system

Abstract

We provide the system. [Solution] The image generation device receives input visual design information and text data, and generates a visual image based on the visual design information and text data. means and A pattern generation device that designs the pattern and texture of a material based on the aforementioned visual image. means and A virtual fitting device that allows users to virtually try on clothes using visual images and designed patterns. means and A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the process of fashion design, there is a problem that it is difficult for users to create designs reflecting their personalities easily, try them on and share them, while connecting to an efficient manufacturing process. In particular, the time and cost in the process of converting abstract design ideas into specific forms and in the process of pattern design have been bottlenecks. Also, it has been difficult for consumers to participate in designs and easily order products with individual specifications, which has led to delays in division of labor and digitalization.

Means for Solving the Problems

[0005] This invention provides a system in which an image generation device generates a visual image based on visual design information and text data input from a user, and a pattern generation device designs material patterns and textures based on this visual image. In addition, by including a virtual fitting device, users can virtually try on the generated design. This allows the generated design to be shared on social media via a communication network, and orders based on the design can be easily accepted and connected to the manufacturing process. With these configurations, users can consistently perform the process from design creation to fitting and ordering, realizing an efficient and participatory process for manufacturing products that reflect individuality.

[0006] An "image generation device" is a device that has the function of generating visual images based on visual design information and text data provided by the user.

[0007] "Visual design information" refers to the information in the form of shapes and images that users input to bring their design concepts to life.

[0008] "Text data" refers to information that expresses design ideas and specifications in words.

[0009] "Visual images" refer to images and graphics that are visualized based on the user's design information.

[0010] A "pattern generation device" is a device used to design the patterns and textures of materials based on visual images.

[0011] "Material pattern" refers to the shape or design that appears on the surface of a fabric or other material.

[0012] "Texture" refers to the feel or visual characteristics of an object's surface.

[0013] A "virtual fitting device" is a device that provides users with the experience of trying on clothes they have designed in a virtual environment.

[0014] A "communication network" refers to the infrastructure used for communication to send and receive information.

[0015] "Social media" refers to online platforms for widely sharing generated designs and information with the public.

[0016] An "order processing unit" is a device that receives and processes orders placed by users and connects them to the manufacturing process. [Brief explanation of the drawing]

[0017] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0020] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0021] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

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

[0027] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0034] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] This invention digitizes the fashion design process and provides a system that allows users to easily create, try on, and order original designs. This system is implemented through an image generation device, a pattern generation device, a virtual fitting device, and integration with social media utilizing a communication network.

[0039] The user inputs their original design as visual design information and text data using a terminal. The server, upon receiving this data, uses an image generation device to generate a visual image based on the input. This visual image is then provided to the user for confirmation.

[0040] Next, the server uses a pattern generator to design the material's pattern and texture in detail from the generated visual image. This designed pattern is then sent to the terminal for the user to review.

[0041] Users can use a virtual fitting system to virtually try on designs and see how they fit them. The server generates and provides the virtual fitting results to the user in real time.

[0042] Furthermore, users can share designs and try-on results generated via communication networks on social media. This sharing feature allows users to widely showcase their designs and deepen their interactions with others.

[0043] Finally, users can use their devices to place orders for the designed products. Once an order is placed, the server sends the order data to the manufacturing process via an order processing unit. The completed products are then delivered to the users.

[0044] As a concrete example, consider a scenario where a user designs an outdoor jacket. The user inputs a rough sketch and text such as "waterproof outdoor jacket" into a terminal. The server receives this and creates a visual image using an image generator. Next, a pattern generator designs a pattern for waterproof material, reproducing a realistic texture. The user tries on the jacket using a virtual fitting device, checks the design, and then shares it on social media. Finally, the user can order the jacket and receive the manufactured product.

[0045] Thus, the present invention enables users to efficiently create and commercialize unique fashion designs.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The user inputs visual design information and text data for the design via their device and clicks the "Send" button. The device then sends this data to the server.

[0049] Step 2:

[0050] The server receives visual design information and text data sent from the user and performs data analysis. Preprocessing is then carried out to prepare the image for use with the image generation device.

[0051] Step 3:

[0052] The server activates the image generation device and generates a visual image based on the received data. This generated visual image is then sent to the user's terminal.

[0053] Step 4:

[0054] The user confirms the visual image received on the device and enters instructions into the device to proceed to the next process.

[0055] Step 5:

[0056] The server uses a pattern generation device to design the material's pattern and texture based on a visual image. This designed pattern data is then transmitted to the terminal.

[0057] Step 6:

[0058] If the user reviews the designed pattern on their device and decides to proceed with a virtual try-on, a virtual try-on request is sent from the device to the server.

[0059] Step 7:

[0060] The server activates the virtual fitting device and performs a virtual try-on for the user. It generates images and videos of the try-on results and sends them to the user's terminal.

[0061] Step 8:

[0062] The user checks the virtual try-on results on their device, and if satisfied, enters information to share the product on social media and sends it to the server.

[0063] Step 9:

[0064] Based on the information the server receives for sharing, it generates links and images for social media such as SNS and provides them to the user.

[0065] Step 10:

[0066] The user uses a device to decide on a product order and sends the order information to the server.

[0067] Step 11:

[0068] The server initiates order processing, transfers the order data to the manufacturing partner, and starts the manufacturing process.

[0069] Step 12:

[0070] The product is manufactured and, once completed, delivered to the user. The user can check the delivery status on their device.

[0071] (Example 1)

[0072] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0073] In the modern fashion design process, there is a problem in that it takes a lot of effort and time for consumers to translate their ideas into concrete forms and try them on to confirm their quality. Furthermore, it is difficult to easily share the generated designs with others. In addition, the process of turning a consumer's favorite design into a product is also becoming increasingly complex.

[0074] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0075] In this invention, the server includes means for receiving design information and text information from an image generation unit and generating visual data, means for a pattern generation unit that sets the material design and texture in detail based on the visual data, and means for a virtual fitting unit that allows the user to virtually try on the visual data and set design. This makes it possible for consumers to quickly try on individual design ideas, receive evaluations, and then commercialize them.

[0076] The "image generation unit" is a device that receives design information and text information and generates visual data based on them.

[0077] The "pattern generation unit" is a device that has the function of setting the design and texture of materials in detail based on the generated visual data.

[0078] A "virtual fitting room" is a device that allows users to virtually try on clothes based on generated visual data and pre-configured designs.

[0079] "Visual data" refers to digital images generated based on design information and textual information.

[0080] "Design information" refers to data that includes visual design elements provided by the user.

[0081] "Textual information" refers to text data entered by users to express descriptions and characteristics related to the design.

[0082] "Material design" is the process of determining the structure and pattern of materials necessary to realize a design.

[0083] "Texture" is a concept that refers to the characteristics of a material in terms of its appearance and feel.

[0084] A "user" refers to an individual who inputs their design ideas into the system, checks the results, tries them on, and shares them.

[0085] To implement this invention, the following system components are used. The system mainly consists of a server, a terminal, and a user.

[0086] User: Users utilize their devices to input their fashion design ideas. The input data consists of visual design information and textual information. Users can take photos of design sketches with their device's camera and upload them as digital images. They can also clarify the design intent by inputting specific textual information, such as "sporty raincoat."

[0087] Server: The server generates visual data based on design and text information received from the user using the image generation unit. Specifically, it analyzes the data using a generation AI model to create a digital image of the design. The AI ​​model utilizes the context generated from the prompt text to draw the most suitable design from the input data. For example, by inputting the prompt "Rough sketch: Hooded long jacket, Text: Waterproof outdoor wear, Color: Dark green," the corresponding visual data will be generated.

[0088] The server then uses a pattern generation unit to design the material and its texture in detail based on this visual data. Specifically, it uses digital fabric simulation to virtually reproduce the cutting and sewing of the fabric and determine the optimal material and texture for the user's design.

[0089] User: Users utilize a virtual fitting room via their device to try on designs onto their 3D models based on generated visual data and design information. During this process, they can check the fit in 360-degree views, taking into account their full body movements and body shape. This allows them to evaluate the suitability of the design before trying it on.

[0090] The completed design can be shared by users through social media via communication networks. This sharing function allows users to get feedback on the design from others and receive new ideas.

[0091] Finally, the user places an order using a terminal to have their preferred design manufactured. This order is managed by the server in the order processing section, and the product is manufactured using a system belonging to the manufacturing process. Once the product is completed, it is sent to the specified delivery address.

[0092] This system allows users to easily bring their unique fashion ideas to life, try them on, share them with others, and ultimately turn them into finished products.

[0093] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0094] Step 1:

[0095] Users input design information using their devices. Specifically, they use the device's camera function to take pictures of sketches and upload them to the system as image data. They also input text information, such as descriptions and characteristics of the design. The input for this process consists of image and text data, which are then sent to the server.

[0096] Step 2:

[0097] The server analyzes the received design information. It utilizes the image generation unit to pass input data to a generation AI model, thereby generating digital visual data. Specifically, the AI ​​model analyzes prompt text and visually depicts the design image. In this process, visual data is output from image and text data inputs.

[0098] Step 3:

[0099] The server sets the material design and texture in detail based on the visual data. A pattern generation unit performs a digital fabric simulation of the material. This ensures the most appropriate fabric cutting and texture reproduction that matches the visual data. Visual data is input, and material design information is output.

[0100] Step 4:

[0101] Users access a virtual fitting room using their device. They can try on designs generated for their own 3D model. The system receives material design information as input and visualizes it, and the output is dynamic fitting data regarding the user's fit. This allows for real-time verification of movement and size evaluation.

[0102] Step 5:

[0103] Users review the generated design and try-on data on their device again and share it on social media via the communication network. In this process, the try-on data is used as input and exported to obtain feedback and comments from others.

[0104] Step 6:

[0105] If the user is satisfied with the design, they confirm their order using the terminal. The final input is order information (quantity, payment information, etc.), which the server receives. This output is instruction data for the manufacturing process, and the actual production of the product begins.

[0106] Step 7:

[0107] The server manages the manufacturing process and ships the manufactured products. The order processing unit sends instruction data to the production line, and the specific products are prepared. The finished products are delivered to the customers. The input is manufacturing instruction data, and the output is the finished product.

[0108] (Application Example 1)

[0109] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0110] There is a lack of platforms that digitize fashion design, allowing users to easily create, try on, and sell their own original designs. Furthermore, it is difficult for users to display and widely share their own designs on digital sales platforms. Solving these challenges is essential.

[0111] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0112] In this invention, the server includes means for receiving input visual design information and string data into an image generation device and generating a visual image based on the visual design information and string data; means for designing the structure and texture of a material based on the visual image; means for virtual fitting where the user can virtually try on the visual image and designed structure; and means for sales promotion where the user can list the generated design as a product on a digital sales platform. This makes it possible for users to create and easily sell original fashion designs.

[0113] 1. An "image generation device" is a device that generates high-quality visual images based on visual design information and string data received from a user.

[0114] 2. "Visual design information" refers to information that includes the visual elements necessary for users to create digital designs.

[0115] 3. "String data" refers to text-based data used to describe the specific content and features of a design.

[0116] 4. A "pattern generation device" is a device used to design the structure and texture of a material in detail based on a visual image.

[0117] 5. A "virtual fitting device" is a device that provides the function of allowing users to virtually try on clothing designed in a digital space.

[0118] 6. A "sales promotion device" is a device that has the function of easily listing user-generated designs on a digital sales platform and promoting sales.

[0119] 7. A "digital sales platform" is an online marketplace for providing and selling products designed by users to a large number of consumers.

[0120] The system implementing this invention provides a platform that allows users to digitize fashion designs and easily create, try on, and sell them. Users input visual design information and text data using a terminal, and the server processes this information to realize original designs.

[0121] The server first generates a visual image based on the visual design information and text data sent by the user, using an image generation device. Specifically, it uses generation AI models such as DALL-E and Stable Diffusion to create high-quality visual images.

[0122] Next, the server uses a pattern generator to design the structure and texture of the material in detail based on the generated visual image. This process uses texture data to perform calculations that reproduce a realistic material feel.

[0123] Next, users can virtually try on the designs using a virtual fitting device to see how they fit them. The virtual fitting device provides a real-time fitting experience using AR technology. Specific examples include Google® ARCore and Apple ARKit.

[0124] Ultimately, the sales promotion system allows users to list the generated designs as products on a digital sales platform and sell them widely. This system enables users to efficiently create digital fashion designs and deliver them directly to consumers.

