system

Abstract

Provide a system. 【Solution means】 Means for receiving a design image generated by an image generation device; Means for analyzing the design image to extract furniture specification information; Means for requesting quotations from a plurality of manufacturers based on the extracted furniture specification information; Means for receiving and comparing and analyzing quotation information from a plurality of manufacturers; Means for presenting the quotation result to the user and allowing the user to select a production request; Means for placing an order for the selected production request with the corresponding manufacturer; Means for tracking the production progress and notifying the user in real time; Means for providing a user interface including augmented reality technology that allows visual confirmation of the furniture design; Means for coordinating the delivery of the finished product with a logistics company; A system including the above.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, image generation technology has advanced, and users can easily generate ideal furniture designs. However, since these generated designs do not exist in reality, there is a problem that they cannot be actually used in daily life. Also, it takes a great deal of labor and time for users to individually request estimates and production from construction companies or furniture craftsmen. Furthermore, it is difficult to collect and compare estimate and production information, making it difficult to purchase at an optimal price.

Means for Solving the Problems

[0005] The present invention solves the above problem by providing a system that receives design images generated by an image generation device, analyzes the design images, and extracts furniture specification information. Based on the extracted furniture specification information, this system requests quotes from multiple manufacturers and compares and analyzes the received quote information. It presents the quote results to the user, allows the user to select a production request, and places an order for the selected production request with the appropriate manufacturer. Furthermore, it has a function to track the production progress and notify the user, making it easier for the user to realize their ideal furniture.

[0006] An "image generation device" is a system that uses computer algorithms to create visual digital content based on user input or instructions.

[0007] A "design image" is a visual design object with a specific shape and color, created by an image generation device.

[0008] "Specification information" refers to data extracted from design images that shows the details of the product, including information such as dimensions, materials, and shape.

[0009] A "manufacturer" refers to a company or individual that designs, assembles, or produces a specific product.

[0010] A "quote" is an assessment of the proposed economic value of a product or service, which typically includes costs, deadlines, and conditions.

[0011] Comparative analysis is the process of evaluating multiple different sets of data, identifying their similarities and differences, and finding the optimal option.

[0012] A "production request" is an official order or request for the production of a product based on a specific design or specifications.

[0013] "Tracking" refers to the act or technique of monitoring the progress of a product or project and keeping track of its current status. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

[0015] 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.

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

[0017] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

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

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0029] 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.

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

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] The system for implementing this invention is constructed using a program deployed in a digital environment. Its core function is to utilize design images created by the user using an image generation device and to efficiently carry out the actual furniture manufacturing based on those images.

[0036] The user first creates a design image using an image generation device and uploads that image to the system via a terminal. The server processes the received image using AI analysis technology and extracts specification information from the image. This specification information includes details such as the shape, size, and material of the furniture.

[0037] After the specification information is extracted, the server automatically requests quotes from multiple partner manufacturers. These quote requests include the extracted specification information, enabling manufacturers to create accurate quotes. The quotes submitted by the manufacturers are received by the server. The server analyzes the multiple quotes and compares the proposals. It determines the best option by considering factors such as price, delivery time, and production capacity.

[0038] The comparison results are displayed on the user's terminal, and the user can select from the displayed quotation options. Based on the user's selection, the server places an order for the confirmed production request with the appropriate manufacturer.

[0039] Once an order is confirmed, the server continuously tracks the production progress and notifies the user of this information in real time. When the product is completed and ready for delivery, the server sends a final notification to the user and coordinates with the logistics company to arrange delivery.

[0040] (Specific example)

[0041] For example, suppose a user designs a new bookshelf. The user completes the design using an image generation device and sends it to the system. This design includes details such as the number of shelves, their height, and the color of the wood to be used.

[0042] The server then extracts the necessary information from the design and sends quote requests to multiple manufacturers. The received quotes include production costs and timelines, which the server compares and displays the best option on the interface for the user to choose from.

[0043] After the user selects the option they deem most suitable, the server notifies the manufacturer to begin production. Meanwhile, the user can track the completion date of the shelves through progress information provided by the system, and the finished shelves are eventually delivered to the specified address.

[0044] In this way, this system allows users to proceed smoothly from the design stage to delivery, automating the conversion from design to physical product and significantly reducing the user's workload.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The user creates their ideal furniture design using an image generation device. They then upload this design image to the system via a terminal.

[0048] Step 2:

[0049] The server receives the uploaded design image and processes it using advanced analysis techniques. Here, it extracts furniture specification information (e.g., shape, size, material, etc.).

[0050] Step 3:

[0051] The server automatically creates a request for quotation based on the extracted specification information and sends it to multiple partner manufacturers. This request includes specific design details.

[0052] Step 4:

[0053] Quotation information is returned to the server from each manufacturer. The server organizes the received quotations and performs a comparative analysis based on factors such as price, delivery time, and quality.

[0054] Step 5:

[0055] The most suitable quote options are displayed for the device. The user browses the multiple options presented through the interface and selects the most appropriate one.

[0056] Step 6:

[0057] Based on the quote selected by the user, the server places a formal order with the appropriate manufacturer. The order includes all necessary details.

[0058] Step 7:

[0059] The server constantly monitors the production progress and notifies the user's terminal of any status updates received from the manufacturer.

[0060] Step 8:

[0061] After the server confirms the product is complete, it arranges for delivery. The terminal is notified of the delivery details along with the final estimated delivery date.

[0062] This series of steps allows users to efficiently complete the process from initial design to receiving the final product.

[0063] (Example 1)

[0064] 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."

[0065] Traditional manufacturing processes required complex procedures and considerable time to actually produce structures designed by users. This made it difficult to smoothly manage the entire process from design to production and delivery, resulting in significant time and effort. Furthermore, quickly comparing quotes from multiple manufacturers and making the optimal choice was also challenging.

[0066] 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.

[0067] In this invention, the server includes means for analyzing configuration data and extracting technical information, means for requesting quotes from multiple manufacturers, and means for presenting the quote results to the user and allowing them to select a production request. This enables the user to efficiently manage a consistent process from design to production and delivery, and to quickly and easily select the optimal option.

[0068] A "data processing device" is a computer system that has the functions of receiving, analyzing, and converting digital data.

[0069] "Configuration data" refers to digital data that includes design information and is used to represent the shape and characteristics of a structure.

[0070] "Technical information" refers to the specifications and detailed information necessary for the fabrication of a structure, including size, materials, and design intent.

[0071] A "manufacturer" refers to an organization or company that has the capability to manufacture and provide structures.

[0072] A "template" is something that provides a basic format or style tailored to a specific purpose or content.

[0073] "Monitoring" refers to the activity of continuously observing a specific process or state and recording or reporting any changes.

[0074] "User" refers to an individual or legal entity that uses this system to manage the design production process.

[0075] This invention provides a system for efficiently manufacturing user-designed structures. The user begins by using design software on a data processing device to generate digital configuration data. A typical example of such software is a general-purpose design program. This design data is then uploaded to the system via the user's terminal.

[0076] The server analyzes the received configuration data using an AI model. This AI model is built on a platform such as TENSORFLOW® or PyTorch. Through this analysis, the server extracts technical information about the structure, including detailed information about its size, shape, and materials.

[0077] Based on the extracted technical information, the server requests quotes from multiple manufacturers. During this process, the server directly accesses the manufacturers' systems via APIs and transmits data electronically. The server automatically compares the received quotes and displays the results on the user's terminal. The user interface is dynamically generated using HTML and JavaScript (registered trademark).

[0078] A concrete example would be a user designing new furniture and using a prompt that reads, "Generate a design image of the furniture based on the following specifications: height 180cm, width 80cm, wood color walnut brown, 5-tier bookshelf." This allows the user to proceed seamlessly from the design stage to manufacturing and delivery.

[0079] This system allows users to easily manage complex production processes and make optimal choices quickly. This streamlines the entire process from design to production and delivery, significantly reducing the user's time and effort.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The user creates configuration data using design software. The user inputs design specifications, and the software generates design image data based on those specifications. Specifically, the user builds the design using drag-and-drop and toolbar selection functions, and finally saves it to the device as an image file.

[0083] Step 2:

[0084] The terminal uploads the generated design image to the server. The input is the user providing an image file they created, initiating the process of sending it to the server. The output is the server receiving this image data. The process is completed when the user clicks the file upload button and selects the image file.

[0085] Step 3:

[0086] The server analyzes the received design images using an AI model. The input is the uploaded image data, and the output is the technical information extracted through the analysis. Specifically, the server inputs data into the model to identify technical elements such as material, size, and shape from the image, and performs a process of extracting features.

[0087] Step 4:

[0088] The server sends quotation requests to multiple manufacturers based on the extracted technical information. The input is technical information, and the output is quotation request messages to each manufacturer. The server connects to the manufacturers' systems using an API and automatically sends the necessary data.

[0089] Step 5:

[0090] The server receives quotation information from manufacturers and compares and analyzes it. The input is quotation data from each manufacturer, and the output is the best quotation option after comparison. Specifically, the server utilizes a database and algorithms to analyze and compare factors such as price and delivery time.

[0091] Step 6:

[0092] The server presents the most suitable quotation options for the user's device. Input is optimized quotation information, and output is information displayed on the user interface. The server dynamically constructs the screen using HTML and JavaScript, providing information in a visually appealing format.

[0093] Step 7:

[0094] The user selects the best option from the presented quotation options. The input is the option selected by the user, and the output is the result of the selection sent to the server. The user completes their selection by clicking buttons or radio buttons on the interface.

[0095] Step 8:

[0096] The server places a formal production order with the appropriate manufacturer based on the user's selection. The input is the user's selection, and the output is the production order message. After confirming the selection, the server sends the order data to the manufacturer using an API.

[0097] Step 9:

[0098] The server monitors production progress in real time and notifies the user. Input is progress information from the manufacturer, and output is a notification to the user's terminal. The server monitors periodic data updates from the manufacturer and automatically reflects the progress status in the user's UI.

[0099] Step 10:

[0100] The server sends a final notification to the user when the product is completed and ready for shipment, and then arranges delivery. The input is product completion information, and the output is a notification to the user and delivery instructions to the logistics company. The server works in conjunction with the logistics system to optimize delivery dates and routes, ensuring rapid shipment.

[0101] (Application Example 1)

[0102] 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."

[0103] To easily produce and quickly begin using furniture designed by users, it is necessary to streamline the entire process from design to production and delivery. However, traditional methods involve complex procedures such as users communicating furniture designs to manufacturers, obtaining quotes, and monitoring progress, which are time-consuming and laborious. Furthermore, the inability to visualize the final product beforehand could potentially lead to decreased user satisfaction.

[0104] 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.

[0105] In this invention, the server includes means for receiving design images generated by an image generation device, means for analyzing the design images to extract furniture specification information, means for providing a user interface including augmented reality technology that allows visual confirmation of the furniture design, means for receiving and comparing quote information from multiple manufacturers, means for placing orders for selected production requests with the relevant manufacturers, and means for providing quote options including a delivery schedule. This makes it possible for users to easily incorporate their designs into production, check the progress in real time, and have the finished products delivered smoothly.

[0106] An "image generation device" is a digital tool used by users to create design images, and it provides the foundation for later analysis of those designs.

[0107] "Design images" are image data created by users using image generation devices that show the shape and specifications of furniture.

[0108] "Specification information" refers to detailed information about the furniture, such as its shape, size, and materials, extracted from the design image.

[0109] A "manufacturer" is a company or organization that has the ability to produce furniture based on the specifications it receives.

[0110] "Quotation information" refers to the proposal provided by the manufacturer, which includes the cost of production, delivery date, and other conditions for the manufactured product.

[0111] Augmented reality technology is a technique that visualizes designed furniture by overlaying it onto real-world space, allowing users to check its actual size and placement.

[0112] A "user interface" is the operating screen or input receiving platform that a user uses to interact with a system.

[0113] "Real-time notification" is a system function that immediately informs users of information as it occurs.

[0114] A "delivery schedule" is information that shows the planned time frame until the finished furniture is delivered to the user.

[0115] The system for implementing this invention involves the coordinated operation of a user, a server, and a client terminal. First, the user creates a design image of furniture using an image generation device and uploads it to the server from the client terminal. This design image includes specifications such as the shape, size, and material of the furniture.

[0116] The server processes the received design images using AI analysis technology to extract specification information. This process utilizes image analysis services such as "Cloud Vision API." Based on the extracted specification information, the server automatically requests quotes from multiple manufacturers. A notification service such as AWS's "SNS (Simple Notification Service)" is used for the quote requests.

[0117] When manufacturers send quotation information to the server, the server analyzes it and provides the user with the best option, taking into account factors such as cost and delivery time. The user reviews the presented quotation through their client terminal and selects the option to request production.

[0118] Based on the user's selection, the server places an order with the appropriate manufacturer, and production begins. The server then tracks the production progress and notifies the user in real time. Google FI® rebase, a real-time database, may be used for tracking.

[0119] Once the finished product is ready at the specified time, the server checks the delivery schedule and coordinates with the logistics company to deliver it to the user. This allows the user to track the entire process from start to finish until the furniture is completed.

[0120] For example, if a user designs a desk perfectly suited to their specifications and orders it through the app, the desk can be completed on the desired date and delivered to their home without any problems. This significantly reduces the user's effort, providing a comfortable purchasing experience.

[0121] Specific examples and prompt statements are as follows:

[0122] Users create new designs using this smartphone app and upload them to an AI model. The AI ​​automatically extracts furniture specifications from the design, finds the best manufacturer, and tracks the production status in real time. Please explain in detail how to streamline the process from online design to production completion.

[0123] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0124] Step 1:

[0125] The user creates a furniture design image using an image generation device and uploads it to the server from their terminal. The input is the design image created by the user, and the output is the completion of the image file upload to the server. At this stage, the system verifies that the design image is in the correct format.

[0126] Step 2:

[0127] The server analyzes the received design images using AI analysis technology and extracts specification information. The input is the uploaded design image, and the output is the extracted specification information of the furniture's shape, size, and material. In this process, an AI tool is used to convert the visual data in the image into text data.

[0128] Step 3:

[0129] The server automatically requests quotes from multiple manufacturers based on the extracted specification information. The input is the specification information, and the output is the status after the quote requests have been sent to the manufacturers. Notifications are sent using AWS's "SNS (Simple Notification Service)".

