System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting conditions for a design desired by a user; means for generating a proposed design based on the conditions inputted by the user using a generative AI model; means for displaying the proposed design generated to the user; means for receiving feedback from the user and modifying the design again using the generative AI model; and means for finalizing a final design and transmitting the final design to a fabrication lab.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] In today's market, there is a growing demand for personalized products that combine traditional craftsmanship with modern design. However, producing such products requires a great deal of time and money, resulting in high prices that are out of reach for many people. This makes it difficult for users to obtain the personalized products they desire, and craftsmen miss out on opportunities to expand into new markets. [Means for solving the problem]

[0005] The present invention solves the above problem by providing a system that includes a means for a user to input desired design conditions, a means for generating a design proposal based on the user's input conditions using a generative AI model, a means for displaying the generated design proposal to the user, a means for receiving user feedback and again revising the design using the generative AI model, and a means for finalizing the final design and sending it to a production workshop.

[0006] Specifically, it includes a means for sending user input conditions to a server, a means for sending design proposals generated from the server to a terminal, and a means for sending the final design data to a production workshop and notifying users of the production and delivery status, making it possible to efficiently design, produce, and provide personalized products. This will significantly reduce costs and time, and make it possible to provide personalized products to many users at affordable prices.

[0007] "User" means any individual or entity that uses the Platform to design and ultimately purchase personalized products.

[0008] "Design requirements" refer to requests related to the style, color, material, and other specific features of the product desired by the user.

[0009] A "generative AI model" refers to an artificial intelligence algorithm that automatically generates designs based on user input.

[0010] "Design Proposal" refers to a product design proposed by a generative AI model.

[0011] "Feedback" refers to requests for corrections or opinions that users give to generated design proposals.

[0012] "Server" refers to the remote system that receives user input and feedback, runs the generative AI model, and generates and stores design proposals.

[0013] "Device" refers to the device (e.g., PC, smartphone, tablet) on which a user enters design criteria, submits feedback, and reviews the generated design proposals.

[0014] "Production workshop" refers to the location or facility where the actual product is produced based on the final design.

[0015] "Production status" refers to the progress of the production of the product by the production workshop.

[0016] "Delivery status" refers to the progress of delivery of the product to the user. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The system of the present invention automates the process of a user inputting desired design conditions, a generative AI model generating design proposals based on those conditions, and then receiving user feedback to further revise the design. The following means and processes are included in order to implement this system.

[0039] System Overview

[0040] 1. User Registration and Login

[0041] The user enters basic information (name, email address, password) on the new registration screen.

[0042] The device collects the information and sends it to the server.

[0043] The server receives this and stores it in a database.

[0044] 2. Initial design settings

[0045] The user inputs the desired design criteria (e.g., style, color, material).

[0046] The terminal receives this information and sends it to the server.

[0047] 3. Design generation using generative AI

[0048] Based on the conditions received by the server, a generative AI model is used to generate design proposals.

[0049] The server transmits the generated design proposal to the terminal and displays it to the user.

[0050] 4. Design feedback and revisions

[0051] The user inputs feedback on the generated design proposal (e.g., color changes or design adjustments).

[0052] The device collects the feedback and sends it to the server.

[0053] The server receives the feedback and uses a generative AI model to refine the design.

[0054] The revised design proposal is then presented to the user again.

[0055] 5. Final design decision

[0056] The user reviews and approves the final design.

[0057] The server stores the final design in a database and generates instructions for the manufacturing workshop.

[0058] 6. Production and Delivery

[0059] The server sends the final design data to the production workshop.

[0060] The production workshop produces the product based on the design.

[0061] After production, the product is shipped to the user.

[0062] The server monitors the progress of production and delivery and notifies the user of the status.

[0063] Specific examples

[0064] 1. User Registration and Login

[0065] A user enters "Yamada Taro", "taro@example.com", and "password123" in the new registration form.

[0066] The terminal receives this and sends it to the server.

[0067] The server saves the data in the database and sends a completion message to the terminal.

[0068] 2. Initial design settings

[0069] The user inputs the desired conditions: "modern Japanese style," "dark red," and "wood."

[0070] The terminal receives this condition and sends it to the server.

[0071] 3. Design generation using generative AI

[0072] The server receives the requirements and generates design proposals using a generative AI model.

[0073] The server sends the design proposal to the terminal and displays it to the user.

[0074] For example, a dark red wooden table design is generated in a modern Japanese style.

[0075] 4. Design feedback and revisions

[0076] The user reviews the design proposal and gives feedback, such as "Make the red a little darker."

[0077] The device sends the feedback to the server.

[0078] The server receives the feedback and uses a generative AI model to adjust the colors and generate a new design.

[0079] The server sends the new design proposal to the terminal and displays it to the user.

[0080] 5. Final design decision

[0081] The user reviews the final design and clicks "Accept."

[0082] The server stores the final design in a database and creates instruction data for the production workshop.

[0083] 6. Production and Delivery

[0084] The production workshop produces the product based on the final design data received from the server.

[0085] Once completed, the product is delivered to the user.

[0086] The server monitors the progress of production and delivery and notifies the user.

[0087] The above detailed process allows users to obtain efficient and personalized products in a short time.

[0088] The processing flow will be explained below.

[0089] Step 1:

[0090] The user enters their name, email address, and password on the new registration screen.

[0091] Step 2:

[0092] The terminal collects the information entered by the user and sends it to the server.

[0093] Step 3:

[0094] The server receives this information and stores it in a database.

[0095] Step 4:

[0096] The server sends a message to the terminal indicating that the save is complete.

[0097] Step 5:

[0098] The terminal displays a save complete message to the user.

[0099] Step 6:

[0100] The user enters an email address and password on the login screen and attempts to log in.

[0101] Step 7:

[0102] The terminal again collects this information and sends it to the server.

[0103] Step 8:

[0104] The server checks the user information against the database and performs authentication. If authentication is successful, it sends a login success message to the terminal.

[0105] Step 9:

[0106] The terminal receives the authentication success message and displays it to the user, who proceeds to the main screen.

[0107] Step 10:

[0108] The user selects "Create a new design" on the main screen and enters the desired design conditions (e.g., style, color, material).

[0109] Step 11:

[0110] The terminal collects the user's input data and sends it to the server.

[0111] Step 12:

[0112] The server receives this data and pre-processes it to feed it into the generative AI model.

[0113] Step 13:

[0114] The server inputs the preprocessed data into a generative AI model to generate design proposals.

[0115] Step 14:

[0116] The server receives the generated design proposal and sends it to the terminal.

[0117] Step 15:

[0118] The terminal receives the design proposal and displays it to the user, who then checks the design proposal.

[0119] Step 16:

[0120] The user enters feedback on the design proposal (e.g., "Make it a darker red").

[0121] Step 17:

[0122] The device collects the feedback and sends it to the server.

[0123] Step 18:

[0124] The server receives the feedback and re-inputs it into the generative AI model to refine the design.

[0125] Step 19:

[0126] The server receives the revised design proposal and sends it to the device.

[0127] Step 20:

[0128] The terminal receives the revised design proposal and displays it again to the user, who then checks the design proposal again.

[0129] Step 21:

[0130] The user approves the final design and clicks the "Approve" button.

[0131] Step 22:

[0132] The terminal collects the authorization information and sends it to the server.

[0133] Step 23:

[0134] The server receives the approval information and stores the final design in a database.

[0135] Step 24:

[0136] The server sends the final design data to the production workshop.

[0137] Step 25:

[0138] The production workshop produces the product based on the design data received from the server.

[0139] Step 26:

[0140] The crafting workshop reports the crafting progress to the server.

[0141] Step 27:

[0142] The server monitors the progress and notifies the user.

[0143] Step 28:

[0144] Once the product is completed, the production workshop will handle the shipping procedures.

[0145] Step 29:

[0146] The server monitors the delivery status and notifies the user of the final delivery status.

[0147] The above are the specific processing steps from user registration to design proposal generation, feedback, final approval, production, and delivery.

[0148] Example 1

[0149] 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."

[0150] Conventional design generation systems have a complicated process from when the user inputs their desired design conditions to when the final design is finalized, and the process of modifying the design based on feedback is time-consuming and laborious. Furthermore, the transmission of the final design to the production studio and notifications of the production and delivery status are sometimes not smooth, which reduces user satisfaction.

[0151] 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.

[0152] In this invention, the server includes a means for inputting the user's desired design conditions, a means for generating a design proposal based on the user's input conditions using a generative AI model, a means for displaying the generated design proposal to the user, a means for receiving user feedback and revising the design using the generative AI model again, and a means for finalizing the design and transmitting the information to the manufacturer. This allows the user to efficiently proceed with the design process, enables rapid design revisions based on feedback, and enables smooth notification of the production workshop and production / delivery status.

[0153] "User" refers to a person who inputs design conditions and provides feedback on the generated design proposal.

[0154] "Design conditions" are elements related to the design desired by the user, including, for example, style, color, material, and the like.

[0155] A "generative AI model" refers to a part of a system that uses artificial intelligence technology to generate design proposals based on user input.

[0156] "Design proposal" refers to a design prototype generated by a generative AI model based on user input conditions.

[0157] "Feedback" refers to opinions and requests for corrections made by the user regarding the generated design proposal.

[0158] "Final design" refers to the design proposal that the generative AI model finalizes based on user feedback.

[0159] "Manufacturer" refers to the workshop or factory that actually produces the product based on the final design and provides it to the user.

[0160] The "information processing device" refers to a server that receives user input conditions and feedback and generates and modifies designs using a generative AI model.

[0161] The "display device" refers to a terminal that displays the generated design proposal to the user.

[0162] "Production Status" refers to the progress of the manufacturer in producing the product based on the final design.

[0163] "Delivery status" refers to the progress of the delivery process until the completed product is delivered to the user.

[0164] The system of the present invention automates the process of a user inputting desired design conditions, a generative AI model generating design proposals based on the input, and then receiving user feedback to further revise the design. To implement this system, the following means and processes are included.

[0165] First, the user enters basic information (name, email address, password) on the new registration screen. The device receives this information and sends it to the server. The server receives this information and stores it in a database. After the user registers, when they enter their email address and password on the login page, the device sends the authentication information to the server, which then performs authentication. If authentication is successful, the user is allowed to log in.

[0166] Next, the user inputs their desired design criteria (e.g., style, color, material). The device receives this information and sends it to the server. The server sends prompts to the generative AI model based on the received criteria. The generative AI model generates design ideas based on the criteria, and the server sends the generated design ideas to the device. The device displays the design ideas to the user.

[0167] For example, when a user enters "Yamada Taro," "taro@example.com," and "password123" in a new registration form, the device sends it to the server, which saves it in the database and returns a message indicating successful registration. When a user enters "taro@example.com" and "password123" on the login screen, the device sends the authentication information to the server, which confirms the user's authentication and then allows the login. Next, the user enters desired design criteria, such as "modern Japanese style," "dark red," and "wood," and the device sends it to the server.

[0168] When the server sends the conditions "modern Japanese style," "dark red," and "wood" as prompts to the generative AI model, the model generates a design proposal for a modern Japanese style dark red wooden table based on the conditions. The server then sends this design proposal to the device and displays it to the user.

[0169] Example prompt sentence:

[0170] Generate a design based on the following criteria: "Modern Japanese style", "Dark red", and "Wood".

[0171] The user then enters feedback about the generated design proposal (e.g., "Make the red a little darker"). The device receives the feedback and sends it to the server. The server verifies the feedback and sends a prompt for correction to the generative AI model. The generative AI model incorporates the feedback to generate a new design proposal, which the server sends to the device and displays again to the user.

[0172] Example prompt sentence:

[0173] Readjust the design: "Make the red a little darker."

[0174] Once the user confirms the final design and clicks "Accept," the server saves the final design in a database and sends the information to the manufacturer. The server then sends the final design data to the manufacturer, who then produces the product based on the design. Once completed, the product is delivered to the user. The server monitors the progress of production and delivery, notifying the user as necessary.

[0175] Through the above detailed process, users can obtain efficient and personalized products in a short period of time.

[0176] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0177] Step 1: User enters basic information on the new registration screen

[0178] Input: The user enters their name, email address, and password.

[0179] Specific operation: The user enters "Yamada Taro", "taro@example.com", and "password123" on the new registration screen.

[0180] Data processing: The device receives the user's input information and sends it to the server as JSON format data.

[0181] Output: The server saves the received data in the database and returns a message to the terminal indicating successful registration.

[0182] Step 2: User enters credentials on login page

[0183] Input: The user enters their email address and password.

[0184] Specific operation: The user enters "taro@example.com" and "password123" on the login screen.

[0185] Data processing: The device receives the authentication information and sends it to the server

[0186] Output: The server checks the authentication information against the database and returns a successful authentication message to the terminal.

[0187] Step 3: User enters design requirements

[0188] Input: The user inputs "design style," "color," and "material."

[0189] Specific operation: The user inputs "Modern Japanese Style", "Dark Red", and "Wood".

[0190] Data processing: The device receives the desired conditions entered and sends them to the server.

[0191] Output: The server receives the desired conditions and sends them as prompts to the generative AI model.

[0192] Step 4: The generative AI model generates design ideas

[0193] Input: Prompt sent from the server ("Modern Japanese Style", "Dark Red", "Wood")

[0194] How it works: The generative AI model generates design ideas based on the conditions.

[0195] Data processing: The generative AI model analyzes the input conditions and generates design proposals

[0196] Output: The server receives the generated design proposal and sends it to the device.

[0197] Step 5: User provides feedback on the design proposal

[0198] Input: User inputs "feedback content"

[0199] Specific behavior: The user reviews the design and enters feedback such as "Make the red a little darker."

[0200] Data processing: The device receives the feedback and sends it to the server

[0201] Output: The server receives the feedback and sends it as prompts to the generative AI model.

[0202] Step 6: The generative AI model refines the design proposal

[0203] Input: Prompt sent from the server ("Make the red a little darker")

[0204] How it works: The generative AI model incorporates feedback and generates new design ideas.

[0205] Data processing: A generative AI model analyzes the feedback and refines the design proposal

[0206] Output: The server receives the revised design proposal and sends it to the device.

[0207] Step 7: User reviews and approves the final design

[0208] Input: The user performs the "final design confirmation" and "approval operation"

[0209] Specific behavior: The user reviews the final design and clicks "Accept."

[0210] Data processing: The device receives the consent operation and sends it to the server

[0211] Output: The server saves the final design to a database and sends instructions to the manufacturer.

[0212] Step 8: The server sends the final design to the manufacturer

[0213] Input: Final design data

[0214] Specific operation: The server sends the final design data to the manufacturer.

[0215] Data processing: The server transfers the final design data to the manufacturer.

[0216] Output: The manufacturer produces the product based on the final design.

[0217] Step 9: The server notifies the user of the progress of production and delivery

[0218] Input: Production status and delivery status

[0219] Specific operation: The server receives production and delivery status from the manufacturer and notifies the user.

[0220] Data processing: The server analyzes the progress data and generates notification messages.

[0221] Output: User receives notification messages to check production and delivery progress

[0222] In this way, the user, the terminal, and the server work together to advance the design process, enabling the user to efficiently receive a product with the design they desire.

[0223] (Application example 1)

[0224] 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."

[0225] In conventional design generation systems, the process of users inputting their desired conditions and the process of revising the design based on feedback on the generated design were separated, resulting in a lack of a mechanism for efficiently reflecting these changes. Furthermore, the process from finalizing the design to sending it to the production workshop was often done manually, which was time-consuming and costly. Furthermore, the user experience in virtual stores was limited, and an environment where users could easily customize designs was not established.

[0226] 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.

[0227] In this invention, the server includes means for inputting user-desired design conditions, means for generating design proposals based on the user-input conditions using a generative AI model, means for displaying the generated design proposals to the user, means for receiving user feedback and again modifying the design using the generative AI model, means for finalizing the design and sending it to a production workshop, and means implemented as a smartphone application and used in a virtual store. This enables users to efficiently customize designs in the virtual store, quickly modify the design based on feedback, and easily manage the entire process from finalizing the design to production and delivery.

[0228] A "user" is an individual or group that uses the system to input desired design conditions and provide feedback on the generated design proposals.

[0229] "Design requirements" are specific requirements such as the style, color, and material of the design desired by the user.

[0230] A "generative AI model" is an artificial intelligence system that automatically generates design proposals based on user input conditions.

[0231] A "smartphone application" is a software program that runs on a smartphone and allows users to create and customize designs.

[0232] A "virtual store" is a virtual sales environment accessible via the Internet, which exists online rather than as a physical store.

[0233] The "server" is a central control unit that receives and processes user design criteria and feedback, and generates and modifies designs using generative AI models.

[0234] "Feedback" refers to the evaluation and correction requests made by the user regarding the generated design proposal.

[0235] A "production workshop" is a facility with physical equipment that produces actual products based on the final design finalized by the user.

[0236] A "prompt" is a specific instruction sentence input to an AI model and is used when generating and modifying designs.

[0237] This invention relates to a system that allows users to input desired design conditions, generates design proposals using a generative AI model, and modifies the design based on user feedback. Specifically, it is implemented as a smartphone application to improve the user experience in a virtual store.

[0238] System Overview

[0239] 1. User Registration and Login

[0240] First, the user installs the smartphone application and registers by entering basic information (e.g., name, email address, password). After registration, the user accesses the server from the login screen and enters the account registration information.

[0241] 2. Initial design settings

[0242] After completing registration and logging in, users enter their desired design criteria (e.g., item category, style, color, material) into the application, and the device sends this information to the server.

[0243] 3. Design generation using generative AI

[0244] The server uses a generative AI model based on the received design criteria to generate an initial design proposal. This generative AI model uses OpenAI's API, etc. The generated design proposal is displayed to the user via their device.

[0245] 4. Design feedback and revisions

[0246] The user inputs feedback based on the generated design proposal (e.g., "make the backrest higher" or "make the color brighter"). This feedback is sent from the device to the server, which then uses the generative AI model to modify the design. The modified design proposal is then displayed to the user again.

[0247] 5. Final design decision

[0248] Once the user has confirmed and approved the final design, the server stores the design data and sends it to the production studio, which then produces the product based on that data.

[0249] 6. Production and Delivery

[0250] After the workshop produces the product based on the final design, the product is delivered to the user. The server monitors the progress of production and delivery in real time and notifies the user.

[0251] Hardware and software used

[0252] Hardware: Smartphones, servers

[0253] Software: Smartphone applications, server-side applications, generative AI models (e.g., OpenAI APIs)

[0254] Specific examples

[0255] If a user wants a "Scandinavian-style white sofa":

[0256] Initial input: "Scandinavian-style white sofa"

[0257] Initial design generation prompt: "Create a design based on the following specifications: Scandinavian-style white sofa"

[0258] Feedback: "Please add a taller backrest."

[0259] Post-feedback prompt: "Revise the following design based on this feedback: Add a taller backrest. Original design: [Initial generated design]"

[0260] In this way, users can efficiently customize designs in the virtual store and quickly revise design proposals using generative AI models.

[0261] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0262] Step 1:

[0263] The user installs the smartphone application and enters their name, email address, and password on the new registration screen. The device collects this information and sends it to the server. The server stores the received data in a database and completes user registration.

[0264] Input: Name, Email Address, Password

[0265] Output: User registration completion message

[0266] Specific operation: Saving data to the database

[0267] Step 2:

[0268] The user enters their account information on the login screen and logs in. The device sends the login information to the server. The server compares the received login information with a database and performs authentication. If authentication is successful, a login success message is sent to the user.

[0269] Input: Account information (email address, password)

[0270] Output: Login successful message

[0271] Specific operation: Database verification and authentication

[0272] Step 3:

[0273] The user inputs design criteria (e.g., item category, style, color, material) into the application. The device sends these criteria to the server, which stores the received design criteria in a database.

[0274] Input: Design criteria (category, style, color, material)

[0275] Output: Message that design conditions have been saved

[0276] Specific operation: Saving data to the database

[0277] Step 4:

[0278] The server sends prompts to the generative AI model based on the saved design conditions, generating design proposals, which are then sent from the server to the device and displayed to the user.

[0279] Input: Design Conditions

[0280] Output: Generated design proposal

[0281] Specific operation: Design generation using generative AI model, sending design proposal

[0282] Step 5:

[0283] The user checks the generated design proposal and inputs feedback (e.g., "Make the back higher," "Make the color brighter," etc.). The device then sends this feedback to the server.

[0284] Input: Feedback

[0285] Output: Feedback received notification

[0286] Specific behavior: Sending feedback data

[0287] Step 6:

[0288] The server receives the feedback and sends new prompts to the generative AI model to revise the design, which is then sent back to the device and displayed to the user.

[0289] Input: Feedback

[0290] Output: Revised design proposal

[0291] Specific operation: Modify the design using the generative AI model and resubmit the design proposal

[0292] Step 7:

[0293] The user checks and approves the final design. The approved design data is sent from the terminal to the server. The server stores the final design in a database and sends it to the production workshop.

[0294] Input: Final approved design data

[0295] Output: Message confirming design saving and sending of production instructions

[0296] Specific operations: saving design data to a database and transferring the data to the production workshop

[0297] Step 8:

[0298] The production workshop produces the product based on the received final design data. Once production is complete, the product is delivered to the user. The server monitors the progress of production and delivery and notifies the user of the progress.

[0299] Input: Final design data

[0300] Output: Production and delivery progress notifications

[0301] Specific operations: Monitoring the progress of the production process, notifying delivery status

[0302] 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.

[0303] The system of the present invention automates the process of a user inputting desired design conditions, a generative AI model generating design proposals based on those conditions, and then receiving user feedback to further revise the design. Furthermore, the system also includes a function to recognize user emotions. A specific embodiment of the system is described below.