[0125] As a concrete example, when a user enters the prompt "Generate a design image for an autumn wool overcoat," the image generator creates an original design, and the pattern generator reproduces the texture of wool. Then, the user can try on the garment on their avatar using a virtual fitting device, and the design can be made available for sale on the digital marketplace.

[0126] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0127] Step 1:

[0128] The user uses a terminal to input visual design information and text data, which are then sent to the server. The input data includes hand-drawn sketches or digital illustrations for visual design information, and text data that describes the specific details of the design (e.g., "Autumn wool overcoat"). It is crucial that this data is sent to the server.

[0129] Step 2:

[0130] Based on the received visual design information and string data, the server uses an image generation device to generate a visual image using a generation AI model (e.g., Stable Diffusion). During this process, data calculations are performed based on prompt statements, resulting in the output of a high-quality visual image. This visual image serves as the foundation for subsequent processes.

[0131] Step 3:

[0132] The server sends the generated visual image to the pattern generator, which designs the structure and texture of the material. Here, realistic material textures are reproduced through calculations using texture data. The output is a material pattern that allows for detailed examination of the texture.

[0133] Step 4:

[0134] The user uses a virtual fitting device to virtually try on clothes designed on their device. The server applies visual images and patterns to the user's avatar using AR technology. The output is a visualization of what the user would look like when trying on the design.

[0135] Step 5:

[0136] The user ultimately lists the designs generated using the sales promotion tool on the digital sales platform. The server processes this information and makes it available for sale in the digital marketplace. The input is the product listing information, and the output is the completion of the listing on the sales platform.

[0137] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0138] This invention provides a system that enables users to more effectively create unique and satisfying fashion designs based on their individual emotions. In addition to an image generation device, a pattern generation device, and a virtual fitting device, this system incorporates an emotion engine that recognizes the user's emotions and reflects them in the design process.

[0139] The user inputs visual design information and text data through a terminal. Once this data is sent to the server, an image generator receives the design information and generates a visual image. During this process, an emotion engine analyzes the user's facial expressions, mouse movements, and input speed to evaluate the user's emotional state. This evaluation is reflected in the style and color scheme of the generated design, resulting in a visual image optimized to the user's preferences and emotions.

[0140] The server further uses a pattern generation device to design material patterns and textures from visual images. Data on the user's emotions provided by the emotion engine influences color selection and texture in pattern design, providing users with more intuitive and appealing choices.

[0141] If a user wishes to virtually try on clothes, they can do so using a virtual fitting device. The server considers the user's emotional tendencies during the virtual fitting and suggests recommended outfits and accessories based on the fitting results. In addition, an emotion engine monitors the user's level of joy and excitement through the terminal and provides feedback to improve the selection of recommended outfits.

[0142] Furthermore, when users share their generated designs on social media, the emotion engine provides advice on how to share the designs in a way that positively impacts others. The device then uses this advice to create and optimize content.

[0143] As a concrete example, when a user begins designing sportswear on their device, if the emotion engine recognizes a high level of excitement, the server suggests energetic colors and dynamic patterns, and generates a design based on them. This design is then virtually tried on and presented as the optimal style for the user's preferred sports activity. The generated design is then shared on social media in a way that satisfies the user.

[0144] This system will allow users to experience an emotionally resonant fashion design process and efficiently create unique and personalized products.

[0145] The following describes the processing flow.

[0146] Step 1:

[0147] The user uses a terminal to input visual design information and text data into the interface. The terminal then prepares to send the input information to the server.

[0148] Step 2:

[0149] The device monitors the user's keyboard input speed, mouse movements, and other data in real time, and sends this data to the emotion engine.

[0150] Step 3:

[0151] The server receives the transmitted design information and emotion data from the emotion engine, and activates the image generation device. The generation device generates a visual image based on the design information.

[0152] Step 4:

[0153] The emotion engine analyzes the user's facial expression and interaction data to identify the user's current emotional state. The identified emotional state is then used as a parameter to determine the style and hue of the visual image.

[0154] Step 5:

[0155] The server generates a visual image and sends it to the terminal. The terminal then displays this image to the user.

[0156] Step 6:

[0157] The user reviews the displayed visual image and then requests a design for the material pattern and texture. The terminal sends this request to the server.

[0158] Step 7:

[0159] The server uses a pattern generation device to design material patterns and textures based on visual images and the user's emotional state. The designed data is then transmitted to the terminal.

[0160] Step 8:

[0161] When a user receives the design results on their device and wishes to virtually try on the product, they send a virtual try-on request from their device to the server.

[0162] Step 9:

[0163] The server activates a virtual fitting system and generates virtual try-on images using the user's design. At this time, the emotion engine re-evaluates the user's emotions and generates recommended styles and accessories.

[0164] Step 10:

[0165] The device displays the user the results of the virtual try-on and suggestions from the emotion engine. If the user reviews the results and is satisfied, they instruct the device to share them on social media.

[0166] Step 11:

[0167] The device requests the server to send content optimized for sharing on social media, the server generates the content, and sends back a link.

[0168] Step 12:

[0169] The user uses a terminal to place an order for the final generated design. This order information is sent to the server, and the manufacturing process is initiated through the order processing unit.

[0170] (Example 2)

[0171] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0172] Traditional fashion design systems have struggled to incorporate user emotions into the design process, making it difficult to generate designs optimized for individual users. Furthermore, they have failed to provide a way to positively influence others when sharing the generated designs on social media. Additionally, they have been unable to provide recommendations for trying on the generated designs that take user emotions into consideration.

[0173] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0174] In this invention, the server includes means for receiving design information and text information input to an image generation device and generating a visual image based on the design information and text information; means for an emotion analysis device that evaluates the user's emotional state from their facial expressions and operation logs and reflects the results in the design style and color scheme; and means for a pattern generation device that designs the material pattern and texture based on the visual image. This enables the generation of designs that are sensitive to the user's emotions, advice on how to share them, and recommendations for virtual try-ons based on emotions.

[0175] An "image generation device" is a device that generates a visual image based on input design information and text information.

[0176] An "emotion analysis device" is a device that evaluates the emotional state of a user from their facial expressions and operation logs, and reflects the evaluation results in the design style and color scheme.

[0177] A "pattern generation device" is a device that designs the patterns and textures of materials based on visual images.

[0178] A "virtual fitting device" is a device that allows users to virtually try on visual images and designed patterns, and then presents recommended outfits based on the results.

[0179] A "communication network" is a network system used for designing, transmitting, and receiving information.

[0180] A "social medium" is a communication platform for sharing generated designs.

[0181] This invention is a system that allows users to create unique and highly satisfying fashion designs based on their own emotional state. The system includes an image generation device, an emotion analysis device, a pattern generation device, and a virtual fitting device, providing a balanced fashion design process.

[0182] The user inputs visual design information and text information using a terminal. This design and text information is transmitted to a server via a communication network. The server uses an image generation device and a generation AI model to generate a visual image based on the received information. In this process, an emotion analysis device is used to take into account the user's emotional state.

[0183] The emotion analysis device analyzes and evaluates information obtained from the user's facial expressions and operation logs. This evaluation is reflected in the selection of color schemes and accent designs, ensuring an attractive design for the user.

[0184] From the generated visual images, the server uses a pattern generation device to design the patterns and textures of the materials. This design is optimized based on the user's emotional state, providing intuitive and original fashion ideas.

[0185] Furthermore, users can try on designs through a virtual fitting system. Based on the fitting results, the server suggests recommended outfits and accessories. These suggestions also reflect the user's emotional evaluation, providing a style that is best suited to the individual user.

[0186] For example, if a user enters a prompt such as "Design some energetic everyday casual wear," the system can generate fashion items with energetic color schemes and design patterns, and provide feedback to the user through a try-on process. In this way, users can gain an emotionally resonant and creative experience through the design process.

[0187] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0188] Step 1:

[0189] Users input visual design information and text data via a terminal. This input data is then transmitted directly to the server via the communication network. The terminal's interface is intuitive and designed to allow users to easily input data.

[0190] Step 2:

[0191] The server uses an image generation device to generate a visual image based on the received design information and text data. Specifically, a generation AI model is utilized, the input data is incorporated into the model, and the design is structured and color suggestions are made. The resulting visual image is then used in the next processing step.

[0192] Step 3:

[0193] The server uses an emotion analysis device to evaluate the user's emotional state. The input for this evaluation includes user facial expression data and operation logs obtained from the terminal. The emotion analysis device analyzes this data to identify what the user is feeling and reflects the results in the design's colors and style.

[0194] Step 4:

[0195] The server uses a pattern generator to design material patterns and textures based on the generated visual images. Visual images and emotional evaluation data are used as input. By suggesting patterns and textures, the server presents the user with optimized design options.

[0196] Step 5:

[0197] When a user performs a virtual try-on, the terminal displays a try-on image of the design generated through the virtual try-on device. Input data includes design and pattern information, and the output shows the tried-on image and recommended outfits. The server provides recommended styles and accessories based on the try-on results.

[0198] Step 6:

[0199] When a user shares a design they've created on social media, the device uses data from an emotion analysis device to advise on how to share it in a way that positively impacts others. The input data for sharing includes detailed design information, and an optimized sharing message is output.

[0200] (Application Example 2)

[0201] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0202] Modern consumers tend to seek unique fashion experiences that align with their individual emotions and personalities. However, traditional online fashion purchasing systems struggle to effectively capture consumer emotions and provide highly satisfying, personalized design suggestions.

[0203] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0204] In this invention, the server includes means for receiving visual design information and language data input to an image generation device and generating a visual representation based on the visual design information and language data; means for a structure generation device that designs the structure and properties of a material based on the visual representation; and means for an emotion analysis device that analyzes the user's emotional state and reflects it in the generated representation. This makes it possible to generate unique and highly satisfying fashion designs based on the user's emotions.

[0205] An "image generation device" is a device that has the function of generating visual representations based on input visual design information and linguistic data.

[0206] A "structure generation device" is a device for designing the structure and properties of a material based on visual representation.

[0207] A "virtual fitting device" is a device that allows users to virtually try on a fitting device using visual representations and designed structures.

[0208] An "emotion analysis device" is a device that analyzes the user's emotions from their facial expressions and actions, and reflects that emotional state in the generated expressions.

[0209] A "communication system" is a system that includes network technology used to share generated expressions across social media.

[0210] An "order processing device" is a device that receives orders based on a designed representation and processes them as manufacturing information.

[0211] To realize this invention, the server provides an integrated system that combines an image generation device, a structure generation device, a virtual wearable device, and an emotion analysis device. The server receives visual design information and language data input from the user via a terminal, and the image generation device generates a visual representation based on this information. Advanced image processing software is used in this process, specifically OpenCV and TENSORFLOW®.

[0212] The emotion analysis device uses machine learning algorithms to analyze the user's camera footage and input data to determine their emotional state. This emotional information influences the generated visual images and structure generation processes.

[0213] The structure generation device designs the appropriate structure and properties of materials based on visual representations, and obtains a virtual sense of wearing the garment through simulation. As a result, users can enjoy an intuitive and engaging fashion experience through a virtual wearing device via a terminal.

[0214] For example, if a user starts designing sportswear, and the emotion analysis device detects an excited state, the server will recommend generating energetic colors and dynamic patterns. This allows for design suggestions that resonate with the user's emotions. Furthermore, appropriate advice is provided to ensure that the generated designs are shared in the most suitable format across social media via the communication system.

[0215] As an example of generative AI use, the AI ​​model can present related items based on prompts such as, "Create a list of related fashion items to suggest when the user smiles." This results in a seamless and personalized user experience.

[0216] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0217] Step 1:

[0218] The user inputs visual design information and linguistic data using a terminal. The input information is received by the server and stored in a database. This information forms the basis for design generation in subsequent processing steps.

[0219] Step 2:

[0220] The server's image generator retrieves stored visual design information and language data. Based on the retrieved information, it generates a visual representation using OpenCV or TensorFlow. Here, the color scheme and style are generated according to user input.

[0221] Step 3:

[0222] The user's device captures video through its camera, and an emotion analysis device analyzes that video. Specifically, it uses a machine learning algorithm to analyze facial expressions and classify emotional states. The analysis results are sent to a server and used in the next stage.

[0223] Step 4:

[0224] The server operates a structure generation device based on the emotion analysis results, setting material structures and properties that reflect the user's emotions in the generated visual representation. This is a crucial step in generating designs optimized for the user's emotional state.

[0225] Step 5:

[0226] Users use a terminal to virtually try on designs using a virtual wearable device. The server provides complementary styling and improvement suggestions based on the user's emotional state. This data is used to enrich the user's visual experience.

