system

Abstract

We provide the system. [Solution] An artificial intelligence-powered method that automatically generates clothing designs based on text prompts entered by the user, An automated manufacturing control system that transmits the generated design information to the manufacturing facility and manufactures clothing, An online sales method that makes finished clothing available for sale on a technical platform, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional clothing design and manufacturing process, a great deal of time and resources were spent on manual work by designers, and it was difficult to quickly and efficiently introduce new designs to the market. Also, there was a lack of a personalized approach based on the preferences and purchase histories of individual customers, making it difficult to enhance customer satisfaction.

Means for Solving the Problems

[0005] This invention provides a means for automatically generating clothing designs using artificial intelligence based on text prompts entered by a user. Furthermore, the generated design information can be transmitted to a manufacturing facility, enabling rapid production of clothing through a fully automated manufacturing management system. The invention also includes a means for selling the finished clothing on a technical platform and providing personalized product suggestions based on customer purchase history and preference information, thereby providing a system that improves customer satisfaction and accelerates time to market.

[0006] A "user" is an individual or legal entity that provides input to a system and requests a specific response or service.

[0007] A "text prompt" is a sentence or keyword that a user enters to give instructions to the system.

[0008] "Clothing design" refers to the design that constitutes elements such as the shape, material, color, and pattern of clothing.

[0009] Artificial intelligence is a technology that learns from large amounts of data and can perform specific tasks in a way that is similar to that of a human.

[0010] A "manufacturing facility" is a facility or system used to carry out the manufacturing process of a product.

[0011] "Manufacturing management means" refers to methods or systems for improving the efficiency of the manufacturing process, controlling quality, and adjusting inventory.

[0012] A "technological platform" is an online foundational system that enables product sales, information provision, and customer management.

[0013] "Online sales methods" refer to the processes and technologies used to sell goods via the internet.

[0014] "Trend data" refers to information about past and present trends and changes in customer preferences.

[0015] "Personalized product recommendation" refers to the act of recommending products that are considered appropriate based on the preferences and purchase histories of individual customers.

Brief Description of the Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is a system that automatically generates clothing designs based on text input from the user and handles the entire process from manufacturing to sales. This system primarily utilizes terminals, servers, and artificial intelligence.

[0038] First, the user uses a terminal to access the system's input screen. The user enters keywords, such as "modern summer tops," into the terminal's interface. The terminal receives this input and sends it to the server.

[0039] Next, the server sends the received text prompt to an artificial intelligence model. The AI ​​analyzes historical trend data and fashion image data to generate a new clothing design based on this prompt. Once the design is generated, it is returned to the server as digital design data.

[0040] The server receives this data and transmits the design information to the manufacturing facility. The manufacturing facility efficiently produces the garments using automated production management systems. After manufacturing is complete, the products are ready for online sale.

[0041] Information on completed clothing items is registered on a technical platform. The server analyzes customers' past purchase history and preference data to implement optimal promotions. Specifically, it can, for example, notify existing customers of new products or suggest related items.

[0042] Finally, the user (customer) accesses the online platform through their device, selects products, and proceeds with the purchase. Once the purchase is complete, the server proceeds with order confirmation and shipping procedures and notifies the user.

[0043] In this way, the system allows users to quickly obtain designs tailored to their preferences, while the manufacturing and sales processes are efficiently managed through automated control systems.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user opens the terminal interface and enters text prompts about the clothing design. For example, they might enter specific keywords such as "casual autumn coat."

[0047] Step 2:

[0048] The terminal receives the input prompt, packets it into a data format (e.g., JSON format), and prepares to send it to the server.

[0049] Step 3:

[0050] The server receives a prompt from the terminal and sends this text data to the artificial intelligence model. At this point, a design generation based on the prompt is requested.

[0051] Step 4:

[0052] The artificial intelligence model references trend data and fashion image databases, analyzes prompts, and generates new designs. During this process, digital design data is created.

[0053] Step 5:

[0054] The AI ​​model sends the generated design back to the server. The server receives and saves this design data.

[0055] Step 6:

[0056] The server transmits the generated design along with the necessary manufacturing information to the manufacturing facility. This information includes instructions regarding material selection and the manufacturing process.

[0057] Step 7:

[0058] The manufacturing facility starts producing products based on instructions from the server. Here, production is carried out efficiently using automated manufacturing management systems.

[0059] Step 8:

[0060] Once manufacturing is complete, the products are registered on a server-managed technical platform. Here, they are ready for online sales.

[0061] Step 9:

[0062] The server analyzes customer preference information and purchase history to generate promotional information for personalized product recommendations.

[0063] Step 10:

[0064] Users access the online platform via their device, browse and select registered products, and proceed with the purchase. They enter their payment information to confirm the purchase.

[0065] Step 11:

[0066] The server verifies the user's purchase procedure and processes the payment. If successful, it notifies the manufacturing department of the shipping instructions and sends a purchase confirmation notification to the user.

[0067] (Example 1)

[0068] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0069] Traditional clothing design and manufacturing systems have struggled to quickly and accurately reflect users' personal preferences. Furthermore, the process from design to manufacturing and sales is complex and time-consuming, requiring efficient management. Additionally, a lack of personalized product suggestions makes improving consumer satisfaction a challenge.

[0070] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0071] In this invention, the server includes means that utilize an intelligent algorithm to automatically generate clothing designs based on language information input by the user, automated production management means that transmit the generated design information to a production facility to produce the clothing, and online distribution means that make the finished clothing available for sale on an information infrastructure. This enables the rapid design, manufacturing, and sale of clothing that reflects the individual preferences of the user.

[0072] A "user" is an individual or group that uses the system to input, select, and purchase clothing designs.

[0073] "Linguistic information" refers to text that expresses the features and style of clothing desired by the user in natural language.

[0074] An "intelligent algorithm" is a sophisticated computing system that analyzes linguistic information entered by a user and automatically generates unique clothing designs based on that analysis.

[0075] "Conceptual information" refers to detailed digital data about clothing designs generated by intelligent algorithms.

[0076] A "production machine" refers to a factory or facility that automatically manufactures clothing based on conceptual information.

[0077] An "automated production management system" is a system or process designed to manufacture clothing efficiently and unmanned, based on conceptual information.

[0078] An "information infrastructure" is an online platform or network established to manage and sell finished clothing items in the digital space.

[0079] "Online distribution methods" refer to electronic commerce systems used to sell and deliver manufactured clothing to customers via an information infrastructure.

[0080] "Preference information" refers to data that shows a customer's past purchasing trends and preferences.

[0081] "Personalized product recommendations" is a marketing technique that proposes the most suitable products to each customer based on their preferences and purchase history.

[0082] This invention begins with the user accessing the system's user interface using their own device. The user inputs a specific prompt, such as "modern summer tops." This linguistic information is then sent to the server by the device.

[0083] The server utilizes a generative AI model to analyze the received linguistic information. This generative AI model includes an algorithm that references a large amount of historical trend information and image datasets to generate clothing designs that meet user requirements. The design generation process involves analyzing existing styles and trendy colors to provide creative and up-to-date styles.

[0084] The generated design, or conceptual information, is returned to the server as digital data. The server transmits this data to the production facility, and an automated production management system initiates an efficient manufacturing process. Once manufacturing is complete, the product is ready to be sold on the information infrastructure via online distribution channels.

[0085] In this information infrastructure, customer preference information and purchase history are analyzed by servers. This analysis leads to personalized product recommendations, displaying the most suitable products for each customer.

[0086] As a concrete example, consider a scenario where a user enters the prompt "elegant autumn jacket." In this case, the generation AI model generates an elegant jacket design incorporating the latest autumn trends based on that sentence. The user can then view the final product through their device and complete the purchase process.

[0087] This invention enables users to quickly obtain clothing tailored to their preferences and facilitates efficient manufacturing and sales processes.

[0088] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0089] Step 1:

[0090] The user accesses the system interface using a terminal and enters a prompt message. This prompt message is text information containing keywords related to a specific clothing item. Upon receiving this prompt message, the terminal validates the input and prepares to send it to the server.

[0091] Step 2:

[0092] The terminal sends prompt messages from the user to the server. At this time, the input information is converted to an appropriate format and forwarded to the server as a prompt message. The server receives this prompt message and prepares for analysis by the AI ​​model.

[0093] Step 3:

[0094] The server passes the received prompt text to the generating AI model. The generating AI model analyzes the input language information and references past trend information and image data in the database. This generates new design data based on the prompt. The analysis and generation process involves processes such as text analysis, design modeling, and optimization.

[0095] Step 4:

[0096] The generative AI model returns the analyzed design as digital data to the server. This output data contains specific clothing design information and can be used in the manufacturing process. The server reviews this digital design data and prepares it for transfer to the production system.

[0097] Step 5:

[0098] The server transmits the generated design data to the production facility. The production facility manufactures garments based on the received design using automated production management systems. Specific operations include analyzing cutting data, creating sewing plans, and executing them on the production line.

[0099] Step 6:

[0100] Manufactured clothing items are prepared for online sale on an information infrastructure. Servers register finished product data on the online distribution platform, making them ready for sale. At this time, product information is organized, pricing is set, and inventory management is performed.

[0101] Step 7:

[0102] Users access the online platform using their devices, select products, and proceed with the purchase. Based on the user's actions, the server verifies the order data and notifies the customer of order confirmation and preparation for shipment. Specific actions performed here include organizing order information, sending it to the customer notification system, and arranging delivery.

[0103] (Application Example 1)

[0104] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0105] Traditional clothing design, manufacturing, and sales processes have made it difficult to quickly create custom-made products based on user requests, and personalized ordering and real-time design suggestions, particularly on online platforms, have not been adequately supported. This has sometimes led to decreased user satisfaction and problems with excess inventory.

[0106] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0107] In this invention, the server includes display means for displaying product previews on the user's terminal and enabling custom orders, electronic transaction means for executing online payments, and means for analyzing past trend data and optimizing the design. This allows the user to select and order the latest design based on their preferences in real time.

[0108] A "user" refers to a consumer who uses the system to input product designs and place an order.

[0109] A "text prompt" refers to the text or keywords that a user enters to give instructions for product design.

[0110] "Product" refers to a product designed and manufactured based on user input, and primarily includes clothing.

[0111] "Artificial intelligence" refers to computational methods that analyze user prompts and automate design generation.

[0112] "Generated design information" refers to the digital design data of a product generated by artificial intelligence.

[0113] A "manufacturing facility" refers to the equipment or organization that produces goods based on the generated design information.

[0114] "Automated manufacturing management systems" refer to management systems designed to streamline the product manufacturing process and minimize human intervention.

[0115] A "technological network" refers to an online platform that enables the sale and trading of goods.

[0116] "Display means" refers to the technical means for displaying the product design and preview on the user's device.

[0117] "Electronic transaction methods" refer to technical means for processing payments for goods via the internet.

[0118] "Trend data" refers to information that shows past fashion trends and market movements.

[0119] "Personalized product recommendations" refer to customized product information generated based on each customer's preferences and purchase history.

[0120] The system for implementing this invention is centered around a terminal, a server, and artificial intelligence.

[0121] A terminal is a device that allows a user to access an input interface to a system. Smartphones and personal computers are prime examples, where the user enters text prompts related to clothing design. For example, they might enter a prompt such as "modern summer tops."

[0122] The server is responsible for receiving input from the user and sending it to the artificial intelligence. The AI ​​used includes, for example, Stable Diffusion and DeepFashion. These are models for analyzing prompts and generating new clothing designs. This process analyzes past fashion data to create the optimal design based on the user's input. The generated design is returned to the server as digital design data.

[0123] Upon receiving this information, the server transmits the design data to the manufacturing facility, where the product is efficiently manufactured by an automated manufacturing management system. Information about the manufactured product is registered on a technical network.

[0124] The user accesses the system again through their device and views a preview of the designed product. During this process, the user can place a custom order.

[0125] The server also analyzes the user's past preferences and purchase history to notify them of personalized product suggestions. For example, if a user enters the prompt "spring formal dress," the AI ​​will suggest formal dresses that take the latest trends into consideration, and the user can view and order them.

[0126] Through electronic transaction methods, users complete payment online, and then the product is delivered. This allows users to quickly and easily obtain personalized designs.

[0127] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0128] Step 1:

[0129] The user uses a terminal to access the system's input screen and enters text prompts such as "modern summer tops." The entered prompts are the initial data necessary to materialize the user's design image.

[0130] Step 2:

[0131] The terminal sends the entered text prompt to the server. The server receives this and converts the data into the appropriate format for a predefined AI model. This format conversion prepares the data for accurate interpretation by the AI.

[0132] Step 3:

[0133] The server delegates processing to an AI model, which generates designs based on prompts. The AI ​​model uses the input prompts to analyze past trend data and image data to generate new designs. The data analysis performed here includes calculations to extract the optimal design from the trend data.