[0304] System Overview

[0305] 1. User Registration and Login

[0306] The user enters basic information (name, email address, password) on the new registration screen.

[0307] The device collects the information and sends it to the server.

[0308] The server receives this and stores it in a database.

[0309] 2. Initial design settings

[0310] The user inputs the desired design criteria (e.g., style, color, material).

[0311] The terminal receives this information and sends it to the server.

[0312] 3. Design generation using generative AI

[0313] Based on the conditions received by the server, a generative AI model is used to generate design proposals.

[0314] The server transmits the generated design proposal to the terminal and displays it to the user.

[0315] 4. Emotion Recognition by Emotion Engine

[0316] The device uses the user's facial expressions, voice, input content, etc. to analyze the user's emotions through an emotion engine.

[0317] The server receives the analysis results and reflects them in design proposals or feedback.

[0318] 5. Design feedback and revisions

[0319] The user inputs feedback on the generated design proposal (e.g., color changes or design adjustments).

[0320] The device collects feedback and performs emotional analysis using an emotion engine.

[0321] The analysis results of the emotion engine are also sent to the server.

[0322] The server receives the feedback and sentiment analysis results, which are then fed back into the generative AI model to refine the design.

[0323] The revised design proposal is then presented to the user again.

[0324] 6. Final design decision

[0325] The user reviews and approves the final design.

[0326] The server stores the final design in a database and generates instructions for the manufacturing workshop.

[0327] 7. Production and Delivery

[0328] The server sends the final design data to the production workshop.

[0329] The production workshop produces the product based on the design.

[0330] After production, the product is shipped to the user.

[0331] The server monitors the progress of production and delivery and notifies the user of the status.

[0332] Specific examples

[0333] 1. User Registration and Login

[0334] A user enters "Hanako Tanaka", "hanako@example.com", and "password123" in a new registration form.

[0335] The terminal receives this and sends it to the server.

[0336] The server saves the data in the database and sends a completion message to the terminal.

[0337] 2. Initial design settings

[0338] The user inputs "modern art," "aqua blue," and "glass" as desired conditions.

[0339] The terminal receives this condition and sends it to the server.

[0340] 3. Design generation using generative AI

[0341] The server receives the requirements and generates design proposals using a generative AI model.

[0342] The server sends the design proposal to the terminal and displays it to the user.

[0343] For example, aqua blue glass objects are generated in a modern art style.

[0344] 4. Emotion Recognition by Emotion Engine

[0345] The device detects positive reactions from the user's facial expressions and sends them to the server.

[0346] 5. Design feedback and revisions

[0347] The user reviews the design proposal and gives feedback, saying, "I'd like the color to be a little darker."

[0348] The device collects feedback and performs emotional analysis using an emotion engine.

[0349] The emotion engine analyzes changes in the user's facial expressions and confirms positive emotions.

[0350] The device sends the feedback and emotion analysis results to the server.

[0351] The server receives the feedback and sentiment analysis results, adjusts the colors using a generative AI model, and generates a new design.

[0352] The server sends the new design proposal to the terminal and displays it to the user.

[0353] 6. Final design decision

[0354] The user reviews the final design and clicks "Accept."

[0355] The server stores the final design in a database and creates instruction data for the production workshop.

[0356] 7. Production and Delivery

[0357] The production workshop produces the product based on the final design data received from the server.

[0358] Once completed, the product is delivered to the user.

[0359] The server monitors the progress of production and delivery and notifies the user.

[0360] The detailed process described above enables users to acquire efficient and personalized products in a short time, and the introduction of an emotion engine increases user satisfaction.

[0361] The processing flow will be explained below.

[0362] Step 1:

[0363] The user enters their name, email address, and password on the new registration screen.

[0364] Step 2:

[0365] The terminal collects the information entered by the user and sends it to the server.

[0366] Step 3:

[0367] The server receives this information and stores it in a database.

[0368] Step 4:

[0369] The server sends a message to the terminal indicating that the save is complete.

[0370] Step 5:

[0371] The terminal displays a save complete message to the user.

[0372] Step 6:

[0373] The user enters an email address and password on the login screen and attempts to log in.

[0374] Step 7:

[0375] The terminal again collects this information and sends it to the server.

[0376] Step 8:

[0377] The server checks the user information against the database and performs authentication. If authentication is successful, it sends a login success message to the terminal.

[0378] Step 9:

[0379] The terminal receives the authentication success message and displays it to the user, who proceeds to the main screen.

[0380] Step 10:

[0381] The user selects "Create a new design" on the main screen and enters the desired design conditions (e.g., style, color, material).

[0382] Step 11:

[0383] The terminal collects the user's input data and sends it to the server.

[0384] Step 12:

[0385] The server receives this data and pre-processes it to feed it into the generative AI model.

[0386] Step 13:

[0387] The server inputs the preprocessed data into a generative AI model to generate design proposals.

[0388] Step 14:

[0389] The server receives the generated design proposal and sends it to the terminal.

[0390] Step 15:

[0391] The terminal receives the design proposal and displays it to the user, who then checks the design proposal.

[0392] Step 16:

[0393] The device captures the user's facial expressions with a camera and sends them to the emotion engine along with their voice and input.

[0394] Step 17:

[0395] The emotion engine analyzes the user's emotions (e.g., joy, confusion, dissatisfaction, etc.) regarding the design proposal.

[0396] Step 18:

[0397] The server receives the emotion analysis results and stores them together with the design proposal.

[0398] Step 19:

[0399] The user enters feedback on the design proposal (e.g., "Make it a darker red").

[0400] Step 20:

[0401] The device collects the feedback and again performs emotion analysis using the emotion engine.

[0402] Step 21:

[0403] The emotion engine analyzes the user's emotions during feedback and sends the results to the server.

[0404] Step 22:

[0405] The server receives the feedback and sentiment analysis results, which are then fed back into the generative AI model to refine the design.

[0406] Step 23:

[0407] The server receives the revised design proposal and sends it back to the device.

[0408] Step 24:

[0409] The terminal receives the revised design proposal and displays it again to the user, who then checks the design proposal again.

[0410] Step 25:

[0411] The user approves the final design and clicks the "Approve" button.

[0412] Step 26:

[0413] The terminal collects the authorization information and sends it to the server.

[0414] Step 27:

[0415] The server receives the approval information and stores the final design in a database.

[0416] Step 28:

[0417] The server sends the final design data to the production workshop.

[0418] Step 29:

[0419] The production workshop produces the product based on the design data received from the server.

[0420] Step 30:

[0421] The crafting workshop reports the crafting progress to the server.

[0422] Step 31:

[0423] The server monitors the progress and notifies the user.

[0424] Step 32:

[0425] Once the product is completed, the production workshop will handle the shipping procedures.

[0426] Step 33:

[0427] The server monitors the delivery status and notifies the user of the final delivery status.

[0428] The above are the specific processing steps from user registration to design proposal generation, sentiment analysis, feedback, final approval, production, and delivery.

[0429] Example 2

[0430] 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."

[0431] Conventional design generation systems have problems such as being unable to appropriately reflect user feedback and difficulty in automatically modifying designs that take the user's emotional state into account. Furthermore, to increase user satisfaction, it is necessary to analyze the user's emotions along with the feedback and reflect them in the design. However, there is currently no effective way to achieve this.

[0432] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for inputting conditions for a design desired by a user; means for generating a design proposal based on the user's input conditions using a generative AI model; means for displaying the generated design proposal to the user; means for the terminal to collect user emotion data and analyze it using an emotion engine; means for transmitting the emotion analysis results to the server and reflecting them in the design proposal and feedback; means for receiving user feedback and again modifying the design using the generative AI model; and means for finalizing the design and transmitting it to a production workshop. This makes it possible to generate and modify designs that reflect the user's emotions, thereby increasing user satisfaction.

[0433] "User" means an individual or organization that utilizes the system to create and modify designs.

[0434] "Desired design conditions" are specific requirements such as style, color, and material that the user desires for the design.

[0435] A "generative AI model" is an artificial intelligence algorithm that automatically generates design proposals based on input conditions.

[0436] "Design Proposal" means an initial or revised design proposal created by a generative AI model.

[0437] A "terminal" is an electronic device that a user uses to access, operate, and input data to the system.

[0438] The "server" is a central processing unit that receives and processes data sent from the device and runs generative AI models and emotion engines as needed.

[0439] "Emotion data" refers to data relating to the user's emotional state collected from the user's facial expressions, voice, input content, and the like.

[0440] An "emotion engine" is a software or hardware component that analyzes collected emotion data and infers the user's emotional state.

[0441] "Emotion analysis results" are information about the user's emotional state analyzed by the emotion engine.

[0442] "Feedback" refers to evaluations and correction requests that users input about the generated design proposals.

[0443] "Manufacturing workshop" means a facility or organization that produces physical products based on a finalized design.

[0444] "Production status" is information about the process and progress of the product being produced by the production workshop.

[0445] "Delivery status" is information about the progress of the product as it is being delivered from the production workshop to the user.

[0446] The system of the present invention automates the process of inputting the user's desired design criteria, generating design proposals based on the input criteria using a generative AI model, and then revising the design based on the user's feedback. Furthermore, the system also includes a function to recognize the user's emotions, which is important for increasing user satisfaction.

[0447] 1. Hardware and Software Configuration

[0448] Hardware

[0449] This system uses the following hardware:

[0450] Device: The device that users use to access the system and provide input and feedback on design criteria. Examples include personal computers and smartphones.

[0451] Server: A central computing unit that runs generative AI models, emotion engines, and processes user input data.

[0452] Camera and microphone: Built into the device and used to collect the user's facial expressions and voice.

[0453] software

[0454] This system uses the following software:

[0455] Generative AI model: An artificial intelligence algorithm that generates design ideas based on user input. Examples include GPT-3 and DALL-E.

[0456] Emotion engine: An algorithm that analyzes a user's emotional data and infers their emotional state. Examples include facial expression analysis software and voice analysis software.

[0457] Database: Built into the server and used to store user registration information and design data.

[0458] 2. Data processing and calculation

[0459] User Registration and Login

[0460] The user uses a device to enter their name, email address, and password, which is then sent to a server, which stores the data in a database.

[0461] Initial design settings

[0462] The user inputs their design preferences (style, color, material), and the device sends this to the server, which then passes this data as input to the generative AI model.

[0463] Design generation using generative AI models

[0464] Based on the conditions received by the server, a generative AI model is used to generate design proposals. For example, the generative AI model uses the following prompt sentence:

[0465] Prompt: "Generate an aqua blue glass object in a modern art style."

[0466] The server sends the generated design proposal to the terminal and displays it to the user.

[0467] Emotion recognition by emotion engine

[0468] The device uses a camera and microphone to collect the user's facial expressions and voice. The emotion engine analyzes this and sends the emotion analysis results to the server. The server then uses the results to create design proposals and provide feedback.

[0469] Design feedback and revisions

[0470] The user provides feedback on the generated design (e.g., "I'd like the color to be a little darker"). The device collects the feedback and analyzes it using an emotion engine. The feedback and emotion analysis results are sent to the server, which then uses this information to further revise the design using the generative AI model.

[0471] Finalize and save the design

[0472] The user checks and approves the final design. The server saves the final design in a database and generates instruction data for the production workshop.

[0473] Production and Delivery

[0474] The production workshop receives the final design data from the server and produces the product. The finished product is delivered to the user. The server monitors the progress of production and delivery and notifies the user.

[0475] Specifically, if a user inputs "modern art," "aqua blue," and "glass" as their desired design criteria, the generative AI model generates a design proposal based on these. If the user reviews the generated design proposal and provides feedback such as "I'd like the color to be a little darker," the emotion engine analyzes the user's facial expression and sends the results to the server. The server uses the generative AI model to generate a new design proposal that reflects the feedback and presents it to the user again. If the user finally approves, the production workshop produces the product based on that design and delivers it to the user.

[0476] The above process is a detailed embodiment of the present invention.

[0477] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0478] Step 1: User registration and login

[0479] Input: The user enters their name, email address, and password on the sign-up screen.

[0480] Specific operation: The user enters "name, email address, password" on the new registration screen and clicks the submit button.

[0481] Data processing: The terminal collects input data in real time and captures the click event of the submit button.

[0482] Output: The device sends the collected data to the server.

[0483] What happens: The server validates the data it receives and stores it in the database if appropriate.

[0484] Output: The server generates a registration completion message and sends it to the terminal.

[0485] Specific behavior: The device displays a completion message on the screen.

[0486] Step 2: Initialize the design

[0487] Input: The user inputs the desired design criteria (style, color, material).

[0488] Specific behavior: The user enters "Style: Modern Art", "Color: Aqua Blue", and "Material: Glass".

[0489] Data processing: The terminal collects input data.

[0490] Output: The terminal sends the collected design conditions to the server.

[0491] Specific operation: The server receives the desired conditions and stores them in a database.

[0492] Step 3: Generate a design using a generative AI model

[0493] Input: The design conditions received by the server.

[0494] What happens: The server calls the generative AI model and passes the design conditions as prompts.

[0495] Prompt: "Generate an aqua blue glass object in a modern art style."

[0496] Data computation: A generative AI model generates design ideas based on input conditions.

[0497] Output: The server sends the generated design proposal to the device.

[0498] Specific operation: The device displays the design proposal on the screen and presents it to the user.

[0499] Step 4: Emotion Recognition with the Emotion Engine

[0500] Input: User facial expressions, voice, and input.

[0501] Specific operation: The device uses the camera and microphone to collect the user's facial expressions and voice.

[0502] Data processing: The emotion data collected by the device is passed to the emotion engine in real time.

[0503] Data calculation: The emotion engine analyzes the emotion data and estimates the user's emotional state.

[0504] Output: The device sends the emotion analysis results to the server.

[0505] Specific operation: The server reflects the results of emotion analysis in design proposals and feedback.

[0506] Step 5: Design feedback and revisions

[0507] Input: User feedback (e.g. "I'd like the color to be a little darker").

[0508] What happens: The user provides feedback on the design proposal.

[0509] Data processing: The device collects feedback and analyzes it again using the emotion engine.

[0510] Output: Send feedback and sentiment analysis results to the server.

[0511] How it works: The server calls the generative AI model and revises the design based on feedback and analysis results.

[0512] Data computation: Generative AI models generate revised design proposals.

[0513] Output: The server sends the revised design proposal to the device and presents it to the user.

[0514] Step 6: Finalize and save your design

[0515] Input: User approval.

[0516] What happens: The user reviews the final design and clicks "Accept."

[0517] Data processing: The server stores the final design in a database.

[0518] Output: Generates instruction data for the production workshop.

[0519] Step 7: Production and Delivery

[0520] Input: Final design data received by the fabrication studio.

[0521] Specific operation: The production workshop produces the product based on the final design data.

[0522] Output: The finished product is delivered to the user.

[0523] What it does: The server monitors the progress of production and delivery and notifies the user.

[0524] (Application example 2)

[0525] 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."

[0526] Conventional design generation systems generate design proposals based on user requirements and incorporate feedback, but lack an automated process that takes user emotions into account. Furthermore, collecting feedback and revising designs takes time, which can lead to reduced user satisfaction. Furthermore, notifications about production and delivery status are uniform, with little consideration given to user emotions. Therefore, achieving an efficient and personalized design generation process remains a challenge.

[0527] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0528] In this invention, the server includes a means for recognizing a user's emotion, a means for modifying a design based on the emotion recognition result, and a means for adjusting the content of notifications to the user based on the emotion recognition result, thereby enabling automatic generation of a design that takes the user's emotion into consideration, modification of the design, and notification of production status and delivery status according to the emotion.

[0529] "Means for inputting the user's desired design conditions" refers to an interface that allows the user to input conditions such as the design style, color, and material that he or she desires.

[0530] "Means for generating design proposals based on user input conditions using a generative AI model" refers to a mechanism for automatically generating appropriate design proposals using a generative AI model based on conditions entered by the user.

[0531] The "means for displaying the generated design proposal to the user" refers to a display device or software interface for providing the generated design proposal to the user in a visible form.

[0532] "Means for receiving user feedback and revising the design using a generative AI model" refers to a mechanism for collecting user feedback such as evaluations and requests for revisions, and then regenerating and revising design proposals using a generative AI model based on that feedback.

[0533] "Means for recognizing user emotions" refers to technology and devices for analyzing and evaluating a user's emotional state from facial expressions, voice, etc.

[0534] "Means for modifying the design based on the emotion recognition results" refers to a mechanism for improving or changing the generated design proposal by referring to the user's emotion recognition results.

[0535] "Means for finalizing the design and transmitting it to the production facility" refers to a system for transmitting the finalized design as data to the production facility and issuing production instructions.

[0536] The "means for transmitting user input conditions to the server" refers to a mechanism for transmitting the design conditions input by the user to the server as data.

[0537] "Means for transmitting the design proposal generated by the server to the terminal" refers to a mechanism for transmitting the design proposal generated by the server as data to the terminal used by the user.

[0538] "Means for sending final design data to the production facility and notifying the user of the production status and delivery status" refers to a system for sending finalized design data to the production facility and notifying the user of the production and delivery status in real time.

[0539] "Means for adjusting the content of notifications to the user based on the emotion recognition results" refers to a mechanism for appropriately changing and adjusting the content of notifications according to the results of the user's emotion recognition, and providing more personalized information to the user.

[0540] This invention is a system that allows a user to easily create a design they desire, and then produces and delivers the product based on a highly accurate design proposal. The following describes a specific embodiment of this system.

[0541] User Registration and Login

[0542] The user enters basic information (name, email address, password) on the new registration screen. This information is collected on the device and sent to the server. The server receives the information and stores it in a database (PostgreSQL). From the next time onwards, the user can access the system using the login information.

[0543] Initial design settings

[0544] Users input their design preferences (e.g., style, color, material) using a React-based interface, and the information is sent by the device to the server.

[0545] Design generation using generative AI

[0546] The server receives the user-entered criteria and generates design proposals using a generative AI model (e.g., OpenAI GPT-3 or DALL-E). The generated design proposals are sent from the server to the device and displayed to the user.

[0547] Emotion recognition by emotion engine

[0548] The device uses the smartphone's camera and microphone to collect the user's facial expressions and voice, and analyzes their emotions using OpenCV and Google Cloud Speech-to-Text. The analysis results are sent to a server and reflected in design proposals and feedback.

[0549] Design feedback and revisions

[0550] The user enters feedback about the generated design proposal (e.g., color changes or design adjustments) using a feedback input form using React. The feedback is analyzed by the emotion engine and sent to the server, where the generative AI model again modifies the design. The modified design proposal is then presented to the user again.

[0551] Final design decision

[0552] The user reviews and approves the final design. The server saves the final design in a database and creates instructions for the manufacturing facility. An example of a prompt is the text "Create a modern art style product with aqua blue color and made of glass."

[0553] Production and Delivery

[0554] The server sends the final design data to the production facility, which then produces the product based on that design. After production, the product is delivered to the user. The server monitors the progress of production and delivery using a notification service such as Firebase, and notifies the user of the status. The content of the notification is also adjusted based on the emotion recognition results.

[0555] The following specific hardware and software are used in each of the above steps:

[0556] Server: Django

[0557] Database: PostgreSQL

[0558] Front-end interface: React

[0559] Generative AI model: OpenAI GPT-3 or DALL-E

[0560] Emotion recognition: OpenCV, Google Cloud Speech-to-Text

[0561] Notification Service: Firebase

[0562] This system allows users to efficiently obtain custom-designed products that reflect their preferences, and adding emotion recognition can further increase user satisfaction.

[0563] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0564] Step 1:

[0565] The user enters basic information (name, email address, password) on the new registration screen. The device collects this information and sends it to the server. The server stores the received information in a database. The input for this step is the basic information entered by the user, and the output is the user information stored in the database.

[0566] Step 2:

[0567] The user inputs desired design conditions (e.g., style, color, material). The terminal receives the input information and sends it to the server. The input of this step is the design conditions entered by the user, and the output is the design conditions sent to the server.

[0568] Step 3:

[0569] The server uses a generative AI model to generate design proposals based on the received design criteria. In this generation process, the AI ​​generates text and image data based on the prompt. The input for this step is the design criteria and prompt, and the output is the generated design proposal.

[0570] Step 4:

[0571] The server sends the generated design proposal to the terminal, which displays it to the user. The user checks the generated design proposal. The input of this step is the generated design proposal, and the output is the design proposal displayed to the user.

[0572] Step 5:

[0573] The device collects the user's facial expressions and voice, and performs emotion analysis using OpenCV and Google Cloud Speech-to-Text. The analysis results are sent to the server. The input of this step is the user's facial expressions and voice data, and the output is the emotion analysis results.

[0574] Step 6:

[0575] The user enters feedback about the design proposal (e.g., color changes or design tweaks), and the device collects the feedback and sends it to the server. The server receives the feedback and sentiment analysis results and uses them to modify the design using a generative AI model. The inputs for this step are the user's feedback and sentiment analysis results, and the output is the modified design proposal.

[0576] Step 7:

[0577] The server sends the revised design proposal to the terminal, which then displays it again to the user. The user confirms the revised design proposal. The input of this step is the revised design proposal, and the output is the revised design proposal displayed to the user.

[0578] Step 8:

[0579] The user reviews and approves the final design. The server stores the final design in a database and generates instructions for the fabrication facility. The input for this step is the final design data, and the output is the instructions sent to the fabrication facility.

[0580] Step 9:

[0581] The server sends the final design data to the production facility, which then produces the product based on that design. After production, the product is delivered to the user. The server uses Firebase to monitor the progress of production and delivery and notify the user of the status. The input for this step is the final design data and production and delivery status data, and the output is the notification content to the user. The notification content is also adjusted based on the emotion recognition results.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] [Second embodiment]

[0586] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0587] 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.