[0130] Step 4:

[0131] When manufacturers send quotation information to the server, the server performs a comparative analysis based on price and delivery time. The input is the quotation information from the manufacturers, and the output is a list of the best quotation options to present to the user. Database queries are used to organize the information.

[0132] Step 5:

[0133] The user reviews the quote results via their terminal and selects a production request. The input consists of quote options sent from the server, and the output is the production request selected by the user. The selection process takes place on the interface.

[0134] Step 6:

[0135] The server places a production order with the appropriate manufacturer based on the user's selection. The input is the user's production request selection, and the output is the status after the production order notification has been sent to the manufacturer.

[0136] Step 7:

[0137] The server tracks production progress and notifies the user in real time. Input is progress information from the manufacturer, and output is progress notifications to the user. Google® Firebase is used to perform real-time data push notifications.

[0138] Step 8:

[0139] Once the product is completed, the server coordinates with the logistics provider to confirm the delivery schedule. The input is the product completion information, and the output is the completion of the delivery arrangement. Delivery information is managed through a logistics API.

[0140] Step 9:

[0141] The user checks the delivery status via a terminal and receives the furniture. The input is delivery tracking information, and the output is a confirmation of furniture receipt. This completes the entire process.

[0142] 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.

[0143] This embodiment of the invention is a furniture manufacturing support system that understands the user's emotions and provides services optimized to them. Based on design images created by an image generation device, this system not only handles furniture manufacturing requests, quotations, and orders, but also identifies the user's emotions and optimizes communication at each step to suit the user.

[0144] When a user generates a design image and uploads it to the system via their device, the server processes the image and extracts the necessary furniture specification information. During this process, the system analyzes the user's emotions using an emotion engine based on their facial expressions and voice input to understand their current feelings.

[0145] For example, if a user is feeling stressed, the server will use that emotional information to select and suggest more relaxing colors and designs when presenting quotation options. Similarly, if detailed technical specifications need to be reviewed, the server will use a relaxed tone and a visually easy-to-understand interface for explanations.

[0146] As the user reviews and selects from the presented quote options, the emotion engine evaluates the user's feedback in real time, providing background support to ensure a smooth selection process. Once the selection is confirmed, the server automatically confirms the production request and places the order with the relevant manufacturer.

[0147] While production is underway, the server manages the progress and sends regular notifications to the user. These notifications are adjusted in terms of content and frequency to be considerate of the user's feelings and reduce the stress of waiting during the production period. Once the product is completed and ready for delivery, a notification is sent to the device, and a delivery date and time are arranged to suit the user's convenience.

[0148] (Specific example)

[0149] Let's consider a scenario where a user designs a new living room table. The user uploads their design image to the system, and the server analyzes the image while using an emotion engine to determine the user's emotions. If the user is feeling cheerful, the system suggests bright-colored wood and positive designs in a friendly manner.

[0150] When the emotion engine detects surprise or anxiety from the user during the selection process, the server provides additional information and past performance data to support the user in making a decision. In this way, flexible responses that are attentive to the user's emotions are provided, enabling the process to be completed with high satisfaction.

[0151] This system allows users to not only place orders but also enjoy a personalized customer experience.

[0152] The following describes the processing flow.

[0153] Step 1:

[0154] Users create their ideal furniture designs using an image generation device and upload the design images to the system via a terminal. A user interface is provided to prompt users to upload their designs.

[0155] Step 2:

[0156] The device uses the user's camera and microphone input to capture the user's facial expressions and voice. This data is used to determine the user's current emotions.

[0157] Step 3:

[0158] The server analyzes the received design image and extracts furniture specification information (e.g., material, size, shape). In parallel, the emotion engine analyzes the user's emotions based on emotion data sent from the terminal.

[0159] Step 4:

[0160] The server takes into account the extracted specification information and the emotional state obtained from the emotion engine, sends quotation requests to multiple manufacturers, and automatically dispatches them in a templated format. The quotation content also incorporates the user's preferred style and color scheme.

[0161] Step 5:

[0162] Once each manufacturer submits a quote, the server receives them and compares and analyzes the price, delivery time, and other factors. Based on information from the emotion engine, it presents options in a way that reduces user stress.

[0163] Step 6:

[0164] The device displays quotation options that it deems most suitable for the user. Based on the user's current sentiment, it adjusts how information is received and displayed to facilitate selection.

[0165] Step 7:

[0166] The server confirms the manufacturing request selected by the user and sends the order data to the chosen manufacturer. The order details include specific specifications and preferences identified by the user.

[0167] Step 8:

[0168] Once production begins, the server periodically retrieves production progress and notifies the user's device of the progress based on a notification schedule that takes into account the user's emotional state. The tone and frequency of notifications are adjusted according to the user's emotions to maintain their interest.

[0169] Step 9:

[0170] Once the product is complete and ready for delivery, the server notifies the terminal of the delivery date and allows for flexible scheduling to suit the user's convenience. The emotion engine completes the entire process by providing an approach to optimize the user's emotions upon receiving the product.

[0171] (Example 2)

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

[0173] Conventional furniture manufacturing systems lack consideration for user emotions in their suggestions and selection support, resulting in a uniform user experience and difficulty in providing flexible services tailored to individual needs. Furthermore, there is a lack of methods to alleviate user anxiety and stress during the estimation and manufacturing processes. Therefore, this invention aims to improve user satisfaction by analyzing user emotions in real time and providing optimal suggestions and support accordingly.

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

[0175] In this invention, the server includes means for analyzing the user's facial expressions and voice input using an emotion analysis function to determine the user's emotions, means for requesting quotation proposals from multiple manufacturers based on the emotion analysis results and extracted furniture specification information, and means for evaluating the emotional feedback the user makes in real time and providing selection support. This enables flexible quotation proposals and selection support that are tailored to the user's emotions, and is expected to improve the user experience.

[0176] An "image generation device" is a device that generates visual information in digital format and is used in design and planning.

[0177] "Design drawings" are visual information that shows the design of a product or structure, providing guidelines for its dimensions, shape, and materials.

[0178] "Specifications" refers to information describing the technical details required for a product or service, including structure, materials, and function.

[0179] "Emotion analysis function" is a technology that recognizes and analyzes a user's emotional state using data such as facial expressions and voice.

[0180] A "quote proposal" is a plan that specifically outlines the costs and conditions for manufacturing a product, and is provided based on specific specifications.

[0181] "Selection support" refers to the act of providing support by presenting information and making suggestions to help users make the best decisions.

[0182] "Production progress" refers to the state indicating the extent to which the manufacturing process of a product or service is complete.

[0183] "Notification" is the act of communicating information in order to inform relevant parties about a certain matter.

[0184] This invention is a system that proposes the optimal furniture production while taking the user's emotions into consideration, and its embodiments are described below.

[0185] Hardware and software configuration

[0186] This system includes terminals used by users to create, save, and upload design images, and a server for data processing. The terminals are equipped with image generation software (e.g., CAD software or design applications), allowing users to save their created designs in JPEG or PNG format and upload them to the system. The server uses image analysis software (e.g., OpenCV or TensorFlow) and sentiment analysis software (e.g., Amazon Rekognition or Microsoft® Azure® Emotion API) to extract image specification information and determine the user's emotional state.

[0187] Data processing and calculation

[0188] The server processes uploaded design images using image analysis software. Specifically, it processes the color information of each pixel and maps information about the furniture's shape, dimensions, and materials to a database. The emotion analysis software takes the user's facial expressions and voice data as input and analyzes them to quantify the user's emotional state. This analysis result is input into the recommendation engine within the system and used to generate the most suitable estimate proposal for the user. The generation AI model (e.g., GPT or DALL-E) generates optimized design proposals based on emotion evaluations and prompts that take into account specification information.

[0189] Specific example

[0190] For example, if a user generates a design image for a living room table, they upload that image to the server via their device. The server analyzes the image and simultaneously determines if the user is feeling cheerful based on their real-time facial expressions. Based on this information, the server uses an AI model to create quotation options, including bright colors and friendly designs, and presents them to the user. The user reviews these and makes their final selection. An example of a prompt used in this process is, "I've designed a coffee table for my living room. Please extract specifications from this image and provide relaxing suggestions based on my mood."

[0191] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0192] Step 1:

[0193] The user generates a design image and uploads it to the system via a terminal. The input is a design image in JPEG or PNG format created by the user. Using the terminal's application software, the user sends this image in a format the system can receive. The output is the state in which the server has received that image data.

[0194] Step 2:

[0195] The server processes the received design image using image analysis software. The input is the design image uploaded to the server in step 1. The server performs object detection and feature analysis based on pixel information to extract specification information regarding the shape, material, and dimensions of the furniture from this image. The output is the analyzed furniture specification information.

[0196] Step 3:

[0197] To analyze user emotions, the server uses emotion analysis software. The input is real-time facial expression or voice data from the user. Based on this data, the server uses facial recognition algorithms or voice analysis algorithms to evaluate the user's emotions. The output is a numerical representation of the user's emotional state.

[0198] Step 4:

[0199] The server generates quotation proposals using a generative AI model. The inputs are furniture specification information obtained as a result of the analysis in step 2 and user emotion data quantified in step 3. The server inputs this data as prompts into the generative AI model, which automatically generates color and design suggestions tailored to the user's emotions. The output is an optimized quotation proposal presented to the user.

[0200] Step 5:

[0201] The user reviews the presented quote proposal and makes a selection. The input is the quote proposal received in step 4. The user makes a selection using a terminal and sends feedback to the server. The server analyzes this feedback in real time and provides additional selection support information as needed. The output is the user's confirmed selection data.

[0202] Step 6:

[0203] After the user confirms their selection, the server automatically places an order with the manufacturer. The input is the user's selection data confirmed in step 5. Based on this, the server uses a communication protocol with the manufacturer to initiate the ordering process. The output is the order instruction sent to the manufacturer.

[0204] Step 7:

[0205] The server manages production progress and provides notifications tailored to the user's emotional state. Inputs include production progress data obtained from the manufacturer and the user's physical stress level. Based on this, the server adjusts the content and frequency of notifications and sends the information to the user via their device. Output is the adjusted notification message.

[0206] (Application Example 2)

[0207] 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".

[0208] Traditional furniture manufacturing processes often involved mechanical design proposals and price quotes without considering user emotions, resulting in a limited user experience and insufficient support, particularly when stress or anxiety arose. A system is needed that understands individual user emotions and provides services optimized based on those emotions.

[0209] 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.

[0210] In this invention, the server includes means for identifying the user's emotions and optimizing design proposals and estimates to those emotions, means for evaluating user feedback in real time and supporting selections, and means for notifying the user of production progress with consideration for their emotions. This enables flexible responses that are attentive to the user's emotions and makes it possible to provide a highly satisfying, customized user experience.

[0211] An "image generation device" is a device that generates visual representations of furniture and other objects designed by the user.

[0212] A "design image" is an image that visually represents the specific design of the furniture and includes specifications for its production.

[0213] "Furniture specification information" refers to information necessary for production, such as materials, size, and shape, extracted from design images.

[0214] A "manufacturer" is a company or organization that actually produces furniture.

[0215] "Quotation information" refers to cost information necessary for production, provided by the manufacturer, and includes proposals that the user can choose from.

[0216] "Means of notifying users" refers to communication methods for providing users with real-time information on production progress and other related matters.

[0217] "Means for identifying user emotions" refers to technologies that analyze a user's emotional state from data such as facial expressions and voice.

[0218] "Methods for evaluating feedback in real time" refer to technologies that instantly analyze user responses and choices to optimize service content.

[0219] "Means of supporting choice" refer to technologies that provide additional information or organize options to help users make the best decision.

[0220] In an embodiment of the present invention, the system realizes a furniture manufacturing support system that understands the user's emotions and provides services accordingly. The server receives a design image created by the user using an image generation device and extracts furniture specification information using image analysis technology. In this case, the specification information includes materials, size, and shape.

[0221] The server requests quotes from multiple manufacturers and compares and analyzes the received quote information. During this process, it analyzes the user's facial expression and voice data using emotion analysis APIs (e.g., Amazon Rekognition or Google Cloud Vision API) to provide design suggestions and quotes tailored to the user's emotions.

[0222] User feedback is evaluated by the system in real time, and guidance and options are adjusted as needed. Furthermore, production progress information is automatically obtained from the manufacturer and communicated in a format that takes into account the user's emotional state. This allows users to proceed through the process with less stress.

[0223] For example, if a user designs a new chair, the system analyzes the user's positive emotions and suggests a colorful and playful design. If the system detects feelings of hesitation, it presents samples of popular designs from the past to reassure the user.

[0224] An example of a prompt sentence to input into a generative AI model is, "When a user designs new furniture, provide the best suggestions based on sentiment data and past popular designs."

[0225] Through this system, users can enjoy a personalized customer experience.

[0226] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0227] Step 1:

[0228] The server receives design images from the terminal as input. Using an image processing algorithm, it extracts furniture specification information such as material, size, and shape from the image. This outputs the information necessary for specific production.

[0229] Step 2:

[0230] The server requests quotes from multiple manufacturers based on the extracted furniture specifications. At this time, the server uses a generative AI model to create a template and outputs a quote request with the specifications added.

[0231] Step 3:

[0232] Manufacturers send quotation information back to the server. The server receives this as input and compares and analyzes multiple quotation pieces. Using data analysis algorithms, it evaluates price and delivery time and outputs the optimal proposal.

[0233] Step 4:

[0234] The server receives user feedback as input and uses an emotion analysis API to determine the user's emotions. Based on these emotions, the server optimizes the presentation of estimate information and adjusts the design options before outputting the results.

[0235] Step 5:

[0236] After the user selects a production request, the server automatically places an order with the manufacturer. The information transmitted includes the specifications selected by the user and the planned production date.

[0237] Step 6:

[0238] As production progresses, the server receives progress information from the manufacturer. Based on this, an AI model generates notifications for the user, and these notifications are output to the device in an emotionally sensitive manner.

[0239] Step 7:

[0240] After the product is completed, the server takes the user's convenience into account, proposes the optimal delivery date and time, and notifies the user's device. The user can then review the proposed schedule and finalize the delivery details by providing feedback.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] [Second Embodiment]

[0245] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0246] 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.

[0247] 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).

[0248] 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.

[0249] 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.

[0250] 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).

[0251] 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.

[0252] 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.

[0253] 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.

[0254] 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.

[0255] 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.

[0256] 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".

