System

Abstract

A system is provided.SOLUTION: A system comprising: means for uploading information of excess inventory by a brand; database means for storing the uploaded product information; means for generating a new proposed design from the stored product information using artificial intelligence; terminal means for a user to select a product and display a plurality of proposed designs; means for customizing the proposed design selected by the user; and means for transmitting manufacturing instructions to a factory based on the final proposed design.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In the apparel industry, the difficulty of forecasting demand results in large amounts of excess inventory and out-of-date products, much of which is discarded. This situation has a negative impact on the environment and has a negative impact on brand image. Furthermore, with consumers increasingly interested in sustainable fashion, new approaches to reducing waste are required. Given this background, efficiently utilizing excess inventory and transforming it into "saleable products" with added value has become an important issue. [Means for solving the problem]

[0005] To solve this problem, the present invention provides the following means: a means for a brand to upload information about excess inventory, a database means for storing the uploaded product information, and a means for generating new design proposals from the stored product information using artificial intelligence. The system also includes a terminal means for a user to select a product and display multiple design proposals, and a means for customizing the design proposal selected by the user. Furthermore, the system provides a means for sending production instructions to a factory based on the final design proposal, thereby achieving an efficient upcycling process. The artificial intelligence learns from the brand's past styles, the latest collections, industry trends, and successful upcycling examples, allowing it to quickly provide high-quality design proposals. Furthermore, the system provides a means for rendering and displaying the customized design proposal results to the user in real time, thereby increasing user satisfaction.

[0006] A "brand" refers to a company or organization that offers a commercial product.

[0007] "Excess inventory" refers to unsold inventory of goods that has been produced in excess of demand.

[0008] "Artificial intelligence" refers to computer systems that mimic human intellectual activity and perform learning and reasoning.

[0009] "Design Proposal" means a proposal for a new or improved design.

[0010] "Terminal" means a device used by a User to enter or receive information.

[0011] A "database" refers to a system for systematically storing and efficiently managing large amounts of data.

[0012] "Customization" refers to the process of modifying the content of a product or service to meet specified requirements or preferences.

[0013] "Production Instructions" means detailed instructions or directions for producing a product.

[0014] "Upload" refers to the act of sending data from a local computer or device to a server.

[0015] "Rendering" in computer graphics refers to the process of generating and displaying an image or scene.

[0016] "Upcycling" refers to the process of transforming waste or unwanted products into something with new value.

[0017] "Real-time" means immediate processing without delay.

[0018] "Learning" refers to the process by which a system or algorithm acquires patterns and knowledge from given data. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0024] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0025] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] The following describes an embodiment of the "UpCycle Hub" system of the present invention. This system aims to enable brands to add value to their excess inventory by upcycling it and converting it into new "sellable products."

[0041] System Configuration

[0042] 1. Server-side components:

[0043] Database: A database for storing excess inventory information uploaded by brands and generated design proposals.

[0044] AI model: Artificial intelligence for generating new design ideas from product information.

[0045] Production instruction function: A function that sends production instructions to partner factories based on the final design proposal.

[0046] 2. User device components:

[0047] Product selection interface: An interface for users to select a brand or product category.

[0048] Design display interface: An interface that displays the generated multiple design proposals to the user.

[0049] Customization feature: A feature that allows users to customize design proposals.

[0050] Rendering function: A function for generating and displaying customization results in real time.

[0051] Purchase function: A function that allows users to select the final design and confirm the order.

[0052] Program processing and specific examples

[0053] First, brands upload their excess inventory information

[0054] The server receives the product list, images, and material information of the excess inventory sent by the brand.

[0055] This information is stored in a database and fed into the AI ​​model.

[0056] Examples:

[0057] If Brand A uploads data on 50 out-of-date shirts to the server, the server stores this data in a database and the AI ​​model begins learning from it.

[0058] Next, the server uses AI to generate design proposals.

[0059] The server generates new design proposals from the stored product information.

[0060] The generated design proposal is sent to the user's device.

[0061] Examples:

[0062] The server uses data from Brand A's shirts to generate five new design ideas and send them to the user's device. The AI ​​model is based on data such as the brand's past styles, latest collections, industry trends, and successful upcycling examples.

[0063] User selects product and sees design options

[0064] The user selects a brand or product category on the user device.

[0065] The server sends relevant design ideas based on the selected brand and category and displays them on the user's device.

[0066] Examples:

[0067] When a user selects a shirt from Brand A, five design ideas sent from the server are displayed on the device.

[0068] Users customize the design

[0069] Users can select the design provided on their device and customize it (changing colors, patterns, etc.).

[0070] The customization results are rendered in real time and displayed to the user.

[0071] Examples:

[0072] If a user selects a blue shirt design and changes the color to red, the change will be reflected in real time.

[0073] The user selects the final design and confirms the order.

[0074] The user selects the final design and confirms the purchase.

[0075] The terminal sends this information to the server.

[0076] Examples:

[0077] Once the user selects a design for the red shirt and confirms the purchase, the information is sent to the server.

[0078] Finally, the server sends the production instructions to the factory.

[0079] The server sends production instructions to affiliated factories based on the received order information.

[0080] The factory receives production instructions, produces the product, and delivers it to the user.

[0081] Examples:

[0082] The server sends production instructions for the red shirt to the factory, which then starts production based on those instructions and delivers the finished product to the user.

[0083] This creates an efficient upcycling process, reducing the costs for brands disposing of excess stock and allowing consumers to acquire unique, sustainable products.

[0084] The processing flow will be explained below.

[0085] Step 1:

[0086] The brand uploads excess inventory information to the server. Specifically, the brand prepares a product list, images, and material information for the excess inventory, and transmits the data to the server.

[0087] Step 2:

[0088] The server stores the received product list, images, and material information in a database, which is then referenced by artificial intelligence (AI) for subsequent processing.

[0089] Step 3:

[0090] The server uses the stored product information to generate new design ideas using an AI model that learns from data such as the brand's past styles, the latest collections, industry trends, and successful upcycling examples.

[0091] Step 4:

[0092] The server transfers the generated design proposals to the user's device, where multiple design proposals are displayed for the user to choose from.

[0093] Step 5:

[0094] The user selects their preferred design from multiple design options displayed on their device, and is then presented with an interface to customize the selected design.

[0095] Step 6:

[0096] Users can customize the design using the customization interface, specifically by changing colors, patterns, etc. The results of this customization are rendered in real time, providing visual feedback to the user.

[0097] Step 7:

[0098] The user finally decides on a design that satisfies them and decides to purchase it. When they click the purchase button, that information is sent to the server.

[0099] Step 8:

[0100] Based on the purchase information received, the server sends production instructions to partner factories, including detailed specifications for the selected design.

[0101] Step 9:

[0102] The factory begins manufacturing the product based on the manufacturing instructions received from the server, and the completed product is shipped to the specified delivery address.

[0103] Step 10:

[0104] The server monitors the entire process, providing status updates to the user as needed, and stores all transaction history in a database for future reference.

[0105] Example 1

[0106] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0107] When a brand has excess inventory, they need a way to effectively utilize that inventory and add value. However, traditional methods make it difficult to efficiently create new designs and offer them to customers, and the process is cumbersome. Therefore, there is a need for a system that can upcycle excess inventory and offer it as new products that customers can customize.

[0108] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0109] In this invention, the server includes a means for brands to upload information about their excess inventory, a database means for storing the uploaded product information, and a means for generating new design ideas from the stored product information using artificial intelligence, thereby enabling excess inventory to be efficiently provided as new customizable design ideas.

[0110] "Excess inventory" is excess inventory of merchandise that has not been sold.

[0111] "Uploading" is the act of transferring data from a computer or device to a server.

[0112] A "database" is a system for systematically storing, managing, and searching large amounts of data.

[0113] "Artificial intelligence" is a technology that allows computers to imitate human intellectual tasks, specifically referring to the technology of data analysis and decision-making.

[0114] A "design proposal" is a proposal for a new look or style for a product.

[0115] A "terminal" is a device operated by a user, including a personal computer or smartphone.

[0116] "Customization" refers to the act of a user making changes to a product, such as its color or pattern.

[0117] "Rendering" is the process of generating images or videos in computer graphics.

[0118] "Production instructions" are orders that instruct the factory on specific work details.

[0119] A "brand" is an identifying name for a company or organization that offers a particular product or service.

[0120] The system of this invention aims to enable brands to effectively utilize excess inventory and offer it as new, customizable products. The program of this system has the following main components and processing steps:

[0121] First, the brand uploads excess inventory information. The user sends a list of excess inventory items (including images, material information, and product descriptions) to the server via the admin screen as a CSV file or via API. The server validates the received data and stores it in a PostgreSQL database.

[0122] The server then uses an artificial intelligence (AI) model based on the stored data to generate new design ideas. This AI model is implemented using TensorFlow and learns from past styles and the latest fashion trends. For example, the server might input the following prompt into the AI ​​model: "Generate five new design ideas based on the image and material information of a shirt. Please consider past styles and the latest fashion trends."

[0123] The generated design proposals are sent from the server to the user's device using React.js. The user can select a brand or product category on the interface and view multiple design proposals. The user then customizes the design proposal in real time using the customization feature powered by Three.js. For example, a user can select a blue shirt design and change the color to red. This change is instantly rendered and displayed to the user.

[0124] Finally, the user selects the customized design and clicks the purchase button to confirm the purchase. This purchase process uses the Stripe API to process payment, and the final design and payment information are sent to the server. The server receives this and sends specific production instructions to partner factories. The factories then produce the product according to those instructions and deliver it to the user.

[0125] The system allows brands to efficiently upcycle excess inventory and offer customers unique, customizable products.

[0126] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0127] Step 1:

[0128] A user uploads surplus inventory information.

[0129] Users access the management screen and send a product list (images, material information, product descriptions, etc.) to the server via a CSV file or API. The input data is a list of excess inventory products, which is saved in the server's database. The CSV file contains detailed information about each product, and when sent via API, the data is sent in JSON format. The output is detailed data about each product saved in the database.

[0130] Step 2:

[0131] The server stores the received surplus inventory information in a database.

[0132] The server validates the data it receives and inserts it into a PostgreSQL database. This data includes images, sizes, materials, and descriptions for each product. The input product data is validated and efficiently stored in the database. The output is the saved product information.

[0133] Step 3:

[0134] The server uses the AI ​​model to generate design proposals.

[0135] The server calls an AI model using TensorFlow based on the saved product information. For example, the AI ​​model receives a prompt such as, "Generate five new design ideas based on shirt images and material information. Consider past styles and the latest fashion trends." In this process, the AI ​​model generates new design ideas using image processing and design generation algorithms. The input data are product images and material information, and the output is the generated design ideas.

[0136] Step 4:

[0137] The server sends the generated design proposal to the user's device.

[0138] The generated design proposals are formatted in JSON format and sent to a user interface built using React.js. Multiple design proposals are displayed on the user's device. The input data are the generated design proposals, and the output is the design proposal displayed on the user's device.

[0139] Step 5:

[0140] The user selects a brand and product category.

[0141] The user selects a brand and product category (e.g., "shirts," "pants," etc.) on the interface. The selection information is sent to the server, which then returns corresponding design proposals. The input data is the user's brand and category selection information, and the output is the corresponding design proposals.

[0142] Step 6:

[0143] The user customizes the design proposal.

[0144] Users can use the customization feature powered by Three.js to change the colors and patterns of the design, and the changes are instantly rendered and displayed to the user. The input data is the user's chosen design and change instructions, and the output is the customized design.

[0145] Step 7:

[0146] The user selects the final design and confirms the purchase.

[0147] The user selects a customized design and clicks the purchase button. The terminal processes the payment using the Stripe API and sends the final design and payment information to the server. The input data is the user's final selection and payment information, and the output is order confirmation.

[0148] Step 8:

[0149] The server sends production instructions to the partner factory.

[0150] The server sends production instructions to partner factories based on the received final design and order information. The factories then use this information to produce the product and deliver it to the user. The input data is the final design and order information, and the output is production instructions.

[0151] (Application example 1)

[0152] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0153] In today's retail industry, brands face a major challenge in effectively handling excess inventory. When excess inventory occurs, they need a way to create new value without wasting it. At the same time, it is important to offer customized products that meet diverse customer needs. Another issue is the difficulty for employees in customer service to instantly grasp inventory information and customization suggestions and make real-time suggestions during interactions. To solve these challenges, an efficient and flexible inventory management system needs to be integrated with customer service systems.

[0154] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0155] In this invention, the server includes: means for brands to upload information about excess inventory; database means for storing the uploaded product information; means for generating new design proposals from the stored product information using artificial intelligence; terminal means for users to select products and display multiple design proposals; means for customizing the design proposal selected by the user; means for sending production instructions to a factory based on the final design proposal; and means for a sales associate wearing smart glasses to check inventory information in real time while interacting with a customer and make customization proposals, thereby enabling efficient upcycling of excess inventory and customization proposals that meet diverse customer needs.

[0156] "Means for brands to upload excess inventory information" is a function that allows brands to input or transmit information about their unsold or overproduced inventory into the system.

[0157] "Database means for storing uploaded product information" refers to a digital database function for organizing and safely storing submitted inventory information.

[0158] "A means of generating new design ideas from stored product information using artificial intelligence" is a function that uses AI technology to automatically generate new design ideas based on stored surplus inventory information.

[0159] "Terminal means for allowing a user to select a product and display multiple design proposals" refers to a function of a digital device that allows a user to select a desired product and display various design proposals for that product.

[0160] "Means for customizing the design proposal selected by the user" refers to a function that allows the user to change and adjust the proposed design proposal to suit their preferences.

[0161] "Means for sending production instructions to the factory based on the final design proposal" is a function for instructing the factory to produce a product based on the final design selected by the user.

[0162] "A means for a sales associate wearing smart glasses to check inventory information in real time while interacting with a customer and make customization suggestions" is a function that allows a sales associate to wear smart glasses, instantly check inventory information while interacting with a customer, and make customization suggestions to the customer.

[0163] MODE FOR CARRYING OUT THE INVENTION

[0164] The system of this invention aims to enable brands to upcycle their excess inventory. Specifically, it enables brands to upload information about their excess inventory, generate new design ideas using that information, and then users can customize and order the products. The system configuration and operating procedures are explained below.

[0165] System Configuration

[0166] 1. Server-side components:

[0167] Database: A database for storing excess inventory information uploaded by brands and generated design proposals. This uses common database software such as MySQL or PostgreSQL.

[0168] AI model: Artificial intelligence for generating new design ideas from product information. Specifically, deep learning frameworks such as TensorFlow and PyTorch are used.

[0169] Production instruction function: A function that sends production instructions to partner factories based on the final design. Network communication is expected to be performed using REST API.

[0170] 2. User device components:

[0171] Product selection interface: An interface for users to select brands and product categories. This is implemented as a web application using a JavaScript framework such as React.

[0172] Design display interface: An interface that displays multiple generated design proposals to the user. This is displayed as a web GUI and uses HTML5 and CSS3.

[0173] Customization feature: A feature that allows users to customize design proposals. The customization results are displayed in real time using a 3D rendering library such as Three.js.

[0174] Rendering function: A function for generating and displaying customization results in real time. This uses WebGL to draw 3D graphics.

[0175] Purchase function: A function that allows users to select the final design and confirm the order. Electronic payment services such as Stripe are integrated.

[0176] 3. Smart Glasses:

[0177] Inventory information confirmation function: A function in which store clerks wear smart glasses and check the store's inventory information in real time. The smart glasses are Google Glass or similar and communicate with the database via Wi-Fi.

[0178] Customization suggestion function: A function that reflects design proposals on the display screen of the smart glasses in real time and makes customization suggestions to customers. This allows for instant suggestions during conversations with customers.

[0179] Specific examples

[0180] First, brands upload excess inventory information to a server, which receives it and stores it in a database. Next, an AI model generates new design ideas based on this data and sends them to users' devices, where they can view and further customize the designs.

[0181] For example, let's say Brand A uploads information about 50 out-of-date shirts. This information is stored in a database, and new design proposals are generated using an AI model. The five resulting design proposals are displayed on the user's device through a React-based web application. The user can choose their favorite design and change the color and pattern using the customization function using Three.js. The final customization result is rendered and displayed in real time using WebGL. Once the purchase is confirmed using Stripe, the information is sent to the manufacturer via the server.

[0182] Furthermore, if a sales associate is wearing smart glasses, they can check inventory information in real time and make customization suggestions while interacting with the customer. For example, if a customer says, "Red is good," the UI of the smart glasses can display a red shirt design in real time and ask for confirmation.

[0183] Example of an input prompt for a generative AI model:

[0184] Shirt details: Blue, cotton, long sleeves, size M.

[0185] Past styles: casual, stylish.

[0186] Upcycling policy: environmentally friendly and in line with the latest Chinese trends.

[0187] The above configuration enables efficient upcycling of excess inventory and customization proposals to meet the diverse needs of customers.

[0188] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0189] Step 1:

[0190] Brands upload excess inventory information. Brands enter information about their unsold or overproduced inventory into the system. This information includes details such as product name, quantity, image, material, and size. The uploaded data is stored in a database.

[0191] Step 2:

[0192] The server provides the stored product information to the AI ​​model, which then generates prompts based on this data to generate new design ideas. These prompts include product information, past styles, industry trends, etc. The generated design ideas are then stored on the server.

[0193] Step 3:

[0194] The server sends the generated design proposal to the user's device, which then displays it to the user through a user interface, such as a React-based web application, using HTML and CSS.

[0195] Step 4:

[0196] Users can select a design and use the customization feature. Based on the design they select, customization is performed in real time using the 3D rendering library Three.js. Color changes, pattern adjustments, and more are possible.

[0197] Step 5:

[0198] The user confirms the final design and checks out. The user confirms the final custom design and completes the payment process using an electronic payment service such as Stripe. This information is sent to the server.

[0199] Step 6:

[0200] The server sends the final design and purchasing information to the factory, which then uses a REST API to send production instructions to the factory, which then produces the product and delivers it to the user.

[0201] Step 7:

[0202] The store associate uses the smart glasses to manage inventory and handle customer service. Wearing the smart glasses, the associate checks inventory information in real time while interacting with the customer and makes customization suggestions based on the design ideas generated by the AI ​​model. If the customer accepts the suggestion, the information is sent to the server in real time, and the process begins again in step 6.

[0203] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0204] The following shows an example of the "UpCycle Hub" system of the present invention, which combines an emotion engine. The system aims to help brands add value to their excess inventory by upcycling it and transforming it into new "sellable products." It also aims to recognize user emotions and adjust the interface in real time to provide a more personalized experience.

[0205] System Configuration

[0206] 1. Server-side components:

[0207] Database: A database for storing excess inventory information uploaded by brands and generated design proposals.

[0208] AI model: Artificial intelligence for generating new design ideas from product information.

[0209] Production instruction function: A function that sends production instructions to partner factories based on the final design proposal.

[0210] Emotion engine: An engine that collects and analyzes user emotional data. This data is used to refine design proposals and interfaces.