[0588] 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).

[0589] 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.

[0590] 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.

[0591] 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).

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] 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."

[0598] The system of the present invention automates the process of a user inputting desired design conditions, a generative AI model generating design proposals based on those conditions, and then receiving user feedback to further revise the design. The following means and processes are included in order to implement this system.

[0599] System Overview

[0600] 1. User Registration and Login

[0601] The user enters basic information (name, email address, password) on the new registration screen.

[0602] The device collects the information and sends it to the server.

[0603] The server receives this and stores it in a database.

[0604] 2. Initial design settings

[0605] The user inputs the desired design criteria (e.g., style, color, material).

[0606] The terminal receives this information and sends it to the server.

[0607] 3. Design generation using generative AI

[0608] Based on the conditions received by the server, a generative AI model is used to generate design proposals.

[0609] The server transmits the generated design proposal to the terminal and displays it to the user.

[0610] 4. Design feedback and revisions

[0611] The user inputs feedback on the generated design proposal (e.g., color changes or design adjustments).

[0612] The device collects the feedback and sends it to the server.

[0613] The server receives the feedback and uses a generative AI model to refine the design.

[0614] The revised design proposal is then presented to the user again.

[0615] 5. Final design decision

[0616] The user reviews and approves the final design.

[0617] The server stores the final design in a database and generates instructions for the manufacturing workshop.

[0618] 6. Production and Delivery

[0619] The server sends the final design data to the production workshop.

[0620] The production workshop produces the product based on the design.

[0621] After production, the product is shipped to the user.

[0622] The server monitors the progress of production and delivery and notifies the user of the status.

[0623] Specific examples

[0624] 1. User Registration and Login

[0625] A user enters "Yamada Taro", "taro@example.com", and "password123" in the new registration form.

[0626] The terminal receives this and sends it to the server.

[0627] The server saves the data in the database and sends a completion message to the terminal.

[0628] 2. Initial design settings

[0629] The user inputs the desired conditions: "modern Japanese style," "dark red," and "wood."

[0630] The terminal receives this condition and sends it to the server.

[0631] 3. Design generation using generative AI

[0632] The server receives the requirements and generates design proposals using a generative AI model.

[0633] The server sends the design proposal to the terminal and displays it to the user.

[0634] For example, a dark red wooden table design is generated in a modern Japanese style.

[0635] 4. Design feedback and revisions

[0636] The user reviews the design proposal and gives feedback, such as "Make the red a little darker."

[0637] The device sends the feedback to the server.

[0638] The server receives the feedback and uses a generative AI model to adjust the colors and generate a new design.

[0639] The server sends the new design proposal to the terminal and displays it to the user.

[0640] 5. Final design decision

[0641] The user reviews the final design and clicks "Accept."

[0642] The server stores the final design in a database and creates instruction data for the production workshop.

[0643] 6. Production and Delivery

[0644] The production workshop produces the product based on the final design data received from the server.

[0645] Once completed, the product is delivered to the user.

[0646] The server monitors the progress of production and delivery and notifies the user.

[0647] The above detailed process allows users to obtain efficient and personalized products in a short time.

[0648] The processing flow will be explained below.

[0649] Step 1:

[0650] The user enters their name, email address, and password on the new registration screen.

[0651] Step 2:

[0652] The terminal collects the information entered by the user and sends it to the server.

[0653] Step 3:

[0654] The server receives this information and stores it in a database.

[0655] Step 4:

[0656] The server sends a message to the terminal indicating that the save is complete.

[0657] Step 5:

[0658] The terminal displays a save complete message to the user.

[0659] Step 6:

[0660] The user enters an email address and password on the login screen and attempts to log in.

[0661] Step 7:

[0662] The terminal again collects this information and sends it to the server.

[0663] Step 8:

[0664] The server checks the user information against the database and performs authentication. If authentication is successful, it sends a login success message to the terminal.

[0665] Step 9:

[0666] The terminal receives the authentication success message and displays it to the user, who proceeds to the main screen.

[0667] Step 10:

[0668] The user selects "Create a new design" on the main screen and enters the desired design conditions (e.g., style, color, material).

[0669] Step 11:

[0670] The terminal collects the user's input data and sends it to the server.

[0671] Step 12:

[0672] The server receives this data and pre-processes it to feed it into the generative AI model.

[0673] Step 13:

[0674] The server inputs the preprocessed data into a generative AI model to generate design proposals.

[0675] Step 14:

[0676] The server receives the generated design proposal and sends it to the terminal.

[0677] Step 15:

[0678] The terminal receives the design proposal and displays it to the user, who then checks the design proposal.

[0679] Step 16:

[0680] The user enters feedback on the design proposal (e.g., "Make it a darker red").

[0681] Step 17:

[0682] The device collects the feedback and sends it to the server.

[0683] Step 18:

[0684] The server receives the feedback and re-inputs it into the generative AI model to refine the design.

[0685] Step 19:

[0686] The server receives the revised design proposal and sends it to the device.

[0687] Step 20:

[0688] The terminal receives the revised design proposal and displays it again to the user, who then checks the design proposal again.

[0689] Step 21:

[0690] The user approves the final design and clicks the "Approve" button.

[0691] Step 22:

[0692] The terminal collects the authorization information and sends it to the server.

[0693] Step 23:

[0694] The server receives the approval information and stores the final design in a database.

[0695] Step 24:

[0696] The server sends the final design data to the production workshop.

[0697] Step 25:

[0698] The production workshop produces the product based on the design data received from the server.

[0699] Step 26:

[0700] The crafting workshop reports the crafting progress to the server.

[0701] Step 27:

[0702] The server monitors the progress and notifies the user.

[0703] Step 28:

[0704] Once the product is completed, the production workshop will handle the shipping procedures.

[0705] Step 29:

[0706] The server monitors the delivery status and notifies the user of the final delivery status.

[0707] The above are the specific processing steps from user registration to design proposal generation, feedback, final approval, production, and delivery.

[0708] Example 1

[0709] 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."

[0710] Conventional design generation systems have a complicated process from when the user inputs their desired design conditions to when the final design is finalized, and the process of modifying the design based on feedback is time-consuming and laborious. Furthermore, the transmission of the final design to the production studio and notifications of the production and delivery status are sometimes not smooth, which reduces user satisfaction.

[0711] 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.

[0712] In this invention, the server includes a means for inputting the user's desired design conditions, a means for generating a design proposal based on the user's input conditions using a generative AI model, a means for displaying the generated design proposal to the user, a means for receiving user feedback and revising the design using the generative AI model again, and a means for finalizing the design and transmitting the information to the manufacturer. This allows the user to efficiently proceed with the design process, enables rapid design revisions based on feedback, and enables smooth notification of the production workshop and production / delivery status.

[0713] "User" refers to a person who inputs design conditions and provides feedback on the generated design proposal.

[0714] "Design conditions" are elements related to the design desired by the user, including, for example, style, color, material, and the like.

[0715] A "generative AI model" refers to a part of a system that uses artificial intelligence technology to generate design proposals based on user input.

[0716] "Design proposal" refers to a design prototype generated by a generative AI model based on user input conditions.

[0717] "Feedback" refers to opinions and requests for corrections made by the user regarding the generated design proposal.

[0718] "Final design" refers to the design proposal that the generative AI model finalizes based on user feedback.

[0719] "Manufacturer" refers to the workshop or factory that actually produces the product based on the final design and provides it to the user.

[0720] The "information processing device" refers to a server that receives user input conditions and feedback and generates and modifies designs using a generative AI model.

[0721] The "display device" refers to a terminal that displays the generated design proposal to the user.

[0722] "Production Status" refers to the progress of the manufacturer in producing the product based on the final design.

[0723] "Delivery status" refers to the progress of the delivery process until the completed product is delivered to the user.

[0724] The system of the present invention automates the process of a user inputting desired design conditions, a generative AI model generating design proposals based on the input, and then receiving user feedback to further revise the design. To implement this system, the following means and processes are included.

[0725] First, the user enters basic information (name, email address, password) on the new registration screen. The device receives this information and sends it to the server. The server receives this information and stores it in a database. After the user registers, when they enter their email address and password on the login page, the device sends the authentication information to the server, which then performs authentication. If authentication is successful, the user is allowed to log in.

[0726] Next, the user inputs their desired design criteria (e.g., style, color, material). The device receives this information and sends it to the server. The server sends prompts to the generative AI model based on the received criteria. The generative AI model generates design ideas based on the criteria, and the server sends the generated design ideas to the device. The device displays the design ideas to the user.

[0727] For example, when a user enters "Yamada Taro," "taro@example.com," and "password123" in a new registration form, the device sends it to the server, which saves it in the database and returns a message indicating successful registration. When a user enters "taro@example.com" and "password123" on the login screen, the device sends the authentication information to the server, which confirms the user's authentication and then allows the login. Next, the user enters desired design criteria, such as "modern Japanese style," "dark red," and "wood," and the device sends it to the server.

[0728] When the server sends the conditions "modern Japanese style," "dark red," and "wood" as prompts to the generative AI model, the model generates a design proposal for a modern Japanese style dark red wooden table based on the conditions. The server then sends this design proposal to the device and displays it to the user.

[0729] Example prompt sentence:

[0730] Generate a design based on the following criteria: "Modern Japanese style", "Dark red", and "Wood".

[0731] The user then enters feedback about the generated design proposal (e.g., "Make the red a little darker"). The device receives the feedback and sends it to the server. The server verifies the feedback and sends a prompt for correction to the generative AI model. The generative AI model incorporates the feedback to generate a new design proposal, which the server sends to the device and displays again to the user.

[0732] Example prompt sentence:

[0733] Readjust the design: "Make the red a little darker."

[0734] Once the user confirms the final design and clicks "Accept," the server saves the final design in a database and sends the information to the manufacturer. The server then sends the final design data to the manufacturer, who then produces the product based on the design. Once completed, the product is delivered to the user. The server monitors the progress of production and delivery, notifying the user as necessary.

[0735] Through the above detailed process, users can obtain efficient and personalized products in a short period of time.

[0736] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0737] Step 1: User enters basic information on the new registration screen

[0738] Input: The user enters their name, email address, and password.

[0739] Specific operation: The user enters "Yamada Taro", "taro@example.com", and "password123" on the new registration screen.

[0740] Data processing: The device receives the user's input information and sends it to the server as JSON format data.

[0741] Output: The server saves the received data in the database and returns a message to the terminal indicating successful registration.

[0742] Step 2: User enters credentials on login page

[0743] Input: The user enters their email address and password.

[0744] Specific operation: The user enters "taro@example.com" and "password123" on the login screen.

[0745] Data processing: The device receives the authentication information and sends it to the server

[0746] Output: The server checks the authentication information against the database and returns a successful authentication message to the terminal.

[0747] Step 3: User enters design requirements

[0748] Input: The user inputs "design style," "color," and "material."

[0749] Specific operation: The user inputs "Modern Japanese Style", "Dark Red", and "Wood".

[0750] Data processing: The device receives the desired conditions entered and sends them to the server.

[0751] Output: The server receives the desired conditions and sends them as prompts to the generative AI model.

[0752] Step 4: The generative AI model generates design ideas

[0753] Input: Prompt sent from the server ("Modern Japanese Style", "Dark Red", "Wood")

[0754] How it works: The generative AI model generates design ideas based on the conditions.

[0755] Data processing: The generative AI model analyzes the input conditions and generates design proposals

[0756] Output: The server receives the generated design proposal and sends it to the device.

[0757] Step 5: User provides feedback on the design proposal

[0758] Input: User inputs "feedback content"

[0759] Specific behavior: The user reviews the design and enters feedback such as "Make the red a little darker."

[0760] Data processing: The device receives the feedback and sends it to the server

[0761] Output: The server receives the feedback and sends it as prompts to the generative AI model.

[0762] Step 6: The generative AI model refines the design proposal

[0763] Input: Prompt sent from the server ("Make the red a little darker")

[0764] How it works: The generative AI model incorporates feedback and generates new design ideas.

[0765] Data processing: A generative AI model analyzes the feedback and refines the design proposal

[0766] Output: The server receives the revised design proposal and sends it to the device.

[0767] Step 7: User reviews and approves the final design

[0768] Input: The user performs the "final design confirmation" and "approval operation"

[0769] Specific behavior: The user reviews the final design and clicks "Accept."

[0770] Data processing: The device receives the consent operation and sends it to the server

[0771] Output: The server saves the final design to a database and sends instructions to the manufacturer.

[0772] Step 8: The server sends the final design to the manufacturer

[0773] Input: Final design data

[0774] Specific operation: The server sends the final design data to the manufacturer.

[0775] Data processing: The server transfers the final design data to the manufacturer.

[0776] Output: The manufacturer produces the product based on the final design.

[0777] Step 9: The server notifies the user of the progress of production and delivery

[0778] Input: Production status and delivery status

[0779] Specific operation: The server receives production and delivery status from the manufacturer and notifies the user.

[0780] Data processing: The server analyzes the progress data and generates notification messages.

[0781] Output: User receives notification messages to check production and delivery progress

[0782] In this way, the user, the terminal, and the server work together to advance the design process, enabling the user to efficiently receive a product with the design they desire.

[0783] (Application example 1)

[0784] 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."

[0785] In conventional design generation systems, the process of users inputting their desired conditions and the process of revising the design based on feedback on the generated design were separated, resulting in a lack of a mechanism for efficiently reflecting these changes. Furthermore, the process from finalizing the design to sending it to the production workshop was often done manually, which was time-consuming and costly. Furthermore, the user experience in virtual stores was limited, and an environment where users could easily customize designs was not established.

[0786] 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.

[0787] In this invention, the server includes means for inputting user-desired design conditions, means for generating design proposals based on the user-input conditions using a generative AI model, means for displaying the generated design proposals to the user, means for receiving user feedback and again modifying the design using the generative AI model, means for finalizing the design and sending it to a production workshop, and means implemented as a smartphone application and used in a virtual store. This enables users to efficiently customize designs in the virtual store, quickly modify the design based on feedback, and easily manage the entire process from finalizing the design to production and delivery.

[0788] A "user" is an individual or group that uses the system to input desired design conditions and provide feedback on the generated design proposals.

[0789] "Design requirements" are specific requirements such as the style, color, and material of the design desired by the user.

[0790] A "generative AI model" is an artificial intelligence system that automatically generates design proposals based on user input conditions.

[0791] A "smartphone application" is a software program that runs on a smartphone and allows users to create and customize designs.

[0792] A "virtual store" is a virtual sales environment accessible via the Internet, which exists online rather than as a physical store.

[0793] The "server" is a central control unit that receives and processes user design criteria and feedback, and generates and modifies designs using generative AI models.

[0794] "Feedback" refers to the evaluation and correction requests made by the user regarding the generated design proposal.

[0795] A "production workshop" is a facility with physical equipment that produces actual products based on the final design finalized by the user.

[0796] A "prompt" is a specific instruction sentence input to an AI model and is used when generating and modifying designs.

[0797] This invention relates to a system that allows users to input desired design conditions, generates design proposals using a generative AI model, and modifies the design based on user feedback. Specifically, it is implemented as a smartphone application to improve the user experience in a virtual store.

[0798] System Overview

[0799] 1. User Registration and Login

[0800] First, the user installs the smartphone application and registers by entering basic information (e.g., name, email address, password). After registration, the user accesses the server from the login screen and enters the account registration information.

[0801] 2. Initial design settings

[0802] After completing registration and logging in, users enter their desired design criteria (e.g., item category, style, color, material) into the application, and the device sends this information to the server.

[0803] 3. Design generation using generative AI

[0804] The server uses a generative AI model based on the received design criteria to generate an initial design proposal. This generative AI model uses OpenAI's API, etc. The generated design proposal is displayed to the user via their device.

[0805] 4. Design feedback and revisions

[0806] The user inputs feedback based on the generated design proposal (e.g., "make the backrest higher" or "make the color brighter"). This feedback is sent from the device to the server, which then uses the generative AI model to modify the design. The modified design proposal is then displayed to the user again.

[0807] 5. Final design decision

[0808] Once the user has confirmed and approved the final design, the server stores the design data and sends it to the production studio, which then produces the product based on that data.

[0809] 6. Production and Delivery

[0810] After the workshop produces the product based on the final design, the product is delivered to the user. The server monitors the progress of production and delivery in real time and notifies the user.

[0811] Hardware and software used

[0812] Hardware: Smartphones, servers

[0813] Software: Smartphone applications, server-side applications, generative AI models (e.g., OpenAI APIs)

[0814] Specific examples

[0815] If a user wants a "Scandinavian-style white sofa":

[0816] Initial input: "Scandinavian-style white sofa"

[0817] Initial design generation prompt: "Create a design based on the following specifications: Scandinavian-style white sofa"

[0818] Feedback: "Please add a taller backrest."

[0819] Post-feedback prompt: "Revise the following design based on this feedback: Add a taller backrest. Original design: [Initial generated design]"

[0820] In this way, users can efficiently customize designs in the virtual store and quickly revise design proposals using generative AI models.

[0821] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0822] Step 1:

[0823] The user installs the smartphone application and enters their name, email address, and password on the new registration screen. The device collects this information and sends it to the server. The server stores the received data in a database and completes user registration.

[0824] Input: Name, Email Address, Password

[0825] Output: User registration completion message

[0826] Specific operation: Saving data to the database

[0827] Step 2:

[0828] The user enters their account information on the login screen and logs in. The device sends the login information to the server. The server compares the received login information with a database and performs authentication. If authentication is successful, a login success message is sent to the user.

[0829] Input: Account information (email address, password)

[0830] Output: Login successful message

[0831] Specific operation: Database verification and authentication

[0832] Step 3:

[0833] The user inputs design criteria (e.g., item category, style, color, material) into the application. The device sends these criteria to the server, which stores the received design criteria in a database.

[0834] Input: Design criteria (category, style, color, material)

[0835] Output: Message that design conditions have been saved

[0836] Specific operation: Saving data to the database

[0837] Step 4:

[0838] The server sends prompts to the generative AI model based on the saved design conditions, generating design proposals, which are then sent from the server to the device and displayed to the user.

[0839] Input: Design Conditions

[0840] Output: Generated design proposal

[0841] Specific operation: Design generation using generative AI model, sending design proposal

[0842] Step 5:

[0843] The user checks the generated design proposal and inputs feedback (e.g., "Make the back higher," "Make the color brighter," etc.). The device then sends this feedback to the server.

[0844] Input: Feedback

[0845] Output: Feedback received notification

[0846] Specific behavior: Sending feedback data

[0847] Step 6:

[0848] The server receives the feedback and sends new prompts to the generative AI model to revise the design, which is then sent back to the device and displayed to the user.

[0849] Input: Feedback

[0850] Output: Revised design proposal

[0851] Specific operation: Modify the design using the generative AI model and resubmit the design proposal

[0852] Step 7:

[0853] The user checks and approves the final design. The approved design data is sent from the terminal to the server. The server stores the final design in a database and sends it to the production workshop.

[0854] Input: Final approved design data

[0855] Output: Message confirming design saving and sending of production instructions

[0856] Specific operations: saving design data to a database and transferring the data to the production workshop

[0857] Step 8:

[0858] The production workshop produces the product based on the received final design data. Once production is complete, the product is delivered to the user. The server monitors the progress of production and delivery and notifies the user of the progress.

[0859] Input: Final design data

[0860] Output: Production and delivery progress notifications

[0861] Specific operations: Monitoring the progress of the production process, notifying delivery status

[0862] 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.

[0863] The system of the present invention automates the process of a user inputting desired design conditions, a generative AI model generating design proposals based on those conditions, and then receiving user feedback to further revise the design. Furthermore, the system also includes a function to recognize user emotions. A specific embodiment of the system is described below.

[0864] System Overview

[0865] 1. User Registration and Login

[0866] The user enters basic information (name, email address, password) on the new registration screen.

[0867] The device collects the information and sends it to the server.

[0868] The server receives this and stores it in a database.

[0869] 2. Initial design settings

[0870] The user inputs the desired design criteria (e.g., style, color, material).

[0871] The terminal receives this information and sends it to the server.

[0872] 3. Design generation using generative AI

[0873] Based on the conditions received by the server, a generative AI model is used to generate design proposals.

[0874] The server transmits the generated design proposal to the terminal and displays it to the user.

[0875] 4. Emotion Recognition by Emotion Engine

[0876] The device uses the user's facial expressions, voice, input content, etc. to analyze the user's emotions through an emotion engine.

[0877] The server receives the analysis results and reflects them in design proposals or feedback.

[0878] 5. Design feedback and revisions

[0879] The user inputs feedback on the generated design proposal (e.g., color changes or design adjustments).

[0880] The device collects feedback and performs emotional analysis using an emotion engine.

[0881] The analysis results of the emotion engine are also sent to the server.

[0882] The server receives the feedback and sentiment analysis results, which are then fed back into the generative AI model to refine the design.

[0883] The revised design proposal is then presented to the user again.

[0884] 6. Final design decision

[0885] The user reviews and approves the final design.

[0886] The server stores the final design in a database and generates instructions for the manufacturing workshop.

[0887] 7. Production and Delivery

[0888] The server sends the final design data to the production workshop.

[0889] The production workshop produces the product based on the design.

[0890] After production, the product is shipped to the user.

[0891] The server monitors the progress of production and delivery and notifies the user of the status.

[0892] Specific examples

[0893] 1. User Registration and Login

[0894] A user enters "Hanako Tanaka", "hanako@example.com", and "password123" in a new registration form.

[0895] The terminal receives this and sends it to the server.

