System

Abstract

A system is provided.SOLUTION: A system comprising: an input means for a user to input an ideal after-sales part condition, usage, and lifestyle; a generation model means for analyzing the user's input data and automatically generating an ideal after-sales part design; an image recognition means for subjecting the generated design to image recognition technology and proposing and designing an optimal part; a manufacturing request means for sending the optimized part design to a manufacturer and requesting manufacturing; and a delivery means for delivering the manufactured part to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Traditional car customization is expensive, time-consuming, and labor-intensive, making it difficult for average users to customize their vehicles. While it is expected to promote reuse of used cars and promote diversity and sustainability, this has not been fully realized. Furthermore, there are still a lack of methods for effectively utilizing waste materials and providing transparency in the manufacturing process. [Means for solving the problem]

[0005] The present invention solves the above problems by the following means.

[0006] The system includes an input means for a user to input ideal after-market part conditions, usage purpose, and lifestyle, a generation model means for analyzing the user's input data and automatically generating an ideal after-market part design, an image recognition means for applying image recognition technology to the generated design to propose and design optimal parts, a manufacturing request means for transmitting the optimized part design to a manufacturer and requesting manufacturing, and a delivery means for delivering the manufactured parts to the user.

[0007] It also includes a comparison tool to compare with past custom photos to evaluate real-world manufacturability, and a recycling tool to reuse waste materials to manufacture parts.

[0008] The generative modeling means has the ability to dynamically adjust the algorithm for generating designs based on user input data, and includes a monitoring means for notifying users of the progress of manufacturing, thereby enabling a highly efficient and highly accurate customization experience while realizing diversity and sustainability.

[0009] "Input means" refers to an interface that allows the user to input their ideal aftermarket parts requirements, intended use, and lifestyle.

[0010] "Generative model means" refers to an artificial intelligence model that analyzes user input data and automatically generates ideal aftermarket part designs.

[0011] "Image recognition means" refers to the technology that applies image recognition technology to the generated design to propose and design the optimal parts.

[0012] The "manufacturing request means" refers to the system portion for transmitting the optimized part design to the manufacturer and requesting manufacturing.

[0013] "Delivery means" refers to a method or system for delivering manufactured parts to a user.

[0014] "Comparison Method" refers to a technique or algorithm used to compare with past custom photographs to assess real-world manufacturability.

[0015] "Recycling methods" refers to methods or techniques for reusing waste materials to create new parts.

[0016] "Monitoring means" refers to the part of the system that notifies the user of the manufacturing progress.

[0017] "Generative AI model" refers to artificial intelligence technology that automatically generates ideal aftermarket part designs based on user data.

[0018] "Image recognition technology" refers to technology that analyzes images to assess the realistic manufacturability of parts. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0027] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0040] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention is a system that allows users to easily design, manufacture, and receive aftermarket parts for automobiles.

[0041] System Configuration

[0042] The system consists of the following main components:

[0043] 1. User Device

[0044] 2. Server

[0045] 3. Generative AI Models

[0046] 4. Image Recognition Technology

[0047] 5. Manufacturer Interface

[0048] 6. EC Service Interface

[0049] Input Method

[0050] The user terminal provides an interface for the user to input their ideal aftermarket parts requirements, usage, and lifestyle. This interface includes text input, drop-down menus, sliders, etc.

[0051] Examples:

[0052] The user enters their vehicle type, daily driving habits, and preferred design tastes into the input screen.

[0053] Generative AI Models

[0054] The server receives input data sent from the user's device and passes it to the generative AI model for analysis. The generative AI model generates the ideal aftermarket part design based on the user's input data.

[0055] Examples:

[0056] The AI ​​model generated from user input designs a spoiler that improves aerodynamic performance at high speeds.

[0057] Image Recognition Method

[0058] The generated design is then run through image recognition technology by the server, which compares it to previous custom photos to match it to realistic manufacturability.

[0059] Examples:

[0060] Image recognition technology has determined that "this spoiler design is highly manufacturable because it is similar to designs that have been successful in the past."

[0061] Manufacturing request method

[0062] After the design is optimized, the server sends a manufacturing request through the manufacturer's interface, which includes CAD data and manufacturing instructions.

[0063] Examples:

[0064] The server transmits request data to the manufacturer, such as "Please manufacture a spoiler with this design."

[0065] Delivery method

[0066] Once the parts are manufactured, they are sent via the EC service interface, and the server notifies the user of the delivery tracking number.

[0067] Examples:

[0068] The server, upon receiving the completion notification from the manufacturer, simultaneously requests delivery from the EC service and notifies the user that "the spoiler is currently being delivered."

[0069] Monitoring Methods

[0070] The server monitors the manufacturing progress and notifies users in real time of the manufacturing status, allowing them to see the current stage of their order.

[0071] Examples:

[0072] The server automatically receives status updates from the manufacturer and notifies the user that "production is currently underway."

[0073] This invention allows users to easily design, manufacture, and receive aftermarket parts that suit their needs, eliminating the high costs and time involved. It also promotes diversity and sustainability by recycling waste materials and monitoring the manufacturing process.

[0074] The processing flow will be explained below.

[0075] Program processing steps

[0076] Step 1: User Input

[0077] User: Fill out the form with requirements for custom car parts, intended use, and lifestyle.

[0078] Terminal: Collects the data entered by the user and sends it to the server.

[0079] Step 2: Data reception and analysis

[0080] Server: Receives user data sent from the device and checks the integrity of the data.

[0081] Step 3: Sending data to the generative AI model

[0082] Server: Passes the received data to the generative AI model engine and requests analysis and design generation.

[0083] Step 4: Aftermarket parts design generation

[0084] Generative AI model: Generates optimal aftermarket part designs based on user input data.

[0085] Server: Receives the generated design data.

[0086] Step 5: Image recognition of the design

[0087] Server: The generated design is analyzed using image recognition technology and compared with previous custom photos to assess realistic manufacturability.

[0088] Step 6: Optimize and suggest

[0089] Image recognition technology: Evaluate whether a design is realistically manufacturable and adjust the design if necessary.

[0090] Server: Prepare the tuned and optimized design.

[0091] Step 7: Prepare the manufacturing order

[0092] Server: Converts the optimized design into data to send to the manufacturer and formats manufacturing instructions.

[0093] Step 8: Submitting a manufacturing request

[0094] Server: Sends manufacturing requests through the manufacturer's interface.

[0095] Step 9: Production monitoring

[0096] Server: Monitors progress and receives status updates from the manufacturer.

[0097] Step 10: Receive notification of manufacturing completion

[0098] Server: Receives "Manufacturing Completion Notification" from the manufacturer and confirms that the part is complete.

[0099] Step 11: Prepare for shipping

[0100] Server: Passes data to the e-commerce service to process the shipping of the completed parts.

[0101] Step 12: Submit a shipping request

[0102] Server: Calls the API of the e-commerce service and sends a delivery request to send the parts to the user.

[0103] Step 13: Notification of shipping tracking information

[0104] Server: Receives the shipping tracking number from the e-commerce service and notifies the user.

[0105] Step 14: User Receipt

[0106] User: Waits for the part to be delivered and then receives it.

[0107] This detailed process step allows users to design, manufacture and receive aftermarket car parts simply and efficiently, and the system also monitors and keeps users informed of the manufacturing process and delivery in real time.

[0108] Example 1

[0109] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0110] In the conventional automotive aftermarket parts market, it has been difficult for users to easily realize their own preferred part designs. Furthermore, users have to manage many steps themselves, such as checking manufacturability and manufacturing progress, and handling delivery procedures, which is time-consuming and costly. There is a need for a system that solves these issues and allows users to easily design, manufacture, and receive their ideal aftermarket parts.

[0111] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0112] In this invention, the server includes input means for a user to input ideal conditions, usage, and lifestyle for automobile aftermarket parts, generation AI model means for analyzing the input data sent from the user terminal and automatically generating an ideal aftermarket part design, image recognition means for applying image recognition technology to the generated design and comparing it with past custom cases to evaluate manufacturability, manufacturing request means for sending the evaluated part design to a manufacturer and requesting manufacturing, and delivery means for delivering the manufactured parts to the user. This allows users to eliminate the problems of high cost and time and easily design, manufacture, and receive aftermarket parts that suit their preferences.

[0113] The "input means" is an interface that allows the user to input ideal conditions, usage purposes, and lifestyles regarding aftermarket parts for automobiles.

[0114] The "generative AI model means" is a model equipped with a highly accurate machine learning algorithm that analyzes input data sent from the user's device and automatically generates the ideal aftermarket part design.

[0115] The "image recognition means" is a technology for subjecting the generated design to image recognition technology, comparing it with past custom examples, and evaluating manufacturability.

[0116] The "manufacturing request means" is an interface and communication means for transmitting the evaluated part design to a manufacturer and making a manufacturing request.

[0117] "Delivery vehicle" is the logistics and notification system for delivering manufactured parts to users.

[0118] The "monitoring means" is a system for monitoring the manufacturing progress in real time and notifying the user of the manufacturing status.

[0119] "Recycling means" refers to processing technologies and systems for reusing waste materials to manufacture aftermarket parts.

[0120] This invention is a system that allows users to easily design, manufacture, and receive aftermarket automotive parts. The system consists of a user terminal, a server, a generative AI model, image recognition technology, a manufacturer interface, and an e-commerce service interface.

[0121] Users use a dedicated interface to input information about their ideal requirements for aftermarket parts, their intended use, and their lifestyle. The interface includes text input, drop-down menus, and sliders. For example, a user can input the following criteria for a Toyota Prius: "daily city driving" and "sporty design."

[0122] The user's device then sends the input information to a server. The server analyzes the received input data and passes it to a generative AI model. The generative AI model uses advanced machine learning algorithms to automatically generate the ideal aftermarket part design based on the user's requirements. For example, the generative AI model can generate a "sporty wheel suitable for city driving."

[0123] The server then uses image recognition technology to compare the generated design with past customizations and evaluate its manufacturability. For example, a generated wheel design may be evaluated to see if it is similar to a previously successful design and be deemed highly manufacturable.

[0124] After the design is evaluated, the server sends a manufacturing request to the manufacturer's interface. This request includes the necessary CAD data and manufacturing instructions. Once manufacturing is complete, the server receives a notification from the manufacturer and processes the delivery through the e-commerce service interface. The user is notified of the delivery tracking number for the completed aftermarket part. For example, the user can receive a notification that "the wheels are currently being delivered."

[0125] The server also monitors the manufacturing progress in real time and notifies the user of the manufacturing status. For example, the server notifies the user of the manufacturing progress status by saying "Currently in manufacturing."

[0126] Furthermore, this system also includes a means for recycling waste materials, promoting a sustainable manufacturing process. Thus, the present invention allows users to easily design, manufacture, and receive aftermarket parts that suit their needs, eliminating the problems of high cost and time.

[0127] Example prompt sentence:

[0128] "Design a sporty wheel for the Toyota Prius that is suitable for city driving."

[0129] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0130] Step 1:

[0131] The user inputs their ideal conditions for aftermarket parts for automobiles, their intended use, and their lifestyle.

[0132] Input: The user inputs their preferences and requirements (e.g., car model, usage scenario, design preferences) into the user terminal.

[0133] What happens: A user uses the device interface (text entry, drop-down menus, sliders, etc.) to enter criteria such as "Toyota Prius," "everyday city driving," and "sporty design."

[0134] Output: The user device is ready to send the entered data to the server.

[0135] Step 2:

[0136] The user terminal sends the input data to the server.

[0137] Input: When the user presses the input completion button, the user terminal generates the data to be sent.

[0138] Specific operation: The user presses the "Send" button, and the device sends data such as "Toyota Prius," "daily city driving," and "sporty design" to the server.

[0139] Output: The input data arrives at the server.

[0140] Step 3:

[0141] The server passes the received data to the generative AI model to generate the design.

[0142] Input: The server analyzes the data sent from the user device and passes it to the generative AI model.

[0143] Specific operation: The server inputs the data "Toyota Prius," "everyday city driving," and "sporty design" into the generative AI model.

[0144] Data processing: The generative AI model runs machine learning algorithms on the received data to generate the ideal aftermarket part design.

[0145] Output: Generated design data (e.g., "sporty wheels suitable for city driving") is generated.

[0146] Step 4:

[0147] The generated design is then subjected to image recognition technology to assess manufacturability.

[0148] Input: The server receives the generated design data.

[0149] What happens: The server passes the generated wheel design to image recognition technology.

[0150] Data processing: Image recognition technology compares the generated design with previous custom examples to assess manufacturability.

[0151] Output: Returns a design that is assessed as having high manufacturability (e.g., "This wheel design is highly manufacturable because it is similar to a previously successful design").

[0152] Step 5:

[0153] The server sends the manufacturing request to the manufacturer's interface.

[0154] Input: The server receives the design data that has been evaluated as manufacturable.

[0155] Specific operation: The server sends data to the manufacturer saying, "Please manufacture a wheel with this design."

[0156] Data processing: Formatting the data to include necessary CAD data and manufacturing instructions.

[0157] Output: Data representing the production request is sent to the manufacturer.

[0158] Step 6:

[0159] Monitor production progress and notify users

[0160] Input: Status update data from the manufacturer is received by the server.

[0161] Specific behavior: The server receives a status update from the manufacturer, such as "Currently in production," and notifies the user of this information.

[0162] Data processing: Converting status information into an appropriate format and sending it to the user interface.

[0163] Output: The user can see the progress (e.g. "Currently in production") in real time.

[0164] Step 7:

[0165] Deliver manufactured parts and notify users

[0166] Input: The server receives notification data of manufacturing completion.

[0167] Specific operation: The server receives a notification of completion of production from the manufacturer and processes the delivery through the EC service interface.

[0168] Data processing: Generate delivery request data and send it to the e-commerce service.

[0169] Output: The user receives a notification with a shipping tracking number (e.g. "Your wheels are on their way").

[0170] (Application example 1)

[0171] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0172] Conventional customization of aftermarket parts for automobiles requires advanced specialized knowledge and skills, making it difficult for ordinary users to design parts to suit their preferences. It is also difficult to check the feasibility and progress of manufacturing, so a system that allows users to easily order and receive custom parts was needed.

[0173] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0174] In this invention, the server includes a data input means for a user to input the ideal after-market part requirements, intended use, and lifestyle, a generation model means for analyzing the user's input data and automatically generating the ideal after-market part design, an image recognition means for applying image recognition technology to the generated design to propose and design the optimal part, a manufacturing request means for transmitting the optimized part design to a manufacturer and requesting manufacturing, a transportation means for delivering the manufactured part to the user, a notification means for notifying the user's mobile information terminal in real time of the design generation and manufacturing progress status, a comparison means for comparing with past custom design data and evaluating actual manufacturability, and a recycling processing means for manufacturing parts using recycled materials. This enables users to easily design, manufacture, and receive their preferred after-market parts without specialized knowledge.

[0175] The "data input means" is an interface that allows users to input their ideal aftermarket parts requirements, intended use, and lifestyle.

[0176] The "generative model means" is an AI model that analyzes user input data and automatically generates ideal aftermarket part designs.

[0177] "Image recognition means" is a technology that applies image recognition technology to the generated design to propose and design the optimal parts.

[0178] The "manufacturing request means" is a system for transmitting the optimized part design to a manufacturer and requesting manufacturing.

[0179] "Transportation" means the means for delivering manufactured parts to users.

[0180] "Notification means" refers to a technology for notifying the user of the design creation and manufacturing progress status in real time via their mobile information terminal.

[0181] "Comparison means" refers to a technique for comparing with past custom design data to evaluate actual manufacturability.

[0182] "Recycling means" refers to technology for reusing waste materials to manufacture parts.

[0183] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention provides a system that allows users to easily design, manufacture, and receive aftermarket parts for automobiles.