[0211] 2. User device components:

[0212] Product selection interface: An interface for users to select a brand or product category.

[0213] Design display interface: An interface that displays the generated multiple design proposals to the user.

[0214] Customization feature: A feature that allows users to customize design proposals.

[0215] Rendering function: A function for generating and displaying customization results in real time.

[0216] Emotion Sensor: A sensor for detecting user emotions in real time and sending them to the emotion engine.

[0217] Purchase function: A function that allows users to select the final design and confirm the order.

[0218] Program processing and specific examples

[0219] First, brands upload their excess inventory information

[0220] Brands send product lists, images, and material information for excess inventory to the server, which stores this data in a database and provides it to the AI ​​model.

[0221] Examples:

[0222] If Brand A uploads data on 50 out-of-date shirts to the server, the server stores this data in a database and the AI ​​model begins learning from it.

[0223] Next, the server uses AI to generate design proposals.

[0224] The server generates new design ideas from stored product information, with the AI ​​model learning from data such as the brand's past styles, latest collections, industry trends, and successful upcycling examples.

[0225] Examples:

[0226] The server uses data from Brand A's shirts to generate five new design ideas and sends them to the user's device.

[0227] User selects product and sees design options

[0228] The user selects a brand or product category on the user device, and the server sends relevant design proposals based on the selected brand or category and displays them on the user device.

[0229] Examples:

[0230] When a user selects a shirt from Brand A, five design ideas sent from the server are displayed on the device.

[0231] Users customize the design

[0232] Users can select from the provided design ideas on their device and customize them (changing colors, patterns, etc.). The customization results are rendered in real time and displayed to the user.

[0233] Examples:

[0234] If a user selects a blue shirt design and changes the color to red, the change will be reflected in real time.

[0235] Recognize user emotions and adjust the interface accordingly

[0236] The emotion sensor captures the user's emotional data in real time and sends it to the emotion engine, which then analyzes the data and adjusts the design and interface according to the user's emotional state.

[0237] Examples:

[0238] As users customize their design proposals, emotion sensors recognize their joys and frustrations, and the emotion engine uses that data to provide new suggestions and customization options.

[0239] The user selects the final design and confirms the order.

[0240] The user selects the final design and confirms the purchase. The device sends this information to the server.

[0241] Examples:

[0242] Once the user selects a design for the red shirt and confirms the purchase, the information is sent to the server.

[0243] Finally, the server sends the production instructions to the factory.

[0244] The server then sends production instructions to partner factories based on the received order information, including detailed specifications for the selected design.

[0245] Examples:

[0246] The server sends production instructions for the red shirt to the factory, which then starts production based on those instructions and delivers the finished product to the user.

[0247] This allows the emotional engine to adjust the interface and make design suggestions based on the user's emotions, providing a more personalized experience, increasing user satisfaction and enabling a more efficient upcycling process for brands' excess inventory.

[0248] The processing flow will be explained below.

[0249] Step 1:

[0250] The brand uploads excess inventory information to the server. Specifically, the brand prepares a product list, images, and material information for the excess inventory, and transmits the data to the server.

[0251] Step 2:

[0252] The server stores the received product list, images, and material information in a database, which is then referenced by artificial intelligence (AI) for subsequent processing.

[0253] Step 3:

[0254] The server uses the stored product information to generate new design ideas using an AI model that learns from data such as the brand's past styles, the latest collections, industry trends, and successful upcycling examples.

[0255] Step 4:

[0256] The server transfers the generated design proposals to the user's device, where multiple design proposals are displayed for the user to choose from.

[0257] Step 5:

[0258] The user selects their preferred design from multiple design options displayed on their device, and is then presented with an interface to customize the selected design.

[0259] Step 6:

[0260] Users can customize the design using the customization interface, specifically by changing colors, patterns, etc. The results of this customization are rendered in real time, providing visual feedback to the user.

[0261] Step 7:

[0262] The user finally decides on a design that satisfies them and decides to purchase it. When they click the purchase button, that information is sent to the server.

[0263] Step 8:

[0264] Based on the purchase information received, the server sends production instructions to partner factories, including detailed specifications for the selected design.

[0265] Step 9:

[0266] The factory begins manufacturing the product based on the manufacturing instructions received from the server, and the completed product is shipped to the specified delivery address.

[0267] Step 10:

[0268] The user device acquires the user's emotional data in real time through an emotion sensor and transmits it to the server. The emotional data includes the user's facial expressions, voice, and body movements.

[0269] Step 11:

[0270] The server-side emotion engine analyzes the acquired emotion data and determines the user's emotional state, for example, whether the user is happy or confused.

[0271] Step 12:

[0272] Based on the emotion engine, the server adjusts the interface and proposes new designs. Based on emotion data, it suggests design ideas and customization options that users are likely to like.

[0273] Step 13:

[0274] The server combines the user's emotional data with their past purchase history and customization history to generate more personalized design proposals and send them to the user's device.

[0275] Step 14:

[0276] The user's device displays the updated design, and the user can customize and select again, and this process is repeated until the user is finally satisfied.

[0277] Step 15:

[0278] The server monitors the entire process, providing status updates to the user as needed, and stores all transaction history in a database for future reference.

[0279] Example 2

[0280] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0281] While existing upcycling systems effectively utilize surplus inventory and generate designs, they lack a personalized experience that responds to individual users' emotions and preferences. Furthermore, they do not use user emotional data to adjust interfaces or design proposals, making it difficult to improve customer satisfaction.

[0282] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0283] In this invention, the server includes: means for brands to upload information about excess inventory; database means for storing uploaded product information; means for generating new design proposals from the stored product information using artificial intelligence; terminal means for users to select products and display multiple design proposals; means for customizing the design proposal selected by the user; means for sending production instructions to a factory based on the final design proposal; emotion engine means for collecting and analyzing user emotion data; and means for adjusting the interface and design proposals based on the emotion data. This makes it possible to adjust the interface and design proposals according to the user's emotions, providing a more personalized experience.

[0284] A "brand" is a concept that includes names, logos, symbols, etc. that allow a company or product to be identified in the market.

[0285] "Excess inventory" refers to goods or materials that remain unsold.

[0286] "Means for uploading information" refers to any method or device for transferring digital data to the system.

[0287] A "database" is a structured collection of information for efficient storage, management, and retrieval of data.

[0288] "Artificial intelligence" refers to computer systems and software that simulate human intelligence, and is a technology that learns and makes inferences based on data.

[0289] "Design proposal" refers to a proposal or concept regarding the appearance and functionality of a product or service.

[0290] "Terminal" means a device that allows a User to access and operate a digital system.

[0291] "Customization means" refers to a method or device that allows a user to change or adjust certain aspects of a product or service.

[0292] "Production instructions" are detailed instructions or instructions for making a particular product.

[0293] "Factory" refers to a facility or building where goods are manufactured or processed.

[0294] "Emotional data" refers to information that expresses a user's emotional state as numbers or categories.

[0295] An "emotion engine" refers to software or algorithms that analyze emotional data and adjust system behavior based on that data.

[0296] An "interface" refers to the screens and input devices that allow a user to interact with a system.

[0297] The present invention combines an emotion engine with the "UpCycle Hub" system. The system aims to enable brands to upcycle excess inventory, add new value to it, and convert it into sellable products. The system also aims to recognize user emotions and adjust the interface in real time to provide a more personalized experience. The following describes specific embodiments of the present invention.

[0298] System Configuration

[0299] 1. Server-side components:

[0300] Database: A database for storing excess inventory information uploaded by brands and generated design proposals.

[0301] AI model: Artificial intelligence to generate new design ideas from product information. The AI ​​model is trained on past styles, the latest collections, industry trends, and successful upcycling examples.

[0302] Production instruction function: A function for sending production instructions to partner factories based on the final design proposal.

[0303] Emotion engine: An engine for collecting and analyzing user emotion data to adjust design proposals and interfaces.

[0304] 2. User device components:

[0305] Product selection interface: An interface for users to select a brand or product category.

[0306] Design display interface: An interface for displaying the generated multiple design proposals to the user.

[0307] Customization: A feature that allows users to customize the design proposal. Colors and patterns can be changed.

[0308] Rendering function: A function for generating and displaying customization results in real time.

[0309] Emotion Sensor: A sensor for detecting user emotions in real time and sending them to the emotion engine.

[0310] Purchase function: A function that allows users to select the final design and confirm the order.

[0311] Specific program description

[0312] Uploading excess inventory information

[0313] The user (brand manager) uploads information about excess inventory (product list, images, material information) to the server, which stores this data in a database and provides it to the AI ​​model.

[0314] Storing data and providing it to AI models

[0315] The server records the received product information in a database and passes it to the AI ​​model, which then learns from it and generates new design proposals.

[0316] Generate design ideas

[0317] The server uses an AI model to generate new design ideas from the stored data, learning from past styles and trends to suggest optimal designs.

[0318] Send and view design ideas

[0319] When a user selects a brand or product category, the server generates design proposals based on the selection and sends them to the user's device for display.

[0320] Customizing the design

[0321] Users can select from the displayed design options and change the colors and patterns, and the device will render and display the customization results in real time.

[0322] Acquiring emotional data and adjusting the interface

[0323] The emotion sensor collects user emotional data in real time and sends it to the emotion engine, which then analyzes it and adjusts the interface and design proposals according to the user's emotional state.

[0324] Final design selection and order confirmation

[0325] The user selects the final design and confirms the purchase, and the device sends this information to the server.

[0326] Sending production instructions

[0327] The server then sends production instructions to partner factories based on the received order information, including detailed specifications for the selected design.

[0328] Specific examples

[0329] If a brand uploads data on 50 out-of-date shirts to the server, the server stores this data in a database, and the AI ​​model begins learning from it. The server generates five new design proposals from the brand's shirt data and sends them to the user's device. When a user selects a brand's shirt and changes the color or pattern, the changes are updated in real time. In addition, an emotion sensor detects the user's emotions and recognizes their joy or dissatisfaction, allowing the server to automatically provide new suggestions and customization options.

[0330] Prompt Sentence Examples

[0331] "Generate five new design ideas from a selection of out-of-date shirts. Use an AI model that adjusts suggestions based on the user's emotional state."

[0332] This allows for a personalized experience for users and an efficient process for upcycling excess inventory for brands.

[0333] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0334] Step 1:

[0335] Input: The user (brand manager) prepares information about excess inventory (product list, images, material information).

[0336] Processing: User enters excess inventory information using the system's upload function.

[0337] Output: Excess inventory data is sent to the server.

[0338] Specific actions: A brand representative enters data for 50 out-of-date shirts according to the format and clicks the submit button.

[0339] Step 2:

[0340] Input: Excess inventory data is sent to the server.

[0341] Processing: The server stores the received surplus inventory data in the database.

[0342] Output: Product information stored in a database.

[0343] Specific operation: The server records the 50 shirt data received from Brand A in the database, and the information is saved in the database.

[0344] Step 3:

[0345] Input: Product information stored in the database.

[0346] Processing: The server invokes the AI ​​model based on the stored product information to generate new design proposals.

[0347] Output: The new design proposal generated.

[0348] Specific operation: The server provides data to the AI ​​model, which generates five new design ideas from Brand A's shirt data.

[0349] Step 4:

[0350] Input: Generated design proposal.

[0351] Process: The user selects a brand or product category on the device. The server sends relevant design ideas to the device based on the selection and displays them.

[0352] Output: Multiple design ideas displayed on the device.

[0353] Specific operation: The user selects a shirt from Brand A, and five design proposals sent from the server are displayed on the device.

[0354] Step 5:

[0355] Input: Design proposal displayed on the device.

[0356] Process: The user selects a design and customizes it with colors, patterns, etc.

[0357] Output: Customized design proposal.

[0358] How it works: A user selects a blue shirt design and changes the color to red. The change is reflected on the device in real time.

[0359] Step 6:

[0360] Input: Emotion data during user customization.

[0361] Processing: The emotion sensor collects user emotion data in real time and sends it to the emotion engine. The server analyzes this data and adjusts the interface and design proposals.

[0362] Output: Emotionally tailored interface and design ideas.

[0363] How it works: As users customize their design proposals, emotion sensors detect their joy or dissatisfaction in real time, and the server provides new suggestions and customization options based on that.

[0364] Step 7:

[0365] Input: Your final customized design proposal.

[0366] Processing: The customer selects the final design and confirms the purchase.

[0367] Output: Confirmed order information.

[0368] What happens: The user selects a design for the red shirt and confirms the purchase, which is then sent to the server.

[0369] Step 8:

[0370] Input: Received order information.

[0371] Processing: Based on the order information received by the server, production instructions are sent to affiliated factories.

[0372] Output: Production instructions sent to the factory.

[0373] Specific operation: The server sends the production instructions for the red shirt to the factory, the factory starts production based on the instructions, and the finished product is delivered to the user.

[0374] (Application example 2)

[0375] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0376] Conventional upcycling systems often lack a personalized user experience when generating design proposals to improve the commercial value of surplus inventory. Furthermore, they lack the ability to adjust the interface based on user sentiment, which hinders user satisfaction. This makes it difficult to efficiently utilize brands' surplus inventory. Furthermore, innovative methods are needed to improve the accuracy and suitability of design proposal generation.

[0377] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a brand to upload information about surplus inventory; database means for storing the uploaded product information; means for generating new design proposals from the stored product information using artificial intelligence; terminal means for a user to select a product and display multiple design proposals; means for customizing the design proposal selected by the user; means for sending production instructions to a production facility based on the final design proposal; emotion sensor means for detecting user emotions and adjusting the design proposals and interface; and emotion engine means for analyzing the detected emotion data and personalizing the interface and suggestions. This makes it possible to provide a personalized experience according to the user's emotions and efficiently upcycle a brand's surplus inventory.

[0378] "Excess inventory" refers to products or materials that remain unsold.

[0379] "Database" refers to an electronic data storage system where brands can store information on excess inventory and generated design ideas.

[0380] "Artificial intelligence" refers to algorithms and analytical methods that mimic human intelligence and generate new design ideas from product information.

[0381] "Terminal" refers to an electronic device that allows a user to select a product and display multiple design options.

[0382] "Customization" refers to the process of changing the colors and patterns of a design proposal selected by the user.

[0383] "Production facility" refers to the factory or manufacturing location where products are produced based on the final design proposal.

[0384] An "emotion sensor" refers to a device or software that detects a user's emotions.

[0385] An "emotion engine" is a system that analyzes detected emotional data and adjusts interface and design proposals according to the user's emotions.

[0386] The system that realizes this invention allows brands to manage excess inventory information, and based on that information, generates new design proposals and provides them to users. Furthermore, it recognizes user emotions in real time and adjusts the interface and design proposals to provide a personalized experience.

[0387] System configuration

[0388] Server-side configuration

[0389] 1. Database

[0390] Brands upload information about their excess inventory to a server and store that data in a database, which includes product lists, images, and material information.

[0391] 2. Artificial Intelligence (AI) Models

[0392] It is an AI model that generates new design ideas based on stored product information, learning from the brand's past styles, latest collections, industry trends, and successful upcycling examples.

[0393] 3. Production instruction function

[0394] This function sends production instructions to a production facility based on the final design proposal, including detailed specifications for the selected design.

[0395] 4. Emotion Engine

[0396] This engine analyzes user emotional data collected from emotion sensors and adjusts interface and design proposals according to the user's emotions.

[0397] Configuring the user device

[0398] 1. Product selection interface

[0399] This is the interface that allows users to select brands and product categories.

[0400] 2. Design display interface

[0401] This is an interface that displays multiple new design ideas to the user generated by the AI ​​model.

[0402] 3. Customization features

[0403] This is a feature that allows users to customize design proposals, allowing them to change colors and patterns.

[0404] 4. Rendering Function

[0405] This function generates customization results in real time and displays them to the user.

[0406] 5. Emotion Sensor

[0407] It is a sensor that uses the smartphone's camera and microphone to detect the user's emotions in real time, and sends this data to the emotion engine.

[0408] 6. Purchase Function

[0409] This is a function that allows users to select the final design and confirm the order.

[0410] Program processing overview

[0411] The server uses an AI model to generate new design proposals from product information stored in the database and sends them to the user's device. The user then uses the device to select a brand or product category and customize the displayed design proposals. The customization results are rendered in real time. The emotion sensor uses the smartphone's camera and an emotion recognition model (e.g., emotion_detection_model.h5) to detect the user's emotions. The emotion engine analyzes the detected emotion data and adjusts the interface and suggestions according to the user's emotions.

[0412] Specific examples

[0413] When a user launches the app and selects a shirt from Brand A, the server displays multiple design proposals that the user can customize. During customization, the user's emotions are detected by the smartphone camera and the data is sent to the emotion engine. If the emotion engine detects "excitement," it will suggest bolder design proposals.

[0414] Prompt Sentence Examples

[0415] "The user selected a shirt from Brand A. When detecting the user's emotion in the video captured by the camera, 'excitement' was recognized. In this case, what kind of design proposal should we suggest?"

[0416] The system allows brands to efficiently upcycle excess inventory while providing a personalized experience that responds to user emotions.

[0417] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0418] Step 1:

[0419] The server provides a means for brands to upload excess inventory information. The input is excess inventory data from the brand (product list, images, material information), and the output is excess inventory information stored in a database. Specifically, brands send product information to the server via a web form or API, and the server stores the data in a database.

[0420] Step 2:

[0421] The server uses the stored product information to run an artificial intelligence (AI) model to generate new design proposals. The input is surplus inventory information stored in the database, and the output is multiple generated design proposals. Specifically, the AI ​​model generates new design proposals by taking into account past style data, industry trends, and successful upcycling examples.

[0422] Step 3:

[0423] The server sends the generated design proposals to the user's device and displays them to the user on the device. The input is the design proposal generated by the AI ​​model, and the output is multiple design proposals displayed on the user's device. Specifically, the server sends the generated design proposals to the user's device in JSON format, which is then parsed and displayed on the device.

[0424] Step 4:

[0425] Users select a brand or product category on their device and browse the displayed design options. The input is the user's selection, and the output is the display of related design options. Specifically, users make selections through the interface, and design options based on those selections are displayed on the user's device.

[0426] Step 5:

[0427] The user customizes the selected design. The input is the user's selected design and customization instructions (color and pattern changes), and the output is the customization result. Specifically, the user changes the color and pattern, and the changes are rendered in real time and displayed to the user.

[0428] Step 6:

[0429] The device uses an emotion sensor to detect the user's emotions. The input is the user's facial and voice data captured by the smartphone's camera and microphone, and the output is the detected emotion data. Specifically, the data acquired by the camera and microphone is input into an emotion recognition model to generate emotion data.

[0430] Step 7:

[0431] The server uses an emotion engine to analyze the detected emotion data and adjust the interface or design proposals. The input is the emotion data sent from the emotion sensor, and the output is an adjusted interface or new proposals. Specifically, the emotion engine analyzes the detected emotion data (e.g., "excitement") and proposes new design proposals or interfaces accordingly.