[0896] The server saves the data in the database and sends a completion message to the terminal.

[0897] 2. Initial design settings

[0898] The user inputs "modern art," "aqua blue," and "glass" as desired conditions.

[0899] The terminal receives this condition and sends it to the server.

[0900] 3. Design generation using generative AI

[0901] The server receives the requirements and generates design proposals using a generative AI model.

[0902] The server sends the design proposal to the terminal and displays it to the user.

[0903] For example, aqua blue glass objects are generated in a modern art style.

[0904] 4. Emotion Recognition by Emotion Engine

[0905] The device detects positive reactions from the user's facial expressions and sends them to the server.

[0906] 5. Design feedback and revisions

[0907] The user reviews the design proposal and gives feedback, saying, "I'd like the color to be a little darker."

[0908] The device collects feedback and performs emotional analysis using an emotion engine.

[0909] The emotion engine analyzes changes in the user's facial expressions and confirms positive emotions.

[0910] The device sends the feedback and emotion analysis results to the server.

[0911] The server receives the feedback and sentiment analysis results, adjusts the colors using a generative AI model, and generates a new design.

[0912] The server sends the new design proposal to the terminal and displays it to the user.

[0913] 6. Final design decision

[0914] The user reviews the final design and clicks "Accept."

[0915] The server stores the final design in a database and creates instruction data for the production workshop.

[0916] 7. Production and Delivery

[0917] The production workshop produces the product based on the final design data received from the server.

[0918] Once completed, the product is delivered to the user.

[0919] The server monitors the progress of production and delivery and notifies the user.

[0920] The detailed process described above enables users to acquire efficient and personalized products in a short time, and the introduction of an emotion engine increases user satisfaction.

[0921] The processing flow will be explained below.

[0922] Step 1:

[0923] The user enters their name, email address, and password on the new registration screen.

[0924] Step 2:

[0925] The terminal collects the information entered by the user and sends it to the server.

[0926] Step 3:

[0927] The server receives this information and stores it in a database.

[0928] Step 4:

[0929] The server sends a message to the terminal indicating that the save is complete.

[0930] Step 5:

[0931] The terminal displays a save complete message to the user.

[0932] Step 6:

[0933] The user enters an email address and password on the login screen and attempts to log in.

[0934] Step 7:

[0935] The terminal again collects this information and sends it to the server.

[0936] Step 8:

[0937] The server checks the user information against the database and performs authentication. If authentication is successful, it sends a login success message to the terminal.

[0938] Step 9:

[0939] The terminal receives the authentication success message and displays it to the user, who proceeds to the main screen.

[0940] Step 10:

[0941] The user selects "Create a new design" on the main screen and enters the desired design conditions (e.g., style, color, material).

[0942] Step 11:

[0943] The terminal collects the user's input data and sends it to the server.

[0944] Step 12:

[0945] The server receives this data and pre-processes it to feed it into the generative AI model.

[0946] Step 13:

[0947] The server inputs the preprocessed data into a generative AI model to generate design proposals.

[0948] Step 14:

[0949] The server receives the generated design proposal and sends it to the terminal.

[0950] Step 15:

[0951] The terminal receives the design proposal and displays it to the user, who then checks the design proposal.

[0952] Step 16:

[0953] The device captures the user's facial expressions with a camera and sends them to the emotion engine along with their voice and input.

[0954] Step 17:

[0955] The emotion engine analyzes the user's emotions (e.g., joy, confusion, dissatisfaction, etc.) regarding the design proposal.

[0956] Step 18:

[0957] The server receives the emotion analysis results and stores them together with the design proposal.

[0958] Step 19:

[0959] The user enters feedback on the design proposal (e.g., "Make it a darker red").

[0960] Step 20:

[0961] The device collects the feedback and again performs emotion analysis using the emotion engine.

[0962] Step 21:

[0963] The emotion engine analyzes the user's emotions during feedback and sends the results to the server.

[0964] Step 22:

[0965] The server receives the feedback and sentiment analysis results, which are then fed back into the generative AI model to refine the design.

[0966] Step 23:

[0967] The server receives the revised design proposal and sends it back to the device.

[0968] Step 24:

[0969] The terminal receives the revised design proposal and displays it again to the user, who then checks the design proposal again.

[0970] Step 25:

[0971] The user approves the final design and clicks the "Approve" button.

[0972] Step 26:

[0973] The terminal collects the authorization information and sends it to the server.

[0974] Step 27:

[0975] The server receives the approval information and stores the final design in a database.

[0976] Step 28:

[0977] The server sends the final design data to the production workshop.

[0978] Step 29:

[0979] The production workshop produces the product based on the design data received from the server.

[0980] Step 30:

[0981] The crafting workshop reports the crafting progress to the server.

[0982] Step 31:

[0983] The server monitors the progress and notifies the user.

[0984] Step 32:

[0985] Once the product is completed, the production workshop will handle the shipping procedures.

[0986] Step 33:

[0987] The server monitors the delivery status and notifies the user of the final delivery status.

[0988] The above are the specific processing steps from user registration to design proposal generation, sentiment analysis, feedback, final approval, production, and delivery.

[0989] Example 2

[0990] 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."

[0991] Conventional design generation systems have problems such as being unable to appropriately reflect user feedback and difficulty in automatically modifying designs that take the user's emotional state into account. Furthermore, to increase user satisfaction, it is necessary to analyze the user's emotions along with the feedback and reflect them in the design. However, there is currently no effective way to achieve this.

[0992] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for inputting conditions for a design desired by a user; means for generating a design proposal based on the user's input conditions using a generative AI model; means for displaying the generated design proposal to the user; means for the terminal to collect user emotion data and analyze it using an emotion engine; means for transmitting the emotion analysis results to the server and reflecting them in the design proposal and feedback; means for receiving user feedback and again modifying the design using the generative AI model; and means for finalizing the design and transmitting it to a production workshop. This makes it possible to generate and modify designs that reflect the user's emotions, thereby increasing user satisfaction.

[0993] "User" means an individual or organization that utilizes the system to create and modify designs.

[0994] "Desired design conditions" are specific requirements such as style, color, and material that the user desires for the design.

[0995] A "generative AI model" is an artificial intelligence algorithm that automatically generates design proposals based on input conditions.

[0996] "Design Proposal" means an initial or revised design proposal created by a generative AI model.

[0997] A "terminal" is an electronic device that a user uses to access, operate, and input data to the system.

[0998] The "server" is a central processing unit that receives and processes data sent from the device and runs generative AI models and emotion engines as needed.

[0999] "Emotion data" refers to data relating to the user's emotional state collected from the user's facial expressions, voice, input content, and the like.

[1000] An "emotion engine" is a software or hardware component that analyzes collected emotion data and infers the user's emotional state.

[1001] "Emotion analysis results" are information about the user's emotional state analyzed by the emotion engine.

[1002] "Feedback" refers to evaluations and correction requests that users input about the generated design proposals.

[1003] "Manufacturing workshop" means a facility or organization that produces physical products based on a finalized design.

[1004] "Production status" is information about the process and progress of the product being produced by the production workshop.

[1005] "Delivery status" is information about the progress of the product as it is being delivered from the production workshop to the user.

[1006] The system of the present invention automates the process of inputting the user's desired design criteria, generating design proposals based on the input criteria using a generative AI model, and then revising the design based on the user's feedback. Furthermore, the system also includes a function to recognize the user's emotions, which is important for increasing user satisfaction.

[1007] 1. Hardware and Software Configuration

[1008] Hardware

[1009] This system uses the following hardware:

[1010] Device: The device that users use to access the system and provide input and feedback on design criteria. Examples include personal computers and smartphones.

[1011] Server: A central computing unit that runs generative AI models, emotion engines, and processes user input data.

[1012] Camera and microphone: Built into the device and used to collect the user's facial expressions and voice.

[1013] software

[1014] This system uses the following software:

[1015] Generative AI model: An artificial intelligence algorithm that generates design ideas based on user input. Examples include GPT-3 and DALL-E.

[1016] Emotion engine: An algorithm that analyzes a user's emotional data and infers their emotional state. Examples include facial expression analysis software and voice analysis software.

[1017] Database: Built into the server and used to store user registration information and design data.

[1018] 2. Data processing and calculation

[1019] User Registration and Login

[1020] The user uses a device to enter their name, email address, and password, which is then sent to a server, which stores the data in a database.

[1021] Initial design settings

[1022] The user inputs their design preferences (style, color, material), and the device sends this to the server, which then passes this data as input to the generative AI model.

[1023] Design generation using generative AI models

[1024] Based on the conditions received by the server, a generative AI model is used to generate design proposals. For example, the generative AI model uses the following prompt sentence:

[1025] Prompt: "Generate an aqua blue glass object in a modern art style."

[1026] The server sends the generated design proposal to the terminal and displays it to the user.

[1027] Emotion recognition by emotion engine

[1028] The device uses a camera and microphone to collect the user's facial expressions and voice. The emotion engine analyzes this and sends the emotion analysis results to the server. The server then uses the results to create design proposals and provide feedback.

[1029] Design feedback and revisions

[1030] The user provides feedback on the generated design (e.g., "I'd like the color to be a little darker"). The device collects the feedback and analyzes it using an emotion engine. The feedback and emotion analysis results are sent to the server, which then uses this information to further revise the design using the generative AI model.

[1031] Finalize and save the design

[1032] The user checks and approves the final design. The server saves the final design in a database and generates instruction data for the production workshop.

[1033] Production and Delivery

[1034] The production workshop receives the final design data from the server and produces the product. The finished product is delivered to the user. The server monitors the progress of production and delivery and notifies the user.

[1035] Specifically, if a user inputs "modern art," "aqua blue," and "glass" as their desired design criteria, the generative AI model generates a design proposal based on these. If the user reviews the generated design proposal and provides feedback such as "I'd like the color to be a little darker," the emotion engine analyzes the user's facial expression and sends the results to the server. The server uses the generative AI model to generate a new design proposal that reflects the feedback and presents it to the user again. If the user finally approves, the production workshop produces the product based on that design and delivers it to the user.

[1036] The above process is a detailed embodiment of the present invention.

[1037] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1038] Step 1: User registration and login

[1039] Input: The user enters their name, email address, and password on the sign-up screen.

[1040] Specific operation: The user enters "name, email address, password" on the new registration screen and clicks the submit button.

[1041] Data processing: The terminal collects input data in real time and captures the click event of the submit button.

[1042] Output: The device sends the collected data to the server.

[1043] What happens: The server validates the data it receives and stores it in the database if appropriate.

[1044] Output: The server generates a registration completion message and sends it to the terminal.

[1045] Specific behavior: The device displays a completion message on the screen.

[1046] Step 2: Initialize the design

[1047] Input: The user inputs the desired design criteria (style, color, material).

[1048] Specific behavior: The user enters "Style: Modern Art", "Color: Aqua Blue", and "Material: Glass".

[1049] Data processing: The terminal collects input data.

[1050] Output: The terminal sends the collected design conditions to the server.

[1051] Specific operation: The server receives the desired conditions and stores them in a database.

[1052] Step 3: Generate a design using a generative AI model

[1053] Input: The design conditions received by the server.

[1054] What happens: The server calls the generative AI model and passes the design conditions as prompts.

[1055] Prompt: "Generate an aqua blue glass object in a modern art style."

[1056] Data computation: A generative AI model generates design ideas based on input conditions.

[1057] Output: The server sends the generated design proposal to the device.

[1058] Specific operation: The device displays the design proposal on the screen and presents it to the user.

[1059] Step 4: Emotion Recognition with the Emotion Engine

[1060] Input: User facial expressions, voice, and input.

[1061] Specific operation: The device uses the camera and microphone to collect the user's facial expressions and voice.

[1062] Data processing: The emotion data collected by the device is passed to the emotion engine in real time.

[1063] Data calculation: The emotion engine analyzes the emotion data and estimates the user's emotional state.

[1064] Output: The device sends the emotion analysis results to the server.

[1065] Specific operation: The server reflects the results of emotion analysis in design proposals and feedback.

[1066] Step 5: Design feedback and revisions

[1067] Input: User feedback (e.g. "I'd like the color to be a little darker").

[1068] What happens: The user provides feedback on the design proposal.

[1069] Data processing: The device collects feedback and analyzes it again using the emotion engine.

[1070] Output: Send feedback and sentiment analysis results to the server.

[1071] How it works: The server calls the generative AI model and revises the design based on feedback and analysis results.

[1072] Data computation: Generative AI models generate revised design proposals.

[1073] Output: The server sends the revised design proposal to the device and presents it to the user.

[1074] Step 6: Finalize and save your design

[1075] Input: User approval.

[1076] What happens: The user reviews the final design and clicks "Accept."

[1077] Data processing: The server stores the final design in a database.

[1078] Output: Generates instruction data for the production workshop.

[1079] Step 7: Production and Delivery

[1080] Input: Final design data received by the fabrication studio.

[1081] Specific operation: The production workshop produces the product based on the final design data.

[1082] Output: The finished product is delivered to the user.

[1083] What it does: The server monitors the progress of production and delivery and notifies the user.

[1084] (Application example 2)

[1085] 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."

[1086] Conventional design generation systems generate design proposals based on user requirements and incorporate feedback, but lack an automated process that takes user emotions into account. Furthermore, collecting feedback and revising designs takes time, which can lead to reduced user satisfaction. Furthermore, notifications about production and delivery status are uniform, with little consideration given to user emotions. Therefore, achieving an efficient and personalized design generation process remains a challenge.

[1087] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1088] In this invention, the server includes a means for recognizing a user's emotion, a means for modifying a design based on the emotion recognition result, and a means for adjusting the content of notifications to the user based on the emotion recognition result, thereby enabling automatic generation of a design that takes the user's emotion into consideration, modification of the design, and notification of production status and delivery status according to the emotion.

[1089] "Means for inputting the user's desired design conditions" refers to an interface that allows the user to input conditions such as the design style, color, and material that he or she desires.

[1090] "Means for generating design proposals based on user input conditions using a generative AI model" refers to a mechanism for automatically generating appropriate design proposals using a generative AI model based on conditions entered by the user.

[1091] The "means for displaying the generated design proposal to the user" refers to a display device or software interface for providing the generated design proposal to the user in a visible form.

[1092] "Means for receiving user feedback and revising the design using a generative AI model" refers to a mechanism for collecting user feedback such as evaluations and requests for revisions, and then regenerating and revising design proposals using a generative AI model based on that feedback.

[1093] "Means for recognizing user emotions" refers to technology and devices for analyzing and evaluating a user's emotional state from facial expressions, voice, etc.

[1094] "Means for modifying the design based on the emotion recognition results" refers to a mechanism for improving or changing the generated design proposal by referring to the user's emotion recognition results.

[1095] "Means for finalizing the design and transmitting it to the production facility" refers to a system for transmitting the finalized design as data to the production facility and issuing production instructions.

[1096] The "means for transmitting user input conditions to the server" refers to a mechanism for transmitting the design conditions input by the user to the server as data.

[1097] "Means for transmitting the design proposal generated by the server to the terminal" refers to a mechanism for transmitting the design proposal generated by the server as data to the terminal used by the user.

[1098] "Means for sending final design data to the production facility and notifying the user of the production status and delivery status" refers to a system for sending finalized design data to the production facility and notifying the user of the production and delivery status in real time.

[1099] "Means for adjusting the content of notifications to the user based on the emotion recognition results" refers to a mechanism for appropriately changing and adjusting the content of notifications according to the results of the user's emotion recognition, and providing more personalized information to the user.

[1100] This invention is a system that allows a user to easily create a design they desire, and then produces and delivers the product based on a highly accurate design proposal. The following describes a specific embodiment of this system.

[1101] User Registration and Login

[1102] The user enters basic information (name, email address, password) on the new registration screen. This information is collected on the device and sent to the server. The server receives the information and stores it in a database (PostgreSQL). From the next time onwards, the user can access the system using the login information.

[1103] Initial design settings

[1104] Users input their design preferences (e.g., style, color, material) using a React-based interface, and the information is sent by the device to the server.

[1105] Design generation using generative AI

[1106] The server receives the user-entered criteria and generates design proposals using a generative AI model (e.g., OpenAI GPT-3 or DALL-E). The generated design proposals are sent from the server to the device and displayed to the user.

[1107] Emotion recognition by emotion engine

[1108] The device uses the smartphone's camera and microphone to collect the user's facial expressions and voice, and analyzes their emotions using OpenCV and Google Cloud Speech-to-Text. The analysis results are sent to a server and reflected in design proposals and feedback.

[1109] Design feedback and revisions

[1110] The user enters feedback about the generated design proposal (e.g., color changes or design adjustments) using a feedback input form using React. The feedback is analyzed by the emotion engine and sent to the server, where the generative AI model again modifies the design. The modified design proposal is then presented to the user again.

[1111] Final design decision

[1112] The user reviews and approves the final design. The server saves the final design in a database and creates instructions for the manufacturing facility. An example of a prompt is the text "Create a modern art style product with aqua blue color and made of glass."

[1113] Production and Delivery

[1114] The server sends the final design data to the production facility, which then produces the product based on that design. After production, the product is delivered to the user. The server monitors the progress of production and delivery using a notification service such as Firebase, and notifies the user of the status. The content of the notification is also adjusted based on the emotion recognition results.

[1115] The following specific hardware and software are used in each of the above steps:

[1116] Server: Django

[1117] Database: PostgreSQL

[1118] Front-end interface: React

[1119] Generative AI model: OpenAI GPT-3 or DALL-E

[1120] Emotion recognition: OpenCV, Google Cloud Speech-to-Text

[1121] Notification Service: Firebase

[1122] This system allows users to efficiently obtain custom-designed products that reflect their preferences, and adding emotion recognition can further increase user satisfaction.

[1123] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1124] Step 1:

[1125] The user enters basic information (name, email address, password) on the new registration screen. The device collects this information and sends it to the server. The server stores the received information in a database. The input for this step is the basic information entered by the user, and the output is the user information stored in the database.

[1126] Step 2:

[1127] The user inputs desired design conditions (e.g., style, color, material). The terminal receives the input information and sends it to the server. The input of this step is the design conditions entered by the user, and the output is the design conditions sent to the server.

[1128] Step 3:

[1129] The server uses a generative AI model to generate design proposals based on the received design criteria. In this generation process, the AI ​​generates text and image data based on the prompt. The input for this step is the design criteria and prompt, and the output is the generated design proposal.

[1130] Step 4:

[1131] The server sends the generated design proposal to the terminal, which displays it to the user. The user checks the generated design proposal. The input of this step is the generated design proposal, and the output is the design proposal displayed to the user.

[1132] Step 5:

[1133] The device collects the user's facial expressions and voice, and performs emotion analysis using OpenCV and Google Cloud Speech-to-Text. The analysis results are sent to the server. The input of this step is the user's facial expressions and voice data, and the output is the emotion analysis results.

[1134] Step 6:

[1135] The user enters feedback about the design proposal (e.g., color changes or design tweaks), and the device collects the feedback and sends it to the server. The server receives the feedback and sentiment analysis results and uses them to modify the design using a generative AI model. The inputs for this step are the user's feedback and sentiment analysis results, and the output is the modified design proposal.

[1136] Step 7:

[1137] The server sends the revised design proposal to the terminal, which then displays it again to the user. The user confirms the revised design proposal. The input of this step is the revised design proposal, and the output is the revised design proposal displayed to the user.

[1138] Step 8:

[1139] The user reviews and approves the final design. The server stores the final design in a database and generates instructions for the fabrication facility. The input for this step is the final design data, and the output is the instructions sent to the fabrication facility.

[1140] Step 9:

[1141] The server sends the final design data to the production facility, which then produces the product based on that design. After production, the product is delivered to the user. The server uses Firebase to monitor the progress of production and delivery and notify the user of the status. The input for this step is the final design data and production and delivery status data, and the output is the notification content to the user. The notification content is also adjusted based on the emotion recognition results.

[1142] 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.

[1143] 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.

[1144] 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.

[1145] [Third embodiment]

[1146] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1147] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1148] 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).

[1149] 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.

[1150] 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.

[1151] 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).

[1152] 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.

[1153] 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.

[1154] 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.

[1155] 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.

[1156] 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.

[1157] 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."

[1158] The system of the present invention automates the process of a user inputting desired design conditions, a generative AI model generating design proposals based on those conditions, and then receiving user feedback to further revise the design. The following means and processes are included in order to implement this system.

[1159] System Overview

[1160] 1. User Registration and Login

[1161] The user enters basic information (name, email address, password) on the new registration screen.

[1162] The device collects the information and sends it to the server.

[1163] The server receives this and stores it in a database.

[1164] 2. Initial design settings

[1165] The user inputs the desired design criteria (e.g., style, color, material).

[1166] The terminal receives this information and sends it to the server.

[1167] 3. Design generation using generative AI

[1168] Based on the conditions received by the server, a generative AI model is used to generate design proposals.

[1169] The server transmits the generated design proposal to the terminal and displays it to the user.

[1170] 4. Design feedback and revisions

[1171] The user inputs feedback on the generated design proposal (e.g., color changes or design adjustments).

[1172] The device collects the feedback and sends it to the server.

[1173] The server receives the feedback and uses a generative AI model to refine the design.

[1174] The revised design proposal is then presented to the user again.

[1175] 5. Final design decision

[1176] The user reviews and approves the final design.

[1177] The server stores the final design in a database and generates instructions for the manufacturing workshop.

[1178] 6. Production and Delivery

[1179] The server sends the final design data to the production workshop.

[1180] The production workshop produces the product based on the design.

[1181] After production, the product is shipped to the user.

[1182] The server monitors the progress of production and delivery and notifies the user of the status.

