system

Abstract

We provide the system. [Solution] A means for receiving images and input information taken by the user, A means including an algorithm for analyzing the attributes of an item from a received image and identifying market demand, A means for determining the processing method of an item based on the analysis results and generating a design drawing for reuse, A means for transmitting the generated design drawings to the processing facility, A means of using smart devices to notify users of the results of market demand analysis and promote online sales, A means of listing finished goods from a processing facility on an online marketplace, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Articles such as furniture are often discarded as unnecessary due to reasons such as moving or replacement, which causes a burden on the environment and waste of resources. It is necessary to reduce such unnecessary items and promote environmental protection and effective use of resources by reusing them. Furthermore, in the process of changing existing unnecessary items into new values, it is required to adopt an efficient method that meets market needs, but there is a problem that the conventional methods are less efficient.

Means for Solving the Problems

[0005] The system of this invention receives images and input information taken by the user, and analyzes this data using AI to clarify the attributes of the item. Based on the analysis, it identifies market demand and generates a design drawing to propose the optimal processing method. This design drawing is sent to a processing facility, where processing and remanufacturing take place. Furthermore, by utilizing an inventory database of unprocessed items, it is possible to utilize existing parts and efficiently remanufacture items while keeping costs down. The processed items are sold through an online marketplace, and the profits earned are distributed to the user and the processing facility. Through this series of processes, the burden on the environment is reduced and resources are used effectively.

[0006] A "user" refers to an individual consumer or organization that uses this system to take photos of unwanted furniture and input the information.

[0007] "Images" refers to photographic data of unwanted furniture taken by the user.

[0008] "Input information" refers to information that users input along with images, such as the material and condition of the furniture, and their desired reuse methods.

[0009] "Items" refers to furniture and other reusable products that the user has photographed.

[0010] "Attributes" refer to the characteristics and features of an item identified through image analysis, such as material, size, and shape.

[0011] "Market demand" refers to information indicating what processing or product form of the analyzed item can be expected to generate consumer demand.

[0012] "AI" refers to algorithms and systems that use artificial intelligence technology to analyze image data and identify market needs.

[0013] "Processing methods" refer to techniques such as repair, modification, and painting necessary to revive an item into a reusable form.

[0014] "Design drawing" refers to drawings or plans that show in detail the reproduction process of an article based on the generated processing method.

[0015] "Processing facility" refers to equipment or places for processing and reproducing articles according to the generated design drawings.

[0016] "Raw article" refers to the inventory of articles that have not yet been processed or reproduced.

[0017] "Inventory database" refers to a system that accumulates information on raw articles and parts and manages them in a searchable state.

[0018] "Online marketplace" refers to a market or platform for selling articles via the Internet.

[0019] "Profit" refers to the profit obtained by subtracting the necessary expenses from the sales when the reproduced article is sold.

Brief Description of Drawings

[0020] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7]It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0021] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0022] First, the language used in the following description will be explained.

[0023] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0024] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0025] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0026] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0028] [First Embodiment]

[0029] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0030] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0031] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0033] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0034] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0036] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0038] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0040] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0041] This invention is a system that uses AI to add new value to unwanted furniture and regenerate it. The system involves a series of processes, from the user taking a photo of the furniture to the system analyzing market needs based on that photo and proposing the optimal processing method.

[0042] At the start of the system, users take photos of unwanted furniture using their smartphones or other devices. Users can also input information such as the material and condition of the furniture and their desired reuse method. This input information, along with the images, is sent from the device to the server.

[0043] The server analyzes the received image data using an AI image recognition model to identify the attributes of the items. This analysis includes characteristics such as the furniture category, material, and dimensions. Next, the server uses historical data from its internal database and external online marketplaces to identify market demand. This demand information indicates what styles and price ranges are preferred for reproduction.

[0044] Next, the server designs the optimal processing method based on the identified market needs and item attributes. A design drawing is generated, and reusable parts are identified by referencing the inventory database of unprocessed items. This enables efficient resource utilization.

[0045] The generated blueprints are sent to partner processing facilities. These facilities then refurbish the furniture based on the received blueprints. This process includes parts replacement, repair, and painting.

[0046] Finally, the revived furniture completed at the processing facility is reported to the server and prepared for listing on the online marketplace. The server shares the progress of the work with the user and the relevant processing facility and distributes the profits earned after the sale. Users can not only create new value from furniture with minimal effort, but also contribute to environmental conservation.

[0047] For example, if a user is looking to dispose of an old wooden table, the system uses photos and information to determine what kind of finish is in demand in the market and suggests an antique-style finish that highlights the wood grain. The processing facility then repaints the table according to these instructions and sells the finished table online. Through this process, both the user and the processing facility can profit while effectively utilizing resources and being environmentally conscious.

[0048] The following describes the processing flow.

[0049] Step 1:

[0050] Users take photos of unwanted furniture using their smartphone or camera-equipped device. After taking the photos, they input information such as the material, condition, and desired use into the application on their device.

[0051] Step 2:

[0052] The terminal combines the information entered by the user and the images taken into a data package and sends it to the server. A secure protocol is used for data transmission via the internet.

[0053] Step 3:

[0054] The server inputs the received data into an AI image recognition algorithm to analyze the attributes of the furniture from the image. Specifically, it identifies the shape, material, and damaged areas, and stores them as digital data.

[0055] Step 4:

[0056] Based on the analysis results, the server uses its internal database and online market trend data to evaluate the market needs for furniture. This helps determine which revival styles are appealing to consumers.

[0057] Step 5:

[0058] The server integrates market needs and item attributes to design the optimal refurbishment method. For example, it specifically determines processing techniques such as partial repair, repainting, and parts replacement, and generates detailed design drawings.

[0059] Step 6:

[0060] The server searches the inventory database of unprocessed materials based on the generated design drawings, identifying reusable and necessary parts. This enables the effective utilization of unused parts.

[0061] Step 7:

[0062] The server transmits the completed design drawings to the processing facility and provides specific instructions for the remanufacturing process. These instructions include processing procedures and details of the parts to be used.

[0063] Step 8:

[0064] The processing facility refurbishes furniture based on design drawings from the server. It handles material procurement, repair, assembly, and painting to produce the final product.

[0065] Step 9:

[0066] The processing facility takes a picture of the finished product and reports it to the server. This report indicates that the product is ready to be listed on the online marketplace.

[0067] Step 10:

[0068] The server receives the completion report and lists the furniture on the online marketplace. It posts product information, price, and photos, and begins sales.

[0069] Step 11:

[0070] The server aggregates sales once a sale is completed, calculates the revenue, and distributes it to the user and processing facility. Transaction details are managed through a transparent, digitally verifiable process.

[0071] (Example 1)

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

[0073] The effort to repurpose furniture destined for disposal by adding new value presents challenges: traditional methods struggle to accurately reflect market demand, and manual processing planning is inefficient. Therefore, there is a need to develop a system that allows for the effective use of resources without burdening users.

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

[0075] In this invention, the server includes means for receiving furniture images and related information taken by the user, means for analyzing the attributes of the item using a generating AI model and identifying market demand, and means for designing an optimal processing method and generating a design drawing based on the analysis results. This makes it possible for users to easily refurbish furniture, create new market value, and efficiently utilize resources.

[0076] A "user" refers to an individual or organization that uses the system to photograph unwanted furniture and provides information about its reuse.

[0077] "Image" refers to photographic data of furniture taken by the user using their device.

[0078] A "generative AI model" refers to an artificial intelligence algorithm used to analyze images of furniture and identify the attributes of the items.

[0079] "Item attributes" refer to characteristics such as the furniture's category, material, and dimensions.

[0080] "Market demand" refers to information indicating what styles and price ranges consumers are in demand for refurbished furniture.

[0081] "Processing method" refers to the specific means and processes used to improve or repair furniture for the purpose of reuse.

[0082] A "design drawing" refers to a drawing or plan that shows the processing procedures and specifications necessary for furniture restoration, generated based on market demand and product characteristics.

[0083] A "processing facility" refers to a facility or business that carries out furniture restoration work based on the generated design drawings.

[0084] An "online platform" refers to an internet marketplace or service used to sell processed furniture.

[0085] "Parts" refers to the constituent elements of furniture, including materials used for necessary repairs and reassembly during restoration.

[0086] "Efficient use of resources" refers to the efficient use of materials and parts in furniture recycling, without waste.

[0087] "Profit sharing" refers to the process of appropriately distributing the profits earned from the sale of furniture to users and manufacturers.

[0088] This invention relates to a system that uses AI technology to add new value to furniture that is scheduled to be discarded and to repurpose it.

[0089] First, the user takes a picture of the furniture they want to reuse using a device such as a smartphone or computer. At this time, the user can input information about the furniture's material, condition, and desired reuse method. For example, if the user wants to reuse an old wooden table in an antique style, they would take a picture of it with their device and input "wood" as the material and "antique style" as the method.

[0090] The terminal sends the captured image and accompanying information to the server. The server inputs the received image into a generating AI model and uses image recognition technology to identify the attributes of the furniture. This analysis reveals the furniture's category, material, and dimensions, and then identifies market demand by referring to internal databases and data from external online markets.

[0091] Based on market demand, the server uses AI-powered prompts to design the optimal processing method that matches the characteristics of the item and the demand, generating a design blueprint aimed at reuse. This blueprint often includes processes such as parts rearrangement, repair, and painting. For example, the analysis might suggest repainting an old table in an antique style that highlights the wood grain.

[0092] The generated blueprints are sent to partner processing facilities. These facilities then proceed with the furniture restoration work based on these blueprints. This process involves the efficient use of parts and the creation of finished products through repainting and repair processes.

[0093] Finished furniture is reported from the processing facility to the server, and preparations are made for listing it on the online platform. The server adjusts sales information, such as photos and prices, and distributes revenue to users and processing facilities depending on sales performance.

[0094] An example of a prompt message could be: "Generate ideas for reusing an old wooden table. Please tell me the best processing method based on market demand." This system allows users to add new value to their home furniture and reuse it in an environmentally friendly way.

[0095] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0096] Step 1:

[0097] Users take pictures of unwanted furniture using their smartphones or computers. They then use the camera function of their device to input information about the furniture's material, condition, and their wishes regarding reuse. The input data consists of images and text information. This data serves as the foundational information necessary for subsequent analysis.

[0098] Step 2:

[0099] The device transmits captured image data and related information to a server via the internet. The transmitted data is used as input for AI analysis on the server. Specifically, the image file and the text description entered by the user reach the server and are stored in the database.

[0100] Step 3:

[0101] The server inputs the received images into a generating AI model for image analysis. The model utilizes image recognition algorithms to identify attributes such as furniture category, material, and dimensions. The analysis output provides analyzed attribute information. This attribute information is used in the next step to analyze market demand.

[0102] Step 4:

[0103] The server analyzes market demand by referencing internal databases and external online data. Based on the received attribute information, it identifies what styles and price ranges are in demand among consumers. This analysis outputs data for product specifications that match market trends.

[0104] Step 5:

[0105] The server designs the optimal processing method based on the analysis results. Using a generative AI model, it generates a design for furniture reuse, taking into account the user's input requirements and market needs. This design shows the processing steps and parts to be used in detail, providing the information necessary for execution in the next step.

[0106] Step 6:

[0107] The server sends the generated design drawings to a partner manufacturing facility. Based on the submitted design drawings, the manufacturing facility carries out the remanufacturing work. Specifically, the work involves selecting parts, repairing them, and painting them, ultimately producing the finished product.

[0108] Step 7:

[0109] Once the furniture is completed at the processing facility, it is reported back to the server. The server then prepares it for sale on online platforms, gathering information for listing on digital marketplaces. Here, product photos and pricing are output, and the sales process begins.

[0110] Step 8:

[0111] After a product is sold, the server distributes revenue to users and processing institutions based on sales. At this stage, sales data is aggregated and the distribution process is executed, and appropriate compensation is paid to each party involved.

[0112] (Application Example 1)

[0113] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0114] There is a need for an efficient system that can increase the added value of reusable furniture. However, traditional methods required users to manually conduct market research and select appropriate recycling methods, which was time-consuming and labor-intensive. Furthermore, users often could not sell the recycled furniture at the optimal price, making it difficult to maximize profits. In addition, there was insufficient consideration for the environment, resulting in ineffective resource utilization.

[0115] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0116] In this invention, the server includes means for receiving images and input information captured by the user, means including an algorithm for analyzing the attributes of an item from the image and identifying market demand, and means for determining a processing method for the item and generating a design drawing based on the analysis results. This reduces the effort required from the user and makes it possible to efficiently provide reusable furniture to the market with added value.

[0117] "Means for receiving images and input information captured by the user" refers to an interface for acquiring image data and related input information captured by the user using a smart device and sending it to the system.

[0118] An "algorithm for analyzing the attributes of goods and identifying market demand" is a computational means for identifying the characteristics of a received item from image data, analyzing market demand trends based on those characteristics, and evaluating the value of the item.

[0119] "Means for determining the processing method of an item based on analysis results and generating a design drawing for reuse" refers to a function that determines the optimal processing method based on identified market needs and generates a design drawing that shows the specific processing procedure and shape.

[0120] "A means of using smart devices to notify users of the results of market demand analysis and promote online sales" refers to a platform for communicating the results of analyzed market demand to users, and an interface for guiding products to be sold appropriately online.

[0121] "A means of listing finished goods from a processing facility on an online marketplace" refers to a function that allows finished goods from a processing facility to be listed on a digital marketplace and made available to consumers in a sales list.

[0122] The system that realizes this invention includes a cloud-based server, a user interface, and an AI analysis module. The server communicates with the user's terminal and receives images of furniture taken by the user and associated input information. To do this, the user uses, for example, a smartphone. The smartphone has a dedicated application installed that allows for easy uploading of captured image data.

[0123] The server analyzes the received images using AI image recognition software (e.g., TENSORFLOW®). This analysis identifies attributes such as the material and design of the items. The server then queries a market database (e.g., MySQL®) to estimate current market demand based on the identified attributes. The market demand analysis results are then communicated to the user via a smart device.

[0124] The user selects a proposed processing method based on market needs presented within the application. This selection is sent to a server, from which a specific design drawing is generated. This design drawing is then sent to a processing facility, where the furniture is processed or restored according to the design drawing.

[0125] The refurbished furniture is automatically listed on an online marketplace by the system. Users are notified regularly about the completion status and sales information of their refurbished furniture.

[0126] For example, if a user wants to restore an old wooden table, they can instruct the system using a prompt such as, "Please suggest ways to give this old wooden table a new style." In response to this request, the AI ​​will suggest antique finishes and renovations, and the processing will be carried out according to the user's selection.

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

[0128] Step 1:

[0129] The user uses a smart device to take pictures of unwanted furniture and inputs relevant information (material, desired style, etc.). This data is sent from the device to the server. The input data consists of image files and text information, and the output of this step is the raw data transferred to the server.

