System

Abstract

A system is provided.SOLUTION: The system includes a means for acquiring design information, a generation and AI means for generating an image on the basis of the design information, a means for generating a design drawing on the basis of the generated image, and a means for receiving an instruction for adjusting the image and the design drawing and regenerating them in accordance with the instruction.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] Creating patterns for clothing requires advanced skills and experience, and even millimeter-level deviations can significantly affect the finished product. Furthermore, since patterns must be remade for each of the numerous designs, it is extremely time-consuming and presents efficiency and economic challenges. Furthermore, since a new pattern must be created for each minor design adjustment, duplication of work is likely to occur. Given this background, there is a demand for a system that can improve the efficiency of clothing production, reduce time, and reduce costs. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means. First, it includes a means for acquiring design information, which acquires specifications such as design drawings uploaded by the user, measurements, body type, and fabric to be used. Next, it includes a means for a generation AI to generate an image based on the acquired design information and convert it into a realistic, photographic image. It also includes a means for automatically generating a design drawing (pattern) based on the generated image. It also includes a means for receiving fine-tuning instructions from the user and regenerating the image and design drawing in accordance with those instructions. This allows for efficient pattern creation without relying on existing technology or experience.

[0006] "Design information" refers to specific specifications and requirements regarding the design of clothing, such as clothing design drawings, measurements, body type, and the type of fabric used.

[0007] "Generative AI" refers to artificial intelligence technology that automatically generates clothing images and blueprints (patterns) based on input design information.

[0008] A "blueprint (pattern)" is a drawing that shows the dimensions and shape of each part of clothing, and refers to the flat pattern required to make clothing.

[0009] "Specified body type and size" refers to the specifications of a specific body type and clothing size entered by the user, and the image and design of the clothing are adjusted based on that.

[0010] "Means for converting images into reality" refers to the means for processing the design images generated by the generative AI to convert them into realistic, photographic-like images.

[0011] "Instructions for fine-tuning the design" refer to specific requests for changes made by the user when the user wishes to modify or improve the generated image or design drawing.

[0012] "Photo data of ready-made products" refers to photographic images of clothes or products that have already been created, and refers to data for generating new designs (patterns) based on those images.

[0013] "User interface" refers to the operating means, such as screens and input forms, that users use to operate and use the system. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] This invention, "AI Pattern Maker," is a system that acquires design information and automatically generates clothing blueprints (patterns) using generative AI. This system allows users to input design information, and the AI ​​generates a realistic image based on that information, and then creates a blueprint from that image. Below, we explain the operation of the program and provide specific examples of this system.

[0036] Program Description

[0037] The system works in the following broad steps:

[0038] 1. Obtaining design information:

[0039] The user uploads a design drawing using the device, which serves as the basis for the user's clothing design.

[0040] The terminal sends the design image file along with information entered by the user, such as body type, size, and fabric specifications, to the server.

[0041] 2. Image generation using generative AI:

[0042] Based on the design information received by the server, an image is generated using generative AI, which creates realistic photo-like images tailored to body shapes and sizes.

[0043] The generated image is stored on the server and displayed to the user.

[0044] 3. Creating a design (pattern):

[0045] The server automatically generates blueprints based on the image data created by the AI, and these blueprints are created based on the dimensions of each part.

[0046] The generated design drawings are stored in a database and provided to the user.

[0047] 4. Tweak and regenerate:

[0048] If the user wishes to make minor adjustments to the image or design, they can input instructions again, including partial corrections and shape changes.

[0049] The device sends this instruction to the server, and the generation AI again generates the corresponding image and blueprint, which the server then stores and provides to the user.

[0050] 5. Providing the final output:

[0051] When the user is satisfied with the final design, he or she requests to download the data in a format such as PDF.

[0052] The server provides the pattern data as a download link, allowing users to obtain the data in the format they require.

[0053] Specific examples

[0054] As a specific example of use, a case will be described where a user uses the system using a design image called "A."

[0055] 1. Obtaining design information:

[0056] The user uploads a design drawing of "A," selects and inputs the size as "M," the body type as "standard," and the fabric as "cotton."

[0057] 2. Image generation using generative AI:

[0058] The server uses generative AI to generate realistic photo-like images based on the uploaded design drawings and specified specifications.

[0059] The generated image is saved and displayed to the user as a preview.

[0060] 3. Creating a design (pattern):

[0061] The server automatically generates a design drawing (pattern) based on the generated realistic image data, calculating the dimensions of each part.

[0062] The generated blueprints are stored in a database and a download link is provided to the user.

[0063] 4. Tweak and regenerate:

[0064] The user requests minor adjustments such as "shortening the sleeves," and sends the instruction to the server from the terminal.

[0065] The server again instructs the generation AI to regenerate the adjusted image and blueprint, which are then displayed to the user as a preview.

[0066] 5. Providing the final output:

[0067] The user finally requests to download the blueprints they are satisfied with, and is provided with a download link in PDF format by the server.

[0068] In this way, the "AI Pattern Maker" can automatically and efficiently generate clothing designs based on the user's design information, simplifying the traditional pattern creation process and reducing time and costs.

[0069] The processing flow will be explained below.

[0070] Step 1:

[0071] The user uploads a design drawing using the device, which serves as the basis for the user's clothing design.

[0072] Step 2:

[0073] The terminal sends the design image file along with information entered by the user, such as body type, size, and fabric specifications, to the server.

[0074] Step 3:

[0075] The server saves the received design file in the directory specified as the save location, and records the save path in the database.

[0076] Step 4:

[0077] The server analyzes the received specification information and creates a request to call the generation AI. The request includes the save path of the design image and the user's specification information.

[0078] Step 5:

[0079] The server sends a request to the generation AI, which generates a realistic photo-like image based on the design drawing and specification information.

[0080] Step 6:

[0081] The AI ​​then sends the generated image data back to the server, which reflects the specified body shape and size.

[0082] Step 7:

[0083] The server receives the generated image data, saves it, and records the path of the saved image in the database.

[0084] Step 8:

[0085] The server generates a preview link for the saved image data and displays it on the user's device, allowing the user to check the generated image.

[0086] Step 9:

[0087] The server generates a design drawing (pattern) based on the generated image data, calculates the dimensions of each part using a design drawing generation algorithm, and creates the pattern.

[0088] Step 10:

[0089] The server stores the generated blueprints in a database and provides a download link to the user's device, allowing the user to obtain the generated pattern.

[0090] Step 11:

[0091] If the user wants to make minor adjustments to the generated image or design, they can input their adjustment instructions. A form is provided to input specific changes (e.g., shorten the sleeves).

[0092] Step 12:

[0093] The device sends instructions for fine-tuning to the server, which then generates data containing the instructions and sends it to the server.

[0094] Step 13:

[0095] The server receives the tweak instructions and sends a re-instruction to the generation AI, which includes the new parameters.

[0096] Step 14:

[0097] The generative AI sends the regenerated images and patterns based on the new parameters back to the server, reflecting the user's adjustment instructions.

[0098] Step 15:

[0099] The server saves the regenerated image and pattern and provides it to the user. It also generates a preview link and displays it on the user's device.

[0100] Step 16:

[0101] When the user is satisfied with the final design, he / she makes a download request by clicking the pattern data download button.

[0102] Step 17:

[0103] The server converts the final pattern data into PDF format and generates a download link, which is then provided to the user's device.

[0104] This series of processes enables efficient generation of design drawings from design drawings, making it possible to create clothes that meet the user's requirements.

[0105] Example 1

[0106] 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."

[0107] In the conventional clothing design process, blueprints (patterns) must be created manually based on design information, which requires time and effort. Furthermore, if fine adjustments are needed, the work must be done again manually, resulting in inefficiency. Furthermore, there are no general-purpose systems, and in many cases, it is difficult to meet the specific requirements of each user. The present invention aims to solve the above-mentioned problems by providing a system that allows users to easily input design information and efficiently generate blueprints.

[0108] 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.

[0109] In this invention, the server includes means for acquiring design information, AI generation means for generating an image based on the design information, means for generating a blueprint based on the generated image, means for receiving instructions for adjusting the image or blueprint and regenerating it in accordance with the instructions, means for saving the generated blueprint and making it accessible to a database, and means for providing the final generated blueprint in a downloadable format. This allows a user to automatically generate a highly accurate blueprint efficiently and quickly make any necessary fine adjustments simply by inputting design information.

[0110] "Design information" is information that the user inputs as the basis for designing clothes, and includes design drawings, body type, size, fabric specifications, and the like.

[0111] "Generative AI" refers to artificial intelligence technology that generates realistic images based on design information obtained from users, such as systems that use generative models and deep learning.

[0112] A "blueprint" is a drawing that shows the dimensions and shape of each part needed to make clothing, based on design information and an image created by generative AI.

[0113] "Image" refers to realistic, photographic image data generated from design information by generative AI.

[0114] A "database" is an information system for storing and managing generated blueprints and images.

[0115] "Regeneration" refers to the process of regenerating an image or design drawing based on instructions from the user.

[0116] "Downloadable format" refers to the format in which a user can obtain the final generated design, such as a PDF or image file format.

[0117] "Preservation" refers to the long-term retention of generated data and making it reusable as needed.

[0118] MODE FOR CARRYING OUT THE INVENTION

[0119] This invention, "AI Pattern Maker," is a system that acquires design information and automatically generates clothing blueprints (patterns) using generative AI. This system allows users to input design information, and the generative AI generates a realistic image based on that information, and then creates a blueprint from that image.

[0120] The system consists of the following:

[0121] Terminal operated by the user

[0122] A server that stores and processes design information

[0123] A generative AI model installed on a server (e.g., OpenAI's DALL-E or MidJourney)

[0124] A database that manages blueprint data (e.g., MySQL or PostgreSQL)

[0125] Program processing

[0126] Get design information

[0127] The user uploads a design drawing using the device. The design drawing is the basis for the user's clothing design. The device sends the design drawing file along with information entered by the user, such as body type, size, and fabric specifications, to the server.

[0128] Image generation by generative AI

[0129] The server uses generative AI to generate realistic photo-realistic images based on the received design information. This AI creates realistic images tailored to body shapes and sizes. The generated images are stored on the server and displayed to the user.

[0130] Creating a blueprint (pattern)

[0131] The server automatically generates blueprints based on the image data created by the AI. These blueprints are created based on the dimensions of each part. The generated blueprints are stored in a database and provided to users.

[0132] Tweaking and Regeneration

[0133] If the user wishes to make minor adjustments to the image or blueprint, they can input instructions again. This can include making partial corrections or changing the shape. The device sends these instructions to the server, and the generation AI regenerates the corresponding image and blueprint. The server then saves them and provides them to the user.

[0134] Providing the final output

[0135] When the user is satisfied with the final design, he / she requests to download the data in a format such as PDF. The server provides the pattern data as a download link so that the user can obtain the data in the required format.

[0136] Specific examples

[0137] As a specific example of use, a case will be described where a user uses the system using a design image called "A."

[0138] 1. Obtaining design information:

[0139] The user uploads a design drawing of "A," selects and inputs the size as "M," the body type as "standard," and the fabric as "cotton."

[0140] 2. Image generation using generative AI:

[0141] The server uses generative AI (e.g., OpenAI's DALL-E) to generate realistic photo-like images based on the uploaded design and specified specifications.

[0142] The generated image is saved and displayed to the user as a preview.

[0143] 3. Creating a design (pattern):

[0144] The server automatically generates a design drawing (pattern) based on the generated realistic image data, calculating the dimensions of each part.

[0145] The generated blueprints are stored in a database and a download link is provided to the user.

[0146] 4. Tweak and regenerate:

[0147] The user requests minor adjustments such as "shortening the sleeves," and sends the instruction to the server from the terminal.

[0148] The server again instructs the generation AI to regenerate the adjusted image and blueprint, which are then displayed to the user as a preview.

[0149] 5. Providing the final output:

[0150] The user finally requests to download the blueprints they are satisfied with, and is provided with a download link in PDF format by the server.

[0151] An example prompt might be:

[0152] "Based on design drawing A, please generate a realistic image of a medium-sized, standard-sized, cotton garment."

[0153] "Please shorten the sleeves of the generated blueprint and regenerate it."

[0154] In this way, the AI ​​pattern maker can automatically and efficiently generate clothing designs based on the user's design information, simplifying the traditional pattern-making process and reducing time and costs.

[0155] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0156] Step 1:

[0157] Get design information

[0158] User: The user uploads a design drawing using the device. The design drawing is the basis for the user to design the clothes.

[0159] Input: Design file, body type, size, fabric specifications

[0160] Terminal: The terminal sends the design file along with information entered by the user, such as body type, size, and fabric specifications, to the server.

[0161] Output: Design information sent to the server

[0162] Specific behavior:

[0163] The user opens the design information input screen.

[0164] Select a design file and enter the required information in the body type, size, and fabric specification fields.

[0165] Click the "Send" button and the device will send the data to the server.

[0166] Step 2:

[0167] Image generation by generative AI

[0168] Server: Based on the received design information, the server uses generative AI (e.g., OpenAI's DALL-E) to generate a realistic photo-like image.

[0169] Input: Design information stored on the server

[0170] Generative AI: Generative AI runs algorithms to generate realistic images.

[0171] Output: Generated photo-like image

[0172] Specific behavior:

[0173] The server inputs the received design file and supplementary information as parameters into the generative AI model.

[0174] Generative AI creates realistic photo-like images.

[0175] The server saves the generated image and returns the data to the user for preview display.

[0176] Step 3:

[0177] Creating a blueprint (pattern)

[0178] Server: The server automatically generates blueprints based on the image data created by the generation AI. The dimensions of each part are calculated based on the design information.

[0179] Input: Generated realistic image data

[0180] Blueprint generation module: Executes an algorithm that calculates the dimensions of each part and automatically generates a blueprint.

[0181] Output: Generated blueprint data

[0182] Specific behavior:

[0183] The server acquires the stored image data and inputs it into the design drawing generation module.

[0184] The design drawing generation module calculates the dimensions of each part and automatically creates a design drawing (pattern).

[0185] The server stores the generated blueprints in a database and provides users with a download link.

[0186] Step 4:

[0187] Tweaking and Regeneration

[0188] User: If the user wishes to make any fine adjustments to the image or design, they input the instructions again from their terminal into the server.

[0189] Input: User instructions for fine tuning

[0190] Terminal: The terminal sends the user's instructions to the server, and the generation AI again generates the corresponding image and blueprint.

[0191] Output: Adjusted images and blueprints

[0192] Specific behavior:

[0193] Users can preview images and blueprints, specify areas they would like tweaked, and provide feedback.

[0194] Click the "Regenerate" button and the terminal will send the instruction to the server.

[0195] The server then sends new prompts reflecting the fine-tuning to the generative AI model, which then generates corresponding images and blueprints.

[0196] The server saves the new images and blueprints and displays them as a preview to the user.

[0197] Step 5:

[0198] Providing the final output

[0199] User: When the user is satisfied with the final design, they request to download it in a format such as PDF.

[0200] Input: User download request

[0201] Server: The server provides the pattern data as a download link, allowing users to retrieve the data in the format they require.

[0202] Output: Download link (e.g. PDF format)

[0203] Specific behavior:

[0204] The user checks the final preview and clicks the "Download" button to send a request to the server.

[0205] The server converts the blueprint data into PDF or other specified format and generates a download link.

[0206] The server provides the generated download link to the user.

[0207] (Application example 1)

[0208] 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."

[0209] Conventional pattern creation systems require a lot of time and effort for design, and it is difficult for users to check and fine-tune the design in real time. In particular, when ordering custom-made clothing, detailed design drawings are required, and there is often a lack of information based on these drawings, making it a challenge to improve the user experience.

[0210] 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.

[0211] In this invention, the server includes means for acquiring design information, generation AI means for generating an image based on the design information, means for generating a blueprint (pattern) based on the generated image, means for receiving instructions for adjusting the image or blueprint and regenerating it according to the instructions, display means for checking the design in real time in a virtual space, and order processing means for ordering custom-made clothing based on the generated blueprint. This allows for efficient automatic generation and adjustment of designs, and enables users to check designs in real time and easily order custom-made clothing.

[0212] "Design information" refers to information such as a design drawing of the clothes the user is trying to design, as well as information about the body type, size, and fabric specifications.

[0213] "Generative AI" is an artificial intelligence system that automatically generates realistic images based on design information.

[0214] A "pattern" is a drawing that shows in detail the dimensions of each part of the clothing based on the generated image.

[0215] A "virtual space" is a virtual environment that users can interact with using a computer or virtual reality device.

[0216] "Display means" refers to devices or software that allow users to visually check designs and blueprints generated in virtual space or the real world.

[0217] The "order processing means" is a system that enables users to order custom-made clothing based on the generated design drawings.

[0218] "Fine-tuning" is an operation that allows the user to make partial corrections or changes to the shape of a generated image or design drawing.

[0219] "Real-time" refers to the state in which changes made by the user are reflected immediately.

[0220] This invention relates to the implementation of a system that allows users to acquire design information, automatically generate clothing blueprints using generative AI, and check and adjust the design in real time in a virtual space.

[0221] The system mainly consists of the following hardware and software: a smartphone, a head-mounted display (e.g., Oculus Rift, HTC Vive), a server (e.g., AWS, Google Cloud), a generative AI model (e.g., OpenAI's DALL-E, MIDAS AI), and Unity or Unreal Engine as a display medium. It also uses Stripe or PayPal as an electronic payment service.

[0222] First, the user inputs design information using a smartphone or head-mounted display, including design sketches, body type, size, fabric characteristics, etc. Once the design information is uploaded to the system, the server receives it and uses the information to generate a realistic image using a generative AI model.

[0223] The generated image is displayed to the user in a virtual space using Unity or Unreal Engine. The user can check the design in this virtual space and make fine adjustments as needed. This adjustment information is then sent back to the server, and new images and blueprints are generated by the generative AI model.

[0224] Once the user is satisfied with the final design, they can order a custom garment based on the generated blueprints, which is processed using electronic payment services such as Stripe or PayPal.

[0225] For example, if a user inputs a prompt such as, "I have uploaded a design drawing for a medium-sized summer dress made of chiffon fabric. Please generate a realistic 3D image of the dress and its blueprints based on these conditions," the server will obtain the design information, and the generative AI model will generate a realistic 3D image of the dress and its blueprints based on those conditions. The generated image and blueprints are displayed to the user in a virtual space, and if the user wishes to make minor adjustments, they will be regenerated based on their instructions.

[0226] In this way, the system provides an efficient way for users to conveniently create designs, generate detailed blueprints based on those designs, and order custom-made garments.

[0227] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0228] Step 1:

[0229] The user inputs design information using a smartphone or head-mounted display.

[0230] The input design information (design image, body type, size, fabric characteristics, etc.) is sent from the terminal to the server.

[0231] Input: Design drawing, body type, size, fabric specifications

[0232] Output: The server receives the design information.

[0233] Step 2:

[0234] The design information received by the server is input into a generative AI model to generate a realistic image.

[0235] Here, a generative AI (e.g., OpenAI's DALL-E) generates a 3D image based on the design drawing and information on body shape, size, and fabric.

[0236] Input: Design information

[0237] Output: Generated 3D image

[0238] Step 3:

[0239] The server generates a blueprint (pattern) based on the image generated by the generative AI model.

[0240] The design drawings include detailed dimensions of each part, and the design drawing data is stored on a server.

[0241] Input: 3D image

[0242] Output: Generated blueprint

[0243] Step 4:

[0244] The server displays the generated 3D images and blueprints to the user in a virtual space.

[0245] This is done using software such as Unity or Unreal Engine, allowing users to see their designs in real time.

[0246] Input: 3D image, blueprint

[0247] Output: 3D images and blueprints displayed in virtual space

[0248] Step 5:

[0249] The user makes minor adjustments to the displayed images and blueprints.

[0250] The device receives the user's adjustment instructions and sends them to the server. For example, shortening the sleeve length or changing the color.

[0251] Input: User adjustment instructions

[0252] Output: Server receives adjustment instructions

[0253] Step 6:

[0254] The server re-inputs the adjustment instructions into the generative AI model to regenerate the image and blueprint.

[0255] The updated design is then resaved and the regenerated images and blueprints are displayed in the virtual space.

