system

Abstract

Provide a system. [Solution] An input method for inputting the brand's unique worldview and concept, an AI selection means for selecting a generative AI model based on an input design concept; A generation means for automatically generating a design proposal using the selected generative AI model; a patterning means for converting the generated design proposal into a pattern and a 3D model; a storage means for storing and providing patterned and 3D designs; A reuse mechanism for multiple uses of the generated 3D assets; a market analysis means for collecting and analyzing market responses to the reused 3D assets; A system including:

Description

[Technical Field]

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

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

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

[0004] Design activities in the apparel industry often involve outsourcing, making it difficult to reflect a brand's unique worldview. Additionally, the lead time from design to manufacturing tends to be long and costs high. Furthermore, market needs cannot be accurately grasped, limiting approaches to new markets. To solve these problems, a means is needed to efficiently and effectively generate designs and provide products that meet market needs. [Means for solving the problem]

[0005] The present invention provides a system that includes an input means for inputting a brand's unique worldview and concept, an AI selection means for selecting a generative AI model based on the input design concept, a generation means for automatically generating design proposals using the selected generative AI model, a patterning means for converting the generated design proposals into patterns and 3D models, a storage means for saving and providing the patterned and 3D designs, a reuse means for reusing the generated 3D assets for multiple purposes, and a market analysis means for collecting and analyzing market responses to the reused 3D assets. This system achieves design efficiency and cost reduction, and enables designs that embody a brand's unique worldview to be quickly brought to market. Furthermore, reusing 3D assets helps understand market needs and develop new markets.

[0006] "Input means" refers to a device or software that allows the user to input the brand's unique worldview or concept.

[0007] The "AI selection means" is a device or software that selects an appropriate generative AI model based on the design concept input by the user.

[0008] A "generative means" is a device or software that automatically generates design proposals based on the brand's worldview using a selected generative AI model.

[0009] The "patterning means" is a device or software for patterning the generated design proposal and converting it into a 3D model.

[0010] "Storage means" means a device or software that stores and optionally provides patterned and 3D designs.

[0011] A "reuse means" is a device or software that allows the generated 3D assets to be reused for multiple purposes, such as advertisements, games, virtual spaces, etc.

[0012] "Market Analysis Tool" means a device or software that collects and analyzes market response to reused 3D assets.

[0013] A "modifier" is a device or software that receives user feedback and modifies the generated design proposal based on that feedback. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0019] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

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

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] This invention is a system that utilizes generative AI to streamline the design process in the apparel industry and understand customer needs. Specific embodiments of this system are described below.

[0036] System configuration

[0037] The system consists of the following main components:

[0038] 1. Input means: A device or software that allows users to input the brand's unique worldview and concept.

[0039] 2. AI selection means: A device or software that selects a generative AI model based on the input design concept.

[0040] 3. Generator: A device or software that automatically generates design proposals using the selected generative AI model.

[0041] 4. Patterning means: A device or software that patterns the generated design proposal and converts it into a 3D model.

[0042] 5. Storage means: A device or software that stores the patterned and 3D designs and provides them as needed.

[0043] 6. Reuse means: A device or software that reuses the generated 3D assets for multiple purposes, such as advertising, games, virtual spaces, etc.

[0044] 7. Market Analysis Tools: Equipment or software that collects and analyzes market response to reused 3D assets.

[0045] 8. Correction tool (optional): A device or software that receives user feedback and corrects the generated design proposals based on it.

[0046] Operational Overview

[0047] The operation of this system will now be outlined.

[0048] User Actions and Input

[0049] Users input their brand's unique worldview and design concept through the device. This input includes specific colors, styles, themes, etc. For example, a design concept based on the theme of "refreshing summer" can be input.

[0050] Server processing

[0051] 1. Data reception and AI model selection:

[0052] The server receives the design concept from the user and selects the optimal generating AI model using the AI ​​selection means.

[0053] This model selection is based on information such as past design data and trend analysis.

[0054] 2. Generate design proposals:

[0055] The server uses the selected generative AI model to automatically generate multiple design proposals. The generator combines colors, shapes, and patterns to create a design that fits the specified concept.

[0056] 3. Patterning and 3D:

[0057] The generated design proposal is patterned by a patterning tool and then converted into a 3D model, which simulates the manufacturing process and significantly reduces lead time.

[0058] View and give feedback on design ideas

[0059] The user checks the generated design proposal via the terminal and provides feedback. The feedback is sent from the terminal to the server. The server then uses a correction means to correct the design proposal based on the user's feedback (this means is optional).

[0060] Save and reuse 3D assets

[0061] The final design proposals are saved and provided as 3D assets to existing advertisements, games, and virtual spaces, enabling new market development and understanding of customer needs.

[0062] Collecting and analyzing market responses

[0063] The server collects and analyzes market responses to reused 3D assets through market analysis tools, and the results of this analysis are reflected in the next design process, allowing for continuous improvement and adaptation.

[0064] Examples:

[0065] Example 1: New summer collection

[0066] User: A brand manager inputs a design concept with the theme "refreshing summer."

[0067] Server: After receiving the concept, select the optimal generative AI model and generate multiple design proposals.

[0068] Terminal: The manager reviews the design proposal and provides feedback, which is used to refine the final design.

[0069] Reuse: The created 3D assets are used in virtual events, and participant responses are collected as data.

[0070] Example 2: Fall / Winter Collection

[0071] User: The designer inputs a design concept based on the theme of "warmth and quality."

[0072] Server: Based on the request, selects an appropriate AI model and generates a design proposal.

[0073] Terminal: The designer evaluates the design proposals and selects the final design. The design is then patterned and converted into 3D, and the final design is made available for download.

[0074] Reuse: The completed 3D assets will appear as in-game items and the reaction from players will be analyzed.

[0075] The processing flow will be explained below.

[0076] Step 1:

[0077] (User)

[0078] Users access the terminal and enter their brand's unique worldview and design concept into a dedicated input form, including detailed information such as theme, color, style, and season. Once the input is complete, they click the submit button to submit their design request.

[0079] Step 2:

[0080] (Terminal)

[0081] The terminal receives the user's input data, formats it, and sends it to the server, validating the data to ensure that the input is accurate.

[0082] Step 3:

[0083] (server)

[0084] The server receives design requests from users. It analyzes the received data and selects a generative AI model that matches the brand's worldview and concept. The selection is made using an AI selection method, referencing past design data and market trend information.

[0085] Step 4:

[0086] (server)

[0087] The server uses the selected generative AI model to generate multiple design proposals based on the user's input concept. Through the generation method, colors, shapes, and patterns are automatically combined to generate a design that matches the concept.

[0088] Step 5:

[0089] (Terminal)

[0090] The device displays the design proposals sent from the server on a user interface, and is equipped with a function to display images and detailed information so that the user can easily check each design proposal.

[0091] Step 6:

[0092] (User)

[0093] The user checks the displayed design proposals, inputs their evaluation and feedback for each design, and sends the feedback, including comments and correction requests, to the server from their device.

[0094] Step 7:

[0095] (server)

[0096] The server receives feedback from the user, modifies the design proposal using the modification means, and then transmits the design proposal to the user again after modification based on the feedback.

[0097] Step 8:

[0098] (server)

[0099] The server automatically patterns the final approved design using a pattern generator and then creates a 3D model, which generates the format and 3D data required for the manufacturing process.

[0100] Step 9:

[0101] (server)

[0102] The server stores the generated patterned data and 3D model using a storage means so that they can be provided to users and manufacturers as needed.

[0103] Step 10:

[0104] (server)

[0105] The server uses a reuse method to store the generated 3D assets in a format that can be reused for various content (advertisements, games, virtual spaces, etc.) and provides them to related applications.

[0106] Step 11:

[0107] (server)

[0108] The server collects and analyzes market responses to the reused 3D assets using market analysis tools. The results of this analysis are fed back into the next design process, enabling designs to be more in line with the market.

[0109] Example 1

[0110] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0111] The traditional apparel design process required a lot of manual work and time, and there was a need for greater efficiency. It was also difficult to accurately grasp customer needs and quickly incorporate them into designs. Furthermore, there was a lack of a system for properly collecting and analyzing market reactions to the designs. It was necessary to solve these problems and achieve greater efficiency in the design process in the apparel industry and appropriate feedback on market reactions.

[0112] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0113] In this invention, the server includes an input means for inputting a brand's unique worldview and concept, an AI selection means for selecting a generative AI model based on the input design concept, a generation means for automatically generating design proposals using the selected generative AI model, a patterning means for converting the generated design proposals into patterns and 3D models, a storage means for saving and providing the patterned and 3D designs, a reuse means for reusing the generated 3D assets for multiple purposes, a market analysis means for collecting and analyzing market responses, and a terminal for users to check the generated design proposals and send feedback. This enables the design process to be more efficient, customer needs to be quickly reflected, and market responses to be appropriately collected and analyzed.

[0114] "A brand's unique worldview and concept" refers to the theme, color, style, and design philosophy that a particular brand wants to express.

[0115] "Input means" refers to a device or software that allows a user to input the brand's unique worldview or concept into the system.

[0116] "AI selection means" refers to a device or software that selects the optimal generative AI model based on the input design concept.

[0117] "Generative means" refers to a device or software that automatically generates design proposals using a selected generative AI model.

[0118] "Patterning means" refers to a device or software that patterns the generated design proposal and then converts it into a 3D model.

[0119] "Storage means" refers to a device or software that stores and, if necessary, provides the patterned and 3D designs.

[0120] "Means for reuse" refers to a device or software that allows the generated 3D assets to be reused for multiple purposes, such as advertising, games, virtual spaces, etc.

[0121] "Market Analysis Tool" means a device or software that collects and analyzes market response to reused 3D assets.

[0122] "Terminal" refers to a device or software that allows a user to review the generated design proposals and provide feedback.

[0123] This invention is a system that uses generative AI to streamline the design process in the apparel industry and understand customer needs. The system consists of the following main components:

[0124] System configuration

[0125] 1. Input method:

[0126] Users input the brand's unique worldview and design concept through a terminal, specifying in detail the specific theme, color, style, etc. For example, a design concept of "resort wear with a blue and white theme" based on the theme of "refreshing summer" can be input.

[0127] 2. AI selection method:

[0128] The server receives the design concept sent by the user and selects the optimal generative AI model using an AI selection method. This uses software to analyze past design data and trends and select the optimal model. For example, DALL-E 2 or StyleGAN 2 is used.

[0129] 3. Generation means:

[0130] The server automatically generates design proposals based on the selected generative AI model. During this process, a prompt is entered to generate the design. For example, the prompt could be "summer resort wear in blue and white."

[0131] 4. Patterning means:

[0132] The generated design proposals are patterned using a pattern generator on the server, and then converted into 3D models using software such as CLO 3D or Blender, allowing users to visualize the design in three dimensions.

[0133] 5. Preservation means:

[0134] The server stores the patterned and 3D designs. This storage is done using cloud storage such as AWS (registered trademark) S3. The stored designs are made available as needed.

[0135] 6. Reuse method:

[0136] The server reuses the generated 3D assets for multiple purposes, such as advertising, online games, and virtual worlds.

[0137] 7. Market analysis tools:

[0138] The server collects and analyzes market reactions to the reused 3D assets using tools such as Google® Analytics, and the results are reflected in the next design process for continuous improvement.

[0139] 8. Terminal:

[0140] The user can review the generated design proposal via their device and provide feedback, which is then sent to the server, where the design is revised as necessary.

[0141] Specific examples

[0142] Example 1: New summer collection

[0143] Example prompt: Refreshing summer resort wear in blue and white.

[0144] 1. User: The brand manager uses the terminal to input a design concept with the theme of "refreshing summer."

[0145] 2. Server: After receiving the concept, select the optimal generative AI model and generate multiple design proposals.

[0146] 3. Terminal: The manager reviews the generated design proposals and provides feedback on additional colors and patterns. The feedback is then sent to the server.

[0147] 4. Server: The server revises the design based on the feedback and finalizes the design. The final design is stored in AWS S3.

[0148] 5. Reuse: The created 3D assets are used in a virtual space (e.g., a VRChat event), and reactions from participants are collected as data.

[0149] Example 2: Fall / Winter Collection

[0150] Example prompt: A warm orange and brown autumn / winter coat

[0151] 1. User: The designer inputs a design concept based on the theme of "warmth and quality" into the terminal.

[0152] 2. Server: Based on the request, select an appropriate generative AI model and generate a design proposal.

[0153] 3. Terminal: The designer evaluates the generated design proposals and selects the final design. The pattern and 3D rendering are carried out in CLO 3D, and the final design is made available for download.

[0154] 4. Reuse: The completed 3D assets are used as items in online games (e.g., skins in Minecraft), and the reactions from players are analyzed.

[0155] This will enable the design process to be more efficient, customer needs to be reflected more quickly, and market responses to be collected and analyzed more appropriately.

[0156] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0157] Step 1:

[0158] The user uses a device to input the brand's unique worldview and design concept. The user details the specific theme, color, style, etc., and this information is sent to the server. An example of input is the theme "refreshing summer" or the prompt "resort wear with a blue and white theme." The input data is sent to the server in JSON format.

[0159] Step 2:

[0160] The server receives the design concept sent by the user. Next, the server uses an AI selection method to select the best generative AI model for this concept. The server analyzes past design data and trends to select the best candidate generative AI model (e.g., DALL-E 2 or StyleGAN2). This selection process involves data calculations based on the AI ​​model's evaluation indicators and trend data. Information on the selected model is passed to the next step.

[0161] Step 3:

[0162] The server uses the selected generative AI model to automatically generate design proposals based on the prompt text. An example of the prompt text is "Summer resort wear with a blue and white theme." The generation method uses the AI ​​model to generate multiple design proposals. In this process, the prompt text is input as a task into the AI ​​model, and multiple generated results are output. The output design proposals are then processed in the next step.

[0163] Step 4:

[0164] The server patterns the generated design proposal using a patterning means. Then, using software such as CLO 3D or Blender, the design proposal is converted into a 3D model. In this process, data processing is performed to convert the generated flat design into a 3D format. The 3D model is generated in a format that can be visually confirmed and sent to a storage means.

[0165] Step 5:

[0166] The server stores the patterned and 3D design proposals using a storage means. Cloud storage such as AWS S3 is used as the storage destination. The stored design data is provided so that users can access it from their devices. The saved data also includes the date and time of creation and version information.

[0167] Step 6:

[0168] The user checks the saved design proposals via their device and sends feedback. Possible feedback content includes requests such as "make the colors more vibrant" or "change the pattern." The feedback data is sent to the server and passed on to the correction tool. The input data may be sent in text format or with an image attached.

[0169] Step 7:

[0170] The server receives the feedback and modifies the design proposal using the modification tool. In this process, modifications based on the user's requests are made using design tools such as Blender or Photoshop. The modified design proposal is repatterned and converted into 3D, and updated using the storage tool. The modifications and the date and time of the update are recorded.

[0171] Step 8:

[0172] The final design proposal is saved in a storage medium and can be reused as a 3D asset in advertisements, games, virtual spaces, etc. When reused, the data format is converted into a format suitable for each use, such as a format for games or a format for VR content.

[0173] Step 9:

[0174] The server uses market analysis tools to gather market responses to the reused 3D assets. Here, Google Analytics and proprietary analysis tools are used to collect data on user responses and usage. The collected data is analyzed and reflected in the next design process. The analysis results are displayed in a dashboard format and provided to users.

[0175] (Application example 1)

[0176] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0177] The traditional apparel design process is time-consuming and costly, and it is difficult to quickly reflect customer needs. Furthermore, the resulting designs are not easily reusable, limiting the means by which they can be effectively used in advertising and virtual environments. The present invention aims to solve these problems and provide a system that allows designs to be effectively reused across multiple media.

[0178] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0179] In this invention, the server includes an input means for inputting a brand's unique worldview and concept, an AI selection means for selecting a generative AI model based on the input design concept, a generation means for automatically generating design proposals using the selected generative AI model, a patterning means for converting the generated design proposals into patterns and three-dimensional models, a storage means for saving and providing the patterned and three-dimensional designs, a reuse means for reusing the generated 3D data as advertising content, and a market analysis means for collecting and analyzing market responses to the reused advertising content, thereby enabling efficient design generation and reuse in a variety of media.

[0180] "Worldview" refers to the unique themes and stories that a brand expresses.

[0181] A "concept" is the basic idea behind the design and image of a brand.

[0182] "Input means" refers to devices and software that allow users to input design information into the system.

[0183] An "AI selection tool" is a device or software that selects the optimal generative AI model based on an input design concept.

[0184] "Generative means" refers to a device or software that automatically creates design proposals using a selected generative AI model.

[0185] "Patterning means" refers to a device or software that patterns the generated design proposal and converts it into a three-dimensional model.

[0186] "Storage means" refers to a device or software that stores the patterned and three-dimensional designs and provides them as needed.

[0187] "Reuse means" refers to devices or software for reusing the generated 3D data as advertising content.

[0188] "Market analysis means" means equipment or software that collects and analyzes market responses to reused advertising content.

[0189] "Advertising content" refers to media such as advertising banners and videos used to convey brand design and information.

[0190] A "three-dimensional model" is model data that represents a design in three-dimensional space.

[0191] This invention provides a system for efficiently inputting and utilizing a brand's unique worldview and concept, and generating and reusing advertising assets, using automated vehicles, logistics centers, factory robots, and user terminals.

[0192] Hardware and Software Use

[0193] The system's main hardware includes a smartphone or tablet to accept user input, and a high-performance server to generate designs using generative AI models. The software includes:

[0194] TENSORFLOW (registered trademark): A library for design generation using AI models.

[0195] Blender: a 3D modeling tool used to convert generated designs into three-dimensional models.

[0196] Firebase: A real-time database and analytics service.

[0197] React Native: A framework for developing cross-platform mobile applications.

[0198] System configuration

[0199] The system consists of the following components:

[0200] 1. Input Method

[0201] An interface that allows users to input a brand's unique worldview and concept. For example, it could be an application that can be operated from a smartphone or tablet.

[0202] 2. AI selection method

[0203] This is a server-side process that selects the optimal generative AI model based on the input design concept. The selection is based on information such as past design data and trend analysis.

[0204] 3. Generation means

[0205] The AI ​​model generates multiple design proposals, using the TensorFlow library in the generation process.

[0206] 4. Patterning Methods

[0207] The process of converting the generated design ideas into patterns and three-dimensional models using Blender.

[0208] 5. Preservation means

[0209] Ability to store and serve patterned and 3D designs using Firebase.

[0210] 6. Reuse methods

[0211] A means to reuse the generated 3D data as advertising content. Reuse the generated 3D data in advertising, virtual environments, and multiple media.

[0212] 7. Market analysis tools

[0213] A function that collects market responses to reused advertising content and analyzes them using Firebase Analytics.

[0214] Specific examples

[0215] Example 1: A collection themed around summer freshness

[0216] User: A brand manager inputs a design concept with the theme "refreshing summer."

[0217] Server: After receiving the concept, the server selects the optimal generative AI model and generates multiple design proposals. Using the generative tools, colors, shapes, and patterns are generated to match the concept.

[0218] Storage and reuse: The generated 3D data is stored in Firebase and can be reused as advertising banners and videos.

[0219] Market analysis: User responses are collected from social media and advertising platforms and fed back into the next design process.

[0220] Prompt Sentence Examples

[0221] "Tell us your design concept for a refreshing summer apparel product. For example, turquoise beachwear or a cool white shirt. Based on this concept, we will use a generative AI model to propose the optimal design."

[0222] This concludes the specific embodiment of the invention. By using this system, designs can be efficiently generated and reused as advertising assets in a variety of media. Furthermore, by analyzing market reactions, it is possible to quickly and accurately reflect these results in the next design.

[0223] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0224] Step 1:

[0225] User input of design concepts

[0226] Users use devices such as smartphones and tablets to input their brand's unique worldview and design concept. For example, they can input the concept "refreshing summer" into a dedicated application on their device. The input design concept is then sent to the server.

[0227] Input: Design concept (e.g. "Summer freshness")

[0228] Data processing: Text data of the design concept entered on the device is sent to the server

[0229] Output: Design concept sent to server

[0230] Step 2:

[0231] AI model selection

[0232] The server selects an appropriate generative AI model based on the received design concept, using past design data and trend analysis, and leveraging the TensorFlow library.

[0233] Input: Design concept (e.g. "Summer freshness")

[0234] Data processing: Concept-based generative AI model selection

[0235] Output: Selected generative AI model

[0236] Step 3:

[0237] Generate design ideas

[0238] The server uses the selected generative AI model to automatically generate multiple design proposals that combine colors, shapes, patterns, etc. to fit the specified concept.

[0239] Input: Selected generative AI model, design concept

[0240] Data Computation: Generating Design Ideas Using AI Models

[0241] Output: Multiple design ideas

[0242] Step 4:

[0243] Patterning and 3D modeling

[0244] The resulting design is then patterned using Blender and then converted into a 3D model, which allows the design to be simulated during the manufacturing process.

[0245] Input: Generated design proposal

[0246] Data calculation: Patterning and 3D modeling of design proposals

[0247] Output: 3D model

[0248] Step 5:

[0249] keep

[0250] The patterned and 3D designs are stored using Firebase, which allows the design data to be served on demand.

[0251] Input: 3D model

[0252] Data calculation: Save data to Firebase

[0253] Output: Saved design data

[0254] Step 6:

[0255] Reusing advertising content

[0256] The generated 3D data can be reused as advertising content, and distributed to social media and advertising platforms in the form of advertising banners or videos.

[0257] Input: 3D model

[0258] Data calculation: Generating advertising content

[0259] Output: Ad banners, videos

[0260] Step 7:

[0261] Collecting and analyzing market responses

[0262] Firebase Analytics will be used to collect and analyze market responses to ads distributed across social media and advertising platforms, providing important insights to inform the next design process.

[0263] Input: Ad banner, video

[0264] Data Computing: Collecting and Analyzing Market Responses

[0265] Output: Analysis results

[0266] The above is the flow of processing in the embodiment of the present invention.

[0267] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0268] The present invention is a system that enables more advanced design proposals and understanding of market needs by combining a system for improving the efficiency of apparel design using generative AI with an emotion engine that recognizes user emotions. Specific embodiments of this system are described below.