[1183] Specific examples

[1184] 1. User Registration and Login

[1185] A user enters "Yamada Taro", "taro@example.com", and "password123" in the new registration form.

[1186] The terminal receives this and sends it to the server.

[1187] The server saves the data in the database and sends a completion message to the terminal.

[1188] 2. Initial design settings

[1189] The user inputs the desired conditions: "modern Japanese style," "dark red," and "wood."

[1190] The terminal receives this condition and sends it to the server.

[1191] 3. Design generation using generative AI

[1192] The server receives the requirements and generates design proposals using a generative AI model.

[1193] The server sends the design proposal to the terminal and displays it to the user.

[1194] For example, a dark red wooden table design is generated in a modern Japanese style.

[1195] 4. Design feedback and revisions

[1196] The user reviews the design proposal and gives feedback, such as "Make the red a little darker."

[1197] The device sends the feedback to the server.

[1198] The server receives the feedback and uses a generative AI model to adjust the colors and generate a new design.

[1199] The server sends the new design proposal to the terminal and displays it to the user.

[1200] 5. Final design decision

[1201] The user reviews the final design and clicks "Accept."

[1202] The server stores the final design in a database and creates instruction data for the production workshop.

[1203] 6. Production and Delivery

[1204] The production workshop produces the product based on the final design data received from the server.

[1205] Once completed, the product is delivered to the user.

[1206] The server monitors the progress of production and delivery and notifies the user.

[1207] The above detailed process allows users to obtain efficient and personalized products in a short time.

[1208] The processing flow will be explained below.

[1209] Step 1:

[1210] The user enters their name, email address, and password on the new registration screen.

[1211] Step 2:

[1212] The terminal collects the information entered by the user and sends it to the server.

[1213] Step 3:

[1214] The server receives this information and stores it in a database.

[1215] Step 4:

[1216] The server sends a message to the terminal indicating that the save is complete.

[1217] Step 5:

[1218] The terminal displays a save complete message to the user.

[1219] Step 6:

[1220] The user enters an email address and password on the login screen and attempts to log in.

[1221] Step 7:

[1222] The terminal again collects this information and sends it to the server.

[1223] Step 8:

[1224] The server checks the user information against the database and performs authentication. If authentication is successful, it sends a login success message to the terminal.

[1225] Step 9:

[1226] The terminal receives the authentication success message and displays it to the user, who proceeds to the main screen.

[1227] Step 10:

[1228] The user selects "Create a new design" on the main screen and enters the desired design conditions (e.g., style, color, material).

[1229] Step 11:

[1230] The terminal collects the user's input data and sends it to the server.

[1231] Step 12:

[1232] The server receives this data and pre-processes it to feed it into the generative AI model.

[1233] Step 13:

[1234] The server inputs the preprocessed data into a generative AI model to generate design proposals.

[1235] Step 14:

[1236] The server receives the generated design proposal and sends it to the terminal.

[1237] Step 15:

[1238] The terminal receives the design proposal and displays it to the user, who then checks the design proposal.

[1239] Step 16:

[1240] The user enters feedback on the design proposal (e.g., "Make it a darker red").

[1241] Step 17:

[1242] The device collects the feedback and sends it to the server.

[1243] Step 18:

[1244] The server receives the feedback and re-inputs it into the generative AI model to refine the design.

[1245] Step 19:

[1246] The server receives the revised design proposal and sends it to the device.

[1247] Step 20:

[1248] The terminal receives the revised design proposal and displays it again to the user, who then checks the design proposal again.

[1249] Step 21:

[1250] The user approves the final design and clicks the "Approve" button.

[1251] Step 22:

[1252] The terminal collects the authorization information and sends it to the server.

[1253] Step 23:

[1254] The server receives the approval information and stores the final design in a database.

[1255] Step 24:

[1256] The server sends the final design data to the production workshop.

[1257] Step 25:

[1258] The production workshop produces the product based on the design data received from the server.

[1259] Step 26:

[1260] The crafting workshop reports the crafting progress to the server.

[1261] Step 27:

[1262] The server monitors the progress and notifies the user.

[1263] Step 28:

[1264] Once the product is completed, the production workshop will handle the shipping procedures.

[1265] Step 29:

[1266] The server monitors the delivery status and notifies the user of the final delivery status.

[1267] The above are the specific processing steps from user registration to design proposal generation, feedback, final approval, production, and delivery.

[1268] Example 1

[1269] 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."

[1270] Conventional design generation systems have a complicated process from when the user inputs their desired design conditions to when the final design is finalized, and the process of modifying the design based on feedback is time-consuming and laborious. Furthermore, the transmission of the final design to the production studio and notifications of the production and delivery status are sometimes not smooth, which reduces user satisfaction.

[1271] 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.

[1272] In this invention, the server includes a means for inputting the user's desired design conditions, a means for generating a design proposal based on the user's input conditions using a generative AI model, a means for displaying the generated design proposal to the user, a means for receiving user feedback and revising the design using the generative AI model again, and a means for finalizing the design and transmitting the information to the manufacturer. This allows the user to efficiently proceed with the design process, enables rapid design revisions based on feedback, and enables smooth notification of the production workshop and production / delivery status.

[1273] "User" refers to a person who inputs design conditions and provides feedback on the generated design proposal.

[1274] "Design conditions" are elements related to the design desired by the user, including, for example, style, color, material, and the like.

[1275] A "generative AI model" refers to a part of a system that uses artificial intelligence technology to generate design proposals based on user input.

[1276] "Design proposal" refers to a design prototype generated by a generative AI model based on user input conditions.

[1277] "Feedback" refers to opinions and requests for corrections made by the user regarding the generated design proposal.

[1278] "Final design" refers to the design proposal that the generative AI model finalizes based on user feedback.

[1279] "Manufacturer" refers to the workshop or factory that actually produces the product based on the final design and provides it to the user.

[1280] The "information processing device" refers to a server that receives user input conditions and feedback and generates and modifies designs using a generative AI model.

[1281] The "display device" refers to a terminal that displays the generated design proposal to the user.

[1282] "Production Status" refers to the progress of the manufacturer in producing the product based on the final design.

[1283] "Delivery status" refers to the progress of the delivery process until the completed product is delivered to the user.

[1284] The system of the present invention automates the process of a user inputting desired design conditions, a generative AI model generating design proposals based on the input, and then receiving user feedback to further revise the design. To implement this system, the following means and processes are included.

[1285] First, the user enters basic information (name, email address, password) on the new registration screen. The device receives this information and sends it to the server. The server receives this information and stores it in a database. After the user registers, when they enter their email address and password on the login page, the device sends the authentication information to the server, which then performs authentication. If authentication is successful, the user is allowed to log in.

[1286] Next, the user inputs their desired design criteria (e.g., style, color, material). The device receives this information and sends it to the server. The server sends prompts to the generative AI model based on the received criteria. The generative AI model generates design ideas based on the criteria, and the server sends the generated design ideas to the device. The device displays the design ideas to the user.

[1287] For example, when a user enters "Yamada Taro," "taro@example.com," and "password123" in a new registration form, the device sends it to the server, which saves it in the database and returns a message indicating successful registration. When a user enters "taro@example.com" and "password123" on the login screen, the device sends the authentication information to the server, which confirms the user's authentication and then allows the login. Next, the user enters desired design criteria, such as "modern Japanese style," "dark red," and "wood," and the device sends it to the server.

[1288] When the server sends the conditions "modern Japanese style," "dark red," and "wood" as prompts to the generative AI model, the model generates a design proposal for a modern Japanese style dark red wooden table based on the conditions. The server then sends this design proposal to the device and displays it to the user.

[1289] Example prompt sentence:

[1290] Generate a design based on the following criteria: "Modern Japanese style", "Dark red", and "Wood".

[1291] The user then enters feedback about the generated design proposal (e.g., "Make the red a little darker"). The device receives the feedback and sends it to the server. The server verifies the feedback and sends a prompt for correction to the generative AI model. The generative AI model incorporates the feedback to generate a new design proposal, which the server sends to the device and displays again to the user.

[1292] Example prompt sentence:

[1293] Readjust the design: "Make the red a little darker."

[1294] Once the user confirms the final design and clicks "Accept," the server saves the final design in a database and sends the information to the manufacturer. The server then sends the final design data to the manufacturer, who then produces the product based on the design. Once completed, the product is delivered to the user. The server monitors the progress of production and delivery, notifying the user as necessary.

[1295] Through the above detailed process, users can obtain efficient and personalized products in a short period of time.

[1296] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1297] Step 1: User enters basic information on the new registration screen

[1298] Input: The user enters their name, email address, and password.

[1299] Specific operation: The user enters "Yamada Taro", "taro@example.com", and "password123" on the new registration screen.

[1300] Data processing: The device receives the user's input information and sends it to the server as JSON format data.

[1301] Output: The server saves the received data in the database and returns a message to the terminal indicating successful registration.

[1302] Step 2: User enters credentials on login page

[1303] Input: The user enters their email address and password.

[1304] Specific operation: The user enters "taro@example.com" and "password123" on the login screen.

[1305] Data processing: The device receives the authentication information and sends it to the server

[1306] Output: The server checks the authentication information against the database and returns a successful authentication message to the terminal.

[1307] Step 3: User enters design requirements

[1308] Input: The user inputs "design style," "color," and "material."

[1309] Specific operation: The user inputs "Modern Japanese Style", "Dark Red", and "Wood".

[1310] Data processing: The device receives the desired conditions entered and sends them to the server.

[1311] Output: The server receives the desired conditions and sends them as prompts to the generative AI model.

[1312] Step 4: The generative AI model generates design ideas

[1313] Input: Prompt sent from the server ("Modern Japanese Style", "Dark Red", "Wood")

[1314] How it works: The generative AI model generates design ideas based on the conditions.

[1315] Data processing: The generative AI model analyzes the input conditions and generates design proposals

[1316] Output: The server receives the generated design proposal and sends it to the device.

[1317] Step 5: User provides feedback on the design proposal

[1318] Input: User inputs "feedback content"

[1319] Specific behavior: The user reviews the design and enters feedback such as "Make the red a little darker."

[1320] Data processing: The device receives the feedback and sends it to the server

[1321] Output: The server receives the feedback and sends it as prompts to the generative AI model.

[1322] Step 6: The generative AI model refines the design proposal

[1323] Input: Prompt sent from the server ("Make the red a little darker")

[1324] How it works: The generative AI model incorporates feedback and generates new design ideas.

[1325] Data processing: A generative AI model analyzes the feedback and refines the design proposal

[1326] Output: The server receives the revised design proposal and sends it to the device.

[1327] Step 7: User reviews and approves the final design

[1328] Input: The user performs the "final design confirmation" and "approval operation"

[1329] Specific behavior: The user reviews the final design and clicks "Accept."

[1330] Data processing: The device receives the consent operation and sends it to the server

[1331] Output: The server saves the final design to a database and sends instructions to the manufacturer.

[1332] Step 8: The server sends the final design to the manufacturer

[1333] Input: Final design data

[1334] Specific operation: The server sends the final design data to the manufacturer.

[1335] Data processing: The server transfers the final design data to the manufacturer.

[1336] Output: The manufacturer produces the product based on the final design.

[1337] Step 9: The server notifies the user of the progress of production and delivery

[1338] Input: Production status and delivery status

[1339] Specific operation: The server receives production and delivery status from the manufacturer and notifies the user.

[1340] Data processing: The server analyzes the progress data and generates notification messages.

[1341] Output: User receives notification messages to check production and delivery progress

[1342] In this way, the user, the terminal, and the server work together to advance the design process, enabling the user to efficiently receive a product with the design they desire.

[1343] (Application example 1)

[1344] 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."

[1345] In conventional design generation systems, the process of users inputting their desired conditions and the process of revising the design based on feedback on the generated design were separated, resulting in a lack of a mechanism for efficiently reflecting these changes. Furthermore, the process from finalizing the design to sending it to the production workshop was often done manually, which was time-consuming and costly. Furthermore, the user experience in virtual stores was limited, and an environment where users could easily customize designs was not established.

[1346] 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.

[1347] In this invention, the server includes means for inputting user-desired design conditions, means for generating design proposals based on the user-input conditions using a generative AI model, means for displaying the generated design proposals to the user, means for receiving user feedback and again modifying the design using the generative AI model, means for finalizing the design and sending it to a production workshop, and means implemented as a smartphone application and used in a virtual store. This enables users to efficiently customize designs in the virtual store, quickly modify the design based on feedback, and easily manage the entire process from finalizing the design to production and delivery.

[1348] A "user" is an individual or group that uses the system to input desired design conditions and provide feedback on the generated design proposals.

[1349] "Design requirements" are specific requirements such as the style, color, and material of the design desired by the user.

[1350] A "generative AI model" is an artificial intelligence system that automatically generates design proposals based on user input conditions.

[1351] A "smartphone application" is a software program that runs on a smartphone and allows users to create and customize designs.

[1352] A "virtual store" is a virtual sales environment accessible via the Internet, which exists online rather than as a physical store.

[1353] The "server" is a central control unit that receives and processes user design criteria and feedback, and generates and modifies designs using generative AI models.

[1354] "Feedback" refers to the evaluation and correction requests made by the user regarding the generated design proposal.

[1355] A "production workshop" is a facility with physical equipment that produces actual products based on the final design finalized by the user.

[1356] A "prompt" is a specific instruction sentence input to an AI model and is used when generating and modifying designs.

[1357] This invention relates to a system that allows users to input desired design conditions, generates design proposals using a generative AI model, and modifies the design based on user feedback. Specifically, it is implemented as a smartphone application to improve the user experience in a virtual store.

[1358] System Overview

[1359] 1. User Registration and Login

[1360] First, the user installs the smartphone application and registers by entering basic information (e.g., name, email address, password). After registration, the user accesses the server from the login screen and enters the account registration information.

[1361] 2. Initial design settings

[1362] After completing registration and logging in, users enter their desired design criteria (e.g., item category, style, color, material) into the application, and the device sends this information to the server.

[1363] 3. Design generation using generative AI

[1364] The server uses a generative AI model based on the received design criteria to generate an initial design proposal. This generative AI model uses OpenAI's API, etc. The generated design proposal is displayed to the user via their device.

[1365] 4. Design feedback and revisions

[1366] The user inputs feedback based on the generated design proposal (e.g., "make the backrest higher" or "make the color brighter"). This feedback is sent from the device to the server, which then uses the generative AI model to modify the design. The modified design proposal is then displayed to the user again.

[1367] 5. Final design decision

[1368] Once the user has confirmed and approved the final design, the server stores the design data and sends it to the production studio, which then produces the product based on that data.

[1369] 6. Production and Delivery

[1370] After the workshop produces the product based on the final design, the product is delivered to the user. The server monitors the progress of production and delivery in real time and notifies the user.

[1371] Hardware and software used

[1372] Hardware: Smartphones, servers

[1373] Software: Smartphone applications, server-side applications, generative AI models (e.g., OpenAI APIs)

[1374] Specific examples

[1375] If a user wants a "Scandinavian-style white sofa":

[1376] Initial input: "Scandinavian-style white sofa"

[1377] Initial design generation prompt: "Create a design based on the following specifications: Scandinavian-style white sofa"

[1378] Feedback: "Please add a taller backrest."

[1379] Post-feedback prompt: "Revise the following design based on this feedback: Add a taller backrest. Original design: [Initial generated design]"

[1380] In this way, users can efficiently customize designs in the virtual store and quickly revise design proposals using generative AI models.

[1381] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1382] Step 1:

[1383] The user installs the smartphone application and enters their name, email address, and password on the new registration screen. The device collects this information and sends it to the server. The server stores the received data in a database and completes user registration.

[1384] Input: Name, Email Address, Password

[1385] Output: User registration completion message

[1386] Specific operation: Saving data to the database

[1387] Step 2:

[1388] The user enters their account information on the login screen and logs in. The device sends the login information to the server. The server compares the received login information with a database and performs authentication. If authentication is successful, a login success message is sent to the user.

[1389] Input: Account information (email address, password)

[1390] Output: Login successful message

[1391] Specific operation: Database verification and authentication

[1392] Step 3:

[1393] The user inputs design criteria (e.g., item category, style, color, material) into the application. The device sends these criteria to the server, which stores the received design criteria in a database.

[1394] Input: Design criteria (category, style, color, material)

[1395] Output: Message that design conditions have been saved

[1396] Specific operation: Saving data to the database

[1397] Step 4:

[1398] The server sends prompts to the generative AI model based on the saved design conditions, generating design proposals, which are then sent from the server to the device and displayed to the user.

[1399] Input: Design Conditions

[1400] Output: Generated design proposal

[1401] Specific operation: Design generation using generative AI model, sending design proposal

[1402] Step 5:

[1403] The user checks the generated design proposal and inputs feedback (e.g., "Make the back higher," "Make the color brighter," etc.). The device then sends this feedback to the server.

[1404] Input: Feedback

[1405] Output: Feedback received notification

[1406] Specific behavior: Sending feedback data

[1407] Step 6:

[1408] The server receives the feedback and sends new prompts to the generative AI model to revise the design, which is then sent back to the device and displayed to the user.

[1409] Input: Feedback

[1410] Output: Revised design proposal

[1411] Specific operation: Modify the design using the generative AI model and resubmit the design proposal

[1412] Step 7:

[1413] The user checks and approves the final design. The approved design data is sent from the terminal to the server. The server stores the final design in a database and sends it to the production workshop.

[1414] Input: Final approved design data

[1415] Output: Message confirming design saving and sending of production instructions

[1416] Specific operations: saving design data to a database and transferring the data to the production workshop

[1417] Step 8:

[1418] The production workshop produces the product based on the received final design data. Once production is complete, the product is delivered to the user. The server monitors the progress of production and delivery and notifies the user of the progress.

[1419] Input: Final design data

[1420] Output: Production and delivery progress notifications

[1421] Specific operations: Monitoring the progress of the production process, notifying delivery status

[1422] 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.

[1423] The system of the present invention automates the process of a user inputting desired design conditions, a generative AI model generating design proposals based on those conditions, and then receiving user feedback to further revise the design. Furthermore, the system also includes a function to recognize user emotions. A specific embodiment of the system is described below.

[1424] System Overview

[1425] 1. User Registration and Login

[1426] The user enters basic information (name, email address, password) on the new registration screen.

[1427] The device collects the information and sends it to the server.

[1428] The server receives this and stores it in a database.

[1429] 2. Initial design settings

[1430] The user inputs the desired design criteria (e.g., style, color, material).

[1431] The terminal receives this information and sends it to the server.

[1432] 3. Design generation using generative AI

[1433] Based on the conditions received by the server, a generative AI model is used to generate design proposals.

[1434] The server transmits the generated design proposal to the terminal and displays it to the user.

[1435] 4. Emotion Recognition by Emotion Engine

[1436] The device uses the user's facial expressions, voice, input content, etc. to analyze the user's emotions through an emotion engine.

[1437] The server receives the analysis results and reflects them in design proposals or feedback.

[1438] 5. Design feedback and revisions

[1439] The user inputs feedback on the generated design proposal (e.g., color changes or design adjustments).

[1440] The device collects feedback and performs emotional analysis using an emotion engine.

[1441] The analysis results of the emotion engine are also sent to the server.

[1442] The server receives the feedback and sentiment analysis results, which are then fed back into the generative AI model to refine the design.

[1443] The revised design proposal is then presented to the user again.

[1444] 6. Final design decision

[1445] The user reviews and approves the final design.

[1446] The server stores the final design in a database and generates instructions for the manufacturing workshop.

[1447] 7. Production and Delivery

[1448] The server sends the final design data to the production workshop.

[1449] The production workshop produces the product based on the design.

[1450] After production, the product is shipped to the user.

[1451] The server monitors the progress of production and delivery and notifies the user of the status.

[1452] Specific examples

[1453] 1. User Registration and Login

[1454] A user enters "Hanako Tanaka", "hanako@example.com", and "password123" in a new registration form.

[1455] The terminal receives this and sends it to the server.

[1456] The server saves the data in the database and sends a completion message to the terminal.

[1457] 2. Initial design settings

[1458] The user inputs "modern art," "aqua blue," and "glass" as desired conditions.

[1459] The terminal receives this condition and sends it to the server.

[1460] 3. Design generation using generative AI

[1461] The server receives the requirements and generates design proposals using a generative AI model.

[1462] The server sends the design proposal to the terminal and displays it to the user.

[1463] For example, aqua blue glass objects are generated in a modern art style.

[1464] 4. Emotion Recognition by Emotion Engine

[1465] The device detects positive reactions from the user's facial expressions and sends them to the server.

[1466] 5. Design feedback and revisions

[1467] The user reviews the design proposal and gives feedback, saying, "I'd like the color to be a little darker."

[1468] The device collects feedback and performs emotional analysis using an emotion engine.

[1469] The emotion engine analyzes changes in the user's facial expressions and confirms positive emotions.

[1470] The device sends the feedback and emotion analysis results to the server.

[1471] The server receives the feedback and sentiment analysis results, adjusts the colors using a generative AI model, and generates a new design.

[1472] The server sends the new design proposal to the terminal and displays it to the user.

[1473] 6. Final design decision

[1474] The user reviews the final design and clicks "Accept."

[1475] The server stores the final design in a database and creates instruction data for the production workshop.

[1476] 7. Production and Delivery

[1477] The production workshop produces the product based on the final design data received from the server.

[1478] Once completed, the product is delivered to the user.

[1479] The server monitors the progress of production and delivery and notifies the user.

[1480] The detailed process described above enables users to acquire efficient and personalized products in a short time, and the introduction of an emotion engine increases user satisfaction.

[1481] The processing flow will be explained below.