[0134] Step 4:

[0135] The digital design data generated from the AI ​​model is returned to the server. The server processes this data into an appropriate format for transmission to the manufacturing facility. The processed data is in a form that can be directly used in the manufacturing process.

[0136] Step 5:

[0137] The manufacturing facility receives design information transmitted from the server and efficiently produces the goods using an automated manufacturing management system. The input is digital design data, and the output is the finished physical product.

[0138] Step 6:

[0139] The user accesses the system again via their terminal to view a preview of the generated product. The server sends a digital image of the product to the terminal to assist the user in confirming it. This preview function allows the user to visually understand the product before making a purchase decision.

[0140] Step 7:

[0141] The user purchases goods through a terminal in a made-to-order format. The server processes the payment through an electronic transaction system. The output of this process is confirmation of transaction completion and commencement of shipping procedures.

[0142] Step 8:

[0143] The server analyzes user preferences and purchase history to provide personalized product recommendations. It sends notifications to the user's device, informing them of new designs and related products. This process enhances the user's purchasing experience.

[0144] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0145] This invention is a system that optimizes clothing design using an emotion engine based on user text prompts. Users input text prompts through a terminal and are provided with an interface to recognize the emotions expressed therein. The emotion engine analyzes the emotional state from the words and context chosen by the user and reflects the results in the design process.

[0146] For example, if a user enters "a glamorous and fun party dress," the emotion engine recognizes positive and uplifting emotions from keywords such as "glamorous" and "fun." This emotion data is sent to the server and used in the design generation process. Based on this emotion data, the artificial intelligence model adjusts the color scheme, style, and direction of decoration to generate the design.

[0147] The generated design data is stored on a server and sent to the manufacturing facility. The manufacturing facility then produces the clothing, incorporating the emotion-based design concept. Once production is complete, the products are registered on a technology platform and sales begin.

[0148] This technological platform provides online sales functionality that takes into account users' emotional and purchase history. The server analyzes what kinds of emotionally charged products users have previously preferred and can provide personalized product recommendations. For example, a user who previously purchased a product based on the emotion of "happiness" will be offered new products that evoke similar emotions.

[0149] This system aims to improve customer satisfaction by enabling users to quickly obtain products with designs customized based on their emotions, and by smoothly integrating the manufacturing and sales processes.

[0150] The following describes the processing flow.

[0151] Step 1:

[0152] The user accesses the system via a terminal and enters text prompts regarding the design of the clothing. For example, they might enter specific details such as "an active and energetic sports jacket."

[0153] Step 2:

[0154] The device sends the entered prompt to the emotion engine. The emotion engine extracts keywords such as "active" and "energetic" from this prompt to recognize the user's emotional state.

[0155] Step 3:

[0156] The emotion engine sends the recognized emotion data to the server. The server receives this emotion information and stores it as contextual information necessary for design generation.

[0157] Step 4:

[0158] The server sends text prompts to the artificial intelligence model based on the user's emotional data, requesting design generation. The AI ​​model then designs clothing using design parameters that take the emotional data into account.

[0159] Step 5:

[0160] The artificial intelligence model sends the generated design data back to the server. The server receives this design and prepares it as necessary documentation for starting manufacturing.

[0161] Step 6:

[0162] The server sends the design data to the manufacturing facility. The manufacturing facility then produces the garments using an automated manufacturing process based on the instructions.

[0163] Step 7:

[0164] Once manufacturing is complete, the garments are registered on the technical platform by a server. This is where the sales process begins.

[0165] Step 8:

[0166] The server generates personalized product recommendations based on the user's sentiment history and purchase history. This information is used on the online sales platform.

[0167] Step 9:

[0168] Users access the technical platform from their devices and view available products. Based on personalized suggestions, users select products and proceed with the purchase.

[0169] Step 10:

[0170] The server sends shipping instructions to the manufacturing department for confirmed purchases. The user receives purchase confirmation and shipping notifications via their device.

[0171] (Example 2)

[0172] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0173] The present invention aims to provide a system for rapidly designing, manufacturing, and selling personalized clothing based on user emotions. Conventional methods have struggled to accurately reflect user emotions in clothing design optimization, and have lacked consistent management throughout the manufacturing and sales process. There is a need to solve these problems and improve customer satisfaction.

[0174] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0175] In this invention, the server includes means for analyzing emotions using natural language processing, means for optimizing and generating clothing designs based on the analyzed emotions using a generative AI model, and means for analyzing the user's emotional history and purchase history to provide personalized product suggestions. This makes it possible to automatically generate clothing designs that reflect the user's emotional state and to efficiently manufacture and sell them.

[0176] "User" refers to the entity that uses this system to input clothing designs and make purchases.

[0177] A "prompt" is text information entered by the user, expressing their requests and feelings regarding the design of the clothing.

[0178] "Natural language processing" refers to the technology used in emotion engines to analyze emotions and intentions from user-inputted prompts.

[0179] "Sentiment analysis" is the process of evaluating a user's emotional state based on prompts and identifying that emotion.

[0180] A "generative AI model" refers to artificial intelligence technology used to automatically generate clothing designs based on the results of emotion analysis.

[0181] "Clothing" refers to fashion items designed and manufactured as a result of emotional analysis and design generation processes.

[0182] A "manufacturing institution" refers to a facility or company that produces actual clothing based on a generated design.

[0183] A "technical platform" refers to an online environment where finished garments are registered and the sales process takes place.

[0184] "Personalized product recommendations" refer to a process that suggests the most suitable products by taking into account the user's past emotional history and purchase history.

[0185] This invention is a system that optimizes clothing design based on user emotions and integrates the manufacturing and sales processes. This system includes the following main components:

[0186] First, the user enters a prompt using the device. This prompt is a textual expression of the user's desired clothing style and mood. For example, a specific prompt might be something like, "A casual shirt I can relax in on the beach." The device has a user interface implemented for entering and sending prompts.

[0187] Next, the server receives a prompt and performs natural language processing using the emotion engine. The emotion engine analyzes the linguistic data contained in the prompt and identifies the underlying emotion. For example, from words like "beach" and "relax," the system recognizes the emotion of relaxation. This analysis result is used as foundational data for the design generation process.

[0188] Based on the analyzed emotion data, the server invokes a generative AI model to generate clothing designs. The generative AI model automatically adjusts design parameters (such as color, style, and material) associated with a specific emotion to output the optimal design. In this process, open-source AI models such as the GPT series and DALL-E are often used.

[0189] The generated design data is sent from the server to the manufacturing facility. The manufacturing facility produces physical clothing based on this data. Once the product is completed, it is registered on a technical platform. This platform serves as the foundation for online sales and enables product recommendations to users.

[0190] Finally, the sales platform provides personalized product recommendations by analyzing users' past emotions and purchase history through its servers. For example, users who have previously purchased products based on the emotion of "relaxation" will be offered new products that evoke similar emotions.

[0191] This system will not only enable users to efficiently acquire clothing that matches their personal preferences, but will also facilitate smooth coordination throughout the entire process from manufacturing to sales. This is expected to significantly improve the customization of clothing and customer satisfaction.

[0192] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0193] Step 1:

[0194] The user enters a prompt using the terminal.

[0195] The user enters text as a prompt, describing the style and occasion of the clothing they want. This data is sent from the terminal to the server for sentiment recognition. For example, "a casual shirt for the beach" might be entered. The input data is structured as text and sent to the server.

[0196] Step 2:

[0197] The server receives a prompt and uses natural language processing to analyze the emotions.

[0198] Upon receiving the prompt, the server uses natural language processing techniques to analyze the text and identify key emotions. A language model is used to extract words like "beach" and "casual" from the text, identifying the relaxed emotion. The analysis results in a label indicating a relaxed emotion.

[0199] Step 3:

[0200] The server generates designs using an AI model based on emotional data.

[0201] Upon receiving the sentiment analysis results, the server invokes a generative AI model to generate a design. The generative AI model takes sentiment data as input and determines relevant design parameters (color, material, style, etc.). As a result, a design for a casual shirt with a blue base color is generated. The output data is saved as a design specification document.

[0202] Step 4:

[0203] The server generates design data which is then sent to the manufacturing facility.

[0204] The generated design specifications are sent from the server to the manufacturing facility and used as production instructions for the actual garments. The data is typically transferred via API and interpreted by automated processes at the manufacturing facility. The manufacturing facility then produces the garments based on this design.

[0205] Step 5:

[0206] Once the server is completed, the product information is registered on the technical platform, and sales begin.

[0207] Information on completed garments is registered on a technical platform and used for data entry into the sales system. The platform provides an environment for making appropriate product recommendations using the user's past sentiment and purchase history. Registered products can be viewed and purchased online.

[0208] This series of steps allows users to obtain personalized clothing through emotion-based design.

[0209] (Application Example 2)

[0210] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0211] Modern consumers want to quickly and easily acquire clothing that reflects their emotions and individuality. However, traditional clothing purchasing processes present challenges, such as difficulty in reflecting their emotions in designs and the inability to quickly try on and select items in physical stores or online. Furthermore, the lack of efficient integration of personalized suggestions and try-on processes significantly limits the customer experience.

[0212] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0213] This invention includes a server that utilizes artificial intelligence to automatically generate clothing designs based on text prompts entered by a user, an automated manufacturing management system that transmits the generated design information to a manufacturing facility for the production of clothing, an online sales system that makes the finished clothing available for sale on a technical platform, and a visualization system that allows users to virtually try on clothing using augmented reality technology. This enables consumers to try on clothing designed based on their own feelings and preferences using AR technology and purchase it directly.

[0214] "User-input text prompts" refer to words and phrases entered by the user in natural language, which are used to analyze emotions and design intent.

[0215] "Clothing design" refers to clothing design information generated by artificial intelligence based on user input.

[0216] "Methods utilizing artificial intelligence" refers to a general term for systems and technologies used to analyze user text prompts and generate appropriate clothing designs.

[0217] "Generated design information" refers to clothing design data created by artificial intelligence in response to user requests.

[0218] A "manufacturing facility" refers to a facility or organization that produces actual garments based on the design information of the garments that have been generated.

[0219] "Automated manufacturing management means" refers to a system that automatically manages and implements the garment manufacturing process based on generated design information.

[0220] A "technical platform" refers to an online marketplace or system that sells finished clothing and is accessible to users.

[0221] "Online sales methods" refer to technologies, including websites and applications, used to sell clothing over the internet.

[0222] Augmented reality technology is a technique that overlays digital information onto the real world environment, allowing users to virtually try on clothing.

[0223] "Visualization means" refers to technologies and methods that visually present the generated clothing designs so that users can confirm them.

[0224] To realize this invention, the user terminal, server, and manufacturing machine each need to play specific roles. The system begins with the user entering text prompts from the terminal. The prompts entered by the user are collected through an interface provided on the terminal.

[0225] The server receives these prompts and analyzes them using an emotion engine. This process utilizes artificial intelligence technologies such as TENSORFLOW® to identify the user's intentions and emotional state. For example, if a user enters "fun and colorful sportswear," the server extracts keywords such as "fun" and "colorful" and analyzes them as emotion data.

[0226] The server automatically generates clothing designs based on the analyzed emotional data. This generated design data is then sent to the user's device using augmented reality (AR) technology, allowing them to virtually try on the clothes. iOS's ARKit is available as the AR technology.

[0227] Meanwhile, the generated design data is sent to the manufacturing facility and reflected in the actual clothing. Users can check the design through AR virtual try-on, and if they are satisfied, they can order it online directly through the technical platform. This allows users to receive products that match their feelings and preferences.

[0228] For example, if a user enters the prompt "a sweater that looks stylish even in winter," the server can use "stylish" and "winter" as keywords to generate designs suitable for the user and allow them to try them on using augmented reality. An example of input to the generation AI model is "a sweater that looks stylish even in winter."

[0229] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0230] Step 1:

[0231] The user enters a text prompt through the interface on the terminal. The entered prompt is natural language text data, such as "fun and colorful sportswear." The terminal sends this prompt as digital data to the server.

[0232] Step 2:

[0233] The server receives text prompts sent by the user. The received prompts are analyzed using a generative AI model such as TensorFlow to extract emotions and keywords. In this process, keywords such as "fun" and "colorful" are extracted as emotion data. The output is the emotion data that forms the basis for design generation.

[0234] Step 3:

[0235] The server generates clothing designs based on emotional data obtained through analysis. AI determines the design elements and outputs that information as 3D design data. The output design information is prepared as data for AR virtual try-on.

[0236] Step 4:

[0237] The server sends the generated design data to the terminal and uses AR technology on the terminal to provide the user with a virtual try-on experience. Specifically, it uses tools such as iOS's ARKit to overlay the clothing design onto the user's visual environment. 3D design data is used as input for the try-on data and is provided to the user as an AR display.