[0432] Step 8:

[0433] The user selects the final design and confirms the order. The input is the final design selected by the user, and the output is data sent to the server as order information. Specifically, the user orders the confirmed design, and the information is sent to the server.

[0434] Step 9:

[0435] The server sends production instructions to the production facility based on the received order information. The input is the order information sent by the user, and the output is the production instructions sent to the production facility. Specifically, the server sends the order information to the production facility, and the production facility produces the product based on the instructions.

[0436] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0437] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0438] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0439] [Second embodiment]

[0440] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0441] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0443] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0444] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

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

[0446] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0447] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0450] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0451] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0452] The following describes an embodiment of the "UpCycle Hub" system of the present invention. This system aims to enable brands to add value to their excess inventory by upcycling it and converting it into new "sellable products."

[0453] System Configuration

[0454] 1. Server-side components:

[0455] Database: A database for storing excess inventory information uploaded by brands and generated design proposals.

[0456] AI model: Artificial intelligence for generating new design ideas from product information.

[0457] Production instruction function: A function that sends production instructions to partner factories based on the final design proposal.

[0458] 2. User device components:

[0459] Product selection interface: An interface for users to select a brand or product category.

[0460] Design display interface: An interface that displays the generated multiple design proposals to the user.

[0461] Customization feature: A feature that allows users to customize design proposals.

[0462] Rendering function: A function for generating and displaying customization results in real time.

[0463] Purchase function: A function that allows users to select the final design and confirm the order.

[0464] Program processing and specific examples

[0465] First, brands upload their excess inventory information

[0466] The server receives the product list, images, and material information of the excess inventory sent by the brand.

[0467] This information is stored in a database and fed into the AI ​​model.

[0468] Examples:

[0469] If Brand A uploads data on 50 out-of-date shirts to the server, the server stores this data in a database and the AI ​​model begins learning from it.

[0470] Next, the server uses AI to generate design proposals.

[0471] The server generates new design proposals from the stored product information.

[0472] The generated design proposal is sent to the user's device.

[0473] Examples:

[0474] The server uses data from Brand A's shirts to generate five new design ideas and send them to the user's device. The AI ​​model is based on data such as the brand's past styles, latest collections, industry trends, and successful upcycling examples.

[0475] User selects product and sees design options

[0476] The user selects a brand or product category on the user device.

[0477] The server sends relevant design ideas based on the selected brand and category and displays them on the user's device.

[0478] Examples:

[0479] When a user selects a shirt from Brand A, five design ideas sent from the server are displayed on the device.

[0480] Users customize the design

[0481] Users can select the design provided on their device and customize it (changing colors, patterns, etc.).

[0482] The customization results are rendered in real time and displayed to the user.

[0483] Examples:

[0484] If a user selects a blue shirt design and changes the color to red, the change will be reflected in real time.

[0485] The user selects the final design and confirms the order.

[0486] The user selects the final design and confirms the purchase.

[0487] The terminal sends this information to the server.

[0488] Examples:

[0489] Once the user selects a design for the red shirt and confirms the purchase, the information is sent to the server.

[0490] Finally, the server sends the production instructions to the factory.

[0491] The server sends production instructions to affiliated factories based on the received order information.

[0492] The factory receives production instructions, produces the product, and delivers it to the user.

[0493] Examples:

[0494] The server sends production instructions for the red shirt to the factory, which then starts production based on those instructions and delivers the finished product to the user.

[0495] This creates an efficient upcycling process, reducing the costs for brands disposing of excess stock and allowing consumers to acquire unique, sustainable products.

[0496] The processing flow will be explained below.

[0497] Step 1:

[0498] The brand uploads excess inventory information to the server. Specifically, the brand prepares a product list, images, and material information for the excess inventory, and transmits the data to the server.

[0499] Step 2:

[0500] The server stores the received product list, images, and material information in a database, which is then referenced by artificial intelligence (AI) for subsequent processing.

[0501] Step 3:

[0502] The server uses the stored product information to generate new design ideas using an AI model that learns from data such as the brand's past styles, the latest collections, industry trends, and successful upcycling examples.

[0503] Step 4:

[0504] The server transfers the generated design proposals to the user's device, where multiple design proposals are displayed for the user to choose from.

[0505] Step 5:

[0506] The user selects their preferred design from multiple design options displayed on their device, and is then presented with an interface to customize the selected design.

[0507] Step 6:

[0508] Users can customize the design using the customization interface, specifically by changing colors, patterns, etc. The results of this customization are rendered in real time, providing visual feedback to the user.

[0509] Step 7:

[0510] The user finally decides on a design that satisfies them and decides to purchase it. When they click the purchase button, that information is sent to the server.

[0511] Step 8:

[0512] Based on the purchase information received, the server sends production instructions to partner factories, including detailed specifications for the selected design.

[0513] Step 9:

[0514] The factory begins manufacturing the product based on the manufacturing instructions received from the server, and the completed product is shipped to the specified delivery address.

[0515] Step 10:

[0516] The server monitors the entire process, providing status updates to the user as needed, and stores all transaction history in a database for future reference.

[0517] Example 1

[0518] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0519] When a brand has excess inventory, they need a way to effectively utilize that inventory and add value. However, traditional methods make it difficult to efficiently create new designs and offer them to customers, and the process is cumbersome. Therefore, there is a need for a system that can upcycle excess inventory and offer it as new products that customers can customize.

[0520] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0521] In this invention, the server includes a means for brands to upload information about their excess inventory, a database means for storing the uploaded product information, and a means for generating new design ideas from the stored product information using artificial intelligence, thereby enabling excess inventory to be efficiently provided as new customizable design ideas.

[0522] "Excess inventory" is excess inventory of merchandise that has not been sold.

[0523] "Uploading" is the act of transferring data from a computer or device to a server.

[0524] A "database" is a system for systematically storing, managing, and searching large amounts of data.

[0525] "Artificial intelligence" is a technology that allows computers to imitate human intellectual tasks, specifically referring to the technology of data analysis and decision-making.

[0526] A "design proposal" is a proposal for a new look or style for a product.

[0527] A "terminal" is a device operated by a user, including a personal computer or smartphone.

[0528] "Customization" refers to the act of a user making changes to a product, such as its color or pattern.

[0529] "Rendering" is the process of generating images or videos in computer graphics.

[0530] "Production instructions" are orders that instruct the factory on specific work details.

[0531] A "brand" is an identifying name for a company or organization that offers a particular product or service.

[0532] The system of this invention aims to enable brands to effectively utilize excess inventory and offer it as new, customizable products. The program of this system has the following main components and processing steps:

[0533] First, the brand uploads excess inventory information. The user sends a list of excess inventory items (including images, material information, and product descriptions) to the server via the admin screen as a CSV file or via API. The server validates the received data and stores it in a PostgreSQL database.

[0534] The server then uses an artificial intelligence (AI) model based on the stored data to generate new design ideas. This AI model is implemented using TensorFlow and learns from past styles and the latest fashion trends. For example, the server might input the following prompt into the AI ​​model: "Generate five new design ideas based on the image and material information of a shirt. Please consider past styles and the latest fashion trends."

[0535] The generated design proposals are sent from the server to the user's device using React.js. The user can select a brand or product category on the interface and view multiple design proposals. The user then customizes the design proposal in real time using the customization feature powered by Three.js. For example, a user can select a blue shirt design and change the color to red. This change is instantly rendered and displayed to the user.

[0536] Finally, the user selects the customized design and clicks the purchase button to confirm the purchase. This purchase process uses the Stripe API to process payment, and the final design and payment information are sent to the server. The server receives this and sends specific production instructions to partner factories. The factories then produce the product according to those instructions and deliver it to the user.

[0537] The system allows brands to efficiently upcycle excess inventory and offer customers unique, customizable products.

[0538] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0539] Step 1:

[0540] A user uploads surplus inventory information.

[0541] Users access the management screen and send a product list (images, material information, product descriptions, etc.) to the server via a CSV file or API. The input data is a list of excess inventory products, which is saved in the server's database. The CSV file contains detailed information about each product, and when sent via API, the data is sent in JSON format. The output is detailed data about each product saved in the database.

[0542] Step 2:

[0543] The server stores the received surplus inventory information in a database.

[0544] The server validates the data it receives and inserts it into a PostgreSQL database. This data includes images, sizes, materials, and descriptions for each product. The input product data is validated and efficiently stored in the database. The output is the saved product information.

[0545] Step 3:

[0546] The server uses the AI ​​model to generate design proposals.

[0547] The server calls an AI model using TensorFlow based on the saved product information. For example, the AI ​​model receives a prompt such as, "Generate five new design ideas based on shirt images and material information. Consider past styles and the latest fashion trends." In this process, the AI ​​model generates new design ideas using image processing and design generation algorithms. The input data are product images and material information, and the output is the generated design ideas.

[0548] Step 4:

[0549] The server sends the generated design proposal to the user's device.

[0550] The generated design proposals are formatted in JSON format and sent to a user interface built using React.js. Multiple design proposals are displayed on the user's device. The input data are the generated design proposals, and the output is the design proposal displayed on the user's device.

[0551] Step 5:

[0552] The user selects a brand and product category.

[0553] The user selects a brand and product category (e.g., "shirts," "pants," etc.) on the interface. The selection information is sent to the server, which then returns corresponding design proposals. The input data is the user's brand and category selection information, and the output is the corresponding design proposals.

[0554] Step 6:

[0555] The user customizes the design proposal.

[0556] Users can use the customization feature powered by Three.js to change the colors and patterns of the design, and the changes are instantly rendered and displayed to the user. The input data is the user's chosen design and change instructions, and the output is the customized design.

[0557] Step 7:

[0558] The user selects the final design and confirms the purchase.

[0559] The user selects a customized design and clicks the purchase button. The terminal processes the payment using the Stripe API and sends the final design and payment information to the server. The input data is the user's final selection and payment information, and the output is order confirmation.

[0560] Step 8:

[0561] The server sends production instructions to the partner factory.

[0562] The server sends production instructions to partner factories based on the received final design and order information. The factories then use this information to produce the product and deliver it to the user. The input data is the final design and order information, and the output is production instructions.

[0563] (Application example 1)

[0564] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0565] In today's retail industry, brands face a major challenge in effectively handling excess inventory. When excess inventory occurs, they need a way to create new value without wasting it. At the same time, it is important to offer customized products that meet diverse customer needs. Another issue is the difficulty for employees in customer service to instantly grasp inventory information and customization suggestions and make real-time suggestions during interactions. To solve these challenges, an efficient and flexible inventory management system needs to be integrated with customer service systems.

[0566] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0567] In this invention, the server includes: means for brands to upload information about excess inventory; database means for storing the uploaded product information; means for generating new design proposals from the stored product information using artificial intelligence; terminal means for users to select products and display multiple design proposals; means for customizing the design proposal selected by the user; means for sending production instructions to a factory based on the final design proposal; and means for a sales associate wearing smart glasses to check inventory information in real time while interacting with a customer and make customization proposals, thereby enabling efficient upcycling of excess inventory and customization proposals that meet diverse customer needs.

[0568] "Means for brands to upload excess inventory information" is a function that allows brands to input or transmit information about their unsold or overproduced inventory into the system.

[0569] "Database means for storing uploaded product information" refers to a digital database function for organizing and safely storing submitted inventory information.

[0570] "A means of generating new design ideas from stored product information using artificial intelligence" is a function that uses AI technology to automatically generate new design ideas based on stored surplus inventory information.

[0571] "Terminal means for allowing a user to select a product and display multiple design proposals" refers to a function of a digital device that allows a user to select a desired product and display various design proposals for that product.

[0572] "Means for customizing the design proposal selected by the user" refers to a function that allows the user to change and adjust the proposed design proposal to suit their preferences.

[0573] "Means for sending production instructions to the factory based on the final design proposal" is a function for instructing the factory to produce a product based on the final design selected by the user.

[0574] "A means for a sales associate wearing smart glasses to check inventory information in real time while interacting with a customer and make customization suggestions" is a function that allows a sales associate to wear smart glasses, instantly check inventory information while interacting with a customer, and make customization suggestions to the customer.

[0575] MODE FOR CARRYING OUT THE INVENTION

[0576] The system of this invention aims to enable brands to upcycle their excess inventory. Specifically, it enables brands to upload information about their excess inventory, generate new design ideas using that information, and then users can customize and order the products. The system configuration and operating procedures are explained below.

[0577] System Configuration

[0578] 1. Server-side components:

[0579] Database: A database for storing excess inventory information uploaded by brands and generated design proposals. This uses common database software such as MySQL or PostgreSQL.

[0580] AI model: Artificial intelligence for generating new design ideas from product information. Specifically, deep learning frameworks such as TensorFlow and PyTorch are used.

[0581] Production instruction function: A function that sends production instructions to partner factories based on the final design. Network communication is expected to be performed using REST API.

[0582] 2. User device components:

[0583] Product selection interface: An interface for users to select brands and product categories. This is implemented as a web application using a JavaScript framework such as React.

[0584] Design display interface: An interface that displays multiple generated design proposals to the user. This is displayed as a web GUI and uses HTML5 and CSS3.

[0585] Customization feature: A feature that allows users to customize design proposals. The customization results are displayed in real time using a 3D rendering library such as Three.js.

[0586] Rendering function: A function for generating and displaying customization results in real time. This uses WebGL to draw 3D graphics.

[0587] Purchase function: A function that allows users to select the final design and confirm the order. Electronic payment services such as Stripe are integrated.

[0588] 3. Smart Glasses:

[0589] Inventory information confirmation function: A function in which store clerks wear smart glasses and check the store's inventory information in real time. The smart glasses are Google Glass or similar and communicate with the database via Wi-Fi.

[0590] Customization suggestion function: A function that reflects design proposals on the display screen of the smart glasses in real time and makes customization suggestions to customers. This allows for instant suggestions during conversations with customers.

[0591] Specific examples

[0592] First, brands upload excess inventory information to a server, which receives it and stores it in a database. Next, an AI model generates new design ideas based on this data and sends them to users' devices, where they can view and further customize the designs.

[0593] For example, let's say Brand A uploads information about 50 out-of-date shirts. This information is stored in a database, and new design proposals are generated using an AI model. The five resulting design proposals are displayed on the user's device through a React-based web application. The user can choose their favorite design and change the color and pattern using the customization function using Three.js. The final customization result is rendered and displayed in real time using WebGL. Once the purchase is confirmed using Stripe, the information is sent to the manufacturer via the server.

[0594] Furthermore, if a sales associate is wearing smart glasses, they can check inventory information in real time and make customization suggestions while interacting with the customer. For example, if a customer says, "Red is good," the UI of the smart glasses can display a red shirt design in real time and ask for confirmation.

[0595] Example of an input prompt for a generative AI model:

[0596] Shirt details: Blue, cotton, long sleeves, size M.

[0597] Past styles: casual, stylish.

[0598] Upcycling policy: environmentally friendly and in line with the latest Chinese trends.

[0599] The above configuration enables efficient upcycling of excess inventory and customization proposals to meet the diverse needs of customers.

[0600] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0601] Step 1:

[0602] Brands upload excess inventory information. Brands enter information about their unsold or overproduced inventory into the system. This information includes details such as product name, quantity, image, material, and size. The uploaded data is stored in a database.

[0603] Step 2:

[0604] The server provides the stored product information to the AI ​​model, which then generates prompts based on this data to generate new design ideas. These prompts include product information, past styles, industry trends, etc. The generated design ideas are then stored on the server.

[0605] Step 3:

[0606] The server sends the generated design proposal to the user's device, which then displays it to the user through a user interface, such as a React-based web application, using HTML and CSS.

[0607] Step 4:

[0608] Users can select a design and use the customization feature. Based on the design they select, customization is performed in real time using the 3D rendering library Three.js. Color changes, pattern adjustments, and more are possible.

[0609] Step 5:

[0610] The user confirms the final design and checks out. The user confirms the final custom design and completes the payment process using an electronic payment service such as Stripe. This information is sent to the server.

[0611] Step 6:

[0612] The server sends the final design and purchasing information to the factory, which then uses a REST API to send production instructions to the factory, which then produces the product and delivers it to the user.

[0613] Step 7:

[0614] The store associate uses the smart glasses to manage inventory and handle customer service. Wearing the smart glasses, the associate checks inventory information in real time while interacting with the customer and makes customization suggestions based on the design ideas generated by the AI ​​model. If the customer accepts the suggestion, the information is sent to the server in real time, and the process begins again in step 6.

[0615] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0616] The following shows an example of the "UpCycle Hub" system of the present invention, which combines an emotion engine. The system aims to help brands add value to their excess inventory by upcycling it and transforming it into new "sellable products." It also aims to recognize user emotions and adjust the interface in real time to provide a more personalized experience.

[0617] System Configuration

[0618] 1. Server-side components:

[0619] Database: A database for storing excess inventory information uploaded by brands and generated design proposals.

[0620] AI model: Artificial intelligence for generating new design ideas from product information.

[0621] Production instruction function: A function that sends production instructions to partner factories based on the final design proposal.

[0622] Emotion engine: An engine that collects and analyzes user emotional data. This data is used to refine design proposals and interfaces.

[0623] 2. User device components:

[0624] Product selection interface: An interface for users to select a brand or product category.

[0625] Design display interface: An interface that displays the generated multiple design proposals to the user.

[0626] Customization feature: A feature that allows users to customize design proposals.

[0627] Rendering function: A function for generating and displaying customization results in real time.

[0628] Emotion Sensor: A sensor for detecting user emotions in real time and sending them to the emotion engine.

[0629] Purchase function: A function that allows users to select the final design and confirm the order.

[0630] Program processing and specific examples

[0631] First, brands upload their excess inventory information

[0632] Brands send product lists, images, and material information for excess inventory to the server, which stores this data in a database and provides it to the AI ​​model.

[0633] Examples:

[0634] If Brand A uploads data on 50 out-of-date shirts to the server, the server stores this data in a database and the AI ​​model begins learning from it.

[0635] Next, the server uses AI to generate design proposals.

[0636] The server generates new design ideas from stored product information, with the AI ​​model learning from data such as the brand's past styles, latest collections, industry trends, and successful upcycling examples.

[0637] Examples:

[0638] The server uses data from Brand A's shirts to generate five new design ideas and sends them to the user's device.

[0639] User selects product and sees design options

[0640] The user selects a brand or product category on the user device, and the server sends relevant design proposals based on the selected brand or category and displays them on the user device.

[0641] Examples:

[0642] When a user selects a shirt from Brand A, five design ideas sent from the server are displayed on the device.

[0643] Users customize the design

[0644] Users can select from the provided design ideas on their device and customize them (changing colors, patterns, etc.). The customization results are rendered in real time and displayed to the user.

[0645] Examples:

[0646] If a user selects a blue shirt design and changes the color to red, the change will be reflected in real time.

[0647] Recognize user emotions and adjust the interface accordingly

[0648] The emotion sensor captures the user's emotional data in real time and sends it to the emotion engine, which then analyzes the data and adjusts the design and interface according to the user's emotional state.