[0269] System configuration

[0270] The system consists of the following main components:

[0271] 1. Input means: A device or software that allows users to input the brand's unique worldview and concept.

[0272] 2. Emotion Engine: A device or software that recognizes and analyzes emotions in user input and feedback.

[0273] 3. AI selection means: A device or software that selects a generative AI model based on the input design concept and analysis results from the emotion engine.

[0274] 4. Generator: A device or software that automatically generates design proposals using the selected generative AI model.

[0275] 5. Patterning means: A device or software that patterns the generated design proposal and converts it into a 3D model.

[0276] 6. Storage means: A device or software that stores the patterned and 3D designs and provides them as needed.

[0277] 7. Reuse means: A device or software that reuses the generated 3D assets for multiple purposes such as advertising, games, virtual spaces, etc.

[0278] 8. Market Analysis Tools: Equipment or software that collects and analyzes market response to reused 3D assets.

[0279] 9. Correction tool (optional): A device or software that receives user feedback and corrects the generated design proposals based on it.

[0280] Operational Overview

[0281] The operation of this system will now be outlined.

[0282] User Actions and Input

[0283] Users input their brand's unique worldview and design concept through the device. This input includes themes, colors, styles, seasons, etc. As users make input, the emotion engine analyzes their facial expressions and voice to recognize their emotional state.

[0284] Server processing

[0285] 1. Data reception and AI model selection:

[0286] The server receives design requests from users and analysis results from the emotion engine. The AI ​​selection means selects a generative AI model based on this information. The selection also takes into account emotion data from the emotion engine, and prioritizes designs predicted to be preferred by users.

[0287] 2. Generate design proposals:

[0288] The server uses the selected generative AI model to generate multiple design proposals based on the user's input concept and emotional data. The generator automatically combines colors, shapes, and patterns to generate designs that match the user's emotional state.

[0289] 3. Patterning and 3D:

[0290] The generated design proposals are patterned by a patterning tool and then converted into a 3D model. This generates the format and 3D data required for the manufacturing process. Designs that match the emotional data are primarily patterned and converted into 3D.

[0291] View and give feedback on design ideas

[0292] The user can review the generated design proposals via their device and provide feedback. The emotion engine monitors the user's emotional state during the feedback process and transmits the results to the server. The proposed design is then adjusted according to the user's emotions.

[0293] Save and reuse 3D assets

[0294] The final design proposals are saved and provided as 3D assets to existing advertisements, games, and virtual spaces, enabling new market development and understanding of customer needs.

[0295] Collecting and analyzing market responses

[0296] The server collects and analyzes market responses to the reused 3D assets using market analysis tools, and the results of this analysis are fed back into the next design process, providing even more accurate market insights.

[0297] Examples:

[0298] Example 1: New summer collection

[0299] User: A brand manager inputs a design concept with the theme of "summer freshness." The emotion engine detects the excitement in the manager's facial expression.

[0300] Server: Receives concept and emotion data, selects generative AI model, and generates refreshing design that matches the emotion.

[0301] Terminal: The manager reviews the design proposal and provides feedback. The emotion engine detects excitement and adjusts the proposal. The final design is approved.

[0302] Reuse: The created 3D assets are used in virtual events, and participant responses are collected as data.

[0303] Example 2: Fall / Winter Collection

[0304] User: The designer inputs a design concept based on the theme of "warmth and quality." The emotion engine recognizes the designer's calm facial expression.

[0305] Server: Based on the request and emotion data, selects an appropriate AI model and generates design proposals.

[0306] Device: Designers evaluate the design proposals and incorporate revisions based on the emotional data. The final design is selected. The designs are then patterned and converted into 3D, and the final design is made available for download.

[0307] Reuse: The completed 3D assets will appear as in-game items and the reaction from players will be analyzed.

[0308] The above is a specific embodiment of the present invention. This system makes it possible to propose sophisticated designs that take user emotions into consideration and to grasp market needs.

[0309] The processing flow will be explained below.

[0310] Step 1:

[0311] (User)

[0312] Users access the device and input their brand's unique worldview and design concept, including details such as theme (e.g., refreshing summer), color, style, and season. Once the input is complete, they click the submit button to submit their design request.

[0313] Step 2:

[0314] (Terminal)

[0315] The terminal receives the user's input data, checks the data for integrity, and then formats and sends it to the server.

[0316] Step 3:

[0317] (server)

[0318] The server receives a design request from the user and begins analysis. The analyzed data is sent to the AI ​​selection process, which selects the optimal generative AI model based on the brand's worldview and concept. Past design data and market trend information are also referenced during this process.

[0319] Step 4:

[0320] (server)

[0321] The server activates the emotion engine based on the design concept input by the user, and the emotion engine analyzes the emotion data obtained during the user's input or feedback to identify the user's current emotional state.

[0322] Step 5:

[0323] (server)

[0324] The server generates multiple design proposals based on the selected generative AI model and emotion data obtained from the emotion engine. The generated design proposals are adjusted to be optimally proposed based on the user's emotional state.

[0325] Step 6:

[0326] (Terminal)

[0327] The device displays multiple design proposals sent from the server on a user interface, and is equipped with a function to display images and detailed information so that the user can easily check each design proposal.

[0328] Step 7:

[0329] (User)

[0330] Users can view the generated design proposals via their devices and provide evaluations and feedback, including specific comments and suggested revisions.

[0331] Step 8:

[0332] (Terminal)

[0333] The device collects user feedback and sends it to the server, where the emotion engine again analyzes the user's emotional state and understands the context of the feedback.

[0334] Step 9:

[0335] (server)

[0336] The server receives the feedback and modifies the design proposal using a modification tool, taking into account the user's emotional state and the feedback content. The modified design proposal is then sent back to the device from the server.

[0337] Step 10:

[0338] (server)

[0339] The server automatically patterns the user's final approved design using a pattern generator and then creates a 3D model, which generates the format and 3D data required for the manufacturing process.

[0340] Step 11:

[0341] (server)

[0342] The server stores the generated patterned data and 3D model using a storage means so that they can be provided to users and manufacturers as needed.

[0343] Step 12:

[0344] (server)

[0345] The server uses a reuse method to store the generated 3D assets in a format that can be reused for various content (advertisements, games, virtual spaces, etc.) and provides them to related applications.

[0346] Step 13:

[0347] (server)

[0348] The server collects and analyzes market responses to the reused 3D assets using market analysis tools. The results of this analysis are fed back into the next design process, enabling designs to be more in line with the market.

[0349] Example 2

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

[0351] Current design generation systems do not take user emotions or feedback into account, making it difficult to provide design proposals that meet user expectations. Furthermore, it is difficult to predict the expected level of demand when releasing generated design proposals directly to the market. This creates a problem of mismatch between design and market needs, lowering the success rate of projects.

[0352] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0353] In this invention, the server includes an input means for inputting a brand's unique worldview and concept, an emotion engine that analyzes the input design concept and user emotions, an AI selection means for selecting a generative AI model based on the analysis results, a generation means for automatically generating design proposals using the selected generative AI model, a patterning means for converting the generated design proposals into patterns and 3D models, a storage means for saving and providing the patterned and 3D designs, a reuse means for reusing the generated 3D assets for multiple purposes, and a market analysis means for collecting and analyzing market reactions to the reused 3D assets. This enables design proposals that take user emotions into consideration and enables effective design development that understands market needs.

[0354] "Input means" refers to a device or software that allows users to input the brand's unique worldview or concept.

[0355] An "emotion engine" is a device or software that recognizes and analyzes emotions during user input and feedback.

[0356] The "AI selection means" is a device or software that selects a generative AI model based on the input design concept and the analysis results from the emotion engine.

[0357] "Generating means" means a device or software that automatically generates design proposals using a selected generative AI model.

[0358] The "patterning means" is a device or software that patterns the generated design proposal and converts it into a 3D model.

[0359] "Storage means" means a device or software that stores and optionally provides patterned and 3D designs.

[0360] A "reuse means" is a device or software that reuses the generated 3D assets for multiple purposes, such as advertisements, games, virtual spaces, etc.

[0361] "Market Analysis Tool" means a device or software that collects and analyzes market response to reused 3D assets.

[0362] "Feedback" refers to the evaluation and opinions that users give to the generated design proposals.

[0363] A "generative AI model" is an artificial intelligence model used to automatically generate design proposals.

[0364] The present invention is a system that enables more advanced design proposals and understanding of market needs by combining an emotion engine that recognizes user emotions with an apparel design efficiency system that uses a generative AI model. Specific embodiments of this system are described below.

[0365] System configuration

[0366] The system consists of the following main components:

[0367] 1. Input means: A device or software that allows users to input the brand's unique worldview or concept. The user interface uses a keyboard or touch screen, and provides text fields and selection menus.

[0368] 2. Emotion engine: A device or software that recognizes emotions by analyzing the user's facial expressions and voice. It can obtain emotional data in real time using a camera or microphone.

[0369] 3. AI selection means: A device or software that selects a generative AI model based on the input design concept and the analysis results from the emotion engine. It selects the optimal AI model based on a specific algorithm.

[0370] 4. Generator: A device or software that automatically generates design proposals using a selected generative AI model. An AI model using deep learning is used, and the design is generated by that model.

[0371] 5. Patterning means: A device or software that patterns the generated design proposal and converts it into a 3D model. The 2D data is converted into a format that can be used by 3D modeling software (e.g., Blender or Maya).

[0372] 6. Storage means: Device or software that stores the patterned and 3D designs and provides them as needed. They can be stored in a database or cloud storage. The storage format should be a general format (e.g., .fbx or .glb) that can be used for advertising, games, and virtual spaces.

[0373] 7. Reuse tools: Devices or software that reuse the generated 3D assets for multiple purposes, such as advertising, games, virtual spaces, etc. It provides APIs and interfaces for reuse.

[0374] 8. Market Analysis Tools: Devices or software that collect and analyze market reactions to reused 3D assets, such as user reviews, online feedback, and social media comments.

[0375] 9. Correction tool (optional): A device or software that receives user feedback and corrects the generated design proposal based on that feedback. It continues to analyze the user's emotions during the feedback process.

[0376] Operational Overview

[0377] The operation of this system can be explained in several steps. First, the user inputs the brand's unique worldview and design concept through the device. This input includes the theme, color, style, season, etc. At this stage, the emotion engine analyzes the user's facial expressions and voice to recognize their emotional state.

[0378] The server receives design requests from users and analysis results from the emotion engine. The AI ​​selection means selects the optimal generative AI model based on this information. This selection also takes into account emotion data from the emotion engine, and prioritizes designs predicted to be preferred by the user.

[0379] The server uses the selected generative AI model to generate multiple design proposals based on the user's input concept and emotional data. The generator automatically combines colors, shapes, and patterns to generate designs that match the user's emotional state.

[0380] The generated design proposals are patterned by a patterning means and then converted into a 3D model. This generates the format and 3D data required for the manufacturing process. Designs that match the emotional data are primarily patterned and converted into 3D.

[0381] The user can then review the generated design proposals and provide feedback via their device. The emotion engine monitors the user's emotional state during the feedback process and transmits the results to the server. The proposed design is then adjusted based on the user's emotions.

[0382] The final design proposal can be saved and provided as a 3D asset for advertisements, games, virtual spaces, etc. This makes it possible to develop new markets and understand customer needs.

[0383] The server collects and analyzes market responses to the reused 3D assets using market analysis tools, and the results of this analysis are fed back into the next design process, providing even more accurate market insights.

[0384] Specific examples

[0385] Example 1: New summer collection

[0386] User: A brand manager inputs a design concept with the theme of "summer freshness." The emotion engine detects the excitement in the manager's facial expression.

[0387] Server: Receives concept and emotion data and selects a generative AI model. Generates a refreshing design that matches the emotion.

[0388] Terminal: Manager reviews design proposals and provides feedback. Emotion engine detects excitement and adjusts proposals. Final design is approved.

[0389] Reuse: The created 3D assets are used in virtual events, and participant responses are collected as data.

[0390] Example prompt:

[0391] Generate design ideas based on the theme of "summer freshness." The emotion engine recognizes the user's sense of excitement.

[0392] Example 2: Fall / Winter Collection

[0393] User: The designer inputs a design concept based on the theme of "warmth and quality." The emotion engine recognizes the designer's calm facial expression.

[0394] Server: Based on the request and emotion data, selects an appropriate AI model and generates design proposals.

[0395] Device: Designers evaluate the design proposals and make revisions based on the emotional data. The final design is selected. The design is then patterned and converted into 3D, and the final design is made available for download.

[0396] Reuse: The completed 3D assets will appear as in-game items and the reaction from players will be analyzed.

[0397] Example prompt:

[0398] Generate design ideas based on the theme of "warmth and quality." The emotion engine recognizes the user's calming emotions.

[0399] The above is a specific embodiment for carrying out the present invention. This system makes it possible to propose sophisticated designs that take into account the user's emotions and to grasp market needs.

[0400] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0401] Step 1:

[0402] Entering design concepts using input devices

[0403] User: Uses the device to input the brand's unique worldview and design concept (theme, color, style, season, etc.).

[0404] Input: Text data entered via a keyboard or touchscreen.

[0405] Output: The text data of the design concept is sent from the device to the server.

[0406] Step 2:

[0407] Emotion analysis using an emotion engine

[0408] Device: Captures the user's facial expressions and voice using a camera and microphone, obtaining emotional data in real time.

[0409] Input: User's facial expressions and voice data.

[0410] Data processing: Emotion analysis algorithms analyze input facial expressions and voice data.

[0411] Output: Emotional data (e.g., elation, calm, etc.) is generated and sent to the server.

[0412] Step 3:

[0413] Data reception and AI model selection

[0414] Server: Receives design concepts from users and analysis results from the emotion engine.

[0415] Input: Text data and sentiment data of design concepts.

[0416] Data processing: Based on the received design concept and emotion data, an algorithm is applied to select the optimal generative AI model.

[0417] Output: An appropriate generative AI model is selected (e.g., a model characterized by vibrant colors).

[0418] Step 4:

[0419] Generate design ideas

[0420] Server: Automatically generate design proposals using the selected generative AI model.

[0421] Input: Text data of design concepts and a selected generative AI model.

[0422] Data calculation: The generative AI model generates multiple design proposals based on the design concept and emotion data. An image generation algorithm using deep learning technology is applied.

[0423] Output: Multiple design options are generated (e.g., a design based on light blue and green).

[0424] Step 5:

[0425] Patterning and 3D design ideas

[0426] Server: Patterns the generated design proposals and converts them into 3D models.

[0427] Input: Generated design proposal.

[0428] Data processing: Converting 2D patterns into a 3D model format that can be used in 3D modeling software.

[0429] Output: 3D model data (e.g. .obj file)

[0430] Step 6:

[0431] View design ideas and receive feedback

[0432] Terminal: The user can view multiple generated design proposals on the screen.

[0433] Input: 3D model data.

[0434] Output: Preview in the user interface.

[0435] User: Enter and submit feedback on the design proposal.

[0436] Input: Feedback content and emotional state during feedback.

[0437] Output: Feedback data and emotion data are sent to the server.

[0438] Step 7:

[0439] Recalibration based on feedback

[0440] Server: Re-adjust design proposals based on feedback data and new sentiment data.

[0441] Input: Feedback data and emotion data.

[0442] Data computation: Algorithms are applied to refine design proposals based on feedback and sentiment data.

[0443] Output: Revised design proposal.

[0444] Step 8:

[0445] Saving 3D assets

[0446] How to save: Save the revised design as a 3D asset.

[0447] Input: 3D model data of the revised design proposal.

[0448] Storage: Stored in a database or cloud storage.

[0449] Output: Saved 3D model data (e.g. .fbx or .glb)

[0450] Step 9:

[0451] 3D Asset Reuse

[0452] Reuse methods: Providing saved 3D assets for advertising, games, virtual spaces, etc.

[0453] Input: Saved 3D model data.

[0454] Output: Providing data for reuse.

[0455] Step 10:

[0456] Collecting and analyzing market responses

[0457] Market Analysis Tools: Collect and analyze market response to the 3D assets provided.

[0458] Input: Market feedback data, online reviews, and social media comments.

[0459] Data calculations: Algorithms are applied to analyze collected market responses and provide feedback for the next design process.

[0460] Output: Market analysis results.

[0461] These are the specific programming steps for this system, which will enable sophisticated design proposals that take user emotions into account and an understanding of market needs.

[0462] (Application example 2)

[0463] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0464] Conventional fashion design proposal systems have had the challenge of making design proposals that fully consider the user's preferences and emotions. Furthermore, they lacked the means to collect and analyze market reactions to the generated designs in real time, making it difficult to reflect these in the next design process. Effectively incorporating user emotions and market reactions is needed to make more suitable design proposals and understand market needs.

[0465] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0466] In this invention, the server includes an input means for inputting a brand's unique worldview and concept, an AI selection means for selecting an AI model, a generation means for automatically generating design proposals using the AI ​​model, a patterning means for converting the generated design proposals into patterns and 3D models, a storage means for saving and providing the generated 3D assets, a reuse means for reusing them, a market analysis means for collecting and analyzing market reactions, an emotion recognition means for recognizing user emotions, and an AI selection means for selecting an AI model based on emotions. This enables advanced design proposals that take user emotions into consideration and an understanding of market needs.

[0467] "Input means" refers to a device or software that allows the user to input the brand's unique worldview or concept.

[0468] An "AI selection means" is a device or software that selects the optimal generative AI model based on the input design concept and emotional data.

[0469] A "generator" is a device or software that automatically generates design proposals using a selected generative AI model.

[0470] The "patterning means" is a device or software that patterns the generated design proposal and converts it into a 3D model.

[0471] "Storage means" means a device or software that stores and optionally provides patterned and 3D designs.

[0472] A "reuse means" is a device or software for reusing the generated 3D assets in multiple ways.

[0473] "Market Analysis Tool" means a device or software that collects and analyzes market response to reused 3D assets.

[0474] "Emotion recognition means" refers to a device or software that recognizes the user's emotions and analyzes their emotional state.

[0475] "Fashion items" are design items such as clothing and accessories that are generated based on emotions recognized by emotion recognition means.

[0476] In this invention, the user inputs the brand's unique worldview and design concept through an input means. The input means is a software application installed on a general computer device such as a personal computer, smartphone, or tablet. The user uses this application to input information such as theme, color, style, and season. The input means has a function to recognize and analyze emotions from the user's facial expressions and voice using an emotion recognition means.

[0477] The server receives the design concept sent by the user and the emotion data obtained by the emotion recognition means. Based on this data, the server's AI selection means selects the optimal generative AI model, and automatically generates multiple design proposals using the selected generative AI model. The generation means uses a deep learning algorithm that utilizes a neural network to generate a design that matches the user's concept and emotion.

[0478] The generated design proposal is patterned by a patterning means and then converted into a 3D model. At this time, software such as Blender or Maya is used as a 3D modeling tool. The patterned and 3D design proposal is stored in a database server by a storage means, so that users can access it as needed.

[0479] The user checks the generated design proposal via the device and provides feedback through the emotion recognition means. This feedback data is also sent to the server, and the design proposal is further adjusted by the generation means. This process results in an optimal design proposal that reflects the user's requests.

[0480] The generated 3D assets can be reused in advertisements, games, and virtual spaces through reuse methods. For example, the 3D assets can be used in virtual events, and market reactions can be collected in real time. This reaction data can be analyzed through market analysis methods and fed back into the next design process.

[0481] To illustrate, the following prompt sentences will explain the system's behavior:

[0482] "Users visit a virtual store, and a camera analyzes their facial expressions to detect happy emotions. Based on that emotion, the virtual store suggests fashion items with bright and fun designs. Users can try on the items and ultimately purchase them."

[0483] In this way, the system of the present invention can propose designs that are appropriate for the user based on their emotions, enabling a new fashion item purchasing experience. Furthermore, by analyzing market reactions to the created designs, more advanced market insights can be obtained and reflected in the next design process.

[0484] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0485] Step 1:

[0486] Users use input devices to input the brand's unique worldview and design concept. Specifically, users input information such as theme, color, style, and season into a dedicated application installed on a PC, smartphone, or tablet. The input design data is then sent to a server.

[0487] Input: Design concept, theme, color, style, season

[0488] Output: Design data sent to the server

[0489] Step 2:

[0490] The emotion recognition means identifies the user's emotional state. Specifically, it uses the device's camera and voice recognition function to capture the user's facial expressions and voice, and analyzes their emotions using an emotion recognition model. The analyzed emotion data is then sent to the server.

[0491] Input: User's facial expressions and voice

[0492] Output: Emotion data sent to the server

[0493] Step 3:

[0494] The server selects an appropriate generative AI model using the AI ​​selection means based on the received design data and emotion data. Specifically, it identifies the generative AI model that is best suited to the user's emotion and design concept from the database in the server.

[0495] Input: Design data, emotion data

[0496] Output: The selected generative AI model

[0497] Step 4:

[0498] The server generates design proposals using the selected generative AI model. Specifically, the generation means uses a deep learning algorithm that utilizes a neural network to generate design proposals that match the user's design concept and emotional data.

[0499] Input: Generative AI model, design data, emotion data

[0500] Output: Generated design proposal

[0501] Step 5:

[0502] The server patterns the generated design proposal using a patterning means and converts it into a 3D model. Specifically, the server uses a 3D modeling tool (e.g., Blender or Maya) to create a 3D model of the generated design proposal.

[0503] Input: Generated design proposal

[0504] Output: Patterned and 3D designs

[0505] Step 6:

[0506] The server stores the patterned and 3D designs in a database using a storage means and provides them to the user as needed. Specifically, the stored data is maintained in a format accessible to the user's terminal.

[0507] Input: Patterned and 3D Design

[0508] Output: Saved design data

[0509] Step 7:

[0510] The user reviews the generated design proposals via their device and provides feedback. The emotion recognition means monitors the user's emotional state even when providing feedback, and transmits that data to the server. Specifically, the user evaluates the design proposals through the application and provides feedback through comments and facial expressions.

[0511] Input: Generated design proposals, user emotion data

[0512] Output: Feedback data sent to the server

[0513] Step 8:

[0514] The server reuses the 3D assets generated using the reuse means in advertisements, games, and virtual spaces, and collects market responses to them. Specifically, the 3D assets are used in virtual events, and response data is collected in real time.