[0257] The system for implementing this invention is constructed using a program deployed in a digital environment. Its core function is to utilize design images created by the user using an image generation device and to efficiently carry out the actual furniture manufacturing based on those images.

[0258] The user first creates a design image using an image generation device and uploads that image to the system via a terminal. The server processes the received image using AI analysis technology and extracts specification information from the image. This specification information includes details such as the shape, size, and material of the furniture.

[0259] After the specification information is extracted, the server automatically requests quotes from multiple partner manufacturers. These quote requests include the extracted specification information, enabling manufacturers to create accurate quotes. The quotes submitted by the manufacturers are received by the server. The server analyzes the multiple quotes and compares the proposals. It determines the best option by considering factors such as price, delivery time, and production capacity.

[0260] The comparison results are displayed on the user's terminal, and the user can select from the displayed quotation options. Based on the user's selection, the server places an order for the confirmed production request with the appropriate manufacturer.

[0261] Once an order is confirmed, the server continuously tracks the production progress and notifies the user of this information in real time. When the product is completed and ready for delivery, the server sends a final notification to the user and coordinates with the logistics company to arrange delivery.

[0262] (Specific example)

[0263] For example, suppose a user designs a new bookshelf. The user completes the design using an image generation device and sends it to the system. This design includes details such as the number of shelves, their height, and the color of the wood to be used.

[0264] The server then extracts the necessary information from the design and sends quote requests to multiple manufacturers. The received quotes include production costs and timelines, which the server compares and displays the best option on the interface for the user to choose from.

[0265] After the user selects the option they deem most suitable, the server notifies the manufacturer to begin production. Meanwhile, the user can track the completion date of the shelves through progress information provided by the system, and the finished shelves are eventually delivered to the specified address.

[0266] In this way, this system allows users to proceed smoothly from the design stage to delivery, automating the conversion from design to physical product and significantly reducing the user's workload.

[0267] The following describes the processing flow.

[0268] Step 1:

[0269] The user creates their ideal furniture design using an image generation device. They then upload this design image to the system via a terminal.

[0270] Step 2:

[0271] The server receives the uploaded design image and processes it using advanced analysis techniques. Here, it extracts furniture specification information (e.g., shape, size, material, etc.).

[0272] Step 3:

[0273] The server automatically creates a request for quotation based on the extracted specification information and sends it to multiple partner manufacturers. This request includes specific design details.

[0274] Step 4:

[0275] Quotation information is returned to the server from each manufacturer. The server organizes the received quotations and performs a comparative analysis based on factors such as price, delivery time, and quality.

[0276] Step 5:

[0277] The most suitable quote options are displayed for the device. The user browses the multiple options presented through the interface and selects the most appropriate one.

[0278] Step 6:

[0279] Based on the quotation selected by the user, the server places an order with the corresponding manufacturer for the formal production request. The order details include the necessary particulars.

[0280] Step 7:

[0281] The server constantly monitors the progress of production and, whenever it receives a status update from the manufacturer, notifies the user's terminal of that information.

[0282] Step 8:

[0283] After the server confirms the completion of the product, it arranges for delivery. The terminal is notified of the delivery details along with the final delivery schedule.

[0284] Through this series of steps, the user can efficiently complete the process from the initial design to receiving the physical item.

[0285] (Example 1)

[0286] 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".

[0287] In the conventional manufacturing process, it took complex procedures and time for the user to actually manufacture the designed structure. Therefore, it was difficult to smoothly progress the process from design to manufacturing and delivery, and there was the problem of taking a lot of time and effort. Furthermore, it was also difficult to quickly compare quotations from multiple manufacturers and make an optimal choice.

[0288] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following respective means.

[0289] In this invention, the server includes means for analyzing configuration data and extracting technical information, means for requesting quotes from multiple manufacturers, and means for presenting the quote results to the user and allowing them to select a production request. This enables the user to efficiently manage a consistent process from design to production and delivery, and to quickly and easily select the optimal option.

[0290] A "data processing device" is a computer system that has the functions of receiving, analyzing, and converting digital data.

[0291] "Configuration data" refers to digital data that includes design information and is used to represent the shape and characteristics of a structure.

[0292] "Technical information" refers to the specifications and detailed information necessary for the fabrication of a structure, including size, materials, and design intent.

[0293] A "manufacturer" refers to an organization or company that has the capability to manufacture and provide structures.

[0294] A "template" is something that provides a basic format or style tailored to a specific purpose or content.

[0295] "Monitoring" refers to the activity of continuously observing a specific process or state and recording or reporting any changes.

[0296] "User" refers to an individual or legal entity that uses this system to manage the design production process.

[0297] This invention provides a system for efficiently manufacturing user-designed structures. The user begins by using design software on a data processing device to generate digital configuration data. A typical example of such software is a general-purpose design program. This design data is then uploaded to the system via the user's terminal.

[0298] The server analyzes the received configuration data using an AI model. This AI model is built on a platform such as TensorFlow or PyTorch. Through this analysis, the server extracts technical information about the structure, including details about its size, shape, and materials.

[0299] Based on the extracted technical information, the server requests quotes from multiple manufacturers. During this process, the server directly accesses the manufacturers' systems via APIs and transmits data electronically. The server automatically compares the received quotes and displays the results on the user's terminal. The user interface is dynamically generated using HTML and JavaScript.

[0300] A concrete example would be a user designing new furniture and using a prompt that reads, "Generate a design image of the furniture based on the following specifications: height 180cm, width 80cm, wood color walnut brown, 5-tier bookshelf." This allows the user to proceed seamlessly from the design stage to manufacturing and delivery.

[0301] This system allows users to easily manage complex production processes and make optimal choices quickly. This streamlines the entire process from design to production and delivery, significantly reducing the user's time and effort.

[0302] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0303] Step 1:

[0304] The user creates configuration data using design software. What the user inputs is specification information regarding the design, and the software generates design image data based on that specification. As specific operations, the user utilizes functions such as drag & drop and toolbar selection to construct the design, and finally saves it as an image file on the terminal.

[0305] Step 2:

[0306] The terminal uploads the generated design image to the server. As input, it provides the image file created by the user and starts the transmission process to the server. As output, the server receives this image data. The specific operation is completed when the user clicks the file upload button and selects the image file.

[0307] Step 3:

[0308] The server analyzes the received design image using an AI model. The input is the uploaded image data, and the output is the technical information extracted through the analysis. Specifically, the server inputs the data into the model to identify technical elements such as materials, sizes, and shapes in the image, and executes the process of extracting features.

[0309] Step 4:

[0310] The server sends a quotation request to multiple manufacturing companies based on the extracted technical information. The input is the technical information, and the output is the quotation request message to each manufacturing company. The server uses an API to connect to the manufacturing company's system and automatically sends the necessary data.

[0311] Step 5:

[0312] The server receives quotation information from manufacturers and compares and analyzes it. The input is quotation data from each manufacturer, and the output is the best quotation option after comparison. Specifically, the server utilizes a database and algorithms to analyze and compare factors such as price and delivery time.

[0313] Step 6:

[0314] The server presents the most suitable quotation options for the user's device. Input is optimized quotation information, and output is information displayed on the user interface. The server dynamically constructs the screen using HTML and JavaScript, providing information in a visually appealing format.

[0315] Step 7:

[0316] The user selects the best option from the presented quotation options. The input is the option selected by the user, and the output is the result of the selection sent to the server. The user completes their selection by clicking buttons or radio buttons on the interface.

[0317] Step 8:

[0318] The server places a formal production order with the appropriate manufacturer based on the user's selection. The input is the user's selection, and the output is the production order message. After confirming the selection, the server sends the order data to the manufacturer using an API.

[0319] Step 9:

[0320] The server monitors production progress in real time and notifies the user. Input is progress information from the manufacturer, and output is a notification to the user's terminal. The server monitors periodic data updates from the manufacturer and automatically reflects the progress status in the user's UI.

[0321] Step 10:

[0322] The server sends a final notification to the user when the product is completed and ready for shipment, and then arranges delivery. The input is product completion information, and the output is a notification to the user and delivery instructions to the logistics company. The server works in conjunction with the logistics system to optimize delivery dates and routes, ensuring rapid shipment.

[0323] (Application Example 1)

[0324] 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."

[0325] To easily produce and quickly begin using furniture designed by users, it is necessary to streamline the entire process from design to production and delivery. However, traditional methods involve complex procedures such as users communicating furniture designs to manufacturers, obtaining quotes, and monitoring progress, which are time-consuming and laborious. Furthermore, the inability to visualize the final product beforehand could potentially lead to decreased user satisfaction.

[0326] 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.

[0327] In this invention, the server includes means for receiving design images generated by an image generation device, means for analyzing the design images to extract furniture specification information, means for providing a user interface including augmented reality technology that allows visual confirmation of the furniture design, means for receiving and comparing quote information from multiple manufacturers, means for placing orders for selected production requests with the relevant manufacturers, and means for providing quote options including a delivery schedule. This makes it possible for users to easily incorporate their designs into production, check the progress in real time, and have the finished products delivered smoothly.

[0328] An "image generation device" is a digital tool used by users to create design images, and it provides the foundation for later analysis of those designs.

[0329] "Design images" are image data created by users using image generation devices that show the shape and specifications of furniture.

[0330] "Specification information" refers to detailed information about the furniture, such as its shape, size, and materials, extracted from the design image.

[0331] A "manufacturer" is a company or organization that has the ability to produce furniture based on the specifications it receives.

[0332] "Quotation information" refers to the proposal provided by the manufacturer, which includes the cost of production, delivery date, and other conditions for the manufactured product.

[0333] Augmented reality technology is a technique that visualizes designed furniture by overlaying it onto real-world space, allowing users to check its actual size and placement.

[0334] A "user interface" is the operating screen or input receiving platform that a user uses to interact with a system.

[0335] "Real-time notification" is a system function that immediately informs users of information as it occurs.

[0336] A "delivery schedule" is information that shows the planned time frame until the finished furniture is delivered to the user.

[0337] The system for implementing this invention involves the coordinated operation of a user, a server, and a client terminal. First, the user creates a design image of furniture using an image generation device and uploads it to the server from the client terminal. This design image includes specifications such as the shape, size, and material of the furniture.

[0338] The server processes the received design images using AI analysis technology to extract specification information. This process utilizes image analysis services such as "Cloud Vision API." Based on the extracted specification information, the server automatically requests quotes from multiple manufacturers. A notification service such as AWS's "SNS (Simple Notification Service)" is used for the quote requests.

[0339] When manufacturers send quotation information to the server, the server analyzes it and provides the user with the best option, taking into account factors such as cost and delivery time. The user reviews the presented quotation through their client terminal and selects the option to request production.

[0340] Based on the user's selection, the server places an order with the appropriate manufacturer, and production begins. The server then tracks the production progress and notifies the user in real time. Google Firebase, a real-time database, may be used for tracking.

[0341] Once the finished product is ready at the specified time, the server checks the delivery schedule and coordinates with the logistics company to deliver it to the user. This allows the user to track the entire process from start to finish until the furniture is completed.

[0342] For example, if a user designs a desk perfectly suited to their specifications and orders it through the app, the desk can be completed on the desired date and delivered to their home without any problems. This significantly reduces the user's effort, providing a comfortable purchasing experience.

[0343] Specific examples and prompt statements are as follows:

[0344] Users create new designs using this smartphone app and upload them to an AI model. The AI ​​automatically extracts furniture specifications from the design, finds the best manufacturer, and tracks the production status in real time. Please explain in detail how to streamline the process from online design to production completion.

[0345] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0346] Step 1:

[0347] The user creates a furniture design image using an image generation device and uploads it to the server from their terminal. The input is the design image created by the user, and the output is the completion of the image file upload to the server. At this stage, the system verifies that the design image is in the correct format.

[0348] Step 2:

[0349] The server analyzes the received design images using AI analysis technology and extracts specification information. The input is the uploaded design image, and the output is the extracted specification information of the furniture's shape, size, and material. In this process, an AI tool is used to convert the visual data in the image into text data.

[0350] Step 3:

[0351] The server automatically requests quotes from multiple manufacturers based on the extracted specification information. The input is the specification information, and the output is the status after the quote requests have been sent to the manufacturers. Notifications are sent using AWS's "SNS (Simple Notification Service)".

[0352] Step 4:

[0353] When manufacturers send quotation information to the server, the server performs a comparative analysis based on price and delivery time. The input is the quotation information from the manufacturers, and the output is a list of the best quotation options to present to the user. Database queries are used to organize the information.

[0354] Step 5:

[0355] The user reviews the quote results via their terminal and selects a production request. The input consists of quote options sent from the server, and the output is the production request selected by the user. The selection process takes place on the interface.

[0356] Step 6:

[0357] The server places a production order with the appropriate manufacturer based on the user's selection. The input is the user's production request selection, and the output is the status after the production order notification has been sent to the manufacturer.

[0358] Step 7:

[0359] The server tracks production progress and notifies the user in real time. Input is progress information from the manufacturer, and output is progress notifications to the user. Google Firebase is used to perform push notifications of real-time data.

[0360] Step 8:

[0361] Once the product is completed, the server coordinates with the logistics provider to confirm the delivery schedule. The input is the product completion information, and the output is the completion of the delivery arrangement. Delivery information is managed through a logistics API.

[0362] Step 9:

[0363] The user checks the delivery status via a terminal and receives the furniture. The input is delivery tracking information, and the output is a confirmation of furniture receipt. This completes the entire process.

[0364] 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.

[0365] This embodiment of the invention is a furniture manufacturing support system that understands the user's emotions and provides services optimized to them. Based on design images created by an image generation device, this system not only handles furniture manufacturing requests, quotations, and orders, but also identifies the user's emotions and optimizes communication at each step to suit the user.

[0366] When a user generates a design image and uploads it to the system via their device, the server processes the image and extracts the necessary furniture specification information. During this process, the system analyzes the user's emotions using an emotion engine based on their facial expressions and voice input to understand their current feelings.

[0367] For example, if a user is feeling stressed, the server will use that emotional information to select and suggest more relaxing colors and designs when presenting quotation options. Similarly, if detailed technical specifications need to be reviewed, the server will use a relaxed tone and a visually easy-to-understand interface for explanations.

[0368] As the user reviews and selects from the presented quote options, the emotion engine evaluates the user's feedback in real time, providing background support to ensure a smooth selection process. Once the selection is confirmed, the server automatically confirms the production request and places the order with the relevant manufacturer.