[0649] Examples:

[0650] As users customize their design proposals, emotion sensors recognize their joys and frustrations, and the emotion engine uses that data to provide new suggestions and customization options.

[0651] The user selects the final design and confirms the order.

[0652] The user selects the final design and confirms the purchase. The device sends this information to the server.

[0653] Examples:

[0654] Once the user selects a design for the red shirt and confirms the purchase, the information is sent to the server.

[0655] Finally, the server sends the production instructions to the factory.

[0656] The server then sends production instructions to partner factories based on the received order information, including detailed specifications for the selected design.

[0657] Examples:

[0658] The server sends production instructions for the red shirt to the factory, which then starts production based on those instructions and delivers the finished product to the user.

[0659] This allows the emotional engine to adjust the interface and make design suggestions based on the user's emotions, providing a more personalized experience, increasing user satisfaction and enabling a more efficient upcycling process for brands' excess inventory.

[0660] The processing flow will be explained below.

[0661] Step 1:

[0662] The brand uploads excess inventory information to the server. Specifically, the brand prepares a product list, images, and material information for the excess inventory, and transmits the data to the server.

[0663] Step 2:

[0664] The server stores the received product list, images, and material information in a database, which is then referenced by artificial intelligence (AI) for subsequent processing.

[0665] Step 3:

[0666] The server uses the stored product information to generate new design ideas using an AI model that learns from data such as the brand's past styles, the latest collections, industry trends, and successful upcycling examples.

[0667] Step 4:

[0668] The server transfers the generated design proposals to the user's device, where multiple design proposals are displayed for the user to choose from.

[0669] Step 5:

[0670] The user selects their preferred design from multiple design options displayed on their device, and is then presented with an interface to customize the selected design.

[0671] Step 6:

[0672] Users can customize the design using the customization interface, specifically by changing colors, patterns, etc. The results of this customization are rendered in real time, providing visual feedback to the user.

[0673] Step 7:

[0674] The user finally decides on a design that satisfies them and decides to purchase it. When they click the purchase button, that information is sent to the server.

[0675] Step 8:

[0676] Based on the purchase information received, the server sends production instructions to partner factories, including detailed specifications for the selected design.

[0677] Step 9:

[0678] The factory begins manufacturing the product based on the manufacturing instructions received from the server, and the completed product is shipped to the specified delivery address.

[0679] Step 10:

[0680] The user device acquires the user's emotional data in real time through an emotion sensor and transmits it to the server. The emotional data includes the user's facial expressions, voice, and body movements.

[0681] Step 11:

[0682] The server-side emotion engine analyzes the acquired emotion data and determines the user's emotional state, for example, whether the user is happy or confused.

[0683] Step 12:

[0684] Based on the emotion engine, the server adjusts the interface and proposes new designs. Based on emotion data, it suggests design ideas and customization options that users are likely to like.

[0685] Step 13:

[0686] The server combines the user's emotional data with their past purchase history and customization history to generate more personalized design proposals and send them to the user's device.

[0687] Step 14:

[0688] The user's device displays the updated design, and the user can customize and select again, and this process is repeated until the user is finally satisfied.

[0689] Step 15:

[0690] The server monitors the entire process, providing status updates to the user as needed, and stores all transaction history in a database for future reference.

[0691] Example 2

[0692] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0693] While existing upcycling systems effectively utilize surplus inventory and generate designs, they lack a personalized experience that responds to individual users' emotions and preferences. Furthermore, they do not use user emotional data to adjust interfaces or design proposals, making it difficult to improve customer satisfaction.

[0694] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0695] In this invention, the server includes: means for brands to upload information about excess inventory; database means for storing uploaded product information; means for generating new design proposals from the stored product information using artificial intelligence; terminal means for users to select products and display multiple design proposals; means for customizing the design proposal selected by the user; means for sending production instructions to a factory based on the final design proposal; emotion engine means for collecting and analyzing user emotion data; and means for adjusting the interface and design proposals based on the emotion data. This makes it possible to adjust the interface and design proposals according to the user's emotions, providing a more personalized experience.

[0696] A "brand" is a concept that includes names, logos, symbols, etc. that allow a company or product to be identified in the market.

[0697] "Excess inventory" refers to goods or materials that remain unsold.

[0698] "Means for uploading information" refers to any method or device for transferring digital data to the system.

[0699] A "database" is a structured collection of information for efficient storage, management, and retrieval of data.

[0700] "Artificial intelligence" refers to computer systems and software that simulate human intelligence, and is a technology that learns and makes inferences based on data.

[0701] "Design proposal" refers to a proposal or concept regarding the appearance and functionality of a product or service.

[0702] "Terminal" means a device that allows a User to access and operate a digital system.

[0703] "Customization means" refers to a method or device that allows a user to change or adjust certain aspects of a product or service.

[0704] "Production instructions" are detailed instructions or instructions for making a particular product.

[0705] "Factory" refers to a facility or building where goods are manufactured or processed.

[0706] "Emotional data" refers to information that expresses a user's emotional state as numbers or categories.

[0707] An "emotion engine" refers to software or algorithms that analyze emotional data and adjust system behavior based on that data.

[0708] An "interface" refers to the screens and input devices that allow a user to interact with a system.

[0709] The present invention combines an emotion engine with the "UpCycle Hub" system. The system aims to enable brands to upcycle excess inventory, add new value to it, and convert it into sellable products. The system also aims to recognize user emotions and adjust the interface in real time to provide a more personalized experience. The following describes specific embodiments of the present invention.

[0710] System Configuration

[0711] 1. Server-side components:

[0712] Database: A database for storing excess inventory information uploaded by brands and generated design proposals.

[0713] AI model: Artificial intelligence to generate new design ideas from product information. The AI ​​model is trained on past styles, the latest collections, industry trends, and successful upcycling examples.

[0714] Production instruction function: A function for sending production instructions to partner factories based on the final design proposal.

[0715] Emotion engine: An engine for collecting and analyzing user emotion data to adjust design proposals and interfaces.

[0716] 2. User device components:

[0717] Product selection interface: An interface for users to select a brand or product category.

[0718] Design display interface: An interface for displaying the generated multiple design proposals to the user.

[0719] Customization: A feature that allows users to customize the design proposal. Colors and patterns can be changed.

[0720] Rendering function: A function for generating and displaying customization results in real time.

[0721] Emotion Sensor: A sensor for detecting user emotions in real time and sending them to the emotion engine.

[0722] Purchase function: A function that allows users to select the final design and confirm the order.

[0723] Specific program description

[0724] Uploading excess inventory information

[0725] The user (brand manager) uploads information about excess inventory (product list, images, material information) to the server, which stores this data in a database and provides it to the AI ​​model.

[0726] Storing data and providing it to AI models

[0727] The server records the received product information in a database and passes it to the AI ​​model, which then learns from it and generates new design proposals.

[0728] Generate design ideas

[0729] The server uses an AI model to generate new design ideas from the stored data, learning from past styles and trends to suggest optimal designs.

[0730] Send and view design ideas

[0731] When a user selects a brand or product category, the server generates design proposals based on the selection and sends them to the user's device for display.

[0732] Customizing the design

[0733] Users can select from the displayed design options and change the colors and patterns, and the device will render and display the customization results in real time.

[0734] Acquiring emotional data and adjusting the interface

[0735] The emotion sensor collects user emotional data in real time and sends it to the emotion engine, which then analyzes it and adjusts the interface and design proposals according to the user's emotional state.

[0736] Final design selection and order confirmation

[0737] The user selects the final design and confirms the purchase, and the device sends this information to the server.

[0738] Sending production instructions

[0739] The server then sends production instructions to partner factories based on the received order information, including detailed specifications for the selected design.

[0740] Specific examples

[0741] If a brand uploads data on 50 out-of-date shirts to the server, the server stores this data in a database, and the AI ​​model begins learning from it. The server generates five new design proposals from the brand's shirt data and sends them to the user's device. When a user selects a brand's shirt and changes the color or pattern, the changes are updated in real time. In addition, an emotion sensor detects the user's emotions and recognizes their joy or dissatisfaction, allowing the server to automatically provide new suggestions and customization options.

[0742] Prompt Sentence Examples

[0743] "Generate five new design ideas from a selection of out-of-date shirts. Use an AI model that adjusts suggestions based on the user's emotional state."

[0744] This allows for a personalized experience for users and an efficient process for upcycling excess inventory for brands.

[0745] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0746] Step 1:

[0747] Input: The user (brand manager) prepares information about excess inventory (product list, images, material information).

[0748] Processing: User enters excess inventory information using the system's upload function.

[0749] Output: Excess inventory data is sent to the server.

[0750] Specific actions: A brand representative enters data for 50 out-of-date shirts according to the format and clicks the submit button.

[0751] Step 2:

[0752] Input: Excess inventory data is sent to the server.

[0753] Processing: The server stores the received surplus inventory data in the database.

[0754] Output: Product information stored in a database.

[0755] Specific operation: The server records the 50 shirt data received from Brand A in the database, and the information is saved in the database.

[0756] Step 3:

[0757] Input: Product information stored in the database.

[0758] Processing: The server invokes the AI ​​model based on the stored product information to generate new design proposals.

[0759] Output: The new design proposal generated.

[0760] Specific operation: The server provides data to the AI ​​model, which generates five new design ideas from Brand A's shirt data.

[0761] Step 4:

[0762] Input: Generated design proposal.

[0763] Process: The user selects a brand or product category on the device. The server sends relevant design ideas to the device based on the selection and displays them.

[0764] Output: Multiple design ideas displayed on the device.

[0765] Specific operation: The user selects a shirt from Brand A, and five design proposals sent from the server are displayed on the device.

[0766] Step 5:

[0767] Input: Design proposal displayed on the device.

[0768] Process: The user selects a design and customizes it with colors, patterns, etc.

[0769] Output: Customized design proposal.

[0770] How it works: A user selects a blue shirt design and changes the color to red. The change is reflected on the device in real time.

[0771] Step 6:

[0772] Input: Emotion data during user customization.

[0773] Processing: The emotion sensor collects user emotion data in real time and sends it to the emotion engine. The server analyzes this data and adjusts the interface and design proposals.

[0774] Output: Emotionally tailored interface and design ideas.

[0775] How it works: As users customize their design proposals, emotion sensors detect their joy or dissatisfaction in real time, and the server provides new suggestions and customization options based on that.

[0776] Step 7:

[0777] Input: Your final customized design proposal.

[0778] Processing: The customer selects the final design and confirms the purchase.

[0779] Output: Confirmed order information.

[0780] What happens: The user selects a design for the red shirt and confirms the purchase, which is then sent to the server.

[0781] Step 8:

[0782] Input: Received order information.

[0783] Processing: Based on the order information received by the server, production instructions are sent to affiliated factories.

[0784] Output: Production instructions sent to the factory.

[0785] Specific operation: The server sends the production instructions for the red shirt to the factory, the factory starts production based on the instructions, and the finished product is delivered to the user.

[0786] (Application example 2)

[0787] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0788] Conventional upcycling systems often lack a personalized user experience when generating design proposals to improve the commercial value of surplus inventory. Furthermore, they lack the ability to adjust the interface based on user sentiment, which hinders user satisfaction. This makes it difficult to efficiently utilize brands' surplus inventory. Furthermore, innovative methods are needed to improve the accuracy and suitability of design proposal generation.

[0789] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a brand to upload information about surplus inventory; database means for storing the uploaded product information; means for generating new design proposals from the stored product information using artificial intelligence; terminal means for a user to select a product and display multiple design proposals; means for customizing the design proposal selected by the user; means for sending production instructions to a production facility based on the final design proposal; emotion sensor means for detecting user emotions and adjusting the design proposals and interface; and emotion engine means for analyzing the detected emotion data and personalizing the interface and suggestions. This makes it possible to provide a personalized experience according to the user's emotions and efficiently upcycle a brand's surplus inventory.

[0790] "Excess inventory" refers to products or materials that remain unsold.

[0791] "Database" refers to an electronic data storage system where brands can store information on excess inventory and generated design ideas.

[0792] "Artificial intelligence" refers to algorithms and analytical methods that mimic human intelligence and generate new design ideas from product information.

[0793] "Terminal" refers to an electronic device that allows a user to select a product and display multiple design options.

[0794] "Customization" refers to the process of changing the colors and patterns of a design proposal selected by the user.

[0795] "Production facility" refers to the factory or manufacturing location where products are produced based on the final design proposal.

[0796] An "emotion sensor" refers to a device or software that detects a user's emotions.

[0797] An "emotion engine" is a system that analyzes detected emotional data and adjusts interface and design proposals according to the user's emotions.

[0798] The system that realizes this invention allows brands to manage excess inventory information, and based on that information, generates new design proposals and provides them to users. Furthermore, it recognizes user emotions in real time and adjusts the interface and design proposals to provide a personalized experience.

[0799] System configuration

[0800] Server-side configuration

[0801] 1. Database

[0802] Brands upload information about their excess inventory to a server and store that data in a database, which includes product lists, images, and material information.

[0803] 2. Artificial Intelligence (AI) Models

[0804] It is an AI model that generates new design ideas based on stored product information, learning from the brand's past styles, latest collections, industry trends, and successful upcycling examples.

[0805] 3. Production instruction function

[0806] This function sends production instructions to a production facility based on the final design proposal, including detailed specifications for the selected design.

[0807] 4. Emotion Engine

[0808] This engine analyzes user emotional data collected from emotion sensors and adjusts interface and design proposals according to the user's emotions.

[0809] Configuring the user device

[0810] 1. Product selection interface

[0811] This is the interface that allows users to select brands and product categories.

[0812] 2. Design display interface

[0813] This is an interface that displays multiple new design ideas to the user generated by the AI ​​model.

[0814] 3. Customization features

[0815] This is a feature that allows users to customize design proposals, allowing them to change colors and patterns.

[0816] 4. Rendering Function

[0817] This function generates customization results in real time and displays them to the user.

[0818] 5. Emotion Sensor

[0819] It is a sensor that uses the smartphone's camera and microphone to detect the user's emotions in real time, and sends this data to the emotion engine.

[0820] 6. Purchase Function

[0821] This is a function that allows users to select the final design and confirm the order.

[0822] Program processing overview

[0823] The server uses an AI model to generate new design proposals from product information stored in the database and sends them to the user's device. The user then uses the device to select a brand or product category and customize the displayed design proposals. The customization results are rendered in real time. The emotion sensor uses the smartphone's camera and an emotion recognition model (e.g., emotion_detection_model.h5) to detect the user's emotions. The emotion engine analyzes the detected emotion data and adjusts the interface and suggestions according to the user's emotions.

[0824] Specific examples

[0825] When a user launches the app and selects a shirt from Brand A, the server displays multiple design proposals that the user can customize. During customization, the user's emotions are detected by the smartphone camera and the data is sent to the emotion engine. If the emotion engine detects "excitement," it will suggest bolder design proposals.

[0826] Prompt Sentence Examples

[0827] "The user selected a shirt from Brand A. When detecting the user's emotion in the video captured by the camera, 'excitement' was recognized. In this case, what kind of design proposal should we suggest?"

[0828] The system allows brands to efficiently upcycle excess inventory while providing a personalized experience that responds to user emotions.

[0829] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0830] Step 1:

[0831] The server provides a means for brands to upload excess inventory information. The input is excess inventory data from the brand (product list, images, material information), and the output is excess inventory information stored in a database. Specifically, brands send product information to the server via a web form or API, and the server stores the data in a database.

[0832] Step 2:

[0833] The server uses the stored product information to run an artificial intelligence (AI) model to generate new design proposals. The input is surplus inventory information stored in the database, and the output is multiple generated design proposals. Specifically, the AI ​​model generates new design proposals by taking into account past style data, industry trends, and successful upcycling examples.

[0834] Step 3:

[0835] The server sends the generated design proposals to the user's device and displays them to the user on the device. The input is the design proposal generated by the AI ​​model, and the output is multiple design proposals displayed on the user's device. Specifically, the server sends the generated design proposals to the user's device in JSON format, which is then parsed and displayed on the device.

[0836] Step 4:

[0837] Users select a brand or product category on their device and browse the displayed design options. The input is the user's selection, and the output is the display of related design options. Specifically, users make selections through the interface, and design options based on those selections are displayed on the user's device.

[0838] Step 5:

[0839] The user customizes the selected design. The input is the user's selected design and customization instructions (color and pattern changes), and the output is the customization result. Specifically, the user changes the color and pattern, and the changes are rendered in real time and displayed to the user.

[0840] Step 6:

[0841] The device uses an emotion sensor to detect the user's emotions. The input is the user's facial and voice data captured by the smartphone's camera and microphone, and the output is the detected emotion data. Specifically, the data acquired by the camera and microphone is input into an emotion recognition model to generate emotion data.

[0842] Step 7:

[0843] The server uses an emotion engine to analyze the detected emotion data and adjust the interface or design proposals. The input is the emotion data sent from the emotion sensor, and the output is an adjusted interface or new proposals. Specifically, the emotion engine analyzes the detected emotion data (e.g., "excitement") and proposes new design proposals or interfaces accordingly.

[0844] Step 8:

[0845] The user selects the final design and confirms the order. The input is the final design selected by the user, and the output is data sent to the server as order information. Specifically, the user orders the confirmed design, and the information is sent to the server.

[0846] Step 9:

[0847] The server sends production instructions to the production facility based on the received order information. The input is the order information sent by the user, and the output is the production instructions sent to the production facility. Specifically, the server sends the order information to the production facility, and the production facility produces the product based on the instructions.

[0848] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0849] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0850] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0851] [Third embodiment]

[0852] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0853] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0854] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0855] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0856] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0857] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0858] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0859] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0860] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0861] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0862] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0863] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0864] The following describes an embodiment of the "UpCycle Hub" system of the present invention. This system aims to enable brands to add value to their excess inventory by upcycling it and converting it into new "sellable products."

[0865] System Configuration

[0866] 1. Server-side components:

[0867] Database: A database for storing excess inventory information uploaded by brands and generated design proposals.

[0868] AI model: Artificial intelligence for generating new design ideas from product information.

[0869] Production instruction function: A function that sends production instructions to partner factories based on the final design proposal.

[0870] 2. User device components:

[0871] Product selection interface: An interface for users to select a brand or product category.

[0872] Design display interface: An interface that displays the generated multiple design proposals to the user.

[0873] Customization feature: A feature that allows users to customize design proposals.

[0874] Rendering function: A function for generating and displaying customization results in real time.

[0875] Purchase function: A function that allows users to select the final design and confirm the order.

[0876] Program processing and specific examples

[0877] First, brands upload their excess inventory information

[0878] The server receives the product list, images, and material information of the excess inventory sent by the brand.

[0879] This information is stored in a database and fed into the AI ​​model.

[0880] Examples:

[0881] If Brand A uploads data on 50 out-of-date shirts to the server, the server stores this data in a database and the AI ​​model begins learning from it.

[0882] Next, the server uses AI to generate design proposals.