[0515] Input: Generated 3D assets

[0516] Output: Collected market response data

[0517] Step 9:

[0518] The server analyzes the market response data collected using the market analysis means and reflects the results in the next design process. Specifically, the analyzed data is used as input when generating the next design proposal.

[0519] Input: Market response data

[0520] Output: Analysis results and points to improve next design data

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

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

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

[0524] [Second embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0537] This invention is a system that utilizes generative AI to streamline the design process in the apparel industry and understand customer needs. Specific embodiments of this system are described below.

[0538] System configuration

[0539] The system consists of the following main components:

[0540] 1. Input means: A device or software that allows users to input the brand's unique worldview and concept.

[0541] 2. AI selection means: A device or software that selects a generative AI model based on the input design concept.

[0542] 3. Generator: A device or software that automatically generates design proposals using the selected generative AI model.

[0543] 4. Patterning means: A device or software that patterns the generated design proposal and converts it into a 3D model.

[0544] 5. Storage means: A device or software that stores the patterned and 3D designs and provides them as needed.

[0545] 6. Reuse means: A device or software that reuses the generated 3D assets for multiple purposes, such as advertising, games, virtual spaces, etc.

[0546] 7. Market Analysis Tools: Equipment or software that collects and analyzes market response to reused 3D assets.

[0547] 8. Correction tool (optional): A device or software that receives user feedback and corrects the generated design proposals based on it.

[0548] Operational Overview

[0549] The operation of this system will now be outlined.

[0550] User Actions and Input

[0551] Users input their brand's unique worldview and design concept through the device. This input includes specific colors, styles, themes, etc. For example, a design concept based on the theme of "refreshing summer" can be input.

[0552] Server processing

[0553] 1. Data reception and AI model selection:

[0554] The server receives the design concept from the user and selects the optimal generating AI model using the AI ​​selection means.

[0555] This model selection is based on information such as past design data and trend analysis.

[0556] 2. Generate design proposals:

[0557] The server uses the selected generative AI model to automatically generate multiple design proposals. The generator combines colors, shapes, and patterns to create a design that fits the specified concept.

[0558] 3. Patterning and 3D:

[0559] The generated design proposal is patterned by a patterning tool and then converted into a 3D model, which simulates the manufacturing process and significantly reduces lead time.

[0560] View and give feedback on design ideas

[0561] The user checks the generated design proposal via the terminal and provides feedback. The feedback is sent from the terminal to the server. The server then uses a correction means to correct the design proposal based on the user's feedback (this means is optional).

[0562] Save and reuse 3D assets

[0563] The final design proposals are saved and provided as 3D assets to existing advertisements, games, and virtual spaces, enabling new market development and understanding of customer needs.

[0564] Collecting and analyzing market responses

[0565] The server collects and analyzes market responses to reused 3D assets through market analysis tools, and the results of this analysis are reflected in the next design process, allowing for continuous improvement and adaptation.

[0566] Examples:

[0567] Example 1: New summer collection

[0568] User: A brand manager inputs a design concept with the theme "refreshing summer."

[0569] Server: After receiving the concept, select the optimal generative AI model and generate multiple design proposals.

[0570] Terminal: The manager reviews the design proposal and provides feedback, which is used to refine the final design.

[0571] Reuse: The created 3D assets are used in virtual events, and participant responses are collected as data.

[0572] Example 2: Fall / Winter Collection

[0573] User: The designer inputs a design concept based on the theme of "warmth and quality."

[0574] Server: Based on the request, selects an appropriate AI model and generates a design proposal.

[0575] Terminal: The designer evaluates the design proposals and selects the final design. The design is then patterned and converted into 3D, and the final design is made available for download.

[0576] Reuse: The completed 3D assets will appear as in-game items and the reaction from players will be analyzed.

[0577] The processing flow will be explained below.

[0578] Step 1:

[0579] (User)

[0580] Users access the terminal and enter their brand's unique worldview and design concept into a dedicated input form, including detailed information such as theme, color, style, and season. Once the input is complete, they click the submit button to submit their design request.

[0581] Step 2:

[0582] (Terminal)

[0583] The terminal receives the user's input data, formats it, and sends it to the server, validating the data to ensure that the input is accurate.

[0584] Step 3:

[0585] (server)

[0586] The server receives design requests from users. It analyzes the received data and selects a generative AI model that matches the brand's worldview and concept. The selection is made using an AI selection method, referencing past design data and market trend information.

[0587] Step 4:

[0588] (server)

[0589] The server uses the selected generative AI model to generate multiple design proposals based on the user's input concept. Through the generation method, colors, shapes, and patterns are automatically combined to generate a design that matches the concept.

[0590] Step 5:

[0591] (Terminal)

[0592] The device displays the design proposals sent from the server on a user interface, and is equipped with a function to display images and detailed information so that the user can easily check each design proposal.

[0593] Step 6:

[0594] (User)

[0595] The user checks the displayed design proposals, inputs their evaluation and feedback for each design, and sends the feedback, including comments and correction requests, to the server from their device.

[0596] Step 7:

[0597] (server)

[0598] The server receives feedback from the user, modifies the design proposal using the modification means, and then transmits the design proposal to the user again after modification based on the feedback.

[0599] Step 8:

[0600] (server)

[0601] The server automatically patterns the final approved design using a pattern generator and then creates a 3D model, which generates the format and 3D data required for the manufacturing process.

[0602] Step 9:

[0603] (server)

[0604] The server stores the generated patterned data and 3D model using a storage means so that they can be provided to users and manufacturers as needed.

[0605] Step 10:

[0606] (server)

[0607] The server uses a reuse method to store the generated 3D assets in a format that can be reused for various content (advertisements, games, virtual spaces, etc.) and provides them to related applications.

[0608] Step 11:

[0609] (server)

[0610] The server collects and analyzes market responses to the reused 3D assets using market analysis tools. The results of this analysis are fed back into the next design process, enabling designs to be more in line with the market.

[0611] Example 1

[0612] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0613] The traditional apparel design process required a lot of manual work and time, and there was a need for greater efficiency. It was also difficult to accurately grasp customer needs and quickly incorporate them into designs. Furthermore, there was a lack of a system for properly collecting and analyzing market reactions to the designs. It was necessary to solve these problems and achieve greater efficiency in the design process in the apparel industry and appropriate feedback on market reactions.

[0614] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0615] In this invention, the server includes an input means for inputting a brand's unique worldview and concept, an AI selection means for selecting a generative AI model based on the input design concept, a generation means for automatically generating design proposals using the selected generative AI model, a patterning means for converting the generated design proposals into patterns and 3D models, a storage means for saving and providing the patterned and 3D designs, a reuse means for reusing the generated 3D assets for multiple purposes, a market analysis means for collecting and analyzing market responses, and a terminal for users to check the generated design proposals and send feedback. This enables the design process to be more efficient, customer needs to be quickly reflected, and market responses to be appropriately collected and analyzed.

[0616] "A brand's unique worldview and concept" refers to the theme, color, style, and design philosophy that a particular brand wants to express.

[0617] "Input means" refers to a device or software that allows a user to input the brand's unique worldview or concept into the system.

[0618] "AI selection means" refers to a device or software that selects the optimal generative AI model based on the input design concept.

[0619] "Generative means" refers to a device or software that automatically generates design proposals using a selected generative AI model.

[0620] "Patterning means" refers to a device or software that patterns the generated design proposal and then converts it into a 3D model.

[0621] "Storage means" refers to a device or software that stores and, if necessary, provides the patterned and 3D designs.

[0622] "Means for reuse" refers to a device or software that allows the generated 3D assets to be reused for multiple purposes, such as advertising, games, virtual spaces, etc.

[0623] "Market Analysis Tool" means a device or software that collects and analyzes market response to reused 3D assets.

[0624] "Terminal" refers to a device or software that allows a user to review the generated design proposals and provide feedback.

[0625] This invention is a system that uses generative AI to streamline the design process in the apparel industry and understand customer needs. The system consists of the following main components:

[0626] System configuration

[0627] 1. Input method:

[0628] Users input the brand's unique worldview and design concept through a terminal, specifying in detail the specific theme, color, style, etc. For example, a design concept of "resort wear with a blue and white theme" based on the theme of "refreshing summer" can be input.

[0629] 2. AI selection method:

[0630] The server receives the design concept sent by the user and selects the optimal generative AI model using an AI selection method. This uses software to analyze past design data and trends and select the optimal model. For example, DALL-E 2 or StyleGAN 2 is used.

[0631] 3. Generation means:

[0632] The server automatically generates design proposals based on the selected generative AI model. During this process, a prompt is entered to generate the design. For example, the prompt could be "summer resort wear in blue and white."

[0633] 4. Patterning means:

[0634] The generated design proposals are patterned using a pattern generator on the server, and then converted into 3D models using software such as CLO 3D or Blender, allowing users to visualize the design in three dimensions.

[0635] 5. Preservation means:

[0636] The server stores the patterned and 3D designs, which are then delivered to the customer via cloud storage such as AWS S3.

[0637] 6. Reuse method:

[0638] The server reuses the generated 3D assets for multiple purposes, such as advertising, online games, and virtual worlds.

[0639] 7. Market analysis tools:

[0640] The server collects and analyzes market reactions to the reused 3D assets using tools such as Google Analytics, and the results are fed back into the next design process for continuous improvement.

[0641] 8. Terminal:

[0642] The user can review the generated design proposal via their device and provide feedback, which is then sent to the server, where the design is revised as necessary.

[0643] Specific examples

[0644] Example 1: New summer collection

[0645] Example prompt: Refreshing summer resort wear in blue and white.

[0646] 1. User: The brand manager uses the terminal to input a design concept with the theme of "refreshing summer."

[0647] 2. Server: After receiving the concept, select the optimal generative AI model and generate multiple design proposals.

[0648] 3. Terminal: The manager reviews the generated design proposals and provides feedback on additional colors and patterns. The feedback is then sent to the server.

[0649] 4. Server: The server revises the design based on the feedback and finalizes the design. The final design is stored in AWS S3.

[0650] 5. Reuse: The created 3D assets are used in a virtual space (e.g., a VRChat event), and reactions from participants are collected as data.

[0651] Example 2: Fall / Winter Collection

[0652] Example prompt: A warm orange and brown autumn / winter coat

[0653] 1. User: The designer inputs a design concept based on the theme of "warmth and quality" into the terminal.

[0654] 2. Server: Based on the request, select an appropriate generative AI model and generate a design proposal.

[0655] 3. Terminal: The designer evaluates the generated design proposals and selects the final design. The pattern and 3D rendering are carried out in CLO 3D, and the final design is made available for download.

[0656] 4. Reuse: The completed 3D assets are used as items in online games (e.g., skins in Minecraft), and the reactions from players are analyzed.

[0657] This will enable the design process to be more efficient, customer needs to be reflected more quickly, and market responses to be collected and analyzed more appropriately.

[0658] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0659] Step 1:

[0660] The user uses a device to input the brand's unique worldview and design concept. The user details the specific theme, color, style, etc., and this information is sent to the server. An example of input is the theme "refreshing summer" or the prompt "resort wear with a blue and white theme." The input data is sent to the server in JSON format.

[0661] Step 2:

[0662] The server receives the design concept sent by the user. Next, the server uses an AI selection method to select the best generative AI model for this concept. The server analyzes past design data and trends to select the best candidate generative AI model (e.g., DALL-E 2 or StyleGAN2). This selection process involves data calculations based on the AI ​​model's evaluation indicators and trend data. Information on the selected model is passed to the next step.

[0663] Step 3:

[0664] The server uses the selected generative AI model to automatically generate design proposals based on the prompt text. An example of the prompt text is "Summer resort wear with a blue and white theme." The generation method uses the AI ​​model to generate multiple design proposals. In this process, the prompt text is input as a task into the AI ​​model, and multiple generated results are output. The output design proposals are then processed in the next step.

[0665] Step 4:

[0666] The server patterns the generated design proposal using a patterning means. Then, using software such as CLO 3D or Blender, the design proposal is converted into a 3D model. In this process, data processing is performed to convert the generated flat design into a 3D format. The 3D model is generated in a format that can be visually confirmed and sent to a storage means.

[0667] Step 5:

[0668] The server stores the patterned and 3D design proposals using a storage means. Cloud storage such as AWS S3 is used as the storage destination. The stored design data is provided so that users can access it from their devices. The saved data also includes the date and time of creation and version information.

[0669] Step 6:

[0670] The user checks the saved design proposals via their device and sends feedback. Possible feedback content includes requests such as "make the colors more vibrant" or "change the pattern." The feedback data is sent to the server and passed on to the correction tool. The input data may be sent in text format or with an image attached.

[0671] Step 7:

[0672] The server receives the feedback and modifies the design proposal using the modification tool. In this process, modifications based on the user's requests are made using design tools such as Blender or Photoshop. The modified design proposal is repatterned and converted into 3D, and updated using the storage tool. The modifications and the date and time of the update are recorded.

[0673] Step 8:

[0674] The final design proposal is saved in a storage medium and can be reused as a 3D asset in advertisements, games, virtual spaces, etc. When reused, the data format is converted into a format suitable for each use, such as a format for games or a format for VR content.

[0675] Step 9:

[0676] The server uses market analysis tools to gather market responses to the reused 3D assets. Here, Google Analytics and proprietary analysis tools are used to collect data on user responses and usage. The collected data is analyzed and reflected in the next design process. The analysis results are displayed in a dashboard format and provided to users.

[0677] (Application example 1)

[0678] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0679] The traditional apparel design process is time-consuming and costly, and it is difficult to quickly reflect customer needs. Furthermore, the resulting designs are not easily reusable, limiting the means by which they can be effectively used in advertising and virtual environments. The present invention aims to solve these problems and provide a system that allows designs to be effectively reused across multiple media.

[0680] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0681] In this invention, the server includes an input means for inputting a brand's unique worldview and concept, an AI selection means for selecting a generative AI model based on the input design concept, a generation means for automatically generating design proposals using the selected generative AI model, a patterning means for converting the generated design proposals into patterns and three-dimensional models, a storage means for saving and providing the patterned and three-dimensional designs, a reuse means for reusing the generated 3D data as advertising content, and a market analysis means for collecting and analyzing market responses to the reused advertising content, thereby enabling efficient design generation and reuse in a variety of media.

[0682] "Worldview" refers to the unique themes and stories that a brand expresses.

[0683] A "concept" is the basic idea behind the design and image of a brand.

[0684] "Input means" refers to devices and software that allow users to input design information into the system.

[0685] An "AI selection tool" is a device or software that selects the optimal generative AI model based on an input design concept.

[0686] "Generative means" refers to a device or software that automatically creates design proposals using a selected generative AI model.

[0687] "Patterning means" refers to a device or software that patterns the generated design proposal and converts it into a three-dimensional model.

[0688] "Storage means" refers to a device or software that stores the patterned and three-dimensional designs and provides them as needed.

[0689] "Reuse means" refers to devices or software for reusing the generated 3D data as advertising content.

[0690] "Market analysis means" means equipment or software that collects and analyzes market responses to reused advertising content.

[0691] "Advertising content" refers to media such as advertising banners and videos used to convey brand design and information.

[0692] A "three-dimensional model" is model data that represents a design in three-dimensional space.

[0693] This invention provides a system for efficiently inputting and utilizing a brand's unique worldview and concept, and generating and reusing advertising assets, using automated vehicles, logistics centers, factory robots, and user terminals.

[0694] Hardware and Software Use

[0695] The system's main hardware includes a smartphone or tablet to accept user input, and a high-performance server to generate designs using generative AI models. The software includes:

[0696] TensorFlow: A library for generating designs using AI models.

[0697] Blender: a 3D modeling tool used to convert generated designs into three-dimensional models.

[0698] Firebase: A real-time database and analytics service.

[0699] React Native: A framework for developing cross-platform mobile applications.

[0700] System configuration

[0701] The system consists of the following components:

[0702] 1. Input Method

[0703] An interface that allows users to input a brand's unique worldview and concept. For example, it could be an application that can be operated from a smartphone or tablet.

[0704] 2. AI selection method

[0705] This is a server-side process that selects the optimal generative AI model based on the input design concept. The selection is based on information such as past design data and trend analysis.

[0706] 3. Generation means

[0707] The AI ​​model generates multiple design proposals, using the TensorFlow library in the generation process.

[0708] 4. Patterning Methods

[0709] The process of converting the generated design ideas into patterns and three-dimensional models using Blender.

[0710] 5. Preservation means

[0711] Ability to store and serve patterned and 3D designs using Firebase.

[0712] 6. Reuse methods

[0713] A means to reuse the generated 3D data as advertising content. Reuse the generated 3D data in advertising, virtual environments, and multiple media.

[0714] 7. Market analysis tools

[0715] A function that collects market responses to reused advertising content and analyzes them using Firebase Analytics.

[0716] Specific examples

[0717] Example 1: A collection themed around summer freshness

[0718] User: A brand manager inputs a design concept with the theme "refreshing summer."

[0719] Server: After receiving the concept, the server selects the optimal generative AI model and generates multiple design proposals. Using the generative tools, colors, shapes, and patterns are generated to match the concept.

[0720] Storage and reuse: The generated 3D data is stored in Firebase and can be reused as advertising banners and videos.

[0721] Market analysis: User responses are collected from social media and advertising platforms and fed back into the next design process.

[0722] Prompt Sentence Examples

[0723] "Tell us your design concept for a refreshing summer apparel product. For example, turquoise beachwear or a cool white shirt. Based on this concept, we will use a generative AI model to propose the optimal design."

[0724] This concludes the specific embodiment of the invention. By using this system, designs can be efficiently generated and reused as advertising assets in a variety of media. Furthermore, by analyzing market reactions, it is possible to quickly and accurately reflect these results in the next design.

[0725] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0726] Step 1:

[0727] User input of design concepts

[0728] Users use devices such as smartphones and tablets to input their brand's unique worldview and design concept. For example, they can input the concept "refreshing summer" into a dedicated application on their device. The input design concept is then sent to the server.

[0729] Input: Design concept (e.g. "Summer freshness")

[0730] Data processing: Text data of the design concept entered on the device is sent to the server

[0731] Output: Design concept sent to server

[0732] Step 2:

[0733] AI model selection

[0734] The server selects an appropriate generative AI model based on the received design concept, using past design data and trend analysis, and leveraging the TensorFlow library.

[0735] Input: Design concept (e.g. "Summer freshness")

[0736] Data processing: Concept-based generative AI model selection

[0737] Output: Selected generative AI model

[0738] Step 3:

[0739] Generate design ideas

[0740] The server uses the selected generative AI model to automatically generate multiple design proposals that combine colors, shapes, patterns, etc. to fit the specified concept.

[0741] Input: Selected generative AI model, design concept

[0742] Data Computation: Generating Design Ideas Using AI Models

[0743] Output: Multiple design ideas

[0744] Step 4:

[0745] Patterning and 3D modeling

[0746] The resulting design is then patterned using Blender and then converted into a 3D model, which allows the design to be simulated during the manufacturing process.

[0747] Input: Generated design proposal

[0748] Data calculation: Patterning and 3D modeling of design proposals

[0749] Output: 3D model

[0750] Step 5:

[0751] keep

[0752] The patterned and 3D designs are stored using Firebase, which allows the design data to be served on demand.

[0753] Input: 3D model

[0754] Data calculation: Save data to Firebase

[0755] Output: Saved design data

[0756] Step 6:

[0757] Reusing advertising content

[0758] The generated 3D data can be reused as advertising content, and distributed to social media and advertising platforms in the form of advertising banners or videos.

[0759] Input: 3D model

[0760] Data calculation: Generating advertising content

[0761] Output: Ad banners, videos

[0762] Step 7:

[0763] Collecting and analyzing market responses

[0764] Firebase Analytics will be used to collect and analyze market responses to ads distributed across social media and advertising platforms, providing important insights to inform the next design process.

[0765] Input: Ad banner, video

[0766] Data Computing: Collecting and Analyzing Market Responses

[0767] Output: Analysis results

[0768] The above is the flow of processing in the embodiment of the present invention.

[0769] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0770] The present invention is a system that enables more advanced design proposals and understanding of market needs by combining a system for improving the efficiency of apparel design using generative AI with an emotion engine that recognizes user emotions. Specific embodiments of this system are described below.

[0771] System configuration

[0772] The system consists of the following main components:

[0773] 1. Input means: A device or software that allows users to input the brand's unique worldview and concept.

[0774] 2. Emotion Engine: A device or software that recognizes and analyzes emotions in user input and feedback.

[0775] 3. AI selection means: A device or software that selects a generative AI model based on the input design concept and analysis results from the emotion engine.

[0776] 4. Generator: A device or software that automatically generates design proposals using the selected generative AI model.

[0777] 5. Patterning means: A device or software that patterns the generated design proposal and converts it into a 3D model.

[0778] 6. Storage means: A device or software that stores the patterned and 3D designs and provides them as needed.

[0779] 7. Reuse means: A device or software that reuses the generated 3D assets for multiple purposes such as advertising, games, virtual spaces, etc.

[0780] 8. Market Analysis Tools: Equipment or software that collects and analyzes market response to reused 3D assets.

[0781] 9. Correction tool (optional): A device or software that receives user feedback and corrects the generated design proposals based on it.

[0782] Operational Overview

[0783] The operation of this system will now be outlined.

[0784] User Actions and Input

[0785] Users input their brand's unique worldview and design concept through the device. This input includes themes, colors, styles, seasons, etc. As users make input, the emotion engine analyzes their facial expressions and voice to recognize their emotional state.

[0786] Server processing

[0787] 1. Data reception and AI model selection:

[0788] The server receives design requests from users and analysis results from the emotion engine. The AI ​​selection means selects a generative AI model based on this information. The selection also takes into account emotion data from the emotion engine, and prioritizes designs predicted to be preferred by users.

[0789] 2. Generate design proposals:

[0790] The server uses the selected generative AI model to generate multiple design proposals based on the user's input concept and emotional data. The generator automatically combines colors, shapes, and patterns to generate designs that match the user's emotional state.

[0791] 3. Patterning and 3D:

[0792] The generated design proposals are patterned by a patterning tool and then converted into a 3D model. This generates the format and 3D data required for the manufacturing process. Designs that match the emotional data are primarily patterned and converted into 3D.