[0238] Step 5:

[0239] Users can view AR virtual try-ons via their devices and rate their satisfaction with the design. If satisfied, they can order the designed product online through the technology platform, and it will be manufactured as a real-world garment. The user's order information is sent to the manufacturing company, and the actual product is produced.

[0240] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0241] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0242] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0243] [Second Embodiment]

[0244] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0245] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0246] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0247] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0248] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0249] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0250] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0251] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0252] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0253] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0254] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0255] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0256] This invention is a system that automatically generates clothing designs based on text input from the user and handles the entire process from manufacturing to sales. This system primarily utilizes terminals, servers, and artificial intelligence.

[0257] First, the user uses a terminal to access the system's input screen. The user enters keywords, such as "modern summer tops," into the terminal's interface. The terminal receives this input and sends it to the server.

[0258] Next, the server sends the received text prompt to an artificial intelligence model. The AI ​​analyzes historical trend data and fashion image data to generate a new clothing design based on this prompt. Once the design is generated, it is returned to the server as digital design data.

[0259] The server receives this data and transmits the design information to the manufacturing facility. The manufacturing facility efficiently produces the garments using automated production management systems. After manufacturing is complete, the products are ready for online sale.

[0260] Information on completed clothing items is registered on a technical platform. The server analyzes customers' past purchase history and preference data to implement optimal promotions. Specifically, it can, for example, notify existing customers of new products or suggest related items.

[0261] Finally, the user (customer) accesses the online platform through their device, selects products, and proceeds with the purchase. Once the purchase is complete, the server proceeds with order confirmation and shipping procedures and notifies the user.

[0262] In this way, the system allows users to quickly obtain designs tailored to their preferences, while the manufacturing and sales processes are efficiently managed through automated control systems.

[0263] The following describes the processing flow.

[0264] Step 1:

[0265] The user opens the terminal interface and enters text prompts about the clothing design. For example, they might enter specific keywords such as "casual autumn coat."

[0266] Step 2:

[0267] The terminal receives the input prompt, packets it into a data format (e.g., JSON format), and prepares to send it to the server.

[0268] Step 3:

[0269] The server receives a prompt from the terminal and sends this text data to the artificial intelligence model. At this point, a design generation based on the prompt is requested.

[0270] Step 4:

[0271] The artificial intelligence model references trend data and fashion image databases, analyzes prompts, and generates new designs. During this process, digital design data is created.

[0272] Step 5:

[0273] The AI ​​model sends the generated design back to the server. The server receives and saves this design data.

[0274] Step 6:

[0275] The server transmits the generated design along with the necessary manufacturing information to the manufacturing facility. This information includes instructions regarding material selection and the manufacturing process.

[0276] Step 7:

[0277] The manufacturing facility starts producing products based on instructions from the server. Here, production is carried out efficiently using automated manufacturing management systems.

[0278] Step 8:

[0279] Once manufacturing is complete, the products are registered on a server-managed technical platform. Here, they are ready for online sales.

[0280] Step 9:

[0281] The server analyzes the customer's preference information and purchase history, and generates promotion information for personalized product recommendations.

[0282] Step 10:

[0283] The user accesses the online platform via the terminal, browses and selects registered products, and proceeds with the purchase. The user enters payment information to confirm the purchase.

[0284] Step 11:

[0285] The server confirms the user's purchase process and performs the payment process. If successful, it notifies the manufacturing department of the shipping instruction and sends a purchase confirmation notice to the user.

[0286] (Example 1)

[0287] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0288] Conventional clothing design and manufacturing systems have difficulty quickly and accurately reflecting the user's personal preferences. Also, the process from design to manufacturing and sales is complex and time-consuming, and efficient management is required. Furthermore, there is a lack of personalized product recommendations, and improving consumer satisfaction has become an issue.

[0289] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0290] In this invention, the server includes means that utilize an intelligent algorithm to automatically generate clothing designs based on language information input by the user, automated production management means that transmit the generated design information to a production facility to produce the clothing, and online distribution means that make the finished clothing available for sale on an information infrastructure. This enables the rapid design, manufacturing, and sale of clothing that reflects the individual preferences of the user.

[0291] A "user" is an individual or group that uses the system to input, select, and purchase clothing designs.

[0292] "Linguistic information" refers to text that expresses the features and style of clothing desired by the user in natural language.

[0293] An "intelligent algorithm" is a sophisticated computing system that analyzes linguistic information entered by a user and automatically generates unique clothing designs based on that analysis.

[0294] "Conceptual information" refers to detailed digital data about clothing designs generated by intelligent algorithms.

[0295] A "production machine" refers to a factory or facility that automatically manufactures clothing based on conceptual information.

[0296] An "automated production management system" is a system or process designed to manufacture clothing efficiently and unmanned, based on conceptual information.

[0297] An "information infrastructure" is an online platform or network established to manage and sell finished clothing items in the digital space.

[0298] "Online distribution methods" refer to electronic commerce systems used to sell and deliver manufactured clothing to customers via an information infrastructure.

[0299] "Preference information" refers to data that shows a customer's past purchasing trends and preferences.

[0300] "Personalized product recommendations" is a marketing technique that proposes the most suitable products to each customer based on their preferences and purchase history.

[0301] This invention begins with the user accessing the system's user interface using their own device. The user inputs a specific prompt, such as "modern summer tops." This linguistic information is then sent to the server by the device.

[0302] The server utilizes a generative AI model to analyze the received linguistic information. This generative AI model includes an algorithm that references a large amount of historical trend information and image datasets to generate clothing designs that meet user requirements. The design generation process involves analyzing existing styles and trendy colors to provide creative and up-to-date styles.

[0303] The generated design, or conceptual information, is returned to the server as digital data. The server transmits this data to the production facility, and an automated production management system initiates an efficient manufacturing process. Once manufacturing is complete, the product is ready to be sold on the information infrastructure via online distribution channels.

[0304] In this information infrastructure, customer preference information and purchase history are analyzed by servers. This analysis leads to personalized product recommendations, displaying the most suitable products for each customer.

[0305] As a concrete example, consider a scenario where a user enters the prompt "elegant autumn jacket." In this case, the generation AI model generates an elegant jacket design incorporating the latest autumn trends based on that sentence. The user can then view the final product through their device and complete the purchase process.

[0306] According to this invention, users can quickly obtain clothing items that suit their preferences, and it becomes possible to realize an efficient manufacturing and sales process.

[0307] The flow of the specific process in Example 1 will be described with reference to FIG. 11.

[0308] Step 1:

[0309] The user uses the terminal to access the interface of the system and inputs a prompt sentence. The input is text information containing keywords related to specific clothing items. The terminal that receives this prompt sentence verifies the input and prepares to send it to the server.

[0310] Step 2:

[0311] The terminal sends the prompt sentence from the user to the server. At this time, the input information is converted into an appropriate format and transferred to the server as a prompt sentence. The server receives this prompt sentence and prepares for analysis by the AI model.

[0312] Step 3:

[0313] The server passes the received prompt sentence to the generating AI model. The generating AI model analyzes the input language information and refers to past fashion information and image data in the database. Thereby, new design data based on the prompt is generated. In the analysis and generation process, processes such as text analysis, design modeling, and optimization are performed.

[0314] Step 4:

[0315] The generating AI model returns the analyzed design to the server as digital data. This output data contains the design information of specific clothing items and is available for the manufacturing process. The server checks this digital design data and prepares to transfer it to the production system.

[0316] Step 5:

[0317] The server transmits the generated design data to the production facility. The production facility manufactures garments based on the received design using automated production management systems. Specific operations include analyzing cutting data, creating sewing plans, and executing them on the production line.

[0318] Step 6:

[0319] Manufactured clothing items are prepared for online sale on an information infrastructure. Servers register finished product data on the online distribution platform, making them ready for sale. At this time, product information is organized, pricing is set, and inventory management is performed.

[0320] Step 7:

[0321] Users access the online platform using their devices, select products, and proceed with the purchase. Based on the user's actions, the server verifies the order data and notifies the customer of order confirmation and preparation for shipment. Specific actions performed here include organizing order information, sending it to the customer notification system, and arranging delivery.

[0322] (Application Example 1)

[0323] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0324] Traditional clothing design, manufacturing, and sales processes have made it difficult to quickly create custom-made products based on user requests, and personalized ordering and real-time design suggestions, particularly on online platforms, have not been adequately supported. This has sometimes led to decreased user satisfaction and problems with excess inventory.

[0325] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0326] In this invention, the server includes display means for displaying product previews on the user's terminal and enabling custom orders, electronic transaction means for executing online payments, and means for analyzing past trend data and optimizing the design. This allows the user to select and order the latest design based on their preferences in real time.

[0327] A "user" refers to a consumer who uses the system to input product designs and place an order.

[0328] A "text prompt" refers to the text or keywords that a user enters to give instructions for product design.

[0329] "Product" refers to a product designed and manufactured based on user input, and primarily includes clothing.

[0330] "Artificial intelligence" refers to computational methods that analyze user prompts and automate design generation.

[0331] "Generated design information" refers to the digital design data of a product generated by artificial intelligence.

[0332] A "manufacturing facility" refers to the equipment or organization that produces goods based on the generated design information.

[0333] "Automated manufacturing management systems" refer to management systems designed to streamline the product manufacturing process and minimize human intervention.

[0334] A "technological network" refers to an online platform that enables the sale and trading of goods.

[0335] "Display means" refers to the technical means for displaying the product design and preview on the user's device.

[0336] "Electronic transaction methods" refer to technical means for processing payments for goods via the internet.

[0337] "Trend data" refers to information that shows past fashion trends and market movements.

[0338] "Personalized product recommendations" refer to customized product information generated based on each customer's preferences and purchase history.

[0339] The system for implementing this invention is centered around a terminal, a server, and artificial intelligence.

[0340] A terminal is a device that allows a user to access an input interface to a system. Smartphones and personal computers are prime examples, where the user enters text prompts related to clothing design. For example, they might enter a prompt such as "modern summer tops."

[0341] The server is responsible for receiving input from the user and sending it to the artificial intelligence. The AI ​​used includes, for example, Stable Diffusion and DeepFashion. These are models for analyzing prompts and generating new clothing designs. This process analyzes past fashion data to create the optimal design based on the user's input. The generated design is returned to the server as digital design data.

[0342] Upon receiving this information, the server transmits the design data to the manufacturing facility, where the product is efficiently manufactured by an automated manufacturing management system. Information about the manufactured product is registered on a technical network.

[0343] The user accesses the system again through their device and views a preview of the designed product. During this process, the user can place a custom order.

[0344] The server also analyzes the user's past preferences and purchase history to notify them of personalized product suggestions. For example, if a user enters the prompt "spring formal dress," the AI ​​will suggest formal dresses that take the latest trends into consideration, and the user can view and order them.

[0345] Through electronic transaction methods, users complete payment online, and then the product is delivered. This allows users to quickly and easily obtain personalized designs.

[0346] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0347] Step 1:

[0348] The user uses a terminal to access the system's input screen and enters text prompts such as "modern summer tops." The entered prompts are the initial data necessary to materialize the user's design image.

[0349] Step 2:

[0350] The terminal sends the entered text prompt to the server. The server receives this and converts the data into the appropriate format for a predefined AI model. This format conversion prepares the data for accurate interpretation by the AI.

[0351] Step 3:

[0352] The server delegates processing to an AI model, which generates designs based on prompts. The AI ​​model uses the input prompts to analyze past trend data and image data to generate new designs. The data analysis performed here includes calculations to extract the optimal design from the trend data.

[0353] Step 4:

[0354] The digital design data generated from the AI ​​model is returned to the server. The server processes this data into an appropriate format for transmission to the manufacturing facility. The processed data is in a form that can be directly used in the manufacturing process.

[0355] Step 5:

[0356] The manufacturing facility receives design information transmitted from the server and efficiently produces the goods using an automated manufacturing management system. The input is digital design data, and the output is the finished physical product.

[0357] Step 6:

[0358] The user accesses the system again via their terminal to view a preview of the generated product. The server sends a digital image of the product to the terminal to assist the user in confirming it. This preview function allows the user to visually understand the product before making a purchase decision.

[0359] Step 7:

[0360] The user purchases goods through a terminal in a made-to-order format. The server processes the payment through an electronic transaction system. The output of this process is confirmation of transaction completion and commencement of shipping procedures.

[0361] Step 8:

[0362] The server analyzes user preferences and purchase history to provide personalized product recommendations. It sends notifications to the user's device, informing them of new designs and related products. This process enhances the user's purchasing experience.

[0363] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0364] This invention is a system that optimizes clothing design using an emotion engine based on user text prompts. Users input text prompts through a terminal and are provided with an interface to recognize the emotions expressed therein. The emotion engine analyzes the emotional state from the words and context chosen by the user and reflects the results in the design process.

[0365] For example, if a user enters "a glamorous and fun party dress," the emotion engine recognizes positive and uplifting emotions from keywords such as "glamorous" and "fun." This emotion data is sent to the server and used in the design generation process. Based on this emotion data, the artificial intelligence model adjusts the color scheme, style, and direction of decoration to generate the design.