[0227] Step 6:

[0228] After the user selects a design they are ultimately satisfied with, the server transmits the generated design via a communication system in a format that can be shared on social media. The AI ​​model also provides recommendations based on prompts regarding the style and message to be used when the user shares the design.

[0229] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0230] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0231] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0232] [Second Embodiment]

[0233] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0234] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0235] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0236] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0237] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0238] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0239] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0240] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0241] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0242] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0243] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0244] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0245] This invention digitizes the fashion design process and provides a system that allows users to easily create, try on, and order original designs. This system is implemented through an image generation device, a pattern generation device, a virtual fitting device, and integration with social media utilizing a communication network.

[0246] The user inputs their original design as visual design information and text data using a terminal. The server, upon receiving this data, uses an image generation device to generate a visual image based on the input. This visual image is then provided to the user for confirmation.

[0247] Next, the server uses a pattern generator to design the material's pattern and texture in detail from the generated visual image. This designed pattern is then sent to the terminal for the user to review.

[0248] Users can use a virtual fitting system to virtually try on designs and see how they fit them. The server generates and provides the virtual fitting results to the user in real time.

[0249] Furthermore, users can share designs and try-on results generated via communication networks on social media. This sharing feature allows users to widely showcase their designs and deepen their interactions with others.

[0250] Finally, users can use their devices to place orders for the designed products. Once an order is placed, the server sends the order data to the manufacturing process via an order processing unit. The completed products are then delivered to the users.

[0251] As a concrete example, consider a scenario where a user designs an outdoor jacket. The user inputs a rough sketch and text such as "waterproof outdoor jacket" into a terminal. The server receives this and creates a visual image using an image generator. Next, a pattern generator designs a pattern for waterproof material, reproducing a realistic texture. The user tries on the jacket using a virtual fitting device, checks the design, and then shares it on social media. Finally, the user can order the jacket and receive the manufactured product.

[0252] Thus, the present invention enables users to efficiently create and commercialize unique fashion designs.

[0253] The following describes the processing flow.

[0254] Step 1:

[0255] The user inputs visual design information and text data for the design via their device and clicks the "Send" button. The device then sends this data to the server.

[0256] Step 2:

[0257] The server receives visual design information and text data sent from the user and performs data analysis. Preprocessing is then carried out to prepare the image for use with the image generation device.

[0258] Step 3:

[0259] The server activates the image generation device and generates a visual image based on the received data. This generated visual image is then sent to the user's terminal.

[0260] Step 4:

[0261] The user confirms the visual image received on the device and enters instructions into the device to proceed to the next process.

[0262] Step 5:

[0263] The server uses a pattern generation device to design the material's pattern and texture based on a visual image. This designed pattern data is then transmitted to the terminal.

[0264] Step 6:

[0265] If the user reviews the designed pattern on their device and decides to proceed with a virtual try-on, a virtual try-on request is sent from the device to the server.

[0266] Step 7:

[0267] The server activates the virtual fitting device and performs a virtual try-on for the user. It generates images and videos of the try-on results and sends them to the user's terminal.

[0268] Step 8:

[0269] The user checks the virtual try-on results on their device, and if satisfied, enters information to share the product on social media and sends it to the server.

[0270] Step 9:

[0271] Based on the information the server receives for sharing, it generates links and images for social media such as SNS and provides them to the user.

[0272] Step 10:

[0273] The user uses a device to decide on a product order and sends the order information to the server.

[0274] Step 11:

[0275] The server initiates order processing, transfers the order data to the manufacturing partner, and starts the manufacturing process.

[0276] Step 12:

[0277] The product is manufactured and, once completed, delivered to the user. The user can check the delivery status on their device.

[0278] (Example 1)

[0279] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0280] In the modern fashion design process, there is a problem in that it takes a lot of effort and time for consumers to translate their ideas into concrete forms and try them on to confirm their quality. Furthermore, it is difficult to easily share the generated designs with others. In addition, the process of turning a consumer's favorite design into a product is also becoming increasingly complex.

[0281] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0282] In this invention, the server includes means for receiving design information and character information by an image generation unit to generate visual data, means for a pattern generation unit to specify in detail the design and texture of a material based on the visual data, and means for a virtual try-on unit for a user to perform virtual try-on with the visual data and the specified design. Thereby, consumers can quickly try on individual design ideas, receive evaluations, and commercialize them.

[0283] The "image generation unit" is a device having a function of receiving design information and character information and generating visual data based on them.

[0284] The "pattern generation unit" is a device having a function of specifying in detail the design and texture of a material based on the generated visual data.

[0285] The "virtual try-on unit" is a device having a function of a user performing virtual try-on based on the generated visual data and the specified design.

[0286] "Visual data" is a digital-formatted image generated based on design information and character information.

[0287] "Design information" is data including visual design elements provided by a user. <​​​​​​​​​​​​​A "user" refers to an individual who inputs their design ideas into the system, checks the results, tries them on, and shares them.

[0292] To implement this invention, the following system components are used. The system mainly consists of a server, a terminal, and a user.

[0293] User: Users utilize their devices to input their fashion design ideas. The input data consists of visual design information and textual information. Users can take photos of design sketches with their device's camera and upload them as digital images. They can also clarify the design intent by inputting specific textual information, such as "sporty raincoat."

[0294] Server: The server generates visual data based on design and text information received from the user using the image generation unit. Specifically, it analyzes the data using a generation AI model to create a digital image of the design. The AI ​​model utilizes the context generated from the prompt text to draw the most suitable design from the input data. For example, by inputting the prompt "Rough sketch: Hooded long jacket, Text: Waterproof outdoor wear, Color: Dark green," the corresponding visual data will be generated.

[0295] The server then uses a pattern generation unit to design the material and its texture in detail based on this visual data. Specifically, it uses digital fabric simulation to virtually reproduce the cutting and sewing of the fabric and determine the optimal material and texture for the user's design.

[0296] User: Users utilize a virtual fitting room via their device to try on designs onto their 3D models based on generated visual data and design information. During this process, they can check the fit in 360-degree views, taking into account their full body movements and body shape. This allows them to evaluate the suitability of the design before trying it on.

[0297] The completed design can be shared by users through social media via communication networks. This sharing function allows users to get feedback on the design from others and receive new ideas.

[0298] Finally, the user places an order using a terminal to have their preferred design manufactured. This order is managed by the server in the order processing section, and the product is manufactured using a system belonging to the manufacturing process. Once the product is completed, it is sent to the specified delivery address.

[0299] This system allows users to easily bring their unique fashion ideas to life, try them on, share them with others, and ultimately turn them into finished products.

[0300] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0301] Step 1:

[0302] Users input design information using their devices. Specifically, they use the device's camera function to take pictures of sketches and upload them to the system as image data. They also input text information, such as descriptions and characteristics of the design. The input for this process consists of image and text data, which are then sent to the server.

[0303] Step 2:

[0304] The server analyzes the received design information. It utilizes the image generation unit to pass input data to a generation AI model, thereby generating digital visual data. Specifically, the AI ​​model analyzes prompt text and visually depicts the design image. In this process, visual data is output from image and text data inputs.

[0305] Step 3:

[0306] The server sets the material design and texture in detail based on visual data. Using the pattern generation unit, it performs a digital fabric simulation of the material. As a result, the most appropriate cutting of the fabric and reproduction of the texture that match the visual data are carried out. Visual data is input and the design information of the material is output.

[0307] Step 4:

[0308] The user uses the terminal to access the virtual fitting part. It is possible to try on the design generated for their 3D model. Receiving the design information of the material as input and visualizing it, the output is the dynamic fitting trial data regarding the user. Thus, the operation check and size evaluation are carried out in real time.

[0309] Step 5:

[0310] The user checks the design and trial data generated on the terminal again and shares them with social media via the communication network. In this process, the trial data is used as input and exported to obtain feedback and comments from others.

[0311] Step 6:

[0312] If the user is satisfied with the design, they use the terminal to confirm the order. The final input is the order information (quantity, payment information, etc.), and the server receives this. This output is the instruction data for the manufacturing process, and the actual manufacturing of the product starts.

[0313] Step 7:

[0314] The server manages the manufacturing process and ships the manufactured product. The order processing unit sends the instruction data to the production line, and the preparation of the specific product is made. The completed product is delivered to the customer. The input is the manufacturing instruction data, and the output is the finished product.

[0315] (Application Example 1)

[0316] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0317] There is a lack of platforms that digitize fashion design, allowing users to easily create, try on, and sell their own original designs. Furthermore, it is difficult for users to display and widely share their own designs on digital sales platforms. Solving these challenges is essential.

[0318] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0319] In this invention, the server includes means for receiving input visual design information and string data into an image generation device and generating a visual image based on the visual design information and string data; means for designing the structure and texture of a material based on the visual image; means for virtual fitting where the user can virtually try on the visual image and designed structure; and means for sales promotion where the user can list the generated design as a product on a digital sales platform. This makes it possible for users to create and easily sell original fashion designs.

[0320] 1. An "image generation device" is a device that generates high-quality visual images based on visual design information and string data received from a user.

[0321] 2. "Visual design information" refers to information that includes the visual elements necessary for users to create digital designs.

[0322] 3. "String data" refers to text-based data used to describe the specific content and features of a design.

[0323] 4. A "pattern generation device" is a device used to design the structure and texture of a material in detail based on a visual image.

[0324] 5. A "virtual fitting device" is a device that provides the function of allowing users to virtually try on clothing designed in a digital space.

[0325] 6. A "sales promotion device" is a device that has the function of easily listing user-generated designs on a digital sales platform and promoting sales.

[0326] 7. A "digital sales platform" is an online marketplace for providing and selling products designed by users to a large number of consumers.

[0327] The system implementing this invention provides a platform that allows users to digitize fashion designs and easily create, try on, and sell them. Users input visual design information and text data using a terminal, and the server processes this information to realize original designs.

[0328] The server first generates a visual image based on the visual design information and text data sent by the user, using an image generation device. Specifically, it uses generation AI models such as DALL-E and Stable Diffusion to create high-quality visual images.

[0329] Next, the server uses a pattern generator to design the structure and texture of the material in detail based on the generated visual image. This process uses texture data to perform calculations that reproduce a realistic material feel.

[0330] Next, users can virtually try on the designs using a virtual fitting device to see how they fit them. The virtual fitting device uses AR technology to provide a real-time try-on experience. Specific examples include Google ARCore and Apple ARKit.

[0331] Ultimately, the sales promotion system allows users to list the generated designs as products on a digital sales platform and sell them widely. This system enables users to efficiently create digital fashion designs and deliver them directly to consumers.

[0332] As a concrete example, when a user enters the prompt "Generate a design image for an autumn wool overcoat," the image generator creates an original design, and the pattern generator reproduces the texture of wool. Then, the user can try on the garment on their avatar using a virtual fitting device, and the design can be made available for sale on the digital marketplace.

[0333] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0334] Step 1:

[0335] The user uses a terminal to input visual design information and text data, which are then sent to the server. The input data includes hand-drawn sketches or digital illustrations for visual design information, and text data that describes the specific details of the design (e.g., "Autumn wool overcoat"). It is crucial that this data is sent to the server.

[0336] Step 2:

[0337] Based on the received visual design information and string data, the server uses an image generation device to generate a visual image using a generation AI model (e.g., Stable Diffusion). During this process, data calculations are performed based on prompt statements, resulting in the output of a high-quality visual image. This visual image serves as the foundation for subsequent processes.

[0338] Step 3:

[0339] The server sends the generated visual image to the pattern generator, which designs the structure and texture of the material. Here, realistic material textures are reproduced through calculations using texture data. The output is a material pattern that allows for detailed examination of the texture.

[0340] Step 4:

[0341] The user uses a virtual fitting device to virtually try on clothes designed on their device. The server applies visual images and patterns to the user's avatar using AR technology. The output is a visualization of what the user would look like when trying on the design.

[0342] Step 5:

[0343] The user ultimately lists the designs generated using the sales promotion tool on the digital sales platform. The server processes this information and makes it available for sale in the digital marketplace. The input is the product listing information, and the output is the completion of the listing on the sales platform.

[0344] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0345] This invention provides a system that enables users to more effectively create unique and satisfying fashion designs based on their individual emotions. In addition to an image generation device, a pattern generation device, and a virtual fitting device, this system incorporates an emotion engine that recognizes the user's emotions and reflects them in the design process.