[0184] System Configuration

[0185] The system consists of the following major components:

[0186] 1. User data entry method

[0187] 2. Server

[0188] 3. Generative AI Model Means

[0189] 4. Image Recognition Methods

[0190] 5. Manufacturing request method

[0191] 6. Means of transportation

[0192] 7. Means of notification

[0193] 8. Means of comparison

[0194] 9. Recycling methods

[0195] Hardware and software used

[0196] 1. Hardware:

[0197] Mobile information terminal (smartphone)

[0198] Servers on the cloud (general cloud service providers)

[0199] 2. Software:

[0200] Mobile app development frameworks (React Native, Swift, Kotlin, etc.)

[0201] Server infrastructure (AWS (registered trademark), Google (registered trademark), Azure (registered trademark), etc.)

[0202] Generative AI models (GPT models, etc.)

[0203] Image recognition technology (OpenCV, TENSORFLOW (registered trademark), etc.)

[0204] EC platform system (general API-based EC system)

[0205] Processing flow

[0206] First, the user uses the data input means to input their ideal aftermarket parts requirements, intended use, and lifestyle into the mobile information terminal application. As an example of input, the user might provide a prompt such as, "I use my Toyota Prius for long-distance weekend drives. Please generate a spoiler design that emphasizes aerodynamic performance."

[0207] The server then receives this input data and passes it to a generative AI model means, which generates a design tailored to the user's needs. The generative AI model used in this step is an advanced text generation model such as GPT-4 (registered trademark).

[0208] The generated design is then processed by the server using image recognition tools and compared with previous custom design data. The image recognition tools use OpenCV and TensorFlow. The comparison tool evaluates the manufacturability of the generated design and proposes a realistic manufacturing plan.

[0209] The server then sends the optimized design to the manufacturer via a manufacturing request mechanism, which includes CAD data and manufacturing instructions. Once manufacturing is complete, a transportation mechanism is activated to deliver the manufactured parts to the user.

[0210] Furthermore, the server uses a notification means to notify the user's mobile information device of the design creation and manufacturing progress in real time, using push notification technology (e.g., Firebase Cloud Messaging).

[0211] Finally, recycling processes provide a mechanism for reusing waste materials to manufacture parts, resulting in an environmentally friendly and sustainable system.

[0212] Specific examples

[0213] The user inputs the following into the application: "I drive a Toyota Prius on long weekend drives. Please generate a spoiler design that emphasizes aerodynamic performance." Based on this prompt, the generative AI model generates a design, which is then evaluated for manufacturability using image recognition. Once the evaluation is complete, a manufacturing request is sent, and once completed, the user receives a real-time notification and the product is delivered.

[0214] This provides a system that allows users to easily design, manufacture, and receive aftermarket parts to their liking, even without specialized knowledge.

[0215] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0216] Step 1:

[0217] The user starts the application on the mobile information terminal and inputs the ideal aftermarket parts requirements, usage, and lifestyle. As an input example, a prompt sentence is provided: "I use a Toyota Prius for long-distance weekend drives. Please generate a spoiler design that emphasizes aerodynamic performance." This input data is sent from the terminal to the server.

[0218] Step 2:

[0219] The server passes the input data received from the terminal to the generative AI model means. The generative AI model (e.g., a GPT-based model) uses this data to generate aftermarket part designs tailored to the user's needs. Here, the model performs analysis and data generation based on the prompt statement, and outputs the design data.

[0220] Step 3:

[0221] The generated design is returned to the server, which then runs it through an image recognition tool (using OpenCV or TensorFlow) to compare the generated design with past custom design data and evaluate its manufacturability. The tool analyzes the image data, performs comparison calculations, and outputs the manufacturability evaluation results.

[0222] Step 4:

[0223] If the server confirms that the design proposed by the generative AI model is manufacturable based on the optimized part design and the manufacturability evaluation results, it sends a manufacturing request to the manufacturer via the manufacturing request means. This request includes CAD data and manufacturing instructions. The manufacturing request data is sent.

[0224] Step 5:

[0225] The manufacturer receives the manufacturing request and manufactures the parts. Once manufacturing is complete, the manufacturer sends a manufacturing completion notification to the server. The server receives this notification and arranges for the parts to be delivered to the user using a transportation means. The delivery procedure data is sent to the transportation service.

[0226] Step 6:

[0227] The server uses the notification means along with the transportation data to notify the user's device of the design creation, manufacturing progress, and delivery status in real time, allowing the user to always be aware of the status of their order. Notification data is sent to the device using push notification technology (e.g., Firebase Cloud Messaging).

[0228] Step 7:

[0229] The recycling process implements a process to promote reuse of waste materials in the manufacturing process. The server collects recycling data and optimizes the recycling process. The recycling data is analyzed and reported.

[0230] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0231] The present invention provides a system that allows users to easily design, manufacture, and receive aftermarket parts for automobiles, and further has a function for recognizing the emotions of users.

[0232] System Configuration

[0233] The system consists of the following main components:

[0234] 1. User Device

[0235] 2. Server

[0236] 3. Generative AI Models

[0237] 4. Image Recognition Technology

[0238] 5. Manufacturer Interface

[0239] 6. EC Service Interface

[0240] 7. Emotion Engine

[0241] Input Method

[0242] The user terminal provides an interface for the user to input their ideal aftermarket parts requirements, usage, and lifestyle. This interface includes text input, drop-down menus, sliders, etc.

[0243] Examples:

[0244] The user enters their vehicle type, daily driving habits, and preferred design tastes into the input screen.

[0245] Emotion Engine

[0246] It is equipped with an emotion engine that analyzes user input and emotional data from the device, allowing it to analyze the user's emotional state in real time, and adjust the output of generative AI models and image recognition technology based on the emotional data.

[0247] Examples:

[0248] If the user is feeling stressed while typing, the emotion engine will detect this and suggest easier options.

[0249] Generative AI Models

[0250] The server receives input data sent from the user's device and the analysis results of the emotion engine, and passes them to the generative AI model for analysis. The generative AI model generates the ideal aftermarket part design based on the user's input data and emotional state.

[0251] Examples:

[0252] Based on user input and emotional data, the generative AI model designs a spoiler that is "highly safe but also takes aesthetics into consideration."

[0253] Image Recognition Method

[0254] The generated design is then run through image recognition technology by the server, which compares it to previous custom photos to match it to realistic manufacturability.

[0255] Examples:

[0256] Image recognition technology has determined that "this spoiler design is similar to past successful designs and has a high manufacturing success rate."

[0257] Manufacturing request method

[0258] After the design is optimized, the server sends a manufacturing request through the manufacturer's interface, which includes CAD data and manufacturing instructions.

[0259] Examples:

[0260] The server transmits request data to the manufacturer, such as "Please manufacture a spoiler with this design."

[0261] Delivery method

[0262] Once the parts are manufactured, they are sent via the EC service interface, and the server notifies the user of the delivery tracking number.

[0263] Examples:

[0264] The server, upon receiving the completion notification from the manufacturer, simultaneously requests delivery from the EC service and notifies the user that "the spoiler is currently being delivered."

[0265] Monitoring Methods

[0266] The server monitors the manufacturing progress and notifies users in real time of the manufacturing status, allowing them to see the current stage of their order.

[0267] Examples:

[0268] The server automatically receives status updates from the manufacturer and notifies the user that "production is currently underway."

[0269] This invention allows users to easily design, manufacture, and receive aftermarket parts that suit their needs, eliminating the high costs and time involved. Emotion recognition capabilities further personalize the user experience, increasing satisfaction. Furthermore, recycling waste materials and monitoring manufacturing processes promote diversity and sustainability.

[0270] The processing flow will be explained below.

[0271] Program processing steps

[0272] Step 1: User Input

[0273] User: Fill out the form with requirements for custom car parts, intended use, and lifestyle.

[0274] Terminal: Collects the data entered by the user and sends it to the server.

[0275] Step 2: Collecting Emotional Data

[0276] Device: Collects emotional data based on sensors on the device and user interactions.

[0277] Device: Sends emotion data to the server.

[0278] Step 3: Data reception and analysis

[0279] Server: Receives input data and emotion data sent by the user and checks the integrity of the data.

[0280] Step 4: Sending data to the generative AI model

[0281] Server: Passes the received input data and emotion data to the generative AI model engine and requests analysis and design generation.

[0282] Step 5: Aftermarket parts design generation

[0283] Generative AI model: Generates optimal aftermarket part designs based on user input and sentiment data.

[0284] Server: Receives the generated design data.

[0285] Step 6: Image recognition of the design

[0286] Server: The generated design is analyzed using image recognition technology and compared with previous custom photos to assess realistic manufacturability.

[0287] Step 7: Optimize and suggest

[0288] Image recognition technology: Evaluate whether a design is realistically manufacturable and adjust the design if necessary.

[0289] Server: Prepare the tuned and optimized design.

[0290] Step 8: Prepare the manufacturing order

[0291] Server: Converts the optimized design into data to send to the manufacturer and formats manufacturing instructions.

[0292] Step 9: Submitting a manufacturing request

[0293] Server: Sends manufacturing requests through the manufacturer's interface.

[0294] Step 10: Production monitoring

[0295] Server: Monitors progress and receives status updates from the manufacturer.

[0296] Step 11: Receive notification of manufacturing completion

[0297] Server: Receives "Manufacturing Completion Notification" from the manufacturer and confirms that the part is complete.

[0298] Step 12: Prepare for shipping

[0299] Server: Passes data to the e-commerce service to process the shipping of the completed parts.

[0300] Step 13: Submit a shipping request

[0301] Server: Calls the API of the e-commerce service and sends a delivery request to send the parts to the user.

[0302] Step 14: Notification of shipping tracking information

[0303] Server: Receives the shipping tracking number from the e-commerce service and notifies the user.

[0304] Step 15: User Receipt

[0305] User: Waits for the part to be delivered and then receives it.

[0306] The above are the specific process steps for implementing the present invention. These detailed process steps allow users to easily and efficiently design, manufacture, and receive sophisticated custom parts. Furthermore, emotion recognition capabilities further personalize the user experience, increasing satisfaction.

[0307] Example 2

[0308] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0309] Modern consumers are increasingly seeking customizable products that fit their personal tastes. However, traditional product design and manufacturing systems have struggled to generate personalized designs that take into account the user's emotional state. Furthermore, efficient systems are needed to confirm manufacturability and reuse waste materials. This has hindered efforts to improve user experience and reduce environmental impact.

[0310] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an input means for the user to input ideal product conditions, usage purpose, and lifestyle; a generative model means for analyzing the user's input data and emotional data and automatically generating an ideal product design; and an image recognition means for applying image recognition technology to the generated design to propose and design an optimal product. This enables the generation of personalized product designs that correspond to the user's preferences and emotional state. Furthermore, by supporting evaluation of the manufacturability of the generated design and the reuse of waste materials during the manufacturing stage, it is possible to simultaneously achieve an improved user experience and a reduced environmental impact.

[0311] "Ideal product conditions" refer to the specific product features and requirements desired by users.

[0312] "Usage" refers to information about how a user will use the product.

[0313] "Lifestyle" refers to information that indicates the user's lifestyle and preferences.

[0314] "Input means" refers to an interface for a user to input data.

[0315] "Input data" refers to information provided by the user, such as product conditions, intended use, and lifestyle.

[0316] "Emotion data" refers to information for analyzing the user's emotional state.

[0317] "Generative model means" refers to a means for analyzing input data and emotional data and automatically generating product designs.

[0318] "Image recognition technology" refers to the technology that analyzes and evaluates the generated design.

[0319] "Image recognition means" refers to a means for optimizing and evaluating designs generated using image recognition technology.

[0320] "Manufacturer Interface" refers to the means by which the generated design is transmitted to the manufacturer.

[0321] "Manufacturing request means" refers to a means for requesting a product design from a manufacturer.

[0322] "Delivery means" refers to a means for delivering manufactured products to users.

[0323] "Comparison means" refers to a means for comparing historical product data with the generated design to evaluate manufacturability.

[0324] "Recycling means" refers to means of reusing waste materials to manufacture products.

[0325] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention provides a system that allows users to easily design, manufacture, and receive products, and further has a function for recognizing user emotions.

[0326] System Configuration

[0327] The system consists of the following main components:

[0328] 1. User Device

[0329] 2. Server

[0330] 3. Generative AI Models

[0331] 4. Image Recognition Technology

[0332] 5. Manufacturer Interface

[0333] 6. Delivery method

[0334] 7. Emotion Engine

[0335] 8. Means of comparison

[0336] 9. Recycling methods

[0337] Input Method

[0338] The user terminal provides an interface for the user to input ideal product requirements, usage purposes, and lifestyles, including text input, drop-down menus, and sliders.

[0339] Examples:

[0340] Based on their needs, users select the product type ("Custom Furniture"), intended use ("Living Room"), and preferred design style ("Modern and Minimal") on the input screen.

[0341] Emotion Engine

[0342] It is equipped with an emotion engine that analyzes user input and emotional data from the device, allowing it to analyze the user's emotional state in real time and adjust the output of generative AI models and image recognition technology.

[0343] Examples:

[0344] If the user is stressed while typing, the emotion engine will detect this and offer easier options.

[0345] Generative AI Models

[0346] The server receives input data sent from the user's device and the analysis results of the emotion engine, and passes them to the generative AI model for analysis. The generative AI model generates an ideal product design based on the user's input data and emotional state.

[0347] Examples:

[0348] Based on user input and emotional data, the generative AI model designs "modern furniture that is highly functional and well-designed."

[0349] Image Recognition Method

[0350] The generated design is then run through image recognition technology by the server, which compares it with historical product data to ensure realistic manufacturability.

[0351] Examples:

[0352] Image recognition technology determines that "this furniture design is similar to past successful designs and has a high manufacturing success rate."

[0353] Manufacturing request method

[0354] After the design is optimized, the server sends a manufacturing request through the manufacturer's interface, which includes the CAD data and manufacturing instructions.

[0355] Examples:

[0356] The server sends request data to the manufacturer saying, "Please manufacture furniture with this design."

[0357] Delivery method

[0358] Once the manufacturing is complete, the server will process the delivery of the product and notify the user of the delivery tracking number.

[0359] Examples:

[0360] The server receives a completion notification from the manufacturer and simultaneously requests delivery, notifying the user that "The furniture is currently being delivered. The tracking number is XXXXXXX."

[0361] Monitoring Methods

[0362] The server monitors the manufacturing progress and notifies users in real time of the manufacturing status, allowing them to see the current stage of their order.

[0363] Examples:

[0364] The server automatically receives status updates from the manufacturer and notifies the user, "Currently in production."

[0365] This allows users to easily design, manufacture, and receive their own personalized products, eliminating the high costs and time required. Emotion recognition capabilities further personalize the user experience, increasing satisfaction. Furthermore, recycling waste materials and monitoring the manufacturing process promote diversity and sustainability.

[0366] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0367] Step 1:

[0368] The user inputs their ideal product requirements, intended use, and lifestyle. Input methods include text entry, drop-down menus, and sliders. When the user selects one of these, the device collects the input data.

[0369] Specific behavior:

[0370] The user inputs the car model "Custom Car," the intended use "daily use," and the design taste "sporty and elegant" into the input screen. The input data includes the car model, intended use, and design taste.

[0371] Input: Data provided by a user through an input interface.

[0372] Output: The user device is ready to send the collected data to the server.

[0373] Step 2:

[0374] The user terminal sends the collected input data to the server using a secure data transfer protocol (e.g., HTTPS).

[0375] Specific behavior:

[0376] After the input data is entered, the terminal encrypts the data and sends it to the server. The transmitted data includes the vehicle model, intended use, and design taste.

[0377] Input: Input data collected by the user terminal.

[0378] Output: The data is encrypted and sent to the server.

[0379] Step 3:

[0380] The server receives the input data along with the user's emotional data, which is then analyzed by the emotion engine. Emotional data is collected from the user's typing speed and other biometric information.

[0381] Specific behavior:

[0382] The server uses an emotion engine to detect emotions such as "stress" or "relaxation" from the user's input speed and operation patterns. The emotion engine analyzes real-time data and uses an algorithm to determine the user's emotional state.

[0383] Input: Input data and emotion data sent from the user device.

[0384] Output: Parsed emotional state (e.g. "High Stress").

[0385] Step 4:

[0386] Based on the analysis results, the server passes the data to a generative AI model for further analysis, which then generates an optimal product design based on the user's input data and emotional state.