[0130] Step 2:

[0131] The server analyzes the received image data using an AI image recognition model. The images are analyzed using TensorFlow, and the attributes of the furniture (material, design characteristics, etc.) are identified. The input is image data, and the output is attribute information as a result of the analysis. In this process, the AI ​​model extracts specific features from the image and records them as numerical data.

[0132] Step 3:

[0133] Based on the analysis results, the server queries the market database to obtain current market demand information. The input is the analyzed attribute information, and the output is information on appropriate processing methods and recommended selling prices according to demand. In this step, statistical analysis of the market data is performed.

[0134] Step 4:

[0135] The server notifies the user of the optimal furniture processing method and estimated selling price based on market demand. Details are communicated to the user via push notifications and in-app displays on their device. The input is market demand information, and the output is a notification message to the user. The system operates using real-time notification functionality via an API.

[0136] Step 5:

[0137] The user selects their preferred processing method from the presented suggestions and sends the selection information to the server via the app. The input is the user's selection information, and the output is the selection received by the server. In this step, the user confirms the processing option using touch controls.

[0138] Step 6:

[0139] The server automatically generates specific design drawings for reuse based on the selected information obtained and sends them to the processing facility. The input is user-selected information, and the output is design drawing data in CAD format. Design proposals are created using a generated AI model.

[0140] Step 7:

[0141] The processing facility manufactures and restores furniture based on the received design drawings. This includes processes using specified materials and paints. The input is the design drawing data, and the output is the finished furniture. Within the facility, parts are assembled and finished using specialized processing machinery.

[0142] Step 8:

[0143] Information about the finished furniture is reported to the server at the processing facility, and the server then lists the finished products on an online marketplace. Input is digital data and photographs of the finished furniture, and output is product information registered on the marketplace. Integration with the e-commerce platform is performed on the server.

[0144] Step 9:

[0145] Once furniture sales are confirmed, the server distributes the revenue to the user and processing facility. The input is sales data, and the output is the distributed revenue information. In this final step, the profits are automatically transferred via an online payment system.

[0146] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0147] This invention is a furniture recycling system that takes user emotions into account, aiming not only to promote environmental protection and efficient resource utilization but also to improve the user experience. The system utilizes an emotion engine in the process of determining the optimal processing method by analyzing market needs based on photos taken by the user of the furniture.

[0148] Users take photos of unwanted furniture using their smartphones or camera-equipped devices and input information such as material, condition, and desired reuse methods. During this process, an emotion engine analyzes and records the user's emotional state. The user's emotions are inferred from their voice, facial expressions, and input content.

[0149] The device sends input information, including captured images and emotional data, to the server. The server passes the image data to an AI analysis system to identify the attributes of the items. Furthermore, while referring to the emotional data, it identifies market demand and considers processing methods that respond to the user's emotions.

[0150] The server integrates item attributes, market needs, and sentiment data to design the optimal reprocessing method. This process prioritizes processing methods that reflect the user's positive emotions and expectations. The generated blueprint includes progressively different designs and processing suggestions, allowing the user to choose according to their preferences.

[0151] The generated design drawings, including the selection of reusable parts based on the drawings, are sent to the processing facility. The processing facility then refurbishes the furniture based on the received design drawings. Reusable parts are selected from a database of unprocessed items, enabling efficient processing.

[0152] Once the revival furniture is completed at the processing facility, it is reported to the server and prepared for listing on the online marketplace. If a sale is made, the profits are distributed to the user and the processing facility, with user satisfaction taking into account based on their sentiment.

[0153] As a concrete example, consider a scenario where a user takes a photo of an old chair and inputs it into the app, and the emotion engine detects a slightly depressed mood. The system would then prioritize suggesting cheerful, uplifting designs or chair modification ideas. In this way, considering the user's emotions provides a more personalized experience and a more satisfying restoration process.

[0154] The following describes the processing flow.

[0155] Step 1:

[0156] Users take photos of unwanted furniture with their smartphones or camera-equipped devices. They also input information such as the furniture's material, current condition, and desired reuse method into the application.

[0157] Step 2:

[0158] The device sends the user's input information to the server along with the captured image. Additionally, an emotion engine analyzes the user's emotions from their voice tone and facial expressions, and transmits that data as well.

[0159] Step 3:

[0160] The server inputs the received image data into an AI algorithm to analyze the attributes of the furniture. This process identifies characteristics such as shape, dimensions, and material.

[0161] Step 4:

[0162] The server combines analyzed furniture attributes with user sentiment data to evaluate market needs. If positive sentiment is detected, it suggests processing methods that reflect the user's emotions.

[0163] Step 5:

[0164] The server integrates item attributes, market needs, and sentiment data to design the optimal regeneration plan. The generated blueprint includes customized processing methods and design options based on sentiment.

[0165] Step 6:

[0166] The server transmits the completed design drawings to the processing facility. At the processing facility, reusable parts are selected from the inventory database based on the design drawings, and efficient processing is carried out.

[0167] Step 7:

[0168] The processing facility restores the furniture according to the design plans and reports to the server upon completion. Images of the final product are also sent to the server in preparation for market listing.

[0169] Step 8:

[0170] The server, upon receiving a report, lists the furniture on the online marketplace. It sets the product information and selling price, and posts a product description based on user sentiment.

[0171] Step 9:

[0172] The server calculates the revenue earned after a sale is completed and distributes it to the user and processing facility. This process manages revenue distribution and feedback to maximize user satisfaction.

[0173] (Example 2)

[0174] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0175] In recent years, with sustainable consumption and environmental protection becoming increasingly important, there has been a demand for effective reuse of unwanted items and improved user experience. However, conventional reuse systems primarily focus on the items themselves and do not adequately consider user emotions or market trends. As a result, challenges exist, such as a lack of personalized service and difficulty in improving user satisfaction.

[0176] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0177] In this invention, the server includes means for receiving images and input information taken by the user and analyzing the emotional state; means including an algorithm for analyzing the attributes of an item from the received image and designing for reuse, taking into account the analyzed emotional information and market demand; and means for determining a method of processing the item based on the analysis results and emotional information and generating a design drawing that corresponds to the user's emotions. This makes it possible to personalize the reuse process and make suggestions that are sensitive to the user's emotions.

[0178] A "user" refers to an individual or corporation that wishes to reuse unwanted furniture using the system.

[0179] "Emotional state" refers to the emotional state analyzed based on the user's voice tone, facial expressions, and input information.

[0180] "Item attributes" refer to information such as the type, material, shape, and condition of the furniture being photographed.

[0181] "Market demand" refers to the demand for goods that reflect current consumer trends and behaviors.

[0182] A "design drawing" refers to a drawing or plan that specifies the processing methods and designs to be generated for reuse.

[0183] A "processing facility" refers to a facility or place that refurbishes goods based on design drawings.

[0184] An "online marketplace" refers to a platform where goods are sold and purchased over the internet.

[0185] A "prompt sentence" refers to an input sentence that a generative AI model uses to make suggestions or give instructions based on the user's emotions and desires.

[0186] The furniture recycling system in this invention efficiently carries out the process of recycling furniture by having the user take pictures of the furniture using a smartphone or camera-equipped terminal and analyzing that information. Specific embodiments are shown below.

[0187] Users take photos of unwanted furniture in their homes or offices using their smartphones or camera-equipped devices. The smartphones have emotion analysis software installed, which detects voice and facial expressions during shooting to analyze the user's emotional state. Users can also enter information about the furniture's material, condition, desired reuse method, and any wishes or comments in a free-text field within the app.

[0188] The device sends this image data, input information, and emotional state to the server. Encrypted communication is used for data transmission to ensure data security. The server passes the received images to an AI analysis system, which uses machine learning frameworks such as TensorFlow to identify the attributes of the items. Simultaneously, an emotion engine that performs natural language processing analyzes the user's emotional data and generates prompts to suggest the optimal reuse methods.

[0189] For example, suppose a user takes a photo of an old desk because they want to reuse it. If the emotion engine detects a depressed mood, it will generate a prompt such as, "Please suggest a design to transform this desk into a more cheerful space." This prompt is then input into a generative AI model, which creates a customized design based on the user's wishes and emotions.

[0190] The server generates optimal blueprints for reuse and selects the most suitable parts by referring to an inventory database of raw materials. The generated blueprints are then sent to the processing facility. Based on the received blueprints, the facility refurbishes the furniture and operates its equipment to ensure efficient processing. Through this process, a personalized refurbishment experience is provided that responds to user sentiment and market demand.

[0191] Furthermore, completed furniture is reported to the server and prepared for listing on the online marketplace. If a sale is made, the revenue is appropriately distributed to the user and the processing facility, and the user's satisfaction level is evaluated considering their sentiment data. Throughout this entire process, the system is guaranteed to operate efficiently and securely.

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

[0193] Step 1:

[0194] Users take photos of unwanted furniture using their smartphones or camera-equipped devices. They input information such as the furniture's material, condition, and desired reuse method into the device's app. The device uses voice recognition and facial expression analysis technology to analyze the user's emotions and records the results as emotional data. This collects detailed information about the user's emotional state and the furniture.

[0195] Step 2:

[0196] The device sends collected image data, input information, and emotion data to the server. As input, this data is encrypted before transmission. As output, the server receives this data and stores it in a database. This process ensures the secure transmission and storage of the data.

[0197] Step 3:

[0198] The server uses an AI analysis system to identify the attributes of items from received image data. Image data and existing image models are used as input. The AI ​​analysis system (e.g., a machine learning framework) matches the type and characteristics of furniture against information learned in a database, generating attribute information as output.

[0199] Step 4:

[0200] The server uses the analyzed item attributes to combine the emotion engine and market demand information to design a reused item. Using item attributes, emotion data, and market demand data as input, the generative AI model constructs prompt sentences. The output is a design blueprint that reflects the user's emotions.

[0201] Step 5:

[0202] The server sends the generated blueprints to the manufacturing facility. Optimized blueprints and inventory database information are used as input. As output, the manufacturing facility selects the necessary parts from inventory and receives manufacturing instructions based on the blueprints. This process ensures smooth preparation and manufacturing of parts.

[0203] Step 6:

[0204] After the furniture refurbishment is complete at the processing facility, the terminal reports the completion to the server. Inputs include detailed data on the finished product and a report of processing success. Outputs include the server preparing the product for listing on an online marketplace and managing sales data.

[0205] Step 7:

[0206] If a sale is completed, the server aggregates sales data and distributes revenue to the user and processing facility. Sales price and contract terms are used as input. The output includes revenue distribution and satisfaction evaluation, reflecting user sentiment data. This process ensures fair revenue distribution and improved user experience.

[0207] (Application Example 2)

[0208] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0209] Currently, conventional systems for recycling unwanted furniture do not take into account the user's emotional state, making it difficult to provide a personalized user experience and level of satisfaction. Furthermore, because user emotions cannot be reflected in the proposed recycling plans, the suggested plans may not always meet user expectations. Therefore, there is a need to develop a system that provides optimal recycling plans while considering user emotions, thereby achieving higher satisfaction.

[0210] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0211] In this invention, the server includes means for receiving images, audio, and facial expression data captured by the user; means including an algorithm for analyzing the attributes of an item and the user's emotional state from the received data and identifying an optimal design based on market demand and the user's emotions; and means for determining a processing method for the item based on the analysis results and generating a design for reuse that is appropriate to the emotions. This makes it possible to present a personalized reuse plan that takes the user's emotions into consideration.

[0212] A "user" is an individual or group that participates in the process of recycling unwanted furniture using this system.

[0213] "Image, audio, and facial expression data" refers to visual and audio information provided by the user, which is used to analyze the user's emotional state.

[0214] "Item attributes" refer to characteristics such as the material, design, condition, and function of furniture and related items, and are information used to evaluate their reusability.

[0215] "User emotional state" refers to the psychological state detected from the user's voice, facial expressions, etc., and is a factor that influences the playback plan.

[0216] "Design plans for reuse" are drawings and specifications that show new furniture designs and manufacturing methods, generated based on data and analysis results obtained from users.

[0217] An "emotion-driven reuse plan" refers to a personalized and proposed method of reusing furniture that takes into account the user's emotional state.

[0218] "Market demand" is a concept that indicates the level of consumer demand or need for a product or service at a specific point in time, and is a factor to consider when developing a reuse plan.

[0219] This invention realizes a system that provides a reuse plan based on the user's emotions. First, the user uses a terminal to take images of unwanted items through a high-resolution camera and also collects audio data using a microphone. This data is immediately transmitted to an emotion recognition engine, which analyzes the user's emotional state from their facial expressions and tone of voice.

[0220] The server analyzes emotional data using the EmotionRecognizer library, a specialized algorithm implemented in Python, and identifies object attributes (material, condition, etc.) from images using the open-source OpenCV library. Furthermore, it evaluates current market needs through an AI model and determines the reuse plan best suited to the user's emotional state.

[0221] Based on the decided plan, a design blueprint for reuse will be created. This blueprint will include emotionally resonant colors and design elements, and will present multiple options that reflect the user's preferences.

[0222] For example, if a user takes a picture of an old table with their device and enters a comment in a tired voice, the system will prioritize suggesting designs that give a relaxed impression. Furthermore, it will adopt a human-touch approach as an emotion-based prompt, such as, "I want to create an even more comfortable space with this table."

[0223] Examples of prompts include, "Propose a furniture revival design based on emotion analysis," and "Analyze the user's emotions from their voice and facial expressions, and design furniture that matches them."

[0224] Once the manufacturing process for reuse is complete, the server uploads the data to the online platform, preparing it for sale. This makes it possible to provide a highly satisfying experience that resonates with the user's emotions.

[0225] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0226] Step 1:

[0227] The user takes pictures of unwanted furniture using the device's high-resolution camera and saves them on the device. They also record comments and their feelings about the furniture using voice input. The input here consists of image and audio data, which are prepared for transmission to the server. The output is the data sent directly to the server.

[0228] Step 2:

[0229] The server analyzes the received image data using the OpenCV library. Specifically, it identifies the material and condition of the furniture through image processing algorithms. The input is image data, and the output is attribute information of the item. This reveals the details of the furniture.

[0230] Step 3:

[0231] The server analyzes the received audio data using the EmotionRecognizer library to identify the user's emotional state. Specifically, it extracts features from the audio waveform and classifies emotions based on them. The input is audio data, and the output is the user's emotional state. The analysis is performed using a generative AI model.

[0232] Step 4:

[0233] The server inputs the attribute information of the analyzed items and the user's emotional state into a generating AI model, and determines the optimal reuse plan while considering market demand. In this process, it refers to market trend data and generates design proposals that are sensitive to emotions. The output is a blueprint for reuse.