[0366] The generated design data is stored on a server and sent to the manufacturing facility. The manufacturing facility then produces the clothing, incorporating the emotion-based design concept. Once production is complete, the products are registered on a technology platform and sales begin.

[0367] This technological platform provides online sales functionality that takes into account users' emotional and purchase history. The server analyzes what kinds of emotionally charged products users have previously preferred and can provide personalized product recommendations. For example, a user who previously purchased a product based on the emotion of "happiness" will be offered new products that evoke similar emotions.

[0368] This system aims to improve customer satisfaction by enabling users to quickly obtain products with designs customized based on their emotions, and by smoothly integrating the manufacturing and sales processes.

[0369] The following describes the processing flow.

[0370] Step 1:

[0371] The user accesses the system via a terminal and enters text prompts regarding the design of the clothing. For example, they might enter specific details such as "an active and energetic sports jacket."

[0372] Step 2:

[0373] The device sends the entered prompt to the emotion engine. The emotion engine extracts keywords such as "active" and "energetic" from this prompt to recognize the user's emotional state.

[0374] Step 3:

[0375] The emotion engine sends the recognized emotion data to the server. The server receives this emotion information and stores it as contextual information necessary for design generation.

[0376] Step 4:

[0377] The server sends text prompts to the artificial intelligence model based on the user's emotional data, requesting design generation. The AI ​​model then designs clothing using design parameters that take the emotional data into account.

[0378] Step 5:

[0379] The artificial intelligence model sends the generated design data back to the server. The server receives this design and prepares it as necessary documentation for starting manufacturing.

[0380] Step 6:

[0381] The server sends the design data to the manufacturing facility. The manufacturing facility then produces the garments using an automated manufacturing process based on the instructions.

[0382] Step 7:

[0383] Once manufacturing is complete, the garments are registered on the technical platform by a server. This is where the sales process begins.

[0384] Step 8:

[0385] The server generates personalized product recommendations based on the user's sentiment history and purchase history. This information is used on the online sales platform.

[0386] Step 9:

[0387] Users access the technical platform from their devices and view available products. Based on personalized suggestions, users select products and proceed with the purchase.

[0388] Step 10:

[0389] The server sends shipping instructions to the manufacturing department for confirmed purchases. The user receives purchase confirmation and shipping notifications via their device.

[0390] (Example 2)

[0391] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0392] The present invention aims to provide a system for rapidly designing, manufacturing, and selling personalized clothing based on user emotions. Conventional methods have struggled to accurately reflect user emotions in clothing design optimization, and have lacked consistent management throughout the manufacturing and sales process. There is a need to solve these problems and improve customer satisfaction.

[0393] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0394] In this invention, the server includes means for analyzing emotions using natural language processing, means for optimizing and generating clothing designs based on the analyzed emotions using a generative AI model, and means for analyzing the user's emotional history and purchase history to provide personalized product suggestions. This makes it possible to automatically generate clothing designs that reflect the user's emotional state and to efficiently manufacture and sell them.

[0395] "User" refers to the entity that uses this system to input clothing designs and make purchases.

[0396] A "prompt" is text information entered by the user, expressing their requests and feelings regarding the design of the clothing.

[0397] "Natural language processing" refers to the technology used in emotion engines to analyze emotions and intentions from user-inputted prompts.

[0398] "Sentiment analysis" is the process of evaluating a user's emotional state based on prompts and identifying that emotion.

[0399] A "generative AI model" refers to artificial intelligence technology used to automatically generate clothing designs based on the results of emotion analysis.

[0400] "Clothing" refers to fashion items designed and manufactured as a result of emotional analysis and design generation processes.

[0401] A "manufacturing institution" refers to a facility or company that produces actual clothing based on a generated design.

[0402] A "technical platform" refers to an online environment where finished garments are registered and the sales process takes place.

[0403] "Personalized product recommendations" refer to a process that suggests the most suitable products by taking into account the user's past emotional history and purchase history.

[0404] This invention is a system that optimizes clothing design based on user emotions and integrates the manufacturing and sales processes. This system includes the following main components:

[0405] First, the user enters a prompt using the device. This prompt is a textual expression of the user's desired clothing style and mood. For example, a specific prompt might be something like, "A casual shirt I can relax in on the beach." The device has a user interface implemented for entering and sending prompts.

[0406] Next, the server receives a prompt and performs natural language processing using the emotion engine. The emotion engine analyzes the linguistic data contained in the prompt and identifies the underlying emotion. For example, from words like "beach" and "relax," the system recognizes the emotion of relaxation. This analysis result is used as foundational data for the design generation process.

[0407] Based on the analyzed emotion data, the server invokes a generative AI model to generate clothing designs. The generative AI model automatically adjusts design parameters (such as color, style, and material) associated with a specific emotion to output the optimal design. In this process, open-source AI models such as the GPT series and DALL-E are often used.

[0408] The generated design data is sent from the server to the manufacturing facility. The manufacturing facility produces physical clothing based on this data. Once the product is completed, it is registered on a technical platform. This platform serves as the foundation for online sales and enables product recommendations to users.

[0409] Finally, the sales platform provides personalized product recommendations by analyzing users' past emotions and purchase history through its servers. For example, users who have previously purchased products based on the emotion of "relaxation" will be offered new products that evoke similar emotions.

[0410] This system will not only enable users to efficiently acquire clothing that matches their personal preferences, but will also facilitate smooth coordination throughout the entire process from manufacturing to sales. This is expected to significantly improve the customization of clothing and customer satisfaction.

[0411] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0412] Step 1:

[0413] The user enters a prompt using the terminal.

[0414] The user enters text as a prompt, describing the style and occasion of the clothing they want. This data is sent from the terminal to the server for sentiment recognition. For example, "a casual shirt for the beach" might be entered. The input data is structured as text and sent to the server.

[0415] Step 2:

[0416] The server receives a prompt and uses natural language processing to analyze the emotions.

[0417] Upon receiving the prompt, the server uses natural language processing techniques to analyze the text and identify key emotions. A language model is used to extract words like "beach" and "casual" from the text, identifying the relaxed emotion. The analysis results in a label indicating a relaxed emotion.

[0418] Step 3:

[0419] The server generates designs using an AI model based on emotional data.

[0420] Upon receiving the sentiment analysis results, the server invokes a generative AI model to generate a design. The generative AI model takes sentiment data as input and determines relevant design parameters (color, material, style, etc.). As a result, a design for a casual shirt with a blue base color is generated. The output data is saved as a design specification document.

[0421] Step 4:

[0422] The server generates design data which is then sent to the manufacturing facility.

[0423] The generated design specifications are sent from the server to the manufacturing facility and used as production instructions for the actual garments. The data is typically transferred via API and interpreted by automated processes at the manufacturing facility. The manufacturing facility then produces the garments based on this design.

[0424] Step 5:

[0425] Once the server is completed, the product information is registered on the technical platform, and sales begin.

[0426] Information on completed garments is registered on a technical platform and used for data entry into the sales system. The platform provides an environment for making appropriate product recommendations using the user's past sentiment and purchase history. Registered products can be viewed and purchased online.

[0427] This series of steps allows users to obtain personalized clothing through emotion-based design.

[0428] (Application Example 2)

[0429] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0430] Modern consumers want to quickly and easily acquire clothing that reflects their emotions and individuality. However, traditional clothing purchasing processes present challenges, such as difficulty in reflecting their emotions in designs and the inability to quickly try on and select items in physical stores or online. Furthermore, the lack of efficient integration of personalized suggestions and try-on processes significantly limits the customer experience.

[0431] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0432] This invention includes a server that utilizes artificial intelligence to automatically generate clothing designs based on text prompts entered by a user, an automated manufacturing management system that transmits the generated design information to a manufacturing facility for the production of clothing, an online sales system that makes the finished clothing available for sale on a technical platform, and a visualization system that allows users to virtually try on clothing using augmented reality technology. This enables consumers to try on clothing designed based on their own feelings and preferences using AR technology and purchase it directly.

[0433] "User-input text prompts" refer to words and phrases entered by the user in natural language, which are used to analyze emotions and design intent.

[0434] "Clothing design" refers to clothing design information generated by artificial intelligence based on user input.

[0435] "Methods utilizing artificial intelligence" refers to a general term for systems and technologies used to analyze user text prompts and generate appropriate clothing designs.

[0436] "Generated design information" refers to clothing design data created by artificial intelligence in response to user requests.

[0437] A "manufacturing facility" refers to a facility or organization that produces actual garments based on the design information of the garments that have been generated.

[0438] "Automated manufacturing management means" refers to a system that automatically manages and implements the garment manufacturing process based on generated design information.

[0439] A "technical platform" refers to an online marketplace or system that sells finished clothing and is accessible to users.

[0440] "Online sales methods" refer to technologies, including websites and applications, used to sell clothing over the internet.

[0441] Augmented reality technology is a technique that overlays digital information onto the real world environment, allowing users to virtually try on clothing.

[0442] "Visualization means" refers to technologies and methods that visually present the generated clothing designs so that users can confirm them.

[0443] To realize this invention, the user terminal, server, and manufacturing machine each need to play specific roles. The system begins with the user entering text prompts from the terminal. The prompts entered by the user are collected through an interface provided on the terminal.

[0444] The server receives these prompts and analyzes them using an emotion engine. This process utilizes artificial intelligence technologies such as TensorFlow to identify the user's intent and emotional state. For example, if a user enters "fun and colorful sportswear," the server extracts keywords such as "fun" and "colorful" and analyzes them as emotion data.

[0445] The server automatically generates clothing designs based on the analyzed emotional data. This generated design data is then sent to the user's device using augmented reality (AR) technology, allowing them to virtually try on the clothes. iOS's ARKit is available as the AR technology.

[0446] Meanwhile, the generated design data is sent to the manufacturing facility and reflected in the actual clothing. Users can check the design through AR virtual try-on, and if they are satisfied, they can order it online directly through the technical platform. This allows users to receive products that match their feelings and preferences.

[0447] For example, if a user enters the prompt "a sweater that looks stylish even in winter," the server can use "stylish" and "winter" as keywords to generate designs suitable for the user and allow them to try them on using augmented reality. An example of input to the generation AI model is "a sweater that looks stylish even in winter."

[0448] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0449] Step 1:

[0450] The user enters a text prompt through the interface on the terminal. The entered prompt is natural language text data, such as "fun and colorful sportswear." The terminal sends this prompt as digital data to the server.

[0451] Step 2:

[0452] The server receives text prompts sent by the user. The received prompts are analyzed using a generative AI model such as TensorFlow to extract emotions and keywords. In this process, keywords such as "fun" and "colorful" are extracted as emotion data. The output is the emotion data that forms the basis for design generation.

[0453] Step 3:

[0454] The server generates clothing designs based on emotional data obtained through analysis. AI determines the design elements and outputs that information as 3D design data. The output design information is prepared as data for AR virtual try-on.

[0455] Step 4:

[0456] The server sends the generated design data to the terminal and uses AR technology on the terminal to provide the user with a virtual try-on experience. Specifically, it uses tools such as iOS's ARKit to overlay the clothing design onto the user's visual environment. 3D design data is used as input for the try-on data and is provided to the user as an AR display.

[0457] Step 5:

[0458] Users can view AR virtual try-ons via their devices and rate their satisfaction with the design. If satisfied, they can order the designed product online through the technology platform, and it will be manufactured as a real-world garment. The user's order information is sent to the manufacturing company, and the actual product is produced.

[0459] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0460] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0461] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0462] [Third Embodiment]

[0463] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0464] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0465] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0466] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0467] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0468] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0469] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0470] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0471] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0472] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0473] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0474] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0475] This invention is a system that automatically generates clothing designs based on text input from the user and handles the entire process from manufacturing to sales. This system primarily utilizes terminals, servers, and artificial intelligence.

[0476] First, the user uses a terminal to access the system's input screen. The user enters keywords, such as "modern summer tops," into the terminal's interface. The terminal receives this input and sends it to the server.

[0477] Next, the server sends the received text prompt to an artificial intelligence model. The AI ​​analyzes historical trend data and fashion image data to generate a new clothing design based on this prompt. Once the design is generated, it is returned to the server as digital design data.

[0478] The server receives this data and transmits the design information to the manufacturing facility. The manufacturing facility efficiently produces the garments using automated production management systems. After manufacturing is complete, the products are ready for online sale.

[0479] Information on completed clothing items is registered on a technical platform. The server analyzes customers' past purchase history and preference data to implement optimal promotions. Specifically, it can, for example, notify existing customers of new products or suggest related items.

[0480] Finally, the user (customer) accesses the online platform through their device, selects products, and proceeds with the purchase. Once the purchase is complete, the server proceeds with order confirmation and shipping procedures and notifies the user.