[0256] Input: User adjustment instructions

[0257] Output: Regenerated 3D images and blueprints

[0258] Step 7:

[0259] If the user is satisfied with the final design, they order the custom garment.

[0260] The terminal transmits the user's order information and electronic payment information to the server, and the server processes the order.

[0261] Once payment is complete, the final design will be available for download in PDF format.

[0262] Input: User's order information, electronic payment information

[0263] Output: Confirmed order and download link for the blueprint in PDF format

[0264] 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.

[0265] This invention, the "Emotion Recognition AI Patternmaker," combines a system that acquires design information and automatically generates clothing blueprints (patterns) using a generative AI with an emotion engine that recognizes the user's emotions. This system uses user input design information, and the generative AI generates a realistic image based on that information. The emotion engine analyzes the user's emotions and provides an optimized blueprint. Below, we explain the operation of the program and provide specific examples of this system.

[0266] Program Description

[0267] The system works in the following broad steps:

[0268] 1. Obtaining design information:

[0269] The user uploads a design drawing using the device, which serves as the basis for the user's clothing design.

[0270] The terminal sends the design image file along with information entered by the user, such as body type, size, and fabric specifications, to the server.

[0271] 2. Image generation using generative AI:

[0272] Based on the design information received by the server, an image is generated using generative AI, which creates realistic photo-like images tailored to body shapes and sizes.

[0273] The generated image is stored on the server and displayed to the user.

[0274] 3. Emotion Recognition with Emotion Engine:

[0275] The device captures the user's facial expressions and tone of voice and sends them to the server.

[0276] The server passes the received data to the emotion engine, which analyzes the user's emotions. The emotion engine recognizes emotional states such as joy, sadness, anger, and surprise.

[0277] 4. Creating a design (pattern):

[0278] The server automatically generates a blueprint (pattern) based on the analysis results of the generation AI and emotion engine. The dimensions of each part are calculated and a pattern is created that takes into account the user's emotional state.

[0279] The generated design drawings are stored in a database and provided to the user.

[0280] 5. Emotion-based optimization:

[0281] The server optimizes the image and blueprint based on the user's emotions. For example, if the user expresses dissatisfaction, it will re-instruct the generating AI to make fine adjustments.

[0282] The regenerated image or blueprint is displayed to the user, and the emotion engine is used to analyze the emotion again and confirm the satisfaction level.

[0283] 6. Providing the final output:

[0284] When the user is satisfied with the final design, he / she requests a download by clicking the pattern data download button.

[0285] The server converts the final pattern data into PDF format and generates a download link, which is then provided to the user's device.

[0286] Specific examples

[0287] As a specific example of use, a case will be described where a user uses the system using a design image of a dress named "B."

[0288] 1. Obtaining design information:

[0289] The user uploads a design drawing of "B," selects and inputs the size "L," the body type "slim," and the fabric "silk."

[0290] 2. Image generation using generative AI:

[0291] The server uses generative AI to generate realistic photo-like images based on the uploaded design drawings and specified specifications.

[0292] The generated image is saved and displayed to the user as a preview.

[0293] 3. Emotion Recognition with Emotion Engine:

[0294] The device captures the user's facial expression data and sends it to the server.

[0295] The server uses an emotion engine to analyze the user's emotional state.

[0296] 4. Creating a design (pattern):

[0297] Based on the results of the generative AI and emotion engine, the server automatically generates a blueprint that matches the user's specified specifications and emotions.

[0298] Save the blueprint and generate a download link to provide to the user.

[0299] 5. Emotion-based optimization:

[0300] If the user is dissatisfied with the image, he or she inputs a command to regenerate it from the terminal.

[0301] The server uses an emotion engine to analyze user satisfaction and fine-tune the design.

[0302] 6. Providing the final output:

[0303] The user finally makes a request to download the design he is satisfied with, and the server converts the pattern data into PDF format.

[0304] The server provides a download link and the user obtains the pattern data.

[0305] In this way, the "Emotion-Aware AI Pattern Maker" automatically and efficiently generates clothing blueprints based on the user's design information and emotional state, providing optimal designs.

[0306] The processing flow will be explained below.

[0307] Step 1:

[0308] The user uploads a design drawing using the device, which serves as the basis for the user's clothing design.

[0309] Step 2:

[0310] The terminal sends the design image file along with information entered by the user, such as body type, size, and fabric specifications, to the server.

[0311] Step 3:

[0312] The server saves the received design file in the directory specified as the save location, and records the save path in the database.

[0313] Step 4:

[0314] The server analyzes the received specification information and creates a request to call the generation AI. The request includes the save path of the design image and the user's specification information.

[0315] Step 5:

[0316] The server sends a request to the generation AI, which generates a realistic photo-like image based on the design drawing and specification information.

[0317] Step 6:

[0318] The AI ​​then sends the generated image data back to the server, which reflects the specified body shape and size.

[0319] Step 7:

[0320] The server receives the generated image data, saves it, and records the path of the saved image in the database.

[0321] Step 8:

[0322] The server generates a preview link for the saved image data and displays it on the user's device, allowing the user to check the generated image.

[0323] Step 9:

[0324] The device captures the user's facial expressions and voice tone and sends them to the server, which is data collection for emotion recognition.

[0325] Step 10:

[0326] The server passes the received facial expression data to the emotion engine means, which analyzes the user's emotions. The emotion engine recognizes emotional states such as joy, sadness, anger, and surprise.

[0327] Step 11:

[0328] The server automatically generates a design (pattern) based on the results of the generation AI and emotion engine. The generated design is based on the dimensional calculations of each part and also takes into account the user's emotional state.

[0329] Step 12:

[0330] The server stores the generated blueprints in a database and provides a download link to the user's device, allowing the user to obtain the generated pattern.

[0331] Step 13:

[0332] The server optimizes the image and blueprint based on the user's emotions. For example, if the user expresses dissatisfaction, it will re-instruct the generating AI to make fine adjustments.

[0333] Step 14:

[0334] The server saves the regenerated image and pattern and provides it to the user. It also generates a preview link and displays it on the user's device.

[0335] Step 15:

[0336] When the user is satisfied with the final design, he / she makes a download request by clicking the pattern data download button.

[0337] Step 16:

[0338] The server converts the final pattern data into PDF format and generates a download link, which is then provided to the user's device.

[0339] Through this series of processes, the "Emotion Recognition AI Pattern Maker" uses emotion recognition to automatically and efficiently generate clothing blueprints based on the user's design information and emotional state, providing optimal designs.

[0340] Example 2

[0341] 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."

[0342] Conventional clothing design generation systems were able to generate images based on design information and specifications provided by the user, but they were unable to generate or adjust designs that took the user's emotional state into account, making it difficult to increase user satisfaction. Furthermore, while it was possible to receive instructions for regeneration when adjusting images and designs, the lack of flexible adjustments that could respond to changes in emotions was an issue.

[0343] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0344] In this invention, the server includes means for acquiring design information, AI generation means for generating an image based on the design information, means for generating a blueprint based on the generated image, means for recognizing a user's emotion, means for adjusting the blueprint or image based on the user's emotion, and means for regenerating the blueprint or image. This makes it possible to generate and adjust blueprints and images that reflect the user's emotional state, thereby improving user satisfaction.

[0345] "Design information" refers to information necessary for designing clothing, and includes design drawings, body types, sizes, fabric specifications, and the like provided by the user.

[0346] "Generative AI" is an artificial intelligence technology that generates realistic photographic images based on design information.

[0347] A "blueprint" is a clothing pattern and detailed design data created based on an image generated by generative AI.

[0348] The "emotion engine" is a system that recognizes a user's emotional state by analyzing the user's facial expression data and voice tone.

[0349] "Regeneration" is the process of regenerating an image or blueprint based on the user's instructions or emotional state.

[0350] "Body type" is information about the user's physical dimensions and shape, and is used to adjust clothing designs and sizes.

[0351] "Size" is a standard for determining the measurements and fit of clothing, and is information specified by the user.

[0352] "Fabric specifications" are information that indicates details such as the material, weave, and texture of the clothing.

[0353] A "user" is someone who uses this system to provide design information and obtain final blueprints and images.

[0354] This invention, "Emotion Recognition AI Patterner," is a system that uses generative AI to generate realistic images based on design information provided by the user, and analyzes the user's emotions using an emotion engine to provide optimal designs. This system is composed of the following main components:

[0355] 1. Obtaining design information:

[0356] Users use a terminal to upload design drawings and enter information such as body type, size, fabric specifications, etc. This inputs the user's desired clothing design into the system.

[0357] The terminal sends the input information along with the design image file to the server. Through this data transfer, the server obtains the necessary information.

[0358] 2. Image generation using generative AI

[0359] The server uses a generative AI to generate realistic, photo-realistic images based on the received design information, sending the AI ​​prompts specific to the design information.

[0360] For example, the prompt may include specific instructions such as "Dress B, size L, slim build, silk fabric."

[0361] The server stores the generated image and displays it to the user as a preview, allowing the user to visually check it.

[0362] 3. Emotion Recognition by Emotion Engine

[0363] The device captures the user's facial expression data with a camera and records their voice tone with a microphone, and this data is sent to the server.

[0364] The server passes the received data to the emotion engine, which analyzes the user's emotional state. The emotion engine detects emotions such as joy, sadness, anger, and surprise, and uses this information to adjust the next step.

[0365] 4. Creating a design (pattern)

[0366] The server combines the image output from the generation AI with the analysis results of the emotion engine, and automatically generates a design that matches the user's specified specifications and emotions. This design calculates the dimensions of each part and reflects user feedback.

[0367] The generated blueprints are stored in a database on the server and provided to users as a download link.

[0368] 5. Emotion-Based Optimization

[0369] The server optimizes the image and blueprint by taking into account the user's emotional state. For example, if the user expresses dissatisfaction, it will re-instruct the generation AI to fine-tune the image and blueprint.

[0370] The regenerated images and blueprints are then displayed to the user again, allowing for repeated confirmation and adjustment of the emotional state.

[0371] 6. Providing the final output

[0372] The user then makes a request to download the final design that they are satisfied with.

[0373] The server converts the blueprint into PDF format and generates a download link for the user, allowing the user to obtain the completed blueprint.

[0374] Specific examples

[0375] For example, a user can upload a design drawing of a "B" dress and select size "L," body type "slim," and fabric "silk." Based on this information, the AI ​​will receive the prompt "Dress B, size L, slim body type, silk fabric" and generate a realistic image.

[0376] The emotion engine then analyzes the user's facial expression data and voice tone to confirm their level of satisfaction. If the emotion engine determines that the user is dissatisfied, it sends new instructions to the generation AI, which then regenerates the image or blueprint.

[0377] Finally, once the user is satisfied with the design, it is provided in PDF format and the user can obtain it via a download link.

[0378] In this way, the "Emotion-Aware AI Pattern Maker" is a system that integrates the user's design information and emotional state, efficiently and effectively generates clothing blueprints, and provides optimal designs.

[0379] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0380] Step 1:

[0381] Get design information

[0382] The user uploads the design using the device, which involves clicking the "upload button" on the screen.

[0383] The user inputs information such as body type, size, and fabric specifications (e.g., size "L", body type "Slim", fabric "Silk") into the terminal. This includes filling out an input form.

[0384] The terminal sends this information to the server. At this time, the design image file and the user's input information are packed into a single data packet and sent to the server as an HTTP POST request.

[0385] Input: Design file, body type, size, fabric specifications

[0386] Output: Design information data packet sent to the server

[0387] Step 2:

[0388] Image generation by generative AI

[0389] The server analyzes the design information it receives and generates prompts for the AI, such as "dress B, size L, slim build, silk fabric."

[0390] The server sends prompts to the generative AI model, which then generates realistic, photo-realistic images tailored to the specified design, body type, size, and fabric specifications.

[0391] The server saves the generated image and displays a preview to the user. At this time, the image file is saved in the server's storage, and the image URL is returned to the user's device via an HTTP response.

[0392] Input: Design information data packet sent to the server

[0393] Output: Generated realistic photo-realistic images

[0394] Step 3:

[0395] Emotion recognition by emotion engine

[0396] The device captures the user's facial expression data with the camera and records the voice tone with the microphone. A pop-up requesting permission to use the camera and microphone is displayed on the device, and the user's permission is obtained.

[0397] The device sends the captured data to the server. This dataset, which includes facial feature points and audio waveform data, is sent to the server as an HTTP POST request.

[0398] The server passes the received data to an emotion engine that analyzes the user's emotional state, running algorithms to classify emotions such as joy, sadness, anger, and surprise from facial expressions and vocal tones.

[0399] Input: User's facial expression data and voice data captured by the device

[0400] Output: Parsed user's emotional state

[0401] Step 4:

[0402] Creating a blueprint (pattern)

[0403] The server integrates the images from the generative AI model with the analysis results of the emotion engine to automatically generate a design, taking into account the user's emotional state and the specified design specifications.

[0404] The measurements for each part are calculated and final adjustments are made based on the user's feedback. For example, if the user requests that the skirt be made a little longer, the measurements will be included in the design.

[0405] The server stores the generated blueprints in a database and provides the user with a download link, which is sent to the user's device in an HTTP response.

[0406] Input: Image from generative AI, analysis results from emotion engine

[0407] Output: Generated blueprints and their download links

[0408] Step 5:

[0409] Emotion-Based Optimization

[0410] The server optimizes the images and blueprints based on the user's emotional state. For example, if the user expresses dissatisfaction, it will re-instruct the generation AI to regenerate the images and blueprints.

[0411] The server displays the regenerated image or blueprint to the user and checks their emotional state again, at which point the regenerated data is also run through the emotion engine.

[0412] Input: Parsed user emotional state, frustration feedback

[0413] Output: Optimized images and blueprints

[0414] Step 6:

[0415] Providing the final output

[0416] When the user is finally satisfied with the design, a request is sent to the server to download it, which involves the user clicking a download button.

[0417] The server converts the blueprints into PDF format and generates a download link, which is provided to the user as an HTTP response.

[0418] Input: User download request

[0419] Output: Download link for the design in PDF format

[0420] In this way, by dividing the program flow into detailed processing steps, the "Emotion Recognition AI Pattern Maker" can automatically generate optimal clothing designs by integrating the user's design information and emotional state.

[0421] (Application example 2)

[0422] 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."

[0423] Conventional design systems have difficulty generating blueprints and images that reflect the user's emotional state, and have faced the challenge of requiring a great deal of time and effort to increase user satisfaction. Other issues include a lack of systems that can optimize designs in real time or instantly reflect user feedback. The present invention aims to solve these issues by providing a system that allows users to easily and quickly obtain optimal designs based on their own emotions.

[0424] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring design information, generation AI means for generating an image based on the design information, means for generating a design drawing (pattern) based on the generated image, emotion analysis means, means for analyzing the user's emotions and optimizing the design drawing or image, means for receiving and regenerating instructions for adjusting the image or design drawing, means for realistically converting the image to match a specified body type and size, and means for optimizing the image based on the user's emotional state. This makes it possible to take the user's emotions into consideration, optimize the design in real time, and quickly generate a design drawing that satisfies the user.

[0425] "Design information" refers to information such as the design image of the clothing or accessories, body type, size, fabric specifications, etc., input by the user.

[0426] "Generative AI" is an artificial intelligence model that generates realistic images based on acquired design information.

[0427] A "blueprint (pattern)" is a production drawing for clothing or accessories generated based on a design.

[0428] "Emotion analysis" is a technology that recognizes emotions from a user's facial expressions and voice and analyzes the results.

[0429] The "optimization means" is a means for adjusting the generated images and blueprints based on the results of the user's emotion analysis.

[0430] "Regeneration means" is a means of receiving instructions to adjust an image or blueprint, and regenerating it using the generation AI.

[0431] "Body type information" is data relating to the user's physical characteristics and size.

[0432] "Realistic transformation" is the process of transforming a design image based on real-world dimensions to fit a specified body type and size.

[0433] "Form for carrying out the invention" of the specification

[0434] This invention, "Emotion Recognition AI Patternmaker," is a system that acquires a user's design information and automatically generates clothing designs (patterns) using a generation AI. By combining this system with emotion analysis technology, it analyzes the user's emotional state and provides optimized designs. An embodiment of the present invention is described in detail below.

[0435] The hardware used to implement this invention includes a terminal (smartphone, tablet, PC, etc.) for inputting design information, a camera and microphone for emotion analysis, and a server for analyzing and generating data. The software used includes a generative AI model that analyzes the generated design image, an emotion engine that analyzes the user's emotion, and a data processing program that links with them.

[0436] Get design information

[0437] Using a device, users can take a photo or upload a design drawing and enter the necessary information, such as size, body type, fabric specifications, etc. This information is sent to a server and stored in a database for use by the generative AI model.

[0438] Image generation by generative AI

[0439] The server uses generative AI to generate realistic photo-realistic images based on the design information received. This AI model converts the design into a realistic representation based on the user's body shape and size. The generated images are stored on the server and displayed on the user's device.

[0440] Emotion analysis

[0441] The device captures the user's facial expressions and voice data and sends them to a server, which then uses an emotion engine to analyze the user's emotional state. Emotion analysis recognizes emotions such as joy, sadness, anger, and surprise in real time, and the results are used for design optimization.

[0442] Design (pattern) generation and optimization

[0443] Based on the analysis results of the generation AI and emotion engine, the server generates a design (pattern) optimized for the user's specified specifications and emotional state. The design is provided in a form that calculates the dimensions of each part and reflects the user's emotional state. If the user expresses dissatisfaction with the image, the server issues further instructions to the generation AI to fine-tune the design and increase user satisfaction.

[0444] Providing the final output

[0445] To allow users to download the final design they are satisfied with, the server converts the pattern data into PDF format and generates a download link, which is then provided to the device, allowing users to easily retrieve the pattern data.

[0446] Specific examples

[0447] For example, suppose a user uploads a design drawing for a "summer dress" in a special corner in the store and selects size "M," body type "regular," and fabric "cotton." The generation AI generates a realistic, photo-like image based on that information and displays it on the smartphone. If the user expresses emotion in relation to this image through facial expressions or voice, the server analyzes it using an emotion engine and regenerates the design based on the results. Finally, the user can download the design that satisfies them in PDF format.

[0448] Prompt Sentence Examples

[0449] For example, a possible prompt for a generative AI model might be:

[0450] Generative AI: Generate a realistic photo-realistic image of this dress design. The body type is "Slim", the size is "L", and the fabric is "Silk."

[0451] In this way, the present invention realizes a system that can automatically and efficiently generate clothing designs based on the user's design information and emotional state, and provide optimal designs.

[0452] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0453] Program processing steps

[0454] Step 1:

[0455] The user uses a terminal to input design information. The input information includes the design image, body type, size, and fabric specifications. The terminal sends this information to the server. The input data includes the design image file, body type information, size information, and fabric information, and this data is sent to the server.

[0456] Step 2:

[0457] The server uses a generative AI model to generate a realistic photo-like image based on the received design information. The input data includes the design information sent by the user (design image file, body type information, size information, fabric information). Based on this data, the generative AI model performs calculations to generate a photo-like image. A realistic photo-like image is obtained as output data.

[0458] Step 3:

[0459] The generated image is stored on the server and sent to the terminal, which displays the image to the user. The input data is a realistic photo-like image stored on the server, and the output data is the image displayed on the user's terminal.

[0460] Step 4:

[0461] The device uses a camera and microphone to capture the user's facial expression and voice data and sends it to the server. The input data includes the user's facial expression images and voice data, which are sent to the server. In operation, the device's camera and microphone capture the user's data in real time.

[0462] Step 5:

[0463] The server analyzes the received facial image and voice data using an emotion engine to recognize the user's emotional state. The input data is the user's facial image and voice data, and the emotion engine processes this data to identify the user's emotional state. The output data is the emotion analysis result (emotional state such as joy, sadness, anger, surprise, etc.).

[0464] Step 6:

[0465] The server generates an optimized design (pattern) based on the analysis results of the generation AI and emotion engine. The input data is the user's design information and emotion analysis results, and the generation AI performs calculations based on these to generate an optimized design. The output data is the optimized design data.

[0466] Step 7:

[0467] When the device receives a command to regenerate, the server issues new commands to the generation AI and fine-tunes the design. The input data includes the user's command to regenerate and the results of the previous generation, and the generation AI generates a new image and blueprint based on these. The output data is the adjusted image and blueprint.