[0234] Step 5:

[0235] Once a blueprint for reuse is generated from the server, the terminal presents it to the user. The blueprint includes colors and designs based on the user's preferences. By viewing this, the user can visualize the optimal reuse plan.

[0236] Step 6:

[0237] The server sends the generated blueprints to the manufacturing facility and provides manufacturing instructions for reuse. Specifically, it sends the blueprints along with a list of available parts to the manufacturing facility. The output here is data containing detailed instruction information for the manufacturing facility.

[0238] Step 7:

[0239] Once manufacturing is complete at the processing facility, the server retrieves the information and prepares to list the revived furniture (recycled furniture) on the online platform. Specific actions include updating product information and incorporating user reviews. The final output is online listing information ready for sale.

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

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

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

[0243] [Second Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0256] This invention is a system that uses AI to add new value to unwanted furniture and regenerate it. The system involves a series of processes, from the user taking a photo of the furniture to the system analyzing market needs based on that photo and proposing the optimal processing method.

[0257] At the start of the system, users take photos of unwanted furniture using their smartphones or other devices. Users can also input information such as the material and condition of the furniture and their desired reuse method. This input information, along with the images, is sent from the device to the server.

[0258] The server analyzes the received image data using an AI image recognition model to identify the attributes of the items. This analysis includes characteristics such as the furniture category, material, and dimensions. Next, the server uses historical data from its internal database and external online marketplaces to identify market demand. This demand information indicates what styles and price ranges are preferred for reproduction.

[0259] Next, the server designs the optimal processing method based on the identified market needs and item attributes. A design drawing is generated, and reusable parts are identified by referencing the inventory database of unprocessed items. This enables efficient resource utilization.

[0260] The generated blueprints are sent to partner processing facilities. These facilities then refurbish the furniture based on the received blueprints. This process includes parts replacement, repair, and painting.

[0261] Finally, the revived furniture completed at the processing facility is reported to the server and prepared for listing on the online marketplace. The server shares the progress of the work with the user and the relevant processing facility and distributes the profits earned after the sale. Users can not only create new value from furniture with minimal effort, but also contribute to environmental conservation.

[0262] For example, if a user is looking to dispose of an old wooden table, the system uses photos and information to determine what kind of finish is in demand in the market and suggests an antique-style finish that highlights the wood grain. The processing facility then repaints the table according to these instructions and sells the finished table online. Through this process, both the user and the processing facility can profit while effectively utilizing resources and being environmentally conscious.

[0263] The following describes the processing flow.

[0264] Step 1:

[0265] Users take photos of unwanted furniture using their smartphone or camera-equipped device. After taking the photos, they input information such as the material, condition, and desired use into the application on their device.

[0266] Step 2:

[0267] The terminal combines the information entered by the user and the images taken into a data package and sends it to the server. A secure protocol is used for data transmission via the internet.

[0268] Step 3:

[0269] The server inputs the received data into an AI image recognition algorithm to analyze the attributes of the furniture from the image. Specifically, it identifies the shape, material, and damaged areas, and stores them as digital data.

[0270] Step 4:

[0271] Based on the analysis results, the server uses its internal database and online market trend data to evaluate the market needs for furniture. This helps determine which revival styles are appealing to consumers.

[0272] Step 5:

[0273] The server integrates market needs and item attributes to design the optimal refurbishment method. For example, it specifically determines processing techniques such as partial repair, repainting, and parts replacement, and generates detailed design drawings.

[0274] Step 6:

[0275] The server searches the inventory database of unprocessed materials based on the generated design drawings, identifying reusable and necessary parts. This enables the effective utilization of unused parts.

[0276] Step 7:

[0277] The server sends the completed design drawings to the processing facility and provides specific instructions for the recycling and processing. The instructions include processing procedures and details of the parts to be used.

[0278] Step 8:

[0279] The processing facility performs the recycling and processing of the furniture based on the design drawings from the server. It procures materials, repairs, assembles, paints, and finishes the final product.

[0280] Step 9:

[0281] The processing facility takes pictures of the completed product and reports to the server. This report indicates that the product is ready to be listed on the online marketplace.

[0282] Step 10:

[0283] The server receives the completion report and lists the furniture on the online marketplace. It posts product information, prices, and photos and starts selling.

[0284] Step 11:

[0285] When a sale is made, the server aggregates the sales, calculates the profits for the users and the processing facility, and distributes them. The details of the transactions are managed in a digital and transparent process that can be verified.

[0286] (Example 1)

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

[0288] In the conventional approach, it is difficult to accurately reflect the market demand in the effort to add new value and recycle furniture scheduled for disposal, and there is also the problem that the manual processing plan is not efficient. Therefore, there is a need to build a system that can effectively utilize resources without burdening the users.

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

[0290] In this invention, the server includes means for receiving furniture images and related information taken by the user, means for analyzing the attributes of the item using a generating AI model and identifying market demand, and means for designing an optimal processing method and generating a design drawing based on the analysis results. This makes it possible for users to easily refurbish furniture, create new market value, and efficiently utilize resources.

[0291] A "user" refers to an individual or organization that uses the system to photograph unwanted furniture and provides information about its reuse.

[0292] "Image" refers to photographic data of furniture taken by the user using their device.

[0293] A "generative AI model" refers to an artificial intelligence algorithm used to analyze images of furniture and identify the attributes of the items.

[0294] "Item attributes" refer to characteristics such as the furniture's category, material, and dimensions.

[0295] "Market demand" refers to information indicating what styles and price ranges consumers are in demand for refurbished furniture.

[0296] "Processing method" refers to the specific means and processes used to improve or repair furniture for the purpose of reuse.

[0297] A "design drawing" refers to a drawing or plan that shows the processing procedures and specifications necessary for furniture restoration, generated based on market demand and product characteristics.

[0298] A "processing facility" refers to a facility or business that carries out furniture restoration work based on the generated design drawings.

[0299] An "online platform" refers to an internet marketplace or service used to sell processed furniture.

[0300] "Parts" refers to the constituent elements of furniture, including materials used for necessary repairs and reassembly during restoration.

[0301] "Efficient use of resources" refers to the efficient use of materials and parts in furniture recycling, without waste.

[0302] "Profit sharing" refers to the process of appropriately distributing the profits earned from the sale of furniture to users and manufacturers.

[0303] This invention relates to a system that uses AI technology to add new value to furniture that is scheduled to be discarded and to repurpose it.

[0304] First, the user takes a picture of the furniture they want to reuse using a device such as a smartphone or computer. At this time, the user can input information about the furniture's material, condition, and desired reuse method. For example, if the user wants to reuse an old wooden table in an antique style, they would take a picture of it with their device and input "wood" as the material and "antique style" as the method.

[0305] The terminal sends the captured image and accompanying information to the server. The server inputs the received image into a generating AI model and uses image recognition technology to identify the attributes of the furniture. This analysis reveals the furniture's category, material, and dimensions, and then identifies market demand by referring to internal databases and data from external online markets.

[0306] Based on market demand, the server uses AI-powered prompts to design the optimal processing method that matches the characteristics of the item and the demand, generating a design blueprint aimed at reuse. This blueprint often includes processes such as parts rearrangement, repair, and painting. For example, the analysis might suggest repainting an old table in an antique style that highlights the wood grain.

[0307] The generated design drawing is sent to the cooperating processing institution. Based on this design drawing, the processing institution proceeds with the reproduction work of the furniture. As a result, the parts are efficiently used, and a finished product that has undergone repainting and repair processes is produced.

[0308] The completed furniture is reported from the processing institution to the server, and preparations for listing on the online platform are made. The server adjusts information related to sales, such as photos and prices, and distributes profits to users and processing institutions depending on the sales situation.

[0309] As an example of the prompt sentence, a form such as "Please generate a reuse plan for an old wooden table. Please tell me the optimal processing method based on market demand." can be used. With this system, users can add new value to the furniture in their homes and achieve reuse in an environmentally friendly way.

[0310] The flow of the specific process in Example 1 will be described using FIG. 11.

[0311] Step 1:

[0312] The user takes a photo of the unwanted furniture using a smartphone or a personal computer. Utilizing the camera function of the terminal used for shooting, the user inputs the material and condition of the furniture and their hopes regarding reuse. The input data consists of an image and text information. This data becomes the basic information necessary for subsequent analysis.

[0313] Step 2:

[0314] The terminal transmits the captured image data and its related information to the server via the Internet. The transmitted data is used as input for AI analysis on the server. Specifically, the image file and the text description input by the user reach the server and are stored in the database.

[0315] Step 3:

[0316] The server inputs the received images into a generating AI model for image analysis. The model utilizes image recognition algorithms to identify attributes such as furniture category, material, and dimensions. The analysis output provides analyzed attribute information. This attribute information is used in the next step to analyze market demand.

[0317] Step 4:

[0318] The server analyzes market demand by referencing internal databases and external online data. Based on the received attribute information, it identifies what styles and price ranges are in demand among consumers. This analysis outputs data for product specifications that match market trends.

[0319] Step 5:

[0320] The server designs the optimal processing method based on the analysis results. Using a generative AI model, it generates a design for furniture reuse, taking into account the user's input requirements and market needs. This design shows the processing steps and parts to be used in detail, providing the information necessary for execution in the next step.

[0321] Step 6:

[0322] The server sends the generated design drawings to a partner manufacturing facility. Based on the submitted design drawings, the manufacturing facility carries out the remanufacturing work. Specifically, the work involves selecting parts, repairing them, and painting them, ultimately producing the finished product.

[0323] Step 7:

[0324] Once the furniture is completed at the processing facility, it is reported back to the server. The server then prepares it for sale on online platforms, gathering information for listing on digital marketplaces. Here, product photos and pricing are output, and the sales process begins.

[0325] Step 8:

[0326] After a product is sold, the server distributes revenue to users and processing institutions based on sales. At this stage, sales data is aggregated and the distribution process is executed, and appropriate compensation is paid to each party involved.

[0327] (Application Example 1)

[0328] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0329] There is a need for an efficient system that can increase the added value of reusable furniture. However, traditional methods required users to manually conduct market research and select appropriate recycling methods, which was time-consuming and labor-intensive. Furthermore, users often could not sell the recycled furniture at the optimal price, making it difficult to maximize profits. In addition, there was insufficient consideration for the environment, resulting in ineffective resource utilization.

[0330] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0331] In this invention, the server includes means for receiving images and input information captured by the user, means including an algorithm for analyzing the attributes of an item from the image and identifying market demand, and means for determining a processing method for the item and generating a design drawing based on the analysis results. This reduces the effort required from the user and makes it possible to efficiently provide reusable furniture to the market with added value.

[0332] "Means for receiving images and input information captured by the user" refers to an interface for acquiring image data and related input information captured by the user using a smart device and sending it to the system.

[0333] An "algorithm for analyzing the attributes of goods and identifying market demand" is a computational means for identifying the characteristics of a received item from image data, analyzing market demand trends based on those characteristics, and evaluating the value of the item.

[0334] "Means for determining the processing method of an item based on analysis results and generating a design drawing for reuse" refers to a function that determines the optimal processing method based on identified market needs and generates a design drawing that shows the specific processing procedure and shape.

[0335] "A means of using smart devices to notify users of the results of market demand analysis and promote online sales" refers to a platform for communicating the results of analyzed market demand to users, and an interface for guiding products to be sold appropriately online.

[0336] "A means of listing finished goods from a processing facility on an online marketplace" refers to a function that allows finished goods from a processing facility to be listed on a digital marketplace and made available to consumers in a sales list.

[0337] The system that realizes this invention includes a cloud-based server, a user interface, and an AI analysis module. The server communicates with the user's terminal and receives images of furniture taken by the user and associated input information. To do this, the user uses, for example, a smartphone. The smartphone has a dedicated application installed that allows for easy uploading of captured image data.

[0338] The server analyzes the received images using AI image recognition software (e.g., TensorFlow). This analysis identifies attributes such as the material and design of the items. The server then queries a market database (e.g., MySQL) to estimate current market demand based on the identified attributes. The market demand analysis results are then communicated to the user via a smart device.

[0339] The user selects a proposed processing method based on market needs presented within the application. This selection is sent to a server, from which a specific design drawing is generated. This design drawing is then sent to a processing facility, where the furniture is processed or restored according to the design drawing.

[0340] The refurbished furniture is automatically listed on an online marketplace by the system. Users are notified regularly about the completion status and sales information of their refurbished furniture.

[0341] For example, if a user wants to restore an old wooden table, they can instruct the system using a prompt such as, "Please suggest ways to give this old wooden table a new style." In response to this request, the AI ​​will suggest antique finishes and renovations, and the processing will be carried out according to the user's selection.

[0342] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0343] Step 1:

[0344] The user uses a smart device to take pictures of unwanted furniture and inputs relevant information (material, desired style, etc.). This data is sent from the device to the server. The input data consists of image files and text information, and the output of this step is the raw data transferred to the server.

[0345] Step 2:

[0346] The server analyzes the received image data using an AI image recognition model. The images are analyzed using TensorFlow, and the attributes of the furniture (material, design characteristics, etc.) are identified. The input is image data, and the output is attribute information as a result of the analysis. In this process, the AI ​​model extracts specific features from the image and records them as numerical data.

[0347] Step 3:

[0348] Based on the analysis results, the server queries the market database to obtain current market demand information. The input is the analyzed attribute information, and the output is information on appropriate processing methods and recommended selling prices according to demand. In this step, statistical analysis of the market data is performed.

[0349] Step 4:

[0350] The server notifies the user of the optimal furniture processing method and estimated selling price based on market demand. Details are communicated to the user via push notifications and in-app displays on their device. The input is market demand information, and the output is a notification message to the user. The system operates using real-time notification functionality via an API.

[0351] Step 5:

[0352] The user selects their preferred processing method from the presented suggestions and sends the selection information to the server via the app. The input is the user's selection information, and the output is the selection received by the server. In this step, the user confirms the processing option using touch controls.

[0353] Step 6:

[0354] The server automatically generates specific design drawings for reuse based on the selected information obtained and sends them to the processing facility. The input is user-selected information, and the output is design drawing data in CAD format. Design proposals are created using a generated AI model.

[0355] Step 7:

[0356] The processing facility manufactures and restores furniture based on the received design drawings. This includes processes using specified materials and paints. The input is the design drawing data, and the output is the finished furniture. Within the facility, parts are assembled and finished using specialized processing machinery.

[0357] Step 8:

[0358] Information about the finished furniture is reported to the server at the processing facility, and the server then lists the finished products on an online marketplace. Input is digital data and photographs of the finished furniture, and output is product information registered on the marketplace. Integration with the e-commerce platform is performed on the server.

[0359] Step 9:

[0360] Once furniture sales are confirmed, the server distributes the revenue to the user and processing facility. The input is sales data, and the output is the distributed revenue information. In this final step, the profits are automatically transferred via an online payment system.