[0481] In this way, the system allows users to quickly obtain designs tailored to their preferences, while the manufacturing and sales processes are efficiently managed through automated control systems.

[0482] The following describes the processing flow.

[0483] Step 1:

[0484] The user opens the terminal interface and enters text prompts about the clothing design. For example, they might enter specific keywords such as "casual autumn coat."

[0485] Step 2:

[0486] The terminal receives the input prompt, packets it into a data format (e.g., JSON format), and prepares to send it to the server.

[0487] Step 3:

[0488] The server receives a prompt from the terminal and sends this text data to the artificial intelligence model. At this point, a design generation based on the prompt is requested.

[0489] Step 4:

[0490] The artificial intelligence model references trend data and fashion image databases, analyzes prompts, and generates new designs. During this process, digital design data is created.

[0491] Step 5:

[0492] The AI ​​model sends the generated design back to the server. The server receives and saves this design data.

[0493] Step 6:

[0494] The server transmits the generated design along with the necessary manufacturing information to the manufacturing facility. This information includes instructions regarding material selection and the manufacturing process.

[0495] Step 7:

[0496] The manufacturing facility starts producing products based on instructions from the server. Here, production is carried out efficiently using automated manufacturing management systems.

[0497] Step 8:

[0498] Once manufacturing is complete, the products are registered on a server-managed technical platform. Here, they are ready for online sales.

[0499] Step 9:

[0500] The server analyzes customer preference information and purchase history to generate promotional information for personalized product recommendations.

[0501] Step 10:

[0502] Users access the online platform via their device, browse and select registered products, and proceed with the purchase. They enter their payment information to confirm the purchase.

[0503] Step 11:

[0504] The server verifies the user's purchase procedure and processes the payment. If successful, it notifies the manufacturing department of the shipping instructions and sends a purchase confirmation notification to the user.

[0505] (Example 1)

[0506] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0507] Traditional clothing design and manufacturing systems have struggled to quickly and accurately reflect users' personal preferences. Furthermore, the process from design to manufacturing and sales is complex and time-consuming, requiring efficient management. Additionally, a lack of personalized product suggestions makes improving consumer satisfaction a challenge.

[0508] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0509] In this invention, the server includes means that utilize an intelligent algorithm to automatically generate clothing designs based on language information input by the user, automated production management means that transmit the generated design information to a production facility to produce the clothing, and online distribution means that make the finished clothing available for sale on an information infrastructure. This enables the rapid design, manufacturing, and sale of clothing that reflects the individual preferences of the user.

[0510] A "user" is an individual or group that uses the system to input, select, and purchase clothing designs.

[0511] "Linguistic information" refers to text that expresses the features and style of clothing desired by the user in natural language.

[0512] An "intelligent algorithm" is a sophisticated computing system that analyzes linguistic information entered by a user and automatically generates unique clothing designs based on that analysis.

[0513] "Conceptual information" refers to detailed digital data about clothing designs generated by intelligent algorithms.

[0514] A "production machine" refers to a factory or facility that automatically manufactures clothing based on conceptual information.

[0515] An "automated production management system" is a system or process designed to manufacture clothing efficiently and unmanned, based on conceptual information.

[0516] An "information infrastructure" is an online platform or network established to manage and sell finished clothing items in the digital space.

[0517] "Online distribution methods" refer to electronic commerce systems used to sell and deliver manufactured clothing to customers via an information infrastructure.

[0518] "Preference information" refers to data that shows a customer's past purchasing trends and preferences.

[0519] "Personalized product recommendations" is a marketing technique that proposes the most suitable products to each customer based on their preferences and purchase history.

[0520] This invention begins with the user accessing the system's user interface using their own device. The user inputs a specific prompt, such as "modern summer tops." This linguistic information is then sent to the server by the device.

[0521] The server utilizes a generative AI model to analyze the received linguistic information. This generative AI model includes an algorithm that references a large amount of historical trend information and image datasets to generate clothing designs that meet user requirements. The design generation process involves analyzing existing styles and trendy colors to provide creative and up-to-date styles.

[0522] The generated design, or conceptual information, is returned to the server as digital data. The server transmits this data to the production facility, and an automated production management system initiates an efficient manufacturing process. Once manufacturing is complete, the product is ready to be sold on the information infrastructure via online distribution channels.

[0523] In this information infrastructure, customer preference information and purchase history are analyzed by servers. This analysis leads to personalized product recommendations, displaying the most suitable products for each customer.

[0524] As a concrete example, consider a scenario where a user enters the prompt "elegant autumn jacket." In this case, the generation AI model generates an elegant jacket design incorporating the latest autumn trends based on that sentence. The user can then view the final product through their device and complete the purchase process.

[0525] This invention enables users to quickly obtain clothing tailored to their preferences and facilitates efficient manufacturing and sales processes.

[0526] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0527] Step 1:

[0528] The user accesses the system interface using a terminal and enters a prompt message. This prompt message is text information containing keywords related to a specific clothing item. Upon receiving this prompt message, the terminal validates the input and prepares to send it to the server.

[0529] Step 2:

[0530] The terminal sends prompt messages from the user to the server. At this time, the input information is converted to an appropriate format and forwarded to the server as a prompt message. The server receives this prompt message and prepares for analysis by the AI ​​model.

[0531] Step 3:

[0532] The server passes the received prompt text to the generating AI model. The generating AI model analyzes the input language information and references past trend information and image data in the database. This generates new design data based on the prompt. The analysis and generation process involves processes such as text analysis, design modeling, and optimization.

[0533] Step 4:

[0534] The generative AI model returns the analyzed design as digital data to the server. This output data contains specific clothing design information and can be used in the manufacturing process. The server reviews this digital design data and prepares it for transfer to the production system.

[0535] Step 5:

[0536] The server transmits the generated design data to the production facility. The production facility manufactures garments based on the received design using automated production management systems. Specific operations include analyzing cutting data, creating sewing plans, and executing them on the production line.

[0537] Step 6:

[0538] Manufactured clothing items are prepared for online sale on an information infrastructure. Servers register finished product data on the online distribution platform, making them ready for sale. At this time, product information is organized, pricing is set, and inventory management is performed.

[0539] Step 7:

[0540] Users access the online platform using their devices, select products, and proceed with the purchase. Based on the user's actions, the server verifies the order data and notifies the customer of order confirmation and preparation for shipment. Specific actions performed here include organizing order information, sending it to the customer notification system, and arranging delivery.

[0541] (Application Example 1)

[0542] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0543] Traditional clothing design, manufacturing, and sales processes have made it difficult to quickly create custom-made products based on user requests, and personalized ordering and real-time design suggestions, particularly on online platforms, have not been adequately supported. This has sometimes led to decreased user satisfaction and problems with excess inventory.

[0544] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0545] In this invention, the server includes display means for displaying product previews on the user's terminal and enabling custom orders, electronic transaction means for executing online payments, and means for analyzing past trend data and optimizing the design. This allows the user to select and order the latest design based on their preferences in real time.

[0546] A "user" refers to a consumer who uses the system to input product designs and place an order.

[0547] A "text prompt" refers to the text or keywords that a user enters to give instructions for product design.

[0548] "Product" refers to a product designed and manufactured based on user input, and primarily includes clothing.

[0549] "Artificial intelligence" refers to computational methods that analyze user prompts and automate design generation.

[0550] "Generated design information" refers to the digital design data of a product generated by artificial intelligence.

[0551] A "manufacturing facility" refers to the equipment or organization that produces goods based on the generated design information.

[0552] "Automated manufacturing management systems" refer to management systems designed to streamline the product manufacturing process and minimize human intervention.

[0553] A "technological network" refers to an online platform that enables the sale and trading of goods.

[0554] "Display means" refers to the technical means for displaying the product design and preview on the user's device.

[0555] "Electronic transaction methods" refer to technical means for processing payments for goods via the internet.

[0556] "Trend data" refers to information that shows past fashion trends and market movements.

[0557] "Personalized product recommendations" refer to customized product information generated based on each customer's preferences and purchase history.

[0558] The system for implementing this invention is centered around a terminal, a server, and artificial intelligence.

[0559] A terminal is a device that allows a user to access an input interface to a system. Smartphones and personal computers are prime examples, where the user enters text prompts related to clothing design. For example, they might enter a prompt such as "modern summer tops."

[0560] The server is responsible for receiving input from the user and sending it to the artificial intelligence. The AI ​​used includes, for example, Stable Diffusion and DeepFashion. These are models for analyzing prompts and generating new clothing designs. This process analyzes past fashion data to create the optimal design based on the user's input. The generated design is returned to the server as digital design data.

[0561] Upon receiving this information, the server transmits the design data to the manufacturing facility, where the product is efficiently manufactured by an automated manufacturing management system. Information about the manufactured product is registered on a technical network.

[0562] The user accesses the system again through their device and views a preview of the designed product. During this process, the user can place a custom order.

[0563] The server also analyzes the user's past preferences and purchase history to notify them of personalized product suggestions. For example, if a user enters the prompt "spring formal dress," the AI ​​will suggest formal dresses that take the latest trends into consideration, and the user can view and order them.

[0564] Through electronic transaction methods, users complete payment online, and then the product is delivered. This allows users to quickly and easily obtain personalized designs.

[0565] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0566] Step 1:

[0567] The user uses a terminal to access the system's input screen and enters text prompts such as "modern summer tops." The entered prompts are the initial data necessary to materialize the user's design image.

[0568] Step 2:

[0569] The terminal sends the entered text prompt to the server. The server receives this and converts the data into the appropriate format for a predefined AI model. This format conversion prepares the data for accurate interpretation by the AI.

[0570] Step 3:

[0571] The server delegates processing to an AI model, which generates designs based on prompts. The AI ​​model uses the input prompts to analyze past trend data and image data to generate new designs. The data analysis performed here includes calculations to extract the optimal design from the trend data.

[0572] Step 4:

[0573] The digital design data generated from the AI ​​model is returned to the server. The server processes this data into an appropriate format for transmission to the manufacturing facility. The processed data is in a form that can be directly used in the manufacturing process.

[0574] Step 5:

[0575] The manufacturing facility receives design information transmitted from the server and efficiently produces the goods using an automated manufacturing management system. The input is digital design data, and the output is the finished physical product.

[0576] Step 6:

[0577] The user accesses the system again via their terminal to view a preview of the generated product. The server sends a digital image of the product to the terminal to assist the user in confirming it. This preview function allows the user to visually understand the product before making a purchase decision.

[0578] Step 7:

[0579] The user purchases goods through a terminal in a made-to-order format. The server processes the payment through an electronic transaction system. The output of this process is confirmation of transaction completion and commencement of shipping procedures.

[0580] Step 8:

[0581] The server analyzes user preferences and purchase history to provide personalized product recommendations. It sends notifications to the user's device, informing them of new designs and related products. This process enhances the user's purchasing experience.

[0582] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0583] This invention is a system that optimizes clothing design using an emotion engine based on user text prompts. Users input text prompts through a terminal and are provided with an interface to recognize the emotions expressed therein. The emotion engine analyzes the emotional state from the words and context chosen by the user and reflects the results in the design process.

[0584] For example, if a user enters "a glamorous and fun party dress," the emotion engine recognizes positive and uplifting emotions from keywords such as "glamorous" and "fun." This emotion data is sent to the server and used in the design generation process. Based on this emotion data, the artificial intelligence model adjusts the color scheme, style, and direction of decoration to generate the design.

[0585] The generated design data is stored on a server and sent to the manufacturing facility. The manufacturing facility then produces the clothing, incorporating the emotion-based design concept. Once production is complete, the products are registered on a technology platform and sales begin.

[0586] This technological platform provides online sales functionality that takes into account users' emotional and purchase history. The server analyzes what kinds of emotionally charged products users have previously preferred and can provide personalized product recommendations. For example, a user who previously purchased a product based on the emotion of "happiness" will be offered new products that evoke similar emotions.

[0587] This system aims to improve customer satisfaction by enabling users to quickly obtain products with designs customized based on their emotions, and by smoothly integrating the manufacturing and sales processes.

[0588] The following describes the processing flow.

[0589] Step 1:

[0590] The user accesses the system via a terminal and enters text prompts regarding the design of the clothing. For example, they might enter specific details such as "an active and energetic sports jacket."

[0591] Step 2:

[0592] The device sends the entered prompt to the emotion engine. The emotion engine extracts keywords such as "active" and "energetic" from this prompt to recognize the user's emotional state.

[0593] Step 3:

[0594] The emotion engine sends the recognized emotion data to the server. The server receives this emotion information and stores it as contextual information necessary for design generation.

[0595] Step 4:

[0596] The server sends text prompts to the artificial intelligence model based on the user's emotional data, requesting design generation. The AI ​​model then designs clothing using design parameters that take the emotional data into account.

[0597] Step 5:

[0598] The artificial intelligence model sends the generated design data back to the server. The server receives this design and prepares it as necessary documentation for starting manufacturing.