[1482] Step 1:

[1483] The user enters their name, email address, and password on the new registration screen.

[1484] Step 2:

[1485] The terminal collects the information entered by the user and sends it to the server.

[1486] Step 3:

[1487] The server receives this information and stores it in a database.

[1488] Step 4:

[1489] The server sends a message to the terminal indicating that the save is complete.

[1490] Step 5:

[1491] The terminal displays a save complete message to the user.

[1492] Step 6:

[1493] The user enters an email address and password on the login screen and attempts to log in.

[1494] Step 7:

[1495] The terminal again collects this information and sends it to the server.

[1496] Step 8:

[1497] The server checks the user information against the database and performs authentication. If authentication is successful, it sends a login success message to the terminal.

[1498] Step 9:

[1499] The terminal receives the authentication success message and displays it to the user, who proceeds to the main screen.

[1500] Step 10:

[1501] The user selects "Create a new design" on the main screen and enters the desired design conditions (e.g., style, color, material).

[1502] Step 11:

[1503] The terminal collects the user's input data and sends it to the server.

[1504] Step 12:

[1505] The server receives this data and pre-processes it to feed it into the generative AI model.

[1506] Step 13:

[1507] The server inputs the preprocessed data into a generative AI model to generate design proposals.

[1508] Step 14:

[1509] The server receives the generated design proposal and sends it to the terminal.

[1510] Step 15:

[1511] The terminal receives the design proposal and displays it to the user, who then checks the design proposal.

[1512] Step 16:

[1513] The device captures the user's facial expressions with a camera and sends them to the emotion engine along with their voice and input.

[1514] Step 17:

[1515] The emotion engine analyzes the user's emotions (e.g., joy, confusion, dissatisfaction, etc.) regarding the design proposal.

[1516] Step 18:

[1517] The server receives the emotion analysis results and stores them together with the design proposal.

[1518] Step 19:

[1519] The user enters feedback on the design proposal (e.g., "Make it a darker red").

[1520] Step 20:

[1521] The device collects the feedback and again performs emotion analysis using the emotion engine.

[1522] Step 21:

[1523] The emotion engine analyzes the user's emotions during feedback and sends the results to the server.

[1524] Step 22:

[1525] The server receives the feedback and sentiment analysis results, which are then fed back into the generative AI model to refine the design.

[1526] Step 23:

[1527] The server receives the revised design proposal and sends it back to the device.

[1528] Step 24:

[1529] The terminal receives the revised design proposal and displays it again to the user, who then checks the design proposal again.

[1530] Step 25:

[1531] The user approves the final design and clicks the "Approve" button.

[1532] Step 26:

[1533] The terminal collects the authorization information and sends it to the server.

[1534] Step 27:

[1535] The server receives the approval information and stores the final design in a database.

[1536] Step 28:

[1537] The server sends the final design data to the production workshop.

[1538] Step 29:

[1539] The production workshop produces the product based on the design data received from the server.

[1540] Step 30:

[1541] The crafting workshop reports the crafting progress to the server.

[1542] Step 31:

[1543] The server monitors the progress and notifies the user.

[1544] Step 32:

[1545] Once the product is completed, the production workshop will handle the shipping procedures.

[1546] Step 33:

[1547] The server monitors the delivery status and notifies the user of the final delivery status.

[1548] The above are the specific processing steps from user registration to design proposal generation, sentiment analysis, feedback, final approval, production, and delivery.

[1549] Example 2

[1550] 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."

[1551] Conventional design generation systems have problems such as being unable to appropriately reflect user feedback and difficulty in automatically modifying designs that take the user's emotional state into account. Furthermore, to increase user satisfaction, it is necessary to analyze the user's emotions along with the feedback and reflect them in the design. However, there is currently no effective way to achieve this.

[1552] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for inputting conditions for a design desired by a user; means for generating a design proposal based on the user's input conditions using a generative AI model; means for displaying the generated design proposal to the user; means for the terminal to collect user emotion data and analyze it using an emotion engine; means for transmitting the emotion analysis results to the server and reflecting them in the design proposal and feedback; means for receiving user feedback and again modifying the design using the generative AI model; and means for finalizing the design and transmitting it to a production workshop. This makes it possible to generate and modify designs that reflect the user's emotions, thereby increasing user satisfaction.

[1553] "User" means an individual or organization that utilizes the system to create and modify designs.

[1554] "Desired design conditions" are specific requirements such as style, color, and material that the user desires for the design.

[1555] A "generative AI model" is an artificial intelligence algorithm that automatically generates design proposals based on input conditions.

[1556] "Design Proposal" means an initial or revised design proposal created by a generative AI model.

[1557] A "terminal" is an electronic device that a user uses to access, operate, and input data to the system.

[1558] The "server" is a central processing unit that receives and processes data sent from the device and runs generative AI models and emotion engines as needed.

[1559] "Emotion data" refers to data relating to the user's emotional state collected from the user's facial expressions, voice, input content, and the like.

[1560] An "emotion engine" is a software or hardware component that analyzes collected emotion data and infers the user's emotional state.

[1561] "Emotion analysis results" are information about the user's emotional state analyzed by the emotion engine.

[1562] "Feedback" refers to evaluations and correction requests that users input about the generated design proposals.

[1563] "Manufacturing workshop" means a facility or organization that produces physical products based on a finalized design.

[1564] "Production status" is information about the process and progress of the product being produced by the production workshop.

[1565] "Delivery status" is information about the progress of the product as it is being delivered from the production workshop to the user.

[1566] The system of the present invention automates the process of inputting the user's desired design criteria, generating design proposals based on the input criteria using a generative AI model, and then revising the design based on the user's feedback. Furthermore, the system also includes a function to recognize the user's emotions, which is important for increasing user satisfaction.

[1567] 1. Hardware and Software Configuration

[1568] Hardware

[1569] This system uses the following hardware:

[1570] Device: The device that users use to access the system and provide input and feedback on design criteria. Examples include personal computers and smartphones.

[1571] Server: A central computing unit that runs generative AI models, emotion engines, and processes user input data.

[1572] Camera and microphone: Built into the device and used to collect the user's facial expressions and voice.

[1573] software

[1574] This system uses the following software:

[1575] Generative AI model: An artificial intelligence algorithm that generates design ideas based on user input. Examples include GPT-3 and DALL-E.

[1576] Emotion engine: An algorithm that analyzes a user's emotional data and infers their emotional state. Examples include facial expression analysis software and voice analysis software.

[1577] Database: Built into the server and used to store user registration information and design data.

[1578] 2. Data processing and calculation

[1579] User Registration and Login

[1580] The user uses a device to enter their name, email address, and password, which is then sent to a server, which stores the data in a database.

[1581] Initial design settings

[1582] The user inputs their design preferences (style, color, material), and the device sends this to the server, which then passes this data as input to the generative AI model.

[1583] Design generation using generative AI models

[1584] Based on the conditions received by the server, a generative AI model is used to generate design proposals. For example, the generative AI model uses the following prompt sentence:

[1585] Prompt: "Generate an aqua blue glass object in a modern art style."

[1586] The server sends the generated design proposal to the terminal and displays it to the user.

[1587] Emotion recognition by emotion engine

[1588] The device uses a camera and microphone to collect the user's facial expressions and voice. The emotion engine analyzes this and sends the emotion analysis results to the server. The server then uses the results to create design proposals and provide feedback.

[1589] Design feedback and revisions

[1590] The user provides feedback on the generated design (e.g., "I'd like the color to be a little darker"). The device collects the feedback and analyzes it using an emotion engine. The feedback and emotion analysis results are sent to the server, which then uses this information to further revise the design using the generative AI model.

[1591] Finalize and save the design

[1592] The user checks and approves the final design. The server saves the final design in a database and generates instruction data for the production workshop.

[1593] Production and Delivery

[1594] The production workshop receives the final design data from the server and produces the product. The finished product is delivered to the user. The server monitors the progress of production and delivery and notifies the user.

[1595] Specifically, if a user inputs "modern art," "aqua blue," and "glass" as their desired design criteria, the generative AI model generates a design proposal based on these. If the user reviews the generated design proposal and provides feedback such as "I'd like the color to be a little darker," the emotion engine analyzes the user's facial expression and sends the results to the server. The server uses the generative AI model to generate a new design proposal that reflects the feedback and presents it to the user again. If the user finally approves, the production workshop produces the product based on that design and delivers it to the user.

[1596] The above process is a detailed embodiment of the present invention.

[1597] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1598] Step 1: User registration and login

[1599] Input: The user enters their name, email address, and password on the sign-up screen.

[1600] Specific operation: The user enters "name, email address, password" on the new registration screen and clicks the submit button.

[1601] Data processing: The terminal collects input data in real time and captures the click event of the submit button.

[1602] Output: The device sends the collected data to the server.

[1603] What happens: The server validates the data it receives and stores it in the database if appropriate.

[1604] Output: The server generates a registration completion message and sends it to the terminal.

[1605] Specific behavior: The device displays a completion message on the screen.

[1606] Step 2: Initialize the design

[1607] Input: The user inputs the desired design criteria (style, color, material).

[1608] Specific behavior: The user enters "Style: Modern Art", "Color: Aqua Blue", and "Material: Glass".

[1609] Data processing: The terminal collects input data.

[1610] Output: The terminal sends the collected design conditions to the server.

[1611] Specific operation: The server receives the desired conditions and stores them in a database.

[1612] Step 3: Generate a design using a generative AI model

[1613] Input: The design conditions received by the server.

[1614] What happens: The server calls the generative AI model and passes the design conditions as prompts.

[1615] Prompt: "Generate an aqua blue glass object in a modern art style."

[1616] Data computation: A generative AI model generates design ideas based on input conditions.

[1617] Output: The server sends the generated design proposal to the device.

[1618] Specific operation: The device displays the design proposal on the screen and presents it to the user.

[1619] Step 4: Emotion Recognition with the Emotion Engine

[1620] Input: User facial expressions, voice, and input.

[1621] Specific operation: The device uses the camera and microphone to collect the user's facial expressions and voice.

[1622] Data processing: The emotion data collected by the device is passed to the emotion engine in real time.

[1623] Data calculation: The emotion engine analyzes the emotion data and estimates the user's emotional state.

[1624] Output: The device sends the emotion analysis results to the server.

[1625] Specific operation: The server reflects the results of emotion analysis in design proposals and feedback.

[1626] Step 5: Design feedback and revisions

[1627] Input: User feedback (e.g. "I'd like the color to be a little darker").

[1628] What happens: The user provides feedback on the design proposal.

[1629] Data processing: The device collects feedback and analyzes it again using the emotion engine.

[1630] Output: Send feedback and sentiment analysis results to the server.

[1631] How it works: The server calls the generative AI model and revises the design based on feedback and analysis results.

[1632] Data computation: Generative AI models generate revised design proposals.

[1633] Output: The server sends the revised design proposal to the device and presents it to the user.

[1634] Step 6: Finalize and save your design

[1635] Input: User approval.

[1636] What happens: The user reviews the final design and clicks "Accept."

[1637] Data processing: The server stores the final design in a database.

[1638] Output: Generates instruction data for the production workshop.

[1639] Step 7: Production and Delivery

[1640] Input: Final design data received by the fabrication studio.

[1641] Specific operation: The production workshop produces the product based on the final design data.

[1642] Output: The finished product is delivered to the user.

[1643] What it does: The server monitors the progress of production and delivery and notifies the user.

[1644] (Application example 2)

[1645] 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."

[1646] Conventional design generation systems generate design proposals based on user requirements and incorporate feedback, but lack an automated process that takes user emotions into account. Furthermore, collecting feedback and revising designs takes time, which can lead to reduced user satisfaction. Furthermore, notifications about production and delivery status are uniform, with little consideration given to user emotions. Therefore, achieving an efficient and personalized design generation process remains a challenge.

[1647] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1648] In this invention, the server includes a means for recognizing a user's emotion, a means for modifying a design based on the emotion recognition result, and a means for adjusting the content of notifications to the user based on the emotion recognition result, thereby enabling automatic generation of a design that takes the user's emotion into consideration, modification of the design, and notification of production status and delivery status according to the emotion.

[1649] "Means for inputting the user's desired design conditions" refers to an interface that allows the user to input conditions such as the design style, color, and material that he or she desires.

[1650] "Means for generating design proposals based on user input conditions using a generative AI model" refers to a mechanism for automatically generating appropriate design proposals using a generative AI model based on conditions entered by the user.

[1651] The "means for displaying the generated design proposal to the user" refers to a display device or software interface for providing the generated design proposal to the user in a visible form.

[1652] "Means for receiving user feedback and revising the design using a generative AI model" refers to a mechanism for collecting user feedback such as evaluations and requests for revisions, and then regenerating and revising design proposals using a generative AI model based on that feedback.

[1653] "Means for recognizing user emotions" refers to technology and devices for analyzing and evaluating a user's emotional state from facial expressions, voice, etc.

[1654] "Means for modifying the design based on the emotion recognition results" refers to a mechanism for improving or changing the generated design proposal by referring to the user's emotion recognition results.

[1655] "Means for finalizing the design and transmitting it to the production facility" refers to a system for transmitting the finalized design as data to the production facility and issuing production instructions.

[1656] The "means for transmitting user input conditions to the server" refers to a mechanism for transmitting the design conditions input by the user to the server as data.

[1657] "Means for transmitting the design proposal generated by the server to the terminal" refers to a mechanism for transmitting the design proposal generated by the server as data to the terminal used by the user.

[1658] "Means for sending final design data to the production facility and notifying the user of the production status and delivery status" refers to a system for sending finalized design data to the production facility and notifying the user of the production and delivery status in real time.

[1659] "Means for adjusting the content of notifications to the user based on the emotion recognition results" refers to a mechanism for appropriately changing and adjusting the content of notifications according to the results of the user's emotion recognition, and providing more personalized information to the user.

[1660] This invention is a system that allows a user to easily create a design they desire, and then produces and delivers the product based on a highly accurate design proposal. The following describes a specific embodiment of this system.

[1661] User Registration and Login

[1662] The user enters basic information (name, email address, password) on the new registration screen. This information is collected on the device and sent to the server. The server receives the information and stores it in a database (PostgreSQL). From the next time onwards, the user can access the system using the login information.

[1663] Initial design settings

[1664] Users input their design preferences (e.g., style, color, material) using a React-based interface, and the information is sent by the device to the server.

[1665] Design generation using generative AI

[1666] The server receives the user-entered criteria and generates design proposals using a generative AI model (e.g., OpenAI GPT-3 or DALL-E). The generated design proposals are sent from the server to the device and displayed to the user.

[1667] Emotion recognition by emotion engine

[1668] The device uses the smartphone's camera and microphone to collect the user's facial expressions and voice, and analyzes their emotions using OpenCV and Google Cloud Speech-to-Text. The analysis results are sent to a server and reflected in design proposals and feedback.

[1669] Design feedback and revisions

[1670] The user enters feedback about the generated design proposal (e.g., color changes or design adjustments) using a feedback input form using React. The feedback is analyzed by the emotion engine and sent to the server, where the generative AI model again modifies the design. The modified design proposal is then presented to the user again.

[1671] Final design decision

[1672] The user reviews and approves the final design. The server saves the final design in a database and creates instructions for the manufacturing facility. An example of a prompt is the text "Create a modern art style product with aqua blue color and made of glass."

[1673] Production and Delivery

[1674] The server sends the final design data to the production facility, which then produces the product based on that design. After production, the product is delivered to the user. The server monitors the progress of production and delivery using a notification service such as Firebase, and notifies the user of the status. The content of the notification is also adjusted based on the emotion recognition results.

[1675] The following specific hardware and software are used in each of the above steps:

[1676] Server: Django

[1677] Database: PostgreSQL

[1678] Front-end interface: React

[1679] Generative AI model: OpenAI GPT-3 or DALL-E

[1680] Emotion recognition: OpenCV, Google Cloud Speech-to-Text

[1681] Notification Service: Firebase

[1682] This system allows users to efficiently obtain custom-designed products that reflect their preferences, and adding emotion recognition can further increase user satisfaction.

[1683] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1684] Step 1:

[1685] The user enters basic information (name, email address, password) on the new registration screen. The device collects this information and sends it to the server. The server stores the received information in a database. The input for this step is the basic information entered by the user, and the output is the user information stored in the database.

[1686] Step 2:

[1687] The user inputs desired design conditions (e.g., style, color, material). The terminal receives the input information and sends it to the server. The input of this step is the design conditions entered by the user, and the output is the design conditions sent to the server.

[1688] Step 3:

[1689] The server uses a generative AI model to generate design proposals based on the received design criteria. In this generation process, the AI ​​generates text and image data based on the prompt. The input for this step is the design criteria and prompt, and the output is the generated design proposal.

[1690] Step 4:

[1691] The server sends the generated design proposal to the terminal, which displays it to the user. The user checks the generated design proposal. The input of this step is the generated design proposal, and the output is the design proposal displayed to the user.

[1692] Step 5:

[1693] The device collects the user's facial expressions and voice, and performs emotion analysis using OpenCV and Google Cloud Speech-to-Text. The analysis results are sent to the server. The input of this step is the user's facial expressions and voice data, and the output is the emotion analysis results.

[1694] Step 6:

[1695] The user enters feedback about the design proposal (e.g., color changes or design tweaks), and the device collects the feedback and sends it to the server. The server receives the feedback and sentiment analysis results and uses them to modify the design using a generative AI model. The inputs for this step are the user's feedback and sentiment analysis results, and the output is the modified design proposal.

[1696] Step 7:

[1697] The server sends the revised design proposal to the terminal, which then displays it again to the user. The user confirms the revised design proposal. The input of this step is the revised design proposal, and the output is the revised design proposal displayed to the user.

[1698] Step 8:

[1699] The user reviews and approves the final design. The server stores the final design in a database and generates instructions for the fabrication facility. The input for this step is the final design data, and the output is the instructions sent to the fabrication facility.

[1700] Step 9:

[1701] The server sends the final design data to the production facility, which then produces the product based on that design. After production, the product is delivered to the user. The server uses Firebase to monitor the progress of production and delivery and notify the user of the status. The input for this step is the final design data and production and delivery status data, and the output is the notification content to the user. The notification content is also adjusted based on the emotion recognition results.

[1702] 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.

[1703] 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.

[1704] 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.

[1705] [Fourth embodiment]

[1706] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1707] 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.

[1708] 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).

[1709] 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.

[1710] 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.

[1711] 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).

[1712] 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.

[1713] 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.

[1714] 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.

[1715] 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.

[1716] 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.

[1717] 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.

[1718] 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."

[1719] The system of the present invention automates the process of a user inputting desired design conditions, a generative AI model generating design proposals based on those conditions, and then receiving user feedback to further revise the design. The following means and processes are included in order to implement this system.

[1720] System Overview

[1721] 1. User Registration and Login

[1722] The user enters basic information (name, email address, password) on the new registration screen.

[1723] The device collects the information and sends it to the server.

[1724] The server receives this and stores it in a database.

[1725] 2. Initial design settings

[1726] The user inputs the desired design criteria (e.g., style, color, material).

[1727] The terminal receives this information and sends it to the server.

[1728] 3. Design generation using generative AI

[1729] Based on the conditions received by the server, a generative AI model is used to generate design proposals.

[1730] The server transmits the generated design proposal to the terminal and displays it to the user.

[1731] 4. Design feedback and revisions

[1732] The user inputs feedback on the generated design proposal (e.g., color changes or design adjustments).

[1733] The device collects the feedback and sends it to the server.

[1734] The server receives the feedback and uses a generative AI model to refine the design.

[1735] The revised design proposal is then presented to the user again.

[1736] 5. Final design decision

[1737] The user reviews and approves the final design.

[1738] The server stores the final design in a database and generates instructions for the manufacturing workshop.

[1739] 6. Production and Delivery

[1740] The server sends the final design data to the production workshop.

[1741] The production workshop produces the product based on the design.

[1742] After production, the product is shipped to the user.

[1743] The server monitors the progress of production and delivery and notifies the user of the status.

[1744] Specific examples

[1745] 1. User Registration and Login

[1746] A user enters "Yamada Taro", "taro@example.com", and "password123" in the new registration form.

[1747] The terminal receives this and sends it to the server.

[1748] The server saves the data in the database and sends a completion message to the terminal.

[1749] 2. Initial design settings

[1750] The user inputs the desired conditions: "modern Japanese style," "dark red," and "wood."

[1751] The terminal receives this condition and sends it to the server.

[1752] 3. Design generation using generative AI

[1753] The server receives the requirements and generates design proposals using a generative AI model.

[1754] The server sends the design proposal to the terminal and displays it to the user.

[1755] For example, a dark red wooden table design is generated in a modern Japanese style.

[1756] 4. Design feedback and revisions

[1757] The user reviews the design proposal and gives feedback, such as "Make the red a little darker."

[1758] The device sends the feedback to the server.

[1759] The server receives the feedback and uses a generative AI model to adjust the colors and generate a new design.

[1760] The server sends the new design proposal to the terminal and displays it to the user.

[1761] 5. Final design decision

[1762] The user reviews the final design and clicks "Accept."

[1763] The server stores the final design in a database and creates instruction data for the production workshop.

[1764] 6. Production and Delivery

[1765] The production workshop produces the product based on the final design data received from the server.

[1766] Once completed, the product is delivered to the user.

[1767] The server monitors the progress of production and delivery and notifies the user.

[1768] The above detailed process allows users to obtain efficient and personalized products in a short time.

[1769] The processing flow will be explained below.

[1770] Step 1:

[1771] The user enters their name, email address, and password on the new registration screen.

[1772] Step 2:

[1773] The terminal collects the information entered by the user and sends it to the server.