[0361] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0362] This invention is a furniture recycling system that takes user emotions into account, aiming not only to promote environmental protection and efficient resource utilization but also to improve the user experience. The system utilizes an emotion engine in the process of determining the optimal processing method by analyzing market needs based on photos taken by the user of the furniture.

[0363] Users take photos of unwanted furniture using their smartphones or camera-equipped devices and input information such as material, condition, and desired reuse methods. During this process, an emotion engine analyzes and records the user's emotional state. The user's emotions are inferred from their voice, facial expressions, and input content.

[0364] The device sends input information, including captured images and emotional data, to the server. The server passes the image data to an AI analysis system to identify the attributes of the items. Furthermore, while referring to the emotional data, it identifies market demand and considers processing methods that respond to the user's emotions.

[0365] The server integrates item attributes, market needs, and sentiment data to design the optimal reprocessing method. This process prioritizes processing methods that reflect the user's positive emotions and expectations. The generated blueprint includes progressively different designs and processing suggestions, allowing the user to choose according to their preferences.

[0366] The generated design drawings, including the selection of reusable parts based on the drawings, are sent to the processing facility. The processing facility then refurbishes the furniture based on the received design drawings. Reusable parts are selected from a database of unprocessed items, enabling efficient processing.

[0367] Once the revival furniture is completed at the processing facility, it is reported to the server and prepared for listing on the online marketplace. If a sale is made, the profits are distributed to the user and the processing facility, with user satisfaction taking into account based on their sentiment.

[0368] As a concrete example, consider a scenario where a user takes a photo of an old chair and inputs it into the app, and the emotion engine detects a slightly depressed mood. The system would then prioritize suggesting cheerful, uplifting designs or chair modification ideas. In this way, considering the user's emotions provides a more personalized experience and a more satisfying restoration process.

[0369] The following describes the processing flow.

[0370] Step 1:

[0371] Users take photos of unwanted furniture with their smartphones or camera-equipped devices. They also input information such as the furniture's material, current condition, and desired reuse method into the application.

[0372] Step 2:

[0373] The device sends the user's input information to the server along with the captured image. Additionally, an emotion engine analyzes the user's emotions from their voice tone and facial expressions, and transmits that data as well.

[0374] Step 3:

[0375] The server inputs the received image data into an AI algorithm to analyze the attributes of the furniture. This process identifies characteristics such as shape, dimensions, and material.

[0376] Step 4:

[0377] The server combines analyzed furniture attributes with user sentiment data to evaluate market needs. If positive sentiment is detected, it suggests processing methods that reflect the user's emotions.

[0378] Step 5:

[0379] The server integrates item attributes, market needs, and sentiment data to design the optimal regeneration plan. The generated blueprint includes customized processing methods and design options based on sentiment.

[0380] Step 6:

[0381] The server transmits the completed design drawings to the processing facility. At the processing facility, reusable parts are selected from the inventory database based on the design drawings, and efficient processing is carried out.

[0382] Step 7:

[0383] The processing facility restores the furniture according to the design plans and reports to the server upon completion. Images of the final product are also sent to the server in preparation for market listing.

[0384] Step 8:

[0385] The server, upon receiving a report, lists the furniture on the online marketplace. It sets the product information and selling price, and posts a product description based on user sentiment.

[0386] Step 9:

[0387] The server calculates the revenue earned after a sale is completed and distributes it to the user and processing facility. This process manages revenue distribution and feedback to maximize user satisfaction.

[0388] (Example 2)

[0389] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0390] In recent years, with sustainable consumption and environmental protection becoming increasingly important, there has been a demand for effective reuse of unwanted items and improved user experience. However, conventional reuse systems primarily focus on the items themselves and do not adequately consider user emotions or market trends. As a result, challenges exist, such as a lack of personalized service and difficulty in improving user satisfaction.

[0391] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0392] In this invention, the server includes means for receiving images and input information taken by the user and analyzing the emotional state; means including an algorithm for analyzing the attributes of an item from the received image and designing for reuse, taking into account the analyzed emotional information and market demand; and means for determining a method of processing the item based on the analysis results and emotional information and generating a design drawing that corresponds to the user's emotions. This makes it possible to personalize the reuse process and make suggestions that are sensitive to the user's emotions.

[0393] A "user" refers to an individual or corporation that wishes to reuse unwanted furniture using the system.

[0394] "Emotional state" refers to the emotional state analyzed based on the user's voice tone, facial expressions, and input information.

[0395] "Item attributes" refer to information such as the type, material, shape, and condition of the furniture being photographed.

[0396] "Market demand" refers to the demand for goods that reflect current consumer trends and behaviors.

[0397] A "design drawing" refers to a drawing or plan that specifies the processing methods and designs to be generated for reuse.

[0398] A "processing facility" refers to a facility or place that refurbishes goods based on design drawings.

[0399] An "online marketplace" refers to a platform where goods are sold and purchased over the internet.

[0400] A "prompt sentence" refers to an input sentence that a generative AI model uses to make suggestions or give instructions based on the user's emotions and desires.

[0401] The furniture recycling system in this invention efficiently carries out the process of recycling furniture by having the user take pictures of the furniture using a smartphone or camera-equipped terminal and analyzing that information. Specific embodiments are shown below.

[0402] Users take photos of unwanted furniture in their homes or offices using their smartphones or camera-equipped devices. The smartphones have emotion analysis software installed, which detects voice and facial expressions during shooting to analyze the user's emotional state. Users can also enter information about the furniture's material, condition, desired reuse method, and any wishes or comments in a free-text field within the app.

[0403] The device sends this image data, input information, and emotional state to the server. Encrypted communication is used for data transmission to ensure data security. The server passes the received images to an AI analysis system, which uses machine learning frameworks such as TensorFlow to identify the attributes of the items. Simultaneously, an emotion engine that performs natural language processing analyzes the user's emotional data and generates prompts to suggest the optimal reuse methods.

[0404] For example, suppose a user takes a photo of an old desk because they want to reuse it. If the emotion engine detects a depressed mood, it will generate a prompt such as, "Please suggest a design to transform this desk into a more cheerful space." This prompt is then input into a generative AI model, which creates a customized design based on the user's wishes and emotions.

[0405] The server generates optimal blueprints for reuse and selects the most suitable parts by referring to an inventory database of raw materials. The generated blueprints are then sent to the processing facility. Based on the received blueprints, the facility refurbishes the furniture and operates its equipment to ensure efficient processing. Through this process, a personalized refurbishment experience is provided that responds to user sentiment and market demand.

[0406] Furthermore, completed furniture is reported to the server and prepared for listing on the online marketplace. If a sale is made, the revenue is appropriately distributed to the user and the processing facility, and the user's satisfaction level is evaluated considering their sentiment data. Throughout this entire process, the system is guaranteed to operate efficiently and securely.

[0407] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0408] Step 1:

[0409] Users take photos of unwanted furniture using their smartphones or camera-equipped devices. They input information such as the furniture's material, condition, and desired reuse method into the device's app. The device uses voice recognition and facial expression analysis technology to analyze the user's emotions and records the results as emotional data. This collects detailed information about the user's emotional state and the furniture.

[0410] Step 2:

[0411] The device sends collected image data, input information, and emotion data to the server. As input, this data is encrypted before transmission. As output, the server receives this data and stores it in a database. This process ensures the secure transmission and storage of the data.

[0412] Step 3:

[0413] The server uses an AI analysis system to identify the attributes of items from received image data. Image data and existing image models are used as input. The AI ​​analysis system (e.g., a machine learning framework) matches the type and characteristics of furniture against information learned in a database, generating attribute information as output.

[0414] Step 4:

[0415] The server uses the analyzed item attributes to combine the emotion engine and market demand information to design a reused item. Using item attributes, emotion data, and market demand data as input, the generative AI model constructs prompt sentences. The output is a design blueprint that reflects the user's emotions.

[0416] Step 5:

[0417] The server sends the generated blueprints to the manufacturing facility. Optimized blueprints and inventory database information are used as input. As output, the manufacturing facility selects the necessary parts from inventory and receives manufacturing instructions based on the blueprints. This process ensures smooth preparation and manufacturing of parts.

[0418] Step 6:

[0419] After the furniture refurbishment is complete at the processing facility, the terminal reports the completion to the server. Inputs include detailed data on the finished product and a report of processing success. Outputs include the server preparing the product for listing on an online marketplace and managing sales data.

[0420] Step 7:

[0421] If a sale is completed, the server aggregates sales data and distributes revenue to the user and processing facility. Sales price and contract terms are used as input. The output includes revenue distribution and satisfaction evaluation, reflecting user sentiment data. This process ensures fair revenue distribution and improved user experience.

[0422] (Application Example 2)

[0423] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0424] Currently, conventional systems for recycling unwanted furniture do not take into account the user's emotional state, making it difficult to provide a personalized user experience and level of satisfaction. Furthermore, because user emotions cannot be reflected in the proposed recycling plans, the suggested plans may not always meet user expectations. Therefore, there is a need to develop a system that provides optimal recycling plans while considering user emotions, thereby achieving higher satisfaction.

[0425] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0426] In this invention, the server includes means for receiving images, audio, and facial expression data captured by the user; means including an algorithm for analyzing the attributes of an item and the user's emotional state from the received data and identifying an optimal design based on market demand and the user's emotions; and means for determining a processing method for the item based on the analysis results and generating a design for reuse that is appropriate to the emotions. This makes it possible to present a personalized reuse plan that takes the user's emotions into consideration.

[0427] A "user" is an individual or group that participates in the process of recycling unwanted furniture using this system.

[0428] "Image, audio, and facial expression data" refers to visual and audio information provided by the user, which is used to analyze the user's emotional state.

[0429] "Item attributes" refer to characteristics such as the material, design, condition, and function of furniture and related items, and are information used to evaluate their reusability.

[0430] "User emotional state" refers to the psychological state detected from the user's voice, facial expressions, etc., and is a factor that influences the playback plan.

[0431] "Design plans for reuse" are drawings and specifications that show new furniture designs and manufacturing methods, generated based on data and analysis results obtained from users.

[0432] An "emotion-driven reuse plan" refers to a personalized and proposed method of reusing furniture that takes into account the user's emotional state.

[0433] "Market demand" is a concept that indicates the level of consumer demand or need for a product or service at a specific point in time, and is a factor to consider when developing a reuse plan.

[0434] This invention realizes a system that provides a reuse plan based on the user's emotions. First, the user uses a terminal to take images of unwanted items through a high-resolution camera and also collects audio data using a microphone. This data is immediately transmitted to an emotion recognition engine, which analyzes the user's emotional state from their facial expressions and tone of voice.

[0435] The server analyzes emotional data using the EmotionRecognizer library, a specialized algorithm implemented in Python, and identifies object attributes (material, condition, etc.) from images using the open-source OpenCV library. Furthermore, it evaluates current market needs through an AI model and determines the reuse plan best suited to the user's emotional state.

[0436] Based on the decided plan, a design blueprint for reuse will be created. This blueprint will include emotionally resonant colors and design elements, and will present multiple options that reflect the user's preferences.

[0437] For example, if a user takes a picture of an old table with their device and enters a comment in a tired voice, the system will prioritize suggesting designs that give a relaxed impression. Furthermore, it will adopt a human-touch approach as an emotion-based prompt, such as, "I want to create an even more comfortable space with this table."

[0438] Examples of prompts include, "Propose a furniture revival design based on emotion analysis," and "Analyze the user's emotions from their voice and facial expressions, and design furniture that matches them."

[0439] Once the manufacturing process for reuse is complete, the server uploads the data to the online platform, preparing it for sale. This makes it possible to provide a highly satisfying experience that resonates with the user's emotions.

[0440] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0441] Step 1:

[0442] The user takes pictures of unwanted furniture using the device's high-resolution camera and saves them on the device. They also record comments and their feelings about the furniture using voice input. The input here consists of image and audio data, which are prepared for transmission to the server. The output is the data sent directly to the server.

[0443] Step 2:

[0444] The server analyzes the received image data using the OpenCV library. Specifically, it identifies the material and condition of the furniture through image processing algorithms. The input is image data, and the output is attribute information of the item. This reveals the details of the furniture.

[0445] Step 3:

[0446] The server analyzes the received audio data using the EmotionRecognizer library to identify the user's emotional state. Specifically, it extracts features from the audio waveform and classifies emotions based on them. The input is audio data, and the output is the user's emotional state. The analysis is performed using a generative AI model.

[0447] Step 4:

[0448] The server inputs the attribute information of the analyzed items and the user's emotional state into a generating AI model, and determines the optimal reuse plan while considering market demand. In this process, it refers to market trend data and generates design proposals that are sensitive to emotions. The output is a blueprint for reuse.

[0449] Step 5:

[0450] Once a blueprint for reuse is generated from the server, the terminal presents it to the user. The blueprint includes colors and designs based on the user's preferences. By viewing this, the user can visualize the optimal reuse plan.

[0451] Step 6:

[0452] The server sends the generated blueprints to the manufacturing facility and provides manufacturing instructions for reuse. Specifically, it sends the blueprints along with a list of available parts to the manufacturing facility. The output here is data containing detailed instruction information for the manufacturing facility.

[0453] Step 7:

[0454] Once manufacturing is complete at the processing facility, the server retrieves the information and prepares to list the revived furniture (recycled furniture) on the online platform. Specific actions include updating product information and incorporating user reviews. The final output is online listing information ready for sale.

[0455] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0456] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0457] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0458] [Third Embodiment]

[0459] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0460] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0461] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0462] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0463] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0464] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0465] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0466] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0467] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0468] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0469] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0470] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0471] This invention is a system that uses AI to add new value to unwanted furniture and regenerate it. The system involves a series of processes, from the user taking a photo of the furniture to the system analyzing market needs based on that photo and proposing the optimal processing method.

[0472] At the start of the system, users take photos of unwanted furniture using their smartphones or other devices. Users can also input information such as the material and condition of the furniture and their desired reuse method. This input information, along with the images, is sent from the device to the server.

[0473] The server analyzes the received image data using an AI image recognition model to identify the attributes of the items. This analysis includes characteristics such as the furniture category, material, and dimensions. Next, the server uses historical data from its internal database and external online marketplaces to identify market demand. This demand information indicates what styles and price ranges are preferred for reproduction.

[0474] Next, the server designs the optimal processing method based on the identified market needs and item attributes. A design drawing is generated, and reusable parts are identified by referencing the inventory database of unprocessed items. This enables efficient resource utilization.

[0475] The generated blueprints are sent to partner processing facilities. These facilities then refurbish the furniture based on the received blueprints. This process includes parts replacement, repair, and painting.

[0476] Finally, the revived furniture completed at the processing facility is reported to the server and prepared for listing on the online marketplace. The server shares the progress of the work with the user and the relevant processing facility and distributes the profits earned after the sale. Users can not only create new value from furniture with minimal effort, but also contribute to environmental conservation.