[0793] View and give feedback on design ideas

[0794] The user can review the generated design proposals via their device and provide feedback. The emotion engine monitors the user's emotional state during the feedback process and transmits the results to the server. The proposed design is then adjusted according to the user's emotions.

[0795] Save and reuse 3D assets

[0796] The final design proposals are saved and provided as 3D assets to existing advertisements, games, and virtual spaces, enabling new market development and understanding of customer needs.

[0797] Collecting and analyzing market responses

[0798] The server collects and analyzes market responses to the reused 3D assets using market analysis tools, and the results of this analysis are fed back into the next design process, providing even more accurate market insights.

[0799] Examples:

[0800] Example 1: New summer collection

[0801] User: A brand manager inputs a design concept with the theme of "summer freshness." The emotion engine detects the excitement in the manager's facial expression.

[0802] Server: Receives concept and emotion data, selects generative AI model, and generates refreshing design that matches the emotion.

[0803] Terminal: The manager reviews the design proposal and provides feedback. The emotion engine detects excitement and adjusts the proposal. The final design is approved.

[0804] Reuse: The created 3D assets are used in virtual events, and participant responses are collected as data.

[0805] Example 2: Fall / Winter Collection

[0806] User: The designer inputs a design concept based on the theme of "warmth and quality." The emotion engine recognizes the designer's calm facial expression.

[0807] Server: Based on the request and emotion data, selects an appropriate AI model and generates design proposals.

[0808] Device: Designers evaluate the design proposals and incorporate revisions based on the emotional data. The final design is selected. The designs are then patterned and converted into 3D, and the final design is made available for download.

[0809] Reuse: The completed 3D assets will appear as in-game items and the reaction from players will be analyzed.

[0810] The above is a specific embodiment of the present invention. This system makes it possible to propose sophisticated designs that take user emotions into consideration and to grasp market needs.

[0811] The processing flow will be explained below.

[0812] Step 1:

[0813] (User)

[0814] Users access the device and input their brand's unique worldview and design concept, including details such as theme (e.g., refreshing summer), color, style, and season. Once the input is complete, they click the submit button to submit their design request.

[0815] Step 2:

[0816] (Terminal)

[0817] The terminal receives the user's input data, checks the data for integrity, and then formats and sends it to the server.

[0818] Step 3:

[0819] (server)

[0820] The server receives a design request from the user and begins analysis. The analyzed data is sent to the AI ​​selection process, which selects the optimal generative AI model based on the brand's worldview and concept. Past design data and market trend information are also referenced during this process.

[0821] Step 4:

[0822] (server)

[0823] The server activates the emotion engine based on the design concept input by the user, and the emotion engine analyzes the emotion data obtained during the user's input or feedback to identify the user's current emotional state.

[0824] Step 5:

[0825] (server)

[0826] The server generates multiple design proposals based on the selected generative AI model and emotion data obtained from the emotion engine. The generated design proposals are adjusted to be optimally proposed based on the user's emotional state.

[0827] Step 6:

[0828] (Terminal)

[0829] The device displays multiple design proposals sent from the server on a user interface, and is equipped with a function to display images and detailed information so that the user can easily check each design proposal.

[0830] Step 7:

[0831] (User)

[0832] Users can view the generated design proposals via their devices and provide evaluations and feedback, including specific comments and suggested revisions.

[0833] Step 8:

[0834] (Terminal)

[0835] The device collects user feedback and sends it to the server, where the emotion engine again analyzes the user's emotional state and understands the context of the feedback.

[0836] Step 9:

[0837] (server)

[0838] The server receives the feedback and modifies the design proposal using a modification tool, taking into account the user's emotional state and the feedback content. The modified design proposal is then sent back to the device from the server.

[0839] Step 10:

[0840] (server)

[0841] The server automatically patterns the user's final approved design using a pattern generator and then creates a 3D model, which generates the format and 3D data required for the manufacturing process.

[0842] Step 11:

[0843] (server)

[0844] The server stores the generated patterned data and 3D model using a storage means so that they can be provided to users and manufacturers as needed.

[0845] Step 12:

[0846] (server)

[0847] The server uses a reuse method to store the generated 3D assets in a format that can be reused for various content (advertisements, games, virtual spaces, etc.) and provides them to related applications.

[0848] Step 13:

[0849] (server)

[0850] The server collects and analyzes market responses to the reused 3D assets using market analysis tools. The results of this analysis are fed back into the next design process, enabling designs to be more in line with the market.

[0851] Example 2

[0852] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0853] Current design generation systems do not take user emotions or feedback into account, making it difficult to provide design proposals that meet user expectations. Furthermore, it is difficult to predict the expected level of demand when releasing generated design proposals directly to the market. This creates a problem of mismatch between design and market needs, lowering the success rate of projects.

[0854] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0855] In this invention, the server includes an input means for inputting a brand's unique worldview and concept, an emotion engine that analyzes the input design concept and user emotions, an AI selection means for selecting a generative AI model based on the analysis results, a generation means for automatically generating design proposals using the selected generative AI model, a patterning means for converting the generated design proposals into patterns and 3D models, a storage means for saving and providing the patterned and 3D designs, a reuse means for reusing the generated 3D assets for multiple purposes, and a market analysis means for collecting and analyzing market reactions to the reused 3D assets. This enables design proposals that take user emotions into consideration and enables effective design development that understands market needs.

[0856] "Input means" refers to a device or software that allows users to input the brand's unique worldview or concept.

[0857] An "emotion engine" is a device or software that recognizes and analyzes emotions during user input and feedback.

[0858] The "AI selection means" is a device or software that selects a generative AI model based on the input design concept and the analysis results from the emotion engine.

[0859] "Generating means" means a device or software that automatically generates design proposals using a selected generative AI model.

[0860] The "patterning means" is a device or software that patterns the generated design proposal and converts it into a 3D model.

[0861] "Storage means" means a device or software that stores and optionally provides patterned and 3D designs.

[0862] A "reuse means" is a device or software that reuses the generated 3D assets for multiple purposes, such as advertisements, games, virtual spaces, etc.

[0863] "Market Analysis Tool" means a device or software that collects and analyzes market response to reused 3D assets.

[0864] "Feedback" refers to the evaluation and opinions that users give to the generated design proposals.

[0865] A "generative AI model" is an artificial intelligence model used to automatically generate design proposals.

[0866] The present invention is a system that enables more advanced design proposals and understanding of market needs by combining an emotion engine that recognizes user emotions with an apparel design efficiency system that uses a generative AI model. Specific embodiments of this system are described below.

[0867] System configuration

[0868] The system consists of the following main components:

[0869] 1. Input means: A device or software that allows users to input the brand's unique worldview or concept. The user interface uses a keyboard or touch screen, and provides text fields and selection menus.

[0870] 2. Emotion engine: A device or software that recognizes emotions by analyzing the user's facial expressions and voice. It can obtain emotional data in real time using a camera or microphone.

[0871] 3. AI selection means: A device or software that selects a generative AI model based on the input design concept and the analysis results from the emotion engine. It selects the optimal AI model based on a specific algorithm.

[0872] 4. Generator: A device or software that automatically generates design proposals using a selected generative AI model. An AI model using deep learning is used, and the design is generated by that model.

[0873] 5. Patterning means: A device or software that patterns the generated design proposal and converts it into a 3D model. The 2D data is converted into a format that can be used by 3D modeling software (e.g., Blender or Maya).

[0874] 6. Storage means: Device or software that stores the patterned and 3D designs and provides them as needed. They can be stored in a database or cloud storage. The storage format should be a general format (e.g., .fbx or .glb) that can be used for advertising, games, and virtual spaces.

[0875] 7. Reuse tools: Devices or software that reuse the generated 3D assets for multiple purposes, such as advertising, games, virtual spaces, etc. It provides APIs and interfaces for reuse.

[0876] 8. Market Analysis Tools: Devices or software that collect and analyze market reactions to reused 3D assets, such as user reviews, online feedback, and social media comments.

[0877] 9. Correction tool (optional): A device or software that receives user feedback and corrects the generated design proposal based on that feedback. It continues to analyze the user's emotions during the feedback process.

[0878] Operational Overview

[0879] The operation of this system can be explained in several steps. First, the user inputs the brand's unique worldview and design concept through the device. This input includes the theme, color, style, season, etc. At this stage, the emotion engine analyzes the user's facial expressions and voice to recognize their emotional state.

[0880] The server receives design requests from users and analysis results from the emotion engine. The AI ​​selection means selects the optimal generative AI model based on this information. This selection also takes into account emotion data from the emotion engine, and prioritizes designs predicted to be preferred by the user.

[0881] The server uses the selected generative AI model to generate multiple design proposals based on the user's input concept and emotional data. The generator automatically combines colors, shapes, and patterns to generate designs that match the user's emotional state.

[0882] The generated design proposals are patterned by a patterning means and then converted into a 3D model. This generates the format and 3D data required for the manufacturing process. Designs that match the emotional data are primarily patterned and converted into 3D.

[0883] The user can then review the generated design proposals and provide feedback via their device. The emotion engine monitors the user's emotional state during the feedback process and transmits the results to the server. The proposed design is then adjusted based on the user's emotions.

[0884] The final design proposal can be saved and provided as a 3D asset for advertisements, games, virtual spaces, etc. This makes it possible to develop new markets and understand customer needs.

[0885] The server collects and analyzes market responses to the reused 3D assets using market analysis tools, and the results of this analysis are fed back into the next design process, providing even more accurate market insights.

[0886] Specific examples

[0887] Example 1: New summer collection

[0888] User: A brand manager inputs a design concept with the theme of "summer freshness." The emotion engine detects the excitement in the manager's facial expression.

[0889] Server: Receives concept and emotion data and selects a generative AI model. Generates a refreshing design that matches the emotion.

[0890] Terminal: Manager reviews design proposals and provides feedback. Emotion engine detects excitement and adjusts proposals. Final design is approved.

[0891] Reuse: The created 3D assets are used in virtual events, and participant responses are collected as data.

[0892] Example prompt:

[0893] Generate design ideas based on the theme of "summer freshness." The emotion engine recognizes the user's sense of excitement.

[0894] Example 2: Fall / Winter Collection

[0895] User: The designer inputs a design concept based on the theme of "warmth and quality." The emotion engine recognizes the designer's calm facial expression.

[0896] Server: Based on the request and emotion data, selects an appropriate AI model and generates design proposals.

[0897] Device: Designers evaluate the design proposals and make revisions based on the emotional data. The final design is selected. The design is then patterned and converted into 3D, and the final design is made available for download.

[0898] Reuse: The completed 3D assets will appear as in-game items and the reaction from players will be analyzed.

[0899] Example prompt:

[0900] Generate design ideas based on the theme of "warmth and quality." The emotion engine recognizes the user's calming emotions.

[0901] The above is a specific embodiment for carrying out the present invention. This system makes it possible to propose sophisticated designs that take into account the user's emotions and to grasp market needs.

[0902] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0903] Step 1:

[0904] Entering design concepts using input devices

[0905] User: Uses the device to input the brand's unique worldview and design concept (theme, color, style, season, etc.).

[0906] Input: Text data entered via a keyboard or touchscreen.

[0907] Output: The text data of the design concept is sent from the device to the server.

[0908] Step 2:

[0909] Emotion analysis using an emotion engine

[0910] Device: Captures the user's facial expressions and voice using a camera and microphone, obtaining emotional data in real time.

[0911] Input: User's facial expressions and voice data.

[0912] Data processing: Emotion analysis algorithms analyze input facial expressions and voice data.

[0913] Output: Emotional data (e.g., elation, calm, etc.) is generated and sent to the server.

[0914] Step 3:

[0915] Data reception and AI model selection

[0916] Server: Receives design concepts from users and analysis results from the emotion engine.

[0917] Input: Text data and sentiment data of design concepts.

[0918] Data processing: Based on the received design concept and emotion data, an algorithm is applied to select the optimal generative AI model.

[0919] Output: An appropriate generative AI model is selected (e.g., a model characterized by vibrant colors).

[0920] Step 4:

[0921] Generate design ideas

[0922] Server: Automatically generate design proposals using the selected generative AI model.

[0923] Input: Text data of design concepts and a selected generative AI model.

[0924] Data calculation: The generative AI model generates multiple design proposals based on the design concept and emotion data. An image generation algorithm using deep learning technology is applied.

[0925] Output: Multiple design options are generated (e.g., a design based on light blue and green).

[0926] Step 5:

[0927] Patterning and 3D design ideas

[0928] Server: Patterns the generated design proposals and converts them into 3D models.

[0929] Input: Generated design proposal.

[0930] Data processing: Converting 2D patterns into a 3D model format that can be used in 3D modeling software.

[0931] Output: 3D model data (e.g. .obj file)

[0932] Step 6:

[0933] View design ideas and receive feedback

[0934] Terminal: The user can view multiple generated design proposals on the screen.

[0935] Input: 3D model data.

[0936] Output: Preview in the user interface.

[0937] User: Enter and submit feedback on the design proposal.

[0938] Input: Feedback content and emotional state during feedback.

[0939] Output: Feedback data and emotion data are sent to the server.

[0940] Step 7:

[0941] Recalibration based on feedback

[0942] Server: Re-adjust design proposals based on feedback data and new sentiment data.

[0943] Input: Feedback data and emotion data.

[0944] Data computation: Algorithms are applied to refine design proposals based on feedback and sentiment data.

[0945] Output: Revised design proposal.

[0946] Step 8:

[0947] Saving 3D assets

[0948] How to save: Save the revised design as a 3D asset.

[0949] Input: 3D model data of the revised design proposal.

[0950] Storage: Stored in a database or cloud storage.

[0951] Output: Saved 3D model data (e.g. .fbx or .glb)

[0952] Step 9:

[0953] 3D Asset Reuse

[0954] Reuse methods: Providing saved 3D assets for advertising, games, virtual spaces, etc.

[0955] Input: Saved 3D model data.

[0956] Output: Providing data for reuse.

[0957] Step 10:

[0958] Collecting and analyzing market responses

[0959] Market Analysis Tools: Collect and analyze market response to the 3D assets provided.

[0960] Input: Market feedback data, online reviews, and social media comments.

[0961] Data calculations: Algorithms are applied to analyze collected market responses and provide feedback for the next design process.

[0962] Output: Market analysis results.

[0963] These are the specific programming steps for this system, which will enable sophisticated design proposals that take user emotions into account and an understanding of market needs.

[0964] (Application example 2)

[0965] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0966] Conventional fashion design proposal systems have had the challenge of making design proposals that fully consider the user's preferences and emotions. Furthermore, they lacked the means to collect and analyze market reactions to the generated designs in real time, making it difficult to reflect these in the next design process. Effectively incorporating user emotions and market reactions is needed to make more suitable design proposals and understand market needs.

[0967] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0968] In this invention, the server includes an input means for inputting a brand's unique worldview and concept, an AI selection means for selecting an AI model, a generation means for automatically generating design proposals using the AI ​​model, a patterning means for converting the generated design proposals into patterns and 3D models, a storage means for saving and providing the generated 3D assets, a reuse means for reusing them, a market analysis means for collecting and analyzing market reactions, an emotion recognition means for recognizing user emotions, and an AI selection means for selecting an AI model based on emotions. This enables advanced design proposals that take user emotions into consideration and an understanding of market needs.

[0969] "Input means" refers to a device or software that allows the user to input the brand's unique worldview or concept.

[0970] An "AI selection means" is a device or software that selects the optimal generative AI model based on the input design concept and emotional data.

[0971] A "generator" is a device or software that automatically generates design proposals using a selected generative AI model.

[0972] The "patterning means" is a device or software that patterns the generated design proposal and converts it into a 3D model.

[0973] "Storage means" means a device or software that stores and optionally provides patterned and 3D designs.

[0974] A "reuse means" is a device or software for reusing the generated 3D assets in multiple ways.

[0975] "Market Analysis Tool" means a device or software that collects and analyzes market response to reused 3D assets.

[0976] "Emotion recognition means" refers to a device or software that recognizes the user's emotions and analyzes their emotional state.

[0977] "Fashion items" are design items such as clothing and accessories that are generated based on emotions recognized by emotion recognition means.

[0978] In this invention, the user inputs the brand's unique worldview and design concept through an input means. The input means is a software application installed on a general computer device such as a personal computer, smartphone, or tablet. The user uses this application to input information such as theme, color, style, and season. The input means has a function to recognize and analyze emotions from the user's facial expressions and voice using an emotion recognition means.

[0979] The server receives the design concept sent by the user and the emotion data obtained by the emotion recognition means. Based on this data, the server's AI selection means selects the optimal generative AI model, and automatically generates multiple design proposals using the selected generative AI model. The generation means uses a deep learning algorithm that utilizes a neural network to generate a design that matches the user's concept and emotion.

[0980] The generated design proposal is patterned by a patterning means and then converted into a 3D model. At this time, software such as Blender or Maya is used as a 3D modeling tool. The patterned and 3D design proposal is stored in a database server by a storage means, so that users can access it as needed.

[0981] The user checks the generated design proposal via the device and provides feedback through the emotion recognition means. This feedback data is also sent to the server, and the design proposal is further adjusted by the generation means. This process results in an optimal design proposal that reflects the user's requests.

[0982] The generated 3D assets can be reused in advertisements, games, and virtual spaces through reuse methods. For example, the 3D assets can be used in virtual events, and market reactions can be collected in real time. This reaction data can be analyzed through market analysis methods and fed back into the next design process.

[0983] To illustrate, the following prompt sentences will explain the system's behavior:

[0984] "Users visit a virtual store, and a camera analyzes their facial expressions to detect happy emotions. Based on that emotion, the virtual store suggests fashion items with bright and fun designs. Users can try on the items and ultimately purchase them."

[0985] In this way, the system of the present invention can propose designs that are appropriate for the user based on their emotions, enabling a new fashion item purchasing experience. Furthermore, by analyzing market reactions to the created designs, more advanced market insights can be obtained and reflected in the next design process.

[0986] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0987] Step 1:

[0988] Users use input devices to input the brand's unique worldview and design concept. Specifically, users input information such as theme, color, style, and season into a dedicated application installed on a PC, smartphone, or tablet. The input design data is then sent to a server.

[0989] Input: Design concept, theme, color, style, season

[0990] Output: Design data sent to the server

[0991] Step 2:

[0992] The emotion recognition means identifies the user's emotional state. Specifically, it uses the device's camera and voice recognition function to capture the user's facial expressions and voice, and analyzes their emotions using an emotion recognition model. The analyzed emotion data is then sent to the server.

[0993] Input: User's facial expressions and voice

[0994] Output: Emotion data sent to the server

[0995] Step 3:

[0996] The server selects an appropriate generative AI model using the AI ​​selection means based on the received design data and emotion data. Specifically, it identifies the generative AI model that is best suited to the user's emotion and design concept from the database in the server.

[0997] Input: Design data, emotion data

[0998] Output: The selected generative AI model

[0999] Step 4:

[1000] The server generates design proposals using the selected generative AI model. Specifically, the generation means uses a deep learning algorithm that utilizes a neural network to generate design proposals that match the user's design concept and emotional data.

[1001] Input: Generative AI model, design data, emotion data

[1002] Output: Generated design proposal

[1003] Step 5:

[1004] The server patterns the generated design proposal using a patterning means and converts it into a 3D model. Specifically, the server uses a 3D modeling tool (e.g., Blender or Maya) to create a 3D model of the generated design proposal.

[1005] Input: Generated design proposal

[1006] Output: Patterned and 3D designs

[1007] Step 6:

[1008] The server stores the patterned and 3D designs in a database using a storage means and provides them to the user as needed. Specifically, the stored data is maintained in a format accessible to the user's terminal.

[1009] Input: Patterned and 3D Design

[1010] Output: Saved design data

[1011] Step 7:

[1012] The user reviews the generated design proposals via their device and provides feedback. The emotion recognition means monitors the user's emotional state even when providing feedback, and transmits that data to the server. Specifically, the user evaluates the design proposals through the application and provides feedback through comments and facial expressions.

[1013] Input: Generated design proposals, user emotion data

[1014] Output: Feedback data sent to the server

[1015] Step 8:

[1016] The server reuses the 3D assets generated using the reuse means in advertisements, games, and virtual spaces, and collects market responses to them. Specifically, the 3D assets are used in virtual events, and response data is collected in real time.

[1017] Input: Generated 3D assets

[1018] Output: Collected market response data

[1019] Step 9:

[1020] The server analyzes the market response data collected using the market analysis means and reflects the results in the next design process. Specifically, the analyzed data is used as input when generating the next design proposal.

[1021] Input: Market response data

[1022] Output: Analysis results and points to improve next design data

[1023] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1024] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1025] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1026] [Third embodiment]

[1027] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1028] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1030] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1031] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1032] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1034] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1035] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1037] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1038] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1039] This invention is a system that utilizes generative AI to streamline the design process in the apparel industry and understand customer needs. Specific embodiments of this system are described below.

[1040] System configuration

[1041] The system consists of the following main components:

[1042] 1. Input means: A device or software that allows users to input the brand's unique worldview and concept.

[1043] 2. AI selection means: A device or software that selects a generative AI model based on the input design concept.

[1044] 3. Generator: A device or software that automatically generates design proposals using the selected generative AI model.

[1045] 4. Patterning means: A device or software that patterns the generated design proposal and converts it into a 3D model.

[1046] 5. Storage means: A device or software that stores the patterned and 3D designs and provides them as needed.

[1047] 6. Reuse means: A device or software that reuses the generated 3D assets for multiple purposes, such as advertising, games, virtual spaces, etc.

[1048] 7. Market Analysis Tools: Equipment or software that collects and analyzes market response to reused 3D assets.

[1049] 8. Correction tool (optional): A device or software that receives user feedback and corrects the generated design proposals based on it.

[1050] Operational Overview

[1051] The operation of this system will now be outlined.

[1052] User Actions and Input

[1053] Users input their brand's unique worldview and design concept through the device. This input includes specific colors, styles, themes, etc. For example, a design concept based on the theme of "refreshing summer" can be input.

[1054] Server processing

[1055] 1. Data reception and AI model selection:

[1056] The server receives the design concept from the user and selects the optimal generating AI model using the AI ​​selection means.

[1057] This model selection is based on information such as past design data and trend analysis.

[1058] 2. Generate design proposals:

[1059] The server uses the selected generative AI model to automatically generate multiple design proposals. The generator combines colors, shapes, and patterns to create a design that fits the specified concept.