[0599] Step 6:

[0600] The server sends the design data to the manufacturing facility. The manufacturing facility then produces the garments using an automated manufacturing process based on the instructions.

[0601] Step 7:

[0602] Once manufacturing is complete, the garments are registered on the technical platform by a server. This is where the sales process begins.

[0603] Step 8:

[0604] The server generates personalized product recommendations based on the user's sentiment history and purchase history. This information is used on the online sales platform.

[0605] Step 9:

[0606] Users access the technical platform from their devices and view available products. Based on personalized suggestions, users select products and proceed with the purchase.

[0607] Step 10:

[0608] The server sends shipping instructions to the manufacturing department for confirmed purchases. The user receives purchase confirmation and shipping notifications via their device.

[0609] (Example 2)

[0610] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0611] The present invention aims to provide a system for rapidly designing, manufacturing, and selling personalized clothing based on user emotions. Conventional methods have struggled to accurately reflect user emotions in clothing design optimization, and have lacked consistent management throughout the manufacturing and sales process. There is a need to solve these problems and improve customer satisfaction.

[0612] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0613] In this invention, the server includes means for analyzing emotions using natural language processing, means for optimizing and generating clothing designs based on the analyzed emotions using a generative AI model, and means for analyzing the user's emotional history and purchase history to provide personalized product suggestions. This makes it possible to automatically generate clothing designs that reflect the user's emotional state and to efficiently manufacture and sell them.

[0614] "User" refers to the entity that uses this system to input clothing designs and make purchases.

[0615] A "prompt" is text information entered by the user, expressing their requests and feelings regarding the design of the clothing.

[0616] "Natural language processing" refers to the technology used in emotion engines to analyze emotions and intentions from user-inputted prompts.

[0617] "Sentiment analysis" is the process of evaluating a user's emotional state based on prompts and identifying that emotion.

[0618] A "generative AI model" refers to artificial intelligence technology used to automatically generate clothing designs based on the results of emotion analysis.

[0619] "Clothing" refers to fashion items designed and manufactured as a result of emotional analysis and design generation processes.

[0620] A "manufacturing institution" refers to a facility or company that produces actual clothing based on a generated design.

[0621] A "technical platform" refers to an online environment where finished garments are registered and the sales process takes place.

[0622] "Personalized product recommendations" refer to a process that suggests the most suitable products by taking into account the user's past emotional history and purchase history.

[0623] This invention is a system that optimizes clothing design based on user emotions and integrates the manufacturing and sales processes. This system includes the following main components:

[0624] First, the user enters a prompt using the device. This prompt is a textual expression of the user's desired clothing style and mood. For example, a specific prompt might be something like, "A casual shirt I can relax in on the beach." The device has a user interface implemented for entering and sending prompts.

[0625] Next, the server receives a prompt and performs natural language processing using the emotion engine. The emotion engine analyzes the linguistic data contained in the prompt and identifies the underlying emotion. For example, from words like "beach" and "relax," the system recognizes the emotion of relaxation. This analysis result is used as foundational data for the design generation process.

[0626] Based on the analyzed emotion data, the server invokes a generative AI model to generate clothing designs. The generative AI model automatically adjusts design parameters (such as color, style, and material) associated with a specific emotion to output the optimal design. In this process, open-source AI models such as the GPT series and DALL-E are often used.

[0627] The generated design data is sent from the server to the manufacturing facility. The manufacturing facility produces physical clothing based on this data. Once the product is completed, it is registered on a technical platform. This platform serves as the foundation for online sales and enables product recommendations to users.

[0628] Finally, the sales platform provides personalized product recommendations by analyzing users' past emotions and purchase history through its servers. For example, users who have previously purchased products based on the emotion of "relaxation" will be offered new products that evoke similar emotions.

[0629] This system will not only enable users to efficiently acquire clothing that matches their personal preferences, but will also facilitate smooth coordination throughout the entire process from manufacturing to sales. This is expected to significantly improve the customization of clothing and customer satisfaction.

[0630] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0631] Step 1:

[0632] The user enters a prompt using the terminal.

[0633] The user enters text as a prompt, describing the style and occasion of the clothing they want. This data is sent from the terminal to the server for sentiment recognition. For example, "a casual shirt for the beach" might be entered. The input data is structured as text and sent to the server.

[0634] Step 2:

[0635] The server receives a prompt and uses natural language processing to analyze the emotions.

[0636] Upon receiving the prompt, the server uses natural language processing techniques to analyze the text and identify key emotions. A language model is used to extract words like "beach" and "casual" from the text, identifying the relaxed emotion. The analysis results in a label indicating a relaxed emotion.

[0637] Step 3:

[0638] The server generates designs using an AI model based on emotional data.

[0639] Upon receiving the sentiment analysis results, the server invokes a generative AI model to generate a design. The generative AI model takes sentiment data as input and determines relevant design parameters (color, material, style, etc.). As a result, a design for a casual shirt with a blue base color is generated. The output data is saved as a design specification document.

[0640] Step 4:

[0641] The server generates design data which is then sent to the manufacturing facility.

[0642] The generated design specifications are sent from the server to the manufacturing facility and used as production instructions for the actual garments. The data is typically transferred via API and interpreted by automated processes at the manufacturing facility. The manufacturing facility then produces the garments based on this design.

[0643] Step 5:

[0644] Once the server is completed, the product information is registered on the technical platform, and sales begin.

[0645] Information on completed garments is registered on a technical platform and used for data entry into the sales system. The platform provides an environment for making appropriate product recommendations using the user's past sentiment and purchase history. Registered products can be viewed and purchased online.

[0646] This series of steps allows users to obtain personalized clothing through emotion-based design.

[0647] (Application Example 2)

[0648] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0649] Modern consumers want to quickly and easily acquire clothing that reflects their emotions and individuality. However, traditional clothing purchasing processes present challenges, such as difficulty in reflecting their emotions in designs and the inability to quickly try on and select items in physical stores or online. Furthermore, the lack of efficient integration of personalized suggestions and try-on processes significantly limits the customer experience.

[0650] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0651] This invention includes a server that utilizes artificial intelligence to automatically generate clothing designs based on text prompts entered by a user, an automated manufacturing management system that transmits the generated design information to a manufacturing facility for the production of clothing, an online sales system that makes the finished clothing available for sale on a technical platform, and a visualization system that allows users to virtually try on clothing using augmented reality technology. This enables consumers to try on clothing designed based on their own feelings and preferences using AR technology and purchase it directly.

[0652] "User-input text prompts" refer to words and phrases entered by the user in natural language, which are used to analyze emotions and design intent.

[0653] "Clothing design" refers to clothing design information generated by artificial intelligence based on user input.

[0654] "Methods utilizing artificial intelligence" refers to a general term for systems and technologies used to analyze user text prompts and generate appropriate clothing designs.

[0655] "Generated design information" refers to clothing design data created by artificial intelligence in response to user requests.

[0656] A "manufacturing facility" refers to a facility or organization that produces actual garments based on the design information of the garments that have been generated.

[0657] "Automated manufacturing management means" refers to a system that automatically manages and implements the garment manufacturing process based on generated design information.

[0658] A "technical platform" refers to an online marketplace or system that sells finished clothing and is accessible to users.

[0659] "Online sales methods" refer to technologies, including websites and applications, used to sell clothing over the internet.

[0660] Augmented reality technology is a technique that overlays digital information onto the real world environment, allowing users to virtually try on clothing.

[0661] "Visualization means" refers to technologies and methods that visually present the generated clothing designs so that users can confirm them.

[0662] To realize this invention, the user terminal, server, and manufacturing machine each need to play specific roles. The system begins with the user entering text prompts from the terminal. The prompts entered by the user are collected through an interface provided on the terminal.

[0663] The server receives these prompts and analyzes them using an emotion engine. This process utilizes artificial intelligence technologies such as TensorFlow to identify the user's intent and emotional state. For example, if a user enters "fun and colorful sportswear," the server extracts keywords such as "fun" and "colorful" and analyzes them as emotion data.

[0664] The server automatically generates clothing designs based on the analyzed emotional data. This generated design data is then sent to the user's device using augmented reality (AR) technology, allowing them to virtually try on the clothes. iOS's ARKit is available as the AR technology.

[0665] Meanwhile, the generated design data is sent to the manufacturing facility and reflected in the actual clothing. Users can check the design through AR virtual try-on, and if they are satisfied, they can order it online directly through the technical platform. This allows users to receive products that match their feelings and preferences.

[0666] For example, if a user enters the prompt "a sweater that looks stylish even in winter," the server can use "stylish" and "winter" as keywords to generate designs suitable for the user and allow them to try them on using augmented reality. An example of input to the generation AI model is "a sweater that looks stylish even in winter."

[0667] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0668] Step 1:

[0669] The user enters a text prompt through the interface on the terminal. The entered prompt is natural language text data, such as "fun and colorful sportswear." The terminal sends this prompt as digital data to the server.

[0670] Step 2:

[0671] The server receives text prompts sent by the user. The received prompts are analyzed using a generative AI model such as TensorFlow to extract emotions and keywords. In this process, keywords such as "fun" and "colorful" are extracted as emotion data. The output is the emotion data that forms the basis for design generation.

[0672] Step 3:

[0673] The server generates clothing designs based on emotional data obtained through analysis. AI determines the design elements and outputs that information as 3D design data. The output design information is prepared as data for AR virtual try-on.

[0674] Step 4:

[0675] The server sends the generated design data to the terminal and uses AR technology on the terminal to provide the user with a virtual try-on experience. Specifically, it uses tools such as iOS's ARKit to overlay the clothing design onto the user's visual environment. 3D design data is used as input for the try-on data and is provided to the user as an AR display.

[0676] Step 5:

[0677] Users can view AR virtual try-ons via their devices and rate their satisfaction with the design. If satisfied, they can order the designed product online through the technology platform, and it will be manufactured as a real-world garment. The user's order information is sent to the manufacturing company, and the actual product is produced.

[0678] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0679] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0680] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0681] [Fourth Embodiment]

[0682] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0683] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0684] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0685] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0686] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0687] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0688] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0689] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0690] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0691] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0692] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0693] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0694] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0695] This invention is a system that automatically generates clothing designs based on text input from the user and handles the entire process from manufacturing to sales. This system primarily utilizes terminals, servers, and artificial intelligence.

[0696] First, the user uses a terminal to access the system's input screen. The user enters keywords, such as "modern summer tops," into the terminal's interface. The terminal receives this input and sends it to the server.

[0697] Next, the server sends the received text prompt to an artificial intelligence model. The AI ​​analyzes historical trend data and fashion image data to generate a new clothing design based on this prompt. Once the design is generated, it is returned to the server as digital design data.

[0698] The server receives this data and transmits the design information to the manufacturing facility. The manufacturing facility efficiently produces the garments using automated production management systems. After manufacturing is complete, the products are ready for online sale.

[0699] Information on completed clothing items is registered on a technical platform. The server analyzes customers' past purchase history and preference data to implement optimal promotions. Specifically, it can, for example, notify existing customers of new products or suggest related items.

[0700] Finally, the user (customer) accesses the online platform through their device, selects products, and proceeds with the purchase. Once the purchase is complete, the server proceeds with order confirmation and shipping procedures and notifies the user.

[0701] In this way, the system allows users to quickly obtain designs tailored to their preferences, while the manufacturing and sales processes are efficiently managed through automated control systems.

[0702] The following describes the processing flow.

[0703] Step 1:

[0704] The user opens the terminal interface and enters text prompts about the clothing design. For example, they might enter specific keywords such as "casual autumn coat."

[0705] Step 2:

[0706] The terminal receives the input prompt, packets it into a data format (e.g., JSON format), and prepares to send it to the server.

[0707] Step 3:

[0708] The server receives a prompt from the terminal and sends this text data to the artificial intelligence model. At this point, a design generation based on the prompt is requested.

[0709] Step 4:

[0710] The artificial intelligence model references trend data and fashion image databases, analyzes prompts, and generates new designs. During this process, digital design data is created.

[0711] Step 5:

[0712] The AI ​​model sends the generated design back to the server. The server receives and saves this design data.

[0713] Step 6:

[0714] The server transmits the generated design along with the necessary manufacturing information to the manufacturing facility. This information includes instructions regarding material selection and the manufacturing process.

[0715] Step 7:

[0716] The manufacturing facility starts producing products based on instructions from the server. Here, production is carried out efficiently using automated manufacturing management systems.

[0717] Step 8:

[0718] Once manufacturing is complete, the products are registered on a server-managed technical platform. Here, they are ready for online sales.

[0719] Step 9:

[0720] The server analyzes customer preference information and purchase history to generate promotional information for personalized product recommendations.

[0721] Step 10:

[0722] Users access the online platform via their device, browse and select registered products, and proceed with the purchase. They enter their payment information to confirm the purchase.