[0468] Step 8:

[0469] In order for the user to finally download the satisfactory design, the server converts the pattern data into PDF format and generates a download link. The input data is the optimized design data, which is processed to convert it into PDF. The output data is a download link that is provided to the terminal.

[0470] 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.

[0471] 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.

[0472] 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.

[0473] [Second embodiment]

[0474] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0475] 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.

[0476] 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).

[0477] 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.

[0478] 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.

[0479] 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).

[0480] 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.

[0481] 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.

[0482] 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.

[0483] 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.

[0484] 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.

[0485] 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."

[0486] This invention, "AI Pattern Maker," is a system that acquires design information and automatically generates clothing blueprints (patterns) using generative AI. This system allows users to input design information, and the AI ​​generates a realistic image based on that information, and then creates a blueprint from that image. Below, we explain the operation of the program and provide specific examples of this system.

[0487] Program Description

[0488] The system works in the following broad steps:

[0489] 1. Obtaining design information:

[0490] The user uploads a design drawing using the device, which serves as the basis for the user's clothing design.

[0491] The terminal sends the design image file along with information entered by the user, such as body type, size, and fabric specifications, to the server.

[0492] 2. Image generation using generative AI:

[0493] Based on the design information received by the server, an image is generated using generative AI, which creates realistic photo-like images tailored to body shapes and sizes.

[0494] The generated image is stored on the server and displayed to the user.

[0495] 3. Creating a design (pattern):

[0496] The server automatically generates blueprints based on the image data created by the AI, and these blueprints are created based on the dimensions of each part.

[0497] The generated design drawings are stored in a database and provided to the user.

[0498] 4. Tweak and regenerate:

[0499] If the user wishes to make minor adjustments to the image or design, they can input instructions again, including partial corrections and shape changes.

[0500] The device sends this instruction to the server, and the generation AI again generates the corresponding image and blueprint, which the server then stores and provides to the user.

[0501] 5. Providing the final output:

[0502] When the user is satisfied with the final design, he or she requests to download the data in a format such as PDF.

[0503] The server provides the pattern data as a download link, allowing users to obtain the data in the format they require.

[0504] Specific examples

[0505] As a specific example of use, a case will be described where a user uses the system using a design image called "A."

[0506] 1. Obtaining design information:

[0507] The user uploads a design drawing of "A," selects and inputs the size as "M," the body type as "standard," and the fabric as "cotton."

[0508] 2. Image generation using generative AI:

[0509] The server uses generative AI to generate realistic photo-like images based on the uploaded design drawings and specified specifications.

[0510] The generated image is saved and displayed to the user as a preview.

[0511] 3. Creating a design (pattern):

[0512] The server automatically generates a design drawing (pattern) based on the generated realistic image data, calculating the dimensions of each part.

[0513] The generated blueprints are stored in a database and a download link is provided to the user.

[0514] 4. Tweak and regenerate:

[0515] The user requests minor adjustments such as "shortening the sleeves," and sends the instruction to the server from the terminal.

[0516] The server again instructs the generation AI to regenerate the adjusted image and blueprint, which are then displayed to the user as a preview.

[0517] 5. Providing the final output:

[0518] The user finally requests to download the blueprints they are satisfied with, and is provided with a download link in PDF format by the server.

[0519] In this way, the "AI Pattern Maker" can automatically and efficiently generate clothing designs based on the user's design information, simplifying the traditional pattern creation process and reducing time and costs.

[0520] The processing flow will be explained below.

[0521] Step 1:

[0522] The user uploads a design drawing using the device, which serves as the basis for the user's clothing design.

[0523] Step 2:

[0524] The terminal sends the design image file along with information entered by the user, such as body type, size, and fabric specifications, to the server.

[0525] Step 3:

[0526] The server saves the received design file in the directory specified as the save location, and records the save path in the database.

[0527] Step 4:

[0528] The server analyzes the received specification information and creates a request to call the generation AI. The request includes the save path of the design image and the user's specification information.

[0529] Step 5:

[0530] The server sends a request to the generation AI, which generates a realistic photo-like image based on the design drawing and specification information.

[0531] Step 6:

[0532] The AI ​​then sends the generated image data back to the server, which reflects the specified body shape and size.

[0533] Step 7:

[0534] The server receives the generated image data, saves it, and records the path of the saved image in the database.

[0535] Step 8:

[0536] The server generates a preview link for the saved image data and displays it on the user's device, allowing the user to check the generated image.

[0537] Step 9:

[0538] The server generates a design drawing (pattern) based on the generated image data, calculates the dimensions of each part using a design drawing generation algorithm, and creates the pattern.

[0539] Step 10:

[0540] The server stores the generated blueprints in a database and provides a download link to the user's device, allowing the user to obtain the generated pattern.

[0541] Step 11:

[0542] If the user wants to make minor adjustments to the generated image or design, they can input their adjustment instructions. A form is provided to input specific changes (e.g., shorten the sleeves).

[0543] Step 12:

[0544] The device sends instructions for fine-tuning to the server, which then generates data containing the instructions and sends it to the server.

[0545] Step 13:

[0546] The server receives the tweak instructions and sends a re-instruction to the generation AI, which includes the new parameters.

[0547] Step 14:

[0548] The generative AI sends the regenerated images and patterns based on the new parameters back to the server, reflecting the user's adjustment instructions.

[0549] Step 15:

[0550] The server saves the regenerated image and pattern and provides it to the user. It also generates a preview link and displays it on the user's device.

[0551] Step 16:

[0552] When the user is satisfied with the final design, he / she makes a download request by clicking the pattern data download button.

[0553] Step 17:

[0554] The server converts the final pattern data into PDF format and generates a download link, which is then provided to the user's device.

[0555] This series of processes enables efficient generation of design drawings from design drawings, making it possible to create clothes that meet the user's requirements.

[0556] Example 1

[0557] 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."

[0558] In the conventional clothing design process, blueprints (patterns) must be created manually based on design information, which requires time and effort. Furthermore, if fine adjustments are needed, the work must be done again manually, resulting in inefficiency. Furthermore, there are no general-purpose systems, and in many cases, it is difficult to meet the specific requirements of each user. The present invention aims to solve the above-mentioned problems by providing a system that allows users to easily input design information and efficiently generate blueprints.

[0559] 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.

[0560] In this invention, the server includes means for acquiring design information, AI generation means for generating an image based on the design information, means for generating a blueprint based on the generated image, means for receiving instructions for adjusting the image or blueprint and regenerating it in accordance with the instructions, means for saving the generated blueprint and making it accessible to a database, and means for providing the final generated blueprint in a downloadable format. This allows a user to automatically generate a highly accurate blueprint efficiently and quickly make any necessary fine adjustments simply by inputting design information.

[0561] "Design information" is information that the user inputs as the basis for designing clothes, and includes design drawings, body type, size, fabric specifications, and the like.

[0562] "Generative AI" refers to artificial intelligence technology that generates realistic images based on design information obtained from users, such as systems that use generative models and deep learning.

[0563] A "blueprint" is a drawing that shows the dimensions and shape of each part needed to make clothing, based on design information and an image created by generative AI.

[0564] "Image" refers to realistic, photographic image data generated from design information by generative AI.

[0565] A "database" is an information system for storing and managing generated blueprints and images.

[0566] "Regeneration" refers to the process of regenerating an image or design drawing based on instructions from the user.

[0567] "Downloadable format" refers to the format in which a user can obtain the final generated design, such as a PDF or image file format.

[0568] "Preservation" refers to the long-term retention of generated data and making it reusable as needed.

[0569] MODE FOR CARRYING OUT THE INVENTION

[0570] This invention, "AI Pattern Maker," is a system that acquires design information and automatically generates clothing blueprints (patterns) using generative AI. This system allows users to input design information, and the generative AI generates a realistic image based on that information, and then creates a blueprint from that image.

[0571] The system consists of the following:

[0572] Terminal operated by the user

[0573] A server that stores and processes design information

[0574] A generative AI model installed on a server (e.g., OpenAI's DALL-E or MidJourney)

[0575] A database that manages blueprint data (e.g., MySQL or PostgreSQL)

[0576] Program processing

[0577] Get design information

[0578] The user uploads a design drawing using the device. The design drawing is the basis for the user's clothing design. The device sends the design drawing file along with information entered by the user, such as body type, size, and fabric specifications, to the server.

[0579] Image generation by generative AI

[0580] The server uses generative AI to generate realistic photo-realistic images based on the received design information. This AI creates realistic images tailored to body shapes and sizes. The generated images are stored on the server and displayed to the user.

[0581] Creating a blueprint (pattern)

[0582] The server automatically generates blueprints based on the image data created by the AI. These blueprints are created based on the dimensions of each part. The generated blueprints are stored in a database and provided to users.

[0583] Tweaking and Regeneration

[0584] If the user wishes to make minor adjustments to the image or blueprint, they can input instructions again. This can include making partial corrections or changing the shape. The device sends these instructions to the server, and the generation AI regenerates the corresponding image and blueprint. The server then saves them and provides them to the user.

[0585] Providing the final output

[0586] When the user is satisfied with the final design, he / she requests to download the data in a format such as PDF. The server provides the pattern data as a download link so that the user can obtain the data in the required format.

[0587] Specific examples

[0588] As a specific example of use, a case will be described where a user uses the system using a design image called "A."

[0589] 1. Obtaining design information:

[0590] The user uploads a design drawing of "A," selects and inputs the size as "M," the body type as "standard," and the fabric as "cotton."

[0591] 2. Image generation using generative AI:

[0592] The server uses generative AI (e.g., OpenAI's DALL-E) to generate realistic photo-like images based on the uploaded design and specified specifications.

[0593] The generated image is saved and displayed to the user as a preview.

[0594] 3. Creating a design (pattern):

[0595] The server automatically generates a design drawing (pattern) based on the generated realistic image data, calculating the dimensions of each part.

[0596] The generated blueprints are stored in a database and a download link is provided to the user.

[0597] 4. Tweak and regenerate:

[0598] The user requests minor adjustments such as "shortening the sleeves," and sends the instruction to the server from the terminal.

[0599] The server again instructs the generation AI to regenerate the adjusted image and blueprint, which are then displayed to the user as a preview.

[0600] 5. Providing the final output:

[0601] The user finally requests to download the blueprints they are satisfied with, and is provided with a download link in PDF format by the server.

[0602] An example prompt might be:

[0603] "Based on design drawing A, please generate a realistic image of a medium-sized, standard-sized, cotton garment."

[0604] "Please shorten the sleeves of the generated blueprint and regenerate it."

[0605] In this way, the AI ​​pattern maker can automatically and efficiently generate clothing designs based on the user's design information, simplifying the traditional pattern-making process and reducing time and costs.

[0606] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0607] Step 1:

[0608] Get design information

[0609] User: The user uploads a design drawing using the device. The design drawing is the basis for the user to design the clothes.

[0610] Input: Design file, body type, size, fabric specifications

[0611] Terminal: The terminal sends the design file along with information entered by the user, such as body type, size, and fabric specifications, to the server.

[0612] Output: Design information sent to the server

[0613] Specific behavior:

[0614] The user opens the design information input screen.

[0615] Select a design file and enter the required information in the body type, size, and fabric specification fields.

[0616] Click the "Send" button and the device will send the data to the server.

[0617] Step 2:

[0618] Image generation by generative AI

[0619] Server: Based on the received design information, the server uses generative AI (e.g., OpenAI's DALL-E) to generate a realistic photo-like image.

[0620] Input: Design information stored on the server

[0621] Generative AI: Generative AI runs algorithms to generate realistic images.

[0622] Output: Generated photo-like image

[0623] Specific behavior:

[0624] The server inputs the received design file and supplementary information as parameters into the generative AI model.

[0625] Generative AI creates realistic photo-like images.

[0626] The server saves the generated image and returns the data to the user for preview display.

[0627] Step 3:

[0628] Creating a blueprint (pattern)

[0629] Server: The server automatically generates blueprints based on the image data created by the generation AI. The dimensions of each part are calculated based on the design information.

[0630] Input: Generated realistic image data

[0631] Blueprint generation module: Executes an algorithm that calculates the dimensions of each part and automatically generates a blueprint.

[0632] Output: Generated blueprint data

[0633] Specific behavior:

[0634] The server acquires the stored image data and inputs it into the design drawing generation module.

[0635] The design drawing generation module calculates the dimensions of each part and automatically creates a design drawing (pattern).

[0636] The server stores the generated blueprints in a database and provides users with a download link.

[0637] Step 4:

[0638] Tweaking and Regeneration

[0639] User: If the user wishes to make any fine adjustments to the image or design, they input the instructions again from their terminal into the server.

[0640] Input: User instructions for fine tuning

[0641] Terminal: The terminal sends the user's instructions to the server, and the generation AI again generates the corresponding image and blueprint.

[0642] Output: Adjusted images and blueprints

[0643] Specific behavior:

[0644] Users can preview images and blueprints, specify areas they would like tweaked, and provide feedback.

[0645] Click the "Regenerate" button and the terminal will send the instruction to the server.

[0646] The server then sends new prompts reflecting the fine-tuning to the generative AI model, which then generates corresponding images and blueprints.

[0647] The server saves the new images and blueprints and displays them as a preview to the user.

[0648] Step 5:

[0649] Providing the final output

[0650] User: When the user is satisfied with the final design, they request to download it in a format such as PDF.

[0651] Input: User download request

[0652] Server: The server provides the pattern data as a download link, allowing users to retrieve the data in the format they require.

[0653] Output: Download link (e.g. PDF format)

[0654] Specific behavior:

[0655] The user checks the final preview and clicks the "Download" button to send a request to the server.

[0656] The server converts the blueprint data into PDF or other specified format and generates a download link.

[0657] The server provides the generated download link to the user.

[0658] (Application example 1)

[0659] 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."

[0660] Conventional pattern creation systems require a lot of time and effort for design, and it is difficult for users to check and fine-tune the design in real time. In particular, when ordering custom-made clothing, detailed design drawings are required, and there is often a lack of information based on these drawings, making it a challenge to improve the user experience.

[0661] 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.

[0662] In this invention, the server includes means for acquiring design information, generation AI means for generating an image based on the design information, means for generating a blueprint (pattern) based on the generated image, means for receiving instructions for adjusting the image or blueprint and regenerating it according to the instructions, display means for checking the design in real time in a virtual space, and order processing means for ordering custom-made clothing based on the generated blueprint. This allows for efficient automatic generation and adjustment of designs, and enables users to check designs in real time and easily order custom-made clothing.

[0663] "Design information" refers to information such as a design drawing of the clothes the user is trying to design, as well as information about the body type, size, and fabric specifications.

[0664] "Generative AI" is an artificial intelligence system that automatically generates realistic images based on design information.

[0665] A "pattern" is a drawing that shows in detail the dimensions of each part of the clothing based on the generated image.

[0666] A "virtual space" is a virtual environment that users can interact with using a computer or virtual reality device.

[0667] "Display means" refers to devices or software that allow users to visually check designs and blueprints generated in virtual space or the real world.

[0668] The "order processing means" is a system that enables users to order custom-made clothing based on the generated design drawings.

[0669] "Fine-tuning" is an operation that allows the user to make partial corrections or changes to the shape of a generated image or design drawing.

[0670] "Real-time" refers to the state in which changes made by the user are reflected immediately.

[0671] This invention relates to the implementation of a system that allows users to acquire design information, automatically generate clothing blueprints using generative AI, and check and adjust the design in real time in a virtual space.

[0672] The system mainly consists of the following hardware and software: a smartphone, a head-mounted display (e.g., Oculus Rift, HTC Vive), a server (e.g., AWS, Google Cloud), a generative AI model (e.g., OpenAI's DALL-E, MIDAS AI), and Unity or Unreal Engine as a display medium. It also uses Stripe or PayPal as an electronic payment service.

[0673] First, the user inputs design information using a smartphone or head-mounted display, including design sketches, body type, size, fabric characteristics, etc. Once the design information is uploaded to the system, the server receives it and uses the information to generate a realistic image using a generative AI model.

[0674] The generated image is displayed to the user in a virtual space using Unity or Unreal Engine. The user can check the design in this virtual space and make fine adjustments as needed. This adjustment information is then sent back to the server, and new images and blueprints are generated by the generative AI model.

[0675] Once the user is satisfied with the final design, they can order a custom garment based on the generated blueprints, which is processed using electronic payment services such as Stripe or PayPal.

[0676] For example, if a user inputs a prompt such as, "I have uploaded a design drawing for a medium-sized summer dress made of chiffon fabric. Please generate a realistic 3D image of the dress and its blueprints based on these conditions," the server will obtain the design information, and the generative AI model will generate a realistic 3D image of the dress and its blueprints based on those conditions. The generated image and blueprints are displayed to the user in a virtual space, and if the user wishes to make minor adjustments, they will be regenerated based on their instructions.

[0677] In this way, the system provides an efficient way for users to conveniently create designs, generate detailed blueprints based on those designs, and order custom-made garments.

[0678] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0679] Step 1:

[0680] The user inputs design information using a smartphone or head-mounted display.

[0681] The input design information (design image, body type, size, fabric characteristics, etc.) is sent from the terminal to the server.

[0682] Input: Design drawing, body type, size, fabric specifications

[0683] Output: The server receives the design information.

[0684] Step 2:

[0685] The design information received by the server is input into a generative AI model to generate a realistic image.

[0686] Here, a generative AI (e.g., OpenAI's DALL-E) generates a 3D image based on the design drawing and information on body shape, size, and fabric.

[0687] Input: Design information

[0688] Output: Generated 3D image

[0689] Step 3:

[0690] The server generates a blueprint (pattern) based on the image generated by the generative AI model.

[0691] The design drawings include detailed dimensions of each part, and the design drawing data is stored on a server.

[0692] Input: 3D image

[0693] Output: Generated blueprint

[0694] Step 4:

[0695] The server displays the generated 3D images and blueprints to the user in a virtual space.

[0696] This is done using software such as Unity or Unreal Engine, allowing users to see their designs in real time.

[0697] Input: 3D image, blueprint

[0698] Output: 3D images and blueprints displayed in virtual space

[0699] Step 5:

[0700] The user makes minor adjustments to the displayed images and blueprints.

[0701] The device receives the user's adjustment instructions and sends them to the server. For example, shortening the sleeve length or changing the color.

[0702] Input: User adjustment instructions

[0703] Output: Server receives adjustment instructions

[0704] Step 6:

[0705] The server re-inputs the adjustment instructions into the generative AI model to regenerate the image and blueprint.

[0706] The updated design is then resaved and the regenerated images and blueprints are displayed in the virtual space.

[0707] Input: User adjustment instructions

[0708] Output: Regenerated 3D images and blueprints

[0709] Step 7:

[0710] If the user is satisfied with the final design, they order the custom garment.

[0711] The terminal transmits the user's order information and electronic payment information to the server, and the server processes the order.

[0712] Once payment is complete, the final design will be available for download in PDF format.

[0713] Input: User's order information, electronic payment information

[0714] Output: Confirmed order and download link for the blueprint in PDF format

[0715] 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.

[0716] This invention, the "Emotion Recognition AI Patternmaker," combines a system that acquires design information and automatically generates clothing blueprints (patterns) using a generative AI with an emotion engine that recognizes the user's emotions. This system uses user input design information, and the generative AI generates a realistic image based on that information. The emotion engine analyzes the user's emotions and provides an optimized blueprint. Below, we explain the operation of the program and provide specific examples of this system.

[0717] Program Description

[0718] The system works in the following broad steps:

[0719] 1. Obtaining design information:

[0720] The user uploads a design drawing using the device, which serves as the basis for the user's clothing design.

[0721] The terminal sends the design image file along with information entered by the user, such as body type, size, and fabric specifications, to the server.

[0722] 2. Image generation using generative AI:

[0723] Based on the design information received by the server, an image is generated using generative AI, which creates realistic photo-like images tailored to body shapes and sizes.

[0724] The generated image is stored on the server and displayed to the user.

[0725] 3. Emotion Recognition with Emotion Engine:

[0726] The device captures the user's facial expressions and tone of voice and sends them to the server.

[0727] The server passes the received data to the emotion engine, which analyzes the user's emotions. The emotion engine recognizes emotional states such as joy, sadness, anger, and surprise.