[1774] Step 3:

[1775] The server receives this information and stores it in a database.

[1776] Step 4:

[1777] The server sends a message to the terminal indicating that the save is complete.

[1778] Step 5:

[1779] The terminal displays a save complete message to the user.

[1780] Step 6:

[1781] The user enters an email address and password on the login screen and attempts to log in.

[1782] Step 7:

[1783] The terminal again collects this information and sends it to the server.

[1784] Step 8:

[1785] The server checks the user information against the database and performs authentication. If authentication is successful, it sends a login success message to the terminal.

[1786] Step 9:

[1787] The terminal receives the authentication success message and displays it to the user, who proceeds to the main screen.

[1788] Step 10:

[1789] The user selects "Create a new design" on the main screen and enters the desired design conditions (e.g., style, color, material).

[1790] Step 11:

[1791] The terminal collects the user's input data and sends it to the server.

[1792] Step 12:

[1793] The server receives this data and pre-processes it to feed it into the generative AI model.

[1794] Step 13:

[1795] The server inputs the preprocessed data into a generative AI model to generate design proposals.

[1796] Step 14:

[1797] The server receives the generated design proposal and sends it to the terminal.

[1798] Step 15:

[1799] The terminal receives the design proposal and displays it to the user, who then checks the design proposal.

[1800] Step 16:

[1801] The user enters feedback on the design proposal (e.g., "Make it a darker red").

[1802] Step 17:

[1803] The device collects the feedback and sends it to the server.

[1804] Step 18:

[1805] The server receives the feedback and re-inputs it into the generative AI model to refine the design.

[1806] Step 19:

[1807] The server receives the revised design proposal and sends it to the device.

[1808] Step 20:

[1809] The terminal receives the revised design proposal and displays it again to the user, who then checks the design proposal again.

[1810] Step 21:

[1811] The user approves the final design and clicks the "Approve" button.

[1812] Step 22:

[1813] The terminal collects the authorization information and sends it to the server.

[1814] Step 23:

[1815] The server receives the approval information and stores the final design in a database.

[1816] Step 24:

[1817] The server sends the final design data to the production workshop.

[1818] Step 25:

[1819] The production workshop produces the product based on the design data received from the server.

[1820] Step 26:

[1821] The crafting workshop reports the crafting progress to the server.

[1822] Step 27:

[1823] The server monitors the progress and notifies the user.

[1824] Step 28:

[1825] Once the product is completed, the production workshop will handle the shipping procedures.

[1826] Step 29:

[1827] The server monitors the delivery status and notifies the user of the final delivery status.

[1828] The above are the specific processing steps from user registration to design proposal generation, feedback, final approval, production, and delivery.

[1829] Example 1

[1830] 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."

[1831] Conventional design generation systems have a complicated process from when the user inputs their desired design conditions to when the final design is finalized, and the process of modifying the design based on feedback is time-consuming and laborious. Furthermore, the transmission of the final design to the production studio and notifications of the production and delivery status are sometimes not smooth, which reduces user satisfaction.

[1832] 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.

[1833] In this invention, the server includes a means for inputting the user's desired design conditions, a means for generating a design proposal based on the user's input conditions using a generative AI model, a means for displaying the generated design proposal to the user, a means for receiving user feedback and revising the design using the generative AI model again, and a means for finalizing the design and transmitting the information to the manufacturer. This allows the user to efficiently proceed with the design process, enables rapid design revisions based on feedback, and enables smooth notification of the production workshop and production / delivery status.

[1834] "User" refers to a person who inputs design conditions and provides feedback on the generated design proposal.

[1835] "Design conditions" are elements related to the design desired by the user, including, for example, style, color, material, and the like.

[1836] A "generative AI model" refers to a part of a system that uses artificial intelligence technology to generate design proposals based on user input.

[1837] "Design proposal" refers to a design prototype generated by a generative AI model based on user input conditions.

[1838] "Feedback" refers to opinions and requests for corrections made by the user regarding the generated design proposal.

[1839] "Final design" refers to the design proposal that the generative AI model finalizes based on user feedback.

[1840] "Manufacturer" refers to the workshop or factory that actually produces the product based on the final design and provides it to the user.

[1841] The "information processing device" refers to a server that receives user input conditions and feedback and generates and modifies designs using a generative AI model.

[1842] The "display device" refers to a terminal that displays the generated design proposal to the user.

[1843] "Production Status" refers to the progress of the manufacturer in producing the product based on the final design.

[1844] "Delivery status" refers to the progress of the delivery process until the completed product is delivered to the user.

[1845] The system of the present invention automates the process of a user inputting desired design conditions, a generative AI model generating design proposals based on the input, and then receiving user feedback to further revise the design. To implement this system, the following means and processes are included.

[1846] First, the user enters basic information (name, email address, password) on the new registration screen. The device receives this information and sends it to the server. The server receives this information and stores it in a database. After the user registers, when they enter their email address and password on the login page, the device sends the authentication information to the server, which then performs authentication. If authentication is successful, the user is allowed to log in.

[1847] Next, the user inputs their desired design criteria (e.g., style, color, material). The device receives this information and sends it to the server. The server sends prompts to the generative AI model based on the received criteria. The generative AI model generates design ideas based on the criteria, and the server sends the generated design ideas to the device. The device displays the design ideas to the user.

[1848] For example, when a user enters "Yamada Taro," "taro@example.com," and "password123" in a new registration form, the device sends it to the server, which saves it in the database and returns a message indicating successful registration. When a user enters "taro@example.com" and "password123" on the login screen, the device sends the authentication information to the server, which confirms the user's authentication and then allows the login. Next, the user enters desired design criteria, such as "modern Japanese style," "dark red," and "wood," and the device sends it to the server.

[1849] When the server sends the conditions "modern Japanese style," "dark red," and "wood" as prompts to the generative AI model, the model generates a design proposal for a modern Japanese style dark red wooden table based on the conditions. The server then sends this design proposal to the device and displays it to the user.

[1850] Example prompt sentence:

[1851] Generate a design based on the following criteria: "Modern Japanese style", "Dark red", and "Wood".

[1852] The user then enters feedback about the generated design proposal (e.g., "Make the red a little darker"). The device receives the feedback and sends it to the server. The server verifies the feedback and sends a prompt for correction to the generative AI model. The generative AI model incorporates the feedback to generate a new design proposal, which the server sends to the device and displays again to the user.

[1853] Example prompt sentence:

[1854] Readjust the design: "Make the red a little darker."

[1855] Once the user confirms the final design and clicks "Accept," the server saves the final design in a database and sends the information to the manufacturer. The server then sends the final design data to the manufacturer, who then produces the product based on the design. Once completed, the product is delivered to the user. The server monitors the progress of production and delivery, notifying the user as necessary.

[1856] Through the above detailed process, users can obtain efficient and personalized products in a short period of time.

[1857] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1858] Step 1: User enters basic information on the new registration screen

[1859] Input: The user enters their name, email address, and password.

[1860] Specific operation: The user enters "Yamada Taro", "taro@example.com", and "password123" on the new registration screen.

[1861] Data processing: The device receives the user's input information and sends it to the server as JSON format data.

[1862] Output: The server saves the received data in the database and returns a message to the terminal indicating successful registration.

[1863] Step 2: User enters credentials on login page

[1864] Input: The user enters their email address and password.

[1865] Specific operation: The user enters "taro@example.com" and "password123" on the login screen.

[1866] Data processing: The device receives the authentication information and sends it to the server

[1867] Output: The server checks the authentication information against the database and returns a successful authentication message to the terminal.

[1868] Step 3: User enters design requirements

[1869] Input: The user inputs "design style," "color," and "material."

[1870] Specific operation: The user inputs "Modern Japanese Style", "Dark Red", and "Wood".

[1871] Data processing: The device receives the desired conditions entered and sends them to the server.

[1872] Output: The server receives the desired conditions and sends them as prompts to the generative AI model.

[1873] Step 4: The generative AI model generates design ideas

[1874] Input: Prompt sent from the server ("Modern Japanese Style", "Dark Red", "Wood")

[1875] How it works: The generative AI model generates design ideas based on the conditions.

[1876] Data processing: The generative AI model analyzes the input conditions and generates design proposals

[1877] Output: The server receives the generated design proposal and sends it to the device.

[1878] Step 5: User provides feedback on the design proposal

[1879] Input: User inputs "feedback content"

[1880] Specific behavior: The user reviews the design and enters feedback such as "Make the red a little darker."

[1881] Data processing: The device receives the feedback and sends it to the server

[1882] Output: The server receives the feedback and sends it as prompts to the generative AI model.

[1883] Step 6: The generative AI model refines the design proposal

[1884] Input: Prompt sent from the server ("Make the red a little darker")

[1885] How it works: The generative AI model incorporates feedback and generates new design ideas.

[1886] Data processing: A generative AI model analyzes the feedback and refines the design proposal

[1887] Output: The server receives the revised design proposal and sends it to the device.

[1888] Step 7: User reviews and approves the final design

[1889] Input: The user performs the "final design confirmation" and "approval operation"

[1890] Specific behavior: The user reviews the final design and clicks "Accept."

[1891] Data processing: The device receives the consent operation and sends it to the server

[1892] Output: The server saves the final design to a database and sends instructions to the manufacturer.

[1893] Step 8: The server sends the final design to the manufacturer

[1894] Input: Final design data

[1895] Specific operation: The server sends the final design data to the manufacturer.

[1896] Data processing: The server transfers the final design data to the manufacturer.

[1897] Output: The manufacturer produces the product based on the final design.

[1898] Step 9: The server notifies the user of the progress of production and delivery

[1899] Input: Production status and delivery status

[1900] Specific operation: The server receives production and delivery status from the manufacturer and notifies the user.

[1901] Data processing: The server analyzes the progress data and generates notification messages.

[1902] Output: User receives notification messages to check production and delivery progress

[1903] In this way, the user, the terminal, and the server work together to advance the design process, enabling the user to efficiently receive a product with the design they desire.

[1904] (Application example 1)

[1905] 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."

[1906] In conventional design generation systems, the process of users inputting their desired conditions and the process of revising the design based on feedback on the generated design were separated, resulting in a lack of a mechanism for efficiently reflecting these changes. Furthermore, the process from finalizing the design to sending it to the production workshop was often done manually, which was time-consuming and costly. Furthermore, the user experience in virtual stores was limited, and an environment where users could easily customize designs was not established.

[1907] 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.

[1908] In this invention, the server includes means for inputting user-desired design conditions, means for generating design proposals based on the user-input conditions using a generative AI model, means for displaying the generated design proposals to the user, means for receiving user feedback and again modifying the design using the generative AI model, means for finalizing the design and sending it to a production workshop, and means implemented as a smartphone application and used in a virtual store. This enables users to efficiently customize designs in the virtual store, quickly modify the design based on feedback, and easily manage the entire process from finalizing the design to production and delivery.

[1909] A "user" is an individual or group that uses the system to input desired design conditions and provide feedback on the generated design proposals.

[1910] "Design requirements" are specific requirements such as the style, color, and material of the design desired by the user.

[1911] A "generative AI model" is an artificial intelligence system that automatically generates design proposals based on user input conditions.

[1912] A "smartphone application" is a software program that runs on a smartphone and allows users to create and customize designs.

[1913] A "virtual store" is a virtual sales environment accessible via the Internet, which exists online rather than as a physical store.

[1914] The "server" is a central control unit that receives and processes user design criteria and feedback, and generates and modifies designs using generative AI models.

[1915] "Feedback" refers to the evaluation and correction requests made by the user regarding the generated design proposal.

[1916] A "production workshop" is a facility with physical equipment that produces actual products based on the final design finalized by the user.

[1917] A "prompt" is a specific instruction sentence input to an AI model and is used when generating and modifying designs.

[1918] This invention relates to a system that allows users to input desired design conditions, generates design proposals using a generative AI model, and modifies the design based on user feedback. Specifically, it is implemented as a smartphone application to improve the user experience in a virtual store.

[1919] System Overview

[1920] 1. User Registration and Login

[1921] First, the user installs the smartphone application and registers by entering basic information (e.g., name, email address, password). After registration, the user accesses the server from the login screen and enters the account registration information.

[1922] 2. Initial design settings

[1923] After completing registration and logging in, users enter their desired design criteria (e.g., item category, style, color, material) into the application, and the device sends this information to the server.

[1924] 3. Design generation using generative AI

[1925] The server uses a generative AI model based on the received design criteria to generate an initial design proposal. This generative AI model uses OpenAI's API, etc. The generated design proposal is displayed to the user via their device.

[1926] 4. Design feedback and revisions

[1927] The user inputs feedback based on the generated design proposal (e.g., "make the backrest higher" or "make the color brighter"). This feedback is sent from the device to the server, which then uses the generative AI model to modify the design. The modified design proposal is then displayed to the user again.

[1928] 5. Final design decision

[1929] Once the user has confirmed and approved the final design, the server stores the design data and sends it to the production studio, which then produces the product based on that data.

[1930] 6. Production and Delivery

[1931] After the workshop produces the product based on the final design, the product is delivered to the user. The server monitors the progress of production and delivery in real time and notifies the user.

[1932] Hardware and software used

[1933] Hardware: Smartphones, servers

[1934] Software: Smartphone applications, server-side applications, generative AI models (e.g., OpenAI APIs)

[1935] Specific examples

[1936] If a user wants a "Scandinavian-style white sofa":

[1937] Initial input: "Scandinavian-style white sofa"

[1938] Initial design generation prompt: "Create a design based on the following specifications: Scandinavian-style white sofa"

[1939] Feedback: "Please add a taller backrest."

[1940] Post-feedback prompt: "Revise the following design based on this feedback: Add a taller backrest. Original design: [Initial generated design]"

[1941] In this way, users can efficiently customize designs in the virtual store and quickly revise design proposals using generative AI models.

[1942] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1943] Step 1:

[1944] The user installs the smartphone application and enters their name, email address, and password on the new registration screen. The device collects this information and sends it to the server. The server stores the received data in a database and completes user registration.

[1945] Input: Name, Email Address, Password

[1946] Output: User registration completion message

[1947] Specific operation: Saving data to the database

[1948] Step 2:

[1949] The user enters their account information on the login screen and logs in. The device sends the login information to the server. The server compares the received login information with a database and performs authentication. If authentication is successful, a login success message is sent to the user.

[1950] Input: Account information (email address, password)

[1951] Output: Login successful message

[1952] Specific operation: Database verification and authentication

[1953] Step 3:

[1954] The user inputs design criteria (e.g., item category, style, color, material) into the application. The device sends these criteria to the server, which stores the received design criteria in a database.

[1955] Input: Design criteria (category, style, color, material)

[1956] Output: Message that design conditions have been saved

[1957] Specific operation: Saving data to the database

[1958] Step 4:

[1959] The server sends prompts to the generative AI model based on the saved design conditions, generating design proposals, which are then sent from the server to the device and displayed to the user.

[1960] Input: Design Conditions

[1961] Output: Generated design proposal

[1962] Specific operation: Design generation using generative AI model, sending design proposal

[1963] Step 5:

[1964] The user checks the generated design proposal and inputs feedback (e.g., "Make the back higher," "Make the color brighter," etc.). The device then sends this feedback to the server.

[1965] Input: Feedback

[1966] Output: Feedback received notification

[1967] Specific behavior: Sending feedback data

[1968] Step 6:

[1969] The server receives the feedback and sends new prompts to the generative AI model to revise the design, which is then sent back to the device and displayed to the user.

[1970] Input: Feedback

[1971] Output: Revised design proposal

[1972] Specific operation: Modify the design using the generative AI model and resubmit the design proposal

[1973] Step 7:

[1974] The user checks and approves the final design. The approved design data is sent from the terminal to the server. The server stores the final design in a database and sends it to the production workshop.

[1975] Input: Final approved design data

[1976] Output: Message confirming design saving and sending of production instructions

[1977] Specific operations: saving design data to a database and transferring the data to the production workshop

[1978] Step 8:

[1979] The production workshop produces the product based on the received final design data. Once production is complete, the product is delivered to the user. The server monitors the progress of production and delivery and notifies the user of the progress.

[1980] Input: Final design data

[1981] Output: Production and delivery progress notifications

[1982] Specific operations: Monitoring the progress of the production process, notifying delivery status

[1983] 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.

[1984] The system of the present invention automates the process of a user inputting desired design conditions, a generative AI model generating design proposals based on those conditions, and then receiving user feedback to further revise the design. Furthermore, the system also includes a function to recognize user emotions. A specific embodiment of the system is described below.

[1985] System Overview

[1986] 1. User Registration and Login

[1987] The user enters basic information (name, email address, password) on the new registration screen.

[1988] The device collects the information and sends it to the server.

[1989] The server receives this and stores it in a database.

[1990] 2. Initial design settings

[1991] The user inputs the desired design criteria (e.g., style, color, material).

[1992] The terminal receives this information and sends it to the server.

[1993] 3. Design generation using generative AI

[1994] Based on the conditions received by the server, a generative AI model is used to generate design proposals.

[1995] The server transmits the generated design proposal to the terminal and displays it to the user.

[1996] 4. Emotion Recognition by Emotion Engine

[1997] The device uses the user's facial expressions, voice, input content, etc. to analyze the user's emotions through an emotion engine.

[1998] The server receives the analysis results and reflects them in design proposals or feedback.

[1999] 5. Design feedback and revisions

[2000] The user inputs feedback on the generated design proposal (e.g., color changes or design adjustments).

[2001] The device collects feedback and performs emotional analysis using an emotion engine.

[2002] The analysis results of the emotion engine are also sent to the server.

[2003] The server receives the feedback and sentiment analysis results, which are then fed back into the generative AI model to refine the design.

[2004] The revised design proposal is then presented to the user again.

[2005] 6. Final design decision

[2006] The user reviews and approves the final design.

[2007] The server stores the final design in a database and generates instructions for the manufacturing workshop.

[2008] 7. Production and Delivery

[2009] The server sends the final design data to the production workshop.

[2010] The production workshop produces the product based on the design.

[2011] After production, the product is shipped to the user.

[2012] The server monitors the progress of production and delivery and notifies the user of the status.

[2013] Specific examples

[2014] 1. User Registration and Login

[2015] A user enters "Hanako Tanaka", "hanako@example.com", and "password123" in a new registration form.

[2016] The terminal receives this and sends it to the server.

[2017] The server saves the data in the database and sends a completion message to the terminal.

[2018] 2. Initial design settings

[2019] The user inputs "modern art," "aqua blue," and "glass" as desired conditions.

[2020] The terminal receives this condition and sends it to the server.

[2021] 3. Design generation using generative AI

[2022] The server receives the requirements and generates design proposals using a generative AI model.

[2023] The server sends the design proposal to the terminal and displays it to the user.

[2024] For example, aqua blue glass objects are generated in a modern art style.

[2025] 4. Emotion Recognition by Emotion Engine

[2026] The device detects positive reactions from the user's facial expressions and sends them to the server.

[2027] 5. Design feedback and revisions

[2028] The user reviews the design proposal and gives feedback, saying, "I'd like the color to be a little darker."

[2029] The device collects feedback and performs emotional analysis using an emotion engine.

[2030] The emotion engine analyzes changes in the user's facial expressions and confirms positive emotions.

[2031] The device sends the feedback and emotion analysis results to the server.

[2032] The server receives the feedback and sentiment analysis results, adjusts the colors using a generative AI model, and generates a new design.

[2033] The server sends the new design proposal to the terminal and displays it to the user.

[2034] 6. Final design decision

[2035] The user reviews the final design and clicks "Accept."

[2036] The server stores the final design in a database and creates instruction data for the production workshop.

[2037] 7. Production and Delivery

[2038] The production workshop produces the product based on the final design data received from the server.

[2039] Once completed, the product is delivered to the user.

[2040] The server monitors the progress of production and delivery and notifies the user.

[2041] The detailed process described above enables users to acquire efficient and personalized products in a short time, and the introduction of an emotion engine increases user satisfaction.

[2042] The processing flow will be explained below.

[2043] Step 1:

[2044] The user enters their name, email address, and password on the new registration screen.

[2045] Step 2:

[2046] The terminal collects the information entered by the user and sends it to the server.

[2047] Step 3:

[2048] The server receives this information and stores it in a database.

[2049] Step 4:

[2050] The server sends a message to the terminal indicating that the save is complete.

[2051] Step 5:

[2052] The terminal displays a save complete message to the user.

[2053] Step 6:

[2054] The user enters an email address and password on the login screen and attempts to log in.

[2055] Step 7:

[2056] The terminal again collects this information and sends it to the server.

[2057] Step 8:

[2058] The server checks the user information against the database and performs authentication. If authentication is successful, it sends a login success message to the terminal.

[2059] Step 9:

[2060] The terminal receives the authentication success message and displays it to the user, who proceeds to the main screen.

[2061] Step 10:

[2062] The user selects "Create a new design" on the main screen and enters the desired design conditions (e.g., style, color, material).

[2063] Step 11:

[2064] The terminal collects the user's input data and sends it to the server.

[2065] Step 12:

[2066] The server receives this data and pre-processes it to feed it into the generative AI model.

[2067] Step 13:

[2068] The server inputs the preprocessed data into a generative AI model to generate design proposals.

[2069] Step 14:

[2070] The server receives the generated design proposal and sends it to the terminal.

[2071] Step 15:

[2072] The terminal receives the design proposal and displays it to the user, who then checks the design proposal.

[2073] Step 16:

[2074] The device captures the user's facial expressions with a camera and sends them to the emotion engine along with their voice and input.

[2075] Step 17:

[2076] The emotion engine analyzes the user's emotions (e.g., joy, confusion, dissatisfaction, etc.) regarding the design proposal.