[0369] While production is underway, the server manages the progress and sends regular notifications to the user. These notifications are adjusted in terms of content and frequency to be considerate of the user's feelings and reduce the stress of waiting during the production period. Once the product is completed and ready for delivery, a notification is sent to the device, and a delivery date and time are arranged to suit the user's convenience.

[0370] (Specific example)

[0371] Let's consider a scenario where a user designs a new living room table. The user uploads their design image to the system, and the server analyzes the image while using an emotion engine to determine the user's emotions. If the user is feeling cheerful, the system suggests bright-colored wood and positive designs in a friendly manner.

[0372] When the emotion engine detects surprise or anxiety from the user during the selection process, the server provides additional information and past performance data to support the user in making a decision. In this way, flexible responses that are attentive to the user's emotions are provided, enabling the process to be completed with high satisfaction.

[0373] This system allows users to not only place orders but also enjoy a personalized customer experience.

[0374] The following describes the processing flow.

[0375] Step 1:

[0376] Users create their ideal furniture designs using an image generation device and upload the design images to the system via a terminal. A user interface is provided to prompt users to upload their designs.

[0377] Step 2:

[0378] The device uses the user's camera and microphone input to capture the user's facial expressions and voice. This data is used to determine the user's current emotions.

[0379] Step 3:

[0380] The server analyzes the received design image and extracts furniture specification information (e.g., material, size, shape). In parallel, the emotion engine analyzes the user's emotions based on emotion data sent from the terminal.

[0381] Step 4:

[0382] The server takes into account the extracted specification information and the emotional state obtained from the emotion engine, sends quotation requests to multiple manufacturers, and automatically dispatches them in a templated format. The quotation content also incorporates the user's preferred style and color scheme.

[0383] Step 5:

[0384] Once each manufacturer submits a quote, the server receives them and compares and analyzes the price, delivery time, and other factors. Based on information from the emotion engine, it presents options in a way that reduces user stress.

[0385] Step 6:

[0386] The device displays quotation options that it deems most suitable for the user. Based on the user's current sentiment, it adjusts how information is received and displayed to facilitate selection.

[0387] Step 7:

[0388] The server confirms the manufacturing request selected by the user and sends the order data to the chosen manufacturer. The order details include specific specifications and preferences identified by the user.

[0389] Step 8:

[0390] Once production begins, the server periodically retrieves production progress and notifies the user's device of the progress based on a notification schedule that takes into account the user's emotional state. The tone and frequency of notifications are adjusted according to the user's emotions to maintain their interest.

[0391] Step 9:

[0392] Once the product is complete and ready for delivery, the server notifies the terminal of the delivery date and allows for flexible scheduling to suit the user's convenience. The emotion engine completes the entire process by providing an approach to optimize the user's emotions upon receiving the product.

[0393] (Example 2)

[0394] 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".

[0395] Conventional furniture manufacturing systems lack consideration for user emotions in their suggestions and selection support, resulting in a uniform user experience and difficulty in providing flexible services tailored to individual needs. Furthermore, there is a lack of methods to alleviate user anxiety and stress during the estimation and manufacturing processes. Therefore, this invention aims to improve user satisfaction by analyzing user emotions in real time and providing optimal suggestions and support accordingly.

[0396] 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.

[0397] In this invention, the server includes means for analyzing the user's facial expressions and voice input using an emotion analysis function to determine the user's emotions, means for requesting quotation proposals from multiple manufacturers based on the emotion analysis results and extracted furniture specification information, and means for evaluating the emotional feedback the user makes in real time and providing selection support. This enables flexible quotation proposals and selection support that are tailored to the user's emotions, and is expected to improve the user experience.

[0398] An "image generation device" is a device that generates visual information in digital format and is used in design and planning.

[0399] "Design drawings" are visual information that shows the design of a product or structure, providing guidelines for its dimensions, shape, and materials.

[0400] "Specifications" refers to information describing the technical details required for a product or service, including structure, materials, and function.

[0401] "Emotion analysis function" is a technology that recognizes and analyzes a user's emotional state using data such as facial expressions and voice.

[0402] A "quote proposal" is a plan that specifically outlines the costs and conditions for manufacturing a product, and is provided based on specific specifications.

[0403] "Selection support" refers to the act of providing support by presenting information and making suggestions to help users make the best decisions.

[0404] "Production progress" refers to the state indicating the extent to which the manufacturing process of a product or service is complete.

[0405] "Notification" is the act of communicating information in order to inform relevant parties about a certain matter.

[0406] This invention is a system that proposes the optimal furniture production while taking the user's emotions into consideration, and its embodiments are described below.

[0407] Hardware and software configuration

[0408] This system includes terminals used by users to create, save, and upload design images, and a server for data processing. The terminals are equipped with image generation software (e.g., CAD software or design applications), allowing users to save their created designs in JPEG or PNG format and upload them to the system. The server uses image analysis software (e.g., OpenCV or TensorFlow) and emotion analysis software (e.g., Amazon Rekognition or Microsoft Azure Emotion API) to extract image specification information and determine the user's emotional state.

[0409] Data processing and calculation

[0410] The server processes uploaded design images using image analysis software. Specifically, it processes the color information of each pixel and maps information about the furniture's shape, dimensions, and materials to a database. The emotion analysis software takes the user's facial expressions and voice data as input and analyzes them to quantify the user's emotional state. This analysis result is input into the recommendation engine within the system and used to generate the most suitable estimate proposal for the user. The generation AI model (e.g., GPT or DALL-E) generates optimized design proposals based on emotion evaluations and prompts that take into account specification information.

[0411] Specific example

[0412] For example, if a user generates a design image for a living room table, they upload that image to the server via their device. The server analyzes the image and simultaneously determines if the user is feeling cheerful based on their real-time facial expressions. Based on this information, the server uses an AI model to create quotation options, including bright colors and friendly designs, and presents them to the user. The user reviews these and makes their final selection. An example of a prompt used in this process is, "I've designed a coffee table for my living room. Please extract specifications from this image and provide relaxing suggestions based on my mood."

[0413] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0414] Step 1:

[0415] The user generates a design image and uploads it to the system via a terminal. The input is a design image in JPEG or PNG format created by the user. Using the terminal's application software, the user sends this image in a format the system can receive. The output is the state in which the server has received that image data.

[0416] Step 2:

[0417] The server processes the received design image using image analysis software. The input is the design image uploaded to the server in step 1. The server performs object detection and feature analysis based on pixel information to extract specification information regarding the shape, material, and dimensions of the furniture from this image. The output is the analyzed furniture specification information.

[0418] Step 3:

[0419] To analyze user emotions, the server uses emotion analysis software. The input is real-time facial expression or voice data from the user. Based on this data, the server uses facial recognition algorithms or voice analysis algorithms to evaluate the user's emotions. The output is a numerical representation of the user's emotional state.

[0420] Step 4:

[0421] The server generates quotation proposals using a generative AI model. The inputs are furniture specification information obtained as a result of the analysis in step 2 and user emotion data quantified in step 3. The server inputs this data as prompts into the generative AI model, which automatically generates color and design suggestions tailored to the user's emotions. The output is an optimized quotation proposal presented to the user.

[0422] Step 5:

[0423] The user reviews the presented quote proposal and makes a selection. The input is the quote proposal received in step 4. The user makes a selection using a terminal and sends feedback to the server. The server analyzes this feedback in real time and provides additional selection support information as needed. The output is the user's confirmed selection data.

[0424] Step 6:

[0425] After the user confirms their selection, the server automatically places an order with the manufacturer. The input is the user's selection data confirmed in step 5. Based on this, the server uses a communication protocol with the manufacturer to initiate the ordering process. The output is the order instruction sent to the manufacturer.

[0426] Step 7:

[0427] The server manages production progress and provides notifications tailored to the user's emotional state. Inputs include production progress data obtained from the manufacturer and the user's physical stress level. Based on this, the server adjusts the content and frequency of notifications and sends the information to the user via their device. Output is the adjusted notification message.

[0428] (Application Example 2)

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

[0430] Traditional furniture manufacturing processes often involved mechanical design proposals and price quotes without considering user emotions, resulting in a limited user experience and insufficient support, particularly when stress or anxiety arose. A system is needed that understands individual user emotions and provides services optimized based on those emotions.

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

[0432] In this invention, the server includes means for identifying the user's emotions and optimizing design proposals and estimates to those emotions, means for evaluating user feedback in real time and supporting selections, and means for notifying the user of production progress with consideration for their emotions. This enables flexible responses that are attentive to the user's emotions and makes it possible to provide a highly satisfying, customized user experience.

[0433] An "image generation device" is a device that generates visual representations of furniture and other objects designed by the user.

[0434] A "design image" is an image that visually represents the specific design of the furniture and includes specifications for its production.

[0435] "Furniture specification information" refers to information necessary for production, such as materials, size, and shape, extracted from design images.

[0436] A "manufacturer" is a company or organization that actually produces furniture.

[0437] "Quotation information" refers to cost information necessary for production, provided by the manufacturer, and includes proposals that the user can choose from.

[0438] "Means of notifying users" refers to communication methods for providing users with real-time information on production progress and other related matters.

[0439] "Means for identifying user emotions" refers to technologies that analyze a user's emotional state from data such as facial expressions and voice.

[0440] "Methods for evaluating feedback in real time" refer to technologies that instantly analyze user responses and choices to optimize service content.

[0441] "Means of supporting choice" refer to technologies that provide additional information or organize options to help users make the best decision.

[0442] In an embodiment of the present invention, the system realizes a furniture manufacturing support system that understands the user's emotions and provides services accordingly. The server receives a design image created by the user using an image generation device and extracts furniture specification information using image analysis technology. In this case, the specification information includes materials, size, and shape.

[0443] The server requests quotes from multiple manufacturers and compares and analyzes the received quote information. During this process, it analyzes the user's facial expression and voice data using emotion analysis APIs (e.g., Amazon Rekognition or Google Cloud Vision API) to provide design suggestions and quotes tailored to the user's emotions.

[0444] User feedback is evaluated by the system in real time, and guidance and options are adjusted as needed. Furthermore, production progress information is automatically obtained from the manufacturer and communicated in a format that takes into account the user's emotional state. This allows users to proceed through the process with less stress.

[0445] For example, if a user designs a new chair, the system analyzes the user's positive emotions and suggests a colorful and playful design. If the system detects feelings of hesitation, it presents samples of popular designs from the past to reassure the user.

[0446] An example of a prompt sentence to input into a generative AI model is, "When a user designs new furniture, provide the best suggestions based on sentiment data and past popular designs."

[0447] Through this system, users can enjoy a personalized customer experience.

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

[0449] Step 1:

[0450] The server receives design images from the terminal as input. Using an image processing algorithm, it extracts furniture specification information such as material, size, and shape from the image. This outputs the information necessary for specific production.

[0451] Step 2:

[0452] The server requests quotes from multiple manufacturers based on the extracted furniture specifications. At this time, the server uses a generative AI model to create a template and outputs a quote request with the specifications added.

[0453] Step 3:

[0454] Manufacturers send quotation information back to the server. The server receives this as input and compares and analyzes multiple quotation pieces. Using data analysis algorithms, it evaluates price and delivery time and outputs the optimal proposal.

[0455] Step 4:

[0456] The server receives user feedback as input and uses an emotion analysis API to determine the user's emotions. Based on these emotions, the server optimizes the presentation of estimate information and adjusts the design options before outputting the results.

[0457] Step 5:

[0458] After the user selects a production request, the server automatically places an order with the manufacturer. The information transmitted includes the specifications selected by the user and the planned production date.

[0459] Step 6:

[0460] As production progresses, the server receives progress information from the manufacturer. Based on this, an AI model generates notifications for the user, and these notifications are output to the device in an emotionally sensitive manner.

[0461] Step 7:

[0462] After the product is completed, the server takes the user's convenience into account, proposes the optimal delivery date and time, and notifies the user's device. The user can then review the proposed schedule and finalize the delivery details by providing feedback.

[0463] 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.

[0464] 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.

[0465] 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.

[0466] [Third Embodiment]

[0467] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0468] 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.

[0469] 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).

[0470] 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.

[0471] 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.

[0472] 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).

[0473] 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.

[0474] 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.

[0475] 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.

[0476] 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.

[0477] 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.

[0478] 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".

[0479] The system for implementing this invention is constructed using a program deployed in a digital environment. Its core function is to utilize design images created by the user using an image generation device and to efficiently carry out the actual furniture manufacturing based on those images.

[0480] The user first creates a design image using an image generation device and uploads that image to the system via a terminal. The server processes the received image using AI analysis technology and extracts specification information from the image. This specification information includes details such as the shape, size, and material of the furniture.

[0481] After the specification information is extracted, the server automatically requests quotes from multiple partner manufacturers. These quote requests include the extracted specification information, enabling manufacturers to create accurate quotes. The quotes submitted by the manufacturers are received by the server. The server analyzes the multiple quotes and compares the proposals. It determines the best option by considering factors such as price, delivery time, and production capacity.

[0482] The comparison results are displayed on the user's terminal, and the user can select from the displayed quotation options. Based on the user's selection, the server places an order for the confirmed production request with the appropriate manufacturer.

[0483] Once an order is confirmed, the server continuously tracks the production progress and notifies the user of this information in real time. When the product is completed and ready for delivery, the server sends a final notification to the user and coordinates with the logistics company to arrange delivery.

[0484] (Specific example)

[0485] For example, suppose a user designs a new bookshelf. The user completes the design using an image generation device and sends it to the system. This design includes details such as the number of shelves, their height, and the color of the wood to be used.

[0486] The server then extracts the necessary information from the design and sends quote requests to multiple manufacturers. The received quotes include production costs and timelines, which the server compares and displays the best option on the interface for the user to choose from.

[0487] After the user selects the option they deem most suitable, the server notifies the manufacturer to begin production. Meanwhile, the user can track the completion date of the shelves through progress information provided by the system, and the finished shelves are eventually delivered to the specified address.

[0488] In this way, this system allows users to proceed smoothly from the design stage to delivery, automating the conversion from design to physical product and significantly reducing the user's workload.

[0489] The following describes the processing flow.

[0490] Step 1:

[0491] The user creates their ideal furniture design using an image generation device. They then upload this design image to the system via a terminal.