[0883] The server generates new design proposals from the stored product information.

[0884] The generated design proposal is sent to the user's device.

[0885] Examples:

[0886] The server uses data from Brand A's shirts to generate five new design ideas and send them to the user's device. The AI ​​model is based on data such as the brand's past styles, latest collections, industry trends, and successful upcycling examples.

[0887] User selects product and sees design options

[0888] The user selects a brand or product category on the user device.

[0889] The server sends relevant design ideas based on the selected brand and category and displays them on the user's device.

[0890] Examples:

[0891] When a user selects a shirt from Brand A, five design ideas sent from the server are displayed on the device.

[0892] Users customize the design

[0893] Users can select the design provided on their device and customize it (changing colors, patterns, etc.).

[0894] The customization results are rendered in real time and displayed to the user.

[0895] Examples:

[0896] If a user selects a blue shirt design and changes the color to red, the change will be reflected in real time.

[0897] The user selects the final design and confirms the order.

[0898] The user selects the final design and confirms the purchase.

[0899] The terminal sends this information to the server.

[0900] Examples:

[0901] Once the user selects a design for the red shirt and confirms the purchase, the information is sent to the server.

[0902] Finally, the server sends the production instructions to the factory.

[0903] The server sends production instructions to affiliated factories based on the received order information.

[0904] The factory receives production instructions, produces the product, and delivers it to the user.

[0905] Examples:

[0906] The server sends production instructions for the red shirt to the factory, which then starts production based on those instructions and delivers the finished product to the user.

[0907] This creates an efficient upcycling process, reducing the costs for brands disposing of excess stock and allowing consumers to acquire unique, sustainable products.

[0908] The processing flow will be explained below.

[0909] Step 1:

[0910] The brand uploads excess inventory information to the server. Specifically, the brand prepares a product list, images, and material information for the excess inventory, and transmits the data to the server.

[0911] Step 2:

[0912] The server stores the received product list, images, and material information in a database, which is then referenced by artificial intelligence (AI) for subsequent processing.

[0913] Step 3:

[0914] The server uses the stored product information to generate new design ideas using an AI model that learns from data such as the brand's past styles, the latest collections, industry trends, and successful upcycling examples.

[0915] Step 4:

[0916] The server transfers the generated design proposals to the user's device, where multiple design proposals are displayed for the user to choose from.

[0917] Step 5:

[0918] The user selects their preferred design from multiple design options displayed on their device, and is then presented with an interface to customize the selected design.

[0919] Step 6:

[0920] Users can customize the design using the customization interface, specifically by changing colors, patterns, etc. The results of this customization are rendered in real time, providing visual feedback to the user.

[0921] Step 7:

[0922] The user finally decides on a design that satisfies them and decides to purchase it. When they click the purchase button, that information is sent to the server.

[0923] Step 8:

[0924] Based on the purchase information received, the server sends production instructions to partner factories, including detailed specifications for the selected design.

[0925] Step 9:

[0926] The factory begins manufacturing the product based on the manufacturing instructions received from the server, and the completed product is shipped to the specified delivery address.

[0927] Step 10:

[0928] The server monitors the entire process, providing status updates to the user as needed, and stores all transaction history in a database for future reference.

[0929] Example 1

[0930] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0931] When a brand has excess inventory, they need a way to effectively utilize that inventory and add value. However, traditional methods make it difficult to efficiently create new designs and offer them to customers, and the process is cumbersome. Therefore, there is a need for a system that can upcycle excess inventory and offer it as new products that customers can customize.

[0932] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0933] In this invention, the server includes a means for brands to upload information about their excess inventory, a database means for storing the uploaded product information, and a means for generating new design ideas from the stored product information using artificial intelligence, thereby enabling excess inventory to be efficiently provided as new customizable design ideas.

[0934] "Excess inventory" is excess inventory of merchandise that has not been sold.

[0935] "Uploading" is the act of transferring data from a computer or device to a server.

[0936] A "database" is a system for systematically storing, managing, and searching large amounts of data.

[0937] "Artificial intelligence" is a technology that allows computers to imitate human intellectual tasks, specifically referring to the technology of data analysis and decision-making.

[0938] A "design proposal" is a proposal for a new look or style for a product.

[0939] A "terminal" is a device operated by a user, including a personal computer or smartphone.

[0940] "Customization" refers to the act of a user making changes to a product, such as its color or pattern.

[0941] "Rendering" is the process of generating images or videos in computer graphics.

[0942] "Production instructions" are orders that instruct the factory on specific work details.

[0943] A "brand" is an identifying name for a company or organization that offers a particular product or service.

[0944] The system of this invention aims to enable brands to effectively utilize excess inventory and offer it as new, customizable products. The program of this system has the following main components and processing steps:

[0945] First, the brand uploads excess inventory information. The user sends a list of excess inventory items (including images, material information, and product descriptions) to the server via the admin screen as a CSV file or via API. The server validates the received data and stores it in a PostgreSQL database.

[0946] The server then uses an artificial intelligence (AI) model based on the stored data to generate new design ideas. This AI model is implemented using TensorFlow and learns from past styles and the latest fashion trends. For example, the server might input the following prompt into the AI ​​model: "Generate five new design ideas based on the image and material information of a shirt. Please consider past styles and the latest fashion trends."

[0947] The generated design proposals are sent from the server to the user's device using React.js. The user can select a brand or product category on the interface and view multiple design proposals. The user then customizes the design proposal in real time using the customization feature powered by Three.js. For example, a user can select a blue shirt design and change the color to red. This change is instantly rendered and displayed to the user.

[0948] Finally, the user selects the customized design and clicks the purchase button to confirm the purchase. This purchase process uses the Stripe API to process payment, and the final design and payment information are sent to the server. The server receives this and sends specific production instructions to partner factories. The factories then produce the product according to those instructions and deliver it to the user.

[0949] The system allows brands to efficiently upcycle excess inventory and offer customers unique, customizable products.

[0950] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0951] Step 1:

[0952] A user uploads surplus inventory information.

[0953] Users access the management screen and send a product list (images, material information, product descriptions, etc.) to the server via a CSV file or API. The input data is a list of excess inventory products, which is saved in the server's database. The CSV file contains detailed information about each product, and when sent via API, the data is sent in JSON format. The output is detailed data about each product saved in the database.

[0954] Step 2:

[0955] The server stores the received surplus inventory information in a database.

[0956] The server validates the data it receives and inserts it into a PostgreSQL database. This data includes images, sizes, materials, and descriptions for each product. The input product data is validated and efficiently stored in the database. The output is the saved product information.

[0957] Step 3:

[0958] The server uses the AI ​​model to generate design proposals.

[0959] The server calls an AI model using TensorFlow based on the saved product information. For example, the AI ​​model receives a prompt such as, "Generate five new design ideas based on shirt images and material information. Consider past styles and the latest fashion trends." In this process, the AI ​​model generates new design ideas using image processing and design generation algorithms. The input data are product images and material information, and the output is the generated design ideas.

[0960] Step 4:

[0961] The server sends the generated design proposal to the user's device.

[0962] The generated design proposals are formatted in JSON format and sent to a user interface built using React.js. Multiple design proposals are displayed on the user's device. The input data are the generated design proposals, and the output is the design proposal displayed on the user's device.

[0963] Step 5:

[0964] The user selects a brand and product category.

[0965] The user selects a brand and product category (e.g., "shirts," "pants," etc.) on the interface. The selection information is sent to the server, which then returns corresponding design proposals. The input data is the user's brand and category selection information, and the output is the corresponding design proposals.

[0966] Step 6:

[0967] The user customizes the design proposal.

[0968] Users can use the customization feature powered by Three.js to change the colors and patterns of the design, and the changes are instantly rendered and displayed to the user. The input data is the user's chosen design and change instructions, and the output is the customized design.

[0969] Step 7:

[0970] The user selects the final design and confirms the purchase.

[0971] The user selects a customized design and clicks the purchase button. The terminal processes the payment using the Stripe API and sends the final design and payment information to the server. The input data is the user's final selection and payment information, and the output is order confirmation.

[0972] Step 8:

[0973] The server sends production instructions to the partner factory.

[0974] The server sends production instructions to partner factories based on the received final design and order information. The factories then use this information to produce the product and deliver it to the user. The input data is the final design and order information, and the output is production instructions.

[0975] (Application example 1)

[0976] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0977] In today's retail industry, brands face a major challenge in effectively handling excess inventory. When excess inventory occurs, they need a way to create new value without wasting it. At the same time, it is important to offer customized products that meet diverse customer needs. Another issue is the difficulty for employees in customer service to instantly grasp inventory information and customization suggestions and make real-time suggestions during interactions. To solve these challenges, an efficient and flexible inventory management system needs to be integrated with customer service systems.

[0978] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0979] In this invention, the server includes: means for brands to upload information about excess inventory; database means for storing the uploaded product information; means for generating new design proposals from the stored product information using artificial intelligence; terminal means for users to select products and display multiple design proposals; means for customizing the design proposal selected by the user; means for sending production instructions to a factory based on the final design proposal; and means for a sales associate wearing smart glasses to check inventory information in real time while interacting with a customer and make customization proposals, thereby enabling efficient upcycling of excess inventory and customization proposals that meet diverse customer needs.

[0980] "Means for brands to upload excess inventory information" is a function that allows brands to input or transmit information about their unsold or overproduced inventory into the system.

[0981] "Database means for storing uploaded product information" refers to a digital database function for organizing and safely storing submitted inventory information.

[0982] "A means of generating new design ideas from stored product information using artificial intelligence" is a function that uses AI technology to automatically generate new design ideas based on stored surplus inventory information.

[0983] "Terminal means for allowing a user to select a product and display multiple design proposals" refers to a function of a digital device that allows a user to select a desired product and display various design proposals for that product.

[0984] "Means for customizing the design proposal selected by the user" refers to a function that allows the user to change and adjust the proposed design proposal to suit their preferences.

[0985] "Means for sending production instructions to the factory based on the final design proposal" is a function for instructing the factory to produce a product based on the final design selected by the user.

[0986] "A means for a sales associate wearing smart glasses to check inventory information in real time while interacting with a customer and make customization suggestions" is a function that allows a sales associate to wear smart glasses, instantly check inventory information while interacting with a customer, and make customization suggestions to the customer.

[0987] MODE FOR CARRYING OUT THE INVENTION

[0988] The system of this invention aims to enable brands to upcycle their excess inventory. Specifically, it enables brands to upload information about their excess inventory, generate new design ideas using that information, and then users can customize and order the products. The system configuration and operating procedures are explained below.

[0989] System Configuration

[0990] 1. Server-side components:

[0991] Database: A database for storing excess inventory information uploaded by brands and generated design proposals. This uses common database software such as MySQL or PostgreSQL.

[0992] AI model: Artificial intelligence for generating new design ideas from product information. Specifically, deep learning frameworks such as TensorFlow and PyTorch are used.

[0993] Production instruction function: A function that sends production instructions to partner factories based on the final design. Network communication is expected to be performed using REST API.

[0994] 2. User device components:

[0995] Product selection interface: An interface for users to select brands and product categories. This is implemented as a web application using a JavaScript framework such as React.

[0996] Design display interface: An interface that displays multiple generated design proposals to the user. This is displayed as a web GUI and uses HTML5 and CSS3.

[0997] Customization feature: A feature that allows users to customize design proposals. The customization results are displayed in real time using a 3D rendering library such as Three.js.

[0998] Rendering function: A function for generating and displaying customization results in real time. This uses WebGL to draw 3D graphics.

[0999] Purchase function: A function that allows users to select the final design and confirm the order. Electronic payment services such as Stripe are integrated.

[1000] 3. Smart Glasses:

[1001] Inventory information confirmation function: A function in which store clerks wear smart glasses and check the store's inventory information in real time. The smart glasses are Google Glass or similar and communicate with the database via Wi-Fi.

[1002] Customization suggestion function: A function that reflects design proposals on the display screen of the smart glasses in real time and makes customization suggestions to customers. This allows for instant suggestions during conversations with customers.

[1003] Specific examples

[1004] First, brands upload excess inventory information to a server, which receives it and stores it in a database. Next, an AI model generates new design ideas based on this data and sends them to users' devices, where they can view and further customize the designs.

[1005] For example, let's say Brand A uploads information about 50 out-of-date shirts. This information is stored in a database, and new design proposals are generated using an AI model. The five resulting design proposals are displayed on the user's device through a React-based web application. The user can choose their favorite design and change the color and pattern using the customization function using Three.js. The final customization result is rendered and displayed in real time using WebGL. Once the purchase is confirmed using Stripe, the information is sent to the manufacturer via the server.

[1006] Furthermore, if a sales associate is wearing smart glasses, they can check inventory information in real time and make customization suggestions while interacting with the customer. For example, if a customer says, "Red is good," the UI of the smart glasses can display a red shirt design in real time and ask for confirmation.

[1007] Example of an input prompt for a generative AI model:

[1008] Shirt details: Blue, cotton, long sleeves, size M.

[1009] Past styles: casual, stylish.

[1010] Upcycling policy: environmentally friendly and in line with the latest Chinese trends.

[1011] The above configuration enables efficient upcycling of excess inventory and customization proposals to meet the diverse needs of customers.

[1012] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1013] Step 1:

[1014] Brands upload excess inventory information. Brands enter information about their unsold or overproduced inventory into the system. This information includes details such as product name, quantity, image, material, and size. The uploaded data is stored in a database.

[1015] Step 2:

[1016] The server provides the stored product information to the AI ​​model, which then generates prompts based on this data to generate new design ideas. These prompts include product information, past styles, industry trends, etc. The generated design ideas are then stored on the server.

[1017] Step 3:

[1018] The server sends the generated design proposal to the user's device, which then displays it to the user through a user interface, such as a React-based web application, using HTML and CSS.

[1019] Step 4:

[1020] Users can select a design and use the customization feature. Based on the design they select, customization is performed in real time using the 3D rendering library Three.js. Color changes, pattern adjustments, and more are possible.

[1021] Step 5:

[1022] The user confirms the final design and checks out. The user confirms the final custom design and completes the payment process using an electronic payment service such as Stripe. This information is sent to the server.

[1023] Step 6:

[1024] The server sends the final design and purchasing information to the factory, which then uses a REST API to send production instructions to the factory, which then produces the product and delivers it to the user.

[1025] Step 7:

[1026] The store associate uses the smart glasses to manage inventory and handle customer service. Wearing the smart glasses, the associate checks inventory information in real time while interacting with the customer and makes customization suggestions based on the design ideas generated by the AI ​​model. If the customer accepts the suggestion, the information is sent to the server in real time, and the process begins again in step 6.

[1027] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1028] The following shows an example of the "UpCycle Hub" system of the present invention, which combines an emotion engine. The system aims to help brands add value to their excess inventory by upcycling it and transforming it into new "sellable products." It also aims to recognize user emotions and adjust the interface in real time to provide a more personalized experience.

[1029] System Configuration

[1030] 1. Server-side components:

[1031] Database: A database for storing excess inventory information uploaded by brands and generated design proposals.

[1032] AI model: Artificial intelligence for generating new design ideas from product information.

[1033] Production instruction function: A function that sends production instructions to partner factories based on the final design proposal.

[1034] Emotion engine: An engine that collects and analyzes user emotional data. This data is used to refine design proposals and interfaces.

[1035] 2. User device components:

[1036] Product selection interface: An interface for users to select a brand or product category.

[1037] Design display interface: An interface that displays the generated multiple design proposals to the user.

[1038] Customization feature: A feature that allows users to customize design proposals.

[1039] Rendering function: A function for generating and displaying customization results in real time.

[1040] Emotion Sensor: A sensor for detecting user emotions in real time and sending them to the emotion engine.

[1041] Purchase function: A function that allows users to select the final design and confirm the order.

[1042] Program processing and specific examples

[1043] First, brands upload their excess inventory information

[1044] Brands send product lists, images, and material information for excess inventory to the server, which stores this data in a database and provides it to the AI ​​model.

[1045] Examples:

[1046] If Brand A uploads data on 50 out-of-date shirts to the server, the server stores this data in a database and the AI ​​model begins learning from it.

[1047] Next, the server uses AI to generate design proposals.

[1048] The server generates new design ideas from stored product information, with the AI ​​model learning from data such as the brand's past styles, latest collections, industry trends, and successful upcycling examples.

[1049] Examples:

[1050] The server uses data from Brand A's shirts to generate five new design ideas and sends them to the user's device.

[1051] User selects product and sees design options

[1052] The user selects a brand or product category on the user device, and the server sends relevant design proposals based on the selected brand or category and displays them on the user device.

[1053] Examples:

[1054] When a user selects a shirt from Brand A, five design ideas sent from the server are displayed on the device.

[1055] Users customize the design

[1056] Users can select from the provided design ideas on their device and customize them (changing colors, patterns, etc.). The customization results are rendered in real time and displayed to the user.

[1057] Examples:

[1058] If a user selects a blue shirt design and changes the color to red, the change will be reflected in real time.

[1059] Recognize user emotions and adjust the interface accordingly

[1060] The emotion sensor captures the user's emotional data in real time and sends it to the emotion engine, which then analyzes the data and adjusts the design and interface according to the user's emotional state.

[1061] Examples:

[1062] As users customize their design proposals, emotion sensors recognize their joys and frustrations, and the emotion engine uses that data to provide new suggestions and customization options.

[1063] The user selects the final design and confirms the order.

[1064] The user selects the final design and confirms the purchase. The device sends this information to the server.

[1065] Examples:

[1066] Once the user selects a design for the red shirt and confirms the purchase, the information is sent to the server.

[1067] Finally, the server sends the production instructions to the factory.

[1068] The server then sends production instructions to partner factories based on the received order information, including detailed specifications for the selected design.

[1069] Examples:

[1070] The server sends production instructions for the red shirt to the factory, which then starts production based on those instructions and delivers the finished product to the user.

[1071] This allows the emotional engine to adjust the interface and make design suggestions based on the user's emotions, providing a more personalized experience, increasing user satisfaction and enabling a more efficient upcycling process for brands' excess inventory.

[1072] The processing flow will be explained below.

[1073] Step 1:

[1074] The brand uploads excess inventory information to the server. Specifically, the brand prepares a product list, images, and material information for the excess inventory, and transmits the data to the server.

[1075] Step 2:

[1076] The server stores the received product list, images, and material information in a database, which is then referenced by artificial intelligence (AI) for subsequent processing.

[1077] Step 3:

[1078] The server uses the stored product information to generate new design ideas using an AI model that learns from data such as the brand's past styles, the latest collections, industry trends, and successful upcycling examples.

[1079] Step 4:

[1080] The server transfers the generated design proposals to the user's device, where multiple design proposals are displayed for the user to choose from.

[1081] Step 5:

[1082] The user selects their preferred design from multiple design options displayed on their device, and is then presented with an interface to customize the selected design.

[1083] Step 6:

[1084] Users can customize the design using the customization interface, specifically by changing colors, patterns, etc. The results of this customization are rendered in real time, providing visual feedback to the user.