[0723] Step 11:

[0724] The server verifies the user's purchase procedure and processes the payment. If successful, it notifies the manufacturing department of the shipping instructions and sends a purchase confirmation notification to the user.

[0725] (Example 1)

[0726] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0727] Traditional clothing design and manufacturing systems have struggled to quickly and accurately reflect users' personal preferences. Furthermore, the process from design to manufacturing and sales is complex and time-consuming, requiring efficient management. Additionally, a lack of personalized product suggestions makes improving consumer satisfaction a challenge.

[0728] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0729] In this invention, the server includes means that utilize an intelligent algorithm to automatically generate clothing designs based on language information input by the user, automated production management means that transmit the generated design information to a production facility to produce the clothing, and online distribution means that make the finished clothing available for sale on an information infrastructure. This enables the rapid design, manufacturing, and sale of clothing that reflects the individual preferences of the user.

[0730] A "user" is an individual or group that uses the system to input, select, and purchase clothing designs.

[0731] "Linguistic information" refers to text that expresses the features and style of clothing desired by the user in natural language.

[0732] An "intelligent algorithm" is a sophisticated computing system that analyzes linguistic information entered by a user and automatically generates unique clothing designs based on that analysis.

[0733] "Conceptual information" refers to detailed digital data about clothing designs generated by intelligent algorithms.

[0734] A "production machine" refers to a factory or facility that automatically manufactures clothing based on conceptual information.

[0735] An "automated production management system" is a system or process designed to manufacture clothing efficiently and unmanned, based on conceptual information.

[0736] An "information infrastructure" is an online platform or network established to manage and sell finished clothing items in the digital space.

[0737] "Online distribution methods" refer to electronic commerce systems used to sell and deliver manufactured clothing to customers via an information infrastructure.

[0738] "Preference information" refers to data that shows a customer's past purchasing trends and preferences.

[0739] "Personalized product recommendations" is a marketing technique that proposes the most suitable products to each customer based on their preferences and purchase history.

[0740] This invention begins with the user accessing the system's user interface using their own device. The user inputs a specific prompt, such as "modern summer tops." This linguistic information is then sent to the server by the device.

[0741] The server utilizes a generative AI model to analyze the received linguistic information. This generative AI model includes an algorithm that references a large amount of historical trend information and image datasets to generate clothing designs that meet user requirements. The design generation process involves analyzing existing styles and trendy colors to provide creative and up-to-date styles.

[0742] The generated design, or conceptual information, is returned to the server as digital data. The server transmits this data to the production facility, and an automated production management system initiates an efficient manufacturing process. Once manufacturing is complete, the product is ready to be sold on the information infrastructure via online distribution channels.

[0743] In this information infrastructure, customer preference information and purchase history are analyzed by servers. This analysis leads to personalized product recommendations, displaying the most suitable products for each customer.

[0744] As a concrete example, consider a scenario where a user enters the prompt "elegant autumn jacket." In this case, the generation AI model generates an elegant jacket design incorporating the latest autumn trends based on that sentence. The user can then view the final product through their device and complete the purchase process.

[0745] This invention enables users to quickly obtain clothing tailored to their preferences and facilitates efficient manufacturing and sales processes.

[0746] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0747] Step 1:

[0748] The user accesses the system interface using a terminal and enters a prompt message. This prompt message is text information containing keywords related to a specific clothing item. Upon receiving this prompt message, the terminal validates the input and prepares to send it to the server.

[0749] Step 2:

[0750] The terminal sends prompt messages from the user to the server. At this time, the input information is converted to an appropriate format and forwarded to the server as a prompt message. The server receives this prompt message and prepares for analysis by the AI ​​model.

[0751] Step 3:

[0752] The server passes the received prompt text to the generating AI model. The generating AI model analyzes the input language information and references past trend information and image data in the database. This generates new design data based on the prompt. The analysis and generation process involves processes such as text analysis, design modeling, and optimization.

[0753] Step 4:

[0754] The generative AI model returns the analyzed design as digital data to the server. This output data contains specific clothing design information and can be used in the manufacturing process. The server reviews this digital design data and prepares it for transfer to the production system.

[0755] Step 5:

[0756] The server transmits the generated design data to the production facility. The production facility manufactures garments based on the received design using automated production management systems. Specific operations include analyzing cutting data, creating sewing plans, and executing them on the production line.

[0757] Step 6:

[0758] Manufactured clothing items are prepared for online sale on an information infrastructure. Servers register finished product data on the online distribution platform, making them ready for sale. At this time, product information is organized, pricing is set, and inventory management is performed.

[0759] Step 7:

[0760] Users access the online platform using their devices, select products, and proceed with the purchase. Based on the user's actions, the server verifies the order data and notifies the customer of order confirmation and preparation for shipment. Specific actions performed here include organizing order information, sending it to the customer notification system, and arranging delivery.

[0761] (Application Example 1)

[0762] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0763] Traditional clothing design, manufacturing, and sales processes have made it difficult to quickly create custom-made products based on user requests, and personalized ordering and real-time design suggestions, particularly on online platforms, have not been adequately supported. This has sometimes led to decreased user satisfaction and problems with excess inventory.

[0764] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0765] In this invention, the server includes display means for displaying product previews on the user's terminal and enabling custom orders, electronic transaction means for executing online payments, and means for analyzing past trend data and optimizing the design. This allows the user to select and order the latest design based on their preferences in real time.

[0766] A "user" refers to a consumer who uses the system to input product designs and place an order.

[0767] A "text prompt" refers to the text or keywords that a user enters to give instructions for product design.

[0768] "Product" refers to a product designed and manufactured based on user input, and primarily includes clothing.

[0769] "Artificial intelligence" refers to computational methods that analyze user prompts and automate design generation.

[0770] "Generated design information" refers to the digital design data of a product generated by artificial intelligence.

[0771] A "manufacturing facility" refers to the equipment or organization that produces goods based on the generated design information.

[0772] "Automated manufacturing management systems" refer to management systems designed to streamline the product manufacturing process and minimize human intervention.

[0773] A "technological network" refers to an online platform that enables the sale and trading of goods.

[0774] "Display means" refers to the technical means for displaying the product design and preview on the user's device.

[0775] "Electronic transaction methods" refer to technical means for processing payments for goods via the internet.

[0776] "Trend data" refers to information that shows past fashion trends and market movements.

[0777] "Personalized product recommendations" refer to customized product information generated based on each customer's preferences and purchase history.

[0778] The system for implementing this invention is centered around a terminal, a server, and artificial intelligence.

[0779] A terminal is a device that allows a user to access an input interface to a system. Smartphones and personal computers are prime examples, where the user enters text prompts related to clothing design. For example, they might enter a prompt such as "modern summer tops."

[0780] The server is responsible for receiving input from the user and sending it to the artificial intelligence. The AI ​​used includes, for example, Stable Diffusion and DeepFashion. These are models for analyzing prompts and generating new clothing designs. This process analyzes past fashion data to create the optimal design based on the user's input. The generated design is returned to the server as digital design data.

[0781] Upon receiving this information, the server transmits the design data to the manufacturing facility, where the product is efficiently manufactured by an automated manufacturing management system. Information about the manufactured product is registered on a technical network.

[0782] The user accesses the system again through their device and views a preview of the designed product. During this process, the user can place a custom order.

[0783] The server also analyzes the user's past preferences and purchase history to notify them of personalized product suggestions. For example, if a user enters the prompt "spring formal dress," the AI ​​will suggest formal dresses that take the latest trends into consideration, and the user can view and order them.

[0784] Through electronic transaction methods, users complete payment online, and then the product is delivered. This allows users to quickly and easily obtain personalized designs.

[0785] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0786] Step 1:

[0787] The user uses a terminal to access the system's input screen and enters text prompts such as "modern summer tops." The entered prompts are the initial data necessary to materialize the user's design image.

[0788] Step 2:

[0789] The terminal sends the entered text prompt to the server. The server receives this and converts the data into the appropriate format for a predefined AI model. This format conversion prepares the data for accurate interpretation by the AI.

[0790] Step 3:

[0791] The server delegates processing to an AI model, which generates designs based on prompts. The AI ​​model uses the input prompts to analyze past trend data and image data to generate new designs. The data analysis performed here includes calculations to extract the optimal design from the trend data.

[0792] Step 4:

[0793] The digital design data generated from the AI ​​model is returned to the server. The server processes this data into an appropriate format for transmission to the manufacturing facility. The processed data is in a form that can be directly used in the manufacturing process.

[0794] Step 5:

[0795] The manufacturing facility receives design information transmitted from the server and efficiently produces the goods using an automated manufacturing management system. The input is digital design data, and the output is the finished physical product.

[0796] Step 6:

[0797] The user accesses the system again via their terminal to view a preview of the generated product. The server sends a digital image of the product to the terminal to assist the user in confirming it. This preview function allows the user to visually understand the product before making a purchase decision.

[0798] Step 7:

[0799] The user purchases goods through a terminal in a made-to-order format. The server processes the payment through an electronic transaction system. The output of this process is confirmation of transaction completion and commencement of shipping procedures.

[0800] Step 8:

[0801] The server analyzes user preferences and purchase history to provide personalized product recommendations. It sends notifications to the user's device, informing them of new designs and related products. This process enhances the user's purchasing experience.

[0802] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0803] This invention is a system that optimizes clothing design using an emotion engine based on user text prompts. Users input text prompts through a terminal and are provided with an interface to recognize the emotions expressed therein. The emotion engine analyzes the emotional state from the words and context chosen by the user and reflects the results in the design process.

[0804] For example, if a user enters "a glamorous and fun party dress," the emotion engine recognizes positive and uplifting emotions from keywords such as "glamorous" and "fun." This emotion data is sent to the server and used in the design generation process. Based on this emotion data, the artificial intelligence model adjusts the color scheme, style, and direction of decoration to generate the design.

[0805] The generated design data is stored on a server and sent to the manufacturing facility. The manufacturing facility then produces the clothing, incorporating the emotion-based design concept. Once production is complete, the products are registered on a technology platform and sales begin.

[0806] This technological platform provides online sales functionality that takes into account users' emotional and purchase history. The server analyzes what kinds of emotionally charged products users have previously preferred and can provide personalized product recommendations. For example, a user who previously purchased a product based on the emotion of "happiness" will be offered new products that evoke similar emotions.

[0807] This system aims to improve customer satisfaction by enabling users to quickly obtain products with designs customized based on their emotions, and by smoothly integrating the manufacturing and sales processes.

[0808] The following describes the processing flow.

[0809] Step 1:

[0810] The user accesses the system via a terminal and enters text prompts regarding the design of the clothing. For example, they might enter specific details such as "an active and energetic sports jacket."

[0811] Step 2:

[0812] The device sends the entered prompt to the emotion engine. The emotion engine extracts keywords such as "active" and "energetic" from this prompt to recognize the user's emotional state.

[0813] Step 3:

[0814] The emotion engine sends the recognized emotion data to the server. The server receives this emotion information and stores it as contextual information necessary for design generation.

[0815] Step 4:

[0816] The server sends text prompts to the artificial intelligence model based on the user's emotional data, requesting design generation. The AI ​​model then designs clothing using design parameters that take the emotional data into account.

[0817] Step 5:

[0818] The artificial intelligence model sends the generated design data back to the server. The server receives this design and prepares it as necessary documentation for starting manufacturing.

[0819] Step 6:

[0820] The server sends the design data to the manufacturing facility. The manufacturing facility then produces the garments using an automated manufacturing process based on the instructions.

[0821] Step 7:

[0822] Once manufacturing is complete, the garments are registered on the technical platform by a server. This is where the sales process begins.

[0823] Step 8:

[0824] The server generates personalized product recommendations based on the user's sentiment history and purchase history. This information is used on the online sales platform.

[0825] Step 9:

[0826] Users access the technical platform from their devices and view available products. Based on personalized suggestions, users select products and proceed with the purchase.

[0827] Step 10:

[0828] The server sends shipping instructions to the manufacturing department for confirmed purchases. The user receives purchase confirmation and shipping notifications via their device.

[0829] (Example 2)

[0830] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0831] The present invention aims to provide a system for rapidly designing, manufacturing, and selling personalized clothing based on user emotions. Conventional methods have struggled to accurately reflect user emotions in clothing design optimization, and have lacked consistent management throughout the manufacturing and sales process. There is a need to solve these problems and improve customer satisfaction.

[0832] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0833] In this invention, the server includes means for analyzing emotions using natural language processing, means for optimizing and generating clothing designs based on the analyzed emotions using a generative AI model, and means for analyzing the user's emotional history and purchase history to provide personalized product suggestions. This makes it possible to automatically generate clothing designs that reflect the user's emotional state and to efficiently manufacture and sell them.

[0834] "User" refers to the entity that uses this system to input clothing designs and make purchases.

