means for analyzing the user's facial expressions and voice in real time to obtain emotion data; means for adjusting the suggestions based on the emotion data; means for analyzing the user's interests and satisfaction via a device worn by the user; and means for modifying the suggestions based on user feedback.

[1207] System Structure

[1208] 1. User information input: Users input their preferences and lifestyle information into a dedicated application via devices such as smart glasses or head-mounted displays. The input includes preferred categories and specific conditions. This allows the user's preferences and lifestyle information to be collected.

[1209] 2. Data collection and transmission: User input data is collected in real time by the device and transmitted to the server using a secure encryption protocol. During this process, the emotion engine analyzes the user's facial expressions and voice, and simultaneously collects emotional data.

[1210] 3. Data storage and analysis preparation: The server decrypts the received encrypted data and stores it in a database. The stored data is labeled with emotion data and used for later analysis.

[1211] 4. Analysis by Generative AI: The Generative AI on the server generates optimal suggestions based on the user's preferences, lifestyle information, and emotional data. The Generative AI analyzes the data and generates suggestions to increase user satisfaction. This analysis includes data analysis using the Google Cloud Vision API and Flask.

[1212] 5. Proposal presentation and feedback collection: The server sends the generated proposals to the device and presents them visually to the user. The user reviews the proposals and provides feedback on their opinions and reactions to each proposal. During this process, the emotion engine continuously analyzes the user's facial expressions and voice to collect emotional data in real time.

[1213] 6. Reflecting Emotional Data and Modifying Proposals: The server analyzes the collected feedback and emotional data and revises the proposals as necessary. It places particular emphasis on emotional data and adjusts the proposals to reflect the user's interests and satisfaction, thereby meeting the user's sophisticated requirements.

[1214] Hardware and software used

[1215] Hardware: smart glasses, head-mounted displays, smartphones

[1216] Software: Google Cloud Vision API, Flask, Emotion Engine

[1217] Specific examples

[1218] Example 1: When the user is shopping

[1219] Suppose User A is wearing smart glasses in a physical store. When he or she looks at a specific product from the gallery, the application detects the gaze and uses an emotion engine to analyze whether the product is interesting to the user. As a result, the application suggests, "Are you interested in this product?" and also presents other related and recommended products.

[1220] Example 2: User is looking for a new product

[1221] Let's say User B is wearing a head-mounted display. While searching for new fashion items, he stops at a specific zone. The emotion engine analyzes the user's facial expression of joy. The application then recommends, "Are you still interested in products in this zone?" and provides related product information.

[1222] Prompt Sentence Examples

[1223] "How can we make optimal shopping recommendations based on user profile information and real-time sentiment data? For example, this could include using recommendation technology to target products that users are interested in, thereby increasing interest and satisfaction."

[1224] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1225] Step 1:

[1226] Through a device worn by the user (smart glasses or a head-mounted display), the user logs into the application and inputs information about their preferences and lifestyle. The input information includes age, hobbies, and categories of purchase interest. This information is collected by the user's device. As an output, the collected user data is encrypted.

[1227] Step 2:

[1228] The device sends the collected user data to the server using an encryption protocol. This process ensures the security of the user information and prevents unauthorized access. The input is the encrypted user data, and the output is the data securely transmitted to the server.

[1229] Step 3:

[1230] The server decrypts the received encrypted data and stores it in a user database, which often contains user profile and lifestyle information for later analysis. The input is the decrypted user data, and the output is the data stored in the database.

[1231] Step 4:

[1232] The server uses a generation AI to analyze the user's input information and generate optimal suggestions, taking into account the user's emotional data. The generation AI uses the Google Cloud Vision API to deeply analyze the user's interests and satisfaction. The input is the user data and emotional data in the database, and the output is the generated suggestion data.

[1233] Step 5:

[1234] The server sends the generated suggestions to the user's device, where the user visually confirms the suggestions through smart glasses or a head-mounted display. The user provides feedback on each suggestion, and the emotion engine analyzes the user's facial expressions and voice to collect emotion data. The input is the generated suggestion data, and the output is the user's feedback and emotion data.

[1235] Step 6:

[1236] The device retransmits the collected feedback and emotion data to the server. This feedback includes the user's specific opinions and reactions to the suggestions. The input is the user's feedback and emotion data, and the output is the state sent to the server.

[1237] Step 7:

[1238] The server analyzes the collected feedback and sentiment data and adjusts the suggestions as needed. It then uses generative AI to generate new suggestions, further improving user satisfaction. This process again uses the Google Cloud Vision API. The input is the collected feedback and sentiment data, and the output is improved suggestions.

[1239] Step 8:

[1240] The server sends the improved proposal data back to the user terminal and provides the final proposal to the user, who can then review it and make a final decision. The input is the improved proposal data, and the output is the improved proposal presented to the user.

[1241] 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.

[1242] 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.

[1243] 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.

[1244] [Fourth embodiment]

[1245] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1246] 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.

[1247] 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).

[1248] 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.

[1249] 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.

[1250] 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).

[1251] 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.

[1252] 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.

[1253] 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.

[1254] 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.

[1255] 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.

[1256] 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.

[1257] 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."

[1258] The system of the present invention proposes optimal home designs based on the user's preferences and lifestyle. A specific embodiment of the system is described below.

[1259] System Overview

[1260] 1. Enter user information

[1261] Users launch a dedicated application on their device (smartphone, tablet, PC, etc.) and enter detailed profile information (age, family composition, hobbies, etc.) and specific requirements for the home (spacious living room, natural light, eco-friendly design, etc.).

[1262] 2. Data collection and transmission

[1263] The terminal collects the data entered by the user and converts it into a data format (JSON, XML, etc.).

[1264] The collected data is encrypted on the device and securely sent to the server.

[1265] 3. Saving data and preparing for analysis

[1266] The server decrypts the received user data and stores it in a database.

[1267] The received data is passed to the generation AI and prepared for detailed analysis.

[1268] 4. Analysis by generative AI

[1269] The AI ​​on the server generates the optimal home design based on the information entered by the user. This AI analyzes data from past home designs and takes into account the user's lifestyle information.

[1270] The generated home design proposals may have multiple variations, and are designed to expand the user's options.

[1271] 5. Presenting design proposals and collecting feedback

[1272] The server transmits the generated multiple housing design plans to the terminal.

[1273] The device visually displays these design proposals to the user and also provides detailed information about each proposal (floor plan, design, materials used, etc.).

[1274] Users can review the proposed design and provide their ratings and feedback, including specific requests such as "I'd like more natural light in the living room."

[1275] 6. Incorporating feedback

[1276] The terminal collects feedback from the user and transmits it back to the server.

[1277] The server runs the generative AI again based on the collected feedback and improves the design proposal.