[0346] The user inputs visual design information and text data through a terminal. Once this data is sent to the server, an image generator receives the design information and generates a visual image. During this process, an emotion engine analyzes the user's facial expressions, mouse movements, and input speed to evaluate the user's emotional state. This evaluation is reflected in the style and color scheme of the generated design, resulting in a visual image optimized to the user's preferences and emotions.

[0347] The server further uses a pattern generation device to design material patterns and textures from visual images. Data on the user's emotions provided by the emotion engine influences color selection and texture in pattern design, providing users with more intuitive and appealing choices.

[0348] If a user wishes to virtually try on clothes, they can do so using a virtual fitting device. The server considers the user's emotional tendencies during the virtual fitting and suggests recommended outfits and accessories based on the fitting results. In addition, an emotion engine monitors the user's level of joy and excitement through the terminal and provides feedback to improve the selection of recommended outfits.

[0349] Furthermore, when users share their generated designs on social media, the emotion engine provides advice on how to share the designs in a way that positively impacts others. The device then uses this advice to create and optimize content.

[0350] As a concrete example, when a user begins designing sportswear on their device, if the emotion engine recognizes a high level of excitement, the server suggests energetic colors and dynamic patterns, and generates a design based on them. This design is then virtually tried on and presented as the optimal style for the user's preferred sports activity. The generated design is then shared on social media in a way that satisfies the user.

[0351] This system will allow users to experience an emotionally resonant fashion design process and efficiently create unique and personalized products.

[0352] The following describes the processing flow.

[0353] Step 1:

[0354] The user uses a terminal to input visual design information and text data into the interface. The terminal then prepares to send the input information to the server.

[0355] Step 2:

[0356] The device monitors the user's keyboard input speed, mouse movements, and other data in real time, and sends this data to the emotion engine.

[0357] Step 3:

[0358] The server receives the transmitted design information and emotion data from the emotion engine, and activates the image generation device. The generation device generates a visual image based on the design information.

[0359] Step 4:

[0360] The emotion engine analyzes the user's facial expression and interaction data to identify the user's current emotional state. The identified emotional state is then used as a parameter to determine the style and hue of the visual image.

[0361] Step 5:

[0362] The server generates a visual image and sends it to the terminal. The terminal then displays this image to the user.

[0363] Step 6:

[0364] The user reviews the displayed visual image and then requests a design for the material pattern and texture. The terminal sends this request to the server.

[0365] Step 7:

[0366] The server uses a pattern generation device to design material patterns and textures based on visual images and the user's emotional state. The designed data is then transmitted to the terminal.

[0367] Step 8:

[0368] When a user receives the design results on their device and wishes to virtually try on the product, they send a virtual try-on request from their device to the server.

[0369] Step 9:

[0370] The server activates a virtual fitting system and generates virtual try-on images using the user's design. At this time, the emotion engine re-evaluates the user's emotions and generates recommended styles and accessories.

[0371] Step 10:

[0372] The device displays the user the results of the virtual try-on and suggestions from the emotion engine. If the user reviews the results and is satisfied, they instruct the device to share them on social media.

[0373] Step 11:

[0374] The device requests the server to send content optimized for sharing on social media, the server generates the content, and sends back a link.

[0375] Step 12:

[0376] The user uses a terminal to place an order for the final generated design. This order information is sent to the server, and the manufacturing process is initiated through the order processing unit.

[0377] (Example 2)

[0378] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0379] Traditional fashion design systems have struggled to incorporate user emotions into the design process, making it difficult to generate designs optimized for individual users. Furthermore, they have failed to provide a way to positively influence others when sharing the generated designs on social media. Additionally, they have been unable to provide recommendations for trying on the generated designs that take user emotions into consideration.

[0380] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0381] In this invention, the server includes means for receiving design information and text information input to an image generation device and generating a visual image based on the design information and text information; means for an emotion analysis device that evaluates the user's emotional state from their facial expressions and operation logs and reflects the results in the design style and color scheme; and means for a pattern generation device that designs the material pattern and texture based on the visual image. This enables the generation of designs that are sensitive to the user's emotions, advice on how to share them, and recommendations for virtual try-ons based on emotions.

[0382] An "image generation device" is a device that generates a visual image based on input design information and text information.

[0383] An "emotion analysis device" is a device that evaluates the emotional state of a user from their facial expressions and operation logs, and reflects the evaluation results in the design style and color scheme.

[0384] A "pattern generation device" is a device that designs the patterns and textures of materials based on visual images.

[0385] A "virtual fitting device" is a device that allows users to virtually try on visual images and designed patterns, and then presents recommended outfits based on the results.

[0386] A "communication network" is a network system used for designing, transmitting, and receiving information.

[0387] A "social medium" is a communication platform for sharing generated designs.

[0388] This invention is a system that allows users to create unique and highly satisfying fashion designs based on their own emotional state. The system includes an image generation device, an emotion analysis device, a pattern generation device, and a virtual fitting device, providing a balanced fashion design process.

[0389] The user inputs visual design information and text information using a terminal. This design and text information is transmitted to a server via a communication network. The server uses an image generation device and a generation AI model to generate a visual image based on the received information. In this process, an emotion analysis device is used to take into account the user's emotional state.

[0390] The emotion analysis device analyzes and evaluates information obtained from the user's facial expressions and operation logs. This evaluation is reflected in the selection of color schemes and accent designs, ensuring an attractive design for the user.

[0391] From the generated visual images, the server uses a pattern generation device to design the patterns and textures of the materials. This design is optimized based on the user's emotional state, providing intuitive and original fashion ideas.

[0392] Furthermore, users can try on designs through a virtual fitting system. Based on the fitting results, the server suggests recommended outfits and accessories. These suggestions also reflect the user's emotional evaluation, providing a style that is best suited to the individual user.

[0393] For example, if a user enters a prompt such as "Design some energetic everyday casual wear," the system can generate fashion items with energetic color schemes and design patterns, and provide feedback to the user through a try-on process. In this way, users can gain an emotionally resonant and creative experience through the design process.

[0394] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0395] Step 1:

[0396] Users input visual design information and text data via a terminal. This input data is then transmitted directly to the server via the communication network. The terminal's interface is intuitive and designed to allow users to easily input data.

[0397] Step 2:

[0398] The server uses an image generation device to generate a visual image based on the received design information and text data. Specifically, a generation AI model is utilized, the input data is incorporated into the model, and the design is structured and color suggestions are made. The resulting visual image is then used in the next processing step.

[0399] Step 3:

[0400] The server uses an emotion analysis device to evaluate the user's emotional state. The input for this evaluation includes user facial expression data and operation logs obtained from the terminal. The emotion analysis device analyzes this data to identify what the user is feeling and reflects the results in the design's colors and style.

[0401] Step 4:

[0402] The server uses a pattern generator to design material patterns and textures based on the generated visual images. Visual images and emotional evaluation data are used as input. By suggesting patterns and textures, the server presents the user with optimized design options.

[0403] Step 5:

[0404] When a user performs a virtual try-on, the terminal displays a try-on image of the design generated through the virtual try-on device. Input data includes design and pattern information, and the output shows the tried-on image and recommended outfits. The server provides recommended styles and accessories based on the try-on results.

[0405] Step 6:

[0406] When a user shares a design they've created on social media, the device uses data from an emotion analysis device to advise on how to share it in a way that positively impacts others. The input data for sharing includes detailed design information, and an optimized sharing message is output.

[0407] (Application Example 2)

[0408] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0409] Modern consumers tend to seek unique fashion experiences that align with their individual emotions and personalities. However, traditional online fashion purchasing systems struggle to effectively capture consumer emotions and provide highly satisfying, personalized design suggestions.

[0410] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0411] In this invention, the server includes means for receiving visual design information and language data input to an image generation device and generating a visual representation based on the visual design information and language data; means for a structure generation device that designs the structure and properties of a material based on the visual representation; and means for an emotion analysis device that analyzes the user's emotional state and reflects it in the generated representation. This makes it possible to generate unique and highly satisfying fashion designs based on the user's emotions.

[0412] An "image generation device" is a device that has the function of generating visual representations based on input visual design information and linguistic data.

[0413] A "structure generation device" is a device for designing the structure and properties of a material based on visual representation.

[0414] A "virtual fitting device" is a device that allows users to virtually try on a fitting device using visual representations and designed structures.

[0415] An "emotion analysis device" is a device that analyzes the user's emotions from their facial expressions and actions, and reflects that emotional state in the generated expressions.

[0416] A "communication system" is a system that includes network technology used to share generated expressions across social media.

[0417] An "order processing device" is a device that receives orders based on a designed representation and processes them as manufacturing information.

[0418] To realize this invention, the server provides an integrated system that combines an image generation device, a structure generation device, a virtual wearable device, and an emotion analysis device. The server receives visual design information and language data input from the user via a terminal, and the image generation device generates a visual representation based on this information. Advanced image processing software is used in this process, specifically OpenCV and TensorFlow.

[0419] The emotion analysis device uses machine learning algorithms to analyze the user's camera footage and input data to determine their emotional state. This emotional information influences the generated visual images and structure generation processes.

[0420] The structure generation device designs the appropriate structure and properties of materials based on visual representations, and obtains a virtual sense of wearing the garment through simulation. As a result, users can enjoy an intuitive and engaging fashion experience through a virtual wearing device via a terminal.

[0421] For example, if a user starts designing sportswear, and the emotion analysis device detects an excited state, the server will recommend generating energetic colors and dynamic patterns. This allows for design suggestions that resonate with the user's emotions. Furthermore, appropriate advice is provided to ensure that the generated designs are shared in the most suitable format across social media via the communication system.

[0422] As an example of generative AI use, the AI ​​model can present related items based on prompts such as, "Create a list of related fashion items to suggest when the user smiles." This results in a seamless and personalized user experience.

[0423] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0424] Step 1:

[0425] The user inputs visual design information and linguistic data using a terminal. The input information is received by the server and stored in a database. This information forms the basis for design generation in subsequent processing steps.

[0426] Step 2:

[0427] The server's image generator retrieves stored visual design information and language data. Based on the retrieved information, it generates a visual representation using OpenCV or TensorFlow. Here, the color scheme and style are generated according to user input.

[0428] Step 3:

[0429] The user's device captures video through its camera, and an emotion analysis device analyzes that video. Specifically, it uses a machine learning algorithm to analyze facial expressions and classify emotional states. The analysis results are sent to a server and used in the next stage.

[0430] Step 4:

[0431] The server operates a structure generation device based on the emotion analysis results, setting material structures and properties that reflect the user's emotions in the generated visual representation. This is a crucial step in generating designs optimized for the user's emotional state.

[0432] Step 5:

[0433] Users use a terminal to virtually try on designs using a virtual wearable device. The server provides complementary styling and improvement suggestions based on the user's emotional state. This data is used to enrich the user's visual experience.

[0434] Step 6:

[0435] After the user selects a design they are ultimately satisfied with, the server transmits the generated design via a communication system in a format that can be shared on social media. The AI ​​model also provides recommendations based on prompts regarding the style and message to be used when the user shares the design.

[0436] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0437] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0438] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0439] [Third Embodiment]

[0440] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0441] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0442] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0443] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0444] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0445] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0446] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0447] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0448] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0449] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0450] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0451] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0452] This invention digitizes the fashion design process and provides a system that allows users to easily create, try on, and order original designs. This system is implemented through an image generation device, a pattern generation device, a virtual fitting device, and integration with social media utilizing a communication network.

[0453] The user inputs their original design as visual design information and text data using a terminal. The server, upon receiving this data, uses an image generation device to generate a visual image based on the input. This visual image is then provided to the user for confirmation.

[0454] Next, the server uses a pattern generator to design the material's pattern and texture in detail from the generated visual image. This designed pattern is then sent to the terminal for the user to review.

[0455] Users can use a virtual fitting system to virtually try on designs and see how they fit them. The server generates and provides the virtual fitting results to the user in real time.

[0456] Furthermore, users can share designs and try-on results generated via communication networks on social media. This sharing feature allows users to widely showcase their designs and deepen their interactions with others.

[0457] Finally, users can use their devices to place orders for the designed products. Once an order is placed, the server sends the order data to the manufacturing process via an order processing unit. The completed products are then delivered to the users.

[0458] As a concrete example, consider a scenario where a user designs an outdoor jacket. The user inputs a rough sketch and text such as "waterproof outdoor jacket" into a terminal. The server receives this and creates a visual image using an image generator. Next, a pattern generator designs a pattern for waterproof material, reproducing a realistic texture. The user tries on the jacket using a virtual fitting device, checks the design, and then shares it on social media. Finally, the user can order the jacket and receive the manufactured product.