[0477] For example, if a user is looking to dispose of an old wooden table, the system uses photos and information to determine what kind of finish is in demand in the market and suggests an antique-style finish that highlights the wood grain. The processing facility then repaints the table according to these instructions and sells the finished table online. Through this process, both the user and the processing facility can profit while effectively utilizing resources and being environmentally conscious.

[0478] The following describes the processing flow.

[0479] Step 1:

[0480] Users take photos of unwanted furniture using their smartphone or camera-equipped device. After taking the photos, they input information such as the material, condition, and desired use into the application on their device.

[0481] Step 2:

[0482] The terminal combines the information entered by the user and the images taken into a data package and sends it to the server. A secure protocol is used for data transmission via the internet.

[0483] Step 3:

[0484] The server inputs the received data into an AI image recognition algorithm to analyze the attributes of the furniture from the image. Specifically, it identifies the shape, material, and damaged areas, and stores them as digital data.

[0485] Step 4:

[0486] Based on the analysis results, the server uses its internal database and online market trend data to evaluate the market needs for furniture. This helps determine which revival styles are appealing to consumers.

[0487] Step 5:

[0488] The server integrates market needs and item attributes to design the optimal refurbishment method. For example, it specifically determines processing techniques such as partial repair, repainting, and parts replacement, and generates detailed design drawings.

[0489] Step 6:

[0490] The server searches the inventory database of unprocessed materials based on the generated design drawings, identifying reusable and necessary parts. This enables the effective utilization of unused parts.

[0491] Step 7:

[0492] The server transmits the completed design drawings to the processing facility and provides specific instructions for the remanufacturing process. These instructions include processing procedures and details of the parts to be used.

[0493] Step 8:

[0494] The processing facility refurbishes furniture based on design drawings from the server. It handles material procurement, repair, assembly, and painting to produce the final product.

[0495] Step 9:

[0496] The processing facility takes a picture of the finished product and reports it to the server. This report indicates that the product is ready to be listed on the online marketplace.

[0497] Step 10:

[0498] The server receives the completion report and lists the furniture on the online marketplace. It posts product information, price, and photos, and begins sales.

[0499] Step 11:

[0500] The server aggregates sales once a sale is completed, calculates the revenue, and distributes it to the user and processing facility. Transaction details are managed through a transparent, digitally verifiable process.

[0501] (Example 1)

[0502] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0503] The effort to repurpose furniture destined for disposal by adding new value presents challenges: traditional methods struggle to accurately reflect market demand, and manual processing planning is inefficient. Therefore, there is a need to develop a system that allows for the effective use of resources without burdening users.

[0504] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0505] In this invention, the server includes means for receiving furniture images and related information taken by the user, means for analyzing the attributes of the item using a generating AI model and identifying market demand, and means for designing an optimal processing method and generating a design drawing based on the analysis results. This makes it possible for users to easily refurbish furniture, create new market value, and efficiently utilize resources.

[0506] A "user" refers to an individual or organization that uses the system to photograph unwanted furniture and provides information about its reuse.

[0507] "Image" refers to photographic data of furniture taken by the user using their device.

[0508] A "generative AI model" refers to an artificial intelligence algorithm used to analyze images of furniture and identify the attributes of the items.

[0509] "Item attributes" refer to characteristics such as the furniture's category, material, and dimensions.

[0510] "Market demand" refers to information indicating what styles and price ranges consumers are in demand for refurbished furniture.

[0511] "Processing method" refers to the specific means and processes used to improve or repair furniture for the purpose of reuse.

[0512] A "design drawing" refers to a drawing or plan that shows the processing procedures and specifications necessary for furniture restoration, generated based on market demand and product characteristics.

[0513] A "processing facility" refers to a facility or business that carries out furniture restoration work based on the generated design drawings.

[0514] An "online platform" refers to an internet marketplace or service used to sell processed furniture.

[0515] "Parts" refers to the constituent elements of furniture, including materials used for necessary repairs and reassembly during restoration.

[0516] "Efficient use of resources" refers to the efficient use of materials and parts in furniture recycling, without waste.

[0517] "Profit sharing" refers to the process of appropriately distributing the profits earned from the sale of furniture to users and manufacturers.

[0518] This invention relates to a system that uses AI technology to add new value to furniture that is scheduled to be discarded and to repurpose it.

[0519] First, the user takes a picture of the furniture they want to reuse using a device such as a smartphone or computer. At this time, the user can input information about the furniture's material, condition, and desired reuse method. For example, if the user wants to reuse an old wooden table in an antique style, they would take a picture of it with their device and input "wood" as the material and "antique style" as the method.

[0520] The terminal sends the captured image and accompanying information to the server. The server inputs the received image into a generating AI model and uses image recognition technology to identify the attributes of the furniture. This analysis reveals the furniture's category, material, and dimensions, and then identifies market demand by referring to internal databases and data from external online markets.

[0521] Based on market demand, the server uses AI-powered prompts to design the optimal processing method that matches the characteristics of the item and the demand, generating a design blueprint aimed at reuse. This blueprint often includes processes such as parts rearrangement, repair, and painting. For example, the analysis might suggest repainting an old table in an antique style that highlights the wood grain.

[0522] The generated blueprints are sent to partner processing facilities. These facilities then proceed with the furniture restoration work based on these blueprints. This process involves the efficient use of parts and the creation of finished products through repainting and repair processes.

[0523] Finished furniture is reported from the processing facility to the server, and preparations are made for listing it on the online platform. The server adjusts sales information, such as photos and prices, and distributes revenue to users and processing facilities depending on sales performance.

[0524] An example of a prompt message could be: "Generate ideas for reusing an old wooden table. Please tell me the best processing method based on market demand." This system allows users to add new value to their home furniture and reuse it in an environmentally friendly way.

[0525] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0526] Step 1:

[0527] Users take pictures of unwanted furniture using their smartphones or computers. They then use the camera function of their device to input information about the furniture's material, condition, and their wishes regarding reuse. The input data consists of images and text information. This data serves as the foundational information necessary for subsequent analysis.

[0528] Step 2:

[0529] The device transmits captured image data and related information to a server via the internet. The transmitted data is used as input for AI analysis on the server. Specifically, the image file and the text description entered by the user reach the server and are stored in the database.

[0530] Step 3:

[0531] The server inputs the received images into a generating AI model for image analysis. The model utilizes image recognition algorithms to identify attributes such as furniture category, material, and dimensions. The analysis output provides analyzed attribute information. This attribute information is used in the next step to analyze market demand.

[0532] Step 4:

[0533] The server analyzes market demand by referencing internal databases and external online data. Based on the received attribute information, it identifies what styles and price ranges are in demand among consumers. This analysis outputs data for product specifications that match market trends.

[0534] Step 5:

[0535] The server designs the optimal processing method based on the analysis results. Using a generative AI model, it generates a design for furniture reuse, taking into account the user's input requirements and market needs. This design shows the processing steps and parts to be used in detail, providing the information necessary for execution in the next step.

[0536] Step 6:

[0537] The server sends the generated design drawings to a partner manufacturing facility. Based on the submitted design drawings, the manufacturing facility carries out the remanufacturing work. Specifically, the work involves selecting parts, repairing them, and painting them, ultimately producing the finished product.

[0538] Step 7:

[0539] Once the furniture is completed at the processing facility, it is reported back to the server. The server then prepares it for sale on online platforms, gathering information for listing on digital marketplaces. Here, product photos and pricing are output, and the sales process begins.

[0540] Step 8:

[0541] After a product is sold, the server distributes revenue to users and processing institutions based on sales. At this stage, sales data is aggregated and the distribution process is executed, and appropriate compensation is paid to each party involved.

[0542] (Application Example 1)

[0543] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0544] There is a need for an efficient system that can increase the added value of reusable furniture. However, traditional methods required users to manually conduct market research and select appropriate recycling methods, which was time-consuming and labor-intensive. Furthermore, users often could not sell the recycled furniture at the optimal price, making it difficult to maximize profits. In addition, there was insufficient consideration for the environment, resulting in ineffective resource utilization.

[0545] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0546] In this invention, the server includes means for receiving images and input information captured by the user, means including an algorithm for analyzing the attributes of an item from the image and identifying market demand, and means for determining a processing method for the item and generating a design drawing based on the analysis results. This reduces the effort required from the user and makes it possible to efficiently provide reusable furniture to the market with added value.

[0547] "Means for receiving images and input information captured by the user" refers to an interface for acquiring image data and related input information captured by the user using a smart device and sending it to the system.

[0548] An "algorithm for analyzing the attributes of goods and identifying market demand" is a computational means for identifying the characteristics of a received item from image data, analyzing market demand trends based on those characteristics, and evaluating the value of the item.

[0549] "Means for determining the processing method of an item based on analysis results and generating a design drawing for reuse" refers to a function that determines the optimal processing method based on identified market needs and generates a design drawing that shows the specific processing procedure and shape.

[0550] "A means of using smart devices to notify users of the results of market demand analysis and promote online sales" refers to a platform for communicating the results of analyzed market demand to users, and an interface for guiding products to be sold appropriately online.

[0551] "A means of listing finished goods from a processing facility on an online marketplace" refers to a function that allows finished goods from a processing facility to be listed on a digital marketplace and made available to consumers in a sales list.

[0552] The system that realizes this invention includes a cloud-based server, a user interface, and an AI analysis module. The server communicates with the user's terminal and receives images of furniture taken by the user and associated input information. To do this, the user uses, for example, a smartphone. The smartphone has a dedicated application installed that allows for easy uploading of captured image data.

[0553] The server analyzes the received images using AI image recognition software (e.g., TensorFlow). This analysis identifies attributes such as the material and design of the items. The server then queries a market database (e.g., MySQL) to estimate current market demand based on the identified attributes. The market demand analysis results are then communicated to the user via a smart device.

[0554] The user selects a proposed processing method based on market needs presented within the application. This selection is sent to a server, from which a specific design drawing is generated. This design drawing is then sent to a processing facility, where the furniture is processed or restored according to the design drawing.

[0555] The refurbished furniture is automatically listed on an online marketplace by the system. Users are notified regularly about the completion status and sales information of their refurbished furniture.

[0556] For example, if a user wants to restore an old wooden table, they can instruct the system using a prompt such as, "Please suggest ways to give this old wooden table a new style." In response to this request, the AI ​​will suggest antique finishes and renovations, and the processing will be carried out according to the user's selection.

[0557] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0558] Step 1:

[0559] The user uses a smart device to take pictures of unwanted furniture and inputs relevant information (material, desired style, etc.). This data is sent from the device to the server. The input data consists of image files and text information, and the output of this step is the raw data transferred to the server.

[0560] Step 2:

[0561] The server analyzes the received image data using an AI image recognition model. The images are analyzed using TensorFlow, and the attributes of the furniture (material, design characteristics, etc.) are identified. The input is image data, and the output is attribute information as a result of the analysis. In this process, the AI ​​model extracts specific features from the image and records them as numerical data.

[0562] Step 3:

[0563] Based on the analysis results, the server queries the market database to obtain current market demand information. The input is the analyzed attribute information, and the output is information on appropriate processing methods and recommended selling prices according to demand. In this step, statistical analysis of the market data is performed.

[0564] Step 4:

[0565] The server notifies the user of the optimal furniture processing method and estimated selling price based on market demand. Details are communicated to the user via push notifications and in-app displays on their device. The input is market demand information, and the output is a notification message to the user. The system operates using real-time notification functionality via an API.

[0566] Step 5:

[0567] The user selects their preferred processing method from the presented suggestions and sends the selection information to the server via the app. The input is the user's selection information, and the output is the selection received by the server. In this step, the user confirms the processing option using touch controls.

[0568] Step 6:

[0569] The server automatically generates specific design drawings for reuse based on the selected information obtained and sends them to the processing facility. The input is user-selected information, and the output is design drawing data in CAD format. Design proposals are created using a generated AI model.

[0570] Step 7:

[0571] The processing facility manufactures and restores furniture based on the received design drawings. This includes processes using specified materials and paints. The input is the design drawing data, and the output is the finished furniture. Within the facility, parts are assembled and finished using specialized processing machinery.

[0572] Step 8:

[0573] Information about the finished furniture is reported to the server at the processing facility, and the server then lists the finished products on an online marketplace. Input is digital data and photographs of the finished furniture, and output is product information registered on the marketplace. Integration with the e-commerce platform is performed on the server.

[0574] Step 9:

[0575] Once furniture sales are confirmed, the server distributes the revenue to the user and processing facility. The input is sales data, and the output is the distributed revenue information. In this final step, the profits are automatically transferred via an online payment system.

[0576] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0577] This invention is a furniture recycling system that takes user emotions into account, aiming not only to promote environmental protection and efficient resource utilization but also to improve the user experience. The system utilizes an emotion engine in the process of determining the optimal processing method by analyzing market needs based on photos taken by the user of the furniture.

[0578] Users take photos of unwanted furniture using their smartphones or camera-equipped devices and input information such as material, condition, and desired reuse methods. During this process, an emotion engine analyzes and records the user's emotional state. The user's emotions are inferred from their voice, facial expressions, and input content.

[0579] The device sends input information, including captured images and emotional data, to the server. The server passes the image data to an AI analysis system to identify the attributes of the items. Furthermore, while referring to the emotional data, it identifies market demand and considers processing methods that respond to the user's emotions.

[0580] The server integrates item attributes, market needs, and sentiment data to design the optimal reprocessing method. This process prioritizes processing methods that reflect the user's positive emotions and expectations. The generated blueprint includes progressively different designs and processing suggestions, allowing the user to choose according to their preferences.

[0581] The generated design drawings, including the selection of reusable parts based on the drawings, are sent to the processing facility. The processing facility then refurbishes the furniture based on the received design drawings. Reusable parts are selected from a database of unprocessed items, enabling efficient processing.

[0582] Once the revival furniture is completed at the processing facility, it is reported to the server and prepared for listing on the online marketplace. If a sale is made, the profits are distributed to the user and the processing facility, with user satisfaction taking into account based on their sentiment.

[0583] As a concrete example, consider a scenario where a user takes a photo of an old chair and inputs it into the app, and the emotion engine detects a slightly depressed mood. The system would then prioritize suggesting cheerful, uplifting designs or chair modification ideas. In this way, considering the user's emotions provides a more personalized experience and a more satisfying restoration process.

[0584] The following describes the processing flow.

[0585] Step 1:

[0586] Users take photos of unwanted furniture with their smartphones or camera-equipped devices. They also input information such as the furniture's material, current condition, and desired reuse method into the application.

[0587] Step 2:

[0588] The device sends the user's input information to the server along with the captured image. Additionally, an emotion engine analyzes the user's emotions from their voice tone and facial expressions, and transmits that data as well.