[1278] The improved design proposal is sent to the terminal as the final design proposal and provided to the user.

[1279] Specific examples

[1280] Example 1: User A's case

[1281] 1. User A enters their preferences for a "large living room" and a "garden with a natural feel" in a dedicated app.

[1282] 2. The device collects this information, encrypts it, and sends it to the server.

[1283] 3. The server analyzes the data using generative AI and generates multiple home design proposals with spacious living rooms and gardens.

[1284] 4. The server sends the design proposal to the terminal, and User A checks the displayed proposal.

[1285] 5. When User A sends feedback such as "I would like more windows in the living room," the server reflects that request and provides the final design proposal.

[1286] Example 2: User B's case

[1287] 1. User B inputs that they need an "eco-friendly design" and a "large home office."

[1288] 2. The device collects the information, encrypts it, and sends it to the server.

[1289] 3. The server uses generative AI to generate multiple design options, including eco-friendly materials and a large home office.

[1290] 4. User B inputs detailed requests based on the presented design proposal and sends feedback to the server.

[1291] 5. The server refines the design based on the feedback and provides the final design.

[1292] In this way, this system can efficiently realize optimal home designs that meet the individual needs of users. By utilizing generative AI, it is possible to promote a user-centered design process and provide a living environment that will satisfy users.

[1293] The processing flow will be explained below.

[1294] Step 1:

[1295] Users launch a dedicated application on their device and enter detailed information about themselves (age, family composition, hobbies, etc.) and the requirements they have for their home (spacious living room, natural light, eco-friendly design, etc.).

[1296] Step 2:

[1297] The terminal collects the data entered by the user and converts it into an appropriate data format, such as JSON. The terminal provides an interface for the user to review and correct the input data as needed.

[1298] Step 3:

[1299] The terminal encrypts the collected data using an encryption protocol such as AES, ensuring security before sending it to the server.

[1300] Step 4:

[1301] The server decrypts the received encrypted data and stores it in a database, after which it performs processing to check the timestamp and data consistency.

[1302] Step 5:

[1303] The server converts the stored user data into a format that can be interpreted by the generation AI and prepares it for delivery to the generation AI, where data cleaning and normalization are performed as necessary.

[1304] Step 6:

[1305] The AI ​​on the server analyzes the user's input in detail and generates the optimal home design. This analysis reflects parameters based on the user's lifestyle and preferences. It also takes into account past design data and trend information.

[1306] Step 7:

[1307] The server organizes the generated multiple home design proposals and converts them into a presentation format, which allows them to be sent to devices in a visually easy-to-understand format.

[1308] Step 8:

[1309] The terminal visually displays the received housing design proposals to the user, providing an interface that includes detailed information about each proposal (floor plan, design, materials used, etc.) to enable the user to easily understand and evaluate them.

[1310] Step 9:

[1311] Users review the proposed design proposals and enter their ratings and feedback (e.g., "I'd like more windows in the living room.") Feedback is provided using simple forms and options on the UI.

[1312] Step 10:

[1313] The device collects feedback from the user, re-encrypts it, and sends it to the server.

[1314] Step 11:

[1315] The server analyzes the collected feedback and runs the generative AI again, generating improvements based on the feedback and updating the design proposal.

[1316] Step 12:

[1317] The server generates the final design proposal and sends it to the terminal, where it is presented to the user, who can approve and confirm the proposal.

[1318] In this way, the system executes a series of processes that generate multiple home design proposals based on information based on the user's preferences and lifestyle, and provides a final design proposal that reflects the user's feedback.

[1319] Example 1

[1320] 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."

[1321] Conventional home design proposal systems have had difficulty efficiently generating optimal designs that correspond to a user's individual lifestyle and preferences. Furthermore, incorporating user feedback is cumbersome, making it difficult to quickly improve the design. The present invention aims to solve these problems and provide a system that efficiently provides optimal home designs that meet the user's needs.

[1322] 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.

[1323] In this invention, the server includes: means for inputting information about a user's preferences and lifestyle; means for collecting the user's input information, converting it into JSON format, encrypting it, and transmitting it; means for receiving the input information, decrypting it, and storing it in a database; means for generating multiple home design proposals using a generation AI based on the user's input information; means for visually presenting the generated multiple home design proposals to the user and collecting feedback according to the user's preferences and lifestyle; and means for collecting the user's feedback and running the generation AI again to modify the home design proposals. This makes it possible to quickly and efficiently generate and improve home design proposals based on the user's input information and feedback.

[1324] "Information about the user's preferences and lifestyle" refers to specific factors that the user places importance on in the living environment, such as the user's age, family structure, hobbies, conditions of the place of residence, and requirements for the home.

[1325] "Input means" refers to devices or software that provide an interface for users to input information. Specifically, this refers to electronic devices such as smartphones, tablets, and PCs, as well as the applications that run on them.

[1326] "Means of converting to a data format, encrypting, and transmitting" refers to the series of processes in which information entered by the user is converted into a standard data format such as JSON or XML, encrypted using an encryption algorithm such as AES-256, and transmitted to a server via a communication line.

[1327] "Means of receiving, decrypting, and storing in a database" refers to the process by which the server receives the encrypted data sent from the terminal, decrypts it using the private key, and then stores the data in a database (e.g., MySQL).

[1328] "Means for generating multiple home design proposals using generative AI" refers to the process of using generative AI (artificial intelligence) to create multiple home design proposals that meet the user's needs based on past home design data and the user's lifestyle information.

[1329] "Means for visually presenting to the user and collecting feedback based on the user's preferences and lifestyle" refers to a process that displays multiple generated design proposals on the user's device and provides an interactive interface that allows the user to review the proposals and input their ratings and suggestions for improvement.

[1330] "Means for re-running the generating AI to modify the housing design proposal" refers to the process of re-running the generating AI and improving the design proposal based on feedback collected from the user, resulting in a final design proposal that more accurately reflects the user's wishes.

[1331] The present invention is a system that proposes optimal home design based on a user's preferences and lifestyle. The system of the present invention mainly operates in cooperation with the user, a terminal, and a server. The processing at each step and the hardware and software used are described in detail below.

[1332] 1. Enter user information

[1333] Users launch a dedicated application using a device such as a smartphone, tablet, or PC. Through this application, users input information such as their age, family composition, hobbies, location, and specific requirements for the home (e.g., a spacious living room, natural light, eco-friendly design, etc.). This input interface is often implemented using front-end frameworks such as React or Angular.

[1334] 2. Data collection and transmission

[1335] The terminal collects the information entered by the user and converts it into JSON format. It then encrypts the data using the AES-256 encryption algorithm. The encrypted data is then sent to the server using a secure communication protocol (e.g., HTTPS). This process typically uses a backend framework such as Node.js or Python.