[1060] 3. Patterning and 3D:

[1061] The generated design proposal is patterned by a patterning tool and then converted into a 3D model, which simulates the manufacturing process and significantly reduces lead time.

[1062] View and give feedback on design ideas

[1063] The user checks the generated design proposal via the terminal and provides feedback. The feedback is sent from the terminal to the server. The server then uses a correction means to correct the design proposal based on the user's feedback (this means is optional).

[1064] Save and reuse 3D assets

[1065] The final design proposals are saved and provided as 3D assets to existing advertisements, games, and virtual spaces, enabling new market development and understanding of customer needs.

[1066] Collecting and analyzing market responses

[1067] The server collects and analyzes market responses to reused 3D assets through market analysis tools, and the results of this analysis are reflected in the next design process, allowing for continuous improvement and adaptation.

[1068] Examples:

[1069] Example 1: New summer collection

[1070] User: A brand manager inputs a design concept with the theme "refreshing summer."

[1071] Server: After receiving the concept, select the optimal generative AI model and generate multiple design proposals.

[1072] Terminal: The manager reviews the design proposal and provides feedback, which is used to refine the final design.

[1073] Reuse: The created 3D assets are used in virtual events, and participant responses are collected as data.

[1074] Example 2: Fall / Winter Collection

[1075] User: The designer inputs a design concept based on the theme of "warmth and quality."

[1076] Server: Based on the request, selects an appropriate AI model and generates a design proposal.

[1077] Terminal: The designer evaluates the design proposals and selects the final design. The design is then patterned and converted into 3D, and the final design is made available for download.

[1078] Reuse: The completed 3D assets will appear as in-game items and the reaction from players will be analyzed.

[1079] The processing flow will be explained below.

[1080] Step 1:

[1081] (User)

[1082] Users access the terminal and enter their brand's unique worldview and design concept into a dedicated input form, including detailed information such as theme, color, style, and season. Once the input is complete, they click the submit button to submit their design request.

[1083] Step 2:

[1084] (Terminal)

[1085] The terminal receives the user's input data, formats it, and sends it to the server, validating the data to ensure that the input is accurate.

[1086] Step 3:

[1087] (server)

[1088] The server receives design requests from users. It analyzes the received data and selects a generative AI model that matches the brand's worldview and concept. The selection is made using an AI selection method, referencing past design data and market trend information.

[1089] Step 4:

[1090] (server)

[1091] The server uses the selected generative AI model to generate multiple design proposals based on the user's input concept. Through the generation method, colors, shapes, and patterns are automatically combined to generate a design that matches the concept.

[1092] Step 5:

[1093] (Terminal)

[1094] The device displays the design proposals sent from the server on a user interface, and is equipped with a function to display images and detailed information so that the user can easily check each design proposal.

[1095] Step 6:

[1096] (User)

[1097] The user checks the displayed design proposals, inputs their evaluation and feedback for each design, and sends the feedback, including comments and correction requests, to the server from their device.

[1098] Step 7:

[1099] (server)

[1100] The server receives feedback from the user, modifies the design proposal using the modification means, and then transmits the design proposal to the user again after modification based on the feedback.

[1101] Step 8:

[1102] (server)

[1103] The server automatically patterns the final approved design using a pattern generator and then creates a 3D model, which generates the format and 3D data required for the manufacturing process.

[1104] Step 9:

[1105] (server)

[1106] The server stores the generated patterned data and 3D model using a storage means so that they can be provided to users and manufacturers as needed.

[1107] Step 10:

[1108] (server)

[1109] The server uses a reuse method to store the generated 3D assets in a format that can be reused for various content (advertisements, games, virtual spaces, etc.) and provides them to related applications.

[1110] Step 11:

[1111] (server)

[1112] The server collects and analyzes market responses to the reused 3D assets using market analysis tools. The results of this analysis are fed back into the next design process, enabling designs to be more in line with the market.

[1113] Example 1

[1114] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1115] The traditional apparel design process required a lot of manual work and time, and there was a need for greater efficiency. It was also difficult to accurately grasp customer needs and quickly incorporate them into designs. Furthermore, there was a lack of a system for properly collecting and analyzing market reactions to the designs. It was necessary to solve these problems and achieve greater efficiency in the design process in the apparel industry and appropriate feedback on market reactions.

[1116] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1117] In this invention, the server includes an input means for inputting a brand's unique worldview and concept, an AI selection means for selecting a generative AI model based on the input design concept, a generation means for automatically generating design proposals using the selected generative AI model, a patterning means for converting the generated design proposals into patterns and 3D models, a storage means for saving and providing the patterned and 3D designs, a reuse means for reusing the generated 3D assets for multiple purposes, a market analysis means for collecting and analyzing market responses, and a terminal for users to check the generated design proposals and send feedback. This enables the design process to be more efficient, customer needs to be quickly reflected, and market responses to be appropriately collected and analyzed.

[1118] "A brand's unique worldview and concept" refers to the theme, color, style, and design philosophy that a particular brand wants to express.

[1119] "Input means" refers to a device or software that allows a user to input the brand's unique worldview or concept into the system.

[1120] "AI selection means" refers to a device or software that selects the optimal generative AI model based on the input design concept.

[1121] "Generative means" refers to a device or software that automatically generates design proposals using a selected generative AI model.

[1122] "Patterning means" refers to a device or software that patterns the generated design proposal and then converts it into a 3D model.

[1123] "Storage means" refers to a device or software that stores and, if necessary, provides the patterned and 3D designs.

[1124] "Means for reuse" refers to a device or software that allows the generated 3D assets to be reused for multiple purposes, such as advertising, games, virtual spaces, etc.

[1125] "Market Analysis Tool" means a device or software that collects and analyzes market response to reused 3D assets.

[1126] "Terminal" refers to a device or software that allows a user to review the generated design proposals and provide feedback.

[1127] This invention is a system that uses generative AI to streamline the design process in the apparel industry and understand customer needs. The system consists of the following main components:

[1128] System configuration

[1129] 1. Input method:

[1130] Users input the brand's unique worldview and design concept through a terminal, specifying in detail the specific theme, color, style, etc. For example, a design concept of "resort wear with a blue and white theme" based on the theme of "refreshing summer" can be input.

[1131] 2. AI selection method:

[1132] The server receives the design concept sent by the user and selects the optimal generative AI model using an AI selection method. This uses software to analyze past design data and trends and select the optimal model. For example, DALL-E 2 or StyleGAN 2 is used.

[1133] 3. Generation means:

[1134] The server automatically generates design proposals based on the selected generative AI model. During this process, a prompt is entered to generate the design. For example, the prompt could be "summer resort wear in blue and white."

[1135] 4. Patterning means:

[1136] The generated design proposals are patterned using a pattern generator on the server, and then converted into 3D models using software such as CLO 3D or Blender, allowing users to visualize the design in three dimensions.

[1137] 5. Preservation means:

[1138] The server stores the patterned and 3D designs, which are then delivered to the customer via cloud storage such as AWS S3.

[1139] 6. Reuse method:

[1140] The server reuses the generated 3D assets for multiple purposes, such as advertising, online games, and virtual worlds.

[1141] 7. Market analysis tools:

[1142] The server collects and analyzes market reactions to the reused 3D assets using tools such as Google Analytics, and the results are fed back into the next design process for continuous improvement.

[1143] 8. Terminal:

[1144] The user can review the generated design proposal via their device and provide feedback, which is then sent to the server, where the design is revised as necessary.

[1145] Specific examples

[1146] Example 1: New summer collection

[1147] Example prompt: Refreshing summer resort wear in blue and white.

[1148] 1. User: The brand manager uses the terminal to input a design concept with the theme of "refreshing summer."

[1149] 2. Server: After receiving the concept, select the optimal generative AI model and generate multiple design proposals.

[1150] 3. Terminal: The manager reviews the generated design proposals and provides feedback on additional colors and patterns. The feedback is then sent to the server.

[1151] 4. Server: The server revises the design based on the feedback and finalizes the design. The final design is stored in AWS S3.

[1152] 5. Reuse: The created 3D assets are used in a virtual space (e.g., a VRChat event), and reactions from participants are collected as data.

[1153] Example 2: Fall / Winter Collection

[1154] Example prompt: A warm orange and brown autumn / winter coat

[1155] 1. User: The designer inputs a design concept based on the theme of "warmth and quality" into the terminal.

[1156] 2. Server: Based on the request, select an appropriate generative AI model and generate a design proposal.

[1157] 3. Terminal: The designer evaluates the generated design proposals and selects the final design. The pattern and 3D rendering are carried out in CLO 3D, and the final design is made available for download.

[1158] 4. Reuse: The completed 3D assets are used as items in online games (e.g., skins in Minecraft), and the reactions from players are analyzed.

[1159] This will enable the design process to be more efficient, customer needs to be reflected more quickly, and market responses to be collected and analyzed more appropriately.

[1160] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1161] Step 1:

[1162] The user uses a device to input the brand's unique worldview and design concept. The user details the specific theme, color, style, etc., and this information is sent to the server. An example of input is the theme "refreshing summer" or the prompt "resort wear with a blue and white theme." The input data is sent to the server in JSON format.

[1163] Step 2:

[1164] The server receives the design concept sent by the user. Next, the server uses an AI selection method to select the best generative AI model for this concept. The server analyzes past design data and trends to select the best candidate generative AI model (e.g., DALL-E 2 or StyleGAN2). This selection process involves data calculations based on the AI ​​model's evaluation indicators and trend data. Information on the selected model is passed to the next step.

[1165] Step 3:

[1166] The server uses the selected generative AI model to automatically generate design proposals based on the prompt text. An example of the prompt text is "Summer resort wear with a blue and white theme." The generation method uses the AI ​​model to generate multiple design proposals. In this process, the prompt text is input as a task into the AI ​​model, and multiple generated results are output. The output design proposals are then processed in the next step.

[1167] Step 4:

[1168] The server patterns the generated design proposal using a patterning means. Then, using software such as CLO 3D or Blender, the design proposal is converted into a 3D model. In this process, data processing is performed to convert the generated flat design into a 3D format. The 3D model is generated in a format that can be visually confirmed and sent to a storage means.

[1169] Step 5:

[1170] The server stores the patterned and 3D design proposals using a storage means. Cloud storage such as AWS S3 is used as the storage destination. The stored design data is provided so that users can access it from their devices. The saved data also includes the date and time of creation and version information.

[1171] Step 6:

[1172] The user checks the saved design proposals via their device and sends feedback. Possible feedback content includes requests such as "make the colors more vibrant" or "change the pattern." The feedback data is sent to the server and passed on to the correction tool. The input data may be sent in text format or with an image attached.

[1173] Step 7:

[1174] The server receives the feedback and modifies the design proposal using the modification tool. In this process, modifications based on the user's requests are made using design tools such as Blender or Photoshop. The modified design proposal is repatterned and converted into 3D, and updated using the storage tool. The modifications and the date and time of the update are recorded.

[1175] Step 8:

[1176] The final design proposal is saved in a storage medium and can be reused as a 3D asset in advertisements, games, virtual spaces, etc. When reused, the data format is converted into a format suitable for each use, such as a format for games or a format for VR content.

[1177] Step 9:

[1178] The server uses market analysis tools to gather market responses to the reused 3D assets. Here, Google Analytics and proprietary analysis tools are used to collect data on user responses and usage. The collected data is analyzed and reflected in the next design process. The analysis results are displayed in a dashboard format and provided to users.

[1179] (Application example 1)

[1180] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1181] The traditional apparel design process is time-consuming and costly, and it is difficult to quickly reflect customer needs. Furthermore, the resulting designs are not easily reusable, limiting the means by which they can be effectively used in advertising and virtual environments. The present invention aims to solve these problems and provide a system that allows designs to be effectively reused across multiple media.

[1182] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1183] In this invention, the server includes an input means for inputting a brand's unique worldview and concept, an AI selection means for selecting a generative AI model based on the input design concept, a generation means for automatically generating design proposals using the selected generative AI model, a patterning means for converting the generated design proposals into patterns and three-dimensional models, a storage means for saving and providing the patterned and three-dimensional designs, a reuse means for reusing the generated 3D data as advertising content, and a market analysis means for collecting and analyzing market responses to the reused advertising content, thereby enabling efficient design generation and reuse in a variety of media.

[1184] "Worldview" refers to the unique themes and stories that a brand expresses.

[1185] A "concept" is the basic idea behind the design and image of a brand.

[1186] "Input means" refers to devices and software that allow users to input design information into the system.

[1187] An "AI selection tool" is a device or software that selects the optimal generative AI model based on an input design concept.

[1188] "Generative means" refers to a device or software that automatically creates design proposals using a selected generative AI model.

[1189] "Patterning means" refers to a device or software that patterns the generated design proposal and converts it into a three-dimensional model.

[1190] "Storage means" refers to a device or software that stores the patterned and three-dimensional designs and provides them as needed.

[1191] "Reuse means" refers to devices or software for reusing the generated 3D data as advertising content.

[1192] "Market analysis means" means equipment or software that collects and analyzes market responses to reused advertising content.

[1193] "Advertising content" refers to media such as advertising banners and videos used to convey brand design and information.

[1194] A "three-dimensional model" is model data that represents a design in three-dimensional space.

[1195] This invention provides a system for efficiently inputting and utilizing a brand's unique worldview and concept, and generating and reusing advertising assets, using automated vehicles, logistics centers, factory robots, and user terminals.

[1196] Hardware and Software Use

[1197] The system's main hardware includes a smartphone or tablet to accept user input, and a high-performance server to generate designs using generative AI models. The software includes:

[1198] TensorFlow: A library for generating designs using AI models.

[1199] Blender: a 3D modeling tool used to convert generated designs into three-dimensional models.

[1200] Firebase: A real-time database and analytics service.

[1201] React Native: A framework for developing cross-platform mobile applications.

[1202] System configuration

[1203] The system consists of the following components:

[1204] 1. Input Method

[1205] An interface that allows users to input a brand's unique worldview and concept. For example, it could be an application that can be operated from a smartphone or tablet.

[1206] 2. AI selection method

[1207] This is a server-side process that selects the optimal generative AI model based on the input design concept. The selection is based on information such as past design data and trend analysis.

[1208] 3. Generation means

[1209] The AI ​​model generates multiple design proposals, using the TensorFlow library in the generation process.

[1210] 4. Patterning Methods

[1211] The process of converting the generated design ideas into patterns and three-dimensional models using Blender.

[1212] 5. Preservation means

[1213] Ability to store and serve patterned and 3D designs using Firebase.

[1214] 6. Reuse methods

[1215] A means to reuse the generated 3D data as advertising content. Reuse the generated 3D data in advertising, virtual environments, and multiple media.

[1216] 7. Market analysis tools

[1217] A function that collects market responses to reused advertising content and analyzes them using Firebase Analytics.

[1218] Specific examples

[1219] Example 1: A collection themed around summer freshness

[1220] User: A brand manager inputs a design concept with the theme "refreshing summer."

[1221] Server: After receiving the concept, the server selects the optimal generative AI model and generates multiple design proposals. Using the generative tools, colors, shapes, and patterns are generated to match the concept.

[1222] Storage and reuse: The generated 3D data is stored in Firebase and can be reused as advertising banners and videos.

[1223] Market analysis: User responses are collected from social media and advertising platforms and fed back into the next design process.

[1224] Prompt Sentence Examples

[1225] "Tell us your design concept for a refreshing summer apparel product. For example, turquoise beachwear or a cool white shirt. Based on this concept, we will use a generative AI model to propose the optimal design."

[1226] This concludes the specific embodiment of the invention. By using this system, designs can be efficiently generated and reused as advertising assets in a variety of media. Furthermore, by analyzing market reactions, it is possible to quickly and accurately reflect these results in the next design.

[1227] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1228] Step 1:

[1229] User input of design concepts

[1230] Users use devices such as smartphones and tablets to input their brand's unique worldview and design concept. For example, they can input the concept "refreshing summer" into a dedicated application on their device. The input design concept is then sent to the server.

[1231] Input: Design concept (e.g. "Summer freshness")

[1232] Data processing: Text data of the design concept entered on the device is sent to the server

[1233] Output: Design concept sent to server

[1234] Step 2:

[1235] AI model selection

[1236] The server selects an appropriate generative AI model based on the received design concept, using past design data and trend analysis, and leveraging the TensorFlow library.

[1237] Input: Design concept (e.g. "Summer freshness")

[1238] Data processing: Concept-based generative AI model selection

[1239] Output: Selected generative AI model

[1240] Step 3:

[1241] Generate design ideas

[1242] The server uses the selected generative AI model to automatically generate multiple design proposals that combine colors, shapes, patterns, etc. to fit the specified concept.

[1243] Input: Selected generative AI model, design concept

[1244] Data Computation: Generating Design Ideas Using AI Models

[1245] Output: Multiple design ideas

[1246] Step 4:

[1247] Patterning and 3D modeling

[1248] The resulting design is then patterned using Blender and then converted into a 3D model, which allows the design to be simulated during the manufacturing process.

[1249] Input: Generated design proposal

[1250] Data calculation: Patterning and 3D modeling of design proposals

[1251] Output: 3D model

[1252] Step 5:

[1253] keep

[1254] The patterned and 3D designs are stored using Firebase, which allows the design data to be served on demand.

[1255] Input: 3D model

[1256] Data calculation: Save data to Firebase

[1257] Output: Saved design data

[1258] Step 6:

[1259] Reusing advertising content

[1260] The generated 3D data can be reused as advertising content, and distributed to social media and advertising platforms in the form of advertising banners or videos.

[1261] Input: 3D model

[1262] Data calculation: Generating advertising content

[1263] Output: Ad banners, videos

[1264] Step 7:

[1265] Collecting and analyzing market responses

[1266] Firebase Analytics will be used to collect and analyze market responses to ads distributed across social media and advertising platforms, providing important insights to inform the next design process.

[1267] Input: Ad banner, video

[1268] Data Computing: Collecting and Analyzing Market Responses

[1269] Output: Analysis results

[1270] The above is the flow of processing in the embodiment of the present invention.

[1271] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1272] The present invention is a system that enables more advanced design proposals and understanding of market needs by combining a system for improving the efficiency of apparel design using generative AI with an emotion engine that recognizes user emotions. Specific embodiments of this system are described below.

[1273] System configuration

[1274] The system consists of the following main components:

[1275] 1. Input means: A device or software that allows users to input the brand's unique worldview and concept.

[1276] 2. Emotion Engine: A device or software that recognizes and analyzes emotions in user input and feedback.

[1277] 3. AI selection means: A device or software that selects a generative AI model based on the input design concept and analysis results from the emotion engine.

[1278] 4. Generator: A device or software that automatically generates design proposals using the selected generative AI model.

[1279] 5. Patterning means: A device or software that patterns the generated design proposal and converts it into a 3D model.

[1280] 6. Storage means: A device or software that stores the patterned and 3D designs and provides them as needed.

[1281] 7. Reuse means: A device or software that reuses the generated 3D assets for multiple purposes such as advertising, games, virtual spaces, etc.

[1282] 8. Market Analysis Tools: Equipment or software that collects and analyzes market response to reused 3D assets.

[1283] 9. Correction tool (optional): A device or software that receives user feedback and corrects the generated design proposals based on it.

[1284] Operational Overview

[1285] The operation of this system will now be outlined.

[1286] User Actions and Input

[1287] Users input their brand's unique worldview and design concept through the device. This input includes themes, colors, styles, seasons, etc. As users make input, the emotion engine analyzes their facial expressions and voice to recognize their emotional state.

[1288] Server processing

[1289] 1. Data reception and AI model selection:

[1290] The server receives design requests from users and analysis results from the emotion engine. The AI ​​selection means selects a generative AI model based on this information. The selection also takes into account emotion data from the emotion engine, and prioritizes designs predicted to be preferred by users.

[1291] 2. Generate design proposals:

[1292] The server uses the selected generative AI model to generate multiple design proposals based on the user's input concept and emotional data. The generator automatically combines colors, shapes, and patterns to generate designs that match the user's emotional state.

[1293] 3. Patterning and 3D:

[1294] The generated design proposals are patterned by a patterning tool and then converted into a 3D model. This generates the format and 3D data required for the manufacturing process. Designs that match the emotional data are primarily patterned and converted into 3D.

[1295] View and give feedback on design ideas

[1296] The user can review the generated design proposals via their device and provide feedback. The emotion engine monitors the user's emotional state during the feedback process and transmits the results to the server. The proposed design is then adjusted according to the user's emotions.

[1297] Save and reuse 3D assets

[1298] The final design proposals are saved and provided as 3D assets to existing advertisements, games, and virtual spaces, enabling new market development and understanding of customer needs.

[1299] Collecting and analyzing market responses

[1300] The server collects and analyzes market responses to the reused 3D assets using market analysis tools, and the results of this analysis are fed back into the next design process, providing even more accurate market insights.

[1301] Examples:

[1302] Example 1: New summer collection

[1303] User: A brand manager inputs a design concept with the theme of "summer freshness." The emotion engine detects the excitement in the manager's facial expression.

[1304] Server: Receives concept and emotion data, selects generative AI model, and generates refreshing design that matches the emotion.

[1305] Terminal: The manager reviews the design proposal and provides feedback. The emotion engine detects excitement and adjusts the proposal. The final design is approved.

[1306] Reuse: The created 3D assets are used in virtual events, and participant responses are collected as data.

[1307] Example 2: Fall / Winter Collection

[1308] User: The designer inputs a design concept based on the theme of "warmth and quality." The emotion engine recognizes the designer's calm facial expression.

[1309] Server: Based on the request and emotion data, selects an appropriate AI model and generates design proposals.

[1310] Device: Designers evaluate the design proposals and incorporate revisions based on the emotional data. The final design is selected. The designs are then patterned and converted into 3D, and the final design is made available for download.

[1311] Reuse: The completed 3D assets will appear as in-game items and the reaction from players will be analyzed.

[1312] The above is a specific embodiment of the present invention. This system makes it possible to propose sophisticated designs that take user emotions into consideration and to grasp market needs.

[1313] The processing flow will be explained below.

[1314] Step 1:

[1315] (User)

[1316] Users access the device and input their brand's unique worldview and design concept, including details such as theme (e.g., refreshing summer), color, style, and season. Once the input is complete, they click the submit button to submit their design request.