[0492] Step 2:

[0493] The server receives the uploaded design image and processes it using advanced analysis techniques. Here, it extracts furniture specification information (e.g., shape, size, material, etc.).

[0494] Step 3:

[0495] The server automatically creates a request for quotation based on the extracted specification information and sends it to multiple partner manufacturers. This request includes specific design details.

[0496] Step 4:

[0497] Quotation information is returned to the server from each manufacturer. The server organizes the received quotations and performs a comparative analysis based on factors such as price, delivery time, and quality.

[0498] Step 5:

[0499] The most suitable quote options are displayed for the device. The user browses the multiple options presented through the interface and selects the most appropriate one.

[0500] Step 6:

[0501] Based on the quote selected by the user, the server places a formal order with the appropriate manufacturer. The order includes all necessary details.

[0502] Step 7:

[0503] The server constantly monitors the production progress and notifies the user's terminal of any status updates received from the manufacturer.

[0504] Step 8:

[0505] After the server confirms the product is complete, it arranges for delivery. The terminal is notified of the delivery details along with the final estimated delivery date.

[0506] This series of steps allows users to efficiently complete the process from initial design to receiving the final product.

[0507] (Example 1)

[0508] 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."

[0509] Traditional manufacturing processes required complex procedures and considerable time to actually produce structures designed by users. This made it difficult to smoothly manage the entire process from design to production and delivery, resulting in significant time and effort. Furthermore, quickly comparing quotes from multiple manufacturers and making the optimal choice was also challenging.

[0510] 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.

[0511] In this invention, the server includes means for analyzing configuration data and extracting technical information, means for requesting quotes from multiple manufacturers, and means for presenting the quote results to the user and allowing them to select a production request. This enables the user to efficiently manage a consistent process from design to production and delivery, and to quickly and easily select the optimal option.

[0512] A "data processing device" is a computer system that has the functions of receiving, analyzing, and converting digital data.

[0513] "Configuration data" refers to digital data that includes design information and is used to represent the shape and characteristics of a structure.

[0514] "Technical information" refers to the specifications and detailed information necessary for the fabrication of a structure, including size, materials, and design intent.

[0515] A "manufacturer" refers to an organization or company that has the capability to manufacture and provide structures.

[0516] A "template" is something that provides a basic format or style tailored to a specific purpose or content.

[0517] "Monitoring" refers to the activity of continuously observing a specific process or state and recording or reporting any changes.

[0518] "User" refers to an individual or legal entity that uses this system to manage the design production process.

[0519] This invention provides a system for efficiently manufacturing user-designed structures. The user begins by using design software on a data processing device to generate digital configuration data. A typical example of such software is a general-purpose design program. This design data is then uploaded to the system via the user's terminal.

[0520] The server analyzes the received configuration data using an AI model. This AI model is built on a platform such as TensorFlow or PyTorch. Through this analysis, the server extracts technical information about the structure, including details about its size, shape, and materials.

[0521] Based on the extracted technical information, the server requests quotes from multiple manufacturers. During this process, the server directly accesses the manufacturers' systems via APIs and transmits data electronically. The server automatically compares the received quotes and displays the results on the user's terminal. The user interface is dynamically generated using HTML and JavaScript.

[0522] A concrete example would be a user designing new furniture and using a prompt that reads, "Generate a design image of the furniture based on the following specifications: height 180cm, width 80cm, wood color walnut brown, 5-tier bookshelf." This allows the user to proceed seamlessly from the design stage to manufacturing and delivery.

[0523] This system allows users to easily manage complex production processes and make optimal choices quickly. This streamlines the entire process from design to production and delivery, significantly reducing the user's time and effort.

[0524] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0525] Step 1:

[0526] The user creates configuration data using design software. The user inputs design specifications, and the software generates design image data based on those specifications. Specifically, the user builds the design using drag-and-drop and toolbar selection functions, and finally saves it to the device as an image file.

[0527] Step 2:

[0528] The terminal uploads the generated design image to the server. The input is the user providing an image file they created, initiating the process of sending it to the server. The output is the server receiving this image data. The process is completed when the user clicks the file upload button and selects the image file.

[0529] Step 3:

[0530] The server analyzes the received design images using an AI model. The input is the uploaded image data, and the output is the technical information extracted through the analysis. Specifically, the server inputs data into the model to identify technical elements such as material, size, and shape from the image, and performs a process of extracting features.

[0531] Step 4:

[0532] The server sends quotation requests to multiple manufacturers based on the extracted technical information. The input is technical information, and the output is quotation request messages to each manufacturer. The server connects to the manufacturers' systems using an API and automatically sends the necessary data.

[0533] Step 5:

[0534] The server receives quotation information from manufacturers and compares and analyzes it. The input is quotation data from each manufacturer, and the output is the best quotation option after comparison. Specifically, the server utilizes a database and algorithms to analyze and compare factors such as price and delivery time.

[0535] Step 6:

[0536] The server presents the most suitable quotation options for the user's device. Input is optimized quotation information, and output is information displayed on the user interface. The server dynamically constructs the screen using HTML and JavaScript, providing information in a visually appealing format.

[0537] Step 7:

[0538] The user selects the best option from the presented quotation options. The input is the option selected by the user, and the output is the result of the selection sent to the server. The user completes their selection by clicking buttons or radio buttons on the interface.

[0539] Step 8:

[0540] The server places a formal production order with the appropriate manufacturer based on the user's selection. The input is the user's selection, and the output is the production order message. After confirming the selection, the server sends the order data to the manufacturer using an API.

[0541] Step 9:

[0542] The server monitors production progress in real time and notifies the user. Input is progress information from the manufacturer, and output is a notification to the user's terminal. The server monitors periodic data updates from the manufacturer and automatically reflects the progress status in the user's UI.

[0543] Step 10:

[0544] The server sends a final notification to the user when the product is completed and ready for shipment, and then arranges delivery. The input is product completion information, and the output is a notification to the user and delivery instructions to the logistics company. The server works in conjunction with the logistics system to optimize delivery dates and routes, ensuring rapid shipment.

[0545] (Application Example 1)

[0546] 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."

[0547] To easily produce and quickly begin using furniture designed by users, it is necessary to streamline the entire process from design to production and delivery. However, traditional methods involve complex procedures such as users communicating furniture designs to manufacturers, obtaining quotes, and monitoring progress, which are time-consuming and laborious. Furthermore, the inability to visualize the final product beforehand could potentially lead to decreased user satisfaction.

[0548] 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.

[0549] In this invention, the server includes means for receiving design images generated by an image generation device, means for analyzing the design images to extract furniture specification information, means for providing a user interface including augmented reality technology that allows visual confirmation of the furniture design, means for receiving and comparing quote information from multiple manufacturers, means for placing orders for selected production requests with the relevant manufacturers, and means for providing quote options including a delivery schedule. This makes it possible for users to easily incorporate their designs into production, check the progress in real time, and have the finished products delivered smoothly.

[0550] An "image generation device" is a digital tool used by users to create design images, and it provides the foundation for later analysis of those designs.

[0551] "Design images" are image data created by users using image generation devices that show the shape and specifications of furniture.

[0552] "Specification information" refers to detailed information about the furniture, such as its shape, size, and materials, extracted from the design image.

[0553] A "manufacturer" is a company or organization that has the ability to produce furniture based on the specifications it receives.

[0554] "Quotation information" refers to the proposal provided by the manufacturer, which includes the cost of production, delivery date, and other conditions for the manufactured product.

[0555] Augmented reality technology is a technique that visualizes designed furniture by overlaying it onto real-world space, allowing users to check its actual size and placement.

[0556] A "user interface" is the operating screen or input receiving platform that a user uses to interact with a system.

[0557] "Real-time notification" is a system function that immediately informs users of information as it occurs.

[0558] A "delivery schedule" is information that shows the planned time frame until the finished furniture is delivered to the user.

[0559] The system for implementing this invention involves the coordinated operation of a user, a server, and a client terminal. First, the user creates a design image of furniture using an image generation device and uploads it to the server from the client terminal. This design image includes specifications such as the shape, size, and material of the furniture.

[0560] The server processes the received design images using AI analysis technology to extract specification information. This process utilizes image analysis services such as "Cloud Vision API." Based on the extracted specification information, the server automatically requests quotes from multiple manufacturers. A notification service such as AWS's "SNS (Simple Notification Service)" is used for the quote requests.

[0561] When manufacturers send quotation information to the server, the server analyzes it and provides the user with the best option, taking into account factors such as cost and delivery time. The user reviews the presented quotation through their client terminal and selects the option to request production.

[0562] Based on the user's selection, the server places an order with the appropriate manufacturer, and production begins. The server then tracks the production progress and notifies the user in real time. Google Firebase, a real-time database, may be used for tracking.

[0563] Once the finished product is ready at the specified time, the server checks the delivery schedule and coordinates with the logistics company to deliver it to the user. This allows the user to track the entire process from start to finish until the furniture is completed.

[0564] For example, if a user designs a desk perfectly suited to their specifications and orders it through the app, the desk can be completed on the desired date and delivered to their home without any problems. This significantly reduces the user's effort, providing a comfortable purchasing experience.

[0565] Specific examples and prompt statements are as follows:

[0566] Users create new designs using this smartphone app and upload them to an AI model. The AI ​​automatically extracts furniture specifications from the design, finds the best manufacturer, and tracks the production status in real time. Please explain in detail how to streamline the process from online design to production completion.

[0567] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0568] Step 1:

[0569] The user creates a furniture design image using an image generation device and uploads it to the server from their terminal. The input is the design image created by the user, and the output is the completion of the image file upload to the server. At this stage, the system verifies that the design image is in the correct format.

[0570] Step 2:

[0571] The server analyzes the received design images using AI analysis technology and extracts specification information. The input is the uploaded design image, and the output is the extracted specification information of the furniture's shape, size, and material. In this process, an AI tool is used to convert the visual data in the image into text data.

[0572] Step 3:

[0573] The server automatically requests quotes from multiple manufacturers based on the extracted specification information. The input is the specification information, and the output is the status after the quote requests have been sent to the manufacturers. Notifications are sent using AWS's "SNS (Simple Notification Service)".

[0574] Step 4:

[0575] When manufacturers send quotation information to the server, the server performs a comparative analysis based on price and delivery time. The input is the quotation information from the manufacturers, and the output is a list of the best quotation options to present to the user. Database queries are used to organize the information.

[0576] Step 5:

[0577] The user reviews the quote results via their terminal and selects a production request. The input consists of quote options sent from the server, and the output is the production request selected by the user. The selection process takes place on the interface.

[0578] Step 6:

[0579] The server places a production order with the appropriate manufacturer based on the user's selection. The input is the user's production request selection, and the output is the status after the production order notification has been sent to the manufacturer.

[0580] Step 7:

[0581] The server tracks production progress and notifies the user in real time. Input is progress information from the manufacturer, and output is progress notifications to the user. Google Firebase is used to perform push notifications of real-time data.

[0582] Step 8:

[0583] Once the product is completed, the server coordinates with the logistics provider to confirm the delivery schedule. The input is the product completion information, and the output is the completion of the delivery arrangement. Delivery information is managed through a logistics API.

[0584] Step 9:

[0585] The user checks the delivery status via a terminal and receives the furniture. The input is delivery tracking information, and the output is a confirmation of furniture receipt. This completes the entire process.

[0586] 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.

[0587] This embodiment of the invention is a furniture manufacturing support system that understands the user's emotions and provides services optimized to them. Based on design images created by an image generation device, this system not only handles furniture manufacturing requests, quotations, and orders, but also identifies the user's emotions and optimizes communication at each step to suit the user.

[0588] When a user generates a design image and uploads it to the system via their device, the server processes the image and extracts the necessary furniture specification information. During this process, the system analyzes the user's emotions using an emotion engine based on their facial expressions and voice input to understand their current feelings.

[0589] For example, if a user is feeling stressed, the server will use that emotional information to select and suggest more relaxing colors and designs when presenting quotation options. Similarly, if detailed technical specifications need to be reviewed, the server will use a relaxed tone and a visually easy-to-understand interface for explanations.

[0590] As the user reviews and selects from the presented quote options, the emotion engine evaluates the user's feedback in real time, providing background support to ensure a smooth selection process. Once the selection is confirmed, the server automatically confirms the production request and places the order with the relevant manufacturer.

[0591] While production is underway, the server manages the progress and sends regular notifications to the user. These notifications are adjusted in terms of content and frequency to be considerate of the user's feelings and reduce the stress of waiting during the production period. Once the product is completed and ready for delivery, a notification is sent to the device, and a delivery date and time are arranged to suit the user's convenience.

[0592] (Specific example)

[0593] Let's consider a scenario where a user designs a new living room table. The user uploads their design image to the system, and the server analyzes the image while using an emotion engine to determine the user's emotions. If the user is feeling cheerful, the system suggests bright-colored wood and positive designs in a friendly manner.

[0594] When the emotion engine detects surprise or anxiety from the user during the selection process, the server provides additional information and past performance data to support the user in making a decision. In this way, flexible responses that are attentive to the user's emotions are provided, enabling the process to be completed with high satisfaction.

[0595] This system allows users to not only place orders but also enjoy a personalized customer experience.

[0596] The following describes the processing flow.

[0597] Step 1:

[0598] Users create their ideal furniture designs using an image generation device and upload the design images to the system via a terminal. A user interface is provided to prompt users to upload their designs.

[0599] Step 2:

[0600] The device uses the user's camera and microphone input to capture the user's facial expressions and voice. This data is used to determine the user's current emotions.

[0601] Step 3:

[0602] The server analyzes the received design image and extracts furniture specification information (e.g., material, size, shape). In parallel, the emotion engine analyzes the user's emotions based on emotion data sent from the terminal.

[0603] Step 4:

[0604] The server takes into account the extracted specification information and the emotional state obtained from the emotion engine, sends quotation requests to multiple manufacturers, and automatically dispatches them in a templated format. The quotation content also incorporates the user's preferred style and color scheme.

[0605] Step 5:

[0606] Once each manufacturer submits a quote, the server receives them and compares and analyzes the price, delivery time, and other factors. Based on information from the emotion engine, it presents options in a way that reduces user stress.

[0607] Step 6:

[0608] The device displays quotation options that it deems most suitable for the user. Based on the user's current sentiment, it adjusts how information is received and displayed to facilitate selection.