[0459] Thus, the present invention enables users to efficiently create and commercialize unique fashion designs.

[0460] The following describes the processing flow.

[0461] Step 1:

[0462] The user inputs visual design information and text data for the design via their device and clicks the "Send" button. The device then sends this data to the server.

[0463] Step 2:

[0464] The server receives visual design information and text data sent from the user and performs data analysis. Preprocessing is then carried out to prepare the image for use with the image generation device.

[0465] Step 3:

[0466] The server activates the image generation device and generates a visual image based on the received data. This generated visual image is then sent to the user's terminal.

[0467] Step 4:

[0468] The user confirms the visual image received on the device and enters instructions into the device to proceed to the next process.

[0469] Step 5:

[0470] The server uses a pattern generation device to design the material's pattern and texture based on a visual image. This designed pattern data is then transmitted to the terminal.

[0471] Step 6:

[0472] If the user reviews the designed pattern on their device and decides to proceed with a virtual try-on, a virtual try-on request is sent from the device to the server.

[0473] Step 7:

[0474] The server activates the virtual fitting device and performs a virtual try-on for the user. It generates images and videos of the try-on results and sends them to the user's terminal.

[0475] Step 8:

[0476] The user checks the virtual try-on results on their device, and if satisfied, enters information to share the product on social media and sends it to the server.

[0477] Step 9:

[0478] Based on the information the server receives for sharing, it generates links and images for social media such as SNS and provides them to the user.

[0479] Step 10:

[0480] The user uses a device to decide on a product order and sends the order information to the server.

[0481] Step 11:

[0482] The server initiates order processing, transfers the order data to the manufacturing partner, and starts the manufacturing process.

[0483] Step 12:

[0484] The product is manufactured and, once completed, delivered to the user. The user can check the delivery status on their device.

[0485] (Example 1)

[0486] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0487] In the modern fashion design process, there is a problem in that it takes a lot of effort and time for consumers to translate their ideas into concrete forms and try them on to confirm their quality. Furthermore, it is difficult to easily share the generated designs with others. In addition, the process of turning a consumer's favorite design into a product is also becoming increasingly complex.

[0488] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0489] In this invention, the server includes means for receiving design information and text information from an image generation unit and generating visual data, means for a pattern generation unit that sets the material design and texture in detail based on the visual data, and means for a virtual fitting unit that allows the user to virtually try on the visual data and set design. This makes it possible for consumers to quickly try on individual design ideas, receive evaluations, and then commercialize them.

[0490] The "image generation unit" is a device that receives design information and text information and generates visual data based on them.

[0491] The "pattern generation unit" is a device that has the function of setting the design and texture of materials in detail based on the generated visual data.

[0492] A "virtual fitting room" is a device that allows users to virtually try on clothes based on generated visual data and pre-configured designs.

[0493] "Visual data" refers to digital images generated based on design information and textual information.

[0494] "Design information" refers to data that includes visual design elements provided by the user.

[0495] "Textual information" refers to text data entered by users to express descriptions and characteristics related to the design.

[0496] "Material design" is the process of determining the structure and pattern of materials necessary to realize a design.

[0497] "Texture" is a concept that refers to the characteristics of a material in terms of its appearance and feel.

[0498] A "user" refers to an individual who inputs their design ideas into the system, checks the results, tries them on, and shares them.

[0499] To implement this invention, the following system components are used. The system mainly consists of a server, a terminal, and a user.

[0500] User: Users utilize their devices to input their fashion design ideas. The input data consists of visual design information and textual information. Users can take photos of design sketches with their device's camera and upload them as digital images. They can also clarify the design intent by inputting specific textual information, such as "sporty raincoat."

[0501] Server: The server generates visual data based on design and text information received from the user using the image generation unit. Specifically, it analyzes the data using a generation AI model to create a digital image of the design. The AI ​​model utilizes the context generated from the prompt text to draw the most suitable design from the input data. For example, by inputting the prompt "Rough sketch: Hooded long jacket, Text: Waterproof outdoor wear, Color: Dark green," the corresponding visual data will be generated.

[0502] The server then uses a pattern generation unit to design the material and its texture in detail based on this visual data. Specifically, it uses digital fabric simulation to virtually reproduce the cutting and sewing of the fabric and determine the optimal material and texture for the user's design.

[0503] User: Users utilize a virtual fitting room via their device to try on designs onto their 3D models based on generated visual data and design information. During this process, they can check the fit in 360-degree views, taking into account their full body movements and body shape. This allows them to evaluate the suitability of the design before trying it on.

[0504] The completed design can be shared by users through social media via communication networks. This sharing function allows users to get feedback on the design from others and receive new ideas.

[0505] Finally, the user places an order using a terminal to have their preferred design manufactured. This order is managed by the server in the order processing section, and the product is manufactured using a system belonging to the manufacturing process. Once the product is completed, it is sent to the specified delivery address.

[0506] This system allows users to easily bring their unique fashion ideas to life, try them on, share them with others, and ultimately turn them into finished products.

[0507] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0508] Step 1:

[0509] Users input design information using their devices. Specifically, they use the device's camera function to take pictures of sketches and upload them to the system as image data. They also input text information, such as descriptions and characteristics of the design. The input for this process consists of image and text data, which are then sent to the server.

[0510] Step 2:

[0511] The server analyzes the received design information. It utilizes the image generation unit to pass input data to a generation AI model, thereby generating digital visual data. Specifically, the AI ​​model analyzes prompt text and visually depicts the design image. In this process, visual data is output from image and text data inputs.

[0512] Step 3:

[0513] The server sets the material design and texture in detail based on the visual data. A pattern generation unit performs a digital fabric simulation of the material. This ensures the most appropriate fabric cutting and texture reproduction that matches the visual data. Visual data is input, and material design information is output.

[0514] Step 4:

[0515] Users access a virtual fitting room using their device. They can try on designs generated for their own 3D model. The system receives material design information as input and visualizes it, and the output is dynamic fitting data regarding the user's fit. This allows for real-time verification of movement and size evaluation.

[0516] Step 5:

[0517] Users review the generated design and try-on data on their device again and share it on social media via the communication network. In this process, the try-on data is used as input and exported to obtain feedback and comments from others.

[0518] Step 6:

[0519] If the user is satisfied with the design, they confirm their order using the terminal. The final input is order information (quantity, payment information, etc.), which the server receives. This output is instruction data for the manufacturing process, and the actual production of the product begins.

[0520] Step 7:

[0521] The server manages the manufacturing process and ships the manufactured products. The order processing unit sends instruction data to the production line, and the specific products are prepared. The finished products are delivered to the customers. The input is manufacturing instruction data, and the output is the finished product.

[0522] (Application Example 1)

[0523] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0524] There is a lack of platforms that digitize fashion design, allowing users to easily create, try on, and sell their own original designs. Furthermore, it is difficult for users to display and widely share their own designs on digital sales platforms. Solving these challenges is essential.

[0525] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0526] In this invention, the server includes means for receiving input visual design information and string data into an image generation device and generating a visual image based on the visual design information and string data; means for designing the structure and texture of a material based on the visual image; means for virtual fitting where the user can virtually try on the visual image and designed structure; and means for sales promotion where the user can list the generated design as a product on a digital sales platform. This makes it possible for users to create and easily sell original fashion designs.

[0527] 1. An "image generation device" is a device that generates high-quality visual images based on visual design information and string data received from a user.

[0528] 2. "Visual design information" refers to information that includes the visual elements necessary for users to create digital designs.

[0529] 3. "String data" refers to text-based data used to describe the specific content and features of a design.

[0530] 4. A "pattern generation device" is a device used to design the structure and texture of a material in detail based on a visual image.

[0531] 5. A "virtual fitting device" is a device that provides the function of allowing users to virtually try on clothing designed in a digital space.

[0532] 6. A "sales promotion device" is a device that has the function of easily listing user-generated designs on a digital sales platform and promoting sales.

[0533] 7. A "digital sales platform" is an online marketplace for providing and selling products designed by users to a large number of consumers.

[0534] The system implementing this invention provides a platform that allows users to digitize fashion designs and easily create, try on, and sell them. Users input visual design information and text data using a terminal, and the server processes this information to realize original designs.

[0535] The server first generates a visual image based on the visual design information and text data sent by the user, using an image generation device. Specifically, it uses generation AI models such as DALL-E and Stable Diffusion to create high-quality visual images.

[0536] Next, the server uses a pattern generator to design the structure and texture of the material in detail based on the generated visual image. This process uses texture data to perform calculations that reproduce a realistic material feel.

[0537] Next, users can virtually try on the designs using a virtual fitting device to see how they fit them. The virtual fitting device uses AR technology to provide a real-time try-on experience. Specific examples include Google ARCore and Apple ARKit.

[0538] Ultimately, the sales promotion system allows users to list the generated designs as products on a digital sales platform and sell them widely. This system enables users to efficiently create digital fashion designs and deliver them directly to consumers.

[0539] As a concrete example, when a user enters the prompt "Generate a design image for an autumn wool overcoat," the image generator creates an original design, and the pattern generator reproduces the texture of wool. Then, the user can try on the garment on their avatar using a virtual fitting device, and the design can be made available for sale on the digital marketplace.

[0540] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0541] Step 1:

[0542] The user uses a terminal to input visual design information and text data, which are then sent to the server. The input data includes hand-drawn sketches or digital illustrations for visual design information, and text data that describes the specific details of the design (e.g., "Autumn wool overcoat"). It is crucial that this data is sent to the server.

[0543] Step 2:

[0544] Based on the received visual design information and string data, the server uses an image generation device to generate a visual image using a generation AI model (e.g., Stable Diffusion). During this process, data calculations are performed based on prompt statements, resulting in the output of a high-quality visual image. This visual image serves as the foundation for subsequent processes.

[0545] Step 3:

[0546] The server sends the generated visual image to the pattern generator, which designs the structure and texture of the material. Here, realistic material textures are reproduced through calculations using texture data. The output is a material pattern that allows for detailed examination of the texture.

[0547] Step 4:

[0548] The user uses a virtual fitting device to virtually try on clothes designed on their device. The server applies visual images and patterns to the user's avatar using AR technology. The output is a visualization of what the user would look like when trying on the design.

[0549] Step 5:

[0550] The user ultimately lists the designs generated using the sales promotion tool on the digital sales platform. The server processes this information and makes it available for sale in the digital marketplace. The input is the product listing information, and the output is the completion of the listing on the sales platform.

[0551] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0552] This invention provides a system that enables users to more effectively create unique and satisfying fashion designs based on their individual emotions. In addition to an image generation device, a pattern generation device, and a virtual fitting device, this system incorporates an emotion engine that recognizes the user's emotions and reflects them in the design process.

[0553] The user inputs visual design information and text data through a terminal. Once this data is sent to the server, an image generator receives the design information and generates a visual image. During this process, an emotion engine analyzes the user's facial expressions, mouse movements, and input speed to evaluate the user's emotional state. This evaluation is reflected in the style and color scheme of the generated design, resulting in a visual image optimized to the user's preferences and emotions.

[0554] The server further uses a pattern generation device to design material patterns and textures from visual images. Data on the user's emotions provided by the emotion engine influences color selection and texture in pattern design, providing users with more intuitive and appealing choices.

[0555] If a user wishes to virtually try on clothes, they can do so using a virtual fitting device. The server considers the user's emotional tendencies during the virtual fitting and suggests recommended outfits and accessories based on the fitting results. In addition, an emotion engine monitors the user's level of joy and excitement through the terminal and provides feedback to improve the selection of recommended outfits.

[0556] Furthermore, when users share their generated designs on social media, the emotion engine provides advice on how to share the designs in a way that positively impacts others. The device then uses this advice to create and optimize content.

[0557] As a concrete example, when a user begins designing sportswear on their device, if the emotion engine recognizes a high level of excitement, the server suggests energetic colors and dynamic patterns, and generates a design based on them. This design is then virtually tried on and presented as the optimal style for the user's preferred sports activity. The generated design is then shared on social media in a way that satisfies the user.

[0558] This system will allow users to experience an emotionally resonant fashion design process and efficiently create unique and personalized products.

[0559] The following describes the processing flow.

[0560] Step 1:

[0561] The user uses a terminal to input visual design information and text data into the interface. The terminal then prepares to send the input information to the server.

[0562] Step 2:

[0563] The device monitors the user's keyboard input speed, mouse movements, and other data in real time, and sends this data to the emotion engine.