[1317] Step 2:

[1318] (Terminal)

[1319] The terminal receives the user's input data, checks the data for integrity, and then formats and sends it to the server.

[1320] Step 3:

[1321] (server)

[1322] The server receives a design request from the user and begins analysis. The analyzed data is sent to the AI ​​selection process, which selects the optimal generative AI model based on the brand's worldview and concept. Past design data and market trend information are also referenced during this process.

[1323] Step 4:

[1324] (server)

[1325] The server activates the emotion engine based on the design concept input by the user, and the emotion engine analyzes the emotion data obtained during the user's input or feedback to identify the user's current emotional state.

[1326] Step 5:

[1327] (server)

[1328] The server generates multiple design proposals based on the selected generative AI model and emotion data obtained from the emotion engine. The generated design proposals are adjusted to be optimally proposed based on the user's emotional state.

[1329] Step 6:

[1330] (Terminal)

[1331] The device displays multiple design proposals sent from the server on a user interface, and is equipped with a function to display images and detailed information so that the user can easily check each design proposal.

[1332] Step 7:

[1333] (User)

[1334] Users can view the generated design proposals via their devices and provide evaluations and feedback, including specific comments and suggested revisions.

[1335] Step 8:

[1336] (Terminal)

[1337] The device collects user feedback and sends it to the server, where the emotion engine again analyzes the user's emotional state and understands the context of the feedback.

[1338] Step 9:

[1339] (server)

[1340] The server receives the feedback and modifies the design proposal using a modification tool, taking into account the user's emotional state and the feedback content. The modified design proposal is then sent back to the device from the server.

[1341] Step 10:

[1342] (server)

[1343] The server automatically patterns the user's final approved design using a pattern generator and then creates a 3D model, which generates the format and 3D data required for the manufacturing process.

[1344] Step 11:

[1345] (server)

[1346] The server stores the generated patterned data and 3D model using a storage means so that they can be provided to users and manufacturers as needed.

[1347] Step 12:

[1348] (server)

[1349] The server uses a reuse method to store the generated 3D assets in a format that can be reused for various content (advertisements, games, virtual spaces, etc.) and provides them to related applications.

[1350] Step 13:

[1351] (server)

[1352] The server collects and analyzes market responses to the reused 3D assets using market analysis tools. The results of this analysis are fed back into the next design process, enabling designs to be more in line with the market.

[1353] Example 2

[1354] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1355] Current design generation systems do not take user emotions or feedback into account, making it difficult to provide design proposals that meet user expectations. Furthermore, it is difficult to predict the expected level of demand when releasing generated design proposals directly to the market. This creates a problem of mismatch between design and market needs, lowering the success rate of projects.

[1356] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1357] In this invention, the server includes an input means for inputting a brand's unique worldview and concept, an emotion engine that analyzes the input design concept and user emotions, an AI selection means for selecting a generative AI model based on the analysis results, a generation means for automatically generating design proposals using the selected generative AI model, a patterning means for converting the generated design proposals into patterns and 3D models, a storage means for saving and providing the patterned and 3D designs, a reuse means for reusing the generated 3D assets for multiple purposes, and a market analysis means for collecting and analyzing market reactions to the reused 3D assets. This enables design proposals that take user emotions into consideration and enables effective design development that understands market needs.

[1358] "Input means" refers to a device or software that allows users to input the brand's unique worldview or concept.

[1359] An "emotion engine" is a device or software that recognizes and analyzes emotions during user input and feedback.

[1360] The "AI selection means" is a device or software that selects a generative AI model based on the input design concept and the analysis results from the emotion engine.

[1361] "Generating means" means a device or software that automatically generates design proposals using a selected generative AI model.

[1362] The "patterning means" is a device or software that patterns the generated design proposal and converts it into a 3D model.

[1363] "Storage means" means a device or software that stores and optionally provides patterned and 3D designs.

[1364] A "reuse means" is a device or software that reuses the generated 3D assets for multiple purposes, such as advertisements, games, virtual spaces, etc.

[1365] "Market Analysis Tool" means a device or software that collects and analyzes market response to reused 3D assets.

[1366] "Feedback" refers to the evaluation and opinions that users give to the generated design proposals.

[1367] A "generative AI model" is an artificial intelligence model used to automatically generate design proposals.

[1368] The present invention is a system that enables more advanced design proposals and understanding of market needs by combining an emotion engine that recognizes user emotions with an apparel design efficiency system that uses a generative AI model. Specific embodiments of this system are described below.

[1369] System configuration

[1370] The system consists of the following main components:

[1371] 1. Input means: A device or software that allows users to input the brand's unique worldview or concept. The user interface uses a keyboard or touch screen, and provides text fields and selection menus.

[1372] 2. Emotion engine: A device or software that recognizes emotions by analyzing the user's facial expressions and voice. It can obtain emotional data in real time using a camera or microphone.

[1373] 3. AI selection means: A device or software that selects a generative AI model based on the input design concept and the analysis results from the emotion engine. It selects the optimal AI model based on a specific algorithm.

[1374] 4. Generator: A device or software that automatically generates design proposals using a selected generative AI model. An AI model using deep learning is used, and the design is generated by that model.

[1375] 5. Patterning means: A device or software that patterns the generated design proposal and converts it into a 3D model. The 2D data is converted into a format that can be used by 3D modeling software (e.g., Blender or Maya).

[1376] 6. Storage means: Device or software that stores the patterned and 3D designs and provides them as needed. They can be stored in a database or cloud storage. The storage format should be a general format (e.g., .fbx or .glb) that can be used for advertising, games, and virtual spaces.

[1377] 7. Reuse tools: Devices or software that reuse the generated 3D assets for multiple purposes, such as advertising, games, virtual spaces, etc. It provides APIs and interfaces for reuse.

[1378] 8. Market Analysis Tools: Devices or software that collect and analyze market reactions to reused 3D assets, such as user reviews, online feedback, and social media comments.

[1379] 9. Correction tool (optional): A device or software that receives user feedback and corrects the generated design proposal based on that feedback. It continues to analyze the user's emotions during the feedback process.

[1380] Operational Overview

[1381] The operation of this system can be explained in several steps. First, the user inputs the brand's unique worldview and design concept through the device. This input includes the theme, color, style, season, etc. At this stage, the emotion engine analyzes the user's facial expressions and voice to recognize their emotional state.

[1382] The server receives design requests from users and analysis results from the emotion engine. The AI ​​selection means selects the optimal generative AI model based on this information. This selection also takes into account emotion data from the emotion engine, and prioritizes designs predicted to be preferred by the user.

[1383] The server uses the selected generative AI model to generate multiple design proposals based on the user's input concept and emotional data. The generator automatically combines colors, shapes, and patterns to generate designs that match the user's emotional state.

[1384] The generated design proposals are patterned by a patterning means and then converted into a 3D model. This generates the format and 3D data required for the manufacturing process. Designs that match the emotional data are primarily patterned and converted into 3D.

[1385] The user can then review the generated design proposals and provide feedback via their device. The emotion engine monitors the user's emotional state during the feedback process and transmits the results to the server. The proposed design is then adjusted based on the user's emotions.

[1386] The final design proposal can be saved and provided as a 3D asset for advertisements, games, virtual spaces, etc. This makes it possible to develop new markets and understand customer needs.

[1387] The server collects and analyzes market responses to the reused 3D assets using market analysis tools, and the results of this analysis are fed back into the next design process, providing even more accurate market insights.

[1388] Specific examples

[1389] Example 1: New summer collection

[1390] User: A brand manager inputs a design concept with the theme of "summer freshness." The emotion engine detects the excitement in the manager's facial expression.

[1391] Server: Receives concept and emotion data and selects a generative AI model. Generates a refreshing design that matches the emotion.

[1392] Terminal: Manager reviews design proposals and provides feedback. Emotion engine detects excitement and adjusts proposals. Final design is approved.

[1393] Reuse: The created 3D assets are used in virtual events, and participant responses are collected as data.

[1394] Example prompt:

[1395] Generate design ideas based on the theme of "summer freshness." The emotion engine recognizes the user's sense of excitement.

[1396] Example 2: Fall / Winter Collection

[1397] User: The designer inputs a design concept based on the theme of "warmth and quality." The emotion engine recognizes the designer's calm facial expression.

[1398] Server: Based on the request and emotion data, selects an appropriate AI model and generates design proposals.

[1399] Device: Designers evaluate the design proposals and make revisions based on the emotional data. The final design is selected. The design is then patterned and converted into 3D, and the final design is made available for download.

[1400] Reuse: The completed 3D assets will appear as in-game items and the reaction from players will be analyzed.

[1401] Example prompt:

[1402] Generate design ideas based on the theme of "warmth and quality." The emotion engine recognizes the user's calming emotions.

[1403] The above is a specific embodiment for carrying out the present invention. This system makes it possible to propose sophisticated designs that take into account the user's emotions and to grasp market needs.

[1404] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1405] Step 1:

[1406] Entering design concepts using input devices

[1407] User: Uses the device to input the brand's unique worldview and design concept (theme, color, style, season, etc.).

[1408] Input: Text data entered via a keyboard or touchscreen.

[1409] Output: The text data of the design concept is sent from the device to the server.

[1410] Step 2:

[1411] Emotion analysis using an emotion engine

[1412] Device: Captures the user's facial expressions and voice using a camera and microphone, obtaining emotional data in real time.

[1413] Input: User's facial expressions and voice data.

[1414] Data processing: Emotion analysis algorithms analyze input facial expressions and voice data.

[1415] Output: Emotional data (e.g., elation, calm, etc.) is generated and sent to the server.

[1416] Step 3:

[1417] Data reception and AI model selection

[1418] Server: Receives design concepts from users and analysis results from the emotion engine.

[1419] Input: Text data and sentiment data of design concepts.

[1420] Data processing: Based on the received design concept and emotion data, an algorithm is applied to select the optimal generative AI model.

[1421] Output: An appropriate generative AI model is selected (e.g., a model characterized by vibrant colors).

[1422] Step 4:

[1423] Generate design ideas

[1424] Server: Automatically generate design proposals using the selected generative AI model.

[1425] Input: Text data of design concepts and a selected generative AI model.

[1426] Data calculation: The generative AI model generates multiple design proposals based on the design concept and emotion data. An image generation algorithm using deep learning technology is applied.

[1427] Output: Multiple design options are generated (e.g., a design based on light blue and green).

[1428] Step 5:

[1429] Patterning and 3D design ideas

[1430] Server: Patterns the generated design proposals and converts them into 3D models.

[1431] Input: Generated design proposal.

[1432] Data processing: Converting 2D patterns into a 3D model format that can be used in 3D modeling software.

[1433] Output: 3D model data (e.g. .obj file)

[1434] Step 6:

[1435] View design ideas and receive feedback

[1436] Terminal: The user can view multiple generated design proposals on the screen.

[1437] Input: 3D model data.

[1438] Output: Preview in the user interface.

[1439] User: Enter and submit feedback on the design proposal.

[1440] Input: Feedback content and emotional state during feedback.

[1441] Output: Feedback data and emotion data are sent to the server.

[1442] Step 7:

[1443] Recalibration based on feedback

[1444] Server: Re-adjust design proposals based on feedback data and new sentiment data.

[1445] Input: Feedback data and emotion data.

[1446] Data computation: Algorithms are applied to refine design proposals based on feedback and sentiment data.

[1447] Output: Revised design proposal.

[1448] Step 8:

[1449] Saving 3D assets

[1450] How to save: Save the revised design as a 3D asset.

[1451] Input: 3D model data of the revised design proposal.

[1452] Storage: Stored in a database or cloud storage.

[1453] Output: Saved 3D model data (e.g. .fbx or .glb)

[1454] Step 9:

[1455] 3D Asset Reuse

[1456] Reuse methods: Providing saved 3D assets for advertising, games, virtual spaces, etc.

[1457] Input: Saved 3D model data.

[1458] Output: Providing data for reuse.

[1459] Step 10:

[1460] Collecting and analyzing market responses

[1461] Market Analysis Tools: Collect and analyze market response to the 3D assets provided.

[1462] Input: Market feedback data, online reviews, and social media comments.

[1463] Data calculations: Algorithms are applied to analyze collected market responses and provide feedback for the next design process.

[1464] Output: Market analysis results.

[1465] These are the specific programming steps for this system, which will enable sophisticated design proposals that take user emotions into account and an understanding of market needs.

[1466] (Application example 2)

[1467] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1468] Conventional fashion design proposal systems have had the challenge of making design proposals that fully consider the user's preferences and emotions. Furthermore, they lacked the means to collect and analyze market reactions to the generated designs in real time, making it difficult to reflect these in the next design process. Effectively incorporating user emotions and market reactions is needed to make more suitable design proposals and understand market needs.

[1469] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1470] In this invention, the server includes an input means for inputting a brand's unique worldview and concept, an AI selection means for selecting an AI model, a generation means for automatically generating design proposals using the AI ​​model, a patterning means for converting the generated design proposals into patterns and 3D models, a storage means for saving and providing the generated 3D assets, a reuse means for reusing them, a market analysis means for collecting and analyzing market reactions, an emotion recognition means for recognizing user emotions, and an AI selection means for selecting an AI model based on emotions. This enables advanced design proposals that take user emotions into consideration and an understanding of market needs.

[1471] "Input means" refers to a device or software that allows the user to input the brand's unique worldview or concept.

[1472] An "AI selection means" is a device or software that selects the optimal generative AI model based on the input design concept and emotional data.

[1473] A "generator" is a device or software that automatically generates design proposals using a selected generative AI model.

[1474] The "patterning means" is a device or software that patterns the generated design proposal and converts it into a 3D model.

[1475] "Storage means" means a device or software that stores and optionally provides patterned and 3D designs.

[1476] A "reuse means" is a device or software for reusing the generated 3D assets in multiple ways.

[1477] "Market Analysis Tool" means a device or software that collects and analyzes market response to reused 3D assets.

[1478] "Emotion recognition means" refers to a device or software that recognizes the user's emotions and analyzes their emotional state.

[1479] "Fashion items" are design items such as clothing and accessories that are generated based on emotions recognized by emotion recognition means.

[1480] In this invention, the user inputs the brand's unique worldview and design concept through an input means. The input means is a software application installed on a general computer device such as a personal computer, smartphone, or tablet. The user uses this application to input information such as theme, color, style, and season. The input means has a function to recognize and analyze emotions from the user's facial expressions and voice using an emotion recognition means.

[1481] The server receives the design concept sent by the user and the emotion data obtained by the emotion recognition means. Based on this data, the server's AI selection means selects the optimal generative AI model, and automatically generates multiple design proposals using the selected generative AI model. The generation means uses a deep learning algorithm that utilizes a neural network to generate a design that matches the user's concept and emotion.

[1482] The generated design proposal is patterned by a patterning means and then converted into a 3D model. At this time, software such as Blender or Maya is used as a 3D modeling tool. The patterned and 3D design proposal is stored in a database server by a storage means, so that users can access it as needed.

[1483] The user checks the generated design proposal via the device and provides feedback through the emotion recognition means. This feedback data is also sent to the server, and the design proposal is further adjusted by the generation means. This process results in an optimal design proposal that reflects the user's requests.

[1484] The generated 3D assets can be reused in advertisements, games, and virtual spaces through reuse methods. For example, the 3D assets can be used in virtual events, and market reactions can be collected in real time. This reaction data can be analyzed through market analysis methods and fed back into the next design process.

[1485] To illustrate, the following prompt sentences will explain the system's behavior:

[1486] "Users visit a virtual store, and a camera analyzes their facial expressions to detect happy emotions. Based on that emotion, the virtual store suggests fashion items with bright and fun designs. Users can try on the items and ultimately purchase them."

[1487] In this way, the system of the present invention can propose designs that are appropriate for the user based on their emotions, enabling a new fashion item purchasing experience. Furthermore, by analyzing market reactions to the created designs, more advanced market insights can be obtained and reflected in the next design process.

[1488] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1489] Step 1:

[1490] Users use input devices to input the brand's unique worldview and design concept. Specifically, users input information such as theme, color, style, and season into a dedicated application installed on a PC, smartphone, or tablet. The input design data is then sent to a server.

[1491] Input: Design concept, theme, color, style, season

[1492] Output: Design data sent to the server

[1493] Step 2:

[1494] The emotion recognition means identifies the user's emotional state. Specifically, it uses the device's camera and voice recognition function to capture the user's facial expressions and voice, and analyzes their emotions using an emotion recognition model. The analyzed emotion data is then sent to the server.

[1495] Input: User's facial expressions and voice

[1496] Output: Emotion data sent to the server

[1497] Step 3:

[1498] The server selects an appropriate generative AI model using the AI ​​selection means based on the received design data and emotion data. Specifically, it identifies the generative AI model that is best suited to the user's emotion and design concept from the database in the server.

[1499] Input: Design data, emotion data

[1500] Output: The selected generative AI model

[1501] Step 4:

[1502] The server generates design proposals using the selected generative AI model. Specifically, the generation means uses a deep learning algorithm that utilizes a neural network to generate design proposals that match the user's design concept and emotional data.

[1503] Input: Generative AI model, design data, emotion data

[1504] Output: Generated design proposal

[1505] Step 5:

[1506] The server patterns the generated design proposal using a patterning means and converts it into a 3D model. Specifically, the server uses a 3D modeling tool (e.g., Blender or Maya) to create a 3D model of the generated design proposal.

[1507] Input: Generated design proposal

[1508] Output: Patterned and 3D designs

[1509] Step 6:

[1510] The server stores the patterned and 3D designs in a database using a storage means and provides them to the user as needed. Specifically, the stored data is maintained in a format accessible to the user's terminal.

[1511] Input: Patterned and 3D Design

[1512] Output: Saved design data

[1513] Step 7:

[1514] The user reviews the generated design proposals via their device and provides feedback. The emotion recognition means monitors the user's emotional state even when providing feedback, and transmits that data to the server. Specifically, the user evaluates the design proposals through the application and provides feedback through comments and facial expressions.

[1515] Input: Generated design proposals, user emotion data

[1516] Output: Feedback data sent to the server

[1517] Step 8:

[1518] The server reuses the 3D assets generated using the reuse means in advertisements, games, and virtual spaces, and collects market responses to them. Specifically, the 3D assets are used in virtual events, and response data is collected in real time.

[1519] Input: Generated 3D assets

[1520] Output: Collected market response data

[1521] Step 9:

[1522] The server analyzes the market response data collected using the market analysis means and reflects the results in the next design process. Specifically, the analyzed data is used as input when generating the next design proposal.

[1523] Input: Market response data

[1524] Output: Analysis results and points to improve next design data

[1525] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1526] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1527] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1528] [Fourth embodiment]

[1529] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1530] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1531] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1532] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1533] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1534] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1535] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1536] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1537] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1538] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1539] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1540] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1541] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1542] This invention is a system that utilizes generative AI to streamline the design process in the apparel industry and understand customer needs. Specific embodiments of this system are described below.

[1543] System configuration

[1544] The system consists of the following main components:

[1545] 1. Input means: A device or software that allows users to input the brand's unique worldview and concept.

[1546] 2. AI selection means: A device or software that selects a generative AI model based on the input design concept.

[1547] 3. Generator: A device or software that automatically generates design proposals using the selected generative AI model.

[1548] 4. Patterning means: A device or software that patterns the generated design proposal and converts it into a 3D model.

[1549] 5. Storage means: A device or software that stores the patterned and 3D designs and provides them as needed.

[1550] 6. Reuse means: A device or software that reuses the generated 3D assets for multiple purposes, such as advertising, games, virtual spaces, etc.

[1551] 7. Market Analysis Tools: Equipment or software that collects and analyzes market response to reused 3D assets.

[1552] 8. Correction tool (optional): A device or software that receives user feedback and corrects the generated design proposals based on it.

[1553] Operational Overview

[1554] The operation of this system will now be outlined.

[1555] User Actions and Input

[1556] Users input their brand's unique worldview and design concept through the device. This input includes specific colors, styles, themes, etc. For example, a design concept based on the theme of "refreshing summer" can be input.

[1557] Server processing

[1558] 1. Data reception and AI model selection:

[1559] The server receives the design concept from the user and selects the optimal generating AI model using the AI ​​selection means.

[1560] This model selection is based on information such as past design data and trend analysis.

[1561] 2. Generate design proposals:

[1562] The server uses the selected generative AI model to automatically generate multiple design proposals. The generator combines colors, shapes, and patterns to create a design that fits the specified concept.

[1563] 3. Patterning and 3D:

[1564] The generated design proposal is patterned by a patterning tool and then converted into a 3D model, which simulates the manufacturing process and significantly reduces lead time.

[1565] View and give feedback on design ideas

[1566] The user checks the generated design proposal via the terminal and provides feedback. The feedback is sent from the terminal to the server. The server then uses a correction means to correct the design proposal based on the user's feedback (this means is optional).

[1567] Save and reuse 3D assets

[1568] The final design proposals are saved and provided as 3D assets to existing advertisements, games, and virtual spaces, enabling new market development and understanding of customer needs.

[1569] Collecting and analyzing market responses

[1570] The server collects and analyzes market responses to reused 3D assets through market analysis tools, and the results of this analysis are reflected in the next design process, allowing for continuous improvement and adaptation.

[1571] Examples:

[1572] Example 1: New summer collection

[1573] User: A brand manager inputs a design concept with the theme "refreshing summer."

[1574] Server: After receiving the concept, select the optimal generative AI model and generate multiple design proposals.

[1575] Terminal: The manager reviews the design proposal and provides feedback, which is used to refine the final design.

[1576] Reuse: The created 3D assets are used in virtual events, and participant responses are collected as data.

[1577] Example 2: Fall / Winter Collection

[1578] User: The designer inputs a design concept based on the theme of "warmth and quality."

[1579] Server: Based on the request, selects an appropriate AI model and generates a design proposal.

[1580] Terminal: The designer evaluates the design proposals and selects the final design. The design is then patterned and converted into 3D, and the final design is made available for download.

[1581] Reuse: The completed 3D assets will appear as in-game items and the reaction from players will be analyzed.

[1582] The processing flow will be explained below.

[1583] Step 1:

[1584] (User)

[1585] Users access the terminal and enter their brand's unique worldview and design concept into a dedicated input form, including detailed information such as theme, color, style, and season. Once the input is complete, they click the submit button to submit their design request.