[0589] Step 3:

[0590] The server inputs the received image data into an AI algorithm to analyze the attributes of the furniture. This process identifies characteristics such as shape, dimensions, and material.

[0591] Step 4:

[0592] The server combines analyzed furniture attributes with user sentiment data to evaluate market needs. If positive sentiment is detected, it suggests processing methods that reflect the user's emotions.

[0593] Step 5:

[0594] The server integrates item attributes, market needs, and sentiment data to design the optimal regeneration plan. The generated blueprint includes customized processing methods and design options based on sentiment.

[0595] Step 6:

[0596] The server transmits the completed design drawings to the processing facility. At the processing facility, reusable parts are selected from the inventory database based on the design drawings, and efficient processing is carried out.

[0597] Step 7:

[0598] The processing facility restores the furniture according to the design plans and reports to the server upon completion. Images of the final product are also sent to the server in preparation for market listing.

[0599] Step 8:

[0600] The server, upon receiving a report, lists the furniture on the online marketplace. It sets the product information and selling price, and posts a product description based on user sentiment.

[0601] Step 9:

[0602] The server calculates the revenue earned after a sale is completed and distributes it to the user and processing facility. This process manages revenue distribution and feedback to maximize user satisfaction.

[0603] (Example 2)

[0604] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0605] In recent years, with sustainable consumption and environmental protection becoming increasingly important, there has been a demand for effective reuse of unwanted items and improved user experience. However, conventional reuse systems primarily focus on the items themselves and do not adequately consider user emotions or market trends. As a result, challenges exist, such as a lack of personalized service and difficulty in improving user satisfaction.

[0606] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0607] In this invention, the server includes means for receiving images and input information taken by the user and analyzing the emotional state; means including an algorithm for analyzing the attributes of an item from the received image and designing for reuse, taking into account the analyzed emotional information and market demand; and means for determining a method of processing the item based on the analysis results and emotional information and generating a design drawing that corresponds to the user's emotions. This makes it possible to personalize the reuse process and make suggestions that are sensitive to the user's emotions.

[0608] A "user" refers to an individual or corporation that wishes to reuse unwanted furniture using the system.

[0609] "Emotional state" refers to the emotional state analyzed based on the user's voice tone, facial expressions, and input information.

[0610] "Item attributes" refer to information such as the type, material, shape, and condition of the furniture being photographed.

[0611] "Market demand" refers to the demand for goods that reflect current consumer trends and behaviors.

[0612] A "design drawing" refers to a drawing or plan that specifies the processing methods and designs to be generated for reuse.

[0613] A "processing facility" refers to a facility or place that refurbishes goods based on design drawings.

[0614] An "online marketplace" refers to a platform where goods are sold and purchased over the internet.

[0615] A "prompt sentence" refers to an input sentence that a generative AI model uses to make suggestions or give instructions based on the user's emotions and desires.

[0616] The furniture recycling system in this invention efficiently carries out the process of recycling furniture by having the user take pictures of the furniture using a smartphone or camera-equipped terminal and analyzing that information. Specific embodiments are shown below.

[0617] Users take photos of unwanted furniture in their homes or offices using their smartphones or camera-equipped devices. The smartphones have emotion analysis software installed, which detects voice and facial expressions during shooting to analyze the user's emotional state. Users can also enter information about the furniture's material, condition, desired reuse method, and any wishes or comments in a free-text field within the app.

[0618] The device sends this image data, input information, and emotional state to the server. Encrypted communication is used for data transmission to ensure data security. The server passes the received images to an AI analysis system, which uses machine learning frameworks such as TensorFlow to identify the attributes of the items. Simultaneously, an emotion engine that performs natural language processing analyzes the user's emotional data and generates prompts to suggest the optimal reuse methods.

[0619] For example, suppose a user takes a photo of an old desk because they want to reuse it. If the emotion engine detects a depressed mood, it will generate a prompt such as, "Please suggest a design to transform this desk into a more cheerful space." This prompt is then input into a generative AI model, which creates a customized design based on the user's wishes and emotions.

[0620] The server generates optimal blueprints for reuse and selects the most suitable parts by referring to an inventory database of raw materials. The generated blueprints are then sent to the processing facility. Based on the received blueprints, the facility refurbishes the furniture and operates its equipment to ensure efficient processing. Through this process, a personalized refurbishment experience is provided that responds to user sentiment and market demand.

[0621] Furthermore, completed furniture is reported to the server and prepared for listing on the online marketplace. If a sale is made, the revenue is appropriately distributed to the user and the processing facility, and the user's satisfaction level is evaluated considering their sentiment data. Throughout this entire process, the system is guaranteed to operate efficiently and securely.

[0622] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0623] Step 1:

[0624] Users take photos of unwanted furniture using their smartphones or camera-equipped devices. They input information such as the furniture's material, condition, and desired reuse method into the device's app. The device uses voice recognition and facial expression analysis technology to analyze the user's emotions and records the results as emotional data. This collects detailed information about the user's emotional state and the furniture.

[0625] Step 2:

[0626] The device sends collected image data, input information, and emotion data to the server. As input, this data is encrypted before transmission. As output, the server receives this data and stores it in a database. This process ensures the secure transmission and storage of the data.

[0627] Step 3:

[0628] The server uses an AI analysis system to identify the attributes of items from received image data. Image data and existing image models are used as input. The AI ​​analysis system (e.g., a machine learning framework) matches the type and characteristics of furniture against information learned in a database, generating attribute information as output.

[0629] Step 4:

[0630] The server uses the analyzed item attributes to combine the emotion engine and market demand information to design a reused item. Using item attributes, emotion data, and market demand data as input, the generative AI model constructs prompt sentences. The output is a design blueprint that reflects the user's emotions.

[0631] Step 5:

[0632] The server sends the generated blueprints to the manufacturing facility. Optimized blueprints and inventory database information are used as input. As output, the manufacturing facility selects the necessary parts from inventory and receives manufacturing instructions based on the blueprints. This process ensures smooth preparation and manufacturing of parts.

[0633] Step 6:

[0634] After the furniture refurbishment is complete at the processing facility, the terminal reports the completion to the server. Inputs include detailed data on the finished product and a report of processing success. Outputs include the server preparing the product for listing on an online marketplace and managing sales data.

[0635] Step 7:

[0636] If a sale is completed, the server aggregates sales data and distributes revenue to the user and processing facility. Sales price and contract terms are used as input. The output includes revenue distribution and satisfaction evaluation, reflecting user sentiment data. This process ensures fair revenue distribution and improved user experience.

[0637] (Application Example 2)

[0638] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0639] Currently, conventional systems for recycling unwanted furniture do not take into account the user's emotional state, making it difficult to provide a personalized user experience and level of satisfaction. Furthermore, because user emotions cannot be reflected in the proposed recycling plans, the suggested plans may not always meet user expectations. Therefore, there is a need to develop a system that provides optimal recycling plans while considering user emotions, thereby achieving higher satisfaction.

[0640] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0641] In this invention, the server includes means for receiving images, audio, and facial expression data captured by the user; means including an algorithm for analyzing the attributes of an item and the user's emotional state from the received data and identifying an optimal design based on market demand and the user's emotions; and means for determining a processing method for the item based on the analysis results and generating a design for reuse that is appropriate to the emotions. This makes it possible to present a personalized reuse plan that takes the user's emotions into consideration.

[0642] A "user" is an individual or group that participates in the process of recycling unwanted furniture using this system.

[0643] "Image, audio, and facial expression data" refers to visual and audio information provided by the user, which is used to analyze the user's emotional state.

[0644] "Item attributes" refer to characteristics such as the material, design, condition, and function of furniture and related items, and are information used to evaluate their reusability.

[0645] "User emotional state" refers to the psychological state detected from the user's voice, facial expressions, etc., and is a factor that influences the playback plan.

[0646] "Design plans for reuse" are drawings and specifications that show new furniture designs and manufacturing methods, generated based on data and analysis results obtained from users.

[0647] An "emotion-driven reuse plan" refers to a personalized and proposed method of reusing furniture that takes into account the user's emotional state.

[0648] "Market demand" is a concept that indicates the level of consumer demand or need for a product or service at a specific point in time, and is a factor to consider when developing a reuse plan.

[0649] This invention realizes a system that provides a reuse plan based on the user's emotions. First, the user uses a terminal to take images of unwanted items through a high-resolution camera and also collects audio data using a microphone. This data is immediately transmitted to an emotion recognition engine, which analyzes the user's emotional state from their facial expressions and tone of voice.

[0650] The server analyzes emotional data using the EmotionRecognizer library, a specialized algorithm implemented in Python, and identifies object attributes (material, condition, etc.) from images using the open-source OpenCV library. Furthermore, it evaluates current market needs through an AI model and determines the reuse plan best suited to the user's emotional state.

[0651] Based on the decided plan, a design blueprint for reuse will be created. This blueprint will include emotionally resonant colors and design elements, and will present multiple options that reflect the user's preferences.

[0652] For example, if a user takes a picture of an old table with their device and enters a comment in a tired voice, the system will prioritize suggesting designs that give a relaxed impression. Furthermore, it will adopt a human-touch approach as an emotion-based prompt, such as, "I want to create an even more comfortable space with this table."

[0653] Examples of prompts include, "Propose a furniture revival design based on emotion analysis," and "Analyze the user's emotions from their voice and facial expressions, and design furniture that matches them."

[0654] Once the manufacturing process for reuse is complete, the server uploads the data to the online platform, preparing it for sale. This makes it possible to provide a highly satisfying experience that resonates with the user's emotions.

[0655] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0656] Step 1:

[0657] The user takes pictures of unwanted furniture using the device's high-resolution camera and saves them on the device. They also record comments and their feelings about the furniture using voice input. The input here consists of image and audio data, which are prepared for transmission to the server. The output is the data sent directly to the server.

[0658] Step 2:

[0659] The server analyzes the received image data using the OpenCV library. Specifically, it identifies the material and condition of the furniture through image processing algorithms. The input is image data, and the output is attribute information of the item. This reveals the details of the furniture.

[0660] Step 3:

[0661] The server analyzes the received audio data using the EmotionRecognizer library to identify the user's emotional state. Specifically, it extracts features from the audio waveform and classifies emotions based on them. The input is audio data, and the output is the user's emotional state. The analysis is performed using a generative AI model.

[0662] Step 4:

[0663] The server inputs the attribute information of the analyzed items and the user's emotional state into a generating AI model, and determines the optimal reuse plan while considering market demand. In this process, it refers to market trend data and generates design proposals that are sensitive to emotions. The output is a blueprint for reuse.

[0664] Step 5:

[0665] Once a blueprint for reuse is generated from the server, the terminal presents it to the user. The blueprint includes colors and designs based on the user's preferences. By viewing this, the user can visualize the optimal reuse plan.

[0666] Step 6:

[0667] The server sends the generated blueprints to the manufacturing facility and provides manufacturing instructions for reuse. Specifically, it sends the blueprints along with a list of available parts to the manufacturing facility. The output here is data containing detailed instruction information for the manufacturing facility.

[0668] Step 7:

[0669] Once manufacturing is complete at the processing facility, the server retrieves the information and prepares to list the revived furniture (recycled furniture) on the online platform. Specific actions include updating product information and incorporating user reviews. The final output is online listing information ready for sale.

[0670] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0671] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0672] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0673] [Fourth Embodiment]

[0674] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0675] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0676] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0677] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0678] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0679] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0680] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0681] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0682] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0683] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0684] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0685] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0686] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0687] This invention is a system that uses AI to add new value to unwanted furniture and regenerate it. The system involves a series of processes, from the user taking a photo of the furniture to the system analyzing market needs based on that photo and proposing the optimal processing method.

[0688] At the start of the system, users take photos of unwanted furniture using their smartphones or other devices. Users can also input information such as the material and condition of the furniture and their desired reuse method. This input information, along with the images, is sent from the device to the server.

[0689] The server analyzes the received image data using an AI image recognition model to identify the attributes of the items. This analysis includes characteristics such as the furniture category, material, and dimensions. Next, the server uses historical data from its internal database and external online marketplaces to identify market demand. This demand information indicates what styles and price ranges are preferred for reproduction.

[0690] Next, the server designs the optimal processing method based on the identified market needs and item attributes. A design drawing is generated, and reusable parts are identified by referencing the inventory database of unprocessed items. This enables efficient resource utilization.

[0691] The generated blueprints are sent to partner processing facilities. These facilities then refurbish the furniture based on the received blueprints. This process includes parts replacement, repair, and painting.

[0692] Finally, the revived furniture completed at the processing facility is reported to the server and prepared for listing on the online marketplace. The server shares the progress of the work with the user and the relevant processing facility and distributes the profits earned after the sale. Users can not only create new value from furniture with minimal effort, but also contribute to environmental conservation.

[0693] For example, if a user is looking to dispose of an old wooden table, the system uses photos and information to determine what kind of finish is in demand in the market and suggests an antique-style finish that highlights the wood grain. The processing facility then repaints the table according to these instructions and sells the finished table online. Through this process, both the user and the processing facility can profit while effectively utilizing resources and being environmentally conscious.

[0694] The following describes the processing flow.

[0695] Step 1:

[0696] Users take photos of unwanted furniture using their smartphone or camera-equipped device. After taking the photos, they input information such as the material, condition, and desired use into the application on their device.

[0697] Step 2:

[0698] The terminal combines the information entered by the user and the images taken into a data package and sends it to the server. A secure protocol is used for data transmission via the internet.

[0699] Step 3:

[0700] The server inputs the received data into an AI image recognition algorithm to analyze the attributes of the furniture from the image. Specifically, it identifies the shape, material, and damaged areas, and stores them as digital data.

[0701] Step 4:

[0702] Based on the analysis results, the server uses its internal database and online market trend data to evaluate the market needs for furniture. This helps determine which revival styles are appealing to consumers.

[0703] Step 5:

[0704] The server integrates market needs and item attributes to design the optimal refurbishment method. For example, it specifically determines processing techniques such as partial repair, repainting, and parts replacement, and generates detailed design drawings.

[0705] Step 6:

[0706] The server searches the inventory database of unprocessed materials based on the generated design drawings, identifying reusable and necessary parts. This enables the effective utilization of unused parts.

[0707] Step 7:

[0708] The server transmits the completed design drawings to the processing facility and provides specific instructions for the remanufacturing process. These instructions include processing procedures and details of the parts to be used.

[0709] Step 8:

[0710] The processing facility refurbishes furniture based on design drawings from the server. It handles material procurement, repair, assembly, and painting to produce the final product.

[0711] Step 9:

[0712] The processing facility takes a picture of the finished product and reports it to the server. This report indicates that the product is ready to be listed on the online marketplace.