[0387] Specific behavior:

[0388] The server sends the emotion analysis results and input data to the generative AI model, which then sets a "relaxing design suitable for users who are feeling stressed" as the generation prompt. The generative AI model then generates the optimal design, taking the user's emotions into consideration.

[0389] Input: Input data and sentiment analysis results.

[0390] Output: Generated product design (e.g. "Relaxed and sporty design").

[0391] Step 5:

[0392] The server then runs the generated design through image recognition technology, which compares it with past successful designs to assess its manufacturability.

[0393] Specific behavior:

[0394] The server processes the generated design using image recognition technology and compares it with a database of past products. An algorithm is then run to obtain an evaluation such as "This design is likely to be manufactured."

[0395] Input: Product design from a generative AI model.

[0396] Output: Manufacturability assessment result (e.g., "Highly manufacturable").

[0397] Step 6:

[0398] The server sends the optimized design to the manufacturer interface and issues a manufacturing request, which includes CAD data and manufacturing instructions.

[0399] Specific behavior:

[0400] After receiving the evaluation results, the server prepares CAD data for the generated design and requests the manufacturer to "manufacture a product with this design."

[0401] Input: Design assessed for manufacturability, CAD data.

[0402] Output: The manufacturing request data is sent to the manufacturer.

[0403] Step 7:

[0404] While the manufacturing is in progress, the server monitors the manufacturing progress and notifies the user in real time.

[0405] Specific behavior:

[0406] The server automatically receives status updates from the manufacturer and notifies the user as soon as a new situation is confirmed, e.g., a message such as "Currently in production."

[0407] Input: Progress updates from the manufacturer.

[0408] Output: Real-time manufacturing progress notification.

[0409] Step 8:

[0410] Once the manufacturing is complete, the server will process the delivery of the product and notify the user of the delivery tracking number.

[0411] Specific behavior:

[0412] The server receives a completion notification from the manufacturer, makes a request to the delivery company, and notifies the user that "The product is on its way. The tracking number is XXXXXXX."

[0413] Input: Notice of completion from manufacturer.

[0414] Output: Notification of shipping process and tracking information.

[0415] (Application example 2)

[0416] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0417] Conventional aftermarket parts design systems generate designs based on static input data without considering the user's emotional state, which prevents them from fully enhancing user satisfaction. Furthermore, they lack the ability to notify users of the manufacturing progress in real time, making it difficult for users to check the current stage of their order. This can result in a poor user experience and an increase in order cancellations and complaints.

[0418] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an input means for the user to input the ideal aftermarket parts conditions, usage purpose, and lifestyle; a generative model means for analyzing the user's input data and emotional state and automatically generating the ideal aftermarket parts design; an image recognition means for applying image recognition technology to the generated design to propose and design the optimal part; a manufacturing request means for sending the optimized part design to a manufacturer and requesting manufacturing; a monitoring means for monitoring the delivery status of the manufactured part and notifying the user in real time; and a delivery means for delivering the manufactured part to the user. This enables the generation of a personalized design based on the user's emotions and the user to be notified of the manufacturing progress in real time.

[0419] The "input means" is a means for providing an interface through which the user can input the desired aftermarket parts requirements, intended use, lifestyle, and the like.

[0420] The "generative model means" is a means for analyzing the user's input data and emotional state, and automatically generating an ideal aftermarket part design.

[0421] "Image recognition means" is a means of applying image recognition technology to the generated design to propose and design the optimal parts.

[0422] The "manufacturing request means" is a means for transmitting the optimized part design to a manufacturer and requesting manufacturing.

[0423] The "monitoring means" is a means for monitoring the delivery status of manufactured parts and notifying the user in real time.

[0424] "Delivery means" refers to a means for delivering manufactured parts to users.

[0425] "Emotional state" is data that indicates the state of a user's emotions as analyzed in real time.

[0426] The system of the present invention is a multi-functional platform for users to design, manufacture, and receive aftermarket automotive parts. The system consists of the following major components:

[0427] System Configuration

[0428] 1. User Input Method

[0429] The user uses the terminal to input the desired aftermarket parts requirements (e.g., vehicle type, driving habits, design preferences), usage, lifestyle, etc. The terminal interface includes text input, drop-down menus, sliders, etc.

[0430] 2. Emotion analysis method

[0431] The system analyzes the user's emotional state in real time from the user's input data and camera footage. This analysis is performed using facial recognition technology and an emotion analysis model. The software used includes OpenCV (facial recognition) and Keras (emotion analysis model).

[0432] 3. Generative Modeling Methods

[0433] The server receives the user's input data and emotional state and inputs it into a generative AI model. This model generates the ideal aftermarket part design incorporating the user's emotional state to increase user satisfaction. The tool used is Keras.

[0434] 4. Image Recognition Methods

[0435] The server then subjects the generated design to image recognition technology to assess its real-world manufacturability. Image recognition technology compares it with past custom photos to determine whether the generated design is manufacturable.

[0436] 5. Manufacturing request method

[0437] The optimized part design is then sent from the server to the manufacturer for manufacturing, along with the CAD data and manufacturing instructions. The manufacturing request is made via an HTTP request using the requests library.

[0438] 6. Monitoring Methods

[0439] The server monitors the manufacturing progress and notifies the user of the status in real time. It automatically receives status updates from the manufacturer and notifies the user every time, improving the user experience.

[0440] 7. Delivery method

[0441] Once manufactured, the parts are delivered to the user via the EC service interface. The server monitors the delivery status in real time and notifies the user of the delivery tracking number.

[0442] Specific examples

[0443] This example shows a user using a smartphone to design an aftermarket car part. The user inputs their car model (Toyota Prius), driving habits (daily short distance driving), and design preferences (modern and simple). At the same time, the user's emotional state (relaxed) is analyzed from camera footage, and a generative AI model uses this data to generate the ideal part design. The generated design is then verified for real-world manufacturability using image recognition technology, and the optimized design is finally sent to the manufacturer. The manufacturing progress is notified to the user in real time via the server, and the completed part is delivered to the user via delivery means.

[0444] Prompt Sentence Examples

[0445] Create the optimal aftermarket part design based on the following information: The user's car model is "Toyota Prius," their driving habit is "daily short distance driving," and their design preference is "modern and simple." Furthermore, the user's emotional state has been detected as "relaxed." Based on all this information, generate the ideal aftermarket part design.

[0446] This invention enables personalized design generation based on the user's emotions and allows the user to be notified of the manufacturing progress in real time, thereby significantly improving user satisfaction.

[0447] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0448] Step 1:

[0449] The user uses a terminal to input their ideal aftermarket parts requirements, intended use, and lifestyle. Input methods include text input, drop-down menus, sliders, etc. The input data is sent from the terminal to the server.

[0450] Input: User data such as car model, driving habits, design preferences, etc.

[0451] Output: User data sent to the server

[0452] Step 2:

[0453] The device captures the user's facial image using a camera and transmits the image in real time to the emotion analysis means, which uses face recognition technology (OpenCV) and emotion analysis model (Keras) to analyze the user's emotional state.

[0454] Input: User's face image

[0455] Output: Parsed emotional state data

[0456] Step 3:

[0457] The server combines the user data received in step 1 with the emotional state data received in step 2 and inputs this into a generative AI model (Keras). The generative AI model generates the ideal aftermarket part design based on the data.

[0458] Input: Integrated user data and emotional state data

[0459] Output: Generated aftermarket parts design

[0460] Step 4:

[0461] The server runs the generated design through image recognition and compares it with past custom photos to assess manufacturability. Image recognition technology is used to compare the realistic manufacturability of the generated design with past success stories.

[0462] Input: Generated aftermarket parts design

[0463] Output: Designs that are evaluated as manufacturable

[0464] Step 5:

[0465] The server sends the optimized design that is evaluated as manufacturable to the manufacturer through the manufacturing request means, including the necessary CAD data and manufacturing instructions, using an HTTP request (requests library).

[0466] Input: Designs that are assessed as manufacturable

[0467] Output: Manufacturing request data sent to the manufacturer

[0468] Step 6:

[0469] The server monitors the manufacturing progress through a monitoring means and automatically receives status updates from the manufacturer, keeping the user informed of the manufacturing progress in real time.

[0470] Input: Production progress data from the manufacturer

[0471] Output: Notify the user of the manufacturing progress

[0472] Step 7:

[0473] Once manufactured, the parts are delivered via the server's EC service interface. The delivery status is monitored in real time, and the user is notified of the delivery tracking number.

[0474] Input: Manufacturing completion notification and delivery request

[0475] Output: Delivery tracking number notification to user

[0476] This processing step allows users to easily create and receive personalized aftermarket part designs based on their emotions.

[0477] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0478] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0479] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0480] [Second embodiment]

[0481] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0482] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0483] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0484] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0485] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0486] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0487] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0488] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0489] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0490] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0491] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0492] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0493] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention is a system that allows users to easily design, manufacture, and receive aftermarket parts for automobiles.

[0494] System Configuration

[0495] The system consists of the following main components:

[0496] 1. User Device

[0497] 2. Server

[0498] 3. Generative AI Models

[0499] 4. Image Recognition Technology

[0500] 5. Manufacturer Interface

[0501] 6. EC Service Interface

[0502] Input Method

[0503] The user terminal provides an interface for the user to input their ideal aftermarket parts requirements, usage, and lifestyle. This interface includes text input, drop-down menus, sliders, etc.

[0504] Examples:

[0505] The user enters their vehicle type, daily driving habits, and preferred design tastes into the input screen.

[0506] Generative AI Models

[0507] The server receives input data sent from the user's device and passes it to the generative AI model for analysis. The generative AI model generates the ideal aftermarket part design based on the user's input data.

[0508] Examples:

[0509] The AI ​​model generated from user input designs a spoiler that improves aerodynamic performance at high speeds.

[0510] Image Recognition Method

[0511] The generated design is then run through image recognition technology by the server, which compares it to previous custom photos to match it to realistic manufacturability.

[0512] Examples:

[0513] Image recognition technology has determined that "this spoiler design is highly manufacturable because it is similar to designs that have been successful in the past."

[0514] Manufacturing request method

[0515] After the design is optimized, the server sends a manufacturing request through the manufacturer's interface, which includes CAD data and manufacturing instructions.

[0516] Examples:

[0517] The server transmits request data to the manufacturer, such as "Please manufacture a spoiler with this design."

[0518] Delivery method

[0519] Once the parts are manufactured, they are sent via the EC service interface, and the server notifies the user of the delivery tracking number.

[0520] Examples:

[0521] The server, upon receiving the completion notification from the manufacturer, simultaneously requests delivery from the EC service and notifies the user that "the spoiler is currently being delivered."

[0522] Monitoring Methods

[0523] The server monitors the manufacturing progress and notifies users in real time of the manufacturing status, allowing them to see the current stage of their order.

[0524] Examples:

[0525] The server automatically receives status updates from the manufacturer and notifies the user that "production is currently underway."

[0526] This invention allows users to easily design, manufacture, and receive aftermarket parts that suit their needs, eliminating the high costs and time involved. It also promotes diversity and sustainability by recycling waste materials and monitoring the manufacturing process.

[0527] The processing flow will be explained below.

[0528] Program processing steps

[0529] Step 1: User Input

[0530] User: Fill out the form with requirements for custom car parts, intended use, and lifestyle.

[0531] Terminal: Collects the data entered by the user and sends it to the server.

[0532] Step 2: Data reception and analysis

[0533] Server: Receives user data sent from the device and checks the integrity of the data.

[0534] Step 3: Sending data to the generative AI model

[0535] Server: Passes the received data to the generative AI model engine and requests analysis and design generation.

[0536] Step 4: Aftermarket parts design generation

[0537] Generative AI model: Generates optimal aftermarket part designs based on user input data.

[0538] Server: Receives the generated design data.

[0539] Step 5: Image recognition of the design

[0540] Server: The generated design is analyzed using image recognition technology and compared with previous custom photos to assess realistic manufacturability.

[0541] Step 6: Optimize and suggest

[0542] Image recognition technology: Evaluate whether a design is realistically manufacturable and adjust the design if necessary.

[0543] Server: Prepare the tuned and optimized design.

[0544] Step 7: Prepare the manufacturing order

[0545] Server: Converts the optimized design into data to send to the manufacturer and formats manufacturing instructions.

[0546] Step 8: Submitting a manufacturing request

[0547] Server: Sends manufacturing requests through the manufacturer's interface.

[0548] Step 9: Production monitoring

[0549] Server: Monitors progress and receives status updates from the manufacturer.

[0550] Step 10: Receive notification of manufacturing completion

[0551] Server: Receives "Manufacturing Completion Notification" from the manufacturer and confirms that the part is complete.

[0552] Step 11: Prepare for shipping

[0553] Server: Passes data to the e-commerce service to process the shipping of the completed parts.

[0554] Step 12: Submit a shipping request

[0555] Server: Calls the API of the e-commerce service and sends a delivery request to send the parts to the user.

[0556] Step 13: Notification of shipping tracking information

[0557] Server: Receives the shipping tracking number from the e-commerce service and notifies the user.

[0558] Step 14: User Receipt

[0559] User: Waits for the part to be delivered and then receives it.

[0560] This detailed process step allows users to design, manufacture and receive aftermarket car parts simply and efficiently, and the system also monitors and keeps users informed of the manufacturing process and delivery in real time.

[0561] Example 1

[0562] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0563] In the conventional automotive aftermarket parts market, it has been difficult for users to easily realize their own preferred part designs. Furthermore, users have to manage many steps themselves, such as checking manufacturability and manufacturing progress, and handling delivery procedures, which is time-consuming and costly. There is a need for a system that solves these issues and allows users to easily design, manufacture, and receive their ideal aftermarket parts.

[0564] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0565] In this invention, the server includes input means for a user to input ideal conditions, usage, and lifestyle for automobile aftermarket parts, generation AI model means for analyzing the input data sent from the user terminal and automatically generating an ideal aftermarket part design, image recognition means for applying image recognition technology to the generated design and comparing it with past custom cases to evaluate manufacturability, manufacturing request means for sending the evaluated part design to a manufacturer and requesting manufacturing, and delivery means for delivering the manufactured parts to the user. This allows users to eliminate the problems of high cost and time and easily design, manufacture, and receive aftermarket parts that suit their preferences.

[0566] The "input means" is an interface that allows the user to input ideal conditions, usage purposes, and lifestyles regarding aftermarket parts for automobiles.

[0567] The "generative AI model means" is a model equipped with a highly accurate machine learning algorithm that analyzes input data sent from the user's device and automatically generates the ideal aftermarket part design.

[0568] The "image recognition means" is a technology for subjecting the generated design to image recognition technology, comparing it with past custom examples, and evaluating manufacturability.

[0569] The "manufacturing request means" is an interface and communication means for transmitting the evaluated part design to a manufacturer and making a manufacturing request.

[0570] "Delivery vehicle" is the logistics and notification system for delivering manufactured parts to users.

[0571] The "monitoring means" is a system for monitoring the manufacturing progress in real time and notifying the user of the manufacturing status.

[0572] "Recycling means" refers to processing technologies and systems for reusing waste materials to manufacture aftermarket parts.

[0573] This invention is a system that allows users to easily design, manufacture, and receive aftermarket automotive parts. The system consists of a user terminal, a server, a generative AI model, image recognition technology, a manufacturer interface, and an e-commerce service interface.

[0574] Users use a dedicated interface to input information about their ideal requirements for aftermarket parts, their intended use, and their lifestyle. The interface includes text input, drop-down menus, and sliders. For example, a user can input the following criteria for a Toyota Prius: "daily city driving" and "sporty design."

[0575] The user's device then sends the input information to a server. The server analyzes the received input data and passes it to a generative AI model. The generative AI model uses advanced machine learning algorithms to automatically generate the ideal aftermarket part design based on the user's requirements. For example, the generative AI model can generate a "sporty wheel suitable for city driving."

[0576] The server then uses image recognition technology to compare the generated design with past customizations and evaluate its manufacturability. For example, a generated wheel design may be evaluated to see if it is similar to a previously successful design and be deemed highly manufacturable.