[0609] Step 7:

[0610] The server confirms the manufacturing request selected by the user and sends the order data to the chosen manufacturer. The order details include specific specifications and preferences identified by the user.

[0611] Step 8:

[0612] Once production begins, the server periodically retrieves production progress and notifies the user's device of the progress based on a notification schedule that takes into account the user's emotional state. The tone and frequency of notifications are adjusted according to the user's emotions to maintain their interest.

[0613] Step 9:

[0614] Once the product is complete and ready for delivery, the server notifies the terminal of the delivery date and allows for flexible scheduling to suit the user's convenience. The emotion engine completes the entire process by providing an approach to optimize the user's emotions upon receiving the product.

[0615] (Example 2)

[0616] 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."

[0617] Conventional furniture manufacturing systems lack consideration for user emotions in their suggestions and selection support, resulting in a uniform user experience and difficulty in providing flexible services tailored to individual needs. Furthermore, there is a lack of methods to alleviate user anxiety and stress during the estimation and manufacturing processes. Therefore, this invention aims to improve user satisfaction by analyzing user emotions in real time and providing optimal suggestions and support accordingly.

[0618] 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.

[0619] In this invention, the server includes means for analyzing the user's facial expressions and voice input using an emotion analysis function to determine the user's emotions, means for requesting quotation proposals from multiple manufacturers based on the emotion analysis results and extracted furniture specification information, and means for evaluating the emotional feedback the user makes in real time and providing selection support. This enables flexible quotation proposals and selection support that are tailored to the user's emotions, and is expected to improve the user experience.

[0620] An "image generation device" is a device that generates visual information in digital format and is used in design and planning.

[0621] "Design drawings" are visual information that shows the design of a product or structure, providing guidelines for its dimensions, shape, and materials.

[0622] "Specifications" refers to information describing the technical details required for a product or service, including structure, materials, and function.

[0623] "Emotion analysis function" is a technology that recognizes and analyzes a user's emotional state using data such as facial expressions and voice.

[0624] A "quote proposal" is a plan that specifically outlines the costs and conditions for manufacturing a product, and is provided based on specific specifications.

[0625] "Selection support" refers to the act of providing support by presenting information and making suggestions to help users make the best decisions.

[0626] "Production progress" refers to the state indicating the extent to which the manufacturing process of a product or service is complete.

[0627] "Notification" is the act of communicating information in order to inform relevant parties about a certain matter.

[0628] This invention is a system that proposes the optimal furniture production while taking the user's emotions into consideration, and its embodiments are described below.

[0629] Hardware and software configuration

[0630] This system includes terminals used by users to create, save, and upload design images, and a server for data processing. The terminals are equipped with image generation software (e.g., CAD software or design applications), allowing users to save their created designs in JPEG or PNG format and upload them to the system. The server uses image analysis software (e.g., OpenCV or TensorFlow) and emotion analysis software (e.g., Amazon Rekognition or Microsoft Azure Emotion API) to extract image specification information and determine the user's emotional state.

[0631] Data processing and calculation

[0632] The server processes uploaded design images using image analysis software. Specifically, it processes the color information of each pixel and maps information about the furniture's shape, dimensions, and materials to a database. The emotion analysis software takes the user's facial expressions and voice data as input and analyzes them to quantify the user's emotional state. This analysis result is input into the recommendation engine within the system and used to generate the most suitable estimate proposal for the user. The generation AI model (e.g., GPT or DALL-E) generates optimized design proposals based on emotion evaluations and prompts that take into account specification information.

[0633] Specific example

[0634] For example, if a user generates a design image for a living room table, they upload that image to the server via their device. The server analyzes the image and simultaneously determines if the user is feeling cheerful based on their real-time facial expressions. Based on this information, the server uses an AI model to create quotation options, including bright colors and friendly designs, and presents them to the user. The user reviews these and makes their final selection. An example of a prompt used in this process is, "I've designed a coffee table for my living room. Please extract specifications from this image and provide relaxing suggestions based on my mood."

[0635] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0636] Step 1:

[0637] The user generates a design image and uploads it to the system via a terminal. The input is a design image in JPEG or PNG format created by the user. Using the terminal's application software, the user sends this image in a format the system can receive. The output is the state in which the server has received that image data.

[0638] Step 2:

[0639] The server processes the received design image using image analysis software. The input is the design image uploaded to the server in step 1. The server performs object detection and feature analysis based on pixel information to extract specification information regarding the shape, material, and dimensions of the furniture from this image. The output is the analyzed furniture specification information.

[0640] Step 3:

[0641] To analyze user emotions, the server uses emotion analysis software. The input is real-time facial expression or voice data from the user. Based on this data, the server uses facial recognition algorithms or voice analysis algorithms to evaluate the user's emotions. The output is a numerical representation of the user's emotional state.

[0642] Step 4:

[0643] The server generates quotation proposals using a generative AI model. The inputs are furniture specification information obtained as a result of the analysis in step 2 and user emotion data quantified in step 3. The server inputs this data as prompts into the generative AI model, which automatically generates color and design suggestions tailored to the user's emotions. The output is an optimized quotation proposal presented to the user.

[0644] Step 5:

[0645] The user reviews the presented quote proposal and makes a selection. The input is the quote proposal received in step 4. The user makes a selection using a terminal and sends feedback to the server. The server analyzes this feedback in real time and provides additional selection support information as needed. The output is the user's confirmed selection data.

[0646] Step 6:

[0647] After the user confirms their selection, the server automatically places an order with the manufacturer. The input is the user's selection data confirmed in step 5. Based on this, the server uses a communication protocol with the manufacturer to initiate the ordering process. The output is the order instruction sent to the manufacturer.

[0648] Step 7:

[0649] The server manages production progress and provides notifications tailored to the user's emotional state. Inputs include production progress data obtained from the manufacturer and the user's physical stress level. Based on this, the server adjusts the content and frequency of notifications and sends the information to the user via their device. Output is the adjusted notification message.

[0650] (Application Example 2)

[0651] 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."

[0652] Traditional furniture manufacturing processes often involved mechanical design proposals and price quotes without considering user emotions, resulting in a limited user experience and insufficient support, particularly when stress or anxiety arose. A system is needed that understands individual user emotions and provides services optimized based on those emotions.

[0653] 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.

[0654] In this invention, the server includes means for identifying the user's emotions and optimizing design proposals and estimates to those emotions, means for evaluating user feedback in real time and supporting selections, and means for notifying the user of production progress with consideration for their emotions. This enables flexible responses that are attentive to the user's emotions and makes it possible to provide a highly satisfying, customized user experience.

[0655] An "image generation device" is a device that generates visual representations of furniture and other objects designed by the user.

[0656] A "design image" is an image that visually represents the specific design of the furniture and includes specifications for its production.

[0657] "Furniture specification information" refers to information necessary for production, such as materials, size, and shape, extracted from design images.

[0658] A "manufacturer" is a company or organization that actually produces furniture.

[0659] "Quotation information" refers to cost information necessary for production, provided by the manufacturer, and includes proposals that the user can choose from.

[0660] "Means of notifying users" refers to communication methods for providing users with real-time information on production progress and other related matters.

[0661] "Means for identifying user emotions" refers to technologies that analyze a user's emotional state from data such as facial expressions and voice.

[0662] "Methods for evaluating feedback in real time" refer to technologies that instantly analyze user responses and choices to optimize service content.

[0663] "Means of supporting choice" refer to technologies that provide additional information or organize options to help users make the best decision.

[0664] In an embodiment of the present invention, the system realizes a furniture manufacturing support system that understands the user's emotions and provides services accordingly. The server receives a design image created by the user using an image generation device and extracts furniture specification information using image analysis technology. In this case, the specification information includes materials, size, and shape.

[0665] The server requests quotes from multiple manufacturers and compares and analyzes the received quote information. During this process, it analyzes the user's facial expression and voice data using emotion analysis APIs (e.g., Amazon Rekognition or Google Cloud Vision API) to provide design suggestions and quotes tailored to the user's emotions.

[0666] User feedback is evaluated by the system in real time, and guidance and options are adjusted as needed. Furthermore, production progress information is automatically obtained from the manufacturer and communicated in a format that takes into account the user's emotional state. This allows users to proceed through the process with less stress.

[0667] For example, if a user designs a new chair, the system analyzes the user's positive emotions and suggests a colorful and playful design. If the system detects feelings of hesitation, it presents samples of popular designs from the past to reassure the user.

[0668] An example of a prompt sentence to input into a generative AI model is, "When a user designs new furniture, provide the best suggestions based on sentiment data and past popular designs."

[0669] Through this system, users can enjoy a personalized customer experience.

[0670] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0671] Step 1:

[0672] The server receives design images from the terminal as input. Using an image processing algorithm, it extracts furniture specification information such as material, size, and shape from the image. This outputs the information necessary for specific production.

[0673] Step 2:

[0674] The server requests quotes from multiple manufacturers based on the extracted furniture specifications. At this time, the server uses a generative AI model to create a template and outputs a quote request with the specifications added.

[0675] Step 3:

[0676] Manufacturers send quotation information back to the server. The server receives this as input and compares and analyzes multiple quotation pieces. Using data analysis algorithms, it evaluates price and delivery time and outputs the optimal proposal.

[0677] Step 4:

[0678] The server receives user feedback as input and uses an emotion analysis API to determine the user's emotions. Based on these emotions, the server optimizes the presentation of estimate information and adjusts the design options before outputting the results.

[0679] Step 5:

[0680] After the user selects a production request, the server automatically places an order with the manufacturer. The information transmitted includes the specifications selected by the user and the planned production date.

[0681] Step 6:

[0682] As production progresses, the server receives progress information from the manufacturer. Based on this, an AI model generates notifications for the user, and these notifications are output to the device in an emotionally sensitive manner.

[0683] Step 7:

[0684] After the product is completed, the server takes the user's convenience into account, proposes the optimal delivery date and time, and notifies the user's device. The user can then review the proposed schedule and finalize the delivery details by providing feedback.

[0685] 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.

[0686] 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.

[0687] 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.

[0688] [Fourth Embodiment]

[0689] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0690] 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.

[0691] 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).

[0692] 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.

[0693] 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.

[0694] 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).

[0695] 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.

[0696] 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.

[0697] 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.

[0698] 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.

[0699] 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.

[0700] 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.

[0701] 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".

[0702] The system for implementing this invention is constructed using a program deployed in a digital environment. Its core function is to utilize design images created by the user using an image generation device and to efficiently carry out the actual furniture manufacturing based on those images.

[0703] The user first creates a design image using an image generation device and uploads that image to the system via a terminal. The server processes the received image using AI analysis technology and extracts specification information from the image. This specification information includes details such as the shape, size, and material of the furniture.

[0704] After the specification information is extracted, the server automatically requests quotes from multiple partner manufacturers. These quote requests include the extracted specification information, enabling manufacturers to create accurate quotes. The quotes submitted by the manufacturers are received by the server. The server analyzes the multiple quotes and compares the proposals. It determines the best option by considering factors such as price, delivery time, and production capacity.

[0705] The comparison results are displayed on the user's terminal, and the user can select from the displayed quotation options. Based on the user's selection, the server places an order for the confirmed production request with the appropriate manufacturer.

[0706] Once an order is confirmed, the server continuously tracks the production progress and notifies the user of this information in real time. When the product is completed and ready for delivery, the server sends a final notification to the user and coordinates with the logistics company to arrange delivery.

[0707] (Specific example)

[0708] For example, suppose a user designs a new bookshelf. The user completes the design using an image generation device and sends it to the system. This design includes details such as the number of shelves, their height, and the color of the wood to be used.

[0709] The server then extracts the necessary information from the design and sends quote requests to multiple manufacturers. The received quotes include production costs and timelines, which the server compares and displays the best option on the interface for the user to choose from.

[0710] After the user selects the option they deem most suitable, the server notifies the manufacturer to begin production. Meanwhile, the user can track the completion date of the shelves through progress information provided by the system, and the finished shelves are eventually delivered to the specified address.

[0711] In this way, this system allows users to proceed smoothly from the design stage to delivery, automating the conversion from design to physical product and significantly reducing the user's workload.

[0712] The following describes the processing flow.

[0713] Step 1:

[0714] The user creates their ideal furniture design using an image generation device. They then upload this design image to the system via a terminal.

[0715] Step 2:

[0716] The server receives the uploaded design image and processes it using advanced analysis techniques. Here, it extracts furniture specification information (e.g., shape, size, material, etc.).

[0717] Step 3:

[0718] The server automatically creates a request for quotation based on the extracted specification information and sends it to multiple partner manufacturers. This request includes specific design details.

[0719] Step 4:

[0720] Quotation information is returned to the server from each manufacturer. The server organizes the received quotations and performs a comparative analysis based on factors such as price, delivery time, and quality.

[0721] Step 5:

[0722] The most suitable quote options are displayed for the device. The user browses the multiple options presented through the interface and selects the most appropriate one.

[0723] Step 6:

[0724] Based on the quote selected by the user, the server places a formal order with the appropriate manufacturer. The order includes all necessary details.

[0725] Step 7:

[0726] The server constantly monitors the production progress and notifies the user's terminal of any status updates received from the manufacturer.

[0727] Step 8:

[0728] After the server confirms the product is complete, it arranges for delivery. The terminal is notified of the delivery details along with the final estimated delivery date.

[0729] This series of steps allows users to efficiently complete the process from initial design to receiving the final product.

[0730] (Example 1)

[0731] 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".

[0732] Traditional manufacturing processes required complex procedures and considerable time to actually produce structures designed by users. This made it difficult to smoothly manage the entire process from design to production and delivery, resulting in significant time and effort. Furthermore, quickly comparing quotes from multiple manufacturers and making the optimal choice was also challenging.

[0733] 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.

[0734] In this invention, the server includes means for analyzing configuration data and extracting technical information, means for requesting quotes from multiple manufacturers, and means for presenting the quote results to the user and allowing them to select a production request. This enables the user to efficiently manage a consistent process from design to production and delivery, and to quickly and easily select the optimal option.

[0735] A "data processing device" is a computer system that has the functions of receiving, analyzing, and converting digital data.

[0736] "Configuration data" refers to digital data that includes design information and is used to represent the shape and characteristics of a structure.

[0737] "Technical information" refers to the specifications and detailed information necessary for the fabrication of a structure, including size, materials, and design intent.

[0738] A "manufacturer" refers to an organization or company that has the capability to manufacture and provide structures.