[2077] Step 18:

[2078] The server receives the emotion analysis results and stores them together with the design proposal.

[2079] Step 19:

[2080] The user enters feedback on the design proposal (e.g., "Make it a darker red").

[2081] Step 20:

[2082] The device collects the feedback and again performs emotion analysis using the emotion engine.

[2083] Step 21:

[2084] The emotion engine analyzes the user's emotions during feedback and sends the results to the server.

[2085] Step 22:

[2086] The server receives the feedback and sentiment analysis results, which are then fed back into the generative AI model to refine the design.

[2087] Step 23:

[2088] The server receives the revised design proposal and sends it back to the device.

[2089] Step 24:

[2090] The terminal receives the revised design proposal and displays it again to the user, who then checks the design proposal again.

[2091] Step 25:

[2092] The user approves the final design and clicks the "Approve" button.

[2093] Step 26:

[2094] The terminal collects the authorization information and sends it to the server.

[2095] Step 27:

[2096] The server receives the approval information and stores the final design in a database.

[2097] Step 28:

[2098] The server sends the final design data to the production workshop.

[2099] Step 29:

[2100] The production workshop produces the product based on the design data received from the server.

[2101] Step 30:

[2102] The crafting workshop reports the crafting progress to the server.

[2103] Step 31:

[2104] The server monitors the progress and notifies the user.

[2105] Step 32:

[2106] Once the product is completed, the production workshop will handle the shipping procedures.

[2107] Step 33:

[2108] The server monitors the delivery status and notifies the user of the final delivery status.

[2109] The above are the specific processing steps from user registration to design proposal generation, sentiment analysis, feedback, final approval, production, and delivery.

[2110] Example 2

[2111] 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."

[2112] Conventional design generation systems have problems such as being unable to appropriately reflect user feedback and difficulty in automatically modifying designs that take the user's emotional state into account. Furthermore, to increase user satisfaction, it is necessary to analyze the user's emotions along with the feedback and reflect them in the design. However, there is currently no effective way to achieve this.

[2113] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for inputting conditions for a design desired by a user; means for generating a design proposal based on the user's input conditions using a generative AI model; means for displaying the generated design proposal to the user; means for the terminal to collect user emotion data and analyze it using an emotion engine; means for transmitting the emotion analysis results to the server and reflecting them in the design proposal and feedback; means for receiving user feedback and again modifying the design using the generative AI model; and means for finalizing the design and transmitting it to a production workshop. This makes it possible to generate and modify designs that reflect the user's emotions, thereby increasing user satisfaction.

[2114] "User" means an individual or organization that utilizes the system to create and modify designs.

[2115] "Desired design conditions" are specific requirements such as style, color, and material that the user desires for the design.

[2116] A "generative AI model" is an artificial intelligence algorithm that automatically generates design proposals based on input conditions.

[2117] "Design Proposal" means an initial or revised design proposal created by a generative AI model.

[2118] A "terminal" is an electronic device that a user uses to access, operate, and input data to the system.

[2119] The "server" is a central processing unit that receives and processes data sent from the device and runs generative AI models and emotion engines as needed.

[2120] "Emotion data" refers to data relating to the user's emotional state collected from the user's facial expressions, voice, input content, and the like.

[2121] An "emotion engine" is a software or hardware component that analyzes collected emotion data and infers the user's emotional state.

[2122] "Emotion analysis results" are information about the user's emotional state analyzed by the emotion engine.

[2123] "Feedback" refers to evaluations and correction requests that users input about the generated design proposals.

[2124] "Manufacturing workshop" means a facility or organization that produces physical products based on a finalized design.

[2125] "Production status" is information about the process and progress of the product being produced by the production workshop.

[2126] "Delivery status" is information about the progress of the product as it is being delivered from the production workshop to the user.

[2127] The system of the present invention automates the process of inputting the user's desired design criteria, generating design proposals based on the input criteria using a generative AI model, and then revising the design based on the user's feedback. Furthermore, the system also includes a function to recognize the user's emotions, which is important for increasing user satisfaction.

[2128] 1. Hardware and Software Configuration

[2129] Hardware

[2130] This system uses the following hardware:

[2131] Device: The device that users use to access the system and provide input and feedback on design criteria. Examples include personal computers and smartphones.

[2132] Server: A central computing unit that runs generative AI models, emotion engines, and processes user input data.

[2133] Camera and microphone: Built into the device and used to collect the user's facial expressions and voice.

[2134] software

[2135] This system uses the following software:

[2136] Generative AI model: An artificial intelligence algorithm that generates design ideas based on user input. Examples include GPT-3 and DALL-E.

[2137] Emotion engine: An algorithm that analyzes a user's emotional data and infers their emotional state. Examples include facial expression analysis software and voice analysis software.

[2138] Database: Built into the server and used to store user registration information and design data.

[2139] 2. Data processing and calculation

[2140] User Registration and Login

[2141] The user uses a device to enter their name, email address, and password, which is then sent to a server, which stores the data in a database.

[2142] Initial design settings

[2143] The user inputs their design preferences (style, color, material), and the device sends this to the server, which then passes this data as input to the generative AI model.

[2144] Design generation using generative AI models

[2145] Based on the conditions received by the server, a generative AI model is used to generate design proposals. For example, the generative AI model uses the following prompt sentence:

[2146] Prompt: "Generate an aqua blue glass object in a modern art style."

[2147] The server sends the generated design proposal to the terminal and displays it to the user.

[2148] Emotion recognition by emotion engine

[2149] The device uses a camera and microphone to collect the user's facial expressions and voice. The emotion engine analyzes this and sends the emotion analysis results to the server. The server then uses the results to create design proposals and provide feedback.

[2150] Design feedback and revisions

[2151] The user provides feedback on the generated design (e.g., "I'd like the color to be a little darker"). The device collects the feedback and analyzes it using an emotion engine. The feedback and emotion analysis results are sent to the server, which then uses this information to further revise the design using the generative AI model.

[2152] Finalize and save the design

[2153] The user checks and approves the final design. The server saves the final design in a database and generates instruction data for the production workshop.

[2154] Production and Delivery

[2155] The production workshop receives the final design data from the server and produces the product. The finished product is delivered to the user. The server monitors the progress of production and delivery and notifies the user.

[2156] Specifically, if a user inputs "modern art," "aqua blue," and "glass" as their desired design criteria, the generative AI model generates a design proposal based on these. If the user reviews the generated design proposal and provides feedback such as "I'd like the color to be a little darker," the emotion engine analyzes the user's facial expression and sends the results to the server. The server uses the generative AI model to generate a new design proposal that reflects the feedback and presents it to the user again. If the user finally approves, the production workshop produces the product based on that design and delivers it to the user.

[2157] The above process is a detailed embodiment of the present invention.

[2158] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2159] Step 1: User registration and login

[2160] Input: The user enters their name, email address, and password on the sign-up screen.

[2161] Specific operation: The user enters "name, email address, password" on the new registration screen and clicks the submit button.

[2162] Data processing: The terminal collects input data in real time and captures the click event of the submit button.

[2163] Output: The device sends the collected data to the server.

[2164] What happens: The server validates the data it receives and stores it in the database if appropriate.

[2165] Output: The server generates a registration completion message and sends it to the terminal.

[2166] Specific behavior: The device displays a completion message on the screen.

[2167] Step 2: Initialize the design

[2168] Input: The user inputs the desired design criteria (style, color, material).

[2169] Specific behavior: The user enters "Style: Modern Art", "Color: Aqua Blue", and "Material: Glass".

[2170] Data processing: The terminal collects input data.

[2171] Output: The terminal sends the collected design conditions to the server.

[2172] Specific operation: The server receives the desired conditions and stores them in a database.

[2173] Step 3: Generate a design using a generative AI model

[2174] Input: The design conditions received by the server.

[2175] What happens: The server calls the generative AI model and passes the design conditions as prompts.

[2176] Prompt: "Generate an aqua blue glass object in a modern art style."

[2177] Data computation: A generative AI model generates design ideas based on input conditions.

[2178] Output: The server sends the generated design proposal to the device.

[2179] Specific operation: The device displays the design proposal on the screen and presents it to the user.

[2180] Step 4: Emotion Recognition with the Emotion Engine

[2181] Input: User facial expressions, voice, and input.

[2182] Specific operation: The device uses the camera and microphone to collect the user's facial expressions and voice.

[2183] Data processing: The emotion data collected by the device is passed to the emotion engine in real time.

[2184] Data calculation: The emotion engine analyzes the emotion data and estimates the user's emotional state.

[2185] Output: The device sends the emotion analysis results to the server.

[2186] Specific operation: The server reflects the results of emotion analysis in design proposals and feedback.

[2187] Step 5: Design feedback and revisions

[2188] Input: User feedback (e.g. "I'd like the color to be a little darker").

[2189] What happens: The user provides feedback on the design proposal.

[2190] Data processing: The device collects feedback and analyzes it again using the emotion engine.

[2191] Output: Send feedback and sentiment analysis results to the server.

[2192] How it works: The server calls the generative AI model and revises the design based on feedback and analysis results.

[2193] Data computation: Generative AI models generate revised design proposals.

[2194] Output: The server sends the revised design proposal to the device and presents it to the user.

[2195] Step 6: Finalize and save your design

[2196] Input: User approval.

[2197] What happens: The user reviews the final design and clicks "Accept."

[2198] Data processing: The server stores the final design in a database.

[2199] Output: Generates instruction data for the production workshop.

[2200] Step 7: Production and Delivery

[2201] Input: Final design data received by the fabrication studio.

[2202] Specific operation: The production workshop produces the product based on the final design data.

[2203] Output: The finished product is delivered to the user.

[2204] What it does: The server monitors the progress of production and delivery and notifies the user.

[2205] (Application example 2)

[2206] 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."

[2207] Conventional design generation systems generate design proposals based on user requirements and incorporate feedback, but lack an automated process that takes user emotions into account. Furthermore, collecting feedback and revising designs takes time, which can lead to reduced user satisfaction. Furthermore, notifications about production and delivery status are uniform, with little consideration given to user emotions. Therefore, achieving an efficient and personalized design generation process remains a challenge.

[2208] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2209] In this invention, the server includes a means for recognizing a user's emotion, a means for modifying a design based on the emotion recognition result, and a means for adjusting the content of notifications to the user based on the emotion recognition result, thereby enabling automatic generation of a design that takes the user's emotion into consideration, modification of the design, and notification of production status and delivery status according to the emotion.

[2210] "Means for inputting the user's desired design conditions" refers to an interface that allows the user to input conditions such as the design style, color, and material that he or she desires.

[2211] "Means for generating design proposals based on user input conditions using a generative AI model" refers to a mechanism for automatically generating appropriate design proposals using a generative AI model based on conditions entered by the user.

[2212] The "means for displaying the generated design proposal to the user" refers to a display device or software interface for providing the generated design proposal to the user in a visible form.

[2213] "Means for receiving user feedback and revising the design using a generative AI model" refers to a mechanism for collecting user feedback such as evaluations and requests for revisions, and then regenerating and revising design proposals using a generative AI model based on that feedback.

[2214] "Means for recognizing user emotions" refers to technology and devices for analyzing and evaluating a user's emotional state from facial expressions, voice, etc.

[2215] "Means for modifying the design based on the emotion recognition results" refers to a mechanism for improving or changing the generated design proposal by referring to the user's emotion recognition results.

[2216] "Means for finalizing the design and transmitting it to the production facility" refers to a system for transmitting the finalized design as data to the production facility and issuing production instructions.

[2217] The "means for transmitting user input conditions to the server" refers to a mechanism for transmitting the design conditions input by the user to the server as data.

[2218] "Means for transmitting the design proposal generated by the server to the terminal" refers to a mechanism for transmitting the design proposal generated by the server as data to the terminal used by the user.

[2219] "Means for sending final design data to the production facility and notifying the user of the production status and delivery status" refers to a system for sending finalized design data to the production facility and notifying the user of the production and delivery status in real time.

[2220] "Means for adjusting the content of notifications to the user based on the emotion recognition results" refers to a mechanism for appropriately changing and adjusting the content of notifications according to the results of the user's emotion recognition, and providing more personalized information to the user.

[2221] This invention is a system that allows a user to easily create a design they desire, and then produces and delivers the product based on a highly accurate design proposal. The following describes a specific embodiment of this system.

[2222] User Registration and Login

[2223] The user enters basic information (name, email address, password) on the new registration screen. This information is collected on the device and sent to the server. The server receives the information and stores it in a database (PostgreSQL). From the next time onwards, the user can access the system using the login information.

[2224] Initial design settings

[2225] Users input their design preferences (e.g., style, color, material) using a React-based interface, and the information is sent by the device to the server.

[2226] Design generation using generative AI

[2227] The server receives the user-entered criteria and generates design proposals using a generative AI model (e.g., OpenAI GPT-3 or DALL-E). The generated design proposals are sent from the server to the device and displayed to the user.

[2228] Emotion recognition by emotion engine

[2229] The device uses the smartphone's camera and microphone to collect the user's facial expressions and voice, and analyzes their emotions using OpenCV and Google Cloud Speech-to-Text. The analysis results are sent to a server and reflected in design proposals and feedback.

[2230] Design feedback and revisions

[2231] The user enters feedback about the generated design proposal (e.g., color changes or design adjustments) using a feedback input form using React. The feedback is analyzed by the emotion engine and sent to the server, where the generative AI model again modifies the design. The modified design proposal is then presented to the user again.

[2232] Final design decision

[2233] The user reviews and approves the final design. The server saves the final design in a database and creates instructions for the manufacturing facility. An example of a prompt is the text "Create a modern art style product with aqua blue color and made of glass."

[2234] Production and Delivery

[2235] The server sends the final design data to the production facility, which then produces the product based on that design. After production, the product is delivered to the user. The server monitors the progress of production and delivery using a notification service such as Firebase, and notifies the user of the status. The content of the notification is also adjusted based on the emotion recognition results.

[2236] The following specific hardware and software are used in each of the above steps:

[2237] Server: Django

[2238] Database: PostgreSQL

[2239] Front-end interface: React

[2240] Generative AI model: OpenAI GPT-3 or DALL-E

[2241] Emotion recognition: OpenCV, Google Cloud Speech-to-Text

[2242] Notification Service: Firebase

[2243] This system allows users to efficiently obtain custom-designed products that reflect their preferences, and adding emotion recognition can further increase user satisfaction.

[2244] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2245] Step 1:

[2246] The user enters basic information (name, email address, password) on the new registration screen. The device collects this information and sends it to the server. The server stores the received information in a database. The input for this step is the basic information entered by the user, and the output is the user information stored in the database.

[2247] Step 2:

[2248] The user inputs desired design conditions (e.g., style, color, material). The terminal receives the input information and sends it to the server. The input of this step is the design conditions entered by the user, and the output is the design conditions sent to the server.

[2249] Step 3:

[2250] The server uses a generative AI model to generate design proposals based on the received design criteria. In this generation process, the AI ​​generates text and image data based on the prompt. The input for this step is the design criteria and prompt, and the output is the generated design proposal.

[2251] Step 4:

[2252] The server sends the generated design proposal to the terminal, which displays it to the user. The user checks the generated design proposal. The input of this step is the generated design proposal, and the output is the design proposal displayed to the user.

[2253] Step 5:

[2254] The device collects the user's facial expressions and voice, and performs emotion analysis using OpenCV and Google Cloud Speech-to-Text. The analysis results are sent to the server. The input of this step is the user's facial expressions and voice data, and the output is the emotion analysis results.

[2255] Step 6:

[2256] The user enters feedback about the design proposal (e.g., color changes or design tweaks), and the device collects the feedback and sends it to the server. The server receives the feedback and sentiment analysis results and uses them to modify the design using a generative AI model. The inputs for this step are the user's feedback and sentiment analysis results, and the output is the modified design proposal.

[2257] Step 7:

[2258] The server sends the revised design proposal to the terminal, which then displays it again to the user. The user confirms the revised design proposal. The input of this step is the revised design proposal, and the output is the revised design proposal displayed to the user.

[2259] Step 8:

[2260] The user reviews and approves the final design. The server stores the final design in a database and generates instructions for the fabrication facility. The input for this step is the final design data, and the output is the instructions sent to the fabrication facility.

[2261] Step 9:

[2262] The server sends the final design data to the production facility, which then produces the product based on that design. After production, the product is delivered to the user. The server uses Firebase to monitor the progress of production and delivery and notify the user of the status. The input for this step is the final design data and production and delivery status data, and the output is the notification content to the user. The notification content is also adjusted based on the emotion recognition results.

[2263] 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.

[2264] 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.

[2265] 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.

[2266] 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.

[2267] 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.

[2268] 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.

[2269] 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).

[2270] 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.

[2271] 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."

[2272] 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.

[2273] 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).

[2274] 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.

[2275] 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.

[2276] 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.

[2277] 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.

[2278] 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.

[2279] 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.

[2280] 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.

[2281] 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.

[2282] 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.

[2283] 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.

[2284] The following is further disclosed regarding the above embodiment.

[2285] (Claim 1)

[2286] A means for a user to input desired design conditions;

[2287] A means for generating design proposals based on user input conditions using a generative AI model;

[2288] means for displaying the generated design proposal to a user;

[2289] A means to receive user feedback and revise the design using the generative AI model again,

[2290] A means to finalize the design and send it to the production workshop;

[2291] A system including:

[2292] (Claim 2)

[2293] means for transmitting user input conditions to a server;

[2294] A means for transmitting the generated design proposal from the server to the terminal;

[2295] The system of claim 1 further comprising:

[2296] (Claim 3)

[2297] A means for transmitting the final design data to the production studio and notifying the user of the production status and delivery status;

[2298] The system of claim 1 further comprising:

[2299] "Example 1"

[2300] (Claim 1)

[2301] A means for a user to input desired design conditions;

[2302] A means for generating design proposals based on user input conditions using a generative AI model;

[2303] means for displaying the generated design proposal to a user;

[2304] A means to receive user feedback and revise the design using the generative AI model again,

[2305] A means to finalize the design and send the information to the manufacturer;

[2306] A system including:

[2307] (Claim 2)

[2308] means for transmitting user input conditions to an information processing device;

[2309] means for transmitting the design proposal generated by the information processing device to the display device;

[2310] The system of claim 1 further comprising:

[2311] (Claim 3)

[2312] A means for transmitting the final design data to the manufacturer and notifying the user of the production and delivery status;

[2313] The system of claim 1 further comprising:

[2314] "Application Example 1"

[2315] (Claim 1)

[2316] A means for a user to input desired design conditions;

[2317] A means for generating design proposals based on user input conditions using a generative AI model;

[2318] means for displaying the generated design proposal to a user;

[2319] A means to receive user feedback and revise the design using the generative AI model again,

[2320] A means to finalize the design and send it to the production workshop;

[2321] A means implemented as a smartphone application and used in a virtual store;

[2322] A system including:

[2323] (Claim 2)

[2324] means for transmitting user input conditions to a server;

[2325] A means for transmitting the generated design proposal from the server to the terminal;

[2326] The system of claim 1 further comprising:

[2327] (Claim 3)

[2328] A means for transmitting the final design data to the production studio and notifying the user of the production status and delivery status;

[2329] means for generating and sending prompts for modifying the design proposal by the generative AI model based on user feedback;

[2330] The system of claim 1 further comprising:

[2331] "Example 2: Combining Emotion Engines"

[2332] (Claim 1)

[2333] A means for a user to input desired design conditions;

[2334] A means for generating design proposals based on user input conditions using a generative AI model;

[2335] means for displaying the generated design proposal to a user;

[2336] A means for the terminal to collect user emotion data and analyze it using an emotion engine;

[2337] A method for sending the results of emotion analysis to a server and reflecting them in design proposals and feedback.

[2338] A means to receive user feedback and revise the design using the generative AI model again,

[2339] A means to finalize the design and send it to the production workshop;

[2340] A system including:

[2341] (Claim 2)

[2342] means for transmitting user input conditions to a server;

[2343] A means for transmitting the generated design proposal from the server to the terminal;

[2344] The system of claim 1 further comprising:

[2345] (Claim 3)

[2346] A means for transmitting the final design data to the production studio and notifying the user of the production status and delivery status;

[2347] The system of claim 1 further comprising:

[2348] "Application example 2 when combining emotion engines"

[2349] (Claim 1)

[2350] A means for a user to input desired design conditions;

[2351] A means for generating design proposals based on user input conditions using a generative AI model;

[2352] means for displaying the generated design proposal to a user;

[2353] A means to receive user feedback and revise the design using the generative AI model again,

[2354] means for recognizing a user's emotion;

[2355] A means to modify the design based on the emotion recognition results,

[2356] a means for finalizing and transmitting the final design to a production facility;

[2357] A system including:

[2358] (Claim 2)

[2359] means for transmitting user input conditions to a server;

[2360] A means for transmitting the generated design proposal from the server to the terminal;

[2361] The system of claim 1 further comprising:

[2362] (Claim 3)

[2363] A means for transmitting the final design data to a production facility and notifying the user of the production status and delivery status, and a means for adjusting the content of notifications to the user based on the emotion recognition results.

[2364] The system of claim 1 further comprising: [Explanation of symbols]

[2365] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for a user to input desired design conditions; A means for generating design proposals based on user input conditions using a generative AI model; means for displaying the generated design proposal to a user; A means to receive user feedback and revise the design using the generative AI model again, A means to finalize the design and send it to the production workshop; A system including:

2. means for transmitting user input conditions to a server; A means for transmitting the generated design proposal from the server to the terminal; The system of claim 1 further comprising:

3. A means for transmitting the final design data to the production studio and notifying the user of the production status and delivery status; The system of claim 1 further comprising:

Images