[0577] After the design is evaluated, the server sends a manufacturing request to the manufacturer's interface. This request includes the necessary CAD data and manufacturing instructions. Once manufacturing is complete, the server receives a notification from the manufacturer and processes the delivery through the e-commerce service interface. The user is notified of the delivery tracking number for the completed aftermarket part. For example, the user can receive a notification that "the wheels are currently being delivered."

[0578] The server also monitors the manufacturing progress in real time and notifies the user of the manufacturing status. For example, the server notifies the user of the manufacturing progress status by saying "Currently in manufacturing."

[0579] Furthermore, this system also includes a means for recycling waste materials, promoting a sustainable manufacturing process. Thus, the present invention allows users to easily design, manufacture, and receive aftermarket parts that suit their needs, eliminating the problems of high cost and time.

[0580] Example prompt sentence:

[0581] "Design a sporty wheel for the Toyota Prius that is suitable for city driving."

[0582] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0583] Step 1:

[0584] The user inputs their ideal conditions for aftermarket parts for automobiles, their intended use, and their lifestyle.

[0585] Input: The user inputs their preferences and requirements (e.g., car model, usage scenario, design preferences) into the user terminal.

[0586] What happens: A user uses the device interface (text entry, drop-down menus, sliders, etc.) to enter criteria such as "Toyota Prius," "everyday city driving," and "sporty design."

[0587] Output: The user device is ready to send the entered data to the server.

[0588] Step 2:

[0589] The user terminal sends the input data to the server.

[0590] Input: When the user presses the input completion button, the user terminal generates the data to be sent.

[0591] Specific operation: The user presses the "Send" button, and the device sends data such as "Toyota Prius," "daily city driving," and "sporty design" to the server.

[0592] Output: The input data arrives at the server.

[0593] Step 3:

[0594] The server passes the received data to the generative AI model to generate the design.

[0595] Input: The server analyzes the data sent from the user device and passes it to the generative AI model.

[0596] Specific operation: The server inputs the data "Toyota Prius," "everyday city driving," and "sporty design" into the generative AI model.

[0597] Data processing: The generative AI model runs machine learning algorithms on the received data to generate the ideal aftermarket part design.

[0598] Output: Generated design data (e.g., "sporty wheels suitable for city driving") is generated.

[0599] Step 4:

[0600] The generated design is then subjected to image recognition technology to assess manufacturability.

[0601] Input: The server receives the generated design data.

[0602] What happens: The server passes the generated wheel design to image recognition technology.

[0603] Data processing: Image recognition technology compares the generated design with previous custom examples to assess manufacturability.

[0604] Output: Returns a design that is assessed as having high manufacturability (e.g., "This wheel design is highly manufacturable because it is similar to a previously successful design").

[0605] Step 5:

[0606] The server sends the manufacturing request to the manufacturer's interface.

[0607] Input: The server receives the design data that has been evaluated as manufacturable.

[0608] Specific operation: The server sends data to the manufacturer saying, "Please manufacture a wheel with this design."

[0609] Data processing: Formatting the data to include necessary CAD data and manufacturing instructions.

[0610] Output: Data representing the production request is sent to the manufacturer.

[0611] Step 6:

[0612] Monitor production progress and notify users

[0613] Input: Status update data from the manufacturer is received by the server.

[0614] Specific behavior: The server receives a status update from the manufacturer, such as "Currently in production," and notifies the user of this information.

[0615] Data processing: Converting status information into an appropriate format and sending it to the user interface.

[0616] Output: The user can see the progress (e.g. "Currently in production") in real time.

[0617] Step 7:

[0618] Deliver manufactured parts and notify users

[0619] Input: The server receives notification data of manufacturing completion.

[0620] Specific operation: The server receives a notification of completion of production from the manufacturer and processes the delivery through the EC service interface.

[0621] Data processing: Generate delivery request data and send it to the e-commerce service.

[0622] Output: The user receives a notification with a shipping tracking number (e.g. "Your wheels are on their way").

[0623] (Application example 1)

[0624] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0625] Conventional customization of aftermarket parts for automobiles requires advanced specialized knowledge and skills, making it difficult for ordinary users to design parts to suit their preferences. It is also difficult to check the feasibility and progress of manufacturing, so a system that allows users to easily order and receive custom parts was needed.

[0626] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0627] In this invention, the server includes a data input means for a user to input the ideal after-market part requirements, intended use, and lifestyle, a generation model means for analyzing the user's input data and automatically generating the ideal after-market part design, an image recognition means for applying image recognition technology to the generated design to propose and design the optimal part, a manufacturing request means for transmitting the optimized part design to a manufacturer and requesting manufacturing, a transportation means for delivering the manufactured part to the user, a notification means for notifying the user's mobile information terminal in real time of the design generation and manufacturing progress status, a comparison means for comparing with past custom design data and evaluating actual manufacturability, and a recycling processing means for manufacturing parts using recycled materials. This enables users to easily design, manufacture, and receive their preferred after-market parts without specialized knowledge.

[0628] The "data input means" is an interface that allows users to input their ideal aftermarket parts requirements, intended use, and lifestyle.

[0629] The "generative model means" is an AI model that analyzes user input data and automatically generates ideal aftermarket part designs.

[0630] "Image recognition means" is a technology that applies image recognition technology to the generated design to propose and design the optimal parts.

[0631] The "manufacturing request means" is a system for transmitting the optimized part design to a manufacturer and requesting manufacturing.

[0632] "Transportation" means the means for delivering manufactured parts to users.

[0633] "Notification means" refers to a technology for notifying the user of the design creation and manufacturing progress status in real time via their mobile information terminal.

[0634] "Comparison means" refers to a technique for comparing with past custom design data to evaluate actual manufacturability.

[0635] "Recycling means" refers to technology for reusing waste materials to manufacture parts.

[0636] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention provides a system that allows users to easily design, manufacture, and receive aftermarket parts for automobiles.

[0637] System Configuration

[0638] The system consists of the following major components:

[0639] 1. User data entry method

[0640] 2. Server

[0641] 3. Generative AI Model Means

[0642] 4. Image Recognition Methods

[0643] 5. Manufacturing request method

[0644] 6. Means of transportation

[0645] 7. Means of notification

[0646] 8. Means of comparison

[0647] 9. Recycling methods

[0648] Hardware and software used

[0649] 1. Hardware:

[0650] Mobile information terminal (smartphone)

[0651] Servers on the cloud (general cloud service providers)

[0652] 2. Software:

[0653] Mobile app development frameworks (React Native, Swift, Kotlin, etc.)

[0654] Server infrastructure (AWS, Google Cloud, Azure, etc.)

[0655] Generative AI models (GPT models, etc.)

[0656] Image recognition technology (OpenCV, TensorFlow, etc.)

[0657] EC platform system (general API-based EC system)

[0658] Processing flow

[0659] First, the user uses the data input means to input their ideal aftermarket parts requirements, intended use, and lifestyle into the mobile information terminal application. As an example of input, the user might provide a prompt such as, "I use my Toyota Prius for long-distance weekend drives. Please generate a spoiler design that emphasizes aerodynamic performance."

[0660] The server then receives this input data and passes it to a generative AI model, which then generates a design tailored to the user's needs. The generative AI model used in this step is an advanced text generation model such as GPT-4.

[0661] The generated design is then processed by the server using image recognition tools and compared with previous custom design data. The image recognition tools use OpenCV and TensorFlow. The comparison tool evaluates the manufacturability of the generated design and proposes a realistic manufacturing plan.

[0662] The server then sends the optimized design to the manufacturer via a manufacturing request mechanism, which includes CAD data and manufacturing instructions. Once manufacturing is complete, a transportation mechanism is activated to deliver the manufactured parts to the user.

[0663] Furthermore, the server uses a notification means to notify the user's mobile information device of the design creation and manufacturing progress in real time, using push notification technology (e.g., Firebase Cloud Messaging).

[0664] Finally, recycling processes provide a mechanism for reusing waste materials to manufacture parts, resulting in an environmentally friendly and sustainable system.

[0665] Specific examples

[0666] The user inputs the following into the application: "I drive a Toyota Prius on long weekend drives. Please generate a spoiler design that emphasizes aerodynamic performance." Based on this prompt, the generative AI model generates a design, which is then evaluated for manufacturability using image recognition. Once the evaluation is complete, a manufacturing request is sent, and once completed, the user receives a real-time notification and the product is delivered.

[0667] This provides a system that allows users to easily design, manufacture, and receive aftermarket parts to their liking, even without specialized knowledge.

[0668] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0669] Step 1:

[0670] The user starts the application on the mobile information terminal and inputs the ideal aftermarket parts requirements, usage, and lifestyle. As an input example, a prompt sentence is provided: "I use a Toyota Prius for long-distance weekend drives. Please generate a spoiler design that emphasizes aerodynamic performance." This input data is sent from the terminal to the server.

[0671] Step 2:

[0672] The server passes the input data received from the terminal to the generative AI model means. The generative AI model (e.g., a GPT-based model) uses this data to generate aftermarket part designs tailored to the user's needs. Here, the model performs analysis and data generation based on the prompt statement, and outputs the design data.

[0673] Step 3:

[0674] The generated design is returned to the server, which then runs it through an image recognition tool (using OpenCV or TensorFlow) to compare the generated design with past custom design data and evaluate its manufacturability. The tool analyzes the image data, performs comparison calculations, and outputs the manufacturability evaluation results.

[0675] Step 4:

[0676] If the server confirms that the design proposed by the generative AI model is manufacturable based on the optimized part design and the manufacturability evaluation results, it sends a manufacturing request to the manufacturer via the manufacturing request means. This request includes CAD data and manufacturing instructions. The manufacturing request data is sent.

[0677] Step 5:

[0678] The manufacturer receives the manufacturing request and manufactures the parts. Once manufacturing is complete, the manufacturer sends a manufacturing completion notification to the server. The server receives this notification and arranges for the parts to be delivered to the user using a transportation means. The delivery procedure data is sent to the transportation service.

[0679] Step 6:

[0680] The server uses the notification means along with the transportation data to notify the user's device of the design creation, manufacturing progress, and delivery status in real time, allowing the user to always be aware of the status of their order. Notification data is sent to the device using push notification technology (e.g., Firebase Cloud Messaging).

[0681] Step 7:

[0682] The recycling process implements a process to promote reuse of waste materials in the manufacturing process. The server collects recycling data and optimizes the recycling process. The recycling data is analyzed and reported.

[0683] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0684] The present invention provides a system that allows users to easily design, manufacture, and receive aftermarket parts for automobiles, and further has a function for recognizing the emotions of users.

[0685] System Configuration

[0686] The system consists of the following main components:

[0687] 1. User Device

[0688] 2. Server

[0689] 3. Generative AI Models

[0690] 4. Image Recognition Technology

[0691] 5. Manufacturer Interface

[0692] 6. EC Service Interface

[0693] 7. Emotion Engine

[0694] Input Method

[0695] The user terminal provides an interface for the user to input their ideal aftermarket parts requirements, usage, and lifestyle. This interface includes text input, drop-down menus, sliders, etc.

[0696] Examples:

[0697] The user enters their vehicle type, daily driving habits, and preferred design tastes into the input screen.

[0698] Emotion Engine

[0699] It is equipped with an emotion engine that analyzes user input and emotional data from the device, allowing it to analyze the user's emotional state in real time, and adjust the output of generative AI models and image recognition technology based on the emotional data.

[0700] Examples:

[0701] If the user is feeling stressed while typing, the emotion engine will detect this and suggest easier options.

[0702] Generative AI Models

[0703] The server receives input data sent from the user's device and the analysis results of the emotion engine, and passes them to the generative AI model for analysis. The generative AI model generates the ideal aftermarket part design based on the user's input data and emotional state.

[0704] Examples:

[0705] Based on user input and emotional data, the generative AI model designs a spoiler that is "highly safe but also takes aesthetics into consideration."

[0706] Image Recognition Method

[0707] The generated design is then run through image recognition technology by the server, which compares it to previous custom photos to match it to realistic manufacturability.

[0708] Examples:

[0709] Image recognition technology has determined that "this spoiler design is similar to past successful designs and has a high manufacturing success rate."

[0710] Manufacturing request method

[0711] After the design is optimized, the server sends a manufacturing request through the manufacturer's interface, which includes CAD data and manufacturing instructions.

[0712] Examples:

[0713] The server transmits request data to the manufacturer, such as "Please manufacture a spoiler with this design."

[0714] Delivery method

[0715] Once the parts are manufactured, they are sent via the EC service interface, and the server notifies the user of the delivery tracking number.

[0716] Examples:

[0717] The server, upon receiving the completion notification from the manufacturer, simultaneously requests delivery from the EC service and notifies the user that "the spoiler is currently being delivered."

[0718] Monitoring Methods

[0719] The server monitors the manufacturing progress and notifies users in real time of the manufacturing status, allowing them to see the current stage of their order.

[0720] Examples:

[0721] The server automatically receives status updates from the manufacturer and notifies the user that "production is currently underway."

[0722] This invention allows users to easily design, manufacture, and receive aftermarket parts that suit their needs, eliminating the high costs and time involved. Emotion recognition capabilities further personalize the user experience, increasing satisfaction. Furthermore, recycling waste materials and monitoring manufacturing processes promote diversity and sustainability.

[0723] The processing flow will be explained below.

[0724] Program processing steps

[0725] Step 1: User Input

[0726] User: Fill out the form with requirements for custom car parts, intended use, and lifestyle.

[0727] Terminal: Collects the data entered by the user and sends it to the server.

[0728] Step 2: Collecting Emotional Data

[0729] Device: Collects emotional data based on sensors on the device and user interactions.

[0730] Device: Sends emotion data to the server.

[0731] Step 3: Data reception and analysis

[0732] Server: Receives input data and emotion data sent by the user and checks the integrity of the data.

[0733] Step 4: Sending data to the generative AI model

[0734] Server: Passes the received input data and emotion data to the generative AI model engine and requests analysis and design generation.

[0735] Step 5: Aftermarket parts design generation

[0736] Generative AI model: Generates optimal aftermarket part designs based on user input and sentiment data.

[0737] Server: Receives the generated design data.

[0738] Step 6: Image recognition of the design

[0739] Server: The generated design is analyzed using image recognition technology and compared with previous custom photos to assess realistic manufacturability.

[0740] Step 7: Optimize and suggest

[0741] Image recognition technology: Evaluate whether a design is realistically manufacturable and adjust the design if necessary.

[0742] Server: Prepare the tuned and optimized design.

[0743] Step 8: Prepare the manufacturing order

[0744] Server: Converts the optimized design into data to send to the manufacturer and formats manufacturing instructions.

[0745] Step 9: Submitting a manufacturing request

[0746] Server: Sends manufacturing requests through the manufacturer's interface.

[0747] Step 10: Production monitoring

[0748] Server: Monitors progress and receives status updates from the manufacturer.

[0749] Step 11: Receive notification of manufacturing completion

[0750] Server: Receives "Manufacturing Completion Notification" from the manufacturer and confirms that the part is complete.

[0751] Step 12: Prepare for shipping

[0752] Server: Passes data to the e-commerce service to process the shipping of the completed parts.

[0753] Step 13: Submit a shipping request

[0754] Server: Calls the API of the e-commerce service and sends a delivery request to send the parts to the user.

[0755] Step 14: Notification of shipping tracking information

[0756] Server: Receives the shipping tracking number from the e-commerce service and notifies the user.

[0757] Step 15: User Receipt

[0758] User: Waits for the part to be delivered and then receives it.

[0759] The above are the specific process steps for implementing the present invention. These detailed process steps allow users to easily and efficiently design, manufacture, and receive sophisticated custom parts. Furthermore, emotion recognition capabilities further personalize the user experience, increasing satisfaction.

[0760] Example 2

[0761] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0762] Modern consumers are increasingly seeking customizable products that fit their personal tastes. However, traditional product design and manufacturing systems have struggled to generate personalized designs that take into account the user's emotional state. Furthermore, efficient systems are needed to confirm manufacturability and reuse waste materials. This has hindered efforts to improve user experience and reduce environmental impact.