[0739] A "template" is something that provides a basic format or style tailored to a specific purpose or content.

[0740] "Monitoring" refers to the activity of continuously observing a specific process or state and recording or reporting any changes.

[0741] "User" refers to an individual or legal entity that uses this system to manage the design production process.

[0742] This invention provides a system for efficiently manufacturing user-designed structures. The user begins by using design software on a data processing device to generate digital configuration data. A typical example of such software is a general-purpose design program. This design data is then uploaded to the system via the user's terminal.

[0743] The server analyzes the received configuration data using an AI model. This AI model is built on a platform such as TensorFlow or PyTorch. Through this analysis, the server extracts technical information about the structure, including details about its size, shape, and materials.

[0744] Based on the extracted technical information, the server requests quotes from multiple manufacturers. During this process, the server directly accesses the manufacturers' systems via APIs and transmits data electronically. The server automatically compares the received quotes and displays the results on the user's terminal. The user interface is dynamically generated using HTML and JavaScript.

[0745] A concrete example would be a user designing new furniture and using a prompt that reads, "Generate a design image of the furniture based on the following specifications: height 180cm, width 80cm, wood color walnut brown, 5-tier bookshelf." This allows the user to proceed seamlessly from the design stage to manufacturing and delivery.

[0746] This system allows users to easily manage complex production processes and make optimal choices quickly. This streamlines the entire process from design to production and delivery, significantly reducing the user's time and effort.

[0747] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0748] Step 1:

[0749] The user creates configuration data using design software. The user inputs design specifications, and the software generates design image data based on those specifications. Specifically, the user builds the design using drag-and-drop and toolbar selection functions, and finally saves it to the device as an image file.

[0750] Step 2:

[0751] The terminal uploads the generated design image to the server. The input is the user providing an image file they created, initiating the process of sending it to the server. The output is the server receiving this image data. The process is completed when the user clicks the file upload button and selects the image file.

[0752] Step 3:

[0753] The server analyzes the received design images using an AI model. The input is the uploaded image data, and the output is the technical information extracted through the analysis. Specifically, the server inputs data into the model to identify technical elements such as material, size, and shape from the image, and performs a process of extracting features.

[0754] Step 4:

[0755] The server sends quotation requests to multiple manufacturers based on the extracted technical information. The input is technical information, and the output is quotation request messages to each manufacturer. The server connects to the manufacturers' systems using an API and automatically sends the necessary data.

[0756] Step 5:

[0757] The server receives quotation information from manufacturers and compares and analyzes it. The input is quotation data from each manufacturer, and the output is the best quotation option after comparison. Specifically, the server utilizes a database and algorithms to analyze and compare factors such as price and delivery time.

[0758] Step 6:

[0759] The server presents the most suitable quotation options for the user's device. Input is optimized quotation information, and output is information displayed on the user interface. The server dynamically constructs the screen using HTML and JavaScript, providing information in a visually appealing format.

[0760] Step 7:

[0761] The user selects the best option from the presented quotation options. The input is the option selected by the user, and the output is the result of the selection sent to the server. The user completes their selection by clicking buttons or radio buttons on the interface.

[0762] Step 8:

[0763] The server places a formal production order with the appropriate manufacturer based on the user's selection. The input is the user's selection, and the output is the production order message. After confirming the selection, the server sends the order data to the manufacturer using an API.

[0764] Step 9:

[0765] The server monitors production progress in real time and notifies the user. Input is progress information from the manufacturer, and output is a notification to the user's terminal. The server monitors periodic data updates from the manufacturer and automatically reflects the progress status in the user's UI.

[0766] Step 10:

[0767] The server sends a final notification to the user when the product is completed and ready for shipment, and then arranges delivery. The input is product completion information, and the output is a notification to the user and delivery instructions to the logistics company. The server works in conjunction with the logistics system to optimize delivery dates and routes, ensuring rapid shipment.

[0768] (Application Example 1)

[0769] 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".

[0770] To easily produce and quickly begin using furniture designed by users, it is necessary to streamline the entire process from design to production and delivery. However, traditional methods involve complex procedures such as users communicating furniture designs to manufacturers, obtaining quotes, and monitoring progress, which are time-consuming and laborious. Furthermore, the inability to visualize the final product beforehand could potentially lead to decreased user satisfaction.

[0771] 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.

[0772] In this invention, the server includes means for receiving design images generated by an image generation device, means for analyzing the design images to extract furniture specification information, means for providing a user interface including augmented reality technology that allows visual confirmation of the furniture design, means for receiving and comparing quote information from multiple manufacturers, means for placing orders for selected production requests with the relevant manufacturers, and means for providing quote options including a delivery schedule. This makes it possible for users to easily incorporate their designs into production, check the progress in real time, and have the finished products delivered smoothly.

[0773] An "image generation device" is a digital tool used by users to create design images, and it provides the foundation for later analysis of those designs.

[0774] "Design images" are image data created by users using image generation devices that show the shape and specifications of furniture.

[0775] "Specification information" refers to detailed information about the furniture, such as its shape, size, and materials, extracted from the design image.

[0776] A "manufacturer" is a company or organization that has the ability to produce furniture based on the specifications it receives.

[0777] "Quotation information" refers to the proposal provided by the manufacturer, which includes the cost of production, delivery date, and other conditions for the manufactured product.

[0778] Augmented reality technology is a technique that visualizes designed furniture by overlaying it onto real-world space, allowing users to check its actual size and placement.

[0779] A "user interface" is the operating screen or input receiving platform that a user uses to interact with a system.

[0780] "Real-time notification" is a system function that immediately informs users of information as it occurs.

[0781] A "delivery schedule" is information that shows the planned time frame until the finished furniture is delivered to the user.

[0782] The system for implementing this invention involves the coordinated operation of a user, a server, and a client terminal. First, the user creates a design image of furniture using an image generation device and uploads it to the server from the client terminal. This design image includes specifications such as the shape, size, and material of the furniture.

[0783] The server processes the received design images using AI analysis technology to extract specification information. This process utilizes image analysis services such as "Cloud Vision API." Based on the extracted specification information, the server automatically requests quotes from multiple manufacturers. A notification service such as AWS's "SNS (Simple Notification Service)" is used for the quote requests.

[0784] When manufacturers send quotation information to the server, the server analyzes it and provides the user with the best option, taking into account factors such as cost and delivery time. The user reviews the presented quotation through their client terminal and selects the option to request production.

[0785] Based on the user's selection, the server places an order with the appropriate manufacturer, and production begins. The server then tracks the production progress and notifies the user in real time. Google Firebase, a real-time database, may be used for tracking.

[0786] Once the finished product is ready at the specified time, the server checks the delivery schedule and coordinates with the logistics company to deliver it to the user. This allows the user to track the entire process from start to finish until the furniture is completed.

[0787] For example, if a user designs a desk perfectly suited to their specifications and orders it through the app, the desk can be completed on the desired date and delivered to their home without any problems. This significantly reduces the user's effort, providing a comfortable purchasing experience.

[0788] Specific examples and prompt statements are as follows:

[0789] Users create new designs using this smartphone app and upload them to an AI model. The AI ​​automatically extracts furniture specifications from the design, finds the best manufacturer, and tracks the production status in real time. Please explain in detail how to streamline the process from online design to production completion.

[0790] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0791] Step 1:

[0792] The user creates a furniture design image using an image generation device and uploads it to the server from their terminal. The input is the design image created by the user, and the output is the completion of the image file upload to the server. At this stage, the system verifies that the design image is in the correct format.

[0793] Step 2:

[0794] The server analyzes the received design images using AI analysis technology and extracts specification information. The input is the uploaded design image, and the output is the extracted specification information of the furniture's shape, size, and material. In this process, an AI tool is used to convert the visual data in the image into text data.

[0795] Step 3:

[0796] The server automatically requests quotes from multiple manufacturers based on the extracted specification information. The input is the specification information, and the output is the status after the quote requests have been sent to the manufacturers. Notifications are sent using AWS's "SNS (Simple Notification Service)".

[0797] Step 4:

[0798] When manufacturers send quotation information to the server, the server performs a comparative analysis based on price and delivery time. The input is the quotation information from the manufacturers, and the output is a list of the best quotation options to present to the user. Database queries are used to organize the information.

[0799] Step 5:

[0800] The user reviews the quote results via their terminal and selects a production request. The input consists of quote options sent from the server, and the output is the production request selected by the user. The selection process takes place on the interface.

[0801] Step 6:

[0802] The server places a production order with the appropriate manufacturer based on the user's selection. The input is the user's production request selection, and the output is the status after the production order notification has been sent to the manufacturer.

[0803] Step 7:

[0804] The server tracks production progress and notifies the user in real time. Input is progress information from the manufacturer, and output is progress notifications to the user. Google Firebase is used to perform push notifications of real-time data.

[0805] Step 8:

[0806] Once the product is completed, the server coordinates with the logistics provider to confirm the delivery schedule. The input is the product completion information, and the output is the completion of the delivery arrangement. Delivery information is managed through a logistics API.

[0807] Step 9:

[0808] The user checks the delivery status via a terminal and receives the furniture. The input is delivery tracking information, and the output is a confirmation of furniture receipt. This completes the entire process.

[0809] 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.

[0810] This embodiment of the invention is a furniture manufacturing support system that understands the user's emotions and provides services optimized to them. Based on design images created by an image generation device, this system not only handles furniture manufacturing requests, quotations, and orders, but also identifies the user's emotions and optimizes communication at each step to suit the user.

[0811] When a user generates a design image and uploads it to the system via their device, the server processes the image and extracts the necessary furniture specification information. During this process, the system analyzes the user's emotions using an emotion engine based on their facial expressions and voice input to understand their current feelings.

[0812] For example, if a user is feeling stressed, the server will use that emotional information to select and suggest more relaxing colors and designs when presenting quotation options. Similarly, if detailed technical specifications need to be reviewed, the server will use a relaxed tone and a visually easy-to-understand interface for explanations.

[0813] As the user reviews and selects from the presented quote options, the emotion engine evaluates the user's feedback in real time, providing background support to ensure a smooth selection process. Once the selection is confirmed, the server automatically confirms the production request and places the order with the relevant manufacturer.

[0814] While production is underway, the server manages the progress and sends regular notifications to the user. These notifications are adjusted in terms of content and frequency to be considerate of the user's feelings and reduce the stress of waiting during the production period. Once the product is completed and ready for delivery, a notification is sent to the device, and a delivery date and time are arranged to suit the user's convenience.

[0815] (Specific example)

[0816] Let's consider a scenario where a user designs a new living room table. The user uploads their design image to the system, and the server analyzes the image while using an emotion engine to determine the user's emotions. If the user is feeling cheerful, the system suggests bright-colored wood and positive designs in a friendly manner.

[0817] When the emotion engine detects surprise or anxiety from the user during the selection process, the server provides additional information and past performance data to support the user in making a decision. In this way, flexible responses that are attentive to the user's emotions are provided, enabling the process to be completed with high satisfaction.

[0818] This system allows users to not only place orders but also enjoy a personalized customer experience.

[0819] The following describes the processing flow.

[0820] Step 1:

[0821] Users create their ideal furniture designs using an image generation device and upload the design images to the system via a terminal. A user interface is provided to prompt users to upload their designs.

[0822] Step 2:

[0823] The device uses the user's camera and microphone input to capture the user's facial expressions and voice. This data is used to determine the user's current emotions.

[0824] Step 3:

[0825] The server analyzes the received design image and extracts furniture specification information (e.g., material, size, shape). In parallel, the emotion engine analyzes the user's emotions based on emotion data sent from the terminal.

[0826] Step 4:

[0827] The server takes into account the extracted specification information and the emotional state obtained from the emotion engine, sends quotation requests to multiple manufacturers, and automatically dispatches them in a templated format. The quotation content also incorporates the user's preferred style and color scheme.

[0828] Step 5:

[0829] Once each manufacturer submits a quote, the server receives them and compares and analyzes the price, delivery time, and other factors. Based on information from the emotion engine, it presents options in a way that reduces user stress.

[0830] Step 6:

[0831] The device displays quotation options that it deems most suitable for the user. Based on the user's current sentiment, it adjusts how information is received and displayed to facilitate selection.

[0832] Step 7:

[0833] The server confirms the manufacturing request selected by the user and sends the order data to the chosen manufacturer. The order details include specific specifications and preferences identified by the user.

[0834] Step 8:

[0835] Once production begins, the server periodically retrieves production progress and notifies the user's device of the progress based on a notification schedule that takes into account the user's emotional state. The tone and frequency of notifications are adjusted according to the user's emotions to maintain their interest.

[0836] Step 9:

[0837] Once the product is complete and ready for delivery, the server notifies the terminal of the delivery date and allows for flexible scheduling to suit the user's convenience. The emotion engine completes the entire process by providing an approach to optimize the user's emotions upon receiving the product.

[0838] (Example 2)

[0839] 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".

[0840] Conventional furniture manufacturing systems lack consideration for user emotions in their suggestions and selection support, resulting in a uniform user experience and difficulty in providing flexible services tailored to individual needs. Furthermore, there is a lack of methods to alleviate user anxiety and stress during the estimation and manufacturing processes. Therefore, this invention aims to improve user satisfaction by analyzing user emotions in real time and providing optimal suggestions and support accordingly.

[0841] 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.

[0842] In this invention, the server includes means for analyzing the user's facial expressions and voice input using an emotion analysis function to determine the user's emotions, means for requesting quotation proposals from multiple manufacturers based on the emotion analysis results and extracted furniture specification information, and means for evaluating the emotional feedback the user makes in real time and providing selection support. This enables flexible quotation proposals and selection support that are tailored to the user's emotions, and is expected to improve the user experience.

[0843] An "image generation device" is a device that generates visual information in digital format and is used in design and planning.

[0844] "Design drawings" are visual information that shows the design of a product or structure, providing guidelines for its dimensions, shape, and materials.

[0845] "Specifications" refers to information describing the technical details required for a product or service, including structure, materials, and function.

[0846] "Emotion analysis function" is a technology that recognizes and analyzes a user's emotional state using data such as facial expressions and voice.

[0847] A "quote proposal" is a plan that specifically outlines the costs and conditions for manufacturing a product, and is provided based on specific specifications.

[0848] "Selection support" refers to the act of providing support by presenting information and making suggestions to help users make the best decisions.