[1085] Step 7:

[1086] The user finally decides on a design that satisfies them and decides to purchase it. When they click the purchase button, that information is sent to the server.

[1087] Step 8:

[1088] Based on the purchase information received, the server sends production instructions to partner factories, including detailed specifications for the selected design.

[1089] Step 9:

[1090] The factory begins manufacturing the product based on the manufacturing instructions received from the server, and the completed product is shipped to the specified delivery address.

[1091] Step 10:

[1092] The user device acquires the user's emotional data in real time through an emotion sensor and transmits it to the server. The emotional data includes the user's facial expressions, voice, and body movements.

[1093] Step 11:

[1094] The server-side emotion engine analyzes the acquired emotion data and determines the user's emotional state, for example, whether the user is happy or confused.

[1095] Step 12:

[1096] Based on the emotion engine, the server adjusts the interface and proposes new designs. Based on emotion data, it suggests design ideas and customization options that users are likely to like.

[1097] Step 13:

[1098] The server combines the user's emotional data with their past purchase history and customization history to generate more personalized design proposals and send them to the user's device.

[1099] Step 14:

[1100] The user's device displays the updated design, and the user can customize and select again, and this process is repeated until the user is finally satisfied.

[1101] Step 15:

[1102] The server monitors the entire process, providing status updates to the user as needed, and stores all transaction history in a database for future reference.

[1103] Example 2

[1104] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1105] While existing upcycling systems effectively utilize surplus inventory and generate designs, they lack a personalized experience that responds to individual users' emotions and preferences. Furthermore, they do not use user emotional data to adjust interfaces or design proposals, making it difficult to improve customer satisfaction.

[1106] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1107] In this invention, the server includes: means for brands to upload information about excess inventory; database means for storing uploaded product information; means for generating new design proposals from the stored product information using artificial intelligence; terminal means for users to select products and display multiple design proposals; means for customizing the design proposal selected by the user; means for sending production instructions to a factory based on the final design proposal; emotion engine means for collecting and analyzing user emotion data; and means for adjusting the interface and design proposals based on the emotion data. This makes it possible to adjust the interface and design proposals according to the user's emotions, providing a more personalized experience.

[1108] A "brand" is a concept that includes names, logos, symbols, etc. that allow a company or product to be identified in the market.

[1109] "Excess inventory" refers to goods or materials that remain unsold.

[1110] "Means for uploading information" refers to any method or device for transferring digital data to the system.

[1111] A "database" is a structured collection of information for efficient storage, management, and retrieval of data.

[1112] "Artificial intelligence" refers to computer systems and software that simulate human intelligence, and is a technology that learns and makes inferences based on data.

[1113] "Design proposal" refers to a proposal or concept regarding the appearance and functionality of a product or service.

[1114] "Terminal" means a device that allows a User to access and operate a digital system.

[1115] "Customization means" refers to a method or device that allows a user to change or adjust certain aspects of a product or service.

[1116] "Production instructions" are detailed instructions or instructions for making a particular product.

[1117] "Factory" refers to a facility or building where goods are manufactured or processed.

[1118] "Emotional data" refers to information that expresses a user's emotional state as numbers or categories.

[1119] An "emotion engine" refers to software or algorithms that analyze emotional data and adjust system behavior based on that data.

[1120] An "interface" refers to the screens and input devices that allow a user to interact with a system.

[1121] The present invention combines an emotion engine with the "UpCycle Hub" system. The system aims to enable brands to upcycle excess inventory, add new value to it, and convert it into sellable products. The system also aims to recognize user emotions and adjust the interface in real time to provide a more personalized experience. The following describes specific embodiments of the present invention.

[1122] System Configuration

[1123] 1. Server-side components:

[1124] Database: A database for storing excess inventory information uploaded by brands and generated design proposals.

[1125] AI model: Artificial intelligence to generate new design ideas from product information. The AI ​​model is trained on past styles, the latest collections, industry trends, and successful upcycling examples.

[1126] Production instruction function: A function for sending production instructions to partner factories based on the final design proposal.

[1127] Emotion engine: An engine for collecting and analyzing user emotion data to adjust design proposals and interfaces.

[1128] 2. User device components:

[1129] Product selection interface: An interface for users to select a brand or product category.

[1130] Design display interface: An interface for displaying the generated multiple design proposals to the user.

[1131] Customization: A feature that allows users to customize the design proposal. Colors and patterns can be changed.

[1132] Rendering function: A function for generating and displaying customization results in real time.

[1133] Emotion Sensor: A sensor for detecting user emotions in real time and sending them to the emotion engine.

[1134] Purchase function: A function that allows users to select the final design and confirm the order.

[1135] Specific program description

[1136] Uploading excess inventory information

[1137] The user (brand manager) uploads information about excess inventory (product list, images, material information) to the server, which stores this data in a database and provides it to the AI ​​model.

[1138] Storing data and providing it to AI models

[1139] The server records the received product information in a database and passes it to the AI ​​model, which then learns from it and generates new design proposals.

[1140] Generate design ideas

[1141] The server uses an AI model to generate new design ideas from the stored data, learning from past styles and trends to suggest optimal designs.

[1142] Send and view design ideas

[1143] When a user selects a brand or product category, the server generates design proposals based on the selection and sends them to the user's device for display.

[1144] Customizing the design

[1145] Users can select from the displayed design options and change the colors and patterns, and the device will render and display the customization results in real time.

[1146] Acquiring emotional data and adjusting the interface

[1147] The emotion sensor collects user emotional data in real time and sends it to the emotion engine, which then analyzes it and adjusts the interface and design proposals according to the user's emotional state.

[1148] Final design selection and order confirmation

[1149] The user selects the final design and confirms the purchase, and the device sends this information to the server.

[1150] Sending production instructions

[1151] The server then sends production instructions to partner factories based on the received order information, including detailed specifications for the selected design.

[1152] Specific examples

[1153] If a brand uploads data on 50 out-of-date shirts to the server, the server stores this data in a database, and the AI ​​model begins learning from it. The server generates five new design proposals from the brand's shirt data and sends them to the user's device. When a user selects a brand's shirt and changes the color or pattern, the changes are updated in real time. In addition, an emotion sensor detects the user's emotions and recognizes their joy or dissatisfaction, allowing the server to automatically provide new suggestions and customization options.

[1154] Prompt Sentence Examples

[1155] "Generate five new design ideas from a selection of out-of-date shirts. Use an AI model that adjusts suggestions based on the user's emotional state."

[1156] This allows for a personalized experience for users and an efficient process for upcycling excess inventory for brands.

[1157] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1158] Step 1:

[1159] Input: The user (brand manager) prepares information about excess inventory (product list, images, material information).

[1160] Processing: User enters excess inventory information using the system's upload function.

[1161] Output: Excess inventory data is sent to the server.

[1162] Specific actions: A brand representative enters data for 50 out-of-date shirts according to the format and clicks the submit button.

[1163] Step 2:

[1164] Input: Excess inventory data is sent to the server.

[1165] Processing: The server stores the received surplus inventory data in the database.

[1166] Output: Product information stored in a database.

[1167] Specific operation: The server records the 50 shirt data received from Brand A in the database, and the information is saved in the database.

[1168] Step 3:

[1169] Input: Product information stored in the database.

[1170] Processing: The server invokes the AI ​​model based on the stored product information to generate new design proposals.

[1171] Output: The new design proposal generated.

[1172] Specific operation: The server provides data to the AI ​​model, which generates five new design ideas from Brand A's shirt data.

[1173] Step 4:

[1174] Input: Generated design proposal.

[1175] Process: The user selects a brand or product category on the device. The server sends relevant design ideas to the device based on the selection and displays them.

[1176] Output: Multiple design ideas displayed on the device.

[1177] Specific operation: The user selects a shirt from Brand A, and five design proposals sent from the server are displayed on the device.

[1178] Step 5:

[1179] Input: Design proposal displayed on the device.

[1180] Process: The user selects a design and customizes it with colors, patterns, etc.

[1181] Output: Customized design proposal.

[1182] How it works: A user selects a blue shirt design and changes the color to red. The change is reflected on the device in real time.

[1183] Step 6:

[1184] Input: Emotion data during user customization.

[1185] Processing: The emotion sensor collects user emotion data in real time and sends it to the emotion engine. The server analyzes this data and adjusts the interface and design proposals.

[1186] Output: Emotionally tailored interface and design ideas.

[1187] How it works: As users customize their design proposals, emotion sensors detect their joy or dissatisfaction in real time, and the server provides new suggestions and customization options based on that.

[1188] Step 7:

[1189] Input: Your final customized design proposal.

[1190] Processing: The customer selects the final design and confirms the purchase.

[1191] Output: Confirmed order information.

[1192] What happens: The user selects a design for the red shirt and confirms the purchase, which is then sent to the server.

[1193] Step 8:

[1194] Input: Received order information.

[1195] Processing: Based on the order information received by the server, production instructions are sent to affiliated factories.

[1196] Output: Production instructions sent to the factory.

[1197] Specific operation: The server sends the production instructions for the red shirt to the factory, the factory starts production based on the instructions, and the finished product is delivered to the user.

[1198] (Application example 2)

[1199] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1200] Conventional upcycling systems often lack a personalized user experience when generating design proposals to improve the commercial value of surplus inventory. Furthermore, they lack the ability to adjust the interface based on user sentiment, which hinders user satisfaction. This makes it difficult to efficiently utilize brands' surplus inventory. Furthermore, innovative methods are needed to improve the accuracy and suitability of design proposal generation.

[1201] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a brand to upload information about surplus inventory; database means for storing the uploaded product information; means for generating new design proposals from the stored product information using artificial intelligence; terminal means for a user to select a product and display multiple design proposals; means for customizing the design proposal selected by the user; means for sending production instructions to a production facility based on the final design proposal; emotion sensor means for detecting user emotions and adjusting the design proposals and interface; and emotion engine means for analyzing the detected emotion data and personalizing the interface and suggestions. This makes it possible to provide a personalized experience according to the user's emotions and efficiently upcycle a brand's surplus inventory.

[1202] "Excess inventory" refers to products or materials that remain unsold.

[1203] "Database" refers to an electronic data storage system where brands can store information on excess inventory and generated design ideas.

[1204] "Artificial intelligence" refers to algorithms and analytical methods that mimic human intelligence and generate new design ideas from product information.

[1205] "Terminal" refers to an electronic device that allows a user to select a product and display multiple design options.

[1206] "Customization" refers to the process of changing the colors and patterns of a design proposal selected by the user.

[1207] "Production facility" refers to the factory or manufacturing location where products are produced based on the final design proposal.

[1208] An "emotion sensor" refers to a device or software that detects a user's emotions.

[1209] An "emotion engine" is a system that analyzes detected emotional data and adjusts interface and design proposals according to the user's emotions.

[1210] The system that realizes this invention allows brands to manage excess inventory information, and based on that information, generates new design proposals and provides them to users. Furthermore, it recognizes user emotions in real time and adjusts the interface and design proposals to provide a personalized experience.

[1211] System configuration

[1212] Server-side configuration

[1213] 1. Database

[1214] Brands upload information about their excess inventory to a server and store that data in a database, which includes product lists, images, and material information.

[1215] 2. Artificial Intelligence (AI) Models

[1216] It is an AI model that generates new design ideas based on stored product information, learning from the brand's past styles, latest collections, industry trends, and successful upcycling examples.

[1217] 3. Production instruction function

[1218] This function sends production instructions to a production facility based on the final design proposal, including detailed specifications for the selected design.

[1219] 4. Emotion Engine

[1220] This engine analyzes user emotional data collected from emotion sensors and adjusts interface and design proposals according to the user's emotions.

[1221] Configuring the user device

[1222] 1. Product selection interface

[1223] This is the interface that allows users to select brands and product categories.

[1224] 2. Design display interface

[1225] This is an interface that displays multiple new design ideas to the user generated by the AI ​​model.

[1226] 3. Customization features

[1227] This is a feature that allows users to customize design proposals, allowing them to change colors and patterns.

[1228] 4. Rendering Function

[1229] This function generates customization results in real time and displays them to the user.

[1230] 5. Emotion Sensor

[1231] It is a sensor that uses the smartphone's camera and microphone to detect the user's emotions in real time, and sends this data to the emotion engine.

[1232] 6. Purchase Function

[1233] This is a function that allows users to select the final design and confirm the order.

[1234] Program processing overview

[1235] The server uses an AI model to generate new design proposals from product information stored in the database and sends them to the user's device. The user then uses the device to select a brand or product category and customize the displayed design proposals. The customization results are rendered in real time. The emotion sensor uses the smartphone's camera and an emotion recognition model (e.g., emotion_detection_model.h5) to detect the user's emotions. The emotion engine analyzes the detected emotion data and adjusts the interface and suggestions according to the user's emotions.

[1236] Specific examples

[1237] When a user launches the app and selects a shirt from Brand A, the server displays multiple design proposals that the user can customize. During customization, the user's emotions are detected by the smartphone camera and the data is sent to the emotion engine. If the emotion engine detects "excitement," it will suggest bolder design proposals.

[1238] Prompt Sentence Examples

[1239] "The user selected a shirt from Brand A. When detecting the user's emotion in the video captured by the camera, 'excitement' was recognized. In this case, what kind of design proposal should we suggest?"

[1240] The system allows brands to efficiently upcycle excess inventory while providing a personalized experience that responds to user emotions.

[1241] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1242] Step 1:

[1243] The server provides a means for brands to upload excess inventory information. The input is excess inventory data from the brand (product list, images, material information), and the output is excess inventory information stored in a database. Specifically, brands send product information to the server via a web form or API, and the server stores the data in a database.

[1244] Step 2:

[1245] The server uses the stored product information to run an artificial intelligence (AI) model to generate new design proposals. The input is surplus inventory information stored in the database, and the output is multiple generated design proposals. Specifically, the AI ​​model generates new design proposals by taking into account past style data, industry trends, and successful upcycling examples.

[1246] Step 3:

[1247] The server sends the generated design proposals to the user's device and displays them to the user on the device. The input is the design proposal generated by the AI ​​model, and the output is multiple design proposals displayed on the user's device. Specifically, the server sends the generated design proposals to the user's device in JSON format, which is then parsed and displayed on the device.

[1248] Step 4:

[1249] Users select a brand or product category on their device and browse the displayed design options. The input is the user's selection, and the output is the display of related design options. Specifically, users make selections through the interface, and design options based on those selections are displayed on the user's device.

[1250] Step 5:

[1251] The user customizes the selected design. The input is the user's selected design and customization instructions (color and pattern changes), and the output is the customization result. Specifically, the user changes the color and pattern, and the changes are rendered in real time and displayed to the user.

[1252] Step 6:

[1253] The device uses an emotion sensor to detect the user's emotions. The input is the user's facial and voice data captured by the smartphone's camera and microphone, and the output is the detected emotion data. Specifically, the data acquired by the camera and microphone is input into an emotion recognition model to generate emotion data.

[1254] Step 7:

[1255] The server uses an emotion engine to analyze the detected emotion data and adjust the interface or design proposals. The input is the emotion data sent from the emotion sensor, and the output is an adjusted interface or new proposals. Specifically, the emotion engine analyzes the detected emotion data (e.g., "excitement") and proposes new design proposals or interfaces accordingly.

[1256] Step 8:

[1257] The user selects the final design and confirms the order. The input is the final design selected by the user, and the output is data sent to the server as order information. Specifically, the user orders the confirmed design, and the information is sent to the server.

[1258] Step 9:

[1259] The server sends production instructions to the production facility based on the received order information. The input is the order information sent by the user, and the output is the production instructions sent to the production facility. Specifically, the server sends the order information to the production facility, and the production facility produces the product based on the instructions.

[1260] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1261] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1262] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1263] [Fourth embodiment]

[1264] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1265] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1266] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1267] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1268] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1269] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1270] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1271] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1272] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1273] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1274] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1275] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1276] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1277] The following describes an embodiment of the "UpCycle Hub" system of the present invention. This system aims to enable brands to add value to their excess inventory by upcycling it and converting it into new "sellable products."

[1278] System Configuration

[1279] 1. Server-side components:

[1280] Database: A database for storing excess inventory information uploaded by brands and generated design proposals.

[1281] AI model: Artificial intelligence for generating new design ideas from product information.

[1282] Production instruction function: A function that sends production instructions to partner factories based on the final design proposal.

[1283] 2. User device components:

[1284] Product selection interface: An interface for users to select a brand or product category.

[1285] Design display interface: An interface that displays the generated multiple design proposals to the user.

[1286] Customization feature: A feature that allows users to customize design proposals.

[1287] Rendering function: A function for generating and displaying customization results in real time.

[1288] Purchase function: A function that allows users to select the final design and confirm the order.

[1289] Program processing and specific examples

[1290] First, brands upload their excess inventory information

[1291] The server receives the product list, images, and material information of the excess inventory sent by the brand.

[1292] This information is stored in a database and fed into the AI ​​model.

[1293] Examples:

[1294] If Brand A uploads data on 50 out-of-date shirts to the server, the server stores this data in a database and the AI ​​model begins learning from it.

[1295] Next, the server uses AI to generate design proposals.

[1296] The server generates new design proposals from the stored product information.

[1297] The generated design proposal is sent to the user's device.

[1298] Examples:

[1299] The server uses data from Brand A's shirts to generate five new design ideas and send them to the user's device. The AI ​​model is based on data such as the brand's past styles, latest collections, industry trends, and successful upcycling examples.

[1300] User selects product and sees design options

[1301] The user selects a brand or product category on the user device.

[1302] The server sends relevant design ideas based on the selected brand and category and displays them on the user's device.

[1303] Examples:

[1304] When a user selects a shirt from Brand A, five design ideas sent from the server are displayed on the device.

[1305] Users customize the design

[1306] Users can select the design provided on their device and customize it (changing colors, patterns, etc.).

[1307] The customization results are rendered in real time and displayed to the user.

[1308] Examples:

[1309] If a user selects a blue shirt design and changes the color to red, the change will be reflected in real time.

[1310] The user selects the final design and confirms the order.

[1311] The user selects the final design and confirms the purchase.

[1312] The terminal sends this information to the server.

[1313] Examples:

[1314] Once the user selects a design for the red shirt and confirms the purchase, the information is sent to the server.

[1315] Finally, the server sends the production instructions to the factory.

[1316] The server sends production instructions to affiliated factories based on the received order information.

[1317] The factory receives production instructions, produces the product, and delivers it to the user.

[1318] Examples:

[1319] The server sends production instructions for the red shirt to the factory, which then starts production based on those instructions and delivers the finished product to the user.

[1320] This creates an efficient upcycling process, reducing the costs for brands disposing of excess stock and allowing consumers to acquire unique, sustainable products.

[1321] The processing flow will be explained below.

[1322] Step 1:

[1323] The brand uploads excess inventory information to the server. Specifically, the brand prepares a product list, images, and material information for the excess inventory, and transmits the data to the server.