[0763] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an input means for the user to input ideal product conditions, usage purpose, and lifestyle; a generative model means for analyzing the user's input data and emotional data and automatically generating an ideal product design; and an image recognition means for applying image recognition technology to the generated design to propose and design an optimal product. This enables the generation of personalized product designs that correspond to the user's preferences and emotional state. Furthermore, by supporting evaluation of the manufacturability of the generated design and the reuse of waste materials during the manufacturing stage, it is possible to simultaneously achieve an improved user experience and a reduced environmental impact.

[0764] "Ideal product conditions" refer to the specific product features and requirements desired by users.

[0765] "Usage" refers to information about how a user will use the product.

[0766] "Lifestyle" refers to information that indicates the user's lifestyle and preferences.

[0767] "Input means" refers to an interface for a user to input data.

[0768] "Input data" refers to information provided by the user, such as product conditions, intended use, and lifestyle.

[0769] "Emotion data" refers to information for analyzing the user's emotional state.

[0770] "Generative model means" refers to a means for analyzing input data and emotional data and automatically generating product designs.

[0771] "Image recognition technology" refers to the technology that analyzes and evaluates the generated design.

[0772] "Image recognition means" refers to a means for optimizing and evaluating designs generated using image recognition technology.

[0773] "Manufacturer Interface" refers to the means by which the generated design is transmitted to the manufacturer.

[0774] "Manufacturing request means" refers to a means for requesting a product design from a manufacturer.

[0775] "Delivery means" refers to a means for delivering manufactured products to users.

[0776] "Comparison means" refers to a means for comparing historical product data with the generated design to evaluate manufacturability.

[0777] "Recycling means" refers to means of reusing waste materials to manufacture products.

[0778] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention provides a system that allows users to easily design, manufacture, and receive products, and further has a function for recognizing user emotions.

[0779] System Configuration

[0780] The system consists of the following main components:

[0781] 1. User Device

[0782] 2. Server

[0783] 3. Generative AI Models

[0784] 4. Image Recognition Technology

[0785] 5. Manufacturer Interface

[0786] 6. Delivery method

[0787] 7. Emotion Engine

[0788] 8. Means of comparison

[0789] 9. Recycling methods

[0790] Input Method

[0791] The user terminal provides an interface for the user to input ideal product requirements, usage purposes, and lifestyles, including text input, drop-down menus, and sliders.

[0792] Examples:

[0793] Based on their needs, users select the product type ("Custom Furniture"), intended use ("Living Room"), and preferred design style ("Modern and Minimal") on the input screen.

[0794] Emotion Engine

[0795] It is equipped with an emotion engine that analyzes user input and emotional data from the device, allowing it to analyze the user's emotional state in real time and adjust the output of generative AI models and image recognition technology.

[0796] Examples:

[0797] If the user is stressed while typing, the emotion engine will detect this and offer easier options.

[0798] Generative AI Models

[0799] The server receives input data sent from the user's device and the analysis results of the emotion engine, and passes them to the generative AI model for analysis. The generative AI model generates an ideal product design based on the user's input data and emotional state.

[0800] Examples:

[0801] Based on user input and emotional data, the generative AI model designs "modern furniture that is highly functional and well-designed."

[0802] Image Recognition Method

[0803] The generated design is then run through image recognition technology by the server, which compares it with historical product data to ensure realistic manufacturability.

[0804] Examples:

[0805] Image recognition technology determines that "this furniture design is similar to past successful designs and has a high manufacturing success rate."

[0806] Manufacturing request method

[0807] After the design is optimized, the server sends a manufacturing request through the manufacturer's interface, which includes the CAD data and manufacturing instructions.

[0808] Examples:

[0809] The server sends request data to the manufacturer saying, "Please manufacture furniture with this design."

[0810] Delivery method

[0811] Once the manufacturing is complete, the server will process the delivery of the product and notify the user of the delivery tracking number.

[0812] Examples:

[0813] The server receives a completion notification from the manufacturer and simultaneously requests delivery, notifying the user that "The furniture is currently being delivered. The tracking number is XXXXXXX."

[0814] Monitoring Methods

[0815] The server monitors the manufacturing progress and notifies users in real time of the manufacturing status, allowing them to see the current stage of their order.

[0816] Examples:

[0817] The server automatically receives status updates from the manufacturer and notifies the user, "Currently in production."

[0818] This allows users to easily design, manufacture, and receive their own personalized products, eliminating the high costs and time required. Emotion recognition capabilities further personalize the user experience, increasing satisfaction. Furthermore, recycling waste materials and monitoring the manufacturing process promote diversity and sustainability.

[0819] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0820] Step 1:

[0821] The user inputs their ideal product requirements, intended use, and lifestyle. Input methods include text entry, drop-down menus, and sliders. When the user selects one of these, the device collects the input data.

[0822] Specific behavior:

[0823] The user inputs the car model "Custom Car," the intended use "daily use," and the design taste "sporty and elegant" into the input screen. The input data includes the car model, intended use, and design taste.

[0824] Input: Data provided by a user through an input interface.

[0825] Output: The user device is ready to send the collected data to the server.

[0826] Step 2:

[0827] The user terminal sends the collected input data to the server using a secure data transfer protocol (e.g., HTTPS).

[0828] Specific behavior:

[0829] After the input data is entered, the terminal encrypts the data and sends it to the server. The transmitted data includes the vehicle model, intended use, and design taste.

[0830] Input: Input data collected by the user terminal.

[0831] Output: The data is encrypted and sent to the server.

[0832] Step 3:

[0833] The server receives the input data along with the user's emotional data, which is then analyzed by the emotion engine. Emotional data is collected from the user's typing speed and other biometric information.

[0834] Specific behavior:

[0835] The server uses an emotion engine to detect emotions such as "stress" or "relaxation" from the user's input speed and operation patterns. The emotion engine analyzes real-time data and uses an algorithm to determine the user's emotional state.

[0836] Input: Input data and emotion data sent from the user device.

[0837] Output: Parsed emotional state (e.g. "High Stress").

[0838] Step 4:

[0839] Based on the analysis results, the server passes the data to a generative AI model for further analysis, which then generates an optimal product design based on the user's input data and emotional state.

[0840] Specific behavior:

[0841] The server sends the emotion analysis results and input data to the generative AI model, which then sets a "relaxing design suitable for users who are feeling stressed" as the generation prompt. The generative AI model then generates the optimal design, taking the user's emotions into consideration.

[0842] Input: Input data and sentiment analysis results.

[0843] Output: Generated product design (e.g. "Relaxed and sporty design").

[0844] Step 5:

[0845] The server then runs the generated design through image recognition technology, which compares it with past successful designs to assess its manufacturability.

[0846] Specific behavior:

[0847] The server processes the generated design using image recognition technology and compares it with a database of past products. An algorithm is then run to obtain an evaluation such as "This design is likely to be manufactured."

[0848] Input: Product design from a generative AI model.

[0849] Output: Manufacturability assessment result (e.g., "Highly manufacturable").

[0850] Step 6:

[0851] The server sends the optimized design to the manufacturer interface and issues a manufacturing request, which includes CAD data and manufacturing instructions.

[0852] Specific behavior:

[0853] After receiving the evaluation results, the server prepares CAD data for the generated design and requests the manufacturer to "manufacture a product with this design."

[0854] Input: Design assessed for manufacturability, CAD data.

[0855] Output: The manufacturing request data is sent to the manufacturer.

[0856] Step 7:

[0857] While the manufacturing is in progress, the server monitors the manufacturing progress and notifies the user in real time.

[0858] Specific behavior:

[0859] The server automatically receives status updates from the manufacturer and notifies the user as soon as a new situation is confirmed, e.g., a message such as "Currently in production."

[0860] Input: Progress updates from the manufacturer.

[0861] Output: Real-time manufacturing progress notification.

[0862] Step 8:

[0863] Once the manufacturing is complete, the server will process the delivery of the product and notify the user of the delivery tracking number.

[0864] Specific behavior:

[0865] The server receives a completion notification from the manufacturer, makes a request to the delivery company, and notifies the user that "The product is on its way. The tracking number is XXXXXXX."

[0866] Input: Notice of completion from manufacturer.

[0867] Output: Notification of shipping process and tracking information.

[0868] (Application example 2)

[0869] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0870] Conventional aftermarket parts design systems generate designs based on static input data without considering the user's emotional state, which prevents them from fully enhancing user satisfaction. Furthermore, they lack the ability to notify users of the manufacturing progress in real time, making it difficult for users to check the current stage of their order. This can result in a poor user experience and an increase in order cancellations and complaints.

[0871] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an input means for the user to input the ideal aftermarket parts conditions, usage purpose, and lifestyle; a generative model means for analyzing the user's input data and emotional state and automatically generating the ideal aftermarket parts design; an image recognition means for applying image recognition technology to the generated design to propose and design the optimal part; a manufacturing request means for sending the optimized part design to a manufacturer and requesting manufacturing; a monitoring means for monitoring the delivery status of the manufactured part and notifying the user in real time; and a delivery means for delivering the manufactured part to the user. This enables the generation of a personalized design based on the user's emotions and the user to be notified of the manufacturing progress in real time.

[0872] The "input means" is a means for providing an interface through which the user can input the desired aftermarket parts requirements, intended use, lifestyle, and the like.

[0873] The "generative model means" is a means for analyzing the user's input data and emotional state, and automatically generating an ideal aftermarket part design.

[0874] "Image recognition means" is a means of applying image recognition technology to the generated design to propose and design the optimal parts.

[0875] The "manufacturing request means" is a means for transmitting the optimized part design to a manufacturer and requesting manufacturing.

[0876] The "monitoring means" is a means for monitoring the delivery status of manufactured parts and notifying the user in real time.

[0877] "Delivery means" refers to a means for delivering manufactured parts to users.

[0878] "Emotional state" is data that indicates the state of a user's emotions as analyzed in real time.

[0879] The system of the present invention is a multi-functional platform for users to design, manufacture, and receive aftermarket automotive parts. The system consists of the following major components:

[0880] System Configuration

[0881] 1. User Input Method

[0882] The user uses the terminal to input the desired aftermarket parts requirements (e.g., vehicle type, driving habits, design preferences), usage, lifestyle, etc. The terminal interface includes text input, drop-down menus, sliders, etc.

[0883] 2. Emotion analysis method

[0884] The system analyzes the user's emotional state in real time from the user's input data and camera footage. This analysis is performed using facial recognition technology and an emotion analysis model. The software used includes OpenCV (facial recognition) and Keras (emotion analysis model).

[0885] 3. Generative Modeling Methods

[0886] The server receives the user's input data and emotional state and inputs it into a generative AI model. This model generates the ideal aftermarket part design incorporating the user's emotional state to increase user satisfaction. The tool used is Keras.

[0887] 4. Image Recognition Methods

[0888] The server then subjects the generated design to image recognition technology to assess its real-world manufacturability. Image recognition technology compares it with past custom photos to determine whether the generated design is manufacturable.

[0889] 5. Manufacturing request method

[0890] The optimized part design is then sent from the server to the manufacturer for manufacturing, along with the CAD data and manufacturing instructions. The manufacturing request is made via an HTTP request using the requests library.

[0891] 6. Monitoring Methods

[0892] The server monitors the manufacturing progress and notifies the user of the status in real time. It automatically receives status updates from the manufacturer and notifies the user every time, improving the user experience.

[0893] 7. Delivery method

[0894] Once manufactured, the parts are delivered to the user via the EC service interface. The server monitors the delivery status in real time and notifies the user of the delivery tracking number.

[0895] Specific examples

[0896] This example shows a user using a smartphone to design an aftermarket car part. The user inputs their car model (Toyota Prius), driving habits (daily short distance driving), and design preferences (modern and simple). At the same time, the user's emotional state (relaxed) is analyzed from camera footage, and a generative AI model uses this data to generate the ideal part design. The generated design is then verified for real-world manufacturability using image recognition technology, and the optimized design is finally sent to the manufacturer. The manufacturing progress is notified to the user in real time via the server, and the completed part is delivered to the user via delivery means.

[0897] Prompt Sentence Examples

[0898] Create the optimal aftermarket part design based on the following information: The user's car model is "Toyota Prius," their driving habit is "daily short distance driving," and their design preference is "modern and simple." Furthermore, the user's emotional state has been detected as "relaxed." Based on all this information, generate the ideal aftermarket part design.

[0899] This invention enables personalized design generation based on the user's emotions and allows the user to be notified of the manufacturing progress in real time, thereby significantly improving user satisfaction.

[0900] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0901] Step 1:

[0902] The user uses a terminal to input their ideal aftermarket parts requirements, intended use, and lifestyle. Input methods include text input, drop-down menus, sliders, etc. The input data is sent from the terminal to the server.

[0903] Input: User data such as car model, driving habits, design preferences, etc.

[0904] Output: User data sent to the server

[0905] Step 2:

[0906] The device captures the user's facial image using a camera and transmits the image in real time to the emotion analysis means, which uses face recognition technology (OpenCV) and emotion analysis model (Keras) to analyze the user's emotional state.

[0907] Input: User's face image

[0908] Output: Parsed emotional state data

[0909] Step 3:

[0910] The server combines the user data received in step 1 with the emotional state data received in step 2 and inputs this into a generative AI model (Keras). The generative AI model generates the ideal aftermarket part design based on the data.

[0911] Input: Integrated user data and emotional state data

[0912] Output: Generated aftermarket parts design

[0913] Step 4:

[0914] The server runs the generated design through image recognition and compares it with past custom photos to assess manufacturability. Image recognition technology is used to compare the realistic manufacturability of the generated design with past success stories.

[0915] Input: Generated aftermarket parts design

[0916] Output: Designs that are evaluated as manufacturable

[0917] Step 5:

[0918] The server sends the optimized design that is evaluated as manufacturable to the manufacturer through the manufacturing request means, including the necessary CAD data and manufacturing instructions, using an HTTP request (requests library).

[0919] Input: Designs that are assessed as manufacturable

[0920] Output: Manufacturing request data sent to the manufacturer

[0921] Step 6:

[0922] The server monitors the manufacturing progress through a monitoring means and automatically receives status updates from the manufacturer, keeping the user informed of the manufacturing progress in real time.

[0923] Input: Production progress data from the manufacturer

[0924] Output: Notify the user of the manufacturing progress

[0925] Step 7:

[0926] Once manufactured, the parts are delivered via the server's EC service interface. The delivery status is monitored in real time, and the user is notified of the delivery tracking number.

[0927] Input: Manufacturing completion notification and delivery request

[0928] Output: Delivery tracking number notification to user

[0929] This processing step allows users to easily create and receive personalized aftermarket part designs based on their emotions.

[0930] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0931] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0932] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0933] [Third embodiment]

[0934] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0935] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0936] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0937] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0938] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0939] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0940] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0941] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0942] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0943] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0944] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0945] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0946] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention is a system that allows users to easily design, manufacture, and receive aftermarket parts for automobiles.

[0947] System Configuration

[0948] The system consists of the following main components:

[0949] 1. User Device

[0950] 2. Server

[0951] 3. Generative AI Models

[0952] 4. Image Recognition Technology

[0953] 5. Manufacturer Interface

[0954] 6. EC Service Interface

[0955] Input Method

[0956] The user terminal provides an interface for the user to input their ideal aftermarket parts requirements, usage, and lifestyle. This interface includes text input, drop-down menus, sliders, etc.

[0957] Examples:

[0958] The user enters their vehicle type, daily driving habits, and preferred design tastes into the input screen.

[0959] Generative AI Models

[0960] The server receives input data sent from the user's device and passes it to the generative AI model for analysis. The generative AI model generates the ideal aftermarket part design based on the user's input data.

[0961] Examples:

[0962] The AI ​​model generated from user input designs a spoiler that improves aerodynamic performance at high speeds.

[0963] Image Recognition Method

[0964] The generated design is then run through image recognition technology by the server, which compares it to previous custom photos to match it to realistic manufacturability.

[0965] Examples:

[0966] Image recognition technology has determined that "this spoiler design is highly manufacturable because it is similar to designs that have been successful in the past."