[0713] Step 10:

[0714] The server receives the completion report and lists the furniture on the online marketplace. It posts product information, price, and photos, and begins sales.

[0715] Step 11:

[0716] The server aggregates sales once a sale is completed, calculates the revenue, and distributes it to the user and processing facility. Transaction details are managed through a transparent, digitally verifiable process.

[0717] (Example 1)

[0718] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0719] The effort to repurpose furniture destined for disposal by adding new value presents challenges: traditional methods struggle to accurately reflect market demand, and manual processing planning is inefficient. Therefore, there is a need to develop a system that allows for the effective use of resources without burdening users.

[0720] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0721] In this invention, the server includes means for receiving furniture images and related information taken by the user, means for analyzing the attributes of the item using a generating AI model and identifying market demand, and means for designing an optimal processing method and generating a design drawing based on the analysis results. This makes it possible for users to easily refurbish furniture, create new market value, and efficiently utilize resources.

[0722] A "user" refers to an individual or organization that uses the system to photograph unwanted furniture and provides information about its reuse.

[0723] "Image" refers to photographic data of furniture taken by the user using their device.

[0724] A "generative AI model" refers to an artificial intelligence algorithm used to analyze images of furniture and identify the attributes of the items.

[0725] "Item attributes" refer to characteristics such as the furniture's category, material, and dimensions.

[0726] "Market demand" refers to information indicating what styles and price ranges consumers are in demand for refurbished furniture.

[0727] "Processing method" refers to the specific means and processes used to improve or repair furniture for the purpose of reuse.

[0728] A "design drawing" refers to a drawing or plan that shows the processing procedures and specifications necessary for furniture restoration, generated based on market demand and product characteristics.

[0729] A "processing facility" refers to a facility or business that carries out furniture restoration work based on the generated design drawings.

[0730] An "online platform" refers to an internet marketplace or service used to sell processed furniture.

[0731] "Parts" refers to the constituent elements of furniture, including materials used for necessary repairs and reassembly during restoration.

[0732] "Efficient use of resources" refers to the efficient use of materials and parts in furniture recycling, without waste.

[0733] "Profit sharing" refers to the process of appropriately distributing the profits earned from the sale of furniture to users and manufacturers.

[0734] This invention relates to a system that uses AI technology to add new value to furniture that is scheduled to be discarded and to repurpose it.

[0735] First, the user takes a picture of the furniture they want to reuse using a device such as a smartphone or computer. At this time, the user can input information about the furniture's material, condition, and desired reuse method. For example, if the user wants to reuse an old wooden table in an antique style, they would take a picture of it with their device and input "wood" as the material and "antique style" as the method.

[0736] The terminal sends the captured image and accompanying information to the server. The server inputs the received image into a generating AI model and uses image recognition technology to identify the attributes of the furniture. This analysis reveals the furniture's category, material, and dimensions, and then identifies market demand by referring to internal databases and data from external online markets.

[0737] Based on market demand, the server uses AI-powered prompts to design the optimal processing method that matches the characteristics of the item and the demand, generating a design blueprint aimed at reuse. This blueprint often includes processes such as parts rearrangement, repair, and painting. For example, the analysis might suggest repainting an old table in an antique style that highlights the wood grain.

[0738] The generated blueprints are sent to partner processing facilities. These facilities then proceed with the furniture restoration work based on these blueprints. This process involves the efficient use of parts and the creation of finished products through repainting and repair processes.

[0739] Finished furniture is reported from the processing facility to the server, and preparations are made for listing it on the online platform. The server adjusts sales information, such as photos and prices, and distributes revenue to users and processing facilities depending on sales performance.

[0740] An example of a prompt message could be: "Generate ideas for reusing an old wooden table. Please tell me the best processing method based on market demand." This system allows users to add new value to their home furniture and reuse it in an environmentally friendly way.

[0741] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0742] Step 1:

[0743] Users take pictures of unwanted furniture using their smartphones or computers. They then use the camera function of their device to input information about the furniture's material, condition, and their wishes regarding reuse. The input data consists of images and text information. This data serves as the foundational information necessary for subsequent analysis.

[0744] Step 2:

[0745] The device transmits captured image data and related information to a server via the internet. The transmitted data is used as input for AI analysis on the server. Specifically, the image file and the text description entered by the user reach the server and are stored in the database.

[0746] Step 3:

[0747] The server inputs the received images into a generating AI model for image analysis. The model utilizes image recognition algorithms to identify attributes such as furniture category, material, and dimensions. The analysis output provides analyzed attribute information. This attribute information is used in the next step to analyze market demand.

[0748] Step 4:

[0749] The server analyzes market demand by referencing internal databases and external online data. Based on the received attribute information, it identifies what styles and price ranges are in demand among consumers. This analysis outputs data for product specifications that match market trends.

[0750] Step 5:

[0751] The server designs the optimal processing method based on the analysis results. Using a generative AI model, it generates a design for furniture reuse, taking into account the user's input requirements and market needs. This design shows the processing steps and parts to be used in detail, providing the information necessary for execution in the next step.

[0752] Step 6:

[0753] The server sends the generated design drawings to a partner manufacturing facility. Based on the submitted design drawings, the manufacturing facility carries out the remanufacturing work. Specifically, the work involves selecting parts, repairing them, and painting them, ultimately producing the finished product.

[0754] Step 7:

[0755] Once the furniture is completed at the processing facility, it is reported back to the server. The server then prepares it for sale on online platforms, gathering information for listing on digital marketplaces. Here, product photos and pricing are output, and the sales process begins.

[0756] Step 8:

[0757] After a product is sold, the server distributes revenue to users and processing institutions based on sales. At this stage, sales data is aggregated and the distribution process is executed, and appropriate compensation is paid to each party involved.

[0758] (Application Example 1)

[0759] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0760] There is a need for an efficient system that can increase the added value of reusable furniture. However, traditional methods required users to manually conduct market research and select appropriate recycling methods, which was time-consuming and labor-intensive. Furthermore, users often could not sell the recycled furniture at the optimal price, making it difficult to maximize profits. In addition, there was insufficient consideration for the environment, resulting in ineffective resource utilization.

[0761] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0762] In this invention, the server includes means for receiving images and input information captured by the user, means including an algorithm for analyzing the attributes of an item from the image and identifying market demand, and means for determining a processing method for the item and generating a design drawing based on the analysis results. This reduces the effort required from the user and makes it possible to efficiently provide reusable furniture to the market with added value.

[0763] "Means for receiving images and input information captured by the user" refers to an interface for acquiring image data and related input information captured by the user using a smart device and sending it to the system.

[0764] An "algorithm for analyzing the attributes of goods and identifying market demand" is a computational means for identifying the characteristics of a received item from image data, analyzing market demand trends based on those characteristics, and evaluating the value of the item.

[0765] "Means for determining the processing method of an item based on analysis results and generating a design drawing for reuse" refers to a function that determines the optimal processing method based on identified market needs and generates a design drawing that shows the specific processing procedure and shape.

[0766] "A means of using smart devices to notify users of the results of market demand analysis and promote online sales" refers to a platform for communicating the results of analyzed market demand to users, and an interface for guiding products to be sold appropriately online.

[0767] "A means of listing finished goods from a processing facility on an online marketplace" refers to a function that allows finished goods from a processing facility to be listed on a digital marketplace and made available to consumers in a sales list.

[0768] The system that realizes this invention includes a cloud-based server, a user interface, and an AI analysis module. The server communicates with the user's terminal and receives images of furniture taken by the user and associated input information. To do this, the user uses, for example, a smartphone. The smartphone has a dedicated application installed that allows for easy uploading of captured image data.

[0769] The server analyzes the received images using AI image recognition software (e.g., TensorFlow). This analysis identifies attributes such as the material and design of the items. The server then queries a market database (e.g., MySQL) to estimate current market demand based on the identified attributes. The market demand analysis results are then communicated to the user via a smart device.

[0770] The user selects a proposed processing method based on market needs presented within the application. This selection is sent to a server, from which a specific design drawing is generated. This design drawing is then sent to a processing facility, where the furniture is processed or restored according to the design drawing.

[0771] The refurbished furniture is automatically listed on an online marketplace by the system. Users are notified regularly about the completion status and sales information of their refurbished furniture.

[0772] For example, if a user wants to restore an old wooden table, they can instruct the system using a prompt such as, "Please suggest ways to give this old wooden table a new style." In response to this request, the AI ​​will suggest antique finishes and renovations, and the processing will be carried out according to the user's selection.

[0773] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0774] Step 1:

[0775] The user uses a smart device to take pictures of unwanted furniture and inputs relevant information (material, desired style, etc.). This data is sent from the device to the server. The input data consists of image files and text information, and the output of this step is the raw data transferred to the server.

[0776] Step 2:

[0777] The server analyzes the received image data using an AI image recognition model. The images are analyzed using TensorFlow, and the attributes of the furniture (material, design characteristics, etc.) are identified. The input is image data, and the output is attribute information as a result of the analysis. In this process, the AI ​​model extracts specific features from the image and records them as numerical data.

[0778] Step 3:

[0779] Based on the analysis results, the server queries the market database to obtain current market demand information. The input is the analyzed attribute information, and the output is information on appropriate processing methods and recommended selling prices according to demand. In this step, statistical analysis of the market data is performed.

[0780] Step 4:

[0781] The server notifies the user of the optimal furniture processing method and estimated selling price based on market demand. Details are communicated to the user via push notifications and in-app displays on their device. The input is market demand information, and the output is a notification message to the user. The system operates using real-time notification functionality via an API.

[0782] Step 5:

[0783] The user selects their preferred processing method from the presented suggestions and sends the selection information to the server via the app. The input is the user's selection information, and the output is the selection received by the server. In this step, the user confirms the processing option using touch controls.

[0784] Step 6:

[0785] The server automatically generates specific design drawings for reuse based on the selected information obtained and sends them to the processing facility. The input is user-selected information, and the output is design drawing data in CAD format. Design proposals are created using a generated AI model.

[0786] Step 7:

[0787] The processing facility manufactures and restores furniture based on the received design drawings. This includes processes using specified materials and paints. The input is the design drawing data, and the output is the finished furniture. Within the facility, parts are assembled and finished using specialized processing machinery.

[0788] Step 8:

[0789] Information about the finished furniture is reported to the server at the processing facility, and the server then lists the finished products on an online marketplace. Input is digital data and photographs of the finished furniture, and output is product information registered on the marketplace. Integration with the e-commerce platform is performed on the server.

[0790] Step 9:

[0791] Once furniture sales are confirmed, the server distributes the revenue to the user and processing facility. The input is sales data, and the output is the distributed revenue information. In this final step, the profits are automatically transferred via an online payment system.

[0792] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0793] This invention is a furniture recycling system that takes user emotions into account, aiming not only to promote environmental protection and efficient resource utilization but also to improve the user experience. The system utilizes an emotion engine in the process of determining the optimal processing method by analyzing market needs based on photos taken by the user of the furniture.

[0794] Users take photos of unwanted furniture using their smartphones or camera-equipped devices and input information such as material, condition, and desired reuse methods. During this process, an emotion engine analyzes and records the user's emotional state. The user's emotions are inferred from their voice, facial expressions, and input content.

[0795] The device sends input information, including captured images and emotional data, to the server. The server passes the image data to an AI analysis system to identify the attributes of the items. Furthermore, while referring to the emotional data, it identifies market demand and considers processing methods that respond to the user's emotions.

[0796] The server integrates item attributes, market needs, and sentiment data to design the optimal reprocessing method. This process prioritizes processing methods that reflect the user's positive emotions and expectations. The generated blueprint includes progressively different designs and processing suggestions, allowing the user to choose according to their preferences.

[0797] The generated design drawings, including the selection of reusable parts based on the drawings, are sent to the processing facility. The processing facility then refurbishes the furniture based on the received design drawings. Reusable parts are selected from a database of unprocessed items, enabling efficient processing.

[0798] Once the revival furniture is completed at the processing facility, it is reported to the server and prepared for listing on the online marketplace. If a sale is made, the profits are distributed to the user and the processing facility, with user satisfaction taking into account based on their sentiment.

[0799] As a concrete example, consider a scenario where a user takes a photo of an old chair and inputs it into the app, and the emotion engine detects a slightly depressed mood. The system would then prioritize suggesting cheerful, uplifting designs or chair modification ideas. In this way, considering the user's emotions provides a more personalized experience and a more satisfying restoration process.

[0800] The following describes the processing flow.

[0801] Step 1:

[0802] Users take photos of unwanted furniture with their smartphones or camera-equipped devices. They also input information such as the furniture's material, current condition, and desired reuse method into the application.

[0803] Step 2:

[0804] The device sends the user's input information to the server along with the captured image. Additionally, an emotion engine analyzes the user's emotions from their voice tone and facial expressions, and transmits that data as well.

[0805] Step 3:

[0806] The server inputs the received image data into an AI algorithm to analyze the attributes of the furniture. This process identifies characteristics such as shape, dimensions, and material.

[0807] Step 4:

[0808] The server combines analyzed furniture attributes with user sentiment data to evaluate market needs. If positive sentiment is detected, it suggests processing methods that reflect the user's emotions.

[0809] Step 5:

[0810] The server integrates item attributes, market needs, and sentiment data to design the optimal regeneration plan. The generated blueprint includes customized processing methods and design options based on sentiment.

[0811] Step 6:

[0812] The server transmits the completed design drawings to the processing facility. At the processing facility, reusable parts are selected from the inventory database based on the design drawings, and efficient processing is carried out.

[0813] Step 7:

[0814] The processing facility restores the furniture according to the design plans and reports to the server upon completion. Images of the final product are also sent to the server in preparation for market listing.

[0815] Step 8:

[0816] The server, upon receiving a report, lists the furniture on the online marketplace. It sets the product information and selling price, and posts a product description based on user sentiment.

[0817] Step 9:

[0818] The server calculates the revenue earned after a sale is completed and distributes it to the user and processing facility. This process manages revenue distribution and feedback to maximize user satisfaction.

[0819] (Example 2)

[0820] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0821] In recent years, with sustainable consumption and environmental protection becoming increasingly important, there has been a demand for effective reuse of unwanted items and improved user experience. However, conventional reuse systems primarily focus on the items themselves and do not adequately consider user emotions or market trends. As a result, challenges exist, such as a lack of personalized service and difficulty in improving user satisfaction.

[0822] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0823] In this invention, the server includes means for receiving images and input information taken by the user and analyzing the emotional state; means including an algorithm for analyzing the attributes of an item from the received image and designing for reuse, taking into account the analyzed emotional information and market demand; and means for determining a method of processing the item based on the analysis results and emotional information and generating a design drawing that corresponds to the user's emotions. This makes it possible to personalize the reuse process and make suggestions that are sensitive to the user's emotions.

[0824] A "user" refers to an individual or corporation that wishes to reuse unwanted furniture using the system.