[0564] Step 3:

[0565] The server receives the transmitted design information and emotion data from the emotion engine, and activates the image generation device. The generation device generates a visual image based on the design information.

[0566] Step 4:

[0567] The emotion engine analyzes the user's facial expression and interaction data to identify the user's current emotional state. The identified emotional state is then used as a parameter to determine the style and hue of the visual image.

[0568] Step 5:

[0569] The server generates a visual image and sends it to the terminal. The terminal then displays this image to the user.

[0570] Step 6:

[0571] The user reviews the displayed visual image and then requests a design for the material pattern and texture. The terminal sends this request to the server.

[0572] Step 7:

[0573] The server uses a pattern generation device to design material patterns and textures based on visual images and the user's emotional state. The designed data is then transmitted to the terminal.

[0574] Step 8:

[0575] When a user receives the design results on their device and wishes to virtually try on the product, they send a virtual try-on request from their device to the server.

[0576] Step 9:

[0577] The server activates a virtual fitting system and generates virtual try-on images using the user's design. At this time, the emotion engine re-evaluates the user's emotions and generates recommended styles and accessories.

[0578] Step 10:

[0579] The device displays the user the results of the virtual try-on and suggestions from the emotion engine. If the user reviews the results and is satisfied, they instruct the device to share them on social media.

[0580] Step 11:

[0581] The device requests the server to send content optimized for sharing on social media, the server generates the content, and sends back a link.

[0582] Step 12:

[0583] The user uses a terminal to place an order for the final generated design. This order information is sent to the server, and the manufacturing process is initiated through the order processing unit.

[0584] (Example 2)

[0585] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0586] Traditional fashion design systems have struggled to incorporate user emotions into the design process, making it difficult to generate designs optimized for individual users. Furthermore, they have failed to provide a way to positively influence others when sharing the generated designs on social media. Additionally, they have been unable to provide recommendations for trying on the generated designs that take user emotions into consideration.

[0587] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0588] In this invention, the server includes means for receiving design information and text information input to an image generation device and generating a visual image based on the design information and text information; means for an emotion analysis device that evaluates the user's emotional state from their facial expressions and operation logs and reflects the results in the design style and color scheme; and means for a pattern generation device that designs the material pattern and texture based on the visual image. This enables the generation of designs that are sensitive to the user's emotions, advice on how to share them, and recommendations for virtual try-ons based on emotions.

[0589] An "image generation device" is a device that generates a visual image based on input design information and text information.

[0590] An "emotion analysis device" is a device that evaluates the emotional state of a user from their facial expressions and operation logs, and reflects the evaluation results in the design style and color scheme.

[0591] A "pattern generation device" is a device that designs the patterns and textures of materials based on visual images.

[0592] A "virtual fitting device" is a device that allows users to virtually try on visual images and designed patterns, and then presents recommended outfits based on the results.

[0593] A "communication network" is a network system used for designing, transmitting, and receiving information.

[0594] A "social medium" is a communication platform for sharing generated designs.

[0595] This invention is a system that allows users to create unique and highly satisfying fashion designs based on their own emotional state. The system includes an image generation device, an emotion analysis device, a pattern generation device, and a virtual fitting device, providing a balanced fashion design process.

[0596] The user inputs visual design information and text information using a terminal. This design and text information is transmitted to a server via a communication network. The server uses an image generation device and a generation AI model to generate a visual image based on the received information. In this process, an emotion analysis device is used to take into account the user's emotional state.

[0597] The emotion analysis device analyzes and evaluates information obtained from the user's facial expressions and operation logs. This evaluation is reflected in the selection of color schemes and accent designs, ensuring an attractive design for the user.

[0598] From the generated visual images, the server uses a pattern generation device to design the patterns and textures of the materials. This design is optimized based on the user's emotional state, providing intuitive and original fashion ideas.

[0599] Furthermore, users can try on designs through a virtual fitting system. Based on the fitting results, the server suggests recommended outfits and accessories. These suggestions also reflect the user's emotional evaluation, providing a style that is best suited to the individual user.

[0600] For example, if a user enters a prompt such as "Design some energetic everyday casual wear," the system can generate fashion items with energetic color schemes and design patterns, and provide feedback to the user through a try-on process. In this way, users can gain an emotionally resonant and creative experience through the design process.

[0601] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0602] Step 1:

[0603] Users input visual design information and text data via a terminal. This input data is then transmitted directly to the server via the communication network. The terminal's interface is intuitive and designed to allow users to easily input data.

[0604] Step 2:

[0605] The server uses an image generation device to generate a visual image based on the received design information and text data. Specifically, a generation AI model is utilized, the input data is incorporated into the model, and the design is structured and color suggestions are made. The resulting visual image is then used in the next processing step.

[0606] Step 3:

[0607] The server uses an emotion analysis device to evaluate the user's emotional state. The input for this evaluation includes user facial expression data and operation logs obtained from the terminal. The emotion analysis device analyzes this data to identify what the user is feeling and reflects the results in the design's colors and style.

[0608] Step 4:

[0609] The server uses a pattern generator to design material patterns and textures based on the generated visual images. Visual images and emotional evaluation data are used as input. By suggesting patterns and textures, the server presents the user with optimized design options.

[0610] Step 5:

[0611] When a user performs a virtual try-on, the terminal displays a try-on image of the design generated through the virtual try-on device. Input data includes design and pattern information, and the output shows the tried-on image and recommended outfits. The server provides recommended styles and accessories based on the try-on results.

[0612] Step 6:

[0613] When a user shares a design they've created on social media, the device uses data from an emotion analysis device to advise on how to share it in a way that positively impacts others. The input data for sharing includes detailed design information, and an optimized sharing message is output.

[0614] (Application Example 2)

[0615] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0616] Modern consumers tend to seek unique fashion experiences that align with their individual emotions and personalities. However, traditional online fashion purchasing systems struggle to effectively capture consumer emotions and provide highly satisfying, personalized design suggestions.

[0617] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0618] In this invention, the server includes means for receiving visual design information and language data input to an image generation device and generating a visual representation based on the visual design information and language data; means for a structure generation device that designs the structure and properties of a material based on the visual representation; and means for an emotion analysis device that analyzes the user's emotional state and reflects it in the generated representation. This makes it possible to generate unique and highly satisfying fashion designs based on the user's emotions.

[0619] An "image generation device" is a device that has the function of generating visual representations based on input visual design information and linguistic data.

[0620] A "structure generation device" is a device for designing the structure and properties of a material based on visual representation.

[0621] A "virtual fitting device" is a device that allows users to virtually try on a fitting device using visual representations and designed structures.

[0622] An "emotion analysis device" is a device that analyzes the user's emotions from their facial expressions and actions, and reflects that emotional state in the generated expressions.

[0623] A "communication system" is a system that includes network technology used to share generated expressions across social media.

[0624] An "order processing device" is a device that receives orders based on a designed representation and processes them as manufacturing information.

[0625] To realize this invention, the server provides an integrated system that combines an image generation device, a structure generation device, a virtual wearable device, and an emotion analysis device. The server receives visual design information and language data input from the user via a terminal, and the image generation device generates a visual representation based on this information. Advanced image processing software is used in this process, specifically OpenCV and TensorFlow.

[0626] The emotion analysis device uses machine learning algorithms to analyze the user's camera footage and input data to determine their emotional state. This emotional information influences the generated visual images and structure generation processes.

[0627] The structure generation device designs the appropriate structure and properties of materials based on visual representations, and obtains a virtual sense of wearing the garment through simulation. As a result, users can enjoy an intuitive and engaging fashion experience through a virtual wearing device via a terminal.

[0628] For example, if a user starts designing sportswear, and the emotion analysis device detects an excited state, the server will recommend generating energetic colors and dynamic patterns. This allows for design suggestions that resonate with the user's emotions. Furthermore, appropriate advice is provided to ensure that the generated designs are shared in the most suitable format across social media via the communication system.

[0629] As an example of generative AI use, the AI ​​model can present related items based on prompts such as, "Create a list of related fashion items to suggest when the user smiles." This results in a seamless and personalized user experience.

[0630] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0631] Step 1:

[0632] The user inputs visual design information and linguistic data using a terminal. The input information is received by the server and stored in a database. This information forms the basis for design generation in subsequent processing steps.

[0633] Step 2:

[0634] The server's image generator retrieves stored visual design information and language data. Based on the retrieved information, it generates a visual representation using OpenCV or TensorFlow. Here, the color scheme and style are generated according to user input.

[0635] Step 3:

[0636] The user's device captures video through its camera, and an emotion analysis device analyzes that video. Specifically, it uses a machine learning algorithm to analyze facial expressions and classify emotional states. The analysis results are sent to a server and used in the next stage.

[0637] Step 4:

[0638] The server operates a structure generation device based on the emotion analysis results, setting material structures and properties that reflect the user's emotions in the generated visual representation. This is a crucial step in generating designs optimized for the user's emotional state.

[0639] Step 5:

[0640] Users use a terminal to virtually try on designs using a virtual wearable device. The server provides complementary styling and improvement suggestions based on the user's emotional state. This data is used to enrich the user's visual experience.

[0641] Step 6:

[0642] After the user selects a design they are ultimately satisfied with, the server transmits the generated design via a communication system in a format that can be shared on social media. The AI ​​model also provides recommendations based on prompts regarding the style and message to be used when the user shares the design.

[0643] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0644] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0645] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0646] [Fourth Embodiment]

[0647] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0648] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0649] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0650] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0651] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0652] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0653] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0654] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0655] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0656] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0657] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0658] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0659] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0660] This invention digitizes the fashion design process and provides a system that allows users to easily create, try on, and order original designs. This system is implemented through an image generation device, a pattern generation device, a virtual fitting device, and integration with social media utilizing a communication network.

[0661] The user inputs their original design as visual design information and text data using a terminal. The server, upon receiving this data, uses an image generation device to generate a visual image based on the input. This visual image is then provided to the user for confirmation.

[0662] Next, the server uses a pattern generator to design the material's pattern and texture in detail from the generated visual image. This designed pattern is then sent to the terminal for the user to review.

[0663] Users can use a virtual fitting system to virtually try on designs and see how they fit them. The server generates and provides the virtual fitting results to the user in real time.

[0664] Furthermore, users can share designs and try-on results generated via communication networks on social media. This sharing feature allows users to widely showcase their designs and deepen their interactions with others.

[0665] Finally, users can use their devices to place orders for the designed products. Once an order is placed, the server sends the order data to the manufacturing process via an order processing unit. The completed products are then delivered to the users.

[0666] As a concrete example, consider a scenario where a user designs an outdoor jacket. The user inputs a rough sketch and text such as "waterproof outdoor jacket" into a terminal. The server receives this and creates a visual image using an image generator. Next, a pattern generator designs a pattern for waterproof material, reproducing a realistic texture. The user tries on the jacket using a virtual fitting device, checks the design, and then shares it on social media. Finally, the user can order the jacket and receive the manufactured product.

[0667] Thus, the present invention enables users to efficiently create and commercialize unique fashion designs.

[0668] The following describes the processing flow.

[0669] Step 1:

[0670] The user inputs visual design information and text data for the design via their device and clicks the "Send" button. The device then sends this data to the server.

[0671] Step 2:

[0672] The server receives visual design information and text data sent from the user and performs data analysis. Preprocessing is then carried out to prepare the image for use with the image generation device.

[0673] Step 3:

[0674] The server activates the image generation device and generates a visual image based on the received data. This generated visual image is then sent to the user's terminal.

[0675] Step 4:

[0676] The user confirms the visual image received on the device and enters instructions into the device to proceed to the next process.

[0677] Step 5:

[0678] The server uses a pattern generation device to design the material's pattern and texture based on a visual image. This designed pattern data is then transmitted to the terminal.

[0679] Step 6:

[0680] If the user reviews the designed pattern on their device and decides to proceed with a virtual try-on, a virtual try-on request is sent from the device to the server.

[0681] Step 7:

[0682] The server activates the virtual fitting device and performs a virtual try-on for the user. It generates images and videos of the try-on results and sends them to the user's terminal.

[0683] Step 8:

[0684] The user checks the virtual try-on results on their device, and if satisfied, enters information to share the product on social media and sends it to the server.

[0685] Step 9:

[0686] Based on the information the server receives for sharing, it generates links and images for social media such as SNS and provides them to the user.

[0687] Step 10:

[0688] The user uses a device to decide on a product order and sends the order information to the server.