[0849] "Production progress" refers to the state indicating the extent to which the manufacturing process of a product or service is complete.

[0850] "Notification" is the act of communicating information in order to inform relevant parties about a certain matter.

[0851] This invention is a system that proposes the optimal furniture production while taking the user's emotions into consideration, and its embodiments are described below.

[0852] Hardware and software configuration

[0853] This system includes terminals used by users to create, save, and upload design images, and a server for data processing. The terminals are equipped with image generation software (e.g., CAD software or design applications), allowing users to save their created designs in JPEG or PNG format and upload them to the system. The server uses image analysis software (e.g., OpenCV or TensorFlow) and emotion analysis software (e.g., Amazon Rekognition or Microsoft Azure Emotion API) to extract image specification information and determine the user's emotional state.

[0854] Data processing and calculation

[0855] The server processes uploaded design images using image analysis software. Specifically, it processes the color information of each pixel and maps information about the furniture's shape, dimensions, and materials to a database. The emotion analysis software takes the user's facial expressions and voice data as input and analyzes them to quantify the user's emotional state. This analysis result is input into the recommendation engine within the system and used to generate the most suitable estimate proposal for the user. The generation AI model (e.g., GPT or DALL-E) generates optimized design proposals based on emotion evaluations and prompts that take into account specification information.

[0856] Specific example

[0857] For example, if a user generates a design image for a living room table, they upload that image to the server via their device. The server analyzes the image and simultaneously determines if the user is feeling cheerful based on their real-time facial expressions. Based on this information, the server uses an AI model to create quotation options, including bright colors and friendly designs, and presents them to the user. The user reviews these and makes their final selection. An example of a prompt used in this process is, "I've designed a coffee table for my living room. Please extract specifications from this image and provide relaxing suggestions based on my mood."

[0858] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0859] Step 1:

[0860] The user generates a design image and uploads it to the system via a terminal. The input is a design image in JPEG or PNG format created by the user. Using the terminal's application software, the user sends this image in a format the system can receive. The output is the state in which the server has received that image data.

[0861] Step 2:

[0862] The server processes the received design image using image analysis software. The input is the design image uploaded to the server in step 1. The server performs object detection and feature analysis based on pixel information to extract specification information regarding the shape, material, and dimensions of the furniture from this image. The output is the analyzed furniture specification information.

[0863] Step 3:

[0864] To analyze user emotions, the server uses emotion analysis software. The input is real-time facial expression or voice data from the user. Based on this data, the server uses facial recognition algorithms or voice analysis algorithms to evaluate the user's emotions. The output is a numerical representation of the user's emotional state.

[0865] Step 4:

[0866] The server generates quotation proposals using a generative AI model. The inputs are furniture specification information obtained as a result of the analysis in step 2 and user emotion data quantified in step 3. The server inputs this data as prompts into the generative AI model, which automatically generates color and design suggestions tailored to the user's emotions. The output is an optimized quotation proposal presented to the user.

[0867] Step 5:

[0868] The user reviews the presented quote proposal and makes a selection. The input is the quote proposal received in step 4. The user makes a selection using a terminal and sends feedback to the server. The server analyzes this feedback in real time and provides additional selection support information as needed. The output is the user's confirmed selection data.

[0869] Step 6:

[0870] After the user confirms their selection, the server automatically places an order with the manufacturer. The input is the user's selection data confirmed in step 5. Based on this, the server uses a communication protocol with the manufacturer to initiate the ordering process. The output is the order instruction sent to the manufacturer.

[0871] Step 7:

[0872] The server manages production progress and provides notifications tailored to the user's emotional state. Inputs include production progress data obtained from the manufacturer and the user's physical stress level. Based on this, the server adjusts the content and frequency of notifications and sends the information to the user via their device. Output is the adjusted notification message.

[0873] (Application Example 2)

[0874] 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".

[0875] Traditional furniture manufacturing processes often involved mechanical design proposals and price quotes without considering user emotions, resulting in a limited user experience and insufficient support, particularly when stress or anxiety arose. A system is needed that understands individual user emotions and provides services optimized based on those emotions.

[0876] 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.

[0877] In this invention, the server includes means for identifying the user's emotions and optimizing design proposals and estimates to those emotions, means for evaluating user feedback in real time and supporting selections, and means for notifying the user of production progress with consideration for their emotions. This enables flexible responses that are attentive to the user's emotions and makes it possible to provide a highly satisfying, customized user experience.

[0878] An "image generation device" is a device that generates visual representations of furniture and other objects designed by the user.

[0879] A "design image" is an image that visually represents the specific design of the furniture and includes specifications for its production.

[0880] "Furniture specification information" refers to information necessary for production, such as materials, size, and shape, extracted from design images.

[0881] A "manufacturer" is a company or organization that actually produces furniture.

[0882] "Quotation information" refers to cost information necessary for production, provided by the manufacturer, and includes proposals that the user can choose from.

[0883] "Means of notifying users" refers to communication methods for providing users with real-time information on production progress and other related matters.

[0884] "Means for identifying user emotions" refers to technologies that analyze a user's emotional state from data such as facial expressions and voice.

[0885] "Methods for evaluating feedback in real time" refer to technologies that instantly analyze user responses and choices to optimize service content.

[0886] "Means of supporting choice" refer to technologies that provide additional information or organize options to help users make the best decision.

[0887] In an embodiment of the present invention, the system realizes a furniture manufacturing support system that understands the user's emotions and provides services accordingly. The server receives a design image created by the user using an image generation device and extracts furniture specification information using image analysis technology. In this case, the specification information includes materials, size, and shape.

[0888] The server requests quotes from multiple manufacturers and compares and analyzes the received quote information. During this process, it analyzes the user's facial expression and voice data using emotion analysis APIs (e.g., Amazon Rekognition or Google Cloud Vision API) to provide design suggestions and quotes tailored to the user's emotions.

[0889] User feedback is evaluated by the system in real time, and guidance and options are adjusted as needed. Furthermore, production progress information is automatically obtained from the manufacturer and communicated in a format that takes into account the user's emotional state. This allows users to proceed through the process with less stress.

[0890] For example, if a user designs a new chair, the system analyzes the user's positive emotions and suggests a colorful and playful design. If the system detects feelings of hesitation, it presents samples of popular designs from the past to reassure the user.

[0891] An example of a prompt sentence to input into a generative AI model is, "When a user designs new furniture, provide the best suggestions based on sentiment data and past popular designs."

[0892] Through this system, users can enjoy a personalized customer experience.

[0893] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0894] Step 1:

[0895] The server receives design images from the terminal as input. Using an image processing algorithm, it extracts furniture specification information such as material, size, and shape from the image. This outputs the information necessary for specific production.

[0896] Step 2:

[0897] The server requests quotes from multiple manufacturers based on the extracted furniture specifications. At this time, the server uses a generative AI model to create a template and outputs a quote request with the specifications added.

[0898] Step 3:

[0899] Manufacturers send quotation information back to the server. The server receives this as input and compares and analyzes multiple quotation pieces. Using data analysis algorithms, it evaluates price and delivery time and outputs the optimal proposal.

[0900] Step 4:

[0901] The server receives user feedback as input and uses an emotion analysis API to determine the user's emotions. Based on these emotions, the server optimizes the presentation of estimate information and adjusts the design options before outputting the results.

[0902] Step 5:

[0903] After the user selects a production request, the server automatically places an order with the manufacturer. The information transmitted includes the specifications selected by the user and the planned production date.

[0904] Step 6:

[0905] As production progresses, the server receives progress information from the manufacturer. Based on this, an AI model generates notifications for the user, and these notifications are output to the device in an emotionally sensitive manner.

[0906] Step 7:

[0907] After the product is completed, the server takes the user's convenience into account, proposes the optimal delivery date and time, and notifies the user's device. The user can then review the proposed schedule and finalize the delivery details by providing feedback.

[0908] 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.

[0909] 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.

[0910] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0911] 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.

[0912] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0913] 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.

[0914] 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.

[0915] 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.

[0916] 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."

[0917] 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.

[0918] 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.

[0919] 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.

[0920] 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.

[0921] 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.

[0922] 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.

[0923] 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.

[0924] 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.

[0925] 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.

[0926] 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.

[0927] 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.

[0928] 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 as being incorporated by reference.

[0929] The following is further disclosed regarding the embodiments described above.

[0930] (Claim 1)

[0931] Means for receiving a design image generated by an image generation device,

[0932] A means for analyzing the design image and extracting furniture specification information,

[0933] A method for requesting quotes from multiple manufacturers based on extracted furniture specifications,

[0934] A means of receiving and comparing quotes from multiple manufacturers,

[0935] A method for presenting the user with a quote and allowing them to choose to proceed with the production,

[0936] A means of placing an order for the selected production request with the relevant manufacturer,

[0937] A means of tracking production progress and notifying users,

[0938] A system that includes this.

[0939] (Claim 2)

[0940] The system according to claim 1, further comprising means for automatically obtaining information regarding the progress of production from the manufacturer after the selection of a production request, and periodically notifying the user.

[0941] (Claim 3)

[0942] The system according to claim 1, comprising means for automatically generating a template for a quotation request based on the materials, size, and shape within a design image.

[0943] "Example 1"

[0944] (Claim 1)

[0945] A means for receiving configuration data generated by a data processing device,

[0946] A means for analyzing the configuration data and extracting technical information of the structure,

[0947] A method for requesting quotes from multiple manufacturers based on extracted technical information,

[0948] A means of receiving and comparing quotes from multiple manufacturers,

[0949] A method of presenting the user with a quote and allowing them to choose whether to proceed with the production,

[0950] A means of placing an order for the selected production request with the relevant manufacturer,

[0951] A means of monitoring production progress and notifying users,

[0952] A system that includes this.

[0953] (Claim 2)

[0954] The system according to claim 1, comprising means for automatically obtaining information regarding the progress of production from the manufacturer after the selection of a production request, and periodically notifying the user.

[0955] (Claim 3)

[0956] The system according to claim 1, comprising means for automatically generating a template for a request for quotation based on the material, size, and shape in the configuration data.

[0957] "Application Example 1"

[0958] (Claim 1)

[0959] Means for receiving a design image generated by an image generation device,

[0960] A means for analyzing the design image and extracting furniture specification information,

[0961] A method for requesting quotes from multiple manufacturers based on extracted furniture specifications,

[0962] A means of receiving and comparing quotes from multiple manufacturers,

[0963] A method for presenting the user with a quote and allowing them to choose to proceed with the production,

[0964] A means of placing an order for the selected production request with the relevant manufacturer,

[0965] A means of tracking production progress and notifying users in real time,

[0966] A means of providing a user interface that includes augmented reality technology that allows for visual confirmation of furniture designs,

[0967] A means of coordinating the delivery of finished products with logistics companies,

[0968] A system that includes this.

[0969] (Claim 2)

[0970] The system according to claim 1, further comprising means for automatically obtaining information regarding the progress of production from the manufacturer after the selection of a production request, and notifying the user in real time.

[0971] (Claim 3)

[0972] The system according to claim 1, comprising means for automatically generating a quote request template based on the materials, size, and shape within a design image, and providing quote options including a delivery schedule.

[0973] "Example 2 of combining an emotion engine"

[0974] (Claim 1)

[0975] Means for receiving design figures generated by an image generation device,

[0976] A means for analyzing the design drawing and extracting furniture specification information,

[0977] A means of determining the user's emotions by analyzing the user's facial expressions and voice input using an emotion analysis function,

[0978] A method for requesting price quotes from multiple manufacturers based on the emotion analysis results and extracted furniture specification information,

[0979] A means of receiving and comparing quotes from multiple manufacturers,

[0980] A method for presenting the user with a quote and allowing them to choose to proceed with the production,

[0981] A means of providing support for user selection by evaluating their emotional feedback in real time,

[0982] A means of placing an order for the selected production request with the relevant manufacturer,

[0983] A means of tracking production progress and providing notifications that are sensitive to user emotions,

[0984] A system that includes this.

[0985] (Claim 2)

[0986] The system according to claim 1, further comprising means for automatically obtaining information regarding the progress of production from the manufacturer after the selection of a production request, and notifying the user at a frequency corresponding to the user's emotional state.

[0987] (Claim 3)

[0988] The system according to claim 1, comprising means for automatically generating a template for a quotation request that also takes into account the user's feelings, based on the materials, dimensions, and shape within the design drawing.

[0989] "Application example 2 when combining with an emotional engine"

[0990] (Claim 1)

[0991] Means for receiving a design image generated by an image generation device,

[0992] A means for analyzing the design image and extracting furniture specification information,

[0993] A method for requesting quotes from multiple manufacturers based on extracted furniture specifications,

[0994] A means of receiving and comparing quotes from multiple manufacturers,

[0995] A method for presenting the user with a quote and allowing them to choose to proceed with the production,

[0996] A means of placing an order for the selected production request with the relevant manufacturer,

[0997] A means of tracking production progress and notifying users,

[0998] A means of identifying user emotions and optimizing design proposals and price quotes to reflect those emotions,

[0999] A means to evaluate user feedback in real time and support their choices,

[1000] A system that includes this.

[1001] (Claim 2)

[1002] The system according to claim 1, comprising means for automatically obtaining information regarding the progress of production from the manufacturer after the selection of a production request, and notifying the user regularly and with emotional consideration.

[1003] (Claim 3)

[1004] The system according to claim 1, comprising means for automatically generating a template for a quote request based on the materials, size, and shape within a design image, and further adjusting the template to take into account the user's feelings. [Explanation of Symbols]

[1005] 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. Means for receiving a design image generated by an image generation device, A means for analyzing the design image and extracting furniture specification information, A method for requesting quotes from multiple manufacturers based on extracted furniture specifications, A means of receiving and comparing quotes from multiple manufacturers, A method for presenting the user with a quote and allowing them to choose to proceed with the production, A means of placing an order for the selected production request with the relevant manufacturer, A means of tracking production progress and notifying users in real time, A means of providing a user interface that includes augmented reality technology that allows for visual confirmation of furniture designs, A means of coordinating the delivery of finished products with logistics companies, A system that includes this.

2. The system according to claim 1, further comprising means for automatically obtaining information regarding the progress of production from the manufacturer after the selection of a production request and notifying the user in real time.

3. The system according to claim 1, comprising means for automatically generating a template for a quote request based on the materials, size, and shape within a design image, and providing quote options including a delivery schedule.

Images