[0728] 4. Creating a design (pattern):

[0729] The server automatically generates a blueprint (pattern) based on the analysis results of the generation AI and emotion engine. The dimensions of each part are calculated and a pattern is created that takes into account the user's emotional state.

[0730] The generated design drawings are stored in a database and provided to the user.

[0731] 5. Emotion-based optimization:

[0732] The server optimizes the image and blueprint based on the user's emotions. For example, if the user expresses dissatisfaction, it will re-instruct the generating AI to make fine adjustments.

[0733] The regenerated image or blueprint is displayed to the user, and the emotion engine is used to analyze the emotion again and confirm the satisfaction level.

[0734] 6. Providing the final output:

[0735] When the user is satisfied with the final design, he / she requests a download by clicking the pattern data download button.

[0736] The server converts the final pattern data into PDF format and generates a download link, which is then provided to the user's device.

[0737] Specific examples

[0738] As a specific example of use, a case will be described where a user uses the system using a design image of a dress named "B."

[0739] 1. Obtaining design information:

[0740] The user uploads a design drawing of "B," selects and inputs the size "L," the body type "slim," and the fabric "silk."

[0741] 2. Image generation using generative AI:

[0742] The server uses generative AI to generate realistic photo-like images based on the uploaded design drawings and specified specifications.

[0743] The generated image is saved and displayed to the user as a preview.

[0744] 3. Emotion Recognition with Emotion Engine:

[0745] The device captures the user's facial expression data and sends it to the server.

[0746] The server uses an emotion engine to analyze the user's emotional state.

[0747] 4. Creating a design (pattern):

[0748] Based on the results of the generative AI and emotion engine, the server automatically generates a blueprint that matches the user's specified specifications and emotions.

[0749] Save the blueprint and generate a download link to provide to the user.

[0750] 5. Emotion-based optimization:

[0751] If the user is dissatisfied with the image, he or she inputs a command to regenerate it from the terminal.

[0752] The server uses an emotion engine to analyze user satisfaction and fine-tune the design.

[0753] 6. Providing the final output:

[0754] The user finally makes a request to download the design he is satisfied with, and the server converts the pattern data into PDF format.

[0755] The server provides a download link and the user obtains the pattern data.

[0756] In this way, the "Emotion-Aware AI Pattern Maker" automatically and efficiently generates clothing blueprints based on the user's design information and emotional state, providing optimal designs.

[0757] The processing flow will be explained below.

[0758] Step 1:

[0759] The user uploads a design drawing using the device, which serves as the basis for the user's clothing design.

[0760] Step 2:

[0761] The terminal sends the design image file along with information entered by the user, such as body type, size, and fabric specifications, to the server.

[0762] Step 3:

[0763] The server saves the received design file in the directory specified as the save location, and records the save path in the database.

[0764] Step 4:

[0765] The server analyzes the received specification information and creates a request to call the generation AI. The request includes the save path of the design image and the user's specification information.

[0766] Step 5:

[0767] The server sends a request to the generation AI, which generates a realistic photo-like image based on the design drawing and specification information.

[0768] Step 6:

[0769] The AI ​​then sends the generated image data back to the server, which reflects the specified body shape and size.

[0770] Step 7:

[0771] The server receives the generated image data, saves it, and records the path of the saved image in the database.

[0772] Step 8:

[0773] The server generates a preview link for the saved image data and displays it on the user's device, allowing the user to check the generated image.

[0774] Step 9:

[0775] The device captures the user's facial expressions and voice tone and sends them to the server, which is data collection for emotion recognition.

[0776] Step 10:

[0777] The server passes the received facial expression data to the emotion engine means, which analyzes the user's emotions. The emotion engine recognizes emotional states such as joy, sadness, anger, and surprise.

[0778] Step 11:

[0779] The server automatically generates a design (pattern) based on the results of the generation AI and emotion engine. The generated design is based on the dimensional calculations of each part and also takes into account the user's emotional state.

[0780] Step 12:

[0781] The server stores the generated blueprints in a database and provides a download link to the user's device, allowing the user to obtain the generated pattern.

[0782] Step 13:

[0783] The server optimizes the image and blueprint based on the user's emotions. For example, if the user expresses dissatisfaction, it will re-instruct the generating AI to make fine adjustments.

[0784] Step 14:

[0785] The server saves the regenerated image and pattern and provides it to the user. It also generates a preview link and displays it on the user's device.

[0786] Step 15:

[0787] When the user is satisfied with the final design, he / she makes a download request by clicking the pattern data download button.

[0788] Step 16:

[0789] The server converts the final pattern data into PDF format and generates a download link, which is then provided to the user's device.

[0790] Through this series of processes, the "Emotion Recognition AI Pattern Maker" uses emotion recognition to automatically and efficiently generate clothing blueprints based on the user's design information and emotional state, providing optimal designs.

[0791] Example 2

[0792] 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."

[0793] Conventional clothing design generation systems were able to generate images based on design information and specifications provided by the user, but they were unable to generate or adjust designs that took the user's emotional state into account, making it difficult to increase user satisfaction. Furthermore, while it was possible to receive instructions for regeneration when adjusting images and designs, the lack of flexible adjustments that could respond to changes in emotions was an issue.

[0794] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0795] In this invention, the server includes means for acquiring design information, AI generation means for generating an image based on the design information, means for generating a blueprint based on the generated image, means for recognizing a user's emotion, means for adjusting the blueprint or image based on the user's emotion, and means for regenerating the blueprint or image. This makes it possible to generate and adjust blueprints and images that reflect the user's emotional state, thereby improving user satisfaction.

[0796] "Design information" refers to information necessary for designing clothing, and includes design drawings, body types, sizes, fabric specifications, and the like provided by the user.

[0797] "Generative AI" is an artificial intelligence technology that generates realistic photographic images based on design information.

[0798] A "blueprint" is a clothing pattern and detailed design data created based on an image generated by generative AI.

[0799] The "emotion engine" is a system that recognizes a user's emotional state by analyzing the user's facial expression data and voice tone.

[0800] "Regeneration" is the process of regenerating an image or blueprint based on the user's instructions or emotional state.

[0801] "Body type" is information about the user's physical dimensions and shape, and is used to adjust clothing designs and sizes.

[0802] "Size" is a standard for determining the measurements and fit of clothing, and is information specified by the user.

[0803] "Fabric specifications" are information that indicates details such as the material, weave, and texture of the clothing.

[0804] A "user" is someone who uses this system to provide design information and obtain final blueprints and images.

[0805] This invention, "Emotion Recognition AI Patterner," is a system that uses generative AI to generate realistic images based on design information provided by the user, and analyzes the user's emotions using an emotion engine to provide optimal designs. This system is composed of the following main components:

[0806] 1. Obtaining design information:

[0807] Users use a terminal to upload design drawings and enter information such as body type, size, fabric specifications, etc. This inputs the user's desired clothing design into the system.

[0808] The terminal sends the input information along with the design image file to the server. Through this data transfer, the server obtains the necessary information.

[0809] 2. Image generation using generative AI

[0810] The server uses a generative AI to generate realistic, photo-realistic images based on the received design information, sending the AI ​​prompts specific to the design information.

[0811] For example, the prompt may include specific instructions such as "Dress B, size L, slim build, silk fabric."

[0812] The server stores the generated image and displays it to the user as a preview, allowing the user to visually check it.

[0813] 3. Emotion Recognition by Emotion Engine

[0814] The device captures the user's facial expression data with a camera and records their voice tone with a microphone, and this data is sent to the server.

[0815] The server passes the received data to the emotion engine, which analyzes the user's emotional state. The emotion engine detects emotions such as joy, sadness, anger, and surprise, and uses this information to adjust the next step.

[0816] 4. Creating a design (pattern)

[0817] The server combines the image output from the generation AI with the analysis results of the emotion engine, and automatically generates a design that matches the user's specified specifications and emotions. This design calculates the dimensions of each part and reflects user feedback.

[0818] The generated blueprints are stored in a database on the server and provided to users as a download link.

[0819] 5. Emotion-Based Optimization

[0820] The server optimizes the image and blueprint by taking into account the user's emotional state. For example, if the user expresses dissatisfaction, it will re-instruct the generation AI to fine-tune the image and blueprint.

[0821] The regenerated images and blueprints are then displayed to the user again, allowing for repeated confirmation and adjustment of the emotional state.

[0822] 6. Providing the final output

[0823] The user then makes a request to download the final design that they are satisfied with.

[0824] The server converts the blueprint into PDF format and generates a download link for the user, allowing the user to obtain the completed blueprint.

[0825] Specific examples

[0826] For example, a user can upload a design drawing of a "B" dress and select size "L," body type "slim," and fabric "silk." Based on this information, the AI ​​will receive the prompt "Dress B, size L, slim body type, silk fabric" and generate a realistic image.

[0827] The emotion engine then analyzes the user's facial expression data and voice tone to confirm their level of satisfaction. If the emotion engine determines that the user is dissatisfied, it sends new instructions to the generation AI, which then regenerates the image or blueprint.

[0828] Finally, once the user is satisfied with the design, it is provided in PDF format and the user can obtain it via a download link.

[0829] In this way, the "Emotion-Aware AI Pattern Maker" is a system that integrates the user's design information and emotional state, efficiently and effectively generates clothing blueprints, and provides optimal designs.

[0830] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0831] Step 1:

[0832] Get design information

[0833] The user uploads the design using the device, which involves clicking the "upload button" on the screen.

[0834] The user inputs information such as body type, size, and fabric specifications (e.g., size "L", body type "Slim", fabric "Silk") into the terminal. This includes filling out an input form.

[0835] The terminal sends this information to the server. At this time, the design image file and the user's input information are packed into a single data packet and sent to the server as an HTTP POST request.

[0836] Input: Design file, body type, size, fabric specifications

[0837] Output: Design information data packet sent to the server

[0838] Step 2:

[0839] Image generation by generative AI

[0840] The server analyzes the design information it receives and generates prompts for the AI, such as "dress B, size L, slim build, silk fabric."

[0841] The server sends prompts to the generative AI model, which then generates realistic, photo-realistic images tailored to the specified design, body type, size, and fabric specifications.

[0842] The server saves the generated image and displays a preview to the user. At this time, the image file is saved in the server's storage, and the image URL is returned to the user's device via an HTTP response.

[0843] Input: Design information data packet sent to the server

[0844] Output: Generated realistic photo-realistic images

[0845] Step 3:

[0846] Emotion recognition by emotion engine

[0847] The device captures the user's facial expression data with the camera and records the voice tone with the microphone. A pop-up requesting permission to use the camera and microphone is displayed on the device, and the user's permission is obtained.

[0848] The device sends the captured data to the server. This dataset, which includes facial feature points and audio waveform data, is sent to the server as an HTTP POST request.

[0849] The server passes the received data to an emotion engine that analyzes the user's emotional state, running algorithms to classify emotions such as joy, sadness, anger, and surprise from facial expressions and vocal tones.

[0850] Input: User's facial expression data and voice data captured by the device

[0851] Output: Parsed user's emotional state

[0852] Step 4:

[0853] Creating a blueprint (pattern)

[0854] The server integrates the images from the generative AI model with the analysis results of the emotion engine to automatically generate a design, taking into account the user's emotional state and the specified design specifications.

[0855] The measurements for each part are calculated and final adjustments are made based on the user's feedback. For example, if the user requests that the skirt be made a little longer, the measurements will be included in the design.

[0856] The server stores the generated blueprints in a database and provides the user with a download link, which is sent to the user's device in an HTTP response.

[0857] Input: Image from generative AI, analysis results from emotion engine

[0858] Output: Generated blueprints and their download links

[0859] Step 5:

[0860] Emotion-Based Optimization

[0861] The server optimizes the images and blueprints based on the user's emotional state. For example, if the user expresses dissatisfaction, it will re-instruct the generation AI to regenerate the images and blueprints.

[0862] The server displays the regenerated image or blueprint to the user and checks their emotional state again, at which point the regenerated data is also run through the emotion engine.

[0863] Input: Parsed user emotional state, frustration feedback

[0864] Output: Optimized images and blueprints

[0865] Step 6:

[0866] Providing the final output

[0867] When the user is finally satisfied with the design, a request is sent to the server to download it, which involves the user clicking a download button.

[0868] The server converts the blueprints into PDF format and generates a download link, which is provided to the user as an HTTP response.

[0869] Input: User download request

[0870] Output: Download link for the design in PDF format

[0871] In this way, by dividing the program flow into detailed processing steps, the "Emotion Recognition AI Pattern Maker" can automatically generate optimal clothing designs by integrating the user's design information and emotional state.

[0872] (Application example 2)

[0873] 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."

[0874] Conventional design systems have difficulty generating blueprints and images that reflect the user's emotional state, and have faced the challenge of requiring a great deal of time and effort to increase user satisfaction. Other issues include a lack of systems that can optimize designs in real time or instantly reflect user feedback. The present invention aims to solve these issues by providing a system that allows users to easily and quickly obtain optimal designs based on their own emotions.

[0875] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring design information, generation AI means for generating an image based on the design information, means for generating a design drawing (pattern) based on the generated image, emotion analysis means, means for analyzing the user's emotions and optimizing the design drawing or image, means for receiving and regenerating instructions for adjusting the image or design drawing, means for realistically converting the image to match a specified body type and size, and means for optimizing the image based on the user's emotional state. This makes it possible to take the user's emotions into consideration, optimize the design in real time, and quickly generate a design drawing that satisfies the user.

[0876] "Design information" refers to information such as the design image of the clothing or accessories, body type, size, fabric specifications, etc., input by the user.

[0877] "Generative AI" is an artificial intelligence model that generates realistic images based on acquired design information.

[0878] A "blueprint (pattern)" is a production drawing for clothing or accessories generated based on a design.

[0879] "Emotion analysis" is a technology that recognizes emotions from a user's facial expressions and voice and analyzes the results.

[0880] The "optimization means" is a means for adjusting the generated images and blueprints based on the results of the user's emotion analysis.

[0881] "Regeneration means" is a means of receiving instructions to adjust an image or blueprint, and regenerating it using the generation AI.

[0882] "Body type information" is data relating to the user's physical characteristics and size.

[0883] "Realistic transformation" is the process of transforming a design image based on real-world dimensions to fit a specified body type and size.

[0884] "Form for carrying out the invention" of the specification

[0885] This invention, "Emotion Recognition AI Patternmaker," is a system that acquires a user's design information and automatically generates clothing designs (patterns) using a generation AI. By combining this system with emotion analysis technology, it analyzes the user's emotional state and provides optimized designs. An embodiment of the present invention is described in detail below.

[0886] The hardware used to implement this invention includes a terminal (smartphone, tablet, PC, etc.) for inputting design information, a camera and microphone for emotion analysis, and a server for analyzing and generating data. The software used includes a generative AI model that analyzes the generated design image, an emotion engine that analyzes the user's emotion, and a data processing program that links with them.

[0887] Get design information

[0888] Using a device, users can take a photo or upload a design drawing and enter the necessary information, such as size, body type, fabric specifications, etc. This information is sent to a server and stored in a database for use by the generative AI model.

[0889] Image generation by generative AI

[0890] The server uses generative AI to generate realistic photo-realistic images based on the design information received. This AI model converts the design into a realistic representation based on the user's body shape and size. The generated images are stored on the server and displayed on the user's device.

[0891] Emotion analysis

[0892] The device captures the user's facial expressions and voice data and sends them to a server, which then uses an emotion engine to analyze the user's emotional state. Emotion analysis recognizes emotions such as joy, sadness, anger, and surprise in real time, and the results are used for design optimization.

[0893] Design (pattern) generation and optimization

[0894] Based on the analysis results of the generation AI and emotion engine, the server generates a design (pattern) optimized for the user's specified specifications and emotional state. The design is provided in a form that calculates the dimensions of each part and reflects the user's emotional state. If the user expresses dissatisfaction with the image, the server issues further instructions to the generation AI to fine-tune the design and increase user satisfaction.

[0895] Providing the final output

[0896] To allow users to download the final design they are satisfied with, the server converts the pattern data into PDF format and generates a download link, which is then provided to the device, allowing users to easily retrieve the pattern data.

[0897] Specific examples

[0898] For example, suppose a user uploads a design drawing for a "summer dress" in a special corner in the store and selects size "M," body type "regular," and fabric "cotton." The generation AI generates a realistic, photo-like image based on that information and displays it on the smartphone. If the user expresses emotion in relation to this image through facial expressions or voice, the server analyzes it using an emotion engine and regenerates the design based on the results. Finally, the user can download the design that satisfies them in PDF format.

[0899] Prompt Sentence Examples

[0900] For example, a possible prompt for a generative AI model might be:

[0901] Generative AI: Generate a realistic photo-realistic image of this dress design. The body type is "Slim", the size is "L", and the fabric is "Silk."

[0902] In this way, the present invention realizes a system that can automatically and efficiently generate clothing designs based on the user's design information and emotional state, and provide optimal designs.

[0903] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0904] Program processing steps

[0905] Step 1:

[0906] The user uses a terminal to input design information. The input information includes the design image, body type, size, and fabric specifications. The terminal sends this information to the server. The input data includes the design image file, body type information, size information, and fabric information, and this data is sent to the server.

[0907] Step 2:

[0908] The server uses a generative AI model to generate a realistic photo-like image based on the received design information. The input data includes the design information sent by the user (design image file, body type information, size information, fabric information). Based on this data, the generative AI model performs calculations to generate a photo-like image. A realistic photo-like image is obtained as output data.

[0909] Step 3:

[0910] The generated image is stored on the server and sent to the terminal, which displays the image to the user. The input data is a realistic photo-like image stored on the server, and the output data is the image displayed on the user's terminal.

[0911] Step 4:

[0912] The device uses a camera and microphone to capture the user's facial expression and voice data and sends it to the server. The input data includes the user's facial expression images and voice data, which are sent to the server. In operation, the device's camera and microphone capture the user's data in real time.

[0913] Step 5:

[0914] The server analyzes the received facial image and voice data using an emotion engine to recognize the user's emotional state. The input data is the user's facial image and voice data, and the emotion engine processes this data to identify the user's emotional state. The output data is the emotion analysis result (emotional state such as joy, sadness, anger, surprise, etc.).

[0915] Step 6:

[0916] The server generates an optimized design (pattern) based on the analysis results of the generation AI and emotion engine. The input data is the user's design information and emotion analysis results, and the generation AI performs calculations based on these to generate an optimized design. The output data is the optimized design data.

[0917] Step 7:

[0918] When the device receives a command to regenerate, the server issues new commands to the generation AI and fine-tunes the design. The input data includes the user's command to regenerate and the results of the previous generation, and the generation AI generates a new image and blueprint based on these. The output data is the adjusted image and blueprint.

[0919] Step 8:

[0920] In order for the user to finally download the satisfactory design, the server converts the pattern data into PDF format and generates a download link. The input data is the optimized design data, which is processed to convert it into PDF. The output data is a download link that is provided to the terminal.

[0921] 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.

[0922] 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.

[0923] 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.

[0924] [Third embodiment]

[0925] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0926] 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.

[0927] 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).

[0928] 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.

[0929] 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.

[0930] 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).

[0931] 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.

[0932] 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.

[0933] 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.

[0934] 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.

[0935] 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.

[0936] 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."

[0937] This invention, "AI Pattern Maker," is a system that acquires design information and automatically generates clothing blueprints (patterns) using generative AI. This system allows users to input design information, and the AI ​​generates a realistic image based on that information, and then creates a blueprint from that image. Below, we explain the operation of the program and provide specific examples of this system.

[0938] Program Description

[0939] The system works in the following broad steps:

[0940] 1. Obtaining design information:

[0941] The user uploads a design drawing using the device, which serves as the basis for the user's clothing design.

[0942] The terminal sends the design image file along with information entered by the user, such as body type, size, and fabric specifications, to the server.

[0943] 2. Image generation using generative AI:

[0944] Based on the design information received by the server, an image is generated using generative AI, which creates realistic photo-like images tailored to body shapes and sizes.

[0945] The generated image is stored on the server and displayed to the user.

[0946] 3. Creating a design (pattern):

[0947] The server automatically generates blueprints based on the image data created by the AI, and these blueprints are created based on the dimensions of each part.