[0967] Manufacturing request method

[0968] After the design is optimized, the server sends a manufacturing request through the manufacturer's interface, which includes CAD data and manufacturing instructions.

[0969] Examples:

[0970] The server transmits request data to the manufacturer, such as "Please manufacture a spoiler with this design."

[0971] Delivery method

[0972] Once the parts are manufactured, they are sent via the EC service interface, and the server notifies the user of the delivery tracking number.

[0973] Examples:

[0974] The server, upon receiving the completion notification from the manufacturer, simultaneously requests delivery from the EC service and notifies the user that "the spoiler is currently being delivered."

[0975] Monitoring Methods

[0976] The server monitors the manufacturing progress and notifies users in real time of the manufacturing status, allowing them to see the current stage of their order.

[0977] Examples:

[0978] The server automatically receives status updates from the manufacturer and notifies the user that "production is currently underway."

[0979] This invention allows users to easily design, manufacture, and receive aftermarket parts that suit their needs, eliminating the high costs and time involved. It also promotes diversity and sustainability by recycling waste materials and monitoring the manufacturing process.

[0980] The processing flow will be explained below.

[0981] Program processing steps

[0982] Step 1: User Input

[0983] User: Fill out the form with requirements for custom car parts, intended use, and lifestyle.

[0984] Terminal: Collects the data entered by the user and sends it to the server.

[0985] Step 2: Data reception and analysis

[0986] Server: Receives user data sent from the device and checks the integrity of the data.

[0987] Step 3: Sending data to the generative AI model

[0988] Server: Passes the received data to the generative AI model engine and requests analysis and design generation.

[0989] Step 4: Aftermarket parts design generation

[0990] Generative AI model: Generates optimal aftermarket part designs based on user input data.

[0991] Server: Receives the generated design data.

[0992] Step 5: Image recognition of the design

[0993] Server: The generated design is analyzed using image recognition technology and compared with previous custom photos to assess realistic manufacturability.

[0994] Step 6: Optimize and suggest

[0995] Image recognition technology: Evaluate whether a design is realistically manufacturable and adjust the design if necessary.

[0996] Server: Prepare the tuned and optimized design.

[0997] Step 7: Prepare the manufacturing order

[0998] Server: Converts the optimized design into data to send to the manufacturer and formats manufacturing instructions.

[0999] Step 8: Submitting a manufacturing request

[1000] Server: Sends manufacturing requests through the manufacturer's interface.

[1001] Step 9: Production monitoring

[1002] Server: Monitors progress and receives status updates from the manufacturer.

[1003] Step 10: Receive notification of manufacturing completion

[1004] Server: Receives "Manufacturing Completion Notification" from the manufacturer and confirms that the part is complete.

[1005] Step 11: Prepare for shipping

[1006] Server: Passes data to the e-commerce service to process the shipping of the completed parts.

[1007] Step 12: Submit a shipping request

[1008] Server: Calls the API of the e-commerce service and sends a delivery request to send the parts to the user.

[1009] Step 13: Notification of shipping tracking information

[1010] Server: Receives the shipping tracking number from the e-commerce service and notifies the user.

[1011] Step 14: User Receipt

[1012] User: Waits for the part to be delivered and then receives it.

[1013] This detailed process step allows users to design, manufacture and receive aftermarket car parts simply and efficiently, and the system also monitors and keeps users informed of the manufacturing process and delivery in real time.

[1014] Example 1

[1015] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1016] In the conventional automotive aftermarket parts market, it has been difficult for users to easily realize their own preferred part designs. Furthermore, users have to manage many steps themselves, such as checking manufacturability and manufacturing progress, and handling delivery procedures, which is time-consuming and costly. There is a need for a system that solves these issues and allows users to easily design, manufacture, and receive their ideal aftermarket parts.

[1017] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1018] In this invention, the server includes input means for a user to input ideal conditions, usage, and lifestyle for automobile aftermarket parts, generation AI model means for analyzing the input data sent from the user terminal and automatically generating an ideal aftermarket part design, image recognition means for applying image recognition technology to the generated design and comparing it with past custom cases to evaluate manufacturability, manufacturing request means for sending the evaluated part design to a manufacturer and requesting manufacturing, and delivery means for delivering the manufactured parts to the user. This allows users to eliminate the problems of high cost and time and easily design, manufacture, and receive aftermarket parts that suit their preferences.

[1019] The "input means" is an interface that allows the user to input ideal conditions, usage purposes, and lifestyles regarding aftermarket parts for automobiles.

[1020] The "generative AI model means" is a model equipped with a highly accurate machine learning algorithm that analyzes input data sent from the user's device and automatically generates the ideal aftermarket part design.

[1021] The "image recognition means" is a technology for subjecting the generated design to image recognition technology, comparing it with past custom examples, and evaluating manufacturability.

[1022] The "manufacturing request means" is an interface and communication means for transmitting the evaluated part design to a manufacturer and making a manufacturing request.

[1023] "Delivery vehicle" is the logistics and notification system for delivering manufactured parts to users.

[1024] The "monitoring means" is a system for monitoring the manufacturing progress in real time and notifying the user of the manufacturing status.

[1025] "Recycling means" refers to processing technologies and systems for reusing waste materials to manufacture aftermarket parts.

[1026] This invention is a system that allows users to easily design, manufacture, and receive aftermarket automotive parts. The system consists of a user terminal, a server, a generative AI model, image recognition technology, a manufacturer interface, and an e-commerce service interface.

[1027] Users use a dedicated interface to input information about their ideal requirements for aftermarket parts, their intended use, and their lifestyle. The interface includes text input, drop-down menus, and sliders. For example, a user can input the following criteria for a Toyota Prius: "daily city driving" and "sporty design."

[1028] The user's device then sends the input information to a server. The server analyzes the received input data and passes it to a generative AI model. The generative AI model uses advanced machine learning algorithms to automatically generate the ideal aftermarket part design based on the user's requirements. For example, the generative AI model can generate a "sporty wheel suitable for city driving."

[1029] The server then uses image recognition technology to compare the generated design with past customizations and evaluate its manufacturability. For example, a generated wheel design may be evaluated to see if it is similar to a previously successful design and be deemed highly manufacturable.

[1030] After the design is evaluated, the server sends a manufacturing request to the manufacturer's interface. This request includes the necessary CAD data and manufacturing instructions. Once manufacturing is complete, the server receives a notification from the manufacturer and processes the delivery through the e-commerce service interface. The user is notified of the delivery tracking number for the completed aftermarket part. For example, the user can receive a notification that "the wheels are currently being delivered."

[1031] The server also monitors the manufacturing progress in real time and notifies the user of the manufacturing status. For example, the server notifies the user of the manufacturing progress status by saying "Currently in manufacturing."

[1032] Furthermore, this system also includes a means for recycling waste materials, promoting a sustainable manufacturing process. Thus, the present invention allows users to easily design, manufacture, and receive aftermarket parts that suit their needs, eliminating the problems of high cost and time.

[1033] Example prompt sentence:

[1034] "Design a sporty wheel for the Toyota Prius that is suitable for city driving."

[1035] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1036] Step 1:

[1037] The user inputs their ideal conditions for aftermarket parts for automobiles, their intended use, and their lifestyle.

[1038] Input: The user inputs their preferences and requirements (e.g., car model, usage scenario, design preferences) into the user terminal.

[1039] What happens: A user uses the device interface (text entry, drop-down menus, sliders, etc.) to enter criteria such as "Toyota Prius," "everyday city driving," and "sporty design."

[1040] Output: The user device is ready to send the entered data to the server.

[1041] Step 2:

[1042] The user terminal sends the input data to the server.

[1043] Input: When the user presses the input completion button, the user terminal generates the data to be sent.

[1044] Specific operation: The user presses the "Send" button, and the device sends data such as "Toyota Prius," "daily city driving," and "sporty design" to the server.

[1045] Output: The input data arrives at the server.

[1046] Step 3:

[1047] The server passes the received data to the generative AI model to generate the design.

[1048] Input: The server analyzes the data sent from the user device and passes it to the generative AI model.

[1049] Specific operation: The server inputs the data "Toyota Prius," "everyday city driving," and "sporty design" into the generative AI model.

[1050] Data processing: The generative AI model runs machine learning algorithms on the received data to generate the ideal aftermarket part design.

[1051] Output: Generated design data (e.g., "sporty wheels suitable for city driving") is generated.

[1052] Step 4:

[1053] The generated design is then subjected to image recognition technology to assess manufacturability.

[1054] Input: The server receives the generated design data.

[1055] What happens: The server passes the generated wheel design to image recognition technology.

[1056] Data processing: Image recognition technology compares the generated design with previous custom examples to assess manufacturability.

[1057] Output: Returns a design that is assessed as having high manufacturability (e.g., "This wheel design is highly manufacturable because it is similar to a previously successful design").

[1058] Step 5:

[1059] The server sends the manufacturing request to the manufacturer's interface.

[1060] Input: The server receives the design data that has been evaluated as manufacturable.

[1061] Specific operation: The server sends data to the manufacturer saying, "Please manufacture a wheel with this design."

[1062] Data processing: Formatting the data to include necessary CAD data and manufacturing instructions.

[1063] Output: Data representing the production request is sent to the manufacturer.

[1064] Step 6:

[1065] Monitor production progress and notify users

[1066] Input: Status update data from the manufacturer is received by the server.

[1067] Specific behavior: The server receives a status update from the manufacturer, such as "Currently in production," and notifies the user of this information.

[1068] Data processing: Converting status information into an appropriate format and sending it to the user interface.

[1069] Output: The user can see the progress (e.g. "Currently in production") in real time.

[1070] Step 7:

[1071] Deliver manufactured parts and notify users

[1072] Input: The server receives notification data of manufacturing completion.

[1073] Specific operation: The server receives a notification of completion of production from the manufacturer and processes the delivery through the EC service interface.

[1074] Data processing: Generate delivery request data and send it to the e-commerce service.

[1075] Output: The user receives a notification with a shipping tracking number (e.g. "Your wheels are on their way").

[1076] (Application example 1)

[1077] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1078] Conventional customization of aftermarket parts for automobiles requires advanced specialized knowledge and skills, making it difficult for ordinary users to design parts to suit their preferences. It is also difficult to check the feasibility and progress of manufacturing, so a system that allows users to easily order and receive custom parts was needed.

[1079] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1080] In this invention, the server includes a data input means for a user to input the ideal after-market part requirements, intended use, and lifestyle, a generation model means for analyzing the user's input data and automatically generating the ideal after-market part design, an image recognition means for applying image recognition technology to the generated design to propose and design the optimal part, a manufacturing request means for transmitting the optimized part design to a manufacturer and requesting manufacturing, a transportation means for delivering the manufactured part to the user, a notification means for notifying the user's mobile information terminal in real time of the design generation and manufacturing progress status, a comparison means for comparing with past custom design data and evaluating actual manufacturability, and a recycling processing means for manufacturing parts using recycled materials. This enables users to easily design, manufacture, and receive their preferred after-market parts without specialized knowledge.

[1081] The "data input means" is an interface that allows users to input their ideal aftermarket parts requirements, intended use, and lifestyle.

[1082] The "generative model means" is an AI model that analyzes user input data and automatically generates ideal aftermarket part designs.

[1083] "Image recognition means" is a technology that applies image recognition technology to the generated design to propose and design the optimal parts.

[1084] The "manufacturing request means" is a system for transmitting the optimized part design to a manufacturer and requesting manufacturing.

[1085] "Transportation" means the means for delivering manufactured parts to users.

[1086] "Notification means" refers to a technology for notifying the user of the design creation and manufacturing progress status in real time via their mobile information terminal.

[1087] "Comparison means" refers to a technique for comparing with past custom design data to evaluate actual manufacturability.

[1088] "Recycling means" refers to technology for reusing waste materials to manufacture parts.

[1089] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention provides a system that allows users to easily design, manufacture, and receive aftermarket parts for automobiles.

[1090] System Configuration

[1091] The system consists of the following major components:

[1092] 1. User data entry method

[1093] 2. Server

[1094] 3. Generative AI Model Means

[1095] 4. Image Recognition Methods

[1096] 5. Manufacturing request method

[1097] 6. Means of transportation

[1098] 7. Means of notification

[1099] 8. Means of comparison

[1100] 9. Recycling methods

[1101] Hardware and software used

[1102] 1. Hardware:

[1103] Mobile information terminal (smartphone)

[1104] Servers on the cloud (general cloud service providers)

[1105] 2. Software:

[1106] Mobile app development frameworks (React Native, Swift, Kotlin, etc.)

[1107] Server infrastructure (AWS, Google Cloud, Azure, etc.)

[1108] Generative AI models (GPT models, etc.)

[1109] Image recognition technology (OpenCV, TensorFlow, etc.)

[1110] EC platform system (general API-based EC system)

[1111] Processing flow

[1112] First, the user uses the data input means to input their ideal aftermarket parts requirements, intended use, and lifestyle into the mobile information terminal application. As an example of input, the user might provide a prompt such as, "I use my Toyota Prius for long-distance weekend drives. Please generate a spoiler design that emphasizes aerodynamic performance."

[1113] The server then receives this input data and passes it to a generative AI model, which then generates a design tailored to the user's needs. The generative AI model used in this step is an advanced text generation model such as GPT-4.

[1114] The generated design is then processed by the server using image recognition tools and compared with previous custom design data. The image recognition tools use OpenCV and TensorFlow. The comparison tool evaluates the manufacturability of the generated design and proposes a realistic manufacturing plan.

[1115] The server then sends the optimized design to the manufacturer via a manufacturing request mechanism, which includes CAD data and manufacturing instructions. Once manufacturing is complete, a transportation mechanism is activated to deliver the manufactured parts to the user.

[1116] Furthermore, the server uses a notification means to notify the user's mobile information device of the design creation and manufacturing progress in real time, using push notification technology (e.g., Firebase Cloud Messaging).

[1117] Finally, recycling processes provide a mechanism for reusing waste materials to manufacture parts, resulting in an environmentally friendly and sustainable system.

[1118] Specific examples

[1119] The user inputs the following into the application: "I drive a Toyota Prius on long weekend drives. Please generate a spoiler design that emphasizes aerodynamic performance." Based on this prompt, the generative AI model generates a design, which is then evaluated for manufacturability using image recognition. Once the evaluation is complete, a manufacturing request is sent, and once completed, the user receives a real-time notification and the product is delivered.

[1120] This provides a system that allows users to easily design, manufacture, and receive aftermarket parts to their liking, even without specialized knowledge.

[1121] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1122] Step 1:

[1123] The user starts the application on the mobile information terminal and inputs the ideal aftermarket parts requirements, usage, and lifestyle. As an input example, a prompt sentence is provided: "I use a Toyota Prius for long-distance weekend drives. Please generate a spoiler design that emphasizes aerodynamic performance." This input data is sent from the terminal to the server.

[1124] Step 2:

[1125] The server passes the input data received from the terminal to the generative AI model means. The generative AI model (e.g., a GPT-based model) uses this data to generate aftermarket part designs tailored to the user's needs. Here, the model performs analysis and data generation based on the prompt statement, and outputs the design data.

[1126] Step 3:

[1127] The generated design is returned to the server, which then runs it through an image recognition tool (using OpenCV or TensorFlow) to compare the generated design with past custom design data and evaluate its manufacturability. The tool analyzes the image data, performs comparison calculations, and outputs the manufacturability evaluation results.

[1128] Step 4:

[1129] If the server confirms that the design proposed by the generative AI model is manufacturable based on the optimized part design and the manufacturability evaluation results, it sends a manufacturing request to the manufacturer via the manufacturing request means. This request includes CAD data and manufacturing instructions. The manufacturing request data is sent.

[1130] Step 5:

[1131] The manufacturer receives the manufacturing request and manufactures the parts. Once manufacturing is complete, the manufacturer sends a manufacturing completion notification to the server. The server receives this notification and arranges for the parts to be delivered to the user using a transportation means. The delivery procedure data is sent to the transportation service.