[1336] 3. Saving data and preparing for analysis

[1337] The server receives the encrypted data sent from the device and decrypts it using the private key. This decrypted data is then returned to its original format using JSON decoding. The data is then stored in a database such as MySQL or PostgreSQL. The server retrieves the user data from the database and prepares it for analysis before passing it to the generation AI.

[1338] 4. Analysis by generative AI

[1339] A generative AI (e.g., OpenAI GPT-4 model) located on the server generates optimal home design proposals based on user input. This generative AI performs analysis while taking into account past home design data and the user's lifestyle information. The generated home design proposals have multiple variations, designed to expand the user's options.

[1340] 5. Presenting design proposals and collecting feedback

[1341] The server sends the generated multiple home design proposals to the terminal. The terminal visually displays these design proposals to the user. 2D or 3D graphics are often used as the display method. Specifically, the interface can be built using libraries such as Three.js or Unity. The user reviews the proposed design proposals and enters their ratings and feedback. This feedback can include specific requests such as "I want more natural light in the living room."

[1342] 6. Incorporating feedback

[1343] The device collects feedback from the user and sends it back to the server. The server then operates the generation AI again based on the collected feedback to improve the design proposal. The improved design proposal is sent to the device as the final design proposal and provided to the user. The final design proposal reflects the user's requests as much as possible, making it possible to achieve a higher level of satisfaction.

[1344] Example prompt sentences

[1345] 1. For User A

[1346] The user wants a "large living room" and a "natural garden." Please propose the optimal house design based on this.

[1347] 2. For User B

[1348] The user wants an "eco-friendly design" and a "large home office." Please generate the optimal home design proposal taking this into consideration.

[1349] The system of this invention can efficiently realize optimal home designs that meet the individual needs of users. Furthermore, by utilizing generative AI, it is possible to promote a user-centered design process and provide a living environment that is highly satisfying.

[1350] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1351] Step 1:

[1352] The user enters information

[1353] Specific operation: The user launches a dedicated application on a device such as a smartphone, tablet, or PC and enters their profile information (age, family composition, hobbies, etc.) and their specific requirements for the home (spacious living room, natural light, eco-friendly design, etc.).

[1354] Input: Manual input of information by the user.

[1355] Output: Data entered by the user that is temporarily stored on the device.

[1356] Step 2:

[1357] The device collects data, converts and encrypts it, and then transmits it.

[1358] What it does: The device collects the information entered by the user, converts it into JSON format, encrypts the data using the AES-256 encryption algorithm, and sends it to the server via a secure communication protocol (HTTPS).

[1359] Input: Information entered by the user (e.g., "We want a large living room with natural light for a 30-year-old couple and their 5-year-old child").

[1360] Output: The encrypted user information is sent to the server.

[1361] Step 3:

[1362] The server receives the data, decrypts it, and stores it in a database

[1363] How it works: The server receives the encrypted data sent from the device and decrypts it using the private key. The decrypted data is then converted back to its original format using JSON decoding. This data is then stored in a database such as MySQL or PostgreSQL.

[1364] Input: Encrypted user information.

[1365] Output: The decrypted user information is stored in the database.

[1366] Step 4:

[1367] The server generates a house design plan using generation AI

[1368] Specific operation: The server retrieves user information stored in the database and passes it to the generation AI. The generation AI (e.g., OpenAI GPT-4 model) generates multiple home design proposals based on past home design data and the user's lifestyle information, tailored to the user's needs.

[1369] Input: User information decrypted and stored in a database.

[1370] Output: Multiple house design proposals generated.

[1371] Step 5:

[1372] The server sends the design proposal to the terminal, which then presents it to the user.

[1373] How it works: The server sends multiple home design proposals generated by the AI ​​to the device. The device then displays these proposals to the user in a visually easy-to-read format (e.g., 2D or 3D graphics). Libraries such as Three.js and Unity are often used.

[1374] Input: Generated multiple house design proposals.

[1375] Output: A visual representation of the design to the user.

[1376] Step 6:

[1377] User enters feedback

[1378] Specific operation: The user reviews the presented design proposals and enters their evaluation and feedback on improvements. An example of feedback might be, "I'd like more windows in the living room."

[1379] Input: Manual input of feedback by the user.

[1380] Output: Feedback data temporarily stored in the device.

[1381] Step 7:

[1382] The device collects feedback and sends it to the server

[1383] Specific operation: The device collects the feedback entered by the user, converts it back into JSON format, encrypts it, and sends it to the server using a secure communication protocol (HTTPS).

[1384] Input: Feedback entered by the user.

[1385] Output: The encrypted feedback data is sent to the server.

[1386] Step 8:

[1387] The server improves the design based on the feedback.

[1388] How it works: The server receives the encrypted feedback, decrypts it, and decodes the JSON. It then passes the feedback to the generation AI, which analyzes it again to generate the optimal design. The improved design is then sent back to the device and provided to the user.

[1389] Input: The decrypted and JSON decoded feedback data.

[1390] Output: An improved final house design.

[1391] (Application example 1)

[1392] 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."

[1393] While conventional systems generally propose home designs based on the user's preferences and lifestyle, they lack support for store layouts. In particular, there are no systems that propose optimal layouts based on the store owner's business requirements. This results in the problem of store owners spending a great deal of time and money designing their ideal store layout.

[1394] 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.

[1395] In this invention, the server includes means for inputting information about a user's preferences and lifestyle, means for collecting the user's input information, encrypting and transmitting it, means for receiving the input information and storing it in a database, means for generating an optimal home design using a generation AI that analyzes the input information, means for presenting the generated home design to the user and collecting user feedback, means for modifying the home design based on the user feedback, means for inputting the user's business requirements, and means for generating a store layout plan based on the business requirements and presenting it to the user. This makes it possible to propose and improve an ideal store layout based on the store owner's business requirements.

[1396] "User" refers to any individual or corporation that requires proposals for home design or store layout.

[1397] "Preferences" refer to specific attributes such as design, layout, and functionality desired by the user.

[1398] "Lifestyle" refers to the habits and behavioral patterns of a user's daily life.

[1399] "Means for inputting information" refers to the interface for collecting information such as user preferences, lifestyle, and business requirements.

[1400] "Means of collection, encryption and transmission" refers to devices or software that electronically collect information entered by users, encrypt it to keep the data secure, and transmit it to a server.

[1401] "Means for receiving and storing in a database" refers to devices or software that receive encrypted user information, decrypt it, and store it in a database.

[1402] "Generative AI" refers to artificial intelligence that automatically generates optimal design proposals based on information provided by the user.

[1403] "Home design" refers to the layout and design of a home that suits the user's preferences and lifestyle.