[0835] A "prompt" is text information entered by the user, expressing their requests and feelings regarding the design of the clothing.

[0836] "Natural language processing" refers to the technology used in emotion engines to analyze emotions and intentions from user-inputted prompts.

[0837] "Sentiment analysis" is the process of evaluating a user's emotional state based on prompts and identifying that emotion.

[0838] A "generative AI model" refers to artificial intelligence technology used to automatically generate clothing designs based on the results of emotion analysis.

[0839] "Clothing" refers to fashion items designed and manufactured as a result of emotional analysis and design generation processes.

[0840] A "manufacturing institution" refers to a facility or company that produces actual clothing based on a generated design.

[0841] A "technical platform" refers to an online environment where finished garments are registered and the sales process takes place.

[0842] "Personalized product recommendations" refer to a process that suggests the most suitable products by taking into account the user's past emotional history and purchase history.

[0843] This invention is a system that optimizes clothing design based on user emotions and integrates the manufacturing and sales processes. This system includes the following main components:

[0844] First, the user enters a prompt using the device. This prompt is a textual expression of the user's desired clothing style and mood. For example, a specific prompt might be something like, "A casual shirt I can relax in on the beach." The device has a user interface implemented for entering and sending prompts.

[0845] Next, the server receives a prompt and performs natural language processing using the emotion engine. The emotion engine analyzes the linguistic data contained in the prompt and identifies the underlying emotion. For example, from words like "beach" and "relax," the system recognizes the emotion of relaxation. This analysis result is used as foundational data for the design generation process.

[0846] Based on the analyzed emotion data, the server invokes a generative AI model to generate clothing designs. The generative AI model automatically adjusts design parameters (such as color, style, and material) associated with a specific emotion to output the optimal design. In this process, open-source AI models such as the GPT series and DALL-E are often used.

[0847] The generated design data is sent from the server to the manufacturing facility. The manufacturing facility produces physical clothing based on this data. Once the product is completed, it is registered on a technical platform. This platform serves as the foundation for online sales and enables product recommendations to users.

[0848] Finally, the sales platform provides personalized product recommendations by analyzing users' past emotions and purchase history through its servers. For example, users who have previously purchased products based on the emotion of "relaxation" will be offered new products that evoke similar emotions.

[0849] This system will not only enable users to efficiently acquire clothing that matches their personal preferences, but will also facilitate smooth coordination throughout the entire process from manufacturing to sales. This is expected to significantly improve the customization of clothing and customer satisfaction.

[0850] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0851] Step 1:

[0852] The user enters a prompt using the terminal.

[0853] The user enters text as a prompt, describing the style and occasion of the clothing they want. This data is sent from the terminal to the server for sentiment recognition. For example, "a casual shirt for the beach" might be entered. The input data is structured as text and sent to the server.

[0854] Step 2:

[0855] The server receives a prompt and uses natural language processing to analyze the emotions.

[0856] Upon receiving the prompt, the server uses natural language processing techniques to analyze the text and identify key emotions. A language model is used to extract words like "beach" and "casual" from the text, identifying the relaxed emotion. The analysis results in a label indicating a relaxed emotion.

[0857] Step 3:

[0858] The server generates designs using an AI model based on emotional data.

[0859] Upon receiving the sentiment analysis results, the server invokes a generative AI model to generate a design. The generative AI model takes sentiment data as input and determines relevant design parameters (color, material, style, etc.). As a result, a design for a casual shirt with a blue base color is generated. The output data is saved as a design specification document.

[0860] Step 4:

[0861] The server generates design data which is then sent to the manufacturing facility.

[0862] The generated design specifications are sent from the server to the manufacturing facility and used as production instructions for the actual garments. The data is typically transferred via API and interpreted by automated processes at the manufacturing facility. The manufacturing facility then produces the garments based on this design.

[0863] Step 5:

[0864] Once the server is completed, the product information is registered on the technical platform, and sales begin.

[0865] Information on completed garments is registered on a technical platform and used for data entry into the sales system. The platform provides an environment for making appropriate product recommendations using the user's past sentiment and purchase history. Registered products can be viewed and purchased online.

[0866] This series of steps allows users to obtain personalized clothing through emotion-based design.

[0867] (Application Example 2)

[0868] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0869] Modern consumers want to quickly and easily acquire clothing that reflects their emotions and individuality. However, traditional clothing purchasing processes present challenges, such as difficulty in reflecting their emotions in designs and the inability to quickly try on and select items in physical stores or online. Furthermore, the lack of efficient integration of personalized suggestions and try-on processes significantly limits the customer experience.

[0870] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0871] This invention includes a server that utilizes artificial intelligence to automatically generate clothing designs based on text prompts entered by a user, an automated manufacturing management system that transmits the generated design information to a manufacturing facility for the production of clothing, an online sales system that makes the finished clothing available for sale on a technical platform, and a visualization system that allows users to virtually try on clothing using augmented reality technology. This enables consumers to try on clothing designed based on their own feelings and preferences using AR technology and purchase it directly.

[0872] "User-input text prompts" refer to words and phrases entered by the user in natural language, which are used to analyze emotions and design intent.

[0873] "Clothing design" refers to clothing design information generated by artificial intelligence based on user input.

[0874] "Methods utilizing artificial intelligence" refers to a general term for systems and technologies used to analyze user text prompts and generate appropriate clothing designs.

[0875] "Generated design information" refers to clothing design data created by artificial intelligence in response to user requests.

[0876] A "manufacturing facility" refers to a facility or organization that produces actual garments based on the design information of the garments that have been generated.

[0877] "Automated manufacturing management means" refers to a system that automatically manages and implements the garment manufacturing process based on generated design information.

[0878] A "technical platform" refers to an online marketplace or system that sells finished clothing and is accessible to users.

[0879] "Online sales methods" refer to technologies, including websites and applications, used to sell clothing over the internet.

[0880] Augmented reality technology is a technique that overlays digital information onto the real world environment, allowing users to virtually try on clothing.

[0881] "Visualization means" refers to technologies and methods that visually present the generated clothing designs so that users can confirm them.

[0882] To realize this invention, the user terminal, server, and manufacturing machine each need to play specific roles. The system begins with the user entering text prompts from the terminal. The prompts entered by the user are collected through an interface provided on the terminal.

[0883] The server receives these prompts and analyzes them using an emotion engine. This process utilizes artificial intelligence technologies such as TensorFlow to identify the user's intent and emotional state. For example, if a user enters "fun and colorful sportswear," the server extracts keywords such as "fun" and "colorful" and analyzes them as emotion data.

[0884] The server automatically generates clothing designs based on the analyzed emotional data. This generated design data is then sent to the user's device using augmented reality (AR) technology, allowing them to virtually try on the clothes. iOS's ARKit is available as the AR technology.

[0885] Meanwhile, the generated design data is sent to the manufacturing facility and reflected in the actual clothing. Users can check the design through AR virtual try-on, and if they are satisfied, they can order it online directly through the technical platform. This allows users to receive products that match their feelings and preferences.

[0886] For example, if a user enters the prompt "a sweater that looks stylish even in winter," the server can use "stylish" and "winter" as keywords to generate designs suitable for the user and allow them to try them on using augmented reality. An example of input to the generation AI model is "a sweater that looks stylish even in winter."

[0887] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0888] Step 1:

[0889] The user enters a text prompt through the interface on the terminal. The entered prompt is natural language text data, such as "fun and colorful sportswear." The terminal sends this prompt as digital data to the server.

[0890] Step 2:

[0891] The server receives text prompts sent by the user. The received prompts are analyzed using a generative AI model such as TensorFlow to extract emotions and keywords. In this process, keywords such as "fun" and "colorful" are extracted as emotion data. The output is the emotion data that forms the basis for design generation.

[0892] Step 3:

[0893] The server generates clothing designs based on emotional data obtained through analysis. AI determines the design elements and outputs that information as 3D design data. The output design information is prepared as data for AR virtual try-on.

[0894] Step 4:

[0895] The server sends the generated design data to the terminal and uses AR technology on the terminal to provide the user with a virtual try-on experience. Specifically, it uses tools such as iOS's ARKit to overlay the clothing design onto the user's visual environment. 3D design data is used as input for the try-on data and is provided to the user as an AR display.

[0896] Step 5:

[0897] Users can view AR virtual try-ons via their devices and rate their satisfaction with the design. If satisfied, they can order the designed product online through the technology platform, and it will be manufactured as a real-world garment. The user's order information is sent to the manufacturing company, and the actual product is produced.

[0898] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0899] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0900] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0901] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0902] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0903] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0904] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0905] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0906] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0907] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0908] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0909] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0910] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0911] 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.

[0912] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0913] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0914] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0915] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0916] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0917] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0918] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0919] The following is further disclosed regarding the embodiments described above.

[0920] (Claim 1)

[0921] An artificial intelligence-powered method that automatically generates clothing designs based on text prompts entered by the user,

[0922] An automated manufacturing control system that transmits the generated design information to the manufacturing facility and manufactures clothing,

[0923] An online sales method that makes finished clothing available for sale on a technical platform,

[0924] A system that includes this.

[0925] (Claim 2)

[0926] The system according to claim 1, comprising means for analyzing historical trend data and optimizing the design for prompt-based design generation.

[0927] (Claim 3)

[0928] The system according to claim 1, comprising means for analyzing customer preference information and purchase history to provide personalized product suggestions.

[0929] "Example 1"

[0930] (Claim 1)

[0931] A method that utilizes an intelligent algorithm to automatically generate clothing designs based on language information entered by the user,

[0932] An automated production management system that transmits the generated concept information to the production facility and produces clothing,

[0933] An online distribution method that makes finished clothing available for sale on an information infrastructure,

[0934] A user interface means for selecting and purchasing products via the user's terminal,

[0935] A system that includes this.

[0936] (Claim 2)

[0937] The system according to claim 1, comprising means for analyzing past trend information and optimizing concept generation based on linguistic information.

[0938] (Claim 3)

[0939] The system according to claim 1, comprising means for analyzing customer preference information and purchase history to provide personalized product recommendations.

[0940] "Application Example 1"

[0941] (Claim 1)

[0942] A method utilizing artificial intelligence to automatically design products based on text prompts entered by the user,

[0943] An automated manufacturing control system that transmits the generated design information to the manufacturing facility and manufactures the product,

[0944] Online trading methods that make finished products available for sale on a technical network,

[0945] A display means that shows a product preview on the user's device and enables custom orders,

[0946] Electronic transaction methods for making online payments,

[0947] A system that includes this.

[0948] (Claim 2)

[0949] The system according to claim 1, comprising means for analyzing past trend data to optimize the design for prompt-based design generation, and means for providing real-time design suggestions to the user.

[0950] (Claim 3)

[0951] The system according to claim 1, comprising means for analyzing customer preference information and transaction history to make personalized product recommendations, and means for notifying the user via a smart device.

[0952] "Example 2 of combining an emotion engine"

[0953] (Claim 1)

[0954] A means of analyzing emotions using natural language processing based on prompts entered by the user,

[0955] A method for optimizing and generating clothing designs based on analyzed emotions using a generative AI model,

[0956] A means of transmitting the generated design information to the manufacturing facility and managing the automated manufacturing process,

[0957] A means of registering the finished garments on a technical sales platform and carrying out sales,

[0958] A means of analyzing a user's emotional history and purchase history to provide personalized product recommendations,

[0959] A system that includes this.

[0960] (Claim 2)

[0961] The system according to claim 1, comprising means for analyzing past sentiment data and trend data to optimize the design in prompt-based design generation.

[0962] (Claim 3)

[0963] The system according to claim 1, comprising means for generating emotion-based clothing designs based on prompt statements provided by a user.

[0964] "Application example 2 of combining emotional engines"

[0965] (Claim 1)

[0966] An artificial intelligence-powered method that automatically generates clothing designs based on text prompts entered by the user,

[0967] An automated manufacturing control system that transmits the generated design information to the manufacturing facility and manufactures clothing,

[0968] An online sales method that makes finished clothing available for sale on a technical platform,

[0969] A visualization method that allows users to virtually try on clothing using augmented reality technology,

[0970] A system that includes this.

[0971] (Claim 2)

[0972] The system according to claim 1, comprising means for analyzing historical trend data and optimizing the design for prompt-based design generation.

[0973] (Claim 3)

[0974] The system according to claim 1, comprising means for analyzing customer preference information and purchase history to provide personalized product suggestions. [Explanation of Symbols]

[0975] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An artificial intelligence-powered method that automatically generates clothing designs based on text prompts entered by the user, An automated manufacturing control system that transmits the generated design information to the manufacturing facility and manufactures clothing, An online sales method that makes finished clothing available for sale on a technical platform, A system that includes this.

2. The system according to claim 1, further comprising means for analyzing historical trend data and optimizing the design for prompt-based design generation.

3. The system according to claim 1, comprising means for analyzing customer preference information and purchase history to provide personalized product suggestions.

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