[1324] Step 2:

[1325] The server stores the received product list, images, and material information in a database, which is then referenced by artificial intelligence (AI) for subsequent processing.

[1326] Step 3:

[1327] The server uses the stored product information to generate new design ideas using an AI model that learns from data such as the brand's past styles, the latest collections, industry trends, and successful upcycling examples.

[1328] Step 4:

[1329] The server transfers the generated design proposals to the user's device, where multiple design proposals are displayed for the user to choose from.

[1330] Step 5:

[1331] The user selects their preferred design from multiple design options displayed on their device, and is then presented with an interface to customize the selected design.

[1332] Step 6:

[1333] Users can customize the design using the customization interface, specifically by changing colors, patterns, etc. The results of this customization are rendered in real time, providing visual feedback to the user.

[1334] Step 7:

[1335] The user finally decides on a design that satisfies them and decides to purchase it. When they click the purchase button, that information is sent to the server.

[1336] Step 8:

[1337] Based on the purchase information received, the server sends production instructions to partner factories, including detailed specifications for the selected design.

[1338] Step 9:

[1339] The factory begins manufacturing the product based on the manufacturing instructions received from the server, and the completed product is shipped to the specified delivery address.

[1340] Step 10:

[1341] The server monitors the entire process, providing status updates to the user as needed, and stores all transaction history in a database for future reference.

[1342] Example 1

[1343] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1344] When a brand has excess inventory, they need a way to effectively utilize that inventory and add value. However, traditional methods make it difficult to efficiently create new designs and offer them to customers, and the process is cumbersome. Therefore, there is a need for a system that can upcycle excess inventory and offer it as new products that customers can customize.

[1345] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1346] In this invention, the server includes a means for brands to upload information about their excess inventory, a database means for storing the uploaded product information, and a means for generating new design ideas from the stored product information using artificial intelligence, thereby enabling excess inventory to be efficiently provided as new customizable design ideas.

[1347] "Excess inventory" is excess inventory of merchandise that has not been sold.

[1348] "Uploading" is the act of transferring data from a computer or device to a server.

[1349] A "database" is a system for systematically storing, managing, and searching large amounts of data.

[1350] "Artificial intelligence" is a technology that allows computers to imitate human intellectual tasks, specifically referring to the technology of data analysis and decision-making.

[1351] A "design proposal" is a proposal for a new look or style for a product.

[1352] A "terminal" is a device operated by a user, including a personal computer or smartphone.

[1353] "Customization" refers to the act of a user making changes to a product, such as its color or pattern.

[1354] "Rendering" is the process of generating images or videos in computer graphics.

[1355] "Production instructions" are orders that instruct the factory on specific work details.

[1356] A "brand" is an identifying name for a company or organization that offers a particular product or service.

[1357] The system of this invention aims to enable brands to effectively utilize excess inventory and offer it as new, customizable products. The program of this system has the following main components and processing steps:

[1358] First, the brand uploads excess inventory information. The user sends a list of excess inventory items (including images, material information, and product descriptions) to the server via the admin screen as a CSV file or via API. The server validates the received data and stores it in a PostgreSQL database.

[1359] The server then uses an artificial intelligence (AI) model based on the stored data to generate new design ideas. This AI model is implemented using TensorFlow and learns from past styles and the latest fashion trends. For example, the server might input the following prompt into the AI ​​model: "Generate five new design ideas based on the image and material information of a shirt. Please consider past styles and the latest fashion trends."

[1360] The generated design proposals are sent from the server to the user's device using React.js. The user can select a brand or product category on the interface and view multiple design proposals. The user then customizes the design proposal in real time using the customization feature powered by Three.js. For example, a user can select a blue shirt design and change the color to red. This change is instantly rendered and displayed to the user.

[1361] Finally, the user selects the customized design and clicks the purchase button to confirm the purchase. This purchase process uses the Stripe API to process payment, and the final design and payment information are sent to the server. The server receives this and sends specific production instructions to partner factories. The factories then produce the product according to those instructions and deliver it to the user.

[1362] The system allows brands to efficiently upcycle excess inventory and offer customers unique, customizable products.

[1363] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1364] Step 1:

[1365] A user uploads surplus inventory information.

[1366] Users access the management screen and send a product list (images, material information, product descriptions, etc.) to the server via a CSV file or API. The input data is a list of excess inventory products, which is saved in the server's database. The CSV file contains detailed information about each product, and when sent via API, the data is sent in JSON format. The output is detailed data about each product saved in the database.

[1367] Step 2:

[1368] The server stores the received surplus inventory information in a database.

[1369] The server validates the data it receives and inserts it into a PostgreSQL database. This data includes images, sizes, materials, and descriptions for each product. The input product data is validated and efficiently stored in the database. The output is the saved product information.

[1370] Step 3:

[1371] The server uses the AI ​​model to generate design proposals.

[1372] The server calls an AI model using TensorFlow based on the saved product information. For example, the AI ​​model receives a prompt such as, "Generate five new design ideas based on shirt images and material information. Consider past styles and the latest fashion trends." In this process, the AI ​​model generates new design ideas using image processing and design generation algorithms. The input data are product images and material information, and the output is the generated design ideas.

[1373] Step 4:

[1374] The server sends the generated design proposal to the user's device.

[1375] The generated design proposals are formatted in JSON format and sent to a user interface built using React.js. Multiple design proposals are displayed on the user's device. The input data are the generated design proposals, and the output is the design proposal displayed on the user's device.

[1376] Step 5:

[1377] The user selects a brand and product category.

[1378] The user selects a brand and product category (e.g., "shirts," "pants," etc.) on the interface. The selection information is sent to the server, which then returns corresponding design proposals. The input data is the user's brand and category selection information, and the output is the corresponding design proposals.

[1379] Step 6:

[1380] The user customizes the design proposal.

[1381] Users can use the customization feature powered by Three.js to change the colors and patterns of the design, and the changes are instantly rendered and displayed to the user. The input data is the user's chosen design and change instructions, and the output is the customized design.

[1382] Step 7:

[1383] The user selects the final design and confirms the purchase.

[1384] The user selects a customized design and clicks the purchase button. The terminal processes the payment using the Stripe API and sends the final design and payment information to the server. The input data is the user's final selection and payment information, and the output is order confirmation.

[1385] Step 8:

[1386] The server sends production instructions to the partner factory.

[1387] The server sends production instructions to partner factories based on the received final design and order information. The factories then use this information to produce the product and deliver it to the user. The input data is the final design and order information, and the output is production instructions.

[1388] (Application example 1)

[1389] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1390] In today's retail industry, brands face a major challenge in effectively handling excess inventory. When excess inventory occurs, they need a way to create new value without wasting it. At the same time, it is important to offer customized products that meet diverse customer needs. Another issue is the difficulty for employees in customer service to instantly grasp inventory information and customization suggestions and make real-time suggestions during interactions. To solve these challenges, an efficient and flexible inventory management system needs to be integrated with customer service systems.

[1391] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1392] In this invention, the server includes: means for brands to upload information about excess inventory; database means for storing the uploaded product information; means for generating new design proposals from the stored product information using artificial intelligence; terminal means for users to select products and display multiple design proposals; means for customizing the design proposal selected by the user; means for sending production instructions to a factory based on the final design proposal; and means for a sales associate wearing smart glasses to check inventory information in real time while interacting with a customer and make customization proposals, thereby enabling efficient upcycling of excess inventory and customization proposals that meet diverse customer needs.

[1393] "Means for brands to upload excess inventory information" is a function that allows brands to input or transmit information about their unsold or overproduced inventory into the system.

[1394] "Database means for storing uploaded product information" refers to a digital database function for organizing and safely storing submitted inventory information.

[1395] "A means of generating new design ideas from stored product information using artificial intelligence" is a function that uses AI technology to automatically generate new design ideas based on stored surplus inventory information.

[1396] "Terminal means for allowing a user to select a product and display multiple design proposals" refers to a function of a digital device that allows a user to select a desired product and display various design proposals for that product.

[1397] "Means for customizing the design proposal selected by the user" refers to a function that allows the user to change and adjust the proposed design proposal to suit their preferences.

[1398] "Means for sending production instructions to the factory based on the final design proposal" is a function for instructing the factory to produce a product based on the final design selected by the user.

[1399] "A means for a sales associate wearing smart glasses to check inventory information in real time while interacting with a customer and make customization suggestions" is a function that allows a sales associate to wear smart glasses, instantly check inventory information while interacting with a customer, and make customization suggestions to the customer.

[1400] MODE FOR CARRYING OUT THE INVENTION

[1401] The system of this invention aims to enable brands to upcycle their excess inventory. Specifically, it enables brands to upload information about their excess inventory, generate new design ideas using that information, and then users can customize and order the products. The system configuration and operating procedures are explained below.

[1402] System Configuration

[1403] 1. Server-side components:

[1404] Database: A database for storing excess inventory information uploaded by brands and generated design proposals. This uses common database software such as MySQL or PostgreSQL.

[1405] AI model: Artificial intelligence for generating new design ideas from product information. Specifically, deep learning frameworks such as TensorFlow and PyTorch are used.

[1406] Production instruction function: A function that sends production instructions to partner factories based on the final design. Network communication is expected to be performed using REST API.

[1407] 2. User device components:

[1408] Product selection interface: An interface for users to select brands and product categories. This is implemented as a web application using a JavaScript framework such as React.

[1409] Design display interface: An interface that displays multiple generated design proposals to the user. This is displayed as a web GUI and uses HTML5 and CSS3.

[1410] Customization feature: A feature that allows users to customize design proposals. The customization results are displayed in real time using a 3D rendering library such as Three.js.

[1411] Rendering function: A function for generating and displaying customization results in real time. This uses WebGL to draw 3D graphics.

[1412] Purchase function: A function that allows users to select the final design and confirm the order. Electronic payment services such as Stripe are integrated.

[1413] 3. Smart Glasses:

[1414] Inventory information confirmation function: A function in which store clerks wear smart glasses and check the store's inventory information in real time. The smart glasses are Google Glass or similar and communicate with the database via Wi-Fi.

[1415] Customization suggestion function: A function that reflects design proposals on the display screen of the smart glasses in real time and makes customization suggestions to customers. This allows for instant suggestions during conversations with customers.

[1416] Specific examples

[1417] First, brands upload excess inventory information to a server, which receives it and stores it in a database. Next, an AI model generates new design ideas based on this data and sends them to users' devices, where they can view and further customize the designs.

[1418] For example, let's say Brand A uploads information about 50 out-of-date shirts. This information is stored in a database, and new design proposals are generated using an AI model. The five resulting design proposals are displayed on the user's device through a React-based web application. The user can choose their favorite design and change the color and pattern using the customization function using Three.js. The final customization result is rendered and displayed in real time using WebGL. Once the purchase is confirmed using Stripe, the information is sent to the manufacturer via the server.

[1419] Furthermore, if a sales associate is wearing smart glasses, they can check inventory information in real time and make customization suggestions while interacting with the customer. For example, if a customer says, "Red is good," the UI of the smart glasses can display a red shirt design in real time and ask for confirmation.

[1420] Example of an input prompt for a generative AI model:

[1421] Shirt details: Blue, cotton, long sleeves, size M.

[1422] Past styles: casual, stylish.

[1423] Upcycling policy: environmentally friendly and in line with the latest Chinese trends.

[1424] The above configuration enables efficient upcycling of excess inventory and customization proposals to meet the diverse needs of customers.

[1425] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1426] Step 1:

[1427] Brands upload excess inventory information. Brands enter information about their unsold or overproduced inventory into the system. This information includes details such as product name, quantity, image, material, and size. The uploaded data is stored in a database.

[1428] Step 2:

[1429] The server provides the stored product information to the AI ​​model, which then generates prompts based on this data to generate new design ideas. These prompts include product information, past styles, industry trends, etc. The generated design ideas are then stored on the server.

[1430] Step 3:

[1431] The server sends the generated design proposal to the user's device, which then displays it to the user through a user interface, such as a React-based web application, using HTML and CSS.

[1432] Step 4:

[1433] Users can select a design and use the customization feature. Based on the design they select, customization is performed in real time using the 3D rendering library Three.js. Color changes, pattern adjustments, and more are possible.

[1434] Step 5:

[1435] The user confirms the final design and checks out. The user confirms the final custom design and completes the payment process using an electronic payment service such as Stripe. This information is sent to the server.

[1436] Step 6:

[1437] The server sends the final design and purchasing information to the factory, which then uses a REST API to send production instructions to the factory, which then produces the product and delivers it to the user.

[1438] Step 7:

[1439] The store associate uses the smart glasses to manage inventory and handle customer service. Wearing the smart glasses, the associate checks inventory information in real time while interacting with the customer and makes customization suggestions based on the design ideas generated by the AI ​​model. If the customer accepts the suggestion, the information is sent to the server in real time, and the process begins again in step 6.

[1440] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1441] The following shows an example of the "UpCycle Hub" system of the present invention, which combines an emotion engine. The system aims to help brands add value to their excess inventory by upcycling it and transforming it into new "sellable products." It also aims to recognize user emotions and adjust the interface in real time to provide a more personalized experience.

[1442] System Configuration

[1443] 1. Server-side components:

[1444] Database: A database for storing excess inventory information uploaded by brands and generated design proposals.

[1445] AI model: Artificial intelligence for generating new design ideas from product information.

[1446] Production instruction function: A function that sends production instructions to partner factories based on the final design proposal.

[1447] Emotion engine: An engine that collects and analyzes user emotional data. This data is used to refine design proposals and interfaces.

[1448] 2. User device components:

[1449] Product selection interface: An interface for users to select a brand or product category.

[1450] Design display interface: An interface that displays the generated multiple design proposals to the user.

[1451] Customization feature: A feature that allows users to customize design proposals.

[1452] Rendering function: A function for generating and displaying customization results in real time.

[1453] Emotion Sensor: A sensor for detecting user emotions in real time and sending them to the emotion engine.

[1454] Purchase function: A function that allows users to select the final design and confirm the order.

[1455] Program processing and specific examples

[1456] First, brands upload their excess inventory information

[1457] Brands send product lists, images, and material information for excess inventory to the server, which stores this data in a database and provides it to the AI ​​model.

[1458] Examples:

[1459] If Brand A uploads data on 50 out-of-date shirts to the server, the server stores this data in a database and the AI ​​model begins learning from it.

[1460] Next, the server uses AI to generate design proposals.

[1461] The server generates new design ideas from stored product information, with the AI ​​model learning from data such as the brand's past styles, latest collections, industry trends, and successful upcycling examples.

[1462] Examples:

[1463] The server uses data from Brand A's shirts to generate five new design ideas and sends them to the user's device.

[1464] User selects product and sees design options

[1465] The user selects a brand or product category on the user device, and the server sends relevant design proposals based on the selected brand or category and displays them on the user device.

[1466] Examples:

[1467] When a user selects a shirt from Brand A, five design ideas sent from the server are displayed on the device.

[1468] Users customize the design

[1469] Users can select from the provided design ideas on their device and customize them (changing colors, patterns, etc.). The customization results are rendered in real time and displayed to the user.

[1470] Examples:

[1471] If a user selects a blue shirt design and changes the color to red, the change will be reflected in real time.

[1472] Recognize user emotions and adjust the interface accordingly

[1473] The emotion sensor captures the user's emotional data in real time and sends it to the emotion engine, which then analyzes the data and adjusts the design and interface according to the user's emotional state.

[1474] Examples:

[1475] As users customize their design proposals, emotion sensors recognize their joys and frustrations, and the emotion engine uses that data to provide new suggestions and customization options.

[1476] The user selects the final design and confirms the order.

[1477] The user selects the final design and confirms the purchase. The device sends this information to the server.

[1478] Examples:

[1479] Once the user selects a design for the red shirt and confirms the purchase, the information is sent to the server.

[1480] Finally, the server sends the production instructions to the factory.

[1481] The server then sends production instructions to partner factories based on the received order information, including detailed specifications for the selected design.

[1482] Examples:

[1483] The server sends production instructions for the red shirt to the factory, which then starts production based on those instructions and delivers the finished product to the user.

[1484] This allows the emotional engine to adjust the interface and make design suggestions based on the user's emotions, providing a more personalized experience, increasing user satisfaction and enabling a more efficient upcycling process for brands' excess inventory.

[1485] The processing flow will be explained below.

[1486] Step 1:

[1487] The brand uploads excess inventory information to the server. Specifically, the brand prepares a product list, images, and material information for the excess inventory, and transmits the data to the server.

[1488] Step 2:

[1489] The server stores the received product list, images, and material information in a database, which is then referenced by artificial intelligence (AI) for subsequent processing.

[1490] Step 3:

[1491] The server uses the stored product information to generate new design ideas using an AI model that learns from data such as the brand's past styles, the latest collections, industry trends, and successful upcycling examples.

[1492] Step 4:

[1493] The server transfers the generated design proposals to the user's device, where multiple design proposals are displayed for the user to choose from.

[1494] Step 5:

[1495] The user selects their preferred design from multiple design options displayed on their device, and is then presented with an interface to customize the selected design.

[1496] Step 6:

[1497] Users can customize the design using the customization interface, specifically by changing colors, patterns, etc. The results of this customization are rendered in real time, providing visual feedback to the user.

[1498] Step 7:

[1499] The user finally decides on a design that satisfies them and decides to purchase it. When they click the purchase button, that information is sent to the server.

[1500] Step 8:

[1501] Based on the purchase information received, the server sends production instructions to partner factories, including detailed specifications for the selected design.

[1502] Step 9:

[1503] The factory begins manufacturing the product based on the manufacturing instructions received from the server, and the completed product is shipped to the specified delivery address.

[1504] Step 10:

[1505] The user device acquires the user's emotional data in real time through an emotion sensor and transmits it to the server. The emotional data includes the user's facial expressions, voice, and body movements.

[1506] Step 11:

[1507] The server-side emotion engine analyzes the acquired emotion data and determines the user's emotional state, for example, whether the user is happy or confused.

[1508] Step 12:

[1509] Based on the emotion engine, the server adjusts the interface and proposes new designs. Based on emotion data, it suggests design ideas and customization options that users are likely to like.

[1510] Step 13:

[1511] The server combines the user's emotional data with their past purchase history and customization history to generate more personalized design proposals and send them to the user's device.

[1512] Step 14:

[1513] The user's device displays the updated design, and the user can customize and select again, and this process is repeated until the user is finally satisfied.

[1514] Step 15:

[1515] The server monitors the entire process, providing status updates to the user as needed, and stores all transaction history in a database for future reference.

[1516] Example 2

[1517] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1518] While existing upcycling systems effectively utilize surplus inventory and generate designs, they lack a personalized experience that responds to individual users' emotions and preferences. Furthermore, they do not use user emotional data to adjust interfaces or design proposals, making it difficult to improve customer satisfaction.