[1404] The "means for presenting and collecting feedback" refers to a device or software that visually displays the generated design proposal to the user and receives evaluations and additional requests from the user.

[1405] "Modification tools" refers to devices or software that refine designs based on feedback collected from users.

[1406] "Business requirements" refer to the specific conditions or goals that a store owner wants to achieve in the design and layout of their store.

[1407] "Store layout proposal" refers to the layout and design of a store generated based on the user's business requirements.

[1408] The present invention includes a system for proposing a home design based on a user's preferences and lifestyle, and a system for proposing a store layout. Specific embodiments for carrying out the invention are described below.

[1409] System Overview

[1410] Enter user information

[1411] Users launch a dedicated application on their device (smartphone, tablet, PC, etc.) and enter their own profile information (age, family composition, hobbies, etc.) and detailed requirements for their specific home (spacious living room, natural light, eco-friendly design, etc.). In the case of store layouts, users also enter business requirements (customer flow, placement of specific product sections, eco-friendly elements, etc.).

[1412] Data collection and transmission

[1413] The terminal collects the data entered by the user and converts it into a data format (JSON, XML, etc.), after which the data is encrypted using a library such as the Fernet encryption library and securely sent to the server.

[1414] Saving data and preparing for analysis

[1415] The server receives the encrypted user data, decrypts it, and stores it in a database, preparing it for analysis using the generative AI.

[1416] Analysis by generative AI

[1417] The AI ​​on the server generates optimal design proposals based on the information entered by the user. This AI analyzes past design data and takes into account the user's lifestyle information. Multiple variations of home designs and store layouts are often generated.

[1418] Presenting design proposals and collecting feedback

[1419] The server sends the generated multiple design proposals to the terminal. The terminal visually displays these proposals to the user and also provides detailed information about each proposal (floor plan, design, materials used, etc.). The user reviews the proposed design proposals and enters their evaluation and feedback. For example, the user can enter specific requests such as "I want more natural light in the living room" or "I want the store's cash register to be located closer to the entrance."

[1420] Reflecting feedback

[1421] The device collects feedback from the user and sends it back to the server. The server then runs the generative AI again based on the collected feedback to improve the design proposal. The improved design proposal is sent to the device as the final design proposal and provided to the user.

[1422] Specific examples

[1423] Home design examples

[1424] User A uses a dedicated app to input their preferences for a "large living room" and a "garden with a natural feel." The device collects this information, encrypts it, and sends it to the server. The server analyzes it using a generative AI and generates multiple home design proposals with large living rooms and gardens. The server sends the design proposals to the device, and User A checks the displayed proposals. When User A sends feedback such as "I would like more windows in the living room," the server reflects this request and provides the final design proposal.

[1425] Store layout example

[1426] Store owner B inputs that he needs an "eco-friendly design" and a "large home office." The device collects the information, encrypts it, and sends it to the server. The server uses generative AI to generate multiple design proposals that include eco-friendly materials and a large home office. User B inputs detailed requests from the presented design proposals and sends feedback to the server. The server uses the feedback to improve the design proposals and provides the final design proposal.

[1427] Prompt Sentence Examples

[1428] Prompts are used to provide user input to the generative AI model and generate optimal design proposals. Examples of prompts are shown below.

[1429] "User profile: Age 35, Business type: Bookstore, Desired wide aisles, Desired eco-friendly design, Considered customer flow. Generate the optimal store layout proposal based on these requirements."

[1430] As described above, the present invention is a system that provides optimal design proposals based on the user's preferences, lifestyle, and business requirements, and realizes a user-centered design process by utilizing generative AI.

[1431] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1432] Step 1:

[1433] Users enter information using a dedicated application, including profile information (age, family composition, hobbies, etc.), housing requirements (spacious living room, natural light, eco-friendly design, etc.), and business requirements for the store layout (customer flow, product placement, eco-friendly design, etc.). This allows users to input their preferences, lifestyle, and business requirements.

[1434] Input: User profile information, housing requirements, business requirements

[1435] Output: User information entered

[1436] Step 2:

[1437] The device collects the input information and converts it into a data format (JSON, XML, etc.), then uses the Fernet encryption library to encrypt the collected data and send it securely to the server.

[1438] Input: User information entered

[1439] Output: Encrypted user data

[1440] Step 3:

[1441] The server receives the encrypted user data, decrypts it and stores useful information in a database.

[1442] Input: Encrypted user data

[1443] Output: User information stored in the database

[1444] Step 4:

[1445] The server passes the information stored in the database to a generative AI model, which analyzes the data to generate home design proposals and store layout proposals. The generative AI used for analysis generates optimal design proposals based on past data and prompts.

[1446] Input: User information stored in the database

[1447] Output: Generated house design proposal or store layout proposal

[1448] Step 5:

[1449] The server sends the generated multiple design proposals to the device, which visually displays the proposals to the user and provides detailed information about each proposal (floor plan, design, materials used, etc.).

[1450] Input: Generated residential design or store layout proposal

[1451] Output: Design proposals presented to the user

[1452] Step 6:

[1453] Users can review the proposed design proposals and enter their ratings and feedback. They can also enter specific requests (e.g., "I want more natural light in the living room" or "I want the cashier counter in the store to be closer to the entrance").

[1454] Input: User feedback

[1455] Output: Feedback information

[1456] Step 7:

[1457] The device collects user feedback information and sends it back to the server, which then runs the generative AI model again and refines the design proposal based on the feedback.

[1458] Input: User feedback information

[1459] Output: Improved design

[1460] Step 8:

[1461] The server transmits the improved design proposal as a final design proposal to the terminal, which then provides the final design proposal to the user.

[1462] Input: Improved design proposal

[1463] Output: Presentation of final design proposal

[1464] This series of processes enables the system to efficiently propose optimal home designs and store layouts based on the user's preferences, lifestyle, and business requirements, thereby increasing user satisfaction.

[1465] 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.

[1466] The system of the present invention combines a user's preferences and lifestyle information with an emotion engine that recognizes the user's emotions to propose optimal home designs. A specific embodiment of this system is described below.

[1467] System Overview

[1468] 1. Enter user information

[1469] Users launch a dedicated application on their device (smartphone, tablet, PC, etc.) and enter detailed profile information (age, family composition, hobbies, etc.) and specific requirements for the home (spacious living room, natural light, eco-friendly design, etc.).

[1470] At this time, the emotion engine analyzes the user's facial expressions, voice, and input data in real time to detect the user's emotional state.

[1471] 2. Data collection and transmission

[1472] The device collects the user's input data and emotion data, converts them into an appropriate data format, and transmits them to the server while ensuring the data security using encryption protocols.