[1132] Step 6:

[1133] The server uses the notification means along with the transportation data to notify the user's device of the design creation, manufacturing progress, and delivery status in real time, allowing the user to always be aware of the status of their order. Notification data is sent to the device using push notification technology (e.g., Firebase Cloud Messaging).

[1134] Step 7:

[1135] The recycling process implements a process to promote reuse of waste materials in the manufacturing process. The server collects recycling data and optimizes the recycling process. The recycling data is analyzed and reported.

[1136] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1137] The present invention provides a system that allows users to easily design, manufacture, and receive aftermarket parts for automobiles, and further has a function for recognizing the emotions of users.

[1138] System Configuration

[1139] The system consists of the following main components:

[1140] 1. User Device

[1141] 2. Server

[1142] 3. Generative AI Models

[1143] 4. Image Recognition Technology

[1144] 5. Manufacturer Interface

[1145] 6. EC Service Interface

[1146] 7. Emotion Engine

[1147] Input Method

[1148] The user terminal provides an interface for the user to input their ideal aftermarket parts requirements, usage, and lifestyle. This interface includes text input, drop-down menus, sliders, etc.

[1149] Examples:

[1150] The user enters their vehicle type, daily driving habits, and preferred design tastes into the input screen.

[1151] Emotion Engine

[1152] It is equipped with an emotion engine that analyzes user input and emotional data from the device, allowing it to analyze the user's emotional state in real time, and adjust the output of generative AI models and image recognition technology based on the emotional data.

[1153] Examples:

[1154] If the user is feeling stressed while typing, the emotion engine will detect this and suggest easier options.

[1155] Generative AI Models

[1156] The server receives input data sent from the user's device and the analysis results of the emotion engine, and passes them to the generative AI model for analysis. The generative AI model generates the ideal aftermarket part design based on the user's input data and emotional state.

[1157] Examples:

[1158] Based on user input and emotional data, the generative AI model designs a spoiler that is "highly safe but also takes aesthetics into consideration."

[1159] Image Recognition Method

[1160] The generated design is then run through image recognition technology by the server, which compares it to previous custom photos to match it to realistic manufacturability.

[1161] Examples:

[1162] Image recognition technology has determined that "this spoiler design is similar to past successful designs and has a high manufacturing success rate."

[1163] Manufacturing request method

[1164] After the design is optimized, the server sends a manufacturing request through the manufacturer's interface, which includes CAD data and manufacturing instructions.

[1165] Examples:

[1166] The server transmits request data to the manufacturer, such as "Please manufacture a spoiler with this design."

[1167] Delivery method

[1168] Once the parts are manufactured, they are sent via the EC service interface, and the server notifies the user of the delivery tracking number.

[1169] Examples:

[1170] The server, upon receiving the completion notification from the manufacturer, simultaneously requests delivery from the EC service and notifies the user that "the spoiler is currently being delivered."

[1171] Monitoring Methods

[1172] The server monitors the manufacturing progress and notifies users in real time of the manufacturing status, allowing them to see the current stage of their order.

[1173] Examples:

[1174] The server automatically receives status updates from the manufacturer and notifies the user that "production is currently underway."

[1175] This invention allows users to easily design, manufacture, and receive aftermarket parts that suit their needs, eliminating the high costs and time involved. Emotion recognition capabilities further personalize the user experience, increasing satisfaction. Furthermore, recycling waste materials and monitoring manufacturing processes promote diversity and sustainability.

[1176] The processing flow will be explained below.

[1177] Program processing steps

[1178] Step 1: User Input

[1179] User: Fill out the form with requirements for custom car parts, intended use, and lifestyle.

[1180] Terminal: Collects the data entered by the user and sends it to the server.

[1181] Step 2: Collecting Emotional Data

[1182] Device: Collects emotional data based on sensors on the device and user interactions.

[1183] Device: Sends emotion data to the server.

[1184] Step 3: Data reception and analysis

[1185] Server: Receives input data and emotion data sent by the user and checks the integrity of the data.

[1186] Step 4: Sending data to the generative AI model

[1187] Server: Passes the received input data and emotion data to the generative AI model engine and requests analysis and design generation.

[1188] Step 5: Aftermarket parts design generation

[1189] Generative AI model: Generates optimal aftermarket part designs based on user input and sentiment data.

[1190] Server: Receives the generated design data.

[1191] Step 6: Image recognition of the design

[1192] Server: The generated design is analyzed using image recognition technology and compared with previous custom photos to assess realistic manufacturability.

[1193] Step 7: Optimize and suggest

[1194] Image recognition technology: Evaluate whether a design is realistically manufacturable and adjust the design if necessary.

[1195] Server: Prepare the tuned and optimized design.

[1196] Step 8: Prepare the manufacturing order

[1197] Server: Converts the optimized design into data to send to the manufacturer and formats manufacturing instructions.

[1198] Step 9: Submitting a manufacturing request

[1199] Server: Sends manufacturing requests through the manufacturer's interface.

[1200] Step 10: Production monitoring

[1201] Server: Monitors progress and receives status updates from the manufacturer.

[1202] Step 11: Receive notification of manufacturing completion

[1203] Server: Receives "Manufacturing Completion Notification" from the manufacturer and confirms that the part is complete.

[1204] Step 12: Prepare for shipping

[1205] Server: Passes data to the e-commerce service to process the shipping of the completed parts.

[1206] Step 13: Submit a shipping request

[1207] Server: Calls the API of the e-commerce service and sends a delivery request to send the parts to the user.

[1208] Step 14: Notification of shipping tracking information

[1209] Server: Receives the shipping tracking number from the e-commerce service and notifies the user.

[1210] Step 15: User Receipt

[1211] User: Waits for the part to be delivered and then receives it.

[1212] The above are the specific process steps for implementing the present invention. These detailed process steps allow users to easily and efficiently design, manufacture, and receive sophisticated custom parts. Furthermore, emotion recognition capabilities further personalize the user experience, increasing satisfaction.

[1213] Example 2

[1214] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1215] Modern consumers are increasingly seeking customizable products that fit their personal tastes. However, traditional product design and manufacturing systems have struggled to generate personalized designs that take into account the user's emotional state. Furthermore, efficient systems are needed to confirm manufacturability and reuse waste materials. This has hindered efforts to improve user experience and reduce environmental impact.

[1216] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an input means for the user to input ideal product conditions, usage purpose, and lifestyle; a generative model means for analyzing the user's input data and emotional data and automatically generating an ideal product design; and an image recognition means for applying image recognition technology to the generated design to propose and design an optimal product. This enables the generation of personalized product designs that correspond to the user's preferences and emotional state. Furthermore, by supporting evaluation of the manufacturability of the generated design and the reuse of waste materials during the manufacturing stage, it is possible to simultaneously achieve an improved user experience and a reduced environmental impact.

[1217] "Ideal product conditions" refer to the specific product features and requirements desired by users.

[1218] "Usage" refers to information about how a user will use the product.

[1219] "Lifestyle" refers to information that indicates the user's lifestyle and preferences.

[1220] "Input means" refers to an interface for a user to input data.

[1221] "Input data" refers to information provided by the user, such as product conditions, intended use, and lifestyle.

[1222] "Emotion data" refers to information for analyzing the user's emotional state.

[1223] "Generative model means" refers to a means for analyzing input data and emotional data and automatically generating product designs.

[1224] "Image recognition technology" refers to the technology that analyzes and evaluates the generated design.

[1225] "Image recognition means" refers to a means for optimizing and evaluating designs generated using image recognition technology.

[1226] "Manufacturer Interface" refers to the means by which the generated design is transmitted to the manufacturer.

[1227] "Manufacturing request means" refers to a means for requesting a product design from a manufacturer.

[1228] "Delivery means" refers to a means for delivering manufactured products to users.

[1229] "Comparison means" refers to a means for comparing historical product data with the generated design to evaluate manufacturability.

[1230] "Recycling means" refers to means of reusing waste materials to manufacture products.

[1231] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention provides a system that allows users to easily design, manufacture, and receive products, and further has a function for recognizing user emotions.

[1232] System Configuration

[1233] The system consists of the following main components:

[1234] 1. User Device

[1235] 2. Server

[1236] 3. Generative AI Models

[1237] 4. Image Recognition Technology

[1238] 5. Manufacturer Interface

[1239] 6. Delivery method

[1240] 7. Emotion Engine

[1241] 8. Means of comparison

[1242] 9. Recycling methods

[1243] Input Method

[1244] The user terminal provides an interface for the user to input ideal product requirements, usage purposes, and lifestyles, including text input, drop-down menus, and sliders.

[1245] Examples:

[1246] Based on their needs, users select the product type ("Custom Furniture"), intended use ("Living Room"), and preferred design style ("Modern and Minimal") on the input screen.

[1247] Emotion Engine

[1248] It is equipped with an emotion engine that analyzes user input and emotional data from the device, allowing it to analyze the user's emotional state in real time and adjust the output of generative AI models and image recognition technology.

[1249] Examples:

[1250] If the user is stressed while typing, the emotion engine will detect this and offer easier options.

[1251] Generative AI Models

[1252] The server receives input data sent from the user's device and the analysis results of the emotion engine, and passes them to the generative AI model for analysis. The generative AI model generates an ideal product design based on the user's input data and emotional state.

[1253] Examples:

[1254] Based on user input and emotional data, the generative AI model designs "modern furniture that is highly functional and well-designed."

[1255] Image Recognition Method

[1256] The generated design is then run through image recognition technology by the server, which compares it with historical product data to ensure realistic manufacturability.

[1257] Examples:

[1258] Image recognition technology determines that "this furniture design is similar to past successful designs and has a high manufacturing success rate."

[1259] Manufacturing request method

[1260] After the design is optimized, the server sends a manufacturing request through the manufacturer's interface, which includes the CAD data and manufacturing instructions.

[1261] Examples:

[1262] The server sends request data to the manufacturer saying, "Please manufacture furniture with this design."

[1263] Delivery method

[1264] Once the manufacturing is complete, the server will process the delivery of the product and notify the user of the delivery tracking number.

[1265] Examples:

[1266] The server receives a completion notification from the manufacturer and simultaneously requests delivery, notifying the user that "The furniture is currently being delivered. The tracking number is XXXXXXX."

[1267] Monitoring Methods

[1268] The server monitors the manufacturing progress and notifies users in real time of the manufacturing status, allowing them to see the current stage of their order.

[1269] Examples:

[1270] The server automatically receives status updates from the manufacturer and notifies the user, "Currently in production."

[1271] This allows users to easily design, manufacture, and receive their own personalized products, eliminating the high costs and time required. Emotion recognition capabilities further personalize the user experience, increasing satisfaction. Furthermore, recycling waste materials and monitoring the manufacturing process promote diversity and sustainability.

[1272] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1273] Step 1:

[1274] The user inputs their ideal product requirements, intended use, and lifestyle. Input methods include text entry, drop-down menus, and sliders. When the user selects one of these, the device collects the input data.

[1275] Specific behavior:

[1276] The user inputs the car model "Custom Car," the intended use "daily use," and the design taste "sporty and elegant" into the input screen. The input data includes the car model, intended use, and design taste.

[1277] Input: Data provided by a user through an input interface.

[1278] Output: The user device is ready to send the collected data to the server.

[1279] Step 2:

[1280] The user terminal sends the collected input data to the server using a secure data transfer protocol (e.g., HTTPS).

[1281] Specific behavior:

[1282] After the input data is entered, the terminal encrypts the data and sends it to the server. The transmitted data includes the vehicle model, intended use, and design taste.

[1283] Input: Input data collected by the user terminal.

[1284] Output: The data is encrypted and sent to the server.

[1285] Step 3:

[1286] The server receives the input data along with the user's emotional data, which is then analyzed by the emotion engine. Emotional data is collected from the user's typing speed and other biometric information.

[1287] Specific behavior:

[1288] The server uses an emotion engine to detect emotions such as "stress" or "relaxation" from the user's input speed and operation patterns. The emotion engine analyzes real-time data and uses an algorithm to determine the user's emotional state.

[1289] Input: Input data and emotion data sent from the user device.

[1290] Output: Parsed emotional state (e.g. "High Stress").

[1291] Step 4:

[1292] Based on the analysis results, the server passes the data to a generative AI model for further analysis, which then generates an optimal product design based on the user's input data and emotional state.

[1293] Specific behavior:

[1294] The server sends the emotion analysis results and input data to the generative AI model, which then sets a "relaxing design suitable for users who are feeling stressed" as the generation prompt. The generative AI model then generates the optimal design, taking the user's emotions into consideration.

[1295] Input: Input data and sentiment analysis results.

[1296] Output: Generated product design (e.g. "Relaxed and sporty design").

[1297] Step 5:

[1298] The server then runs the generated design through image recognition technology, which compares it with past successful designs to assess its manufacturability.

[1299] Specific behavior:

[1300] The server processes the generated design using image recognition technology and compares it with a database of past products. An algorithm is then run to obtain an evaluation such as "This design is likely to be manufactured."

[1301] Input: Product design from a generative AI model.

[1302] Output: Manufacturability assessment result (e.g., "Highly manufacturable").

[1303] Step 6:

[1304] The server sends the optimized design to the manufacturer interface and issues a manufacturing request, which includes CAD data and manufacturing instructions.

[1305] Specific behavior:

[1306] After receiving the evaluation results, the server prepares CAD data for the generated design and requests the manufacturer to "manufacture a product with this design."

[1307] Input: Design assessed for manufacturability, CAD data.

[1308] Output: The manufacturing request data is sent to the manufacturer.

[1309] Step 7:

[1310] While the manufacturing is in progress, the server monitors the manufacturing progress and notifies the user in real time.

[1311] Specific behavior:

[1312] The server automatically receives status updates from the manufacturer and notifies the user as soon as a new situation is confirmed, e.g., a message such as "Currently in production."

[1313] Input: Progress updates from the manufacturer.

[1314] Output: Real-time manufacturing progress notification.

[1315] Step 8:

[1316] Once the manufacturing is complete, the server will process the delivery of the product and notify the user of the delivery tracking number.

[1317] Specific behavior:

[1318] The server receives a completion notification from the manufacturer, makes a request to the delivery company, and notifies the user that "The product is on its way. The tracking number is XXXXXXX."

[1319] Input: Notice of completion from manufacturer.

[1320] Output: Notification of shipping process and tracking information.

[1321] (Application example 2)

[1322] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1323] Conventional aftermarket parts design systems generate designs based on static input data without considering the user's emotional state, which prevents them from fully enhancing user satisfaction. Furthermore, they lack the ability to notify users of the manufacturing progress in real time, making it difficult for users to check the current stage of their order. This can result in a poor user experience and an increase in order cancellations and complaints.

[1324] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an input means for the user to input the ideal aftermarket parts conditions, usage purpose, and lifestyle; a generative model means for analyzing the user's input data and emotional state and automatically generating the ideal aftermarket parts design; an image recognition means for applying image recognition technology to the generated design to propose and design the optimal part; a manufacturing request means for sending the optimized part design to a manufacturer and requesting manufacturing; a monitoring means for monitoring the delivery status of the manufactured part and notifying the user in real time; and a delivery means for delivering the manufactured part to the user. This enables the generation of a personalized design based on the user's emotions and the user to be notified of the manufacturing progress in real time.

[1325] The "input means" is a means for providing an interface through which the user can input the desired aftermarket parts requirements, intended use, lifestyle, and the like.

[1326] The "generative model means" is a means for analyzing the user's input data and emotional state, and automatically generating an ideal aftermarket part design.

[1327] "Image recognition means" is a means of applying image recognition technology to the generated design to propose and design the optimal parts.

[1328] The "manufacturing request means" is a means for transmitting the optimized part design to a manufacturer and requesting manufacturing.

[1329] The "monitoring means" is a means for monitoring the delivery status of manufactured parts and notifying the user in real time.

[1330] "Delivery means" refers to a means for delivering manufactured parts to users.

[1331] "Emotional state" is data that indicates the state of a user's emotions as analyzed in real time.