[1586] Step 2:

[1587] (Terminal)

[1588] The terminal receives the user's input data, formats it, and sends it to the server, validating the data to ensure that the input is accurate.

[1589] Step 3:

[1590] (server)

[1591] The server receives design requests from users. It analyzes the received data and selects a generative AI model that matches the brand's worldview and concept. The selection is made using an AI selection method, referencing past design data and market trend information.

[1592] Step 4:

[1593] (server)

[1594] The server uses the selected generative AI model to generate multiple design proposals based on the user's input concept. Through the generation method, colors, shapes, and patterns are automatically combined to generate a design that matches the concept.

[1595] Step 5:

[1596] (Terminal)

[1597] The device displays the design proposals sent from the server on a user interface, and is equipped with a function to display images and detailed information so that the user can easily check each design proposal.

[1598] Step 6:

[1599] (User)

[1600] The user checks the displayed design proposals, inputs their evaluation and feedback for each design, and sends the feedback, including comments and correction requests, to the server from their device.

[1601] Step 7:

[1602] (server)

[1603] The server receives feedback from the user, modifies the design proposal using the modification means, and then transmits the design proposal to the user again after modification based on the feedback.

[1604] Step 8:

[1605] (server)

[1606] The server automatically patterns the final approved design using a pattern generator and then creates a 3D model, which generates the format and 3D data required for the manufacturing process.

[1607] Step 9:

[1608] (server)

[1609] The server stores the generated patterned data and 3D model using a storage means so that they can be provided to users and manufacturers as needed.

[1610] Step 10:

[1611] (server)

[1612] The server uses a reuse method to store the generated 3D assets in a format that can be reused for various content (advertisements, games, virtual spaces, etc.) and provides them to related applications.

[1613] Step 11:

[1614] (server)

[1615] The server collects and analyzes market responses to the reused 3D assets using market analysis tools. The results of this analysis are fed back into the next design process, enabling designs to be more in line with the market.

[1616] Example 1

[1617] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1618] The traditional apparel design process required a lot of manual work and time, and there was a need for greater efficiency. It was also difficult to accurately grasp customer needs and quickly incorporate them into designs. Furthermore, there was a lack of a system for properly collecting and analyzing market reactions to the designs. It was necessary to solve these problems and achieve greater efficiency in the design process in the apparel industry and appropriate feedback on market reactions.

[1619] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1620] In this invention, the server includes an input means for inputting a brand's unique worldview and concept, an AI selection means for selecting a generative AI model based on the input design concept, a generation means for automatically generating design proposals using the selected generative AI model, a patterning means for converting the generated design proposals into patterns and 3D models, a storage means for saving and providing the patterned and 3D designs, a reuse means for reusing the generated 3D assets for multiple purposes, a market analysis means for collecting and analyzing market responses, and a terminal for users to check the generated design proposals and send feedback. This enables the design process to be more efficient, customer needs to be quickly reflected, and market responses to be appropriately collected and analyzed.

[1621] "A brand's unique worldview and concept" refers to the theme, color, style, and design philosophy that a particular brand wants to express.

[1622] "Input means" refers to a device or software that allows a user to input the brand's unique worldview or concept into the system.

[1623] "AI selection means" refers to a device or software that selects the optimal generative AI model based on the input design concept.

[1624] "Generative means" refers to a device or software that automatically generates design proposals using a selected generative AI model.

[1625] "Patterning means" refers to a device or software that patterns the generated design proposal and then converts it into a 3D model.

[1626] "Storage means" refers to a device or software that stores and, if necessary, provides the patterned and 3D designs.

[1627] "Means for reuse" refers to a device or software that allows the generated 3D assets to be reused for multiple purposes, such as advertising, games, virtual spaces, etc.

[1628] "Market Analysis Tool" means a device or software that collects and analyzes market response to reused 3D assets.

[1629] "Terminal" refers to a device or software that allows a user to review the generated design proposals and provide feedback.

[1630] This invention is a system that uses generative AI to streamline the design process in the apparel industry and understand customer needs. The system consists of the following main components:

[1631] System configuration

[1632] 1. Input method:

[1633] Users input the brand's unique worldview and design concept through a terminal, specifying in detail the specific theme, color, style, etc. For example, a design concept of "resort wear with a blue and white theme" based on the theme of "refreshing summer" can be input.

[1634] 2. AI selection method:

[1635] The server receives the design concept sent by the user and selects the optimal generative AI model using an AI selection method. This uses software to analyze past design data and trends and select the optimal model. For example, DALL-E 2 or StyleGAN 2 is used.

[1636] 3. Generation means:

[1637] The server automatically generates design proposals based on the selected generative AI model. During this process, a prompt is entered to generate the design. For example, the prompt could be "summer resort wear in blue and white."

[1638] 4. Patterning means:

[1639] The generated design proposals are patterned using a pattern generator on the server, and then converted into 3D models using software such as CLO 3D or Blender, allowing users to visualize the design in three dimensions.

[1640] 5. Preservation means:

[1641] The server stores the patterned and 3D designs, which are then delivered to the customer via cloud storage such as AWS S3.

[1642] 6. Reuse method:

[1643] The server reuses the generated 3D assets for multiple purposes, such as advertising, online games, and virtual worlds.

[1644] 7. Market analysis tools:

[1645] The server collects and analyzes market reactions to the reused 3D assets using tools such as Google Analytics, and the results are fed back into the next design process for continuous improvement.

[1646] 8. Terminal:

[1647] The user can review the generated design proposal via their device and provide feedback, which is then sent to the server, where the design is revised as necessary.

[1648] Specific examples

[1649] Example 1: New summer collection

[1650] Example prompt: Refreshing summer resort wear in blue and white.

[1651] 1. User: The brand manager uses the terminal to input a design concept with the theme of "refreshing summer."

[1652] 2. Server: After receiving the concept, select the optimal generative AI model and generate multiple design proposals.

[1653] 3. Terminal: The manager reviews the generated design proposals and provides feedback on additional colors and patterns. The feedback is then sent to the server.

[1654] 4. Server: The server revises the design based on the feedback and finalizes the design. The final design is stored in AWS S3.

[1655] 5. Reuse: The created 3D assets are used in a virtual space (e.g., a VRChat event), and reactions from participants are collected as data.

[1656] Example 2: Fall / Winter Collection

[1657] Example prompt: A warm orange and brown autumn / winter coat

[1658] 1. User: The designer inputs a design concept based on the theme of "warmth and quality" into the terminal.

[1659] 2. Server: Based on the request, select an appropriate generative AI model and generate a design proposal.

[1660] 3. Terminal: The designer evaluates the generated design proposals and selects the final design. The pattern and 3D rendering are carried out in CLO 3D, and the final design is made available for download.

[1661] 4. Reuse: The completed 3D assets are used as items in online games (e.g., skins in Minecraft), and the reactions from players are analyzed.

[1662] This will enable the design process to be more efficient, customer needs to be reflected more quickly, and market responses to be collected and analyzed more appropriately.

[1663] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1664] Step 1:

[1665] The user uses a device to input the brand's unique worldview and design concept. The user details the specific theme, color, style, etc., and this information is sent to the server. An example of input is the theme "refreshing summer" or the prompt "resort wear with a blue and white theme." The input data is sent to the server in JSON format.

[1666] Step 2:

[1667] The server receives the design concept sent by the user. Next, the server uses an AI selection method to select the best generative AI model for this concept. The server analyzes past design data and trends to select the best candidate generative AI model (e.g., DALL-E 2 or StyleGAN2). This selection process involves data calculations based on the AI ​​model's evaluation indicators and trend data. Information on the selected model is passed to the next step.

[1668] Step 3:

[1669] The server uses the selected generative AI model to automatically generate design proposals based on the prompt text. An example of the prompt text is "Summer resort wear with a blue and white theme." The generation method uses the AI ​​model to generate multiple design proposals. In this process, the prompt text is input as a task into the AI ​​model, and multiple generated results are output. The output design proposals are then processed in the next step.

[1670] Step 4:

[1671] The server patterns the generated design proposal using a patterning means. Then, using software such as CLO 3D or Blender, the design proposal is converted into a 3D model. In this process, data processing is performed to convert the generated flat design into a 3D format. The 3D model is generated in a format that can be visually confirmed and sent to a storage means.

[1672] Step 5:

[1673] The server stores the patterned and 3D design proposals using a storage means. Cloud storage such as AWS S3 is used as the storage destination. The stored design data is provided so that users can access it from their devices. The saved data also includes the date and time of creation and version information.

[1674] Step 6:

[1675] The user checks the saved design proposals via their device and sends feedback. Possible feedback content includes requests such as "make the colors more vibrant" or "change the pattern." The feedback data is sent to the server and passed on to the correction tool. The input data may be sent in text format or with an image attached.

[1676] Step 7:

[1677] The server receives the feedback and modifies the design proposal using the modification tool. In this process, modifications based on the user's requests are made using design tools such as Blender or Photoshop. The modified design proposal is repatterned and converted into 3D, and updated using the storage tool. The modifications and the date and time of the update are recorded.

[1678] Step 8:

[1679] The final design proposal is saved in a storage medium and can be reused as a 3D asset in advertisements, games, virtual spaces, etc. When reused, the data format is converted into a format suitable for each use, such as a format for games or a format for VR content.

[1680] Step 9:

[1681] The server uses market analysis tools to gather market responses to the reused 3D assets. Here, Google Analytics and proprietary analysis tools are used to collect data on user responses and usage. The collected data is analyzed and reflected in the next design process. The analysis results are displayed in a dashboard format and provided to users.

[1682] (Application example 1)

[1683] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1684] The traditional apparel design process is time-consuming and costly, and it is difficult to quickly reflect customer needs. Furthermore, the resulting designs are not easily reusable, limiting the means by which they can be effectively used in advertising and virtual environments. The present invention aims to solve these problems and provide a system that allows designs to be effectively reused across multiple media.

[1685] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1686] In this invention, the server includes an input means for inputting a brand's unique worldview and concept, an AI selection means for selecting a generative AI model based on the input design concept, a generation means for automatically generating design proposals using the selected generative AI model, a patterning means for converting the generated design proposals into patterns and three-dimensional models, a storage means for saving and providing the patterned and three-dimensional designs, a reuse means for reusing the generated 3D data as advertising content, and a market analysis means for collecting and analyzing market responses to the reused advertising content, thereby enabling efficient design generation and reuse in a variety of media.

[1687] "Worldview" refers to the unique themes and stories that a brand expresses.

[1688] A "concept" is the basic idea behind the design and image of a brand.

[1689] "Input means" refers to devices and software that allow users to input design information into the system.

[1690] An "AI selection tool" is a device or software that selects the optimal generative AI model based on an input design concept.

[1691] "Generative means" refers to a device or software that automatically creates design proposals using a selected generative AI model.

[1692] "Patterning means" refers to a device or software that patterns the generated design proposal and converts it into a three-dimensional model.

[1693] "Storage means" refers to a device or software that stores the patterned and three-dimensional designs and provides them as needed.

[1694] "Reuse means" refers to devices or software for reusing the generated 3D data as advertising content.

[1695] "Market analysis means" means equipment or software that collects and analyzes market responses to reused advertising content.

[1696] "Advertising content" refers to media such as advertising banners and videos used to convey brand design and information.

[1697] A "three-dimensional model" is model data that represents a design in three-dimensional space.

[1698] This invention provides a system for efficiently inputting and utilizing a brand's unique worldview and concept, and generating and reusing advertising assets, using automated vehicles, logistics centers, factory robots, and user terminals.

[1699] Hardware and Software Use

[1700] The system's main hardware includes a smartphone or tablet to accept user input, and a high-performance server to generate designs using generative AI models. The software includes:

[1701] TensorFlow: A library for generating designs using AI models.

[1702] Blender: a 3D modeling tool used to convert generated designs into three-dimensional models.

[1703] Firebase: A real-time database and analytics service.

[1704] React Native: A framework for developing cross-platform mobile applications.

[1705] System configuration

[1706] The system consists of the following components:

[1707] 1. Input Method

[1708] An interface that allows users to input a brand's unique worldview and concept. For example, it could be an application that can be operated from a smartphone or tablet.

[1709] 2. AI selection method

[1710] This is a server-side process that selects the optimal generative AI model based on the input design concept. The selection is based on information such as past design data and trend analysis.

[1711] 3. Generation means

[1712] The AI ​​model generates multiple design proposals, using the TensorFlow library in the generation process.

[1713] 4. Patterning Methods

[1714] The process of converting the generated design ideas into patterns and three-dimensional models using Blender.

[1715] 5. Preservation means

[1716] Ability to store and serve patterned and 3D designs using Firebase.

[1717] 6. Reuse methods

[1718] A means to reuse the generated 3D data as advertising content. Reuse the generated 3D data in advertising, virtual environments, and multiple media.

[1719] 7. Market analysis tools

[1720] A function that collects market responses to reused advertising content and analyzes them using Firebase Analytics.

[1721] Specific examples

[1722] Example 1: A collection themed around summer freshness

[1723] User: A brand manager inputs a design concept with the theme "refreshing summer."

[1724] Server: After receiving the concept, the server selects the optimal generative AI model and generates multiple design proposals. Using the generative tools, colors, shapes, and patterns are generated to match the concept.

[1725] Storage and reuse: The generated 3D data is stored in Firebase and can be reused as advertising banners and videos.

[1726] Market analysis: User responses are collected from social media and advertising platforms and fed back into the next design process.

[1727] Prompt Sentence Examples

[1728] "Tell us your design concept for a refreshing summer apparel product. For example, turquoise beachwear or a cool white shirt. Based on this concept, we will use a generative AI model to propose the optimal design."

[1729] This concludes the specific embodiment of the invention. By using this system, designs can be efficiently generated and reused as advertising assets in a variety of media. Furthermore, by analyzing market reactions, it is possible to quickly and accurately reflect these results in the next design.

[1730] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1731] Step 1:

[1732] User input of design concepts

[1733] Users use devices such as smartphones and tablets to input their brand's unique worldview and design concept. For example, they can input the concept "refreshing summer" into a dedicated application on their device. The input design concept is then sent to the server.

[1734] Input: Design concept (e.g. "Summer freshness")

[1735] Data processing: Text data of the design concept entered on the device is sent to the server

[1736] Output: Design concept sent to server

[1737] Step 2:

[1738] AI model selection

[1739] The server selects an appropriate generative AI model based on the received design concept, using past design data and trend analysis, and leveraging the TensorFlow library.

[1740] Input: Design concept (e.g. "Summer freshness")

[1741] Data processing: Concept-based generative AI model selection

[1742] Output: Selected generative AI model

[1743] Step 3:

[1744] Generate design ideas

[1745] The server uses the selected generative AI model to automatically generate multiple design proposals that combine colors, shapes, patterns, etc. to fit the specified concept.

[1746] Input: Selected generative AI model, design concept

[1747] Data Computation: Generating Design Ideas Using AI Models

[1748] Output: Multiple design ideas

[1749] Step 4:

[1750] Patterning and 3D modeling

[1751] The resulting design is then patterned using Blender and then converted into a 3D model, which allows the design to be simulated during the manufacturing process.

[1752] Input: Generated design proposal

[1753] Data calculation: Patterning and 3D modeling of design proposals

[1754] Output: 3D model

[1755] Step 5:

[1756] keep

[1757] The patterned and 3D designs are stored using Firebase, which allows the design data to be served on demand.

[1758] Input: 3D model

[1759] Data calculation: Save data to Firebase

[1760] Output: Saved design data

[1761] Step 6:

[1762] Reusing advertising content

[1763] The generated 3D data can be reused as advertising content, and distributed to social media and advertising platforms in the form of advertising banners or videos.

[1764] Input: 3D model

[1765] Data calculation: Generating advertising content

[1766] Output: Ad banners, videos

[1767] Step 7:

[1768] Collecting and analyzing market responses

[1769] Firebase Analytics will be used to collect and analyze market responses to ads distributed across social media and advertising platforms, providing important insights to inform the next design process.

[1770] Input: Ad banner, video

[1771] Data Computing: Collecting and Analyzing Market Responses

[1772] Output: Analysis results

[1773] The above is the flow of processing in the embodiment of the present invention.

[1774] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1775] The present invention is a system that enables more advanced design proposals and understanding of market needs by combining a system for improving the efficiency of apparel design using generative AI with an emotion engine that recognizes user emotions. Specific embodiments of this system are described below.

[1776] System configuration

[1777] The system consists of the following main components:

[1778] 1. Input means: A device or software that allows users to input the brand's unique worldview and concept.

[1779] 2. Emotion Engine: A device or software that recognizes and analyzes emotions in user input and feedback.

[1780] 3. AI selection means: A device or software that selects a generative AI model based on the input design concept and analysis results from the emotion engine.

[1781] 4. Generator: A device or software that automatically generates design proposals using the selected generative AI model.

[1782] 5. Patterning means: A device or software that patterns the generated design proposal and converts it into a 3D model.

[1783] 6. Storage means: A device or software that stores the patterned and 3D designs and provides them as needed.

[1784] 7. Reuse means: A device or software that reuses the generated 3D assets for multiple purposes such as advertising, games, virtual spaces, etc.

[1785] 8. Market Analysis Tools: Equipment or software that collects and analyzes market response to reused 3D assets.

[1786] 9. Correction tool (optional): A device or software that receives user feedback and corrects the generated design proposals based on it.

[1787] Operational Overview

[1788] The operation of this system will now be outlined.

[1789] User Actions and Input

[1790] Users input their brand's unique worldview and design concept through the device. This input includes themes, colors, styles, seasons, etc. As users make input, the emotion engine analyzes their facial expressions and voice to recognize their emotional state.

[1791] Server processing

[1792] 1. Data reception and AI model selection:

[1793] The server receives design requests from users and analysis results from the emotion engine. The AI ​​selection means selects a generative AI model based on this information. The selection also takes into account emotion data from the emotion engine, and prioritizes designs predicted to be preferred by users.

[1794] 2. Generate design proposals:

[1795] The server uses the selected generative AI model to generate multiple design proposals based on the user's input concept and emotional data. The generator automatically combines colors, shapes, and patterns to generate designs that match the user's emotional state.

[1796] 3. Patterning and 3D:

[1797] The generated design proposals are patterned by a patterning tool and then converted into a 3D model. This generates the format and 3D data required for the manufacturing process. Designs that match the emotional data are primarily patterned and converted into 3D.

[1798] View and give feedback on design ideas

[1799] The user can review the generated design proposals via their device and provide feedback. The emotion engine monitors the user's emotional state during the feedback process and transmits the results to the server. The proposed design is then adjusted according to the user's emotions.

[1800] Save and reuse 3D assets

[1801] The final design proposals are saved and provided as 3D assets to existing advertisements, games, and virtual spaces, enabling new market development and understanding of customer needs.

[1802] Collecting and analyzing market responses

[1803] The server collects and analyzes market responses to the reused 3D assets using market analysis tools, and the results of this analysis are fed back into the next design process, providing even more accurate market insights.

[1804] Examples:

[1805] Example 1: New summer collection

[1806] User: A brand manager inputs a design concept with the theme of "summer freshness." The emotion engine detects the excitement in the manager's facial expression.

[1807] Server: Receives concept and emotion data, selects generative AI model, and generates refreshing design that matches the emotion.

[1808] Terminal: The manager reviews the design proposal and provides feedback. The emotion engine detects excitement and adjusts the proposal. The final design is approved.

[1809] Reuse: The created 3D assets are used in virtual events, and participant responses are collected as data.

[1810] Example 2: Fall / Winter Collection

[1811] User: The designer inputs a design concept based on the theme of "warmth and quality." The emotion engine recognizes the designer's calm facial expression.

[1812] Server: Based on the request and emotion data, selects an appropriate AI model and generates design proposals.

[1813] Device: Designers evaluate the design proposals and incorporate revisions based on the emotional data. The final design is selected. The designs are then patterned and converted into 3D, and the final design is made available for download.

[1814] Reuse: The completed 3D assets will appear as in-game items and the reaction from players will be analyzed.

[1815] The above is a specific embodiment of the present invention. This system makes it possible to propose sophisticated designs that take user emotions into consideration and to grasp market needs.

[1816] The processing flow will be explained below.

[1817] Step 1:

[1818] (User)

[1819] Users access the device and input their brand's unique worldview and design concept, including details such as theme (e.g., refreshing summer), color, style, and season. Once the input is complete, they click the submit button to submit their design request.

[1820] Step 2:

[1821] (Terminal)

[1822] The terminal receives the user's input data, checks the data for integrity, and then formats and sends it to the server.

[1823] Step 3:

[1824] (server)

[1825] The server receives a design request from the user and begins analysis. The analyzed data is sent to the AI ​​selection process, which selects the optimal generative AI model based on the brand's worldview and concept. Past design data and market trend information are also referenced during this process.

[1826] Step 4:

[1827] (server)

[1828] The server activates the emotion engine based on the design concept input by the user, and the emotion engine analyzes the emotion data obtained during the user's input or feedback to identify the user's current emotional state.

[1829] Step 5:

[1830] (server)

[1831] The server generates multiple design proposals based on the selected generative AI model and emotion data obtained from the emotion engine. The generated design proposals are adjusted to be optimally proposed based on the user's emotional state.

[1832] Step 6:

[1833] (Terminal)

[1834] The device displays multiple design proposals sent from the server on a user interface, and is equipped with a function to display images and detailed information so that the user can easily check each design proposal.

[1835] Step 7:

[1836] (User)

[1837] Users can view the generated design proposals via their devices and provide evaluations and feedback, including specific comments and suggested revisions.

[1838] Step 8:

[1839] (Terminal)

[1840] The device collects user feedback and sends it to the server, where the emotion engine again analyzes the user's emotional state and understands the context of the feedback.

[1841] Step 9:

[1842] (server)

[1843] The server receives the feedback and modifies the design proposal using a modification tool, taking into account the user's emotional state and the feedback content. The modified design proposal is then sent back to the device from the server.

[1844] Step 10:

[1845] (server)

[1846] The server automatically patterns the user's final approved design using a pattern generator and then creates a 3D model, which generates the format and 3D data required for the manufacturing process.

[1847] Step 11:

[1848] (server)

[1849] The server stores the generated patterned data and 3D model using a storage means so that they can be provided to users and manufacturers as needed.