[1473] 3. Saving data and preparing for analysis

[1474] The server decrypts the received encrypted data and stores it in a database. Emotional data in particular is specially tagged and managed to provide useful information for analysis.

[1475] After storage, the data is passed to a generative AI, ready for further analysis.

[1476] 4. Analysis by generative AI

[1477] The AI ​​on the server generates optimal home designs based on the user's preferences, lifestyle information, and emotional data. Emotional data is evaluated as a particularly important factor, generating multiple design proposals that will satisfy the user.

[1478] 5. Presenting design proposals and collecting feedback

[1479] The server transmits the generated multiple house design plans to the terminal.

[1480] The device visually displays these design proposals to the user and provides detailed information about each proposal (floor plan, design, materials used, etc.). In addition, an emotion engine analyzes users' reactions and comments in real time.

[1481] The user reviews the proposed design proposals and enters their evaluation and feedback. The emotion engine also records the user's emotional state when providing feedback, and sends it to the server.

[1482] 6. Incorporating feedback

[1483] The device collects feedback and emotional data from the user, encrypts it, and sends it to the server.

[1484] The server analyzes the collected feedback and emotional data and runs the generative AI again, generating improvement proposals that place particular emphasis on emotional data and updating the design proposals.

[1485] The final design is sent to the terminal and presented to the user.

[1486] Specific examples

[1487] Example 1: For user A

[1488] 1. User A enters their preferences for a "large living room" and a "natural garden" in the dedicated app. At this time, the emotion engine detects User A's relaxed facial expression.

[1489] 2. The device collects input information and emotional data, encrypts it, and sends it to the server.

[1490] 3. The server uses a generative AI to generate multiple home design proposals with spacious living rooms and gardens. By referencing the emotional data, design proposals that emphasize spacious, relaxing spaces are prioritized.

[1491] 4. The server sends the design proposal to the device, where User A confirms the displayed proposal. The emotion engine analyzes the user's facial expressions while the proposal is displayed and collects further feedback.

[1492] 5. When User A sends feedback such as "I want more windows in the living room," the emotion engine detects a satisfied facial expression, and the server reflects that request and provides a final design proposal.

[1493] Example 2: For user B

[1494] 1. User B inputs that he needs "eco-friendly design" and "large home office." The emotion engine detects User B's interesting facial expression.

[1495] 2. The device collects the information, encrypts it, and sends it to the server.

[1496] 3. The server uses generative AI to generate multiple design proposals, including eco-friendly materials and a large home office. Based on interesting facial expressions, design proposals with particularly innovative ideas are presented.

[1497] 4. User B inputs feedback based on the presented design proposal, and the emotion engine analyzes the user's emotions in real time and sends them to the server.

[1498] 5. The server refines the design proposal based on the feedback and emotion data and provides the final design proposal.

[1499] In this way, by incorporating user emotional data, the system of the present invention can propose more precise and satisfying home design proposals than ever before. The combination of the emotion engine and generative AI realizes a more advanced, user-centered design process.

[1500] The processing flow will be explained below.

[1501] Step 1:

[1502] Users launch a dedicated application on their device and enter their profile information (age, family composition, hobbies, etc.) and detailed requirements for their home (spacious living room, natural light, eco-friendly design, etc.). As they enter their information, the emotion engine analyzes the user's facial expressions and voice in real time and records emotional data.

[1503] Step 2:

[1504] The device combines the data entered by the user and the emotional data collected by the emotion engine, converting it into an appropriate data format, such as JSON, and then labeling the converted data for easy reading.

[1505] Step 3:

[1506] The terminal encrypts the collected data using an encryption protocol such as AES, ensuring the security of the data before sending it to the server.

[1507] Step 4:

[1508] The server decrypts the received encrypted data and stores it in a database. At this time, the emotion data is given a special tag and managed so that it can be used for analysis.

[1509] Step 5:

[1510] The server passes the stored user data and emotion data to the generative AI and prepares it for analysis. Data cleaning and normalization are also performed at this stage.

[1511] Step 6:

[1512] The AI ​​on the server generates optimal home designs based on user input and emotional data. Emotional data is particularly important, as it serves as an indicator for evaluating which design the user will be most satisfied with.

[1513] Step 7:

[1514] The server organizes the generated multiple housing design proposals, converts them into a presentation format, and sends them to the terminal, where they are prepared to be displayed in an easy-to-understand manner for the user.

[1515] Step 8:

[1516] The terminal visually displays the received housing design proposal to the user, and the interface includes detailed information about the design proposal (floor plan, design, materials used, etc.) to make it easy for the user to understand and evaluate.

[1517] Step 9:

[1518] The user reviews the proposed design and provides their evaluation and feedback. At this time, the emotion engine again analyzes the user's facial expressions and voice and records the emotional data at the time of feedback.

[1519] Step 10:

[1520] The device collects the feedback from the user and the recollected emotional data, re-encrypts it, and sends it to the server.

[1521] Step 11:

[1522] The server analyzes the collected feedback and additional emotional data and runs the generative AI again, generating an improved proposal that emphasizes elements that the user expressed a high sensitivity to, taking into account the emotional data in particular.

[1523] Step 12:

[1524] The server generates a final design proposal and sends it to the device, which is individually adjusted based on the emotion data and feedback. The final design proposal is presented to the user, who can approve and confirm it.

[1525] This process utilizes emotional data in addition to user preferences and lifestyle information to produce highly accurate home design proposals. The combination of the emotion engine and generative AI provides a living environment that is more satisfying to users than conventional systems.

[1526] Example 2

[1527] 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."

[1528] Conventional home design proposal systems make proposals based on the user's preferences and lifestyle, but are unable to incorporate data on the user's emotions. This makes it difficult to propose designs that reflect the user's true satisfaction. Furthermore, when modifying home designs based solely on feedback, the user's emotional state cannot be taken into account, resulting in a high likelihood of the design proposals not meeting the user's expectations. There was a need to solve these problems and realize more precise home design proposals that would provide greater user satisfaction.

[1529] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting, encrypting, and transmitting user input information and emotional data, means for receiving the input information and emotional data and storing them in a database, and means for generating an optimal home design using a generative AI model that analyzes the input information and emotional data. This makes it possible to propose a precise and satisfying home design that reflects the user's emotions.

[1530] A "user" is an individual or group that utilizes the system to input their preferences, lifestyle information, and emotional data.

[1531] "Information about preferences and lifestyle" is data about personal preferences and lifestyle habits such as the user's age, family structure, hobbies, and housing requirements.

[1532] "Emotion data" refers to data relating to the emotional state of a user that is acquired in real time from the user's facial expressions, voice, and behavior.

[1533] A "terminal" is a device used by a user to input information and communicate collected data, including smartphones, tablets, and personal computers.