[0689] Step 11:

[0690] The server initiates order processing, transfers the order data to the manufacturing partner, and starts the manufacturing process.

[0691] Step 12:

[0692] The product is manufactured and, once completed, delivered to the user. The user can check the delivery status on their device.

[0693] (Example 1)

[0694] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0695] In the modern fashion design process, there is a problem in that it takes a lot of effort and time for consumers to translate their ideas into concrete forms and try them on to confirm their quality. Furthermore, it is difficult to easily share the generated designs with others. In addition, the process of turning a consumer's favorite design into a product is also becoming increasingly complex.

[0696] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0697] In this invention, the server includes means for receiving design information and text information from an image generation unit and generating visual data, means for a pattern generation unit that sets the material design and texture in detail based on the visual data, and means for a virtual fitting unit that allows the user to virtually try on the visual data and set design. This makes it possible for consumers to quickly try on individual design ideas, receive evaluations, and then commercialize them.

[0698] The "image generation unit" is a device that receives design information and text information and generates visual data based on them.

[0699] The "pattern generation unit" is a device that has the function of setting the design and texture of materials in detail based on the generated visual data.

[0700] A "virtual fitting room" is a device that allows users to virtually try on clothes based on generated visual data and pre-configured designs.

[0701] "Visual data" refers to digital images generated based on design information and textual information.

[0702] "Design information" refers to data that includes visual design elements provided by the user.

[0703] "Textual information" refers to text data entered by users to express descriptions and characteristics related to the design.

[0704] "Material design" is the process of determining the structure and pattern of materials necessary to realize a design.

[0705] "Texture" is a concept that refers to the characteristics of a material in terms of its appearance and feel.

[0706] A "user" refers to an individual who inputs their design ideas into the system, checks the results, tries them on, and shares them.

[0707] To implement this invention, the following system components are used. The system mainly consists of a server, a terminal, and a user.

[0708] User: Users utilize their devices to input their fashion design ideas. The input data consists of visual design information and textual information. Users can take photos of design sketches with their device's camera and upload them as digital images. They can also clarify the design intent by inputting specific textual information, such as "sporty raincoat."

[0709] Server: The server generates visual data based on design and text information received from the user using the image generation unit. Specifically, it analyzes the data using a generation AI model to create a digital image of the design. The AI ​​model utilizes the context generated from the prompt text to draw the most suitable design from the input data. For example, by inputting the prompt "Rough sketch: Hooded long jacket, Text: Waterproof outdoor wear, Color: Dark green," the corresponding visual data will be generated.

[0710] The server then uses a pattern generation unit to design the material and its texture in detail based on this visual data. Specifically, it uses digital fabric simulation to virtually reproduce the cutting and sewing of the fabric and determine the optimal material and texture for the user's design.

[0711] User: Users utilize a virtual fitting room via their device to try on designs onto their 3D models based on generated visual data and design information. During this process, they can check the fit in 360-degree views, taking into account their full body movements and body shape. This allows them to evaluate the suitability of the design before trying it on.

[0712] The completed design can be shared by users through social media via communication networks. This sharing function allows users to get feedback on the design from others and receive new ideas.

[0713] Finally, the user places an order using a terminal to have their preferred design manufactured. This order is managed by the server in the order processing section, and the product is manufactured using a system belonging to the manufacturing process. Once the product is completed, it is sent to the specified delivery address.

[0714] This system allows users to easily bring their unique fashion ideas to life, try them on, share them with others, and ultimately turn them into finished products.

[0715] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0716] Step 1:

[0717] Users input design information using their devices. Specifically, they use the device's camera function to take pictures of sketches and upload them to the system as image data. They also input text information, such as descriptions and characteristics of the design. The input for this process consists of image and text data, which are then sent to the server.

[0718] Step 2:

[0719] The server analyzes the received design information. It utilizes the image generation unit to pass input data to a generation AI model, thereby generating digital visual data. Specifically, the AI ​​model analyzes prompt text and visually depicts the design image. In this process, visual data is output from image and text data inputs.

[0720] Step 3:

[0721] The server sets the material design and texture in detail based on the visual data. A pattern generation unit performs a digital fabric simulation of the material. This ensures the most appropriate fabric cutting and texture reproduction that matches the visual data. Visual data is input, and material design information is output.

[0722] Step 4:

[0723] Users access a virtual fitting room using their device. They can try on designs generated for their own 3D model. The system receives material design information as input and visualizes it, and the output is dynamic fitting data regarding the user's fit. This allows for real-time verification of movement and size evaluation.

[0724] Step 5:

[0725] Users review the generated design and try-on data on their device again and share it on social media via the communication network. In this process, the try-on data is used as input and exported to obtain feedback and comments from others.

[0726] Step 6:

[0727] If the user is satisfied with the design, they confirm their order using the terminal. The final input is order information (quantity, payment information, etc.), which the server receives. This output is instruction data for the manufacturing process, and the actual production of the product begins.

[0728] Step 7:

[0729] The server manages the manufacturing process and ships the manufactured products. The order processing unit sends instruction data to the production line, and the specific products are prepared. The finished products are delivered to the customers. The input is manufacturing instruction data, and the output is the finished product.

[0730] (Application Example 1)

[0731] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0732] There is a lack of platforms that digitize fashion design, allowing users to easily create, try on, and sell their own original designs. Furthermore, it is difficult for users to display and widely share their own designs on digital sales platforms. Solving these challenges is essential.

[0733] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0734] In this invention, the server includes means for receiving input visual design information and string data into an image generation device and generating a visual image based on the visual design information and string data; means for designing the structure and texture of a material based on the visual image; means for virtual fitting where the user can virtually try on the visual image and designed structure; and means for sales promotion where the user can list the generated design as a product on a digital sales platform. This makes it possible for users to create and easily sell original fashion designs.

[0735] 1. An "image generation device" is a device that generates high-quality visual images based on visual design information and string data received from a user.

[0736] 2. "Visual design information" refers to information that includes the visual elements necessary for users to create digital designs.

[0737] 3. "String data" refers to text-based data used to describe the specific content and features of a design.

[0738] 4. A "pattern generation device" is a device used to design the structure and texture of a material in detail based on a visual image.

[0739] 5. A "virtual fitting device" is a device that provides the function of allowing users to virtually try on clothing designed in a digital space.

[0740] 6. A "sales promotion device" is a device that has the function of easily listing user-generated designs on a digital sales platform and promoting sales.

[0741] 7. A "digital sales platform" is an online marketplace for providing and selling products designed by users to a large number of consumers.

[0742] The system implementing this invention provides a platform that allows users to digitize fashion designs and easily create, try on, and sell them. Users input visual design information and text data using a terminal, and the server processes this information to realize original designs.

[0743] The server first generates a visual image based on the visual design information and text data sent by the user, using an image generation device. Specifically, it uses generation AI models such as DALL-E and Stable Diffusion to create high-quality visual images.

[0744] Next, the server uses a pattern generator to design the structure and texture of the material in detail based on the generated visual image. This process uses texture data to perform calculations that reproduce a realistic material feel.

[0745] Next, users can virtually try on the designs using a virtual fitting device to see how they fit them. The virtual fitting device uses AR technology to provide a real-time try-on experience. Specific examples include Google ARCore and Apple ARKit.

[0746] Ultimately, the sales promotion system allows users to list the generated designs as products on a digital sales platform and sell them widely. This system enables users to efficiently create digital fashion designs and deliver them directly to consumers.

[0747] As a concrete example, when a user enters the prompt "Generate a design image for an autumn wool overcoat," the image generator creates an original design, and the pattern generator reproduces the texture of wool. Then, the user can try on the garment on their avatar using a virtual fitting device, and the design can be made available for sale on the digital marketplace.

[0748] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0749] Step 1:

[0750] The user uses a terminal to input visual design information and text data, which are then sent to the server. The input data includes hand-drawn sketches or digital illustrations for visual design information, and text data that describes the specific details of the design (e.g., "Autumn wool overcoat"). It is crucial that this data is sent to the server.

[0751] Step 2:

[0752] Based on the received visual design information and string data, the server uses an image generation device to generate a visual image using a generation AI model (e.g., Stable Diffusion). During this process, data calculations are performed based on prompt statements, resulting in the output of a high-quality visual image. This visual image serves as the foundation for subsequent processes.

[0753] Step 3:

[0754] The server sends the generated visual image to the pattern generator, which designs the structure and texture of the material. Here, realistic material textures are reproduced through calculations using texture data. The output is a material pattern that allows for detailed examination of the texture.

[0755] Step 4:

[0756] The user uses a virtual fitting device to virtually try on clothes designed on their device. The server applies visual images and patterns to the user's avatar using AR technology. The output is a visualization of what the user would look like when trying on the design.

[0757] Step 5:

[0758] The user ultimately lists the designs generated using the sales promotion tool on the digital sales platform. The server processes this information and makes it available for sale in the digital marketplace. The input is the product listing information, and the output is the completion of the listing on the sales platform.

[0759] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0760] This invention provides a system that enables users to more effectively create unique and satisfying fashion designs based on their individual emotions. In addition to an image generation device, a pattern generation device, and a virtual fitting device, this system incorporates an emotion engine that recognizes the user's emotions and reflects them in the design process.

[0761] The user inputs visual design information and text data through a terminal. Once this data is sent to the server, an image generator receives the design information and generates a visual image. During this process, an emotion engine analyzes the user's facial expressions, mouse movements, and input speed to evaluate the user's emotional state. This evaluation is reflected in the style and color scheme of the generated design, resulting in a visual image optimized to the user's preferences and emotions.

[0762] The server further uses a pattern generation device to design material patterns and textures from visual images. Data on the user's emotions provided by the emotion engine influences color selection and texture in pattern design, providing users with more intuitive and appealing choices.

[0763] If a user wishes to virtually try on clothes, they can do so using a virtual fitting device. The server considers the user's emotional tendencies during the virtual fitting and suggests recommended outfits and accessories based on the fitting results. In addition, an emotion engine monitors the user's level of joy and excitement through the terminal and provides feedback to improve the selection of recommended outfits.

[0764] Furthermore, when users share their generated designs on social media, the emotion engine provides advice on how to share the designs in a way that positively impacts others. The device then uses this advice to create and optimize content.

[0765] As a concrete example, when a user begins designing sportswear on their device, if the emotion engine recognizes a high level of excitement, the server suggests energetic colors and dynamic patterns, and generates a design based on them. This design is then virtually tried on and presented as the optimal style for the user's preferred sports activity. The generated design is then shared on social media in a way that satisfies the user.

[0766] This system will allow users to experience an emotionally resonant fashion design process and efficiently create unique and personalized products.

[0767] The following describes the processing flow.

[0768] Step 1:

[0769] The user uses a terminal to input visual design information and text data into the interface. The terminal then prepares to send the input information to the server.

[0770] Step 2:

[0771] The device monitors the user's keyboard input speed, mouse movements, and other data in real time, and sends this data to the emotion engine.

[0772] Step 3:

[0773] The server receives the transmitted design information and emotion data from the emotion engine, and activates the image generation device. The generation device generates a visual image based on the design information.

[0774] Step 4:

[0775] The emotion engine analyzes the user's facial expression and interaction data to identify the user's current emotional state. The identified emotional state is then used as a parameter to determine the style and hue of the visual image.

[0776] Step 5:

[0777] The server generates a visual image and sends it to the terminal. The terminal then displays this image to the user.

[0778] Step 6:

[0779] The user reviews the displayed visual image and then requests a design for the material pattern and texture. The terminal sends this request to the server.

[0780] Step 7:

[0781] The server uses a pattern generation device to design material patterns and textures based on visual images and the user's emotional state. The designed data is then transmitted to the terminal.

[0782] Step 8:

[0783] When a user receives the design results on their device and wishes to virtually try on the product, they send a virtual try-on request from their device to the server.

[0784] Step 9:

[0785] The server activates a virtual fitting system and generates virtual try-on images using the user's design. At this time, the emotion engine re-evaluates the user's emotions and generates recommended styles and accessories.

[0786] Step 10:

[0787] The device displays the user the results of the virtual try-on and suggestions from the emotion engine. If the user reviews the results and is satisfied, they instruct the device to share them on social media.

[0788] Step 11:

[0789] The device requests the server to send content optimized for sharing on social media, the server generates the content, and sends back a link.

[0790] Step 12:

[0791] The user uses a terminal to place an order for the final generated design. This order information is sent to the server, and the manufacturing process is initiated through the order processing unit.