[0948] The generated design drawings are stored in a database and provided to the user.

[0949] 4. Tweak and regenerate:

[0950] If the user wishes to make minor adjustments to the image or design, they can input instructions again, including partial corrections and shape changes.

[0951] The device sends this instruction to the server, and the generation AI again generates the corresponding image and blueprint, which the server then stores and provides to the user.

[0952] 5. Providing the final output:

[0953] When the user is satisfied with the final design, he or she requests to download the data in a format such as PDF.

[0954] The server provides the pattern data as a download link, allowing users to obtain the data in the format they require.

[0955] Specific examples

[0956] As a specific example of use, a case will be described where a user uses the system using a design image called "A."

[0957] 1. Obtaining design information:

[0958] The user uploads a design drawing of "A," selects and inputs the size as "M," the body type as "standard," and the fabric as "cotton."

[0959] 2. Image generation using generative AI:

[0960] The server uses generative AI to generate realistic photo-like images based on the uploaded design drawings and specified specifications.

[0961] The generated image is saved and displayed to the user as a preview.

[0962] 3. Creating a design (pattern):

[0963] The server automatically generates a design drawing (pattern) based on the generated realistic image data, calculating the dimensions of each part.

[0964] The generated blueprints are stored in a database and a download link is provided to the user.

[0965] 4. Tweak and regenerate:

[0966] The user requests minor adjustments such as "shortening the sleeves," and sends the instruction to the server from the terminal.

[0967] The server again instructs the generation AI to regenerate the adjusted image and blueprint, which are then displayed to the user as a preview.

[0968] 5. Providing the final output:

[0969] The user finally requests to download the blueprints they are satisfied with, and is provided with a download link in PDF format by the server.

[0970] In this way, the "AI Pattern Maker" can automatically and efficiently generate clothing designs based on the user's design information, simplifying the traditional pattern creation process and reducing time and costs.

[0971] The processing flow will be explained below.

[0972] Step 1:

[0973] The user uploads a design drawing using the device, which serves as the basis for the user's clothing design.

[0974] Step 2:

[0975] The terminal sends the design image file along with information entered by the user, such as body type, size, and fabric specifications, to the server.

[0976] Step 3:

[0977] The server saves the received design file in the directory specified as the save location, and records the save path in the database.

[0978] Step 4:

[0979] The server analyzes the received specification information and creates a request to call the generation AI. The request includes the save path of the design image and the user's specification information.

[0980] Step 5:

[0981] The server sends a request to the generation AI, which generates a realistic photo-like image based on the design drawing and specification information.

[0982] Step 6:

[0983] The AI ​​then sends the generated image data back to the server, which reflects the specified body shape and size.

[0984] Step 7:

[0985] The server receives the generated image data, saves it, and records the path of the saved image in the database.

[0986] Step 8:

[0987] The server generates a preview link for the saved image data and displays it on the user's device, allowing the user to check the generated image.

[0988] Step 9:

[0989] The server generates a design drawing (pattern) based on the generated image data, calculates the dimensions of each part using a design drawing generation algorithm, and creates the pattern.

[0990] Step 10:

[0991] The server stores the generated blueprints in a database and provides a download link to the user's device, allowing the user to obtain the generated pattern.

[0992] Step 11:

[0993] If the user wants to make minor adjustments to the generated image or design, they can input their adjustment instructions. A form is provided to input specific changes (e.g., shorten the sleeves).

[0994] Step 12:

[0995] The device sends instructions for fine-tuning to the server, which then generates data containing the instructions and sends it to the server.

[0996] Step 13:

[0997] The server receives the tweak instructions and sends a re-instruction to the generation AI, which includes the new parameters.

[0998] Step 14:

[0999] The generative AI sends the regenerated images and patterns based on the new parameters back to the server, reflecting the user's adjustment instructions.

[1000] Step 15:

[1001] The server saves the regenerated image and pattern and provides it to the user. It also generates a preview link and displays it on the user's device.

[1002] Step 16:

[1003] When the user is satisfied with the final design, he / she makes a download request by clicking the pattern data download button.

[1004] Step 17:

[1005] The server converts the final pattern data into PDF format and generates a download link, which is then provided to the user's device.

[1006] This series of processes enables efficient generation of design drawings from design drawings, making it possible to create clothes that meet the user's requirements.

[1007] Example 1

[1008] 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."

[1009] In the conventional clothing design process, blueprints (patterns) must be created manually based on design information, which requires time and effort. Furthermore, if fine adjustments are needed, the work must be done again manually, resulting in inefficiency. Furthermore, there are no general-purpose systems, and in many cases, it is difficult to meet the specific requirements of each user. The present invention aims to solve the above-mentioned problems by providing a system that allows users to easily input design information and efficiently generate blueprints.

[1010] 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.

[1011] In this invention, the server includes means for acquiring design information, AI generation means for generating an image based on the design information, means for generating a blueprint based on the generated image, means for receiving instructions for adjusting the image or blueprint and regenerating it in accordance with the instructions, means for saving the generated blueprint and making it accessible to a database, and means for providing the final generated blueprint in a downloadable format. This allows a user to automatically generate a highly accurate blueprint efficiently and quickly make any necessary fine adjustments simply by inputting design information.

[1012] "Design information" is information that the user inputs as the basis for designing clothes, and includes design drawings, body type, size, fabric specifications, and the like.

[1013] "Generative AI" refers to artificial intelligence technology that generates realistic images based on design information obtained from users, such as systems that use generative models and deep learning.

[1014] A "blueprint" is a drawing that shows the dimensions and shape of each part needed to make clothing, based on design information and an image created by generative AI.

[1015] "Image" refers to realistic, photographic image data generated from design information by generative AI.

[1016] A "database" is an information system for storing and managing generated blueprints and images.

[1017] "Regeneration" refers to the process of regenerating an image or design drawing based on instructions from the user.

[1018] "Downloadable format" refers to the format in which a user can obtain the final generated design, such as a PDF or image file format.

[1019] "Preservation" refers to the long-term retention of generated data and making it reusable as needed.

[1020] MODE FOR CARRYING OUT THE INVENTION

[1021] This invention, "AI Pattern Maker," is a system that acquires design information and automatically generates clothing blueprints (patterns) using generative AI. This system allows users to input design information, and the generative AI generates a realistic image based on that information, and then creates a blueprint from that image.

[1022] The system consists of the following:

[1023] Terminal operated by the user

[1024] A server that stores and processes design information

[1025] A generative AI model installed on a server (e.g., OpenAI's DALL-E or MidJourney)

[1026] A database that manages blueprint data (e.g., MySQL or PostgreSQL)

[1027] Program processing

[1028] Get design information

[1029] The user uploads a design drawing using the device. The design drawing is the basis for the user's clothing design. The device sends the design drawing file along with information entered by the user, such as body type, size, and fabric specifications, to the server.

[1030] Image generation by generative AI

[1031] The server uses generative AI to generate realistic photo-realistic images based on the received design information. This AI creates realistic images tailored to body shapes and sizes. The generated images are stored on the server and displayed to the user.

[1032] Creating a blueprint (pattern)

[1033] The server automatically generates blueprints based on the image data created by the AI. These blueprints are created based on the dimensions of each part. The generated blueprints are stored in a database and provided to users.

[1034] Tweaking and Regeneration

[1035] If the user wishes to make minor adjustments to the image or blueprint, they can input instructions again. This can include making partial corrections or changing the shape. The device sends these instructions to the server, and the generation AI regenerates the corresponding image and blueprint. The server then saves them and provides them to the user.

[1036] Providing the final output

[1037] When the user is satisfied with the final design, he / she requests to download the data in a format such as PDF. The server provides the pattern data as a download link so that the user can obtain the data in the required format.

[1038] Specific examples

[1039] As a specific example of use, a case will be described where a user uses the system using a design image called "A."

[1040] 1. Obtaining design information:

[1041] The user uploads a design drawing of "A," selects and inputs the size as "M," the body type as "standard," and the fabric as "cotton."

[1042] 2. Image generation using generative AI:

[1043] The server uses generative AI (e.g., OpenAI's DALL-E) to generate realistic photo-like images based on the uploaded design and specified specifications.

[1044] The generated image is saved and displayed to the user as a preview.

[1045] 3. Creating a design (pattern):

[1046] The server automatically generates a design drawing (pattern) based on the generated realistic image data, calculating the dimensions of each part.

[1047] The generated blueprints are stored in a database and a download link is provided to the user.

[1048] 4. Tweak and regenerate:

[1049] The user requests minor adjustments such as "shortening the sleeves," and sends the instruction to the server from the terminal.

[1050] The server again instructs the generation AI to regenerate the adjusted image and blueprint, which are then displayed to the user as a preview.

[1051] 5. Providing the final output:

[1052] The user finally requests to download the blueprints they are satisfied with, and is provided with a download link in PDF format by the server.

[1053] An example prompt might be:

[1054] "Based on design drawing A, please generate a realistic image of a medium-sized, standard-sized, cotton garment."

[1055] "Please shorten the sleeves of the generated blueprint and regenerate it."

[1056] In this way, the AI ​​pattern maker can automatically and efficiently generate clothing designs based on the user's design information, simplifying the traditional pattern-making process and reducing time and costs.

[1057] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1058] Step 1:

[1059] Get design information

[1060] User: The user uploads a design drawing using the device. The design drawing is the basis for the user to design the clothes.

[1061] Input: Design file, body type, size, fabric specifications

[1062] Terminal: The terminal sends the design file along with information entered by the user, such as body type, size, and fabric specifications, to the server.

[1063] Output: Design information sent to the server

[1064] Specific behavior:

[1065] The user opens the design information input screen.

[1066] Select a design file and enter the required information in the body type, size, and fabric specification fields.

[1067] Click the "Send" button and the device will send the data to the server.

[1068] Step 2:

[1069] Image generation by generative AI

[1070] Server: Based on the received design information, the server uses generative AI (e.g., OpenAI's DALL-E) to generate a realistic photo-like image.

[1071] Input: Design information stored on the server

[1072] Generative AI: Generative AI runs algorithms to generate realistic images.

[1073] Output: Generated photo-like image

[1074] Specific behavior:

[1075] The server inputs the received design file and supplementary information as parameters into the generative AI model.

[1076] Generative AI creates realistic photo-like images.

[1077] The server saves the generated image and returns the data to the user for preview display.

[1078] Step 3:

[1079] Creating a blueprint (pattern)

[1080] Server: The server automatically generates blueprints based on the image data created by the generation AI. The dimensions of each part are calculated based on the design information.

[1081] Input: Generated realistic image data

[1082] Blueprint generation module: Executes an algorithm that calculates the dimensions of each part and automatically generates a blueprint.

[1083] Output: Generated blueprint data

[1084] Specific behavior:

[1085] The server acquires the stored image data and inputs it into the design drawing generation module.

[1086] The design drawing generation module calculates the dimensions of each part and automatically creates a design drawing (pattern).

[1087] The server stores the generated blueprints in a database and provides users with a download link.

[1088] Step 4:

[1089] Tweaking and Regeneration

[1090] User: If the user wishes to make any fine adjustments to the image or design, they input the instructions again from their terminal into the server.

[1091] Input: User instructions for fine tuning

[1092] Terminal: The terminal sends the user's instructions to the server, and the generation AI again generates the corresponding image and blueprint.

[1093] Output: Adjusted images and blueprints

[1094] Specific behavior:

[1095] Users can preview images and blueprints, specify areas they would like tweaked, and provide feedback.

[1096] Click the "Regenerate" button and the terminal will send the instruction to the server.

[1097] The server then sends new prompts reflecting the fine-tuning to the generative AI model, which then generates corresponding images and blueprints.

[1098] The server saves the new images and blueprints and displays them as a preview to the user.

[1099] Step 5:

[1100] Providing the final output

[1101] User: When the user is satisfied with the final design, they request to download it in a format such as PDF.

[1102] Input: User download request

[1103] Server: The server provides the pattern data as a download link, allowing users to retrieve the data in the format they require.

[1104] Output: Download link (e.g. PDF format)

[1105] Specific behavior:

[1106] The user checks the final preview and clicks the "Download" button to send a request to the server.

[1107] The server converts the blueprint data into PDF or other specified format and generates a download link.

[1108] The server provides the generated download link to the user.

[1109] (Application example 1)

[1110] 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."

[1111] Conventional pattern creation systems require a lot of time and effort for design, and it is difficult for users to check and fine-tune the design in real time. In particular, when ordering custom-made clothing, detailed design drawings are required, and there is often a lack of information based on these drawings, making it a challenge to improve the user experience.

[1112] 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.

[1113] In this invention, the server includes means for acquiring design information, generation AI means for generating an image based on the design information, means for generating a blueprint (pattern) based on the generated image, means for receiving instructions for adjusting the image or blueprint and regenerating it according to the instructions, display means for checking the design in real time in a virtual space, and order processing means for ordering custom-made clothing based on the generated blueprint. This allows for efficient automatic generation and adjustment of designs, and enables users to check designs in real time and easily order custom-made clothing.

[1114] "Design information" refers to information such as a design drawing of the clothes the user is trying to design, as well as information about the body type, size, and fabric specifications.

[1115] "Generative AI" is an artificial intelligence system that automatically generates realistic images based on design information.

[1116] A "pattern" is a drawing that shows in detail the dimensions of each part of the clothing based on the generated image.

[1117] A "virtual space" is a virtual environment that users can interact with using a computer or virtual reality device.

[1118] "Display means" refers to devices or software that allow users to visually check designs and blueprints generated in virtual space or the real world.

[1119] The "order processing means" is a system that enables users to order custom-made clothing based on the generated design drawings.

[1120] "Fine-tuning" is an operation that allows the user to make partial corrections or changes to the shape of a generated image or design drawing.

[1121] "Real-time" refers to the state in which changes made by the user are reflected immediately.

[1122] This invention relates to the implementation of a system that allows users to acquire design information, automatically generate clothing blueprints using generative AI, and check and adjust the design in real time in a virtual space.

[1123] The system mainly consists of the following hardware and software: a smartphone, a head-mounted display (e.g., Oculus Rift, HTC Vive), a server (e.g., AWS, Google Cloud), a generative AI model (e.g., OpenAI's DALL-E, MIDAS AI), and Unity or Unreal Engine as a display medium. It also uses Stripe or PayPal as an electronic payment service.

[1124] First, the user inputs design information using a smartphone or head-mounted display, including design sketches, body type, size, fabric characteristics, etc. Once the design information is uploaded to the system, the server receives it and uses the information to generate a realistic image using a generative AI model.

[1125] The generated image is displayed to the user in a virtual space using Unity or Unreal Engine. The user can check the design in this virtual space and make fine adjustments as needed. This adjustment information is then sent back to the server, and new images and blueprints are generated by the generative AI model.

[1126] Once the user is satisfied with the final design, they can order a custom garment based on the generated blueprints, which is processed using electronic payment services such as Stripe or PayPal.

[1127] For example, if a user inputs a prompt such as, "I have uploaded a design drawing for a medium-sized summer dress made of chiffon fabric. Please generate a realistic 3D image of the dress and its blueprints based on these conditions," the server will obtain the design information, and the generative AI model will generate a realistic 3D image of the dress and its blueprints based on those conditions. The generated image and blueprints are displayed to the user in a virtual space, and if the user wishes to make minor adjustments, they will be regenerated based on their instructions.

[1128] In this way, the system provides an efficient way for users to conveniently create designs, generate detailed blueprints based on those designs, and order custom-made garments.

[1129] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1130] Step 1:

[1131] The user inputs design information using a smartphone or head-mounted display.

[1132] The input design information (design image, body type, size, fabric characteristics, etc.) is sent from the terminal to the server.

[1133] Input: Design drawing, body type, size, fabric specifications

[1134] Output: The server receives the design information.

[1135] Step 2:

[1136] The design information received by the server is input into a generative AI model to generate a realistic image.

[1137] Here, a generative AI (e.g., OpenAI's DALL-E) generates a 3D image based on the design drawing and information on body shape, size, and fabric.

[1138] Input: Design information

[1139] Output: Generated 3D image

[1140] Step 3:

[1141] The server generates a blueprint (pattern) based on the image generated by the generative AI model.

[1142] The design drawings include detailed dimensions of each part, and the design drawing data is stored on a server.

[1143] Input: 3D image

[1144] Output: Generated blueprint

[1145] Step 4:

[1146] The server displays the generated 3D images and blueprints to the user in a virtual space.

[1147] This is done using software such as Unity or Unreal Engine, allowing users to see their designs in real time.

[1148] Input: 3D image, blueprint

[1149] Output: 3D images and blueprints displayed in virtual space

[1150] Step 5:

[1151] The user makes minor adjustments to the displayed images and blueprints.

[1152] The device receives the user's adjustment instructions and sends them to the server. For example, shortening the sleeve length or changing the color.

[1153] Input: User adjustment instructions

[1154] Output: Server receives adjustment instructions

[1155] Step 6:

[1156] The server re-inputs the adjustment instructions into the generative AI model to regenerate the image and blueprint.

[1157] The updated design is then resaved and the regenerated images and blueprints are displayed in the virtual space.

[1158] Input: User adjustment instructions

[1159] Output: Regenerated 3D images and blueprints

[1160] Step 7:

[1161] If the user is satisfied with the final design, they order the custom garment.

[1162] The terminal transmits the user's order information and electronic payment information to the server, and the server processes the order.

[1163] Once payment is complete, the final design will be available for download in PDF format.

[1164] Input: User's order information, electronic payment information

[1165] Output: Confirmed order and download link for the blueprint in PDF format

[1166] 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.

[1167] This invention, the "Emotion Recognition AI Patternmaker," combines a system that acquires design information and automatically generates clothing blueprints (patterns) using a generative AI with an emotion engine that recognizes the user's emotions. This system uses user input design information, and the generative AI generates a realistic image based on that information. The emotion engine analyzes the user's emotions and provides an optimized blueprint. Below, we explain the operation of the program and provide specific examples of this system.

[1168] Program Description

[1169] The system works in the following broad steps:

[1170] 1. Obtaining design information:

[1171] The user uploads a design drawing using the device, which serves as the basis for the user's clothing design.

[1172] The terminal sends the design image file along with information entered by the user, such as body type, size, and fabric specifications, to the server.

[1173] 2. Image generation using generative AI:

[1174] Based on the design information received by the server, an image is generated using generative AI, which creates realistic photo-like images tailored to body shapes and sizes.

[1175] The generated image is stored on the server and displayed to the user.

[1176] 3. Emotion Recognition with Emotion Engine:

[1177] The device captures the user's facial expressions and tone of voice and sends them to the server.

[1178] The server passes the received data to the emotion engine, which analyzes the user's emotions. The emotion engine recognizes emotional states such as joy, sadness, anger, and surprise.

[1179] 4. Creating a design (pattern):

[1180] The server automatically generates a blueprint (pattern) based on the analysis results of the generation AI and emotion engine. The dimensions of each part are calculated and a pattern is created that takes into account the user's emotional state.

[1181] The generated design drawings are stored in a database and provided to the user.

[1182] 5. Emotion-based optimization:

[1183] The server optimizes the image and blueprint based on the user's emotions. For example, if the user expresses dissatisfaction, it will re-instruct the generating AI to make fine adjustments.

[1184] The regenerated image or blueprint is displayed to the user, and the emotion engine is used to analyze the emotion again and confirm the satisfaction level.

[1185] 6. Providing the final output:

[1186] When the user is satisfied with the final design, he / she requests a download by clicking the pattern data download button.

[1187] The server converts the final pattern data into PDF format and generates a download link, which is then provided to the user's device.

[1188] Specific examples

[1189] As a specific example of use, a case will be described where a user uses the system using a design image of a dress named "B."

[1190] 1. Obtaining design information:

[1191] The user uploads a design drawing of "B," selects and inputs the size "L," the body type "slim," and the fabric "silk."

[1192] 2. Image generation using generative AI:

[1193] The server uses generative AI to generate realistic photo-like images based on the uploaded design drawings and specified specifications.

[1194] The generated image is saved and displayed to the user as a preview.

[1195] 3. Emotion Recognition with Emotion Engine:

[1196] The device captures the user's facial expression data and sends it to the server.

[1197] The server uses an emotion engine to analyze the user's emotional state.