[1534] "Encryption" is the process of transforming data according to a specific algorithm to ensure security during transmission.

[1535] A "server" is a computer system that decrypts the data it receives, stores it, and analyzes it using a generative AI model.

[1536] A "database" is a system for systematically storing and managing user input information and emotional data.

[1537] A "generative AI model" is an artificial intelligence model that analyzes user input information and emotional data to generate optimal home designs.

[1538] "Feedback" refers to opinions and evaluations provided by users regarding proposed home design plans.

[1539] "Revising" is the process of improving the proposed home design based on user feedback and sentiment data.

[1540] A "design proposal" is a specific design plan for a house created by a generative AI model that responds to the user's requests and emotions.

[1541] The system of the present invention combines a user's preferences and lifestyle information with an emotion engine that recognizes the user's emotions to propose optimal home designs. A specific embodiment of this system is described below.

[1542] Enter user information

[1543] Users launch a dedicated application on their device (smartphone, tablet, personal computer, etc.). They then enter their own profile information (e.g., age, family composition, hobbies, etc.) and detailed information about their specific home requirements (e.g., spacious living room, natural light, eco-friendly design, etc.). The emotion engine analyzes the user's facial expressions, voice, and input data in real time to detect their emotional state. This emotional data is an important element in the system's design proposals.

[1544] Data collection and transmission

[1545] The device collects the information and emotion data entered by the user and converts it into an appropriate data format, such as JSON. The converted data is then sent to the server using an encryption protocol (e.g., TLS). This encryption ensures the security of the data.

[1546] Saving data and preparing for analysis

[1547] After receiving the encrypted data, the server decrypts it and extracts it as JSON data. The extracted data is then stored in a database. Emotional data in particular is tagged and stored in a format that is easy to use during analysis. After all the collected data has been saved, it is passed to the generation AI and prepared for analysis.

[1548] Analysis by generative AI

[1549] The server-based AI generates multiple optimal home design proposals based on the user's saved preferences, lifestyle information, and emotional data. Emotional data is evaluated as a particularly important factor in this analysis process. Design proposals are generated using prompt sentences.

[1550] Prompt Sentence Examples

[1551] "The user has a relaxed expression and wants a spacious living room and a garden that allows for a natural feel. Please propose the optimal home design based on this."

[1552] "The user is looking for an eco-friendly design and a large home office, which is an interesting look. Please suggest the best home design based on this."

[1553] Presenting design proposals and collecting feedback

[1554] The server sends the generated house design proposal to the terminal. The terminal visually displays the design proposal to the user and provides detailed information (e.g., floor plan, design, materials used, etc.). The emotion engine analyzes the user's reactions (facial expressions and voice) again and collects feedback from the user in real time. The user's emotional state at the time of feedback is also recorded.

[1555] Reflecting feedback

[1556] The device collects feedback and emotional data from the user, re-encrypts it, and sends it to the server. The server analyzes the collected data and uses generative AI to improve the design proposal, placing particular emphasis on emotional data to consider modifications to the design proposal. After the final design proposal is generated, it is sent back to the device and presented to the user.

[1557] Specific examples

[1558] Example 1: For user A

[1559] 1. User A enters their preferences for a "large living room" and a "natural garden" into the app. The emotion engine detects a relaxed facial expression.

[1560] 2. The device encrypts this information and emotion data and sends it to the server.

[1561] 3. The server uses a generative AI to generate multiple home design proposals with spacious living rooms and gardens. By referencing the emotional data, design proposals that emphasize spacious, relaxing spaces are prioritized.

[1562] 4. The server sends the design proposal to the device, where User A confirms it. The emotion engine analyzes the facial expressions displayed and collects feedback.

[1563] 5. When User A provides feedback such as "I want more windows in the living room," the server reflects that request and provides a final design proposal.

[1564] Example 2: For user B

[1565] 1. User B inputs that they want an "eco-friendly design" and a "large home office," and the emotion engine detects an interesting facial expression.

[1566] 2. The device collects this information, encrypts it, and sends it to the server.

[1567] 3. The server uses generative AI to generate multiple eco-friendly design proposals, and proposals including innovative ideas are presented based on interesting facial expressions.

[1568] 4. User B enters feedback on the design proposal, and the emotion engine analyzes the emotions in real time and sends them to the server.

[1569] 5. Finally, the server refines the design based on feedback and emotional data and provides the final design.

[1570] By incorporating user emotional data, the system of this invention can provide more accurate and satisfying home design proposals than ever before. The collaboration between the emotion engine and generative AI realizes an advanced, user-centered design process.

[1571] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1572] Step 1: Enter your information

[1573] The user launches a dedicated application on their device. Here, the user enters their profile information (age, family composition, hobbies, etc.) and specific requirements for the home (spacious living room, natural light, eco-friendly design, etc.). The emotion engine then analyzes the user's facial expressions and voice in real time to obtain data on their emotional state (relaxed, interested, etc.). The input data is saved on the device as profile information, requirement data, and emotion data.

[1574] Input: User profile information, housing requirements, user emotion data

[1575] Output: User information and emotion data stored on the device

[1576] Step 2: Collect and send data

[1577] The device collects the user-entered data and emotion data together, converts them into an appropriate data format, such as JSON, and then securely transmits the converted data to the server using an encryption protocol (e.g., TLS), while maintaining the confidentiality and integrity of the data.

[1578] Input: User information and emotional data stored on the device

[1579] Output: Data sent to the server using an encryption protocol

[1580] Step 3: Save the data and prepare it for analysis

[1581] The server receives the encrypted data and decrypts it. The decrypted data is extracted as JSON data and stored in a database as user information and emotional data. In particular, the emotional data is tagged and managed as useful information for analysis. After all the data is stored, it is passed to the generation AI and prepared for analysis.

[1582] Input: Encrypted data sent to the server

[1583] Output: User information and emotion data stored in a database

[1584] Step 4: Analysis by generative AI

[1585] The generation AI on the server receives the saved user information and emotional data as input and analyzes it using natural language processing and machine learning algorithms. Based on this analysis, it generates multiple optimal home design proposals that reflect the user's preferences and emotional data. Specific design proposals are generated using prompt sentences.

[1586] Input: User information and emotion data in a database

[1587] Output: Multiple house design proposals

[1588] Step 5: Present your design and gather feedback

[1589] The server sends the generated house design proposals to the terminal. The terminal visually displays the proposed multiple design proposals to the user and provides detailed information (floor plan, design, materials used, etc.). The emotion engine analyzes the user's reactions again and collects feedback from the user in real time. The user's feedback and emotional state data are recorded on the terminal.