[0825] "Emotional state" refers to the emotional state analyzed based on the user's voice tone, facial expressions, and input information.

[0826] "Item attributes" refer to information such as the type, material, shape, and condition of the furniture being photographed.

[0827] "Market demand" refers to the demand for goods that reflect current consumer trends and behaviors.

[0828] A "design drawing" refers to a drawing or plan that specifies the processing methods and designs to be generated for reuse.

[0829] A "processing facility" refers to a facility or place that refurbishes goods based on design drawings.

[0830] An "online marketplace" refers to a platform where goods are sold and purchased over the internet.

[0831] A "prompt sentence" refers to an input sentence that a generative AI model uses to make suggestions or give instructions based on the user's emotions and desires.

[0832] The furniture recycling system in this invention efficiently carries out the process of recycling furniture by having the user take pictures of the furniture using a smartphone or camera-equipped terminal and analyzing that information. Specific embodiments are shown below.

[0833] Users take photos of unwanted furniture in their homes or offices using their smartphones or camera-equipped devices. The smartphones have emotion analysis software installed, which detects voice and facial expressions during shooting to analyze the user's emotional state. Users can also enter information about the furniture's material, condition, desired reuse method, and any wishes or comments in a free-text field within the app.

[0834] The device sends this image data, input information, and emotional state to the server. Encrypted communication is used for data transmission to ensure data security. The server passes the received images to an AI analysis system, which uses machine learning frameworks such as TensorFlow to identify the attributes of the items. Simultaneously, an emotion engine that performs natural language processing analyzes the user's emotional data and generates prompts to suggest the optimal reuse methods.

[0835] For example, suppose a user takes a photo of an old desk because they want to reuse it. If the emotion engine detects a depressed mood, it will generate a prompt such as, "Please suggest a design to transform this desk into a more cheerful space." This prompt is then input into a generative AI model, which creates a customized design based on the user's wishes and emotions.

[0836] The server generates optimal blueprints for reuse and selects the most suitable parts by referring to an inventory database of raw materials. The generated blueprints are then sent to the processing facility. Based on the received blueprints, the facility refurbishes the furniture and operates its equipment to ensure efficient processing. Through this process, a personalized refurbishment experience is provided that responds to user sentiment and market demand.

[0837] Furthermore, completed furniture is reported to the server and prepared for listing on the online marketplace. If a sale is made, the revenue is appropriately distributed to the user and the processing facility, and the user's satisfaction level is evaluated considering their sentiment data. Throughout this entire process, the system is guaranteed to operate efficiently and securely.

[0838] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0839] Step 1:

[0840] Users take photos of unwanted furniture using their smartphones or camera-equipped devices. They input information such as the furniture's material, condition, and desired reuse method into the device's app. The device uses voice recognition and facial expression analysis technology to analyze the user's emotions and records the results as emotional data. This collects detailed information about the user's emotional state and the furniture.

[0841] Step 2:

[0842] The device sends collected image data, input information, and emotion data to the server. As input, this data is encrypted before transmission. As output, the server receives this data and stores it in a database. This process ensures the secure transmission and storage of the data.

[0843] Step 3:

[0844] The server uses an AI analysis system to identify the attributes of items from received image data. Image data and existing image models are used as input. The AI ​​analysis system (e.g., a machine learning framework) matches the type and characteristics of furniture against information learned in a database, generating attribute information as output.

[0845] Step 4:

[0846] The server uses the analyzed item attributes to combine the emotion engine and market demand information to design a reused item. Using item attributes, emotion data, and market demand data as input, the generative AI model constructs prompt sentences. The output is a design blueprint that reflects the user's emotions.

[0847] Step 5:

[0848] The server sends the generated blueprints to the manufacturing facility. Optimized blueprints and inventory database information are used as input. As output, the manufacturing facility selects the necessary parts from inventory and receives manufacturing instructions based on the blueprints. This process ensures smooth preparation and manufacturing of parts.

[0849] Step 6:

[0850] After the furniture refurbishment is complete at the processing facility, the terminal reports the completion to the server. Inputs include detailed data on the finished product and a report of processing success. Outputs include the server preparing the product for listing on an online marketplace and managing sales data.

[0851] Step 7:

[0852] If a sale is completed, the server aggregates sales data and distributes revenue to the user and processing facility. Sales price and contract terms are used as input. The output includes revenue distribution and satisfaction evaluation, reflecting user sentiment data. This process ensures fair revenue distribution and improved user experience.

[0853] (Application Example 2)

[0854] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0855] Currently, conventional systems for recycling unwanted furniture do not take into account the user's emotional state, making it difficult to provide a personalized user experience and level of satisfaction. Furthermore, because user emotions cannot be reflected in the proposed recycling plans, the suggested plans may not always meet user expectations. Therefore, there is a need to develop a system that provides optimal recycling plans while considering user emotions, thereby achieving higher satisfaction.

[0856] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0857] In this invention, the server includes means for receiving images, audio, and facial expression data captured by the user; means including an algorithm for analyzing the attributes of an item and the user's emotional state from the received data and identifying an optimal design based on market demand and the user's emotions; and means for determining a processing method for the item based on the analysis results and generating a design for reuse that is appropriate to the emotions. This makes it possible to present a personalized reuse plan that takes the user's emotions into consideration.

[0858] A "user" is an individual or group that participates in the process of recycling unwanted furniture using this system.

[0859] "Image, audio, and facial expression data" refers to visual and audio information provided by the user, which is used to analyze the user's emotional state.

[0860] "Item attributes" refer to characteristics such as the material, design, condition, and function of furniture and related items, and are information used to evaluate their reusability.

[0861] "User emotional state" refers to the psychological state detected from the user's voice, facial expressions, etc., and is a factor that influences the playback plan.

[0862] "Design plans for reuse" are drawings and specifications that show new furniture designs and manufacturing methods, generated based on data and analysis results obtained from users.

[0863] An "emotion-driven reuse plan" refers to a personalized and proposed method of reusing furniture that takes into account the user's emotional state.

[0864] "Market demand" is a concept that indicates the level of consumer demand or need for a product or service at a specific point in time, and is a factor to consider when developing a reuse plan.

[0865] This invention realizes a system that provides a reuse plan based on the user's emotions. First, the user uses a terminal to take images of unwanted items through a high-resolution camera and also collects audio data using a microphone. This data is immediately transmitted to an emotion recognition engine, which analyzes the user's emotional state from their facial expressions and tone of voice.

[0866] The server analyzes emotional data using the EmotionRecognizer library, a specialized algorithm implemented in Python, and identifies object attributes (material, condition, etc.) from images using the open-source OpenCV library. Furthermore, it evaluates current market needs through an AI model and determines the reuse plan best suited to the user's emotional state.

[0867] Based on the decided plan, a design blueprint for reuse will be created. This blueprint will include emotionally resonant colors and design elements, and will present multiple options that reflect the user's preferences.

[0868] For example, if a user takes a picture of an old table with their device and enters a comment in a tired voice, the system will prioritize suggesting designs that give a relaxed impression. Furthermore, it will adopt a human-touch approach as an emotion-based prompt, such as, "I want to create an even more comfortable space with this table."

[0869] Examples of prompts include, "Propose a furniture revival design based on emotion analysis," and "Analyze the user's emotions from their voice and facial expressions, and design furniture that matches them."

[0870] Once the manufacturing process for reuse is complete, the server uploads the data to the online platform, preparing it for sale. This makes it possible to provide a highly satisfying experience that resonates with the user's emotions.

[0871] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0872] Step 1:

[0873] The user takes pictures of unwanted furniture using the device's high-resolution camera and saves them on the device. They also record comments and their feelings about the furniture using voice input. The input here consists of image and audio data, which are prepared for transmission to the server. The output is the data sent directly to the server.

[0874] Step 2:

[0875] The server analyzes the received image data using the OpenCV library. Specifically, it identifies the material and condition of the furniture through image processing algorithms. The input is image data, and the output is attribute information of the item. This reveals the details of the furniture.

[0876] Step 3:

[0877] The server analyzes the received audio data using the EmotionRecognizer library to identify the user's emotional state. Specifically, it extracts features from the audio waveform and classifies emotions based on them. The input is audio data, and the output is the user's emotional state. The analysis is performed using a generative AI model.

[0878] Step 4:

[0879] The server inputs the attribute information of the analyzed items and the user's emotional state into a generating AI model, and determines the optimal reuse plan while considering market demand. In this process, it refers to market trend data and generates design proposals that are sensitive to emotions. The output is a blueprint for reuse.

[0880] Step 5:

[0881] Once a blueprint for reuse is generated from the server, the terminal presents it to the user. The blueprint includes colors and designs based on the user's preferences. By viewing this, the user can visualize the optimal reuse plan.

[0882] Step 6:

[0883] The server sends the generated blueprints to the manufacturing facility and provides manufacturing instructions for reuse. Specifically, it sends the blueprints along with a list of available parts to the manufacturing facility. The output here is data containing detailed instruction information for the manufacturing facility.

[0884] Step 7:

[0885] Once manufacturing is complete at the processing facility, the server retrieves the information and prepares to list the revived furniture (recycled furniture) on the online platform. Specific actions include updating product information and incorporating user reviews. The final output is online listing information ready for sale.

[0886] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0887] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0888] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0889] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0890] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0891] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0892] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0893] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0894] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0895] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0896] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0897] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0898] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0899] 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.

[0900] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0901] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0902] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0903] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0904] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0905] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0906] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0907] The following is further disclosed regarding the embodiments described above.

[0908] (Claim 1)

[0909] A means for receiving images and input information taken by the user,

[0910] A means including an algorithm for analyzing the attributes of an item from a received image and identifying market demand,

[0911] A means for determining the processing method of an item based on the analysis results and generating a design drawing for reuse,

[0912] A means for transmitting the generated design drawings to the processing facility,

[0913] A means of listing finished goods from a processing facility on an online marketplace,

[0914] A system that includes this.

[0915] (Claim 2)

[0916] The system according to claim 1, further comprising means for selecting available parts from an inventory database of raw materials based on the generated design drawings.

[0917] (Claim 3)

[0918] The system according to claim 1, further comprising means for confirming the completion of goods at a processing facility and for distributing revenue to users and processing facilities based on sales.

[0919] "Example 1"

[0920] (Claim 1)

[0921] A means for receiving images taken by the user, input information such as the material, condition, and desired reuse method of the item,

[0922] A means of inputting received images into a generation AI model, analyzing the attributes of the items, and further identifying market demand using an internal database and external online market data,

[0923] A means for designing an optimal processing method that reflects market needs and product characteristics based on analysis results, and for generating a design for reuse that considers the efficient use of resources,

[0924] A means of sending the generated design drawings to a processing plant to have parts rearranged, repaired, and repainted according to the instructions,

[0925] Means of preparing finished goods from processing facilities for sale on online platforms,

[0926] A system that includes this.

[0927] (Claim 2)

[0928] The system according to claim 1, which selects reusable parts from an internal inventory database based on the generated design drawings, thereby promoting efficient resource utilization.

[0929] (Claim 3)

[0930] The system according to claim 1, which verifies the completed state of goods at a processing facility and distributes revenue to users and processing facilities based on sales status on an online platform.

[0931] "Application Example 1"

[0932] (Claim 1)

[0933] A means for receiving images and input information taken by the user,

[0934] A means including an algorithm for analyzing the attributes of an item from a received image and identifying market demand,

[0935] A means for determining the processing method of an item based on the analysis results and generating a design drawing for reuse,

[0936] A means for transmitting the generated design drawings to the processing facility,

[0937] A means of using smart devices to notify users of the results of market demand analysis and promote online sales,

[0938] A means of listing finished goods from a processing facility on an online marketplace,

[0939] A system that includes this.

[0940] (Claim 2)

[0941] The system according to claim 1, further comprising means for selecting available parts from an inventory database of raw materials based on the generated design drawings.

[0942] (Claim 3)

[0943] The system according to claim 1, further comprising means for confirming the completion of goods at a processing facility and for distributing revenue to users and processing facilities based on sales.

[0944] "Example 2 of combining an emotion engine"

[0945] (Claim 1)

[0946] A means of receiving images and input information taken by the user and analyzing their emotional state,

[0947] A means including an algorithm for analyzing the attributes of an item from a received image and designing it for reuse, taking into account the analyzed sentiment information and market demand,

[0948] A means for determining the processing method of an item based on analysis results and emotional information, and for generating a design drawing that corresponds to the user's emotions,

[0949] A means of sending the generated design drawings to a processing facility and efficiently selecting parts from the inventory of unprocessed materials,

[0950] A method of listing finished goods from a processing facility on an online marketplace and distributing post-sale revenue based on sentiment data,

[0951] A system that includes this.

[0952] (Claim 2)

[0953] The system according to claim 1, further comprising means for selecting usable parts from an inventory database based on the generated blueprints and for generating prompt statements to provide processing suggestions that suit the user's preferences.

[0954] (Claim 3)

[0955] The system according to claim 1, further comprising means for evaluating user satisfaction, taking into account user sentiment data, when confirming the completion of goods at a processing facility and distributing revenue to users and processing facilities based on sales.

[0956] "Application example 2 when combining with an emotional engine"

[0957] (Claim 1)

[0958] A means for receiving images, audio, and facial expression data captured by the user,

[0959] A means including an algorithm for analyzing the attributes of an item and the emotional state of a user from received data, and for identifying the optimal design based on market demand and user sentiment,

[0960] A means for determining the processing method of an item based on the analysis results and generating a design for reuse that corresponds to emotions,

[0961] A means of transmitting the generated design drawings to a processing facility and updating the information of the finished product in accordance with the user's emotions,

[0962] A means of completing emotionally resonant revival items at a processing facility and listing them on an online marketplace,

[0963] A system that includes this.

[0964] (Claim 2)

[0965] The system according to claim 1, further comprising means for selecting available components from inventory information of raw materials based on the generated blueprints and setting processing priorities based on the user's sentiment.

[0966] (Claim 3)

[0967] The system according to claim 1, further comprising means for confirming the completion of goods in a processing facility and for distributing revenue based on sales, taking into account user sentiment information. [Explanation of Symbols]

[0968] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving images and input information taken by the user, A means including an algorithm for analyzing the attributes of an item from a received image and identifying market demand, A means for determining the processing method of an item based on the analysis results and generating a design drawing for reuse, A means for transmitting the generated design drawings to the processing facility, A means of using smart devices to notify users of the results of market demand analysis and promote online sales, A means of listing finished goods from a processing facility on an online marketplace, A system that includes this.

2. The system according to claim 1, further comprising means for selecting available parts from an inventory database of unprocessed goods based on the generated design drawings.

3. The system according to claim 1, further comprising means for confirming the completion of goods at a processing facility and for distributing revenue to users and processing facilities based on sales.

Images