[1198] 4. Creating a design (pattern):

[1199] Based on the results of the generative AI and emotion engine, the server automatically generates a blueprint that matches the user's specified specifications and emotions.

[1200] Save the blueprint and generate a download link to provide to the user.

[1201] 5. Emotion-based optimization:

[1202] If the user is dissatisfied with the image, he or she inputs a command to regenerate it from the terminal.

[1203] The server uses an emotion engine to analyze user satisfaction and fine-tune the design.

[1204] 6. Providing the final output:

[1205] The user finally makes a request to download the design he is satisfied with, and the server converts the pattern data into PDF format.

[1206] The server provides a download link and the user obtains the pattern data.

[1207] In this way, the "Emotion-Aware AI Pattern Maker" automatically and efficiently generates clothing blueprints based on the user's design information and emotional state, providing optimal designs.

[1208] The processing flow will be explained below.

[1209] Step 1:

[1210] The user uploads a design drawing using the device, which serves as the basis for the user's clothing design.

[1211] Step 2:

[1212] The terminal sends the design image file along with information entered by the user, such as body type, size, and fabric specifications, to the server.

[1213] Step 3:

[1214] The server saves the received design file in the directory specified as the save location, and records the save path in the database.

[1215] Step 4:

[1216] The server analyzes the received specification information and creates a request to call the generation AI. The request includes the save path of the design image and the user's specification information.

[1217] Step 5:

[1218] The server sends a request to the generation AI, which generates a realistic photo-like image based on the design drawing and specification information.

[1219] Step 6:

[1220] The AI ​​then sends the generated image data back to the server, which reflects the specified body shape and size.

[1221] Step 7:

[1222] The server receives the generated image data, saves it, and records the path of the saved image in the database.

[1223] Step 8:

[1224] The server generates a preview link for the saved image data and displays it on the user's device, allowing the user to check the generated image.

[1225] Step 9:

[1226] The device captures the user's facial expressions and voice tone and sends them to the server, which is data collection for emotion recognition.

[1227] Step 10:

[1228] The server passes the received facial expression data to the emotion engine means, which analyzes the user's emotions. The emotion engine recognizes emotional states such as joy, sadness, anger, and surprise.

[1229] Step 11:

[1230] The server automatically generates a design (pattern) based on the results of the generation AI and emotion engine. The generated design is based on the dimensional calculations of each part and also takes into account the user's emotional state.

[1231] Step 12:

[1232] The server stores the generated blueprints in a database and provides a download link to the user's device, allowing the user to obtain the generated pattern.

[1233] Step 13:

[1234] The server optimizes the image and blueprint based on the user's emotions. For example, if the user expresses dissatisfaction, it will re-instruct the generating AI to make fine adjustments.

[1235] Step 14:

[1236] The server saves the regenerated image and pattern and provides it to the user. It also generates a preview link and displays it on the user's device.

[1237] Step 15:

[1238] When the user is satisfied with the final design, he / she makes a download request by clicking the pattern data download button.

[1239] Step 16:

[1240] The server converts the final pattern data into PDF format and generates a download link, which is then provided to the user's device.

[1241] Through this series of processes, the "Emotion Recognition AI Pattern Maker" uses emotion recognition to automatically and efficiently generate clothing blueprints based on the user's design information and emotional state, providing optimal designs.

[1242] Example 2

[1243] 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."

[1244] Conventional clothing design generation systems were able to generate images based on design information and specifications provided by the user, but they were unable to generate or adjust designs that took the user's emotional state into account, making it difficult to increase user satisfaction. Furthermore, while it was possible to receive instructions for regeneration when adjusting images and designs, the lack of flexible adjustments that could respond to changes in emotions was an issue.

[1245] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1246] In this invention, the server includes means for acquiring design information, AI generation means for generating an image based on the design information, means for generating a blueprint based on the generated image, means for recognizing a user's emotion, means for adjusting the blueprint or image based on the user's emotion, and means for regenerating the blueprint or image. This makes it possible to generate and adjust blueprints and images that reflect the user's emotional state, thereby improving user satisfaction.

[1247] "Design information" refers to information necessary for designing clothing, and includes design drawings, body types, sizes, fabric specifications, and the like provided by the user.

[1248] "Generative AI" is an artificial intelligence technology that generates realistic photographic images based on design information.

[1249] A "blueprint" is a clothing pattern and detailed design data created based on an image generated by generative AI.

[1250] The "emotion engine" is a system that recognizes a user's emotional state by analyzing the user's facial expression data and voice tone.

[1251] "Regeneration" is the process of regenerating an image or blueprint based on the user's instructions or emotional state.

[1252] "Body type" is information about the user's physical dimensions and shape, and is used to adjust clothing designs and sizes.

[1253] "Size" is a standard for determining the measurements and fit of clothing, and is information specified by the user.

[1254] "Fabric specifications" are information that indicates details such as the material, weave, and texture of the clothing.

[1255] A "user" is someone who uses this system to provide design information and obtain final blueprints and images.

[1256] This invention, "Emotion Recognition AI Patterner," is a system that uses generative AI to generate realistic images based on design information provided by the user, and analyzes the user's emotions using an emotion engine to provide optimal designs. This system is composed of the following main components:

[1257] 1. Obtaining design information:

[1258] Users use a terminal to upload design drawings and enter information such as body type, size, fabric specifications, etc. This inputs the user's desired clothing design into the system.

[1259] The terminal sends the input information along with the design image file to the server. Through this data transfer, the server obtains the necessary information.

[1260] 2. Image generation using generative AI

[1261] The server uses a generative AI to generate realistic, photo-realistic images based on the received design information, sending the AI ​​prompts specific to the design information.

[1262] For example, the prompt may include specific instructions such as "Dress B, size L, slim build, silk fabric."

[1263] The server stores the generated image and displays it to the user as a preview, allowing the user to visually check it.

[1264] 3. Emotion Recognition by Emotion Engine

[1265] The device captures the user's facial expression data with a camera and records their voice tone with a microphone, and this data is sent to the server.

[1266] The server passes the received data to the emotion engine, which analyzes the user's emotional state. The emotion engine detects emotions such as joy, sadness, anger, and surprise, and uses this information to adjust the next step.

[1267] 4. Creating a design (pattern)

[1268] The server combines the image output from the generation AI with the analysis results of the emotion engine, and automatically generates a design that matches the user's specified specifications and emotions. This design calculates the dimensions of each part and reflects user feedback.

[1269] The generated blueprints are stored in a database on the server and provided to users as a download link.

[1270] 5. Emotion-Based Optimization

[1271] The server optimizes the image and blueprint by taking into account the user's emotional state. For example, if the user expresses dissatisfaction, it will re-instruct the generation AI to fine-tune the image and blueprint.

[1272] The regenerated images and blueprints are then displayed to the user again, allowing for repeated confirmation and adjustment of the emotional state.

[1273] 6. Providing the final output

[1274] The user then makes a request to download the final design that they are satisfied with.

[1275] The server converts the blueprint into PDF format and generates a download link for the user, allowing the user to obtain the completed blueprint.

[1276] Specific examples

[1277] For example, a user can upload a design drawing of a "B" dress and select size "L," body type "slim," and fabric "silk." Based on this information, the AI ​​will receive the prompt "Dress B, size L, slim body type, silk fabric" and generate a realistic image.

[1278] The emotion engine then analyzes the user's facial expression data and voice tone to confirm their level of satisfaction. If the emotion engine determines that the user is dissatisfied, it sends new instructions to the generation AI, which then regenerates the image or blueprint.

[1279] Finally, once the user is satisfied with the design, it is provided in PDF format and the user can obtain it via a download link.

[1280] In this way, the "Emotion-Aware AI Pattern Maker" is a system that integrates the user's design information and emotional state, efficiently and effectively generates clothing blueprints, and provides optimal designs.

[1281] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1282] Step 1:

[1283] Get design information

[1284] The user uploads the design using the device, which involves clicking the "upload button" on the screen.

[1285] The user inputs information such as body type, size, and fabric specifications (e.g., size "L", body type "Slim", fabric "Silk") into the terminal. This includes filling out an input form.

[1286] The terminal sends this information to the server. At this time, the design image file and the user's input information are packed into a single data packet and sent to the server as an HTTP POST request.

[1287] Input: Design file, body type, size, fabric specifications

[1288] Output: Design information data packet sent to the server

[1289] Step 2:

[1290] Image generation by generative AI

[1291] The server analyzes the design information it receives and generates prompts for the AI, such as "dress B, size L, slim build, silk fabric."

[1292] The server sends prompts to the generative AI model, which then generates realistic, photo-realistic images tailored to the specified design, body type, size, and fabric specifications.

[1293] The server saves the generated image and displays a preview to the user. At this time, the image file is saved in the server's storage, and the image URL is returned to the user's device via an HTTP response.

[1294] Input: Design information data packet sent to the server

[1295] Output: Generated realistic photo-realistic images

[1296] Step 3:

[1297] Emotion recognition by emotion engine

[1298] The device captures the user's facial expression data with the camera and records the voice tone with the microphone. A pop-up requesting permission to use the camera and microphone is displayed on the device, and the user's permission is obtained.

[1299] The device sends the captured data to the server. This dataset, which includes facial feature points and audio waveform data, is sent to the server as an HTTP POST request.

[1300] The server passes the received data to an emotion engine that analyzes the user's emotional state, running algorithms to classify emotions such as joy, sadness, anger, and surprise from facial expressions and vocal tones.

[1301] Input: User's facial expression data and voice data captured by the device

[1302] Output: Parsed user's emotional state

[1303] Step 4:

[1304] Creating a blueprint (pattern)

[1305] The server integrates the images from the generative AI model with the analysis results of the emotion engine to automatically generate a design, taking into account the user's emotional state and the specified design specifications.

[1306] The measurements for each part are calculated and final adjustments are made based on the user's feedback. For example, if the user requests that the skirt be made a little longer, the measurements will be included in the design.

[1307] The server stores the generated blueprints in a database and provides the user with a download link, which is sent to the user's device in an HTTP response.

[1308] Input: Image from generative AI, analysis results from emotion engine

[1309] Output: Generated blueprints and their download links

[1310] Step 5:

[1311] Emotion-Based Optimization

[1312] The server optimizes the images and blueprints based on the user's emotional state. For example, if the user expresses dissatisfaction, it will re-instruct the generation AI to regenerate the images and blueprints.

[1313] The server displays the regenerated image or blueprint to the user and checks their emotional state again, at which point the regenerated data is also run through the emotion engine.

[1314] Input: Parsed user emotional state, frustration feedback

[1315] Output: Optimized images and blueprints

[1316] Step 6:

[1317] Providing the final output

[1318] When the user is finally satisfied with the design, a request is sent to the server to download it, which involves the user clicking a download button.

[1319] The server converts the blueprints into PDF format and generates a download link, which is provided to the user as an HTTP response.

[1320] Input: User download request

[1321] Output: Download link for the design in PDF format

[1322] In this way, by dividing the program flow into detailed processing steps, the "Emotion Recognition AI Pattern Maker" can automatically generate optimal clothing designs by integrating the user's design information and emotional state.

[1323] (Application example 2)

[1324] 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."

[1325] Conventional design systems have difficulty generating blueprints and images that reflect the user's emotional state, and have faced the challenge of requiring a great deal of time and effort to increase user satisfaction. Other issues include a lack of systems that can optimize designs in real time or instantly reflect user feedback. The present invention aims to solve these issues by providing a system that allows users to easily and quickly obtain optimal designs based on their own emotions.

[1326] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring design information, generation AI means for generating an image based on the design information, means for generating a design drawing (pattern) based on the generated image, emotion analysis means, means for analyzing the user's emotions and optimizing the design drawing or image, means for receiving and regenerating instructions for adjusting the image or design drawing, means for realistically converting the image to match a specified body type and size, and means for optimizing the image based on the user's emotional state. This makes it possible to take the user's emotions into consideration, optimize the design in real time, and quickly generate a design drawing that satisfies the user.

[1327] "Design information" refers to information such as the design image of the clothing or accessories, body type, size, fabric specifications, etc., input by the user.

[1328] "Generative AI" is an artificial intelligence model that generates realistic images based on acquired design information.

[1329] A "blueprint (pattern)" is a production drawing for clothing or accessories generated based on a design.

[1330] "Emotion analysis" is a technology that recognizes emotions from a user's facial expressions and voice and analyzes the results.

[1331] The "optimization means" is a means for adjusting the generated images and blueprints based on the results of the user's emotion analysis.

[1332] "Regeneration means" is a means of receiving instructions to adjust an image or blueprint, and regenerating it using the generation AI.

[1333] "Body type information" is data relating to the user's physical characteristics and size.

[1334] "Realistic transformation" is the process of transforming a design image based on real-world dimensions to fit a specified body type and size.

[1335] "Form for carrying out the invention" of the specification

[1336] This invention, "Emotion Recognition AI Patternmaker," is a system that acquires a user's design information and automatically generates clothing designs (patterns) using a generation AI. By combining this system with emotion analysis technology, it analyzes the user's emotional state and provides optimized designs. An embodiment of the present invention is described in detail below.

[1337] The hardware used to implement this invention includes a terminal (smartphone, tablet, PC, etc.) for inputting design information, a camera and microphone for emotion analysis, and a server for analyzing and generating data. The software used includes a generative AI model that analyzes the generated design image, an emotion engine that analyzes the user's emotion, and a data processing program that links with them.

[1338] Get design information

[1339] Using a device, users can take a photo or upload a design drawing and enter the necessary information, such as size, body type, fabric specifications, etc. This information is sent to a server and stored in a database for use by the generative AI model.

[1340] Image generation by generative AI

[1341] The server uses generative AI to generate realistic photo-realistic images based on the design information received. This AI model converts the design into a realistic representation based on the user's body shape and size. The generated images are stored on the server and displayed on the user's device.

[1342] Emotion analysis

[1343] The device captures the user's facial expressions and voice data and sends them to a server, which then uses an emotion engine to analyze the user's emotional state. Emotion analysis recognizes emotions such as joy, sadness, anger, and surprise in real time, and the results are used for design optimization.

[1344] Design (pattern) generation and optimization

[1345] Based on the analysis results of the generation AI and emotion engine, the server generates a design (pattern) optimized for the user's specified specifications and emotional state. The design is provided in a form that calculates the dimensions of each part and reflects the user's emotional state. If the user expresses dissatisfaction with the image, the server issues further instructions to the generation AI to fine-tune the design and increase user satisfaction.

[1346] Providing the final output

[1347] To allow users to download the final design they are satisfied with, the server converts the pattern data into PDF format and generates a download link, which is then provided to the device, allowing users to easily retrieve the pattern data.

[1348] Specific examples

[1349] For example, suppose a user uploads a design drawing for a "summer dress" in a special corner in the store and selects size "M," body type "regular," and fabric "cotton." The generation AI generates a realistic, photo-like image based on that information and displays it on the smartphone. If the user expresses emotion in relation to this image through facial expressions or voice, the server analyzes it using an emotion engine and regenerates the design based on the results. Finally, the user can download the design that satisfies them in PDF format.

[1350] Prompt Sentence Examples

[1351] For example, a possible prompt for a generative AI model might be:

[1352] Generative AI: Generate a realistic photo-realistic image of this dress design. The body type is "Slim", the size is "L", and the fabric is "Silk."

[1353] In this way, the present invention realizes a system that can automatically and efficiently generate clothing designs based on the user's design information and emotional state, and provide optimal designs.

[1354] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1355] Program processing steps

[1356] Step 1:

[1357] The user uses a terminal to input design information. The input information includes the design image, body type, size, and fabric specifications. The terminal sends this information to the server. The input data includes the design image file, body type information, size information, and fabric information, and this data is sent to the server.

[1358] Step 2:

[1359] The server uses a generative AI model to generate a realistic photo-like image based on the received design information. The input data includes the design information sent by the user (design image file, body type information, size information, fabric information). Based on this data, the generative AI model performs calculations to generate a photo-like image. A realistic photo-like image is obtained as output data.

[1360] Step 3:

[1361] The generated image is stored on the server and sent to the terminal, which displays the image to the user. The input data is a realistic photo-like image stored on the server, and the output data is the image displayed on the user's terminal.

[1362] Step 4:

[1363] The device uses a camera and microphone to capture the user's facial expression and voice data and sends it to the server. The input data includes the user's facial expression images and voice data, which are sent to the server. In operation, the device's camera and microphone capture the user's data in real time.

[1364] Step 5:

[1365] The server analyzes the received facial image and voice data using an emotion engine to recognize the user's emotional state. The input data is the user's facial image and voice data, and the emotion engine processes this data to identify the user's emotional state. The output data is the emotion analysis result (emotional state such as joy, sadness, anger, surprise, etc.).

[1366] Step 6:

[1367] The server generates an optimized design (pattern) based on the analysis results of the generation AI and emotion engine. The input data is the user's design information and emotion analysis results, and the generation AI performs calculations based on these to generate an optimized design. The output data is the optimized design data.

[1368] Step 7:

[1369] When the device receives a command to regenerate, the server issues new commands to the generation AI and fine-tunes the design. The input data includes the user's command to regenerate and the results of the previous generation, and the generation AI generates a new image and blueprint based on these. The output data is the adjusted image and blueprint.

[1370] Step 8:

[1371] In order for the user to finally download the satisfactory design, the server converts the pattern data into PDF format and generates a download link. The input data is the optimized design data, which is processed to convert it into PDF. The output data is a download link that is provided to the terminal.

[1372] 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.

[1373] 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.

[1374] 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.

[1375] [Fourth embodiment]

[1376] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1377] 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.

[1378] 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).

[1379] 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.

[1380] 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.

[1381] 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).

[1382] 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.

[1383] 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.

[1384] 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.

[1385] 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.

[1386] 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.

[1387] 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.

[1388] 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."

[1389] This invention, "AI Pattern Maker," is a system that acquires design information and automatically generates clothing blueprints (patterns) using generative AI. This system allows users to input design information, and the AI ​​generates a realistic image based on that information, and then creates a blueprint from that image. Below, we explain the operation of the program and provide specific examples of this system.

[1390] Program Description

[1391] The system works in the following broad steps:

[1392] 1. Obtaining design information:

[1393] The user uploads a design drawing using the device, which serves as the basis for the user's clothing design.

[1394] The terminal sends the design image file along with information entered by the user, such as body type, size, and fabric specifications, to the server.

[1395] 2. Image generation using generative AI:

[1396] Based on the design information received by the server, an image is generated using generative AI, which creates realistic photo-like images tailored to body shapes and sizes.

[1397] The generated image is stored on the server and displayed to the user.

[1398] 3. Creating a design (pattern):

[1399] The server automatically generates blueprints based on the image data created by the AI, and these blueprints are created based on the dimensions of each part.

[1400] The generated design drawings are stored in a database and provided to the user.

[1401] 4. Tweak and regenerate:

[1402] If the user wishes to make minor adjustments to the image or design, they can input instructions again, including partial corrections and shape changes.

[1403] The device sends this instruction to the server, and the generation AI again generates the corresponding image and blueprint, which the server then stores and provides to the user.

[1404] 5. Providing the final output:

[1405] When the user is satisfied with the final design, he or she requests to download the data in a format such as PDF.

[1406] The server provides the pattern data as a download link, allowing users to obtain the data in the format they require.

[1407] Specific examples

[1408] As a specific example of use, a case will be described where a user uses the system using a design image called "A."

[1409] 1. Obtaining design information:

[1410] The user uploads a design drawing of "A," selects and inputs the size as "M," the body type as "standard," and the fabric as "cotton."

[1411] 2. Image generation using generative AI:

[1412] The server uses generative AI to generate realistic photo-like images based on the uploaded design drawings and specified specifications.

[1413] The generated image is saved and displayed to the user as a preview.

[1414] 3. Creating a design (pattern):

[1415] The server automatically generates a design drawing (pattern) based on the generated realistic image data, calculating the dimensions of each part.

[1416] The generated blueprints are stored in a database and a download link is provided to the user.

[1417] 4. Tweak and regenerate:

[1418] The user requests minor adjustments such as "shortening the sleeves," and sends the instruction to the server from the terminal.

[1419] The server again instructs the generation AI to regenerate the adjusted image and blueprint, which are then displayed to the user as a preview.