[1519] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1520] In this invention, the server includes: means for brands to upload information about excess inventory; database means for storing uploaded product information; means for generating new design proposals from the stored product information using artificial intelligence; terminal means for users to select products and display multiple design proposals; means for customizing the design proposal selected by the user; means for sending production instructions to a factory based on the final design proposal; emotion engine means for collecting and analyzing user emotion data; and means for adjusting the interface and design proposals based on the emotion data. This makes it possible to adjust the interface and design proposals according to the user's emotions, providing a more personalized experience.

[1521] A "brand" is a concept that includes names, logos, symbols, etc. that allow a company or product to be identified in the market.

[1522] "Excess inventory" refers to goods or materials that remain unsold.

[1523] "Means for uploading information" refers to any method or device for transferring digital data to the system.

[1524] A "database" is a structured collection of information for efficient storage, management, and retrieval of data.

[1525] "Artificial intelligence" refers to computer systems and software that simulate human intelligence, and is a technology that learns and makes inferences based on data.

[1526] "Design proposal" refers to a proposal or concept regarding the appearance and functionality of a product or service.

[1527] "Terminal" means a device that allows a User to access and operate a digital system.

[1528] "Customization means" refers to a method or device that allows a user to change or adjust certain aspects of a product or service.

[1529] "Production instructions" are detailed instructions or instructions for making a particular product.

[1530] "Factory" refers to a facility or building where goods are manufactured or processed.

[1531] "Emotional data" refers to information that expresses a user's emotional state as numbers or categories.

[1532] An "emotion engine" refers to software or algorithms that analyze emotional data and adjust system behavior based on that data.

[1533] An "interface" refers to the screens and input devices that allow a user to interact with a system.

[1534] The present invention combines an emotion engine with the "UpCycle Hub" system. The system aims to enable brands to upcycle excess inventory, add new value to it, and convert it into sellable products. The system also aims to recognize user emotions and adjust the interface in real time to provide a more personalized experience. The following describes specific embodiments of the present invention.

[1535] System Configuration

[1536] 1. Server-side components:

[1537] Database: A database for storing excess inventory information uploaded by brands and generated design proposals.

[1538] AI model: Artificial intelligence to generate new design ideas from product information. The AI ​​model is trained on past styles, the latest collections, industry trends, and successful upcycling examples.

[1539] Production instruction function: A function for sending production instructions to partner factories based on the final design proposal.

[1540] Emotion engine: An engine for collecting and analyzing user emotion data to adjust design proposals and interfaces.

[1541] 2. User device components:

[1542] Product selection interface: An interface for users to select a brand or product category.

[1543] Design display interface: An interface for displaying the generated multiple design proposals to the user.

[1544] Customization: A feature that allows users to customize the design proposal. Colors and patterns can be changed.

[1545] Rendering function: A function for generating and displaying customization results in real time.

[1546] Emotion Sensor: A sensor for detecting user emotions in real time and sending them to the emotion engine.

[1547] Purchase function: A function that allows users to select the final design and confirm the order.

[1548] Specific program description

[1549] Uploading excess inventory information

[1550] The user (brand manager) uploads information about excess inventory (product list, images, material information) to the server, which stores this data in a database and provides it to the AI ​​model.

[1551] Storing data and providing it to AI models

[1552] The server records the received product information in a database and passes it to the AI ​​model, which then learns from it and generates new design proposals.

[1553] Generate design ideas

[1554] The server uses an AI model to generate new design ideas from the stored data, learning from past styles and trends to suggest optimal designs.

[1555] Send and view design ideas

[1556] When a user selects a brand or product category, the server generates design proposals based on the selection and sends them to the user's device for display.

[1557] Customizing the design

[1558] Users can select from the displayed design options and change the colors and patterns, and the device will render and display the customization results in real time.

[1559] Acquiring emotional data and adjusting the interface

[1560] The emotion sensor collects user emotional data in real time and sends it to the emotion engine, which then analyzes it and adjusts the interface and design proposals according to the user's emotional state.

[1561] Final design selection and order confirmation

[1562] The user selects the final design and confirms the purchase, and the device sends this information to the server.

[1563] Sending production instructions

[1564] The server then sends production instructions to partner factories based on the received order information, including detailed specifications for the selected design.

[1565] Specific examples

[1566] If a brand uploads data on 50 out-of-date shirts to the server, the server stores this data in a database, and the AI ​​model begins learning from it. The server generates five new design proposals from the brand's shirt data and sends them to the user's device. When a user selects a brand's shirt and changes the color or pattern, the changes are updated in real time. In addition, an emotion sensor detects the user's emotions and recognizes their joy or dissatisfaction, allowing the server to automatically provide new suggestions and customization options.

[1567] Prompt Sentence Examples

[1568] "Generate five new design ideas from a selection of out-of-date shirts. Use an AI model that adjusts suggestions based on the user's emotional state."

[1569] This allows for a personalized experience for users and an efficient process for upcycling excess inventory for brands.

[1570] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1571] Step 1:

[1572] Input: The user (brand manager) prepares information about excess inventory (product list, images, material information).

[1573] Processing: User enters excess inventory information using the system's upload function.

[1574] Output: Excess inventory data is sent to the server.

[1575] Specific actions: A brand representative enters data for 50 out-of-date shirts according to the format and clicks the submit button.

[1576] Step 2:

[1577] Input: Excess inventory data is sent to the server.

[1578] Processing: The server stores the received surplus inventory data in the database.

[1579] Output: Product information stored in a database.

[1580] Specific operation: The server records the 50 shirt data received from Brand A in the database, and the information is saved in the database.

[1581] Step 3:

[1582] Input: Product information stored in the database.

[1583] Processing: The server invokes the AI ​​model based on the stored product information to generate new design proposals.

[1584] Output: The new design proposal generated.

[1585] Specific operation: The server provides data to the AI ​​model, which generates five new design ideas from Brand A's shirt data.

[1586] Step 4:

[1587] Input: Generated design proposal.

[1588] Process: The user selects a brand or product category on the device. The server sends relevant design ideas to the device based on the selection and displays them.

[1589] Output: Multiple design ideas displayed on the device.

[1590] Specific operation: The user selects a shirt from Brand A, and five design proposals sent from the server are displayed on the device.

[1591] Step 5:

[1592] Input: Design proposal displayed on the device.

[1593] Process: The user selects a design and customizes it with colors, patterns, etc.

[1594] Output: Customized design proposal.

[1595] How it works: A user selects a blue shirt design and changes the color to red. The change is reflected on the device in real time.

[1596] Step 6:

[1597] Input: Emotion data during user customization.

[1598] Processing: The emotion sensor collects user emotion data in real time and sends it to the emotion engine. The server analyzes this data and adjusts the interface and design proposals.

[1599] Output: Emotionally tailored interface and design ideas.

[1600] How it works: As users customize their design proposals, emotion sensors detect their joy or dissatisfaction in real time, and the server provides new suggestions and customization options based on that.

[1601] Step 7:

[1602] Input: Your final customized design proposal.

[1603] Processing: The customer selects the final design and confirms the purchase.

[1604] Output: Confirmed order information.

[1605] What happens: The user selects a design for the red shirt and confirms the purchase, which is then sent to the server.

[1606] Step 8:

[1607] Input: Received order information.

[1608] Processing: Based on the order information received by the server, production instructions are sent to affiliated factories.

[1609] Output: Production instructions sent to the factory.

[1610] Specific operation: The server sends the production instructions for the red shirt to the factory, the factory starts production based on the instructions, and the finished product is delivered to the user.

[1611] (Application example 2)

[1612] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1613] Conventional upcycling systems often lack a personalized user experience when generating design proposals to improve the commercial value of surplus inventory. Furthermore, they lack the ability to adjust the interface based on user sentiment, which hinders user satisfaction. This makes it difficult to efficiently utilize brands' surplus inventory. Furthermore, innovative methods are needed to improve the accuracy and suitability of design proposal generation.

[1614] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a brand to upload information about surplus inventory; database means for storing the uploaded product information; means for generating new design proposals from the stored product information using artificial intelligence; terminal means for a user to select a product and display multiple design proposals; means for customizing the design proposal selected by the user; means for sending production instructions to a production facility based on the final design proposal; emotion sensor means for detecting user emotions and adjusting the design proposals and interface; and emotion engine means for analyzing the detected emotion data and personalizing the interface and suggestions. This makes it possible to provide a personalized experience according to the user's emotions and efficiently upcycle a brand's surplus inventory.

[1615] "Excess inventory" refers to products or materials that remain unsold.

[1616] "Database" refers to an electronic data storage system where brands can store information on excess inventory and generated design ideas.

[1617] "Artificial intelligence" refers to algorithms and analytical methods that mimic human intelligence and generate new design ideas from product information.

[1618] "Terminal" refers to an electronic device that allows a user to select a product and display multiple design options.

[1619] "Customization" refers to the process of changing the colors and patterns of a design proposal selected by the user.

[1620] "Production facility" refers to the factory or manufacturing location where products are produced based on the final design proposal.

[1621] An "emotion sensor" refers to a device or software that detects a user's emotions.

[1622] An "emotion engine" is a system that analyzes detected emotional data and adjusts interface and design proposals according to the user's emotions.

[1623] The system that realizes this invention allows brands to manage excess inventory information, and based on that information, generates new design proposals and provides them to users. Furthermore, it recognizes user emotions in real time and adjusts the interface and design proposals to provide a personalized experience.

[1624] System configuration

[1625] Server-side configuration

[1626] 1. Database

[1627] Brands upload information about their excess inventory to a server and store that data in a database, which includes product lists, images, and material information.

[1628] 2. Artificial Intelligence (AI) Models

[1629] It is an AI model that generates new design ideas based on stored product information, learning from the brand's past styles, latest collections, industry trends, and successful upcycling examples.

[1630] 3. Production instruction function

[1631] This function sends production instructions to a production facility based on the final design proposal, including detailed specifications for the selected design.

[1632] 4. Emotion Engine

[1633] This engine analyzes user emotional data collected from emotion sensors and adjusts interface and design proposals according to the user's emotions.

[1634] Configuring the user device

[1635] 1. Product selection interface

[1636] This is the interface that allows users to select brands and product categories.

[1637] 2. Design display interface

[1638] This is an interface that displays multiple new design ideas to the user generated by the AI ​​model.

[1639] 3. Customization features

[1640] This is a feature that allows users to customize design proposals, allowing them to change colors and patterns.

[1641] 4. Rendering Function

[1642] This function generates customization results in real time and displays them to the user.

[1643] 5. Emotion Sensor

[1644] It is a sensor that uses the smartphone's camera and microphone to detect the user's emotions in real time, and sends this data to the emotion engine.

[1645] 6. Purchase Function

[1646] This is a function that allows users to select the final design and confirm the order.

[1647] Program processing overview

[1648] The server uses an AI model to generate new design proposals from product information stored in the database and sends them to the user's device. The user then uses the device to select a brand or product category and customize the displayed design proposals. The customization results are rendered in real time. The emotion sensor uses the smartphone's camera and an emotion recognition model (e.g., emotion_detection_model.h5) to detect the user's emotions. The emotion engine analyzes the detected emotion data and adjusts the interface and suggestions according to the user's emotions.

[1649] Specific examples

[1650] When a user launches the app and selects a shirt from Brand A, the server displays multiple design proposals that the user can customize. During customization, the user's emotions are detected by the smartphone camera and the data is sent to the emotion engine. If the emotion engine detects "excitement," it will suggest bolder design proposals.

[1651] Prompt Sentence Examples

[1652] "The user selected a shirt from Brand A. When detecting the user's emotion in the video captured by the camera, 'excitement' was recognized. In this case, what kind of design proposal should we suggest?"

[1653] The system allows brands to efficiently upcycle excess inventory while providing a personalized experience that responds to user emotions.

[1654] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1655] Step 1:

[1656] The server provides a means for brands to upload excess inventory information. The input is excess inventory data from the brand (product list, images, material information), and the output is excess inventory information stored in a database. Specifically, brands send product information to the server via a web form or API, and the server stores the data in a database.

[1657] Step 2:

[1658] The server uses the stored product information to run an artificial intelligence (AI) model to generate new design proposals. The input is surplus inventory information stored in the database, and the output is multiple generated design proposals. Specifically, the AI ​​model generates new design proposals by taking into account past style data, industry trends, and successful upcycling examples.

[1659] Step 3:

[1660] The server sends the generated design proposals to the user's device and displays them to the user on the device. The input is the design proposal generated by the AI ​​model, and the output is multiple design proposals displayed on the user's device. Specifically, the server sends the generated design proposals to the user's device in JSON format, which is then parsed and displayed on the device.

[1661] Step 4:

[1662] Users select a brand or product category on their device and browse the displayed design options. The input is the user's selection, and the output is the display of related design options. Specifically, users make selections through the interface, and design options based on those selections are displayed on the user's device.

[1663] Step 5:

[1664] The user customizes the selected design. The input is the user's selected design and customization instructions (color and pattern changes), and the output is the customization result. Specifically, the user changes the color and pattern, and the changes are rendered in real time and displayed to the user.

[1665] Step 6:

[1666] The device uses an emotion sensor to detect the user's emotions. The input is the user's facial and voice data captured by the smartphone's camera and microphone, and the output is the detected emotion data. Specifically, the data acquired by the camera and microphone is input into an emotion recognition model to generate emotion data.

[1667] Step 7:

[1668] The server uses an emotion engine to analyze the detected emotion data and adjust the interface or design proposals. The input is the emotion data sent from the emotion sensor, and the output is an adjusted interface or new proposals. Specifically, the emotion engine analyzes the detected emotion data (e.g., "excitement") and proposes new design proposals or interfaces accordingly.

[1669] Step 8:

[1670] The user selects the final design and confirms the order. The input is the final design selected by the user, and the output is data sent to the server as order information. Specifically, the user orders the confirmed design, and the information is sent to the server.

[1671] Step 9:

[1672] The server sends production instructions to the production facility based on the received order information. The input is the order information sent by the user, and the output is the production instructions sent to the production facility. Specifically, the server sends the order information to the production facility, and the production facility produces the product based on the instructions.

[1673] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1674] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1675] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1676] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1677] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1678] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1679] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1680] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1681] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1682] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1683] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1684] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1685] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1686] 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.

[1687] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1688] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1689] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1690] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1691] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1692] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1693] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1694] The following is further disclosed regarding the above embodiment.

[1695] (Claim 1)

[1696] A way for brands to upload information about excess inventory;

[1697] a database means for storing the uploaded product information;

[1698] A means for generating new design proposals from stored product information using artificial intelligence;

[1699] a terminal means for allowing a user to select a product and display multiple design proposals;

[1700] A means for users to customize their selected design proposal; and

[1701] A means for sending production instructions to the factory based on the final design proposal;

[1702] A system including:

[1703] (Claim 2)

[1704] The system of claim 1, wherein the artificial intelligence generates design proposals by learning from a brand's past styles, latest collections, industry trends, and successful upcycling examples.

[1705] (Claim 3)

[1706] 10. The system of claim 1, further comprising means for rendering and displaying the customized design proposal to the user in real time.

[1707] "Example 1"

[1708] (Claim 1)

[1709] A way for brands to upload information about excess inventory;

[1710] a database means for storing the uploaded product information;

[1711] A means for generating new design proposals from stored product information using artificial intelligence;

[1712] a terminal means for allowing a user to select a product and display multiple design proposals;

[1713] A means for users to customize their selected design proposal; and

[1714] A means to render the customization results in real time and display them to the user;

[1715] A means for sending production instructions to the factory based on the final design proposal;

[1716] A system including:

[1717] (Claim 2)

[1718] The system of claim 1, wherein the artificial intelligence generates design suggestions by learning from past styles, latest collections, industry trends, and successful upcycling examples.

[1719] (Claim 3)

[1720] 10. The system of claim 1, further comprising means for selecting the generated design proposals in the user interface and finalizing the purchase.

[1721] "Application Example 1"

[1722] (Claim 1)

[1723] A way for brands to upload information about excess inventory;

[1724] a database means for storing the uploaded product information;

[1725] A means for generating new design proposals from stored product information using artificial intelligence;

[1726] a terminal means for allowing a user to select a product and display multiple design proposals;

[1727] A means for users to customize their selected design proposal; and

[1728] A means for sending production instructions to the factory based on the final design proposal;

[1729] A method for store clerks wearing smart glasses to check inventory information in real time and make customization suggestions while interacting with customers.

[1730] A system including:

[1731] (Claim 2)

[1732] The system of claim 1, wherein the artificial intelligence generates design proposals by learning from a brand's past styles, latest collections, industry trends, and successful upcycling examples.

[1733] (Claim 3)

[1734] 10. The system of claim 1, further comprising means for rendering and displaying the customized design proposal to the user in real time.

[1735] "Example 2: Combining Emotion Engines"

[1736] (Claim 1)

[1737] A way for brands to upload information about excess inventory;

[1738] a database means for storing the uploaded product information;

[1739] A means for generating new design proposals from stored product information using artificial intelligence;

[1740] a terminal means for allowing a user to select a product and display multiple design proposals;

[1741] A means for users to customize their selected design proposal; and

[1742] a means for transmitting production instructions to a factory based on the final design proposal;

[1743] an emotion engine means for collecting and analyzing user emotion data;

[1744] A means to adjust interface and design proposals based on emotional data;

[1745] A system including:

[1746] (Claim 2)

[1747] The system of claim 1, wherein the artificial intelligence generates design proposals by learning from a brand's past styles, latest collections, industry trends, and successful upcycling examples.

[1748] (Claim 3)

[1749] 10. The system of claim 1, further comprising means for rendering and displaying the customized design proposal to the user in real time.

[1750] "Application example 2 when combining emotion engines"

[1751] (Claim 1)

[1752] A way for brands to upload information about excess inventory;

[1753] a database means for storing the uploaded product information;

[1754] A means for generating new design proposals from stored product information using artificial intelligence;

[1755] a terminal means for allowing a user to select a product and display multiple design proposals;

[1756] A means for users to customize their selected design proposal; and

[1757] means for transmitting production instructions to a production facility based on the final design proposal;

[1758] an emotion sensor means for detecting user emotions and adjusting design proposals and interfaces;

[1759] an emotion engine means for analyzing the detected emotion data and personalizing the interface and suggestions;

[1760] A system including:

[1761] (Claim 2)

[1762] The system of claim 1, wherein the artificial intelligence generates design proposals by learning from a brand's past styles, latest collections, industry trends, and successful upcycling examples.

[1763] (Claim 3)

[1764] 10. The system of claim 1, further comprising means for rendering and displaying the customized design proposal to the user in real time. [Explanation of symbols]

[1765] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A way for brands to upload information about excess inventory; a database means for storing the uploaded product information; A means for generating new design proposals from stored product information using artificial intelligence; a terminal means for allowing a user to select a product and display multiple design proposals; A means for users to customize their selected design proposal; and A means for sending production instructions to the factory based on the final design proposal; A system including:

2. The system of claim 1, wherein the artificial intelligence generates design proposals by learning from the brand's past styles, latest collections, industry trends, and successful upcycling examples.

3. The system according to claim 1, further comprising means for rendering the customized design proposal in real time and displaying it to the user.

Images