[1590] Input: Generated house design plan

[1591] Output: User feedback and emotion data

[1592] Step 6: Incorporating feedback

[1593] The device re-encrypts the feedback and emotional data collected from the user and sends it to the server. The server analyzes this data and uses generative AI to improve the design proposal. It places particular emphasis on emotional data to generate a final design proposal that reflects the user's feedback. The final design proposal is then sent to the device and presented to the user.

[1594] Input: User feedback and emotional data

[1595] Output: Revised final house design

[1596] (Application example 2)

[1597] 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."

[1598] There is a demand for a system that can provide optimal suggestions to individual users by taking into account not only the user's preferences and lifestyle information but also real-time emotional data. However, conventional systems lack the means to analyze the user's emotions, making it difficult to further improve user satisfaction. The present invention aims to provide a system that can provide suggestions with higher satisfaction by acquiring real-time emotional data via a device worn by the user and adjusting the content of the suggestions based on that data.

[1599] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting information about a user's preferences and lifestyle; means for collecting, encrypting, and transmitting the user's input information; means for receiving the input information and storing it in a database; means for generating optimal suggestions using a generation AI that analyzes the input information; means for presenting the generated suggestions to the user and collecting user feedback; means for analyzing the user's facial expressions and voice in real time and acquiring emotional data; means for adjusting the suggestions based on the emotional data; means for analyzing the user's interests and satisfaction via a device worn by the user; and means for modifying the suggestions based on the user's feedback. This enables suggestions that reflect the user's emotions in real time.

[1600] "User" refers to the consumer or customer who uses the system.

[1601] "Preferences" is information that indicates a user's personal tastes and preferences.

[1602] "Lifestyle" is information about the user's daily habits and behavior patterns.

[1603] "Means for inputting information" refers to a function that allows a user to provide their own profile information through a terminal.

[1604] "Means for encryption and transmission" refers to the function of encrypting collected data to protect it from unauthorized access from outside and transmitting it safely to a server.

[1605] A "database" is a system for storing and managing information obtained from users.

[1606] "Generative AI" refers to artificial intelligence algorithms used to analyze collected data and generate optimal recommendations.

[1607] "Suggestions" refer to recommended options or plans that generative AI creates based on user information and emotional data.

[1608] "Means for collecting feedback" refers to the functionality for incorporating user-provided opinions and reactions into the system.

[1609] "Emotion data" refers to data that indicates the emotional state of a user, obtained by analyzing the user's facial expressions, voice, etc. in real time.

[1610] A "wearable device" is a device that is worn by the user, such as smart glasses or a head-mounted display.

[1611] "Means of adjustment" refers to the ability to change and optimize proposal content in real time based on emotional data and feedback.

[1612] "Means for analyzing interest and satisfaction" refers to a function that measures user interest and satisfaction based on their reactions.

[1613] "Means to modify" refers to the ability to improve existing suggestions based on user feedback.

[1614] An embodiment of the present invention will be described. The system of the present invention combines a user's preference and lifestyle information with an emotion engine that recognizes the user's emotions to provide optimal suggestions. The system includes: means for inputting information about a user's preferences and lifestyle; means for collecting, encrypting, and transmitting the input information; means for receiving the input information and storing it in a database; means for generating optimal suggestions using a generation AI that analyzes the input information; means for presenting the generated suggestions to the user and collecting user feedback; means for analyzing the user's facial expressions and voice in real time to obtain emotion data; means for adjusting the suggestions based on the emotion data; means for analyzing the user's interests and satisfaction via a device worn by the user; and means for modifying the suggestions based on user feedback.

[1615] System Structure

[1616] 1. User information input: Users input their preferences and lifestyle information into a dedicated application via devices such as smart glasses or head-mounted displays. The input includes preferred categories and specific conditions. This allows the user's preferences and lifestyle information to be collected.

[1617] 2. Data collection and transmission: User input data is collected in real time by the device and transmitted to the server using a secure encryption protocol. During this process, the emotion engine analyzes the user's facial expressions and voice, and simultaneously collects emotional data.

[1618] 3. Data storage and analysis preparation: The server decrypts the received encrypted data and stores it in a database. The stored data is labeled with emotion data and used for later analysis.

[1619] 4. Analysis by Generative AI: The Generative AI on the server generates optimal suggestions based on the user's preferences, lifestyle information, and emotional data. The Generative AI analyzes the data and generates suggestions to increase user satisfaction. This analysis includes data analysis using the Google Cloud Vision API and Flask.

[1620] 5. Proposal presentation and feedback collection: The server sends the generated proposals to the device and presents them visually to the user. The user reviews the proposals and provides feedback on their opinions and reactions to each proposal. During this process, the emotion engine continuously analyzes the user's facial expressions and voice to collect emotional data in real time.

[1621] 6. Reflecting Emotional Data and Modifying Proposals: The server analyzes the collected feedback and emotional data and revises the proposals as necessary. It places particular emphasis on emotional data and adjusts the proposals to reflect the user's interests and satisfaction, thereby meeting the user's sophisticated requirements.

[1622] Hardware and software used

[1623] Hardware: smart glasses, head-mounted displays, smartphones

[1624] Software: Google Cloud Vision API, Flask, Emotion Engine

[1625] Specific examples

[1626] Example 1: When the user is shopping

[1627] Suppose User A is wearing smart glasses in a physical store. When he or she looks at a specific product from the gallery, the application detects the gaze and uses an emotion engine to analyze whether the product is interesting to the user. As a result, the application suggests, "Are you interested in this product?" and also presents other related and recommended products.

[1628] Example 2: User is looking for a new product

[1629] Let's say User B is wearing a head-mounted display. While searching for new fashion items, he stops at a specific zone. The emotion engine analyzes the user's facial expression of joy. The application then recommends, "Are you still interested in products in this zone?" and provides related product information.

[1630] Prompt Sentence Examples

[1631] "How can we make optimal shopping recommendations based on user profile information and real-time sentiment data? For example, this could include using recommendation technology to target products that users are interested in, thereby increasing interest and satisfaction."

[1632] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1633] Step 1:

[1634] Through a device worn by the user (smart glasses or a head-mounted display), the user logs into the application and inputs information about their preferences and lifestyle. The input information includes age, hobbies, and categories of purchase interest. This information is collected by the user's device. As an output, the collected user data is encrypted.

[1635] Step 2:

[1636] The device sends the collected user data to the server using an encryption protocol. This process ensures the security of the user information and prevents unauthorized access. The input is the encrypted user data, and the output is the data securely transmitted to the server.

[1637] Step 3:

[1638] The server decrypts the received encrypted data and stores it in a user database, which often contains user profile and lifestyle information for later analysis. The input is the decrypted user data, and the output is the data stored in the database.