[1420] 5. Providing the final output:

[1421] The user finally requests to download the blueprints they are satisfied with, and is provided with a download link in PDF format by the server.

[1422] In this way, the "AI Pattern Maker" can automatically and efficiently generate clothing designs based on the user's design information, simplifying the traditional pattern creation process and reducing time and costs.

[1423] The processing flow will be explained below.

[1424] Step 1:

[1425] The user uploads a design drawing using the device, which serves as the basis for the user's clothing design.

[1426] Step 2:

[1427] The terminal sends the design image file along with information entered by the user, such as body type, size, and fabric specifications, to the server.

[1428] Step 3:

[1429] The server saves the received design file in the directory specified as the save location, and records the save path in the database.

[1430] Step 4:

[1431] The server analyzes the received specification information and creates a request to call the generation AI. The request includes the save path of the design image and the user's specification information.

[1432] Step 5:

[1433] The server sends a request to the generation AI, which generates a realistic photo-like image based on the design drawing and specification information.

[1434] Step 6:

[1435] The AI ​​then sends the generated image data back to the server, which reflects the specified body shape and size.

[1436] Step 7:

[1437] The server receives the generated image data, saves it, and records the path of the saved image in the database.

[1438] Step 8:

[1439] The server generates a preview link for the saved image data and displays it on the user's device, allowing the user to check the generated image.

[1440] Step 9:

[1441] The server generates a design drawing (pattern) based on the generated image data, calculates the dimensions of each part using a design drawing generation algorithm, and creates the pattern.

[1442] Step 10:

[1443] The server stores the generated blueprints in a database and provides a download link to the user's device, allowing the user to obtain the generated pattern.

[1444] Step 11:

[1445] If the user wants to make minor adjustments to the generated image or design, they can input their adjustment instructions. A form is provided to input specific changes (e.g., shorten the sleeves).

[1446] Step 12:

[1447] The device sends instructions for fine-tuning to the server, which then generates data containing the instructions and sends it to the server.

[1448] Step 13:

[1449] The server receives the tweak instructions and sends a re-instruction to the generation AI, which includes the new parameters.

[1450] Step 14:

[1451] The generative AI sends the regenerated images and patterns based on the new parameters back to the server, reflecting the user's adjustment instructions.

[1452] Step 15:

[1453] The server saves the regenerated image and pattern and provides it to the user. It also generates a preview link and displays it on the user's device.

[1454] Step 16:

[1455] When the user is satisfied with the final design, he / she makes a download request by clicking the pattern data download button.

[1456] Step 17:

[1457] The server converts the final pattern data into PDF format and generates a download link, which is then provided to the user's device.

[1458] This series of processes enables efficient generation of design drawings from design drawings, making it possible to create clothes that meet the user's requirements.

[1459] Example 1

[1460] 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."

[1461] In the conventional clothing design process, blueprints (patterns) must be created manually based on design information, which requires time and effort. Furthermore, if fine adjustments are needed, the work must be done again manually, resulting in inefficiency. Furthermore, there are no general-purpose systems, and in many cases, it is difficult to meet the specific requirements of each user. The present invention aims to solve the above-mentioned problems by providing a system that allows users to easily input design information and efficiently generate blueprints.

[1462] 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.

[1463] In this invention, the server includes means for acquiring design information, AI generation means for generating an image based on the design information, means for generating a blueprint based on the generated image, means for receiving instructions for adjusting the image or blueprint and regenerating it in accordance with the instructions, means for saving the generated blueprint and making it accessible to a database, and means for providing the final generated blueprint in a downloadable format. This allows a user to automatically generate a highly accurate blueprint efficiently and quickly make any necessary fine adjustments simply by inputting design information.

[1464] "Design information" is information that the user inputs as the basis for designing clothes, and includes design drawings, body type, size, fabric specifications, and the like.

[1465] "Generative AI" refers to artificial intelligence technology that generates realistic images based on design information obtained from users, such as systems that use generative models and deep learning.

[1466] A "blueprint" is a drawing that shows the dimensions and shape of each part needed to make clothing, based on design information and an image created by generative AI.

[1467] "Image" refers to realistic, photographic image data generated from design information by generative AI.

[1468] A "database" is an information system for storing and managing generated blueprints and images.

[1469] "Regeneration" refers to the process of regenerating an image or design drawing based on instructions from the user.

[1470] "Downloadable format" refers to the format in which a user can obtain the final generated design, such as a PDF or image file format.

[1471] "Preservation" refers to the long-term retention of generated data and making it reusable as needed.

[1472] MODE FOR CARRYING OUT THE INVENTION

[1473] This invention, "AI Pattern Maker," is a system that acquires design information and automatically generates clothing blueprints (patterns) using generative AI. This system allows users to input design information, and the generative AI generates a realistic image based on that information, and then creates a blueprint from that image.

[1474] The system consists of the following:

[1475] Terminal operated by the user

[1476] A server that stores and processes design information

[1477] A generative AI model installed on a server (e.g., OpenAI's DALL-E or MidJourney)

[1478] A database that manages blueprint data (e.g., MySQL or PostgreSQL)

[1479] Program processing

[1480] Get design information

[1481] The user uploads a design drawing using the device. The design drawing is the basis for the user's clothing design. The device sends the design drawing file along with information entered by the user, such as body type, size, and fabric specifications, to the server.

[1482] Image generation by generative AI

[1483] The server uses generative AI to generate realistic photo-realistic images based on the received design information. This AI creates realistic images tailored to body shapes and sizes. The generated images are stored on the server and displayed to the user.

[1484] Creating a blueprint (pattern)

[1485] The server automatically generates blueprints based on the image data created by the AI. These blueprints are created based on the dimensions of each part. The generated blueprints are stored in a database and provided to users.

[1486] Tweaking and Regeneration

[1487] If the user wishes to make minor adjustments to the image or blueprint, they can input instructions again. This can include making partial corrections or changing the shape. The device sends these instructions to the server, and the generation AI regenerates the corresponding image and blueprint. The server then saves them and provides them to the user.

[1488] Providing the final output

[1489] When the user is satisfied with the final design, he / she requests to download the data in a format such as PDF. The server provides the pattern data as a download link so that the user can obtain the data in the required format.

[1490] Specific examples

[1491] As a specific example of use, a case will be described where a user uses the system using a design image called "A."

[1492] 1. Obtaining design information:

[1493] The user uploads a design drawing of "A," selects and inputs the size as "M," the body type as "standard," and the fabric as "cotton."

[1494] 2. Image generation using generative AI:

[1495] The server uses generative AI (e.g., OpenAI's DALL-E) to generate realistic photo-like images based on the uploaded design and specified specifications.

[1496] The generated image is saved and displayed to the user as a preview.

[1497] 3. Creating a design (pattern):

[1498] The server automatically generates a design drawing (pattern) based on the generated realistic image data, calculating the dimensions of each part.

[1499] The generated blueprints are stored in a database and a download link is provided to the user.

[1500] 4. Tweak and regenerate:

[1501] The user requests minor adjustments such as "shortening the sleeves," and sends the instruction to the server from the terminal.

[1502] The server again instructs the generation AI to regenerate the adjusted image and blueprint, which are then displayed to the user as a preview.

[1503] 5. Providing the final output:

[1504] The user finally requests to download the blueprints they are satisfied with, and is provided with a download link in PDF format by the server.

[1505] An example prompt might be:

[1506] "Based on design drawing A, please generate a realistic image of a medium-sized, standard-sized, cotton garment."

[1507] "Please shorten the sleeves of the generated blueprint and regenerate it."

[1508] In this way, the AI ​​pattern maker can automatically and efficiently generate clothing designs based on the user's design information, simplifying the traditional pattern-making process and reducing time and costs.

[1509] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1510] Step 1:

[1511] Get design information

[1512] User: The user uploads a design drawing using the device. The design drawing is the basis for the user to design the clothes.

[1513] Input: Design file, body type, size, fabric specifications

[1514] Terminal: The terminal sends the design file along with information entered by the user, such as body type, size, and fabric specifications, to the server.

[1515] Output: Design information sent to the server

[1516] Specific behavior:

[1517] The user opens the design information input screen.

[1518] Select a design file and enter the required information in the body type, size, and fabric specification fields.

[1519] Click the "Send" button and the device will send the data to the server.

[1520] Step 2:

[1521] Image generation by generative AI

[1522] Server: Based on the received design information, the server uses generative AI (e.g., OpenAI's DALL-E) to generate a realistic photo-like image.

[1523] Input: Design information stored on the server

[1524] Generative AI: Generative AI runs algorithms to generate realistic images.

[1525] Output: Generated photo-like image

[1526] Specific behavior:

[1527] The server inputs the received design file and supplementary information as parameters into the generative AI model.

[1528] Generative AI creates realistic photo-like images.

[1529] The server saves the generated image and returns the data to the user for preview display.

[1530] Step 3:

[1531] Creating a blueprint (pattern)

[1532] Server: The server automatically generates blueprints based on the image data created by the generation AI. The dimensions of each part are calculated based on the design information.

[1533] Input: Generated realistic image data

[1534] Blueprint generation module: Executes an algorithm that calculates the dimensions of each part and automatically generates a blueprint.

[1535] Output: Generated blueprint data

[1536] Specific behavior:

[1537] The server acquires the stored image data and inputs it into the design drawing generation module.

[1538] The design drawing generation module calculates the dimensions of each part and automatically creates a design drawing (pattern).

[1539] The server stores the generated blueprints in a database and provides users with a download link.

[1540] Step 4:

[1541] Tweaking and Regeneration

[1542] User: If the user wishes to make any fine adjustments to the image or design, they input the instructions again from their terminal into the server.

[1543] Input: User instructions for fine tuning

[1544] Terminal: The terminal sends the user's instructions to the server, and the generation AI again generates the corresponding image and blueprint.

[1545] Output: Adjusted images and blueprints

[1546] Specific behavior:

[1547] Users can preview images and blueprints, specify areas they would like tweaked, and provide feedback.

[1548] Click the "Regenerate" button and the terminal will send the instruction to the server.

[1549] The server then sends new prompts reflecting the fine-tuning to the generative AI model, which then generates corresponding images and blueprints.

[1550] The server saves the new images and blueprints and displays them as a preview to the user.

[1551] Step 5:

[1552] Providing the final output

[1553] User: When the user is satisfied with the final design, they request to download it in a format such as PDF.

[1554] Input: User download request

[1555] Server: The server provides the pattern data as a download link, allowing users to retrieve the data in the format they require.

[1556] Output: Download link (e.g. PDF format)

[1557] Specific behavior:

[1558] The user checks the final preview and clicks the "Download" button to send a request to the server.

[1559] The server converts the blueprint data into PDF or other specified format and generates a download link.

[1560] The server provides the generated download link to the user.

[1561] (Application example 1)

[1562] 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."

[1563] Conventional pattern creation systems require a lot of time and effort for design, and it is difficult for users to check and fine-tune the design in real time. In particular, when ordering custom-made clothing, detailed design drawings are required, and there is often a lack of information based on these drawings, making it a challenge to improve the user experience.

[1564] 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.

[1565] In this invention, the server includes means for acquiring design information, generation AI means for generating an image based on the design information, means for generating a blueprint (pattern) based on the generated image, means for receiving instructions for adjusting the image or blueprint and regenerating it according to the instructions, display means for checking the design in real time in a virtual space, and order processing means for ordering custom-made clothing based on the generated blueprint. This allows for efficient automatic generation and adjustment of designs, and enables users to check designs in real time and easily order custom-made clothing.

[1566] "Design information" refers to information such as a design drawing of the clothes the user is trying to design, as well as information about the body type, size, and fabric specifications.

[1567] "Generative AI" is an artificial intelligence system that automatically generates realistic images based on design information.

[1568] A "pattern" is a drawing that shows in detail the dimensions of each part of the clothing based on the generated image.

[1569] A "virtual space" is a virtual environment that users can interact with using a computer or virtual reality device.

[1570] "Display means" refers to devices or software that allow users to visually check designs and blueprints generated in virtual space or the real world.

[1571] The "order processing means" is a system that enables users to order custom-made clothing based on the generated design drawings.

[1572] "Fine-tuning" is an operation that allows the user to make partial corrections or changes to the shape of a generated image or design drawing.

[1573] "Real-time" refers to the state in which changes made by the user are reflected immediately.

[1574] This invention relates to the implementation of a system that allows users to acquire design information, automatically generate clothing blueprints using generative AI, and check and adjust the design in real time in a virtual space.

[1575] The system mainly consists of the following hardware and software: a smartphone, a head-mounted display (e.g., Oculus Rift, HTC Vive), a server (e.g., AWS, Google Cloud), a generative AI model (e.g., OpenAI's DALL-E, MIDAS AI), and Unity or Unreal Engine as a display medium. It also uses Stripe or PayPal as an electronic payment service.

[1576] First, the user inputs design information using a smartphone or head-mounted display, including design sketches, body type, size, fabric characteristics, etc. Once the design information is uploaded to the system, the server receives it and uses the information to generate a realistic image using a generative AI model.

[1577] The generated image is displayed to the user in a virtual space using Unity or Unreal Engine. The user can check the design in this virtual space and make fine adjustments as needed. This adjustment information is then sent back to the server, and new images and blueprints are generated by the generative AI model.

[1578] Once the user is satisfied with the final design, they can order a custom garment based on the generated blueprints, which is processed using electronic payment services such as Stripe or PayPal.

[1579] For example, if a user inputs a prompt such as, "I have uploaded a design drawing for a medium-sized summer dress made of chiffon fabric. Please generate a realistic 3D image of the dress and its blueprints based on these conditions," the server will obtain the design information, and the generative AI model will generate a realistic 3D image of the dress and its blueprints based on those conditions. The generated image and blueprints are displayed to the user in a virtual space, and if the user wishes to make minor adjustments, they will be regenerated based on their instructions.

[1580] In this way, the system provides an efficient way for users to conveniently create designs, generate detailed blueprints based on those designs, and order custom-made garments.

[1581] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1582] Step 1:

[1583] The user inputs design information using a smartphone or head-mounted display.

[1584] The input design information (design image, body type, size, fabric characteristics, etc.) is sent from the terminal to the server.

[1585] Input: Design drawing, body type, size, fabric specifications

[1586] Output: The server receives the design information.

[1587] Step 2:

[1588] The design information received by the server is input into a generative AI model to generate a realistic image.

[1589] Here, a generative AI (e.g., OpenAI's DALL-E) generates a 3D image based on the design drawing and information on body shape, size, and fabric.

[1590] Input: Design information

[1591] Output: Generated 3D image

[1592] Step 3:

[1593] The server generates a blueprint (pattern) based on the image generated by the generative AI model.

[1594] The design drawings include detailed dimensions of each part, and the design drawing data is stored on a server.

[1595] Input: 3D image

[1596] Output: Generated blueprint

[1597] Step 4:

[1598] The server displays the generated 3D images and blueprints to the user in a virtual space.

[1599] This is done using software such as Unity or Unreal Engine, allowing users to see their designs in real time.

[1600] Input: 3D image, blueprint

[1601] Output: 3D images and blueprints displayed in virtual space

[1602] Step 5:

[1603] The user makes minor adjustments to the displayed images and blueprints.

[1604] The device receives the user's adjustment instructions and sends them to the server. For example, shortening the sleeve length or changing the color.

[1605] Input: User adjustment instructions

[1606] Output: Server receives adjustment instructions

[1607] Step 6:

[1608] The server re-inputs the adjustment instructions into the generative AI model to regenerate the image and blueprint.

[1609] The updated design is then resaved and the regenerated images and blueprints are displayed in the virtual space.

[1610] Input: User adjustment instructions

[1611] Output: Regenerated 3D images and blueprints

[1612] Step 7:

[1613] If the user is satisfied with the final design, they order the custom garment.

[1614] The terminal transmits the user's order information and electronic payment information to the server, and the server processes the order.

[1615] Once payment is complete, the final design will be available for download in PDF format.

[1616] Input: User's order information, electronic payment information

[1617] Output: Confirmed order and download link for the blueprint in PDF format

[1618] 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.

[1619] This invention, the "Emotion Recognition AI Patternmaker," combines a system that acquires design information and automatically generates clothing blueprints (patterns) using a generative AI with an emotion engine that recognizes the user's emotions. This system uses user input design information, and the generative AI generates a realistic image based on that information. The emotion engine analyzes the user's emotions and provides an optimized blueprint. Below, we explain the operation of the program and provide specific examples of this system.

[1620] Program Description

[1621] The system works in the following broad steps:

[1622] 1. Obtaining design information:

[1623] The user uploads a design drawing using the device, which serves as the basis for the user's clothing design.

[1624] The terminal sends the design image file along with information entered by the user, such as body type, size, and fabric specifications, to the server.

[1625] 2. Image generation using generative AI:

[1626] Based on the design information received by the server, an image is generated using generative AI, which creates realistic photo-like images tailored to body shapes and sizes.

[1627] The generated image is stored on the server and displayed to the user.

[1628] 3. Emotion Recognition with Emotion Engine:

[1629] The device captures the user's facial expressions and tone of voice and sends them to the server.

[1630] The server passes the received data to the emotion engine, which analyzes the user's emotions. The emotion engine recognizes emotional states such as joy, sadness, anger, and surprise.

[1631] 4. Creating a design (pattern):

[1632] The server automatically generates a blueprint (pattern) based on the analysis results of the generation AI and emotion engine. The dimensions of each part are calculated and a pattern is created that takes into account the user's emotional state.

[1633] The generated design drawings are stored in a database and provided to the user.

[1634] 5. Emotion-based optimization:

[1635] The server optimizes the image and blueprint based on the user's emotions. For example, if the user expresses dissatisfaction, it will re-instruct the generating AI to make fine adjustments.

[1636] The regenerated image or blueprint is displayed to the user, and the emotion engine is used to analyze the emotion again and confirm the satisfaction level.

[1637] 6. Providing the final output:

[1638] When the user is satisfied with the final design, he / she requests a download by clicking the pattern data download button.

[1639] The server converts the final pattern data into PDF format and generates a download link, which is then provided to the user's device.

[1640] Specific examples

[1641] As a specific example of use, a case will be described where a user uses the system using a design image of a dress named "B."

[1642] 1. Obtaining design information:

[1643] The user uploads a design drawing of "B," selects and inputs the size "L," the body type "slim," and the fabric "silk."

[1644] 2. Image generation using generative AI:

[1645] The server uses generative AI to generate realistic photo-like images based on the uploaded design drawings and specified specifications.

[1646] The generated image is saved and displayed to the user as a preview.

[1647] 3. Emotion Recognition with Emotion Engine:

[1648] The device captures the user's facial expression data and sends it to the server.

[1649] The server uses an emotion engine to analyze the user's emotional state.

[1650] 4. Creating a design (pattern):

[1651] Based on the results of the generative AI and emotion engine, the server automatically generates a blueprint that matches the user's specified specifications and emotions.

[1652] Save the blueprint and generate a download link to provide to the user.

[1653] 5. Emotion-based optimization:

[1654] If the user is dissatisfied with the image, he or she inputs a command to regenerate it from the terminal.

[1655] The server uses an emotion engine to analyze user satisfaction and fine-tune the design.

[1656] 6. Providing the final output:

[1657] The user finally makes a request to download the design he is satisfied with, and the server converts the pattern data into PDF format.

[1658] The server provides a download link and the user obtains the pattern data.

[1659] In this way, the "Emotion-Aware AI Pattern Maker" automatically and efficiently generates clothing blueprints based on the user's design information and emotional state, providing optimal designs.

[1660] The processing flow will be explained below.

[1661] Step 1:

[1662] The user uploads a design drawing using the device, which serves as the basis for the user's clothing design.

[1663] Step 2:

[1664] The terminal sends the design image file along with information entered by the user, such as body type, size, and fabric specifications, to the server.

[1665] Step 3:

[1666] The server saves the received design file in the directory specified as the save location, and records the save path in the database.

[1667] Step 4:

[1668] The server analyzes the received specification information and creates a request to call the generation AI. The request includes the save path of the design image and the user's specification information.

[1669] Step 5:

[1670] The server sends a request to the generation AI, which generates a realistic photo-like image based on the design drawing and specification information.

[1671] Step 6:

[1672] The AI ​​then sends the generated image data back to the server, which reflects the specified body shape and size.