[0792] (Example 2)

[0793] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0794] Traditional fashion design systems have struggled to incorporate user emotions into the design process, making it difficult to generate designs optimized for individual users. Furthermore, they have failed to provide a way to positively influence others when sharing the generated designs on social media. Additionally, they have been unable to provide recommendations for trying on the generated designs that take user emotions into consideration.

[0795] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0796] In this invention, the server includes means for receiving design information and text information input to an image generation device and generating a visual image based on the design information and text information; means for an emotion analysis device that evaluates the user's emotional state from their facial expressions and operation logs and reflects the results in the design style and color scheme; and means for a pattern generation device that designs the material pattern and texture based on the visual image. This enables the generation of designs that are sensitive to the user's emotions, advice on how to share them, and recommendations for virtual try-ons based on emotions.

[0797] An "image generation device" is a device that generates a visual image based on input design information and text information.

[0798] An "emotion analysis device" is a device that evaluates the emotional state of a user from their facial expressions and operation logs, and reflects the evaluation results in the design style and color scheme.

[0799] A "pattern generation device" is a device that designs the patterns and textures of materials based on visual images.

[0800] A "virtual fitting device" is a device that allows users to virtually try on visual images and designed patterns, and then presents recommended outfits based on the results.

[0801] A "communication network" is a network system used for designing, transmitting, and receiving information.

[0802] A "social medium" is a communication platform for sharing generated designs.

[0803] This invention is a system that allows users to create unique and highly satisfying fashion designs based on their own emotional state. The system includes an image generation device, an emotion analysis device, a pattern generation device, and a virtual fitting device, providing a balanced fashion design process.

[0804] The user inputs visual design information and text information using a terminal. This design and text information is transmitted to a server via a communication network. The server uses an image generation device and a generation AI model to generate a visual image based on the received information. In this process, an emotion analysis device is used to take into account the user's emotional state.

[0805] The emotion analysis device analyzes and evaluates information obtained from the user's facial expressions and operation logs. This evaluation is reflected in the selection of color schemes and accent designs, ensuring an attractive design for the user.

[0806] From the generated visual images, the server uses a pattern generation device to design the patterns and textures of the materials. This design is optimized based on the user's emotional state, providing intuitive and original fashion ideas.

[0807] Furthermore, users can try on designs through a virtual fitting system. Based on the fitting results, the server suggests recommended outfits and accessories. These suggestions also reflect the user's emotional evaluation, providing a style that is best suited to the individual user.

[0808] For example, if a user enters a prompt such as "Design some energetic everyday casual wear," the system can generate fashion items with energetic color schemes and design patterns, and provide feedback to the user through a try-on process. In this way, users can gain an emotionally resonant and creative experience through the design process.

[0809] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0810] Step 1:

[0811] Users input visual design information and text data via a terminal. This input data is then transmitted directly to the server via the communication network. The terminal's interface is intuitive and designed to allow users to easily input data.

[0812] Step 2:

[0813] The server uses an image generation device to generate a visual image based on the received design information and text data. Specifically, a generation AI model is utilized, the input data is incorporated into the model, and the design is structured and color suggestions are made. The resulting visual image is then used in the next processing step.

[0814] Step 3:

[0815] The server uses an emotion analysis device to evaluate the user's emotional state. The input for this evaluation includes user facial expression data and operation logs obtained from the terminal. The emotion analysis device analyzes this data to identify what the user is feeling and reflects the results in the design's colors and style.

[0816] Step 4:

[0817] The server uses a pattern generator to design material patterns and textures based on the generated visual images. Visual images and emotional evaluation data are used as input. By suggesting patterns and textures, the server presents the user with optimized design options.

[0818] Step 5:

[0819] When a user performs a virtual try-on, the terminal displays a try-on image of the design generated through the virtual try-on device. Input data includes design and pattern information, and the output shows the tried-on image and recommended outfits. The server provides recommended styles and accessories based on the try-on results.

[0820] Step 6:

[0821] When a user shares a design they've created on social media, the device uses data from an emotion analysis device to advise on how to share it in a way that positively impacts others. The input data for sharing includes detailed design information, and an optimized sharing message is output.

[0822] (Application Example 2)

[0823] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0824] Modern consumers tend to seek unique fashion experiences that align with their individual emotions and personalities. However, traditional online fashion purchasing systems struggle to effectively capture consumer emotions and provide highly satisfying, personalized design suggestions.

[0825] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0826] In this invention, the server includes means for receiving visual design information and language data input to an image generation device and generating a visual representation based on the visual design information and language data; means for a structure generation device that designs the structure and properties of a material based on the visual representation; and means for an emotion analysis device that analyzes the user's emotional state and reflects it in the generated representation. This makes it possible to generate unique and highly satisfying fashion designs based on the user's emotions.

[0827] An "image generation device" is a device that has the function of generating visual representations based on input visual design information and linguistic data.

[0828] A "structure generation device" is a device for designing the structure and properties of a material based on visual representation.

[0829] A "virtual fitting device" is a device that allows users to virtually try on a fitting device using visual representations and designed structures.

[0830] An "emotion analysis device" is a device that analyzes the user's emotions from their facial expressions and actions, and reflects that emotional state in the generated expressions.

[0831] A "communication system" is a system that includes network technology used to share generated expressions across social media.

[0832] An "order processing device" is a device that receives orders based on a designed representation and processes them as manufacturing information.

[0833] To realize this invention, the server provides an integrated system that combines an image generation device, a structure generation device, a virtual wearable device, and an emotion analysis device. The server receives visual design information and language data input from the user via a terminal, and the image generation device generates a visual representation based on this information. Advanced image processing software is used in this process, specifically OpenCV and TensorFlow.

[0834] The emotion analysis device uses machine learning algorithms to analyze the user's camera footage and input data to determine their emotional state. This emotional information influences the generated visual images and structure generation processes.

[0835] The structure generation device designs the appropriate structure and properties of materials based on visual representations, and obtains a virtual sense of wearing the garment through simulation. As a result, users can enjoy an intuitive and engaging fashion experience through a virtual wearing device via a terminal.

[0836] For example, if a user starts designing sportswear, and the emotion analysis device detects an excited state, the server will recommend generating energetic colors and dynamic patterns. This allows for design suggestions that resonate with the user's emotions. Furthermore, appropriate advice is provided to ensure that the generated designs are shared in the most suitable format across social media via the communication system.

[0837] As an example of generative AI use, the AI ​​model can present related items based on prompts such as, "Create a list of related fashion items to suggest when the user smiles." This results in a seamless and personalized user experience.

[0838] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0839] Step 1:

[0840] The user inputs visual design information and linguistic data using a terminal. The input information is received by the server and stored in a database. This information forms the basis for design generation in subsequent processing steps.

[0841] Step 2:

[0842] The server's image generator retrieves stored visual design information and language data. Based on the retrieved information, it generates a visual representation using OpenCV or TensorFlow. Here, the color scheme and style are generated according to user input.

[0843] Step 3:

[0844] The user's device captures video through its camera, and an emotion analysis device analyzes that video. Specifically, it uses a machine learning algorithm to analyze facial expressions and classify emotional states. The analysis results are sent to a server and used in the next stage.

[0845] Step 4:

[0846] The server operates a structure generation device based on the emotion analysis results, setting material structures and properties that reflect the user's emotions in the generated visual representation. This is a crucial step in generating designs optimized for the user's emotional state.

[0847] Step 5:

[0848] Users use a terminal to virtually try on designs using a virtual wearable device. The server provides complementary styling and improvement suggestions based on the user's emotional state. This data is used to enrich the user's visual experience.

[0849] Step 6:

[0850] After the user selects a design they are ultimately satisfied with, the server transmits the generated design via a communication system in a format that can be shared on social media. The AI ​​model also provides recommendations based on prompts regarding the style and message to be used when the user shares the design.

[0851] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0852] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0853] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0854] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0855] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0856] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0857] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0858] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0859] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0860] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0861] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0862] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0863] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0864] 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.

[0865] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0866] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0867] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0868] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0869] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0870] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0871] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0872] The following is further disclosed regarding the embodiments described above.

[0873] (Claim 1)

[0874] The image generation device receives input visual design information and text data, and generates a visual image based on the visual design information and text data.

[0875] means and

[0876] A pattern generation device that designs the pattern and texture of a material based on the aforementioned visual image.

[0877] means and

[0878] A virtual fitting device that allows users to virtually try on clothes using visual images and designed patterns.

[0879] means and

[0880] A system that includes this.

[0881] (Claim 2)

[0882] The system according to claim 1, which enables users to share their generated designs with social media via a communication network.

[0883] (Claim 3)

[0884] The system according to claim 1, further comprising an order processing device that receives orders via a communication network based on a designed order and processes them as manufacturing information.

[0885] "Example 1"

[0886] (Claim 1)

[0887] The image generation unit receives the input design information and text information, and generates visual data based on the design information and text information.

[0888] means and

[0889] A pattern generation unit sets the design and texture of the material in detail based on the aforementioned visual data.

[0890] means and

[0891] A virtual fitting room where users can virtually try on clothes using visual data and pre-set designs.

[0892] means and

[0893] A system that includes this.

[0894] (Claim 2)

[0895] The system according to claim 1, which enables users to share generated designs with social media via a communication network.

[0896] (Claim 3)

[0897] The system according to claim 1, further comprising an order processing unit that receives orders via a communication network based on a set design and processes them as manufacturing information.

[0898] "Application Example 1"

[0899] (Claim 1)

[0900] The image generation device receives input visual design information and string data, and generates a visual image based on the visual design information and string data.

[0901] means and

[0902] A pattern generation device that designs the structure and texture of a material based on the aforementioned visual image.

[0903] means and

[0904] A virtual fitting device that allows users to virtually try on clothes based on visual images and designed structures.

[0905] means and

[0906] A sales promotion device that allows users to list their generated designs as products on a digital sales platform.

[0907] means and

[0908] A system that includes this.

[0909] (Claim 2)

[0910] The system according to claim 1, which enables users to share their generated designs with social information media via a digital communication network.

[0911] (Claim 3)

[0912] The system according to claim 1, further comprising an order processing device that receives orders via a digital communication network based on a designed order and processes them as manufacturing information.

[0913] "Example 2 of combining an emotion engine"

[0914] (Claim 1)

[0915] The image generation device receives input design information and text information, and generates a visual image based on the design information and text information.

[0916] means and

[0917] An emotion analysis device that evaluates the user's emotional state from their facial expressions and operation logs, and reflects the results in the design style and color scheme.

[0918] means and

[0919] A pattern generation device that designs the pattern and texture of a material based on the aforementioned visual image.

[0920] means and

[0921] A virtual fitting device that allows users to virtually try on clothes using visual images and designed patterns, and then presents recommended outfits based on the results.

[0922] means and

[0923] A system that includes this.

[0924] (Claim 2)

[0925] The system according to claim 1, wherein, in the process of a user sharing a generated design to a social medium via a communication network, an emotion analysis device advises on a sharing method that has a positive impact.

[0926] (Claim 3)

[0927] The system according to claim 1, further comprising an order processing device that receives orders via a communication network based on a designed order and processes them as manufacturing information.

[0928] "Application example 2 when combining with an emotional engine"

[0929] (Claim 1)

[0930] The image generation device receives input visual design information and language data, and generates a visual representation based on the visual design information and language data.

[0931] means and

[0932] A structure generation device that designs the structure and properties of a material based on the aforementioned visual representation.

[0933] means and

[0934] A virtual fitting device in which the user virtually wears the device through visual representations and designed structures.

[0935] means and

[0936] An emotion analysis device that analyzes the user's emotional state and reflects it in the generated expressions.

[0937] means and

[0938] A system that includes this.

[0939] (Claim 2)

[0940] The system according to claim 1, which enables users to share generated expressions with social media via a communication system, and proposes a method for such sharing.

[0941] (Claim 3)

[0942] The system according to claim 1, further comprising an order processing device that receives orders reflecting the results of sentiment analysis based on designed expressions via a communication system and processes them as manufacturing information. [Explanation of Symbols]

[0943] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. The image generation device receives input visual design information and text data, and generates a visual image based on the visual design information and text data. means and A pattern generation device that designs the pattern and texture of a material based on the aforementioned visual image. means and A virtual fitting device that allows users to virtually try on clothes using visual images and designed patterns. means and A system that includes this.

2. The system according to claim 1, which enables users to share their generated designs with social media via a communication network.

3. The system according to claim 1, further comprising an order processing device that receives orders via a communication network based on a designed order and processes them as manufacturing information.

Images