[1850] Step 12:

[1851] (server)

[1852] The server uses a reuse method to store the generated 3D assets in a format that can be reused for various content (advertisements, games, virtual spaces, etc.) and provides them to related applications.

[1853] Step 13:

[1854] (server)

[1855] The server collects and analyzes market responses to the reused 3D assets using market analysis tools. The results of this analysis are fed back into the next design process, enabling designs to be more in line with the market.

[1856] Example 2

[1857] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1858] Current design generation systems do not take user emotions or feedback into account, making it difficult to provide design proposals that meet user expectations. Furthermore, it is difficult to predict the expected level of demand when releasing generated design proposals directly to the market. This creates a problem of mismatch between design and market needs, lowering the success rate of projects.

[1859] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1860] In this invention, the server includes an input means for inputting a brand's unique worldview and concept, an emotion engine that analyzes the input design concept and user emotions, an AI selection means for selecting a generative AI model based on the analysis results, a generation means for automatically generating design proposals using the selected generative AI model, a patterning means for converting the generated design proposals into patterns and 3D models, a storage means for saving and providing the patterned and 3D designs, a reuse means for reusing the generated 3D assets for multiple purposes, and a market analysis means for collecting and analyzing market reactions to the reused 3D assets. This enables design proposals that take user emotions into consideration and enables effective design development that understands market needs.

[1861] "Input means" refers to a device or software that allows users to input the brand's unique worldview or concept.

[1862] An "emotion engine" is a device or software that recognizes and analyzes emotions during user input and feedback.

[1863] The "AI selection means" is a device or software that selects a generative AI model based on the input design concept and the analysis results from the emotion engine.

[1864] "Generating means" means a device or software that automatically generates design proposals using a selected generative AI model.

[1865] The "patterning means" is a device or software that patterns the generated design proposal and converts it into a 3D model.

[1866] "Storage means" means a device or software that stores and optionally provides patterned and 3D designs.

[1867] A "reuse means" is a device or software that reuses the generated 3D assets for multiple purposes, such as advertisements, games, virtual spaces, etc.

[1868] "Market Analysis Tool" means a device or software that collects and analyzes market response to reused 3D assets.

[1869] "Feedback" refers to the evaluation and opinions that users give to the generated design proposals.

[1870] A "generative AI model" is an artificial intelligence model used to automatically generate design proposals.

[1871] The present invention is a system that enables more advanced design proposals and understanding of market needs by combining an emotion engine that recognizes user emotions with an apparel design efficiency system that uses a generative AI model. Specific embodiments of this system are described below.

[1872] System configuration

[1873] The system consists of the following main components:

[1874] 1. Input means: A device or software that allows users to input the brand's unique worldview or concept. The user interface uses a keyboard or touch screen, and provides text fields and selection menus.

[1875] 2. Emotion engine: A device or software that recognizes emotions by analyzing the user's facial expressions and voice. It can obtain emotional data in real time using a camera or microphone.

[1876] 3. AI selection means: A device or software that selects a generative AI model based on the input design concept and the analysis results from the emotion engine. It selects the optimal AI model based on a specific algorithm.

[1877] 4. Generator: A device or software that automatically generates design proposals using a selected generative AI model. An AI model using deep learning is used, and the design is generated by that model.

[1878] 5. Patterning means: A device or software that patterns the generated design proposal and converts it into a 3D model. The 2D data is converted into a format that can be used by 3D modeling software (e.g., Blender or Maya).

[1879] 6. Storage means: Device or software that stores the patterned and 3D designs and provides them as needed. They can be stored in a database or cloud storage. The storage format should be a general format (e.g., .fbx or .glb) that can be used for advertising, games, and virtual spaces.

[1880] 7. Reuse tools: Devices or software that reuse the generated 3D assets for multiple purposes, such as advertising, games, virtual spaces, etc. It provides APIs and interfaces for reuse.

[1881] 8. Market Analysis Tools: Devices or software that collect and analyze market reactions to reused 3D assets, such as user reviews, online feedback, and social media comments.

[1882] 9. Correction tool (optional): A device or software that receives user feedback and corrects the generated design proposal based on that feedback. It continues to analyze the user's emotions during the feedback process.

[1883] Operational Overview

[1884] The operation of this system can be explained in several steps. First, the user inputs the brand's unique worldview and design concept through the device. This input includes the theme, color, style, season, etc. At this stage, the emotion engine analyzes the user's facial expressions and voice to recognize their emotional state.

[1885] The server receives design requests from users and analysis results from the emotion engine. The AI ​​selection means selects the optimal generative AI model based on this information. This selection also takes into account emotion data from the emotion engine, and prioritizes designs predicted to be preferred by the user.

[1886] The server uses the selected generative AI model to generate multiple design proposals based on the user's input concept and emotional data. The generator automatically combines colors, shapes, and patterns to generate designs that match the user's emotional state.

[1887] The generated design proposals are patterned by a patterning means and then converted into a 3D model. This generates the format and 3D data required for the manufacturing process. Designs that match the emotional data are primarily patterned and converted into 3D.

[1888] The user can then review the generated design proposals and provide feedback via their device. The emotion engine monitors the user's emotional state during the feedback process and transmits the results to the server. The proposed design is then adjusted based on the user's emotions.

[1889] The final design proposal can be saved and provided as a 3D asset for advertisements, games, virtual spaces, etc. This makes it possible to develop new markets and understand customer needs.

[1890] The server collects and analyzes market responses to the reused 3D assets using market analysis tools, and the results of this analysis are fed back into the next design process, providing even more accurate market insights.

[1891] Specific examples

[1892] Example 1: New summer collection

[1893] User: A brand manager inputs a design concept with the theme of "summer freshness." The emotion engine detects the excitement in the manager's facial expression.

[1894] Server: Receives concept and emotion data and selects a generative AI model. Generates a refreshing design that matches the emotion.

[1895] Terminal: Manager reviews design proposals and provides feedback. Emotion engine detects excitement and adjusts proposals. Final design is approved.

[1896] Reuse: The created 3D assets are used in virtual events, and participant responses are collected as data.

[1897] Example prompt:

[1898] Generate design ideas based on the theme of "summer freshness." The emotion engine recognizes the user's sense of excitement.

[1899] Example 2: Fall / Winter Collection

[1900] User: The designer inputs a design concept based on the theme of "warmth and quality." The emotion engine recognizes the designer's calm facial expression.

[1901] Server: Based on the request and emotion data, selects an appropriate AI model and generates design proposals.

[1902] Device: Designers evaluate the design proposals and make revisions based on the emotional data. The final design is selected. The design is then patterned and converted into 3D, and the final design is made available for download.

[1903] Reuse: The completed 3D assets will appear as in-game items and the reaction from players will be analyzed.

[1904] Example prompt:

[1905] Generate design ideas based on the theme of "warmth and quality." The emotion engine recognizes the user's calming emotions.

[1906] The above is a specific embodiment for carrying out the present invention. This system makes it possible to propose sophisticated designs that take into account the user's emotions and to grasp market needs.

[1907] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1908] Step 1:

[1909] Entering design concepts using input devices

[1910] User: Uses the device to input the brand's unique worldview and design concept (theme, color, style, season, etc.).

[1911] Input: Text data entered via a keyboard or touchscreen.

[1912] Output: The text data of the design concept is sent from the device to the server.

[1913] Step 2:

[1914] Emotion analysis using an emotion engine

[1915] Device: Captures the user's facial expressions and voice using a camera and microphone, obtaining emotional data in real time.

[1916] Input: User's facial expressions and voice data.

[1917] Data processing: Emotion analysis algorithms analyze input facial expressions and voice data.

[1918] Output: Emotional data (e.g., elation, calm, etc.) is generated and sent to the server.

[1919] Step 3:

[1920] Data reception and AI model selection

[1921] Server: Receives design concepts from users and analysis results from the emotion engine.

[1922] Input: Text data and sentiment data of design concepts.

[1923] Data processing: Based on the received design concept and emotion data, an algorithm is applied to select the optimal generative AI model.

[1924] Output: An appropriate generative AI model is selected (e.g., a model characterized by vibrant colors).

[1925] Step 4:

[1926] Generate design ideas

[1927] Server: Automatically generate design proposals using the selected generative AI model.

[1928] Input: Text data of design concepts and a selected generative AI model.

[1929] Data calculation: The generative AI model generates multiple design proposals based on the design concept and emotion data. An image generation algorithm using deep learning technology is applied.

[1930] Output: Multiple design options are generated (e.g., a design based on light blue and green).

[1931] Step 5:

[1932] Patterning and 3D design ideas

[1933] Server: Patterns the generated design proposals and converts them into 3D models.

[1934] Input: Generated design proposal.

[1935] Data processing: Converting 2D patterns into a 3D model format that can be used in 3D modeling software.

[1936] Output: 3D model data (e.g. .obj file)

[1937] Step 6:

[1938] View design ideas and receive feedback

[1939] Terminal: The user can view multiple generated design proposals on the screen.

[1940] Input: 3D model data.

[1941] Output: Preview in the user interface.

[1942] User: Enter and submit feedback on the design proposal.

[1943] Input: Feedback content and emotional state during feedback.

[1944] Output: Feedback data and emotion data are sent to the server.

[1945] Step 7:

[1946] Recalibration based on feedback

[1947] Server: Re-adjust design proposals based on feedback data and new sentiment data.

[1948] Input: Feedback data and emotion data.

[1949] Data computation: Algorithms are applied to refine design proposals based on feedback and sentiment data.

[1950] Output: Revised design proposal.

[1951] Step 8:

[1952] Saving 3D assets

[1953] How to save: Save the revised design as a 3D asset.

[1954] Input: 3D model data of the revised design proposal.

[1955] Storage: Stored in a database or cloud storage.

[1956] Output: Saved 3D model data (e.g. .fbx or .glb)

[1957] Step 9:

[1958] 3D Asset Reuse

[1959] Reuse methods: Providing saved 3D assets for advertising, games, virtual spaces, etc.

[1960] Input: Saved 3D model data.

[1961] Output: Providing data for reuse.

[1962] Step 10:

[1963] Collecting and analyzing market responses

[1964] Market Analysis Tools: Collect and analyze market response to the 3D assets provided.

[1965] Input: Market feedback data, online reviews, and social media comments.

[1966] Data calculations: Algorithms are applied to analyze collected market responses and provide feedback for the next design process.

[1967] Output: Market analysis results.

[1968] These are the specific programming steps for this system, which will enable sophisticated design proposals that take user emotions into account and an understanding of market needs.

[1969] (Application example 2)

[1970] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1971] Conventional fashion design proposal systems have had the challenge of making design proposals that fully consider the user's preferences and emotions. Furthermore, they lacked the means to collect and analyze market reactions to the generated designs in real time, making it difficult to reflect these in the next design process. Effectively incorporating user emotions and market reactions is needed to make more suitable design proposals and understand market needs.

[1972] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1973] In this invention, the server includes an input means for inputting a brand's unique worldview and concept, an AI selection means for selecting an AI model, a generation means for automatically generating design proposals using the AI ​​model, a patterning means for converting the generated design proposals into patterns and 3D models, a storage means for saving and providing the generated 3D assets, a reuse means for reusing them, a market analysis means for collecting and analyzing market reactions, an emotion recognition means for recognizing user emotions, and an AI selection means for selecting an AI model based on emotions. This enables advanced design proposals that take user emotions into consideration and an understanding of market needs.

[1974] "Input means" refers to a device or software that allows the user to input the brand's unique worldview or concept.

[1975] An "AI selection means" is a device or software that selects the optimal generative AI model based on the input design concept and emotional data.

[1976] A "generator" is a device or software that automatically generates design proposals using a selected generative AI model.

[1977] The "patterning means" is a device or software that patterns the generated design proposal and converts it into a 3D model.

[1978] "Storage means" means a device or software that stores and optionally provides patterned and 3D designs.

[1979] A "reuse means" is a device or software for reusing the generated 3D assets in multiple ways.

[1980] "Market Analysis Tool" means a device or software that collects and analyzes market response to reused 3D assets.

[1981] "Emotion recognition means" refers to a device or software that recognizes the user's emotions and analyzes their emotional state.

[1982] "Fashion items" are design items such as clothing and accessories that are generated based on emotions recognized by emotion recognition means.

[1983] In this invention, the user inputs the brand's unique worldview and design concept through an input means. The input means is a software application installed on a general computer device such as a personal computer, smartphone, or tablet. The user uses this application to input information such as theme, color, style, and season. The input means has a function to recognize and analyze emotions from the user's facial expressions and voice using an emotion recognition means.

[1984] The server receives the design concept sent by the user and the emotion data obtained by the emotion recognition means. Based on this data, the server's AI selection means selects the optimal generative AI model, and automatically generates multiple design proposals using the selected generative AI model. The generation means uses a deep learning algorithm that utilizes a neural network to generate a design that matches the user's concept and emotion.

[1985] The generated design proposal is patterned by a patterning means and then converted into a 3D model. At this time, software such as Blender or Maya is used as a 3D modeling tool. The patterned and 3D design proposal is stored in a database server by a storage means, so that users can access it as needed.

[1986] The user checks the generated design proposal via the device and provides feedback through the emotion recognition means. This feedback data is also sent to the server, and the design proposal is further adjusted by the generation means. This process results in an optimal design proposal that reflects the user's requests.

[1987] The generated 3D assets can be reused in advertisements, games, and virtual spaces through reuse methods. For example, the 3D assets can be used in virtual events, and market reactions can be collected in real time. This reaction data can be analyzed through market analysis methods and fed back into the next design process.

[1988] To illustrate, the following prompt sentences will explain the system's behavior:

[1989] "Users visit a virtual store, and a camera analyzes their facial expressions to detect happy emotions. Based on that emotion, the virtual store suggests fashion items with bright and fun designs. Users can try on the items and ultimately purchase them."

[1990] In this way, the system of the present invention can propose designs that are appropriate for the user based on their emotions, enabling a new fashion item purchasing experience. Furthermore, by analyzing market reactions to the created designs, more advanced market insights can be obtained and reflected in the next design process.

[1991] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1992] Step 1:

[1993] Users use input devices to input the brand's unique worldview and design concept. Specifically, users input information such as theme, color, style, and season into a dedicated application installed on a PC, smartphone, or tablet. The input design data is then sent to a server.

[1994] Input: Design concept, theme, color, style, season

[1995] Output: Design data sent to the server

[1996] Step 2:

[1997] The emotion recognition means identifies the user's emotional state. Specifically, it uses the device's camera and voice recognition function to capture the user's facial expressions and voice, and analyzes their emotions using an emotion recognition model. The analyzed emotion data is then sent to the server.

[1998] Input: User's facial expressions and voice

[1999] Output: Emotion data sent to the server

[2000] Step 3:

[2001] The server selects an appropriate generative AI model using the AI ​​selection means based on the received design data and emotion data. Specifically, it identifies the generative AI model that is best suited to the user's emotion and design concept from the database in the server.

[2002] Input: Design data, emotion data

[2003] Output: The selected generative AI model

[2004] Step 4:

[2005] The server generates design proposals using the selected generative AI model. Specifically, the generation means uses a deep learning algorithm that utilizes a neural network to generate design proposals that match the user's design concept and emotional data.

[2006] Input: Generative AI model, design data, emotion data

[2007] Output: Generated design proposal

[2008] Step 5:

[2009] The server patterns the generated design proposal using a patterning means and converts it into a 3D model. Specifically, the server uses a 3D modeling tool (e.g., Blender or Maya) to create a 3D model of the generated design proposal.

[2010] Input: Generated design proposal

[2011] Output: Patterned and 3D designs

[2012] Step 6:

[2013] The server stores the patterned and 3D designs in a database using a storage means and provides them to the user as needed. Specifically, the stored data is maintained in a format accessible to the user's terminal.

[2014] Input: Patterned and 3D Design

[2015] Output: Saved design data

[2016] Step 7:

[2017] The user reviews the generated design proposals via their device and provides feedback. The emotion recognition means monitors the user's emotional state even when providing feedback, and transmits that data to the server. Specifically, the user evaluates the design proposals through the application and provides feedback through comments and facial expressions.

[2018] Input: Generated design proposals, user emotion data

[2019] Output: Feedback data sent to the server

[2020] Step 8:

[2021] The server reuses the 3D assets generated using the reuse means in advertisements, games, and virtual spaces, and collects market responses to them. Specifically, the 3D assets are used in virtual events, and response data is collected in real time.

[2022] Input: Generated 3D assets

[2023] Output: Collected market response data

[2024] Step 9:

[2025] The server analyzes the market response data collected using the market analysis means and reflects the results in the next design process. Specifically, the analyzed data is used as input when generating the next design proposal.

[2026] Input: Market response data

[2027] Output: Analysis results and points to improve next design data

[2028] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2029] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2030] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2031] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2032] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2033] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2034] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2035] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2036] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2037] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2038] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2039] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2040] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2041] 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.

[2042] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2043] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2044] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2045] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2046] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2047] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2048] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2049] The following is further disclosed regarding the above embodiment.

[2050] (Claim 1)

[2051] An input method for inputting the brand's unique worldview and concept,

[2052] an AI selection means for selecting a generative AI model based on an input design concept;

[2053] A generation means for automatically generating a design proposal using the selected generative AI model;

[2054] a patterning means for converting the generated design proposal into a pattern and a 3D model;

[2055] a storage means for storing and providing patterned and 3D designs;

[2056] A reuse mechanism for multiple uses of the generated 3D assets;

[2057] a market analysis means for collecting and analyzing market responses to the reused 3D assets;

[2058] A system including:

[2059] (Claim 2)

[2060] 10. The system of claim 1, further comprising a modifying means for receiving user feedback and modifying the generated design proposal.

[2061] (Claim 3)

[2062] 10. The system of claim 1, further comprising means for reusing the generated 3D assets in advertisements, games, and virtual spaces.

[2063] "Example 1"

[2064] (Claim 1)

[2065] An input method for inputting the brand's unique worldview and concept,

[2066] an AI selection means for selecting a generative AI model based on an input design concept;

[2067] A generation means for automatically generating a design proposal using the selected generative AI model;

[2068] a patterning means for converting the generated design proposal into a pattern and a 3D model;

[2069] a storage means for storing and providing patterned and 3D designs;

[2070] A reuse mechanism for multiple uses of the generated 3D assets;

[2071] a market analysis means for collecting and analyzing market responses to the reused 3D assets;

[2072] a terminal for the user to review the generated design proposal and send feedback;

[2073] A system including:

[2074] (Claim 2)

[2075] 10. The system of claim 1, further comprising a modifying means for receiving user feedback and modifying the generated design proposal.

[2076] (Claim 3)

[2077] 10. The system of claim 1, further comprising means for reusing the generated 3D assets in advertisements, virtual spaces, and online games.

[2078] "Application Example 1"

[2079] (Claim 1)

[2080] An input method for inputting the brand's unique worldview and concept,

[2081] an AI selection means for selecting a generative AI model based on an input design concept;

[2082] A generation means for automatically generating design proposals using the selected generative AI model;

[2083] a patterning means for converting the generated design proposal into a pattern and a three-dimensional model;

[2084] a storage means for storing and providing patterned and three-dimensional designs;

[2085] A reuse means for reusing the generated 3D data as advertising content;

[2086] a market analysis means for collecting and analyzing market responses to the reused advertising content;

[2087] A system including:

[2088] (Claim 2)

[2089] 10. The system of claim 1, further comprising a modifying means for receiving user feedback and modifying the generated design proposal.

[2090] (Claim 3)

[2091] 10. The system of claim 1, further comprising means for reusing the generated 3D data for advertising, virtual environments, and multiple media.

[2092] "Example 2: Combining Emotion Engines"

[2093] (Claim 1)

[2094] An input method for inputting the brand's unique worldview and concept,

[2095] an emotion engine that analyzes input design concepts and user emotions;

[2096] An AI selection means for selecting a generative AI model based on the analyzed results;

[2097] A generation means for automatically generating a design proposal using the selected generative AI model;

[2098] a patterning means for converting the generated design proposal into a pattern and a 3D model;

[2099] a storage means for storing and providing patterned and 3D designs;

[2100] A reuse mechanism for multiple uses of the generated 3D assets;

[2101] a market analysis means for collecting and analyzing market responses to the reused 3D assets;

[2102] A system including:

[2103] (Claim 2)

[2104] 10. The system of claim 1, further comprising an emotion engine that receives the user's feedback and reanalyzes the user's emotion in the feedback.

[2105] (Claim 3)

[2106] 10. The system of claim 1, further comprising means for reusing the generated 3D assets in advertisements, games, and virtual spaces.

[2107] "Application example 2 when combining emotion engines"

[2108] (Claim 1)

[2109] An input method for inputting the brand's unique worldview and concept,

[2110] an AI selection means for selecting a generative AI model based on an input design concept;

[2111] A generation means for automatically generating a design proposal using the selected generative AI model;

[2112] a patterning means for converting the generated design proposal into a pattern and a 3D model;

[2113] a storage means for storing and providing patterned and 3D designs;

[2114] A reuse mechanism for multiple uses of the generated 3D assets;

[2115] a market analysis means for collecting and analyzing market responses to the reused 3D assets;

[2116] emotion recognition means for recognizing an emotion of a user and suggesting fashion items based on the emotion;

[2117] an AI selection means for selecting a generative AI model for generating fashion items suggested by the emotion recognition means;

[2118] A system including:

[2119] (Claim 2)

[2120] 10. The system of claim 1, further comprising a modifying means for receiving user feedback and modifying the generated design proposal.

[2121] (Claim 3)

[2122] 10. The system of claim 1, further comprising means for reusing the generated 3D assets in advertisements, games, and virtual spaces. [Explanation of symbols]

[2123] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. An input method for inputting the brand's unique worldview and concept, an AI selection means for selecting a generative AI model based on an input design concept; A generation means for automatically generating a design proposal using the selected generative AI model; a patterning means for converting the generated design proposal into a pattern and a 3D model; a storage means for storing and providing patterned and 3D designs; A reuse mechanism for multiple uses of the generated 3D assets; a market analysis means for collecting and analyzing market responses to the reused 3D assets; A system including:

2. The system of claim 1 further comprising a modifying means for receiving user feedback and modifying the generated design proposal.

3. The system of claim 1 , further comprising means for reusing the generated 3D assets in advertisements, games, and virtual spaces.

Images