[1639] Step 4:

[1640] The server uses a generation AI to analyze the user's input information and generate optimal suggestions, taking into account the user's emotional data. The generation AI uses the Google Cloud Vision API to deeply analyze the user's interests and satisfaction. The input is the user data and emotional data in the database, and the output is the generated suggestion data.

[1641] Step 5:

[1642] The server sends the generated suggestions to the user's device, where the user visually confirms the suggestions through smart glasses or a head-mounted display. The user provides feedback on each suggestion, and the emotion engine analyzes the user's facial expressions and voice to collect emotion data. The input is the generated suggestion data, and the output is the user's feedback and emotion data.

[1643] Step 6:

[1644] The device retransmits the collected feedback and emotion data to the server. This feedback includes the user's specific opinions and reactions to the suggestions. The input is the user's feedback and emotion data, and the output is the state sent to the server.

[1645] Step 7:

[1646] The server analyzes the collected feedback and sentiment data and adjusts the suggestions as needed. It then uses generative AI to generate new suggestions, further improving user satisfaction. This process again uses the Google Cloud Vision API. The input is the collected feedback and sentiment data, and the output is improved suggestions.

[1647] Step 8:

[1648] The server sends the improved proposal data back to the user terminal and provides the final proposal to the user, who can then review it and make a final decision. The input is the improved proposal data, and the output is the improved proposal presented to the user.

[1649] 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.

[1650] 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.

[1651] 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.

[1652] 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.

[1653] 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.

[1654] 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.

[1655] 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).

[1656] 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.

[1657] 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."

[1658] 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.

[1659] 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).

[1660] 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.

[1661] 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.

[1662] 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.

[1663] 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.

[1664] 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.

[1665] 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.

[1666] 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.

[1667] 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.

[1668] 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.

[1669] 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.

[1670] The following is further disclosed regarding the above embodiment.

[1671] (Claim 1)

[1672] means for inputting information about a user's preferences and lifestyle;

[1673] means for collecting, encrypting and transmitting the user's input information;

[1674] means for receiving the input information and storing it in a database;

[1675] A means for generating an optimal home design using a generation AI that analyzes the input information;

[1676] means for presenting the generated home design to a user and collecting user feedback;

[1677] means for modifying the home design based on the user's feedback;

[1678] A system including:

[1679] (Claim 2)

[1680] 10. The system of claim 1, wherein the system generates a plurality of home design proposals based on a user's preferences and lifestyle.

[1681] (Claim 3)

[1682] The system of claim 1, further comprising: improving the proposed home design after collecting the feedback; and presenting a final proposed design.

[1683] "Example 1"

[1684] (Claim 1)

[1685] means for inputting information about a user's preferences and lifestyle;

[1686] means for collecting the user's input information, converting it into a JSON format, encrypting it, and transmitting it;

[1687] means for receiving, decrypting and storing said input information in a database;

[1688] A means for generating a plurality of housing design proposals using a generation AI based on the user's input information;

[1689] a means for visually presenting the generated plurality of house design proposals to a user and collecting feedback according to the user's preferences and lifestyle;

[1690] A means for collecting feedback from the user and re-operating the generating AI to modify the house design plan;

[1691] A system including:

[1692] (Claim 2)

[1693] 2. The system according to claim 1, wherein the generated plurality of house design proposals are presented to the user in a visually easy-to-understand format.

[1694] (Claim 3)

[1695] The system of claim 1, wherein after collecting the feedback, a generative AI is used to refine the housing design proposal and present a final design proposal.

[1696] "Application Example 1"

[1697] (Claim 1)

[1698] means for inputting information about a user's preferences and lifestyle;

[1699] means for collecting, encrypting and transmitting the user's input information;

[1700] means for receiving the input information and storing it in a database;

[1701] A means for generating an optimal home design using a generation AI that analyzes the input information;

[1702] means for presenting the generated home design to a user and collecting user feedback;

[1703] means for modifying the home design based on the user's feedback;

[1704] a means for inputting user business requirements;

[1705] means for generating a store layout plan based on the business requirements and presenting the plan to a user;

[1706] A system including:

[1707] (Claim 2)

[1708] 10. The system of claim 1, wherein the system generates a plurality of home design proposals based on a user's preferences and lifestyle.

[1709] (Claim 3)

[1710] The system of claim 1, further comprising: improving the proposed home design after collecting the feedback; and presenting a final proposed design.

[1711] "Example 2: Combining Emotion Engines"

[1712] (Claim 1)

[1713] means for inputting information about a user's preferences and lifestyle;

[1714] means for collecting, encrypting and transmitting the input information and emotion data of the user;

[1715] means for receiving the input information and emotion data and storing them in a database;

[1716] means for generating an optimal home design using a generative AI model that analyzes the input information and emotion data;

[1717] means for presenting the generated home design to a user and collecting user feedback and sentiment data;

[1718] means for modifying the home design based on the user's feedback and sentiment data;

[1719] A system including:

[1720] (Claim 2)

[1721] 10. The system of claim 1, wherein the system generates a plurality of home design proposals based on a user's preferences, lifestyle, and emotional data.

[1722] (Claim 3)

[1723] 10. The system of claim 1, further comprising: refining the proposed home design after collecting the feedback and sentiment data; and presenting a final proposed design.

[1724] "Application example 2 when combining emotion engines"

[1725] (Claim 1)

[1726] means for inputting information about a user's preferences and lifestyle;

[1727] means for collecting, encrypting and transmitting the user's input information;

[1728] means for receiving the input information and storing it in a database;

[1729] A means for generating an optimal proposal using a generation AI that analyzes the input information;

[1730] means for presenting the generated suggestions to a user and collecting user feedback;

[1731] A means of analyzing the user's facial expressions and voice in real time to obtain emotional data;

[1732] means for adjusting suggestions based on the emotion data;

[1733] means for analyzing user interests and satisfaction via a device worn by the user;

[1734] means for modifying the suggestions based on the user's feedback;

[1735] A system including:

[1736] (Claim 2)

[1737] 10. The system of claim 1, wherein the system generates a plurality of suggestions based on the user's preferences and lifestyle.

[1738] (Claim 3)

[1739] The system of claim 1 , further comprising: refining the recommendations after collecting the feedback and sentiment data; and presenting a final recommendation. [Explanation of symbols]

[1740] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for inputting information about a user's preferences and lifestyle; means for collecting, encrypting and transmitting the user's input information; means for receiving the input information and storing it in a database; A means for generating an optimal home design using a generation AI that analyzes the input information; means for presenting the generated home design to a user and collecting user feedback; means for modifying the home design based on the user's feedback; A system including:

2. The system of claim 1 , wherein the system generates a plurality of home design proposals based on the user's preferences and lifestyle.

3. The system of claim 1 , further comprising: improving the proposed home design after collecting the feedback; and presenting a final proposed design.

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