[1673] Step 7:

[1674] The server receives the generated image data, saves it, and records the path of the saved image in the database.

[1675] Step 8:

[1676] The server generates a preview link for the saved image data and displays it on the user's device, allowing the user to check the generated image.

[1677] Step 9:

[1678] The device captures the user's facial expressions and voice tone and sends them to the server, which is data collection for emotion recognition.

[1679] Step 10:

[1680] The server passes the received facial expression data to the emotion engine means, which analyzes the user's emotions. The emotion engine recognizes emotional states such as joy, sadness, anger, and surprise.

[1681] Step 11:

[1682] The server automatically generates a design (pattern) based on the results of the generation AI and emotion engine. The generated design is based on the dimensional calculations of each part and also takes into account the user's emotional state.

[1683] Step 12:

[1684] The server stores the generated blueprints in a database and provides a download link to the user's device, allowing the user to obtain the generated pattern.

[1685] Step 13:

[1686] The server optimizes the image and blueprint based on the user's emotions. For example, if the user expresses dissatisfaction, it will re-instruct the generating AI to make fine adjustments.

[1687] Step 14:

[1688] The server saves the regenerated image and pattern and provides it to the user. It also generates a preview link and displays it on the user's device.

[1689] Step 15:

[1690] When the user is satisfied with the final design, he / she makes a download request by clicking the pattern data download button.

[1691] Step 16:

[1692] The server converts the final pattern data into PDF format and generates a download link, which is then provided to the user's device.

[1693] Through this series of processes, the "Emotion Recognition AI Pattern Maker" uses emotion recognition to automatically and efficiently generate clothing blueprints based on the user's design information and emotional state, providing optimal designs.

[1694] Example 2

[1695] 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."

[1696] Conventional clothing design generation systems were able to generate images based on design information and specifications provided by the user, but they were unable to generate or adjust designs that took the user's emotional state into account, making it difficult to increase user satisfaction. Furthermore, while it was possible to receive instructions for regeneration when adjusting images and designs, the lack of flexible adjustments that could respond to changes in emotions was an issue.

[1697] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1698] In this invention, the server includes means for acquiring design information, AI generation means for generating an image based on the design information, means for generating a blueprint based on the generated image, means for recognizing a user's emotion, means for adjusting the blueprint or image based on the user's emotion, and means for regenerating the blueprint or image. This makes it possible to generate and adjust blueprints and images that reflect the user's emotional state, thereby improving user satisfaction.

[1699] "Design information" refers to information necessary for designing clothing, and includes design drawings, body types, sizes, fabric specifications, and the like provided by the user.

[1700] "Generative AI" is an artificial intelligence technology that generates realistic photographic images based on design information.

[1701] A "blueprint" is a clothing pattern and detailed design data created based on an image generated by generative AI.

[1702] The "emotion engine" is a system that recognizes a user's emotional state by analyzing the user's facial expression data and voice tone.

[1703] "Regeneration" is the process of regenerating an image or blueprint based on the user's instructions or emotional state.

[1704] "Body type" is information about the user's physical dimensions and shape, and is used to adjust clothing designs and sizes.

[1705] "Size" is a standard for determining the measurements and fit of clothing, and is information specified by the user.

[1706] "Fabric specifications" are information that indicates details such as the material, weave, and texture of the clothing.

[1707] A "user" is someone who uses this system to provide design information and obtain final blueprints and images.

[1708] This invention, "Emotion Recognition AI Patterner," is a system that uses generative AI to generate realistic images based on design information provided by the user, and analyzes the user's emotions using an emotion engine to provide optimal designs. This system is composed of the following main components:

[1709] 1. Obtaining design information:

[1710] Users use a terminal to upload design drawings and enter information such as body type, size, fabric specifications, etc. This inputs the user's desired clothing design into the system.

[1711] The terminal sends the input information along with the design image file to the server. Through this data transfer, the server obtains the necessary information.

[1712] 2. Image generation using generative AI

[1713] The server uses a generative AI to generate realistic, photo-realistic images based on the received design information, sending the AI ​​prompts specific to the design information.

[1714] For example, the prompt may include specific instructions such as "Dress B, size L, slim build, silk fabric."

[1715] The server stores the generated image and displays it to the user as a preview, allowing the user to visually check it.

[1716] 3. Emotion Recognition by Emotion Engine

[1717] The device captures the user's facial expression data with a camera and records their voice tone with a microphone, and this data is sent to the server.

[1718] The server passes the received data to the emotion engine, which analyzes the user's emotional state. The emotion engine detects emotions such as joy, sadness, anger, and surprise, and uses this information to adjust the next step.

[1719] 4. Creating a design (pattern)

[1720] The server combines the image output from the generation AI with the analysis results of the emotion engine, and automatically generates a design that matches the user's specified specifications and emotions. This design calculates the dimensions of each part and reflects user feedback.

[1721] The generated blueprints are stored in a database on the server and provided to users as a download link.

[1722] 5. Emotion-Based Optimization

[1723] The server optimizes the image and blueprint by taking into account the user's emotional state. For example, if the user expresses dissatisfaction, it will re-instruct the generation AI to fine-tune the image and blueprint.

[1724] The regenerated images and blueprints are then displayed to the user again, allowing for repeated confirmation and adjustment of the emotional state.

[1725] 6. Providing the final output

[1726] The user then makes a request to download the final design that they are satisfied with.

[1727] The server converts the blueprint into PDF format and generates a download link for the user, allowing the user to obtain the completed blueprint.

[1728] Specific examples

[1729] For example, a user can upload a design drawing of a "B" dress and select size "L," body type "slim," and fabric "silk." Based on this information, the AI ​​will receive the prompt "Dress B, size L, slim body type, silk fabric" and generate a realistic image.

[1730] The emotion engine then analyzes the user's facial expression data and voice tone to confirm their level of satisfaction. If the emotion engine determines that the user is dissatisfied, it sends new instructions to the generation AI, which then regenerates the image or blueprint.

[1731] Finally, once the user is satisfied with the design, it is provided in PDF format and the user can obtain it via a download link.

[1732] In this way, the "Emotion-Aware AI Pattern Maker" is a system that integrates the user's design information and emotional state, efficiently and effectively generates clothing blueprints, and provides optimal designs.

[1733] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1734] Step 1:

[1735] Get design information

[1736] The user uploads the design using the device, which involves clicking the "upload button" on the screen.

[1737] The user inputs information such as body type, size, and fabric specifications (e.g., size "L", body type "Slim", fabric "Silk") into the terminal. This includes filling out an input form.

[1738] The terminal sends this information to the server. At this time, the design image file and the user's input information are packed into a single data packet and sent to the server as an HTTP POST request.

[1739] Input: Design file, body type, size, fabric specifications

[1740] Output: Design information data packet sent to the server

[1741] Step 2:

[1742] Image generation by generative AI

[1743] The server analyzes the design information it receives and generates prompts for the AI, such as "dress B, size L, slim build, silk fabric."

[1744] The server sends prompts to the generative AI model, which then generates realistic, photo-realistic images tailored to the specified design, body type, size, and fabric specifications.

[1745] The server saves the generated image and displays a preview to the user. At this time, the image file is saved in the server's storage, and the image URL is returned to the user's device via an HTTP response.

[1746] Input: Design information data packet sent to the server

[1747] Output: Generated realistic photo-realistic images

[1748] Step 3:

[1749] Emotion recognition by emotion engine

[1750] The device captures the user's facial expression data with the camera and records the voice tone with the microphone. A pop-up requesting permission to use the camera and microphone is displayed on the device, and the user's permission is obtained.

[1751] The device sends the captured data to the server. This dataset, which includes facial feature points and audio waveform data, is sent to the server as an HTTP POST request.

[1752] The server passes the received data to an emotion engine that analyzes the user's emotional state, running algorithms to classify emotions such as joy, sadness, anger, and surprise from facial expressions and vocal tones.

[1753] Input: User's facial expression data and voice data captured by the device

[1754] Output: Parsed user's emotional state

[1755] Step 4:

[1756] Creating a blueprint (pattern)

[1757] The server integrates the images from the generative AI model with the analysis results of the emotion engine to automatically generate a design, taking into account the user's emotional state and the specified design specifications.

[1758] The measurements for each part are calculated and final adjustments are made based on the user's feedback. For example, if the user requests that the skirt be made a little longer, the measurements will be included in the design.

[1759] The server stores the generated blueprints in a database and provides the user with a download link, which is sent to the user's device in an HTTP response.

[1760] Input: Image from generative AI, analysis results from emotion engine

[1761] Output: Generated blueprints and their download links

[1762] Step 5:

[1763] Emotion-Based Optimization

[1764] The server optimizes the images and blueprints based on the user's emotional state. For example, if the user expresses dissatisfaction, it will re-instruct the generation AI to regenerate the images and blueprints.

[1765] The server displays the regenerated image or blueprint to the user and checks their emotional state again, at which point the regenerated data is also run through the emotion engine.

[1766] Input: Parsed user emotional state, frustration feedback

[1767] Output: Optimized images and blueprints

[1768] Step 6:

[1769] Providing the final output

[1770] When the user is finally satisfied with the design, a request is sent to the server to download it, which involves the user clicking a download button.

[1771] The server converts the blueprints into PDF format and generates a download link, which is provided to the user as an HTTP response.

[1772] Input: User download request

[1773] Output: Download link for the design in PDF format

[1774] In this way, by dividing the program flow into detailed processing steps, the "Emotion Recognition AI Pattern Maker" can automatically generate optimal clothing designs by integrating the user's design information and emotional state.

[1775] (Application example 2)

[1776] 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."

[1777] Conventional design systems have difficulty generating blueprints and images that reflect the user's emotional state, and have faced the challenge of requiring a great deal of time and effort to increase user satisfaction. Other issues include a lack of systems that can optimize designs in real time or instantly reflect user feedback. The present invention aims to solve these issues by providing a system that allows users to easily and quickly obtain optimal designs based on their own emotions.

[1778] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring design information, generation AI means for generating an image based on the design information, means for generating a design drawing (pattern) based on the generated image, emotion analysis means, means for analyzing the user's emotions and optimizing the design drawing or image, means for receiving and regenerating instructions for adjusting the image or design drawing, means for realistically converting the image to match a specified body type and size, and means for optimizing the image based on the user's emotional state. This makes it possible to take the user's emotions into consideration, optimize the design in real time, and quickly generate a design drawing that satisfies the user.

[1779] "Design information" refers to information such as the design image of the clothing or accessories, body type, size, fabric specifications, etc., input by the user.

[1780] "Generative AI" is an artificial intelligence model that generates realistic images based on acquired design information.

[1781] A "blueprint (pattern)" is a production drawing for clothing or accessories generated based on a design.

[1782] "Emotion analysis" is a technology that recognizes emotions from a user's facial expressions and voice and analyzes the results.

[1783] The "optimization means" is a means for adjusting the generated images and blueprints based on the results of the user's emotion analysis.

[1784] "Regeneration means" is a means of receiving instructions to adjust an image or blueprint, and regenerating it using the generation AI.

[1785] "Body type information" is data relating to the user's physical characteristics and size.

[1786] "Realistic transformation" is the process of transforming a design image based on real-world dimensions to fit a specified body type and size.

[1787] "Form for carrying out the invention" of the specification

[1788] This invention, "Emotion Recognition AI Patternmaker," is a system that acquires a user's design information and automatically generates clothing designs (patterns) using a generation AI. By combining this system with emotion analysis technology, it analyzes the user's emotional state and provides optimized designs. An embodiment of the present invention is described in detail below.

[1789] The hardware used to implement this invention includes a terminal (smartphone, tablet, PC, etc.) for inputting design information, a camera and microphone for emotion analysis, and a server for analyzing and generating data. The software used includes a generative AI model that analyzes the generated design image, an emotion engine that analyzes the user's emotion, and a data processing program that links with them.

[1790] Get design information

[1791] Using a device, users can take a photo or upload a design drawing and enter the necessary information, such as size, body type, fabric specifications, etc. This information is sent to a server and stored in a database for use by the generative AI model.

[1792] Image generation by generative AI

[1793] The server uses generative AI to generate realistic photo-realistic images based on the design information received. This AI model converts the design into a realistic representation based on the user's body shape and size. The generated images are stored on the server and displayed on the user's device.

[1794] Emotion analysis

[1795] The device captures the user's facial expressions and voice data and sends them to a server, which then uses an emotion engine to analyze the user's emotional state. Emotion analysis recognizes emotions such as joy, sadness, anger, and surprise in real time, and the results are used for design optimization.

[1796] Design (pattern) generation and optimization

[1797] Based on the analysis results of the generation AI and emotion engine, the server generates a design (pattern) optimized for the user's specified specifications and emotional state. The design is provided in a form that calculates the dimensions of each part and reflects the user's emotional state. If the user expresses dissatisfaction with the image, the server issues further instructions to the generation AI to fine-tune the design and increase user satisfaction.

[1798] Providing the final output

[1799] To allow users to download the final design they are satisfied with, the server converts the pattern data into PDF format and generates a download link, which is then provided to the device, allowing users to easily retrieve the pattern data.

[1800] Specific examples

[1801] For example, suppose a user uploads a design drawing for a "summer dress" in a special corner in the store and selects size "M," body type "regular," and fabric "cotton." The generation AI generates a realistic, photo-like image based on that information and displays it on the smartphone. If the user expresses emotion in relation to this image through facial expressions or voice, the server analyzes it using an emotion engine and regenerates the design based on the results. Finally, the user can download the design that satisfies them in PDF format.

[1802] Prompt Sentence Examples

[1803] For example, a possible prompt for a generative AI model might be:

[1804] Generative AI: Generate a realistic photo-realistic image of this dress design. The body type is "Slim", the size is "L", and the fabric is "Silk."

[1805] In this way, the present invention realizes a system that can automatically and efficiently generate clothing designs based on the user's design information and emotional state, and provide optimal designs.

[1806] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1807] Program processing steps

[1808] Step 1:

[1809] The user uses a terminal to input design information. The input information includes the design image, body type, size, and fabric specifications. The terminal sends this information to the server. The input data includes the design image file, body type information, size information, and fabric information, and this data is sent to the server.

[1810] Step 2:

[1811] The server uses a generative AI model to generate a realistic photo-like image based on the received design information. The input data includes the design information sent by the user (design image file, body type information, size information, fabric information). Based on this data, the generative AI model performs calculations to generate a photo-like image. A realistic photo-like image is obtained as output data.

[1812] Step 3:

[1813] The generated image is stored on the server and sent to the terminal, which displays the image to the user. The input data is a realistic photo-like image stored on the server, and the output data is the image displayed on the user's terminal.

[1814] Step 4:

[1815] The device uses a camera and microphone to capture the user's facial expression and voice data and sends it to the server. The input data includes the user's facial expression images and voice data, which are sent to the server. In operation, the device's camera and microphone capture the user's data in real time.

[1816] Step 5:

[1817] The server analyzes the received facial image and voice data using an emotion engine to recognize the user's emotional state. The input data is the user's facial image and voice data, and the emotion engine processes this data to identify the user's emotional state. The output data is the emotion analysis result (emotional state such as joy, sadness, anger, surprise, etc.).

[1818] Step 6:

[1819] The server generates an optimized design (pattern) based on the analysis results of the generation AI and emotion engine. The input data is the user's design information and emotion analysis results, and the generation AI performs calculations based on these to generate an optimized design. The output data is the optimized design data.

[1820] Step 7:

[1821] When the device receives a command to regenerate, the server issues new commands to the generation AI and fine-tunes the design. The input data includes the user's command to regenerate and the results of the previous generation, and the generation AI generates a new image and blueprint based on these. The output data is the adjusted image and blueprint.

[1822] Step 8:

[1823] In order for the user to finally download the satisfactory design, the server converts the pattern data into PDF format and generates a download link. The input data is the optimized design data, which is processed to convert it into PDF. The output data is a download link that is provided to the terminal.

[1824] 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.

[1825] 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.

[1826] 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.

[1827] 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.

[1828] 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.

[1829] 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.

[1830] 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).

[1831] 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.

[1832] 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."

[1833] 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.

[1834] 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).

[1835] 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.

[1836] 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.

[1837] 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.

[1838] 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.

[1839] 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.

[1840] 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.

[1841] 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.

[1842] 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.

[1843] 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.

[1844] 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.

[1845] The following is further disclosed regarding the above embodiment.

[1846] (Claim 1)

[1847] A means for obtaining design information;

[1848] a generative AI means for generating an image based on the design information;

[1849] A means for generating a design drawing (pattern) based on the generated image;

[1850] A system including means for receiving instructions for adjusting an image or design and regenerating it in response to the instructions.

[1851] (Claim 2)

[1852] 10. The system of claim 1, further comprising means for realistically transforming the image to fit a specified body shape and size.

[1853] (Claim 3)

[1854] The system according to claim 1, further comprising means for uploading photographic data of an existing product and generating a design drawing (pattern) based thereon.

[1855] "Example 1"

[1856] (Claim 1)

[1857] A means for obtaining design information;

[1858] a generative AI means for generating an image based on the design information;

[1859] A means for generating a design drawing based on the generated image;

[1860] a means for receiving instructions for adjusting the image or blueprint and regenerating it in response to the instructions;

[1861] means for storing the generated design drawings and making them accessible to a database;

[1862] The system includes a means for providing the final generated design in a downloadable format.

[1863] (Claim 2)

[1864] 10. The system of claim 1, further comprising means for realistically transforming the image to fit a specified body shape and size.

[1865] (Claim 3)

[1866] 10. The system according to claim 1, further comprising means for uploading photographic data of an existing product and generating a design drawing based thereon.

[1867] "Application Example 1"

[1868] (Claim 1)

[1869] A means for obtaining design information;

[1870] a generative AI means for generating an image based on the design information;

[1871] A means for generating a design drawing (pattern) based on the generated image;

[1872] a means for receiving instructions for adjusting the image or blueprint and regenerating it in response to the instructions;

[1873] A display method for checking designs in real time in a virtual space,

[1874] An order processing means for ordering custom-made clothing based on the generated design drawings

[1875] A system including:

[1876] (Claim 2)

[1877] 10. The system of claim 1, further comprising means for realistically transforming the image to fit a specified body shape and size.

[1878] (Claim 3)

[1879] The system according to claim 1, further comprising means for uploading photographic data of an existing product and generating a design drawing (pattern) based thereon.

[1880] "Example 2: Combining Emotion Engines"

[1881] (Claim 1)

[1882] A means for obtaining design information;

[1883] a generative AI means for generating an image based on the design information;

[1884] A means for generating a design drawing based on the generated image;

[1885] means for recognizing a user's emotion;

[1886] a means for adjusting blueprints and images based on user emotions;

[1887] A system that includes a means to reproduce blueprints and images.

[1888] (Claim 2)

[1889] 10. The system of claim 1, further comprising means for realistically transforming the image to fit a specified user's body shape and size.

[1890] (Claim 3)

[1891] 10. The system of claim 1, further comprising means for uploading image data of an off-the-shelf product and generating a design drawing based thereon.

[1892] "Application example 2 when combining emotion engines"

[1893] New Claims

[1894] (Claim 1)

[1895] A means for obtaining design information;

[1896] a generative AI means for generating an image based on the design information;

[1897] A means for generating a design drawing (pattern) based on the generated image;

[1898] A sentiment analysis means;

[1899] A means of analyzing user emotions and optimizing blueprints and images,

[1900] A system including a means for receiving and regenerating instructions for adjusting an image or blueprint.

[1901] (Claim 2)

[1902] A means to realistically transform images to fit a specified body type and size,

[1903] 10. The system of claim 1, further comprising means for optimizing the image based on the emotional state of the user.

[1904] (Claim 3)

[1905] A means to upload photo data of existing products and generate design drawings (patterns) based on them;

[1906] 10. The system according to claim 1, further comprising means for acquiring and analyzing user emotion data. [Explanation of symbols]

[1907] 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 obtaining design information; a generative AI means for generating an image based on the design information; A means for generating a design drawing based on the generated image; A system including means for receiving instructions for adjusting an image or design and regenerating it in response to the instructions.

2. 10. The system of claim 1, further comprising means for realistically transforming the image to fit a specified body type and size.

3. 2. The system according to claim 1, further comprising means for uploading photographic data of an existing product and generating a design drawing based thereon.

Images