[1332] The system of the present invention is a multi-functional platform for users to design, manufacture, and receive aftermarket automotive parts. The system consists of the following major components:

[1333] System Configuration

[1334] 1. User Input Method

[1335] The user uses the terminal to input the desired aftermarket parts requirements (e.g., vehicle type, driving habits, design preferences), usage, lifestyle, etc. The terminal interface includes text input, drop-down menus, sliders, etc.

[1336] 2. Emotion analysis method

[1337] The system analyzes the user's emotional state in real time from the user's input data and camera footage. This analysis is performed using facial recognition technology and an emotion analysis model. The software used includes OpenCV (facial recognition) and Keras (emotion analysis model).

[1338] 3. Generative Modeling Methods

[1339] The server receives the user's input data and emotional state and inputs it into a generative AI model. This model generates the ideal aftermarket part design incorporating the user's emotional state to increase user satisfaction. The tool used is Keras.

[1340] 4. Image Recognition Methods

[1341] The server then subjects the generated design to image recognition technology to assess its real-world manufacturability. Image recognition technology compares it with past custom photos to determine whether the generated design is manufacturable.

[1342] 5. Manufacturing request method

[1343] The optimized part design is then sent from the server to the manufacturer for manufacturing, along with the CAD data and manufacturing instructions. The manufacturing request is made via an HTTP request using the requests library.

[1344] 6. Monitoring Methods

[1345] The server monitors the manufacturing progress and notifies the user of the status in real time. It automatically receives status updates from the manufacturer and notifies the user every time, improving the user experience.

[1346] 7. Delivery method

[1347] Once manufactured, the parts are delivered to the user via the EC service interface. The server monitors the delivery status in real time and notifies the user of the delivery tracking number.

[1348] Specific examples

[1349] This example shows a user using a smartphone to design an aftermarket car part. The user inputs their car model (Toyota Prius), driving habits (daily short distance driving), and design preferences (modern and simple). At the same time, the user's emotional state (relaxed) is analyzed from camera footage, and a generative AI model uses this data to generate the ideal part design. The generated design is then verified for real-world manufacturability using image recognition technology, and the optimized design is finally sent to the manufacturer. The manufacturing progress is notified to the user in real time via the server, and the completed part is delivered to the user via delivery means.

[1350] Prompt Sentence Examples

[1351] Create the optimal aftermarket part design based on the following information: The user's car model is "Toyota Prius," their driving habit is "daily short distance driving," and their design preference is "modern and simple." Furthermore, the user's emotional state has been detected as "relaxed." Based on all this information, generate the ideal aftermarket part design.

[1352] This invention enables personalized design generation based on the user's emotions and allows the user to be notified of the manufacturing progress in real time, thereby significantly improving user satisfaction.

[1353] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1354] Step 1:

[1355] The user uses a terminal to input their ideal aftermarket parts requirements, intended use, and lifestyle. Input methods include text input, drop-down menus, sliders, etc. The input data is sent from the terminal to the server.

[1356] Input: User data such as car model, driving habits, design preferences, etc.

[1357] Output: User data sent to the server

[1358] Step 2:

[1359] The device captures the user's facial image using a camera and transmits the image in real time to the emotion analysis means, which uses face recognition technology (OpenCV) and emotion analysis model (Keras) to analyze the user's emotional state.

[1360] Input: User's face image

[1361] Output: Parsed emotional state data

[1362] Step 3:

[1363] The server combines the user data received in step 1 with the emotional state data received in step 2 and inputs this into a generative AI model (Keras). The generative AI model generates the ideal aftermarket part design based on the data.

[1364] Input: Integrated user data and emotional state data

[1365] Output: Generated aftermarket parts design

[1366] Step 4:

[1367] The server runs the generated design through image recognition and compares it with past custom photos to assess manufacturability. Image recognition technology is used to compare the realistic manufacturability of the generated design with past success stories.

[1368] Input: Generated aftermarket parts design

[1369] Output: Designs that are evaluated as manufacturable

[1370] Step 5:

[1371] The server sends the optimized design that is evaluated as manufacturable to the manufacturer through the manufacturing request means, including the necessary CAD data and manufacturing instructions, using an HTTP request (requests library).

[1372] Input: Designs that are assessed as manufacturable

[1373] Output: Manufacturing request data sent to the manufacturer

[1374] Step 6:

[1375] The server monitors the manufacturing progress through a monitoring means and automatically receives status updates from the manufacturer, keeping the user informed of the manufacturing progress in real time.

[1376] Input: Production progress data from the manufacturer

[1377] Output: Notify the user of the manufacturing progress

[1378] Step 7:

[1379] Once manufactured, the parts are delivered via the server's EC service interface. The delivery status is monitored in real time, and the user is notified of the delivery tracking number.

[1380] Input: Manufacturing completion notification and delivery request

[1381] Output: Delivery tracking number notification to user

[1382] This processing step allows users to easily create and receive personalized aftermarket part designs based on their emotions.

[1383] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1384] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1385] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1386] [Fourth embodiment]

[1387] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1388] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1389] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1390] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1391] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1392] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1393] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1394] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1395] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1396] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1397] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1398] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1399] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1400] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention is a system that allows users to easily design, manufacture, and receive aftermarket parts for automobiles.

[1401] System Configuration

[1402] The system consists of the following main components:

[1403] 1. User Device

[1404] 2. Server

[1405] 3. Generative AI Models

[1406] 4. Image Recognition Technology

[1407] 5. Manufacturer Interface

[1408] 6. EC Service Interface

[1409] Input Method

[1410] The user terminal provides an interface for the user to input their ideal aftermarket parts requirements, usage, and lifestyle. This interface includes text input, drop-down menus, sliders, etc.

[1411] Examples:

[1412] The user enters their vehicle type, daily driving habits, and preferred design tastes into the input screen.

[1413] Generative AI Models

[1414] The server receives input data sent from the user's device and passes it to the generative AI model for analysis. The generative AI model generates the ideal aftermarket part design based on the user's input data.

[1415] Examples:

[1416] The AI ​​model generated from user input designs a spoiler that improves aerodynamic performance at high speeds.

[1417] Image Recognition Method

[1418] The generated design is then run through image recognition technology by the server, which compares it to previous custom photos to match it to realistic manufacturability.

[1419] Examples:

[1420] Image recognition technology has determined that "this spoiler design is highly manufacturable because it is similar to designs that have been successful in the past."

[1421] Manufacturing request method

[1422] After the design is optimized, the server sends a manufacturing request through the manufacturer's interface, which includes CAD data and manufacturing instructions.

[1423] Examples:

[1424] The server transmits request data to the manufacturer, such as "Please manufacture a spoiler with this design."

[1425] Delivery method

[1426] Once the parts are manufactured, they are sent via the EC service interface, and the server notifies the user of the delivery tracking number.

[1427] Examples:

[1428] The server, upon receiving the completion notification from the manufacturer, simultaneously requests delivery from the EC service and notifies the user that "the spoiler is currently being delivered."

[1429] Monitoring Methods

[1430] The server monitors the manufacturing progress and notifies users in real time of the manufacturing status, allowing them to see the current stage of their order.

[1431] Examples:

[1432] The server automatically receives status updates from the manufacturer and notifies the user that "production is currently underway."

[1433] This invention allows users to easily design, manufacture, and receive aftermarket parts that suit their needs, eliminating the high costs and time involved. It also promotes diversity and sustainability by recycling waste materials and monitoring the manufacturing process.

[1434] The processing flow will be explained below.

[1435] Program processing steps

[1436] Step 1: User Input

[1437] User: Fill out the form with requirements for custom car parts, intended use, and lifestyle.

[1438] Terminal: Collects the data entered by the user and sends it to the server.

[1439] Step 2: Data reception and analysis

[1440] Server: Receives user data sent from the device and checks the integrity of the data.

[1441] Step 3: Sending data to the generative AI model

[1442] Server: Passes the received data to the generative AI model engine and requests analysis and design generation.

[1443] Step 4: Aftermarket parts design generation

[1444] Generative AI model: Generates optimal aftermarket part designs based on user input data.

[1445] Server: Receives the generated design data.

[1446] Step 5: Image recognition of the design

[1447] Server: The generated design is analyzed using image recognition technology and compared with previous custom photos to assess realistic manufacturability.

[1448] Step 6: Optimize and suggest

[1449] Image recognition technology: Evaluate whether a design is realistically manufacturable and adjust the design if necessary.

[1450] Server: Prepare the tuned and optimized design.

[1451] Step 7: Prepare the manufacturing order

[1452] Server: Converts the optimized design into data to send to the manufacturer and formats manufacturing instructions.

[1453] Step 8: Submitting a manufacturing request

[1454] Server: Sends manufacturing requests through the manufacturer's interface.

[1455] Step 9: Production monitoring

[1456] Server: Monitors progress and receives status updates from the manufacturer.

[1457] Step 10: Receive notification of manufacturing completion

[1458] Server: Receives "Manufacturing Completion Notification" from the manufacturer and confirms that the part is complete.

[1459] Step 11: Prepare for shipping

[1460] Server: Passes data to the e-commerce service to process the shipping of the completed parts.

[1461] Step 12: Submit a shipping request

[1462] Server: Calls the API of the e-commerce service and sends a delivery request to send the parts to the user.

[1463] Step 13: Notification of shipping tracking information

[1464] Server: Receives the shipping tracking number from the e-commerce service and notifies the user.

[1465] Step 14: User Receipt

[1466] User: Waits for the part to be delivered and then receives it.

[1467] This detailed process step allows users to design, manufacture and receive aftermarket car parts simply and efficiently, and the system also monitors and keeps users informed of the manufacturing process and delivery in real time.

[1468] Example 1

[1469] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1470] In the conventional automotive aftermarket parts market, it has been difficult for users to easily realize their own preferred part designs. Furthermore, users have to manage many steps themselves, such as checking manufacturability and manufacturing progress, and handling delivery procedures, which is time-consuming and costly. There is a need for a system that solves these issues and allows users to easily design, manufacture, and receive their ideal aftermarket parts.

[1471] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1472] In this invention, the server includes input means for a user to input ideal conditions, usage, and lifestyle for automobile aftermarket parts, generation AI model means for analyzing the input data sent from the user terminal and automatically generating an ideal aftermarket part design, image recognition means for applying image recognition technology to the generated design and comparing it with past custom cases to evaluate manufacturability, manufacturing request means for sending the evaluated part design to a manufacturer and requesting manufacturing, and delivery means for delivering the manufactured parts to the user. This allows users to eliminate the problems of high cost and time and easily design, manufacture, and receive aftermarket parts that suit their preferences.

[1473] The "input means" is an interface that allows the user to input ideal conditions, usage purposes, and lifestyles regarding aftermarket parts for automobiles.

[1474] The "generative AI model means" is a model equipped with a highly accurate machine learning algorithm that analyzes input data sent from the user's device and automatically generates the ideal aftermarket part design.

[1475] The "image recognition means" is a technology for subjecting the generated design to image recognition technology, comparing it with past custom examples, and evaluating manufacturability.

[1476] The "manufacturing request means" is an interface and communication means for transmitting the evaluated part design to a manufacturer and making a manufacturing request.

[1477] "Delivery vehicle" is the logistics and notification system for delivering manufactured parts to users.

[1478] The "monitoring means" is a system for monitoring the manufacturing progress in real time and notifying the user of the manufacturing status.

[1479] "Recycling means" refers to processing technologies and systems for reusing waste materials to manufacture aftermarket parts.

[1480] This invention is a system that allows users to easily design, manufacture, and receive aftermarket automotive parts. The system consists of a user terminal, a server, a generative AI model, image recognition technology, a manufacturer interface, and an e-commerce service interface.

[1481] Users use a dedicated interface to input information about their ideal requirements for aftermarket parts, their intended use, and their lifestyle. The interface includes text input, drop-down menus, and sliders. For example, a user can input the following criteria for a Toyota Prius: "daily city driving" and "sporty design."

[1482] The user's device then sends the input information to a server. The server analyzes the received input data and passes it to a generative AI model. The generative AI model uses advanced machine learning algorithms to automatically generate the ideal aftermarket part design based on the user's requirements. For example, the generative AI model can generate a "sporty wheel suitable for city driving."

[1483] The server then uses image recognition technology to compare the generated design with past customizations and evaluate its manufacturability. For example, a generated wheel design may be evaluated to see if it is similar to a previously successful design and be deemed highly manufacturable.

[1484] After the design is evaluated, the server sends a manufacturing request to the manufacturer's interface. This request includes the necessary CAD data and manufacturing instructions. Once manufacturing is complete, the server receives a notification from the manufacturer and processes the delivery through the e-commerce service interface. The user is notified of the delivery tracking number for the completed aftermarket part. For example, the user can receive a notification that "the wheels are currently being delivered."

[1485] The server also monitors the manufacturing progress in real time and notifies the user of the manufacturing status. For example, the server notifies the user of the manufacturing progress status by saying "Currently in manufacturing."

[1486] Furthermore, this system also includes a means for recycling waste materials, promoting a sustainable manufacturing process. Thus, the present invention allows users to easily design, manufacture, and receive aftermarket parts that suit their needs, eliminating the problems of high cost and time.

[1487] Example prompt sentence:

[1488] "Design a sporty wheel for the Toyota Prius that is suitable for city driving."

[1489] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1490] Step 1:

[1491] The user inputs their ideal conditions for aftermarket parts for automobiles, their intended use, and their lifestyle.

[1492] Input: The user inputs their preferences and requirements (e.g., car model, usage scenario, design preferences) into the user terminal.

[1493] What happens: A user uses the device interface (text entry, drop-down menus, sliders, etc.) to enter criteria such as "Toyota Prius," "everyday city driving," and "sporty design."

[1494] Output: The user device is ready to send the entered data to the server.

[1495] Step 2:

[1496] The user terminal sends the input data to the server.

[1497] Input: When the user presses the input completion button, the user terminal generates the data to be sent.

[1498] Specific operation: The user presses the "Send" button, and the device sends data such as "Toyota Prius," "daily city driving," and "sporty design" to the server.

[1499] Output: The input data arrives at the server.

[1500] Step 3:

[1501] The server passes the received data to the generative AI model to generate the design.

[1502] Input: The server analyzes the data sent from the user device and passes it to the generative AI model.

[1503] Specific operation: The server inputs the data "Toyota Prius," "everyday city driving," and "sporty design" into the generative AI model.

[1504] Data processing: The generative AI model runs machine learning algorithms on the received data to generate the ideal aftermarket part design.

[1505] Output: Generated design data (e.g., "sporty wheels suitable for city driving") is generated.

[1506] Step 4:

[1507] The generated design is then subjected to image recognition technology to assess manufacturability.

[1508] Input: The server receives the generated design data.

[1509] What happens: The server passes the generated wheel design to image recognition technology.

[1510] Data processing: Image recognition technology compares the generated design with previous custom examples to assess manufacturability.

[1511] Output: Returns a design that is assessed as having high manufacturability (e.g., "This wheel design is highly manufacturable because it is similar to a previously successful design").

[1512] Step 5:

[1513] The server sends the manufacturing request to the manufacturer's interface.

[1514] Input: The server receives the design data that has been evaluated as manufacturable.

[1515] Specific operation: The server sends data to the manufacturer saying, "Please manufacture a wheel with this design."

[1516] Data processing: Formatting the data to include necessary CAD data and manufacturing instructions.

[1517] Output: Data representing the production request is sent to the manufacturer.

[1518] Step 6:

[1519] Monitor production progress and notify users

[1520] Input: Status update data from the manufacturer is received by the server.

[1521] Specific behavior: The server receives a status update from the manufacturer, such as "Currently in production," and notifies the user of this information.

[1522] Data processing: Converting status information into an appropriate format and sending it to the user interface.

[1523] Output: The user can see the progress (e.g. "Currently in production") in real time.

[1524] Step 7:

[1525] Deliver manufactured parts and notify users

[1526] Input: The server receives notification data of manufacturing completion.

[1527] Specific operation: The server receives a notification of completion of production from the manufacturer and processes the delivery through the EC service interface.

[1528] Data processing: Generate delivery request data and send it to the e-commerce service.

[1529] Output: The user receives a notification with a shipping tracking number (e.g. "Your wheels are on their way").

[1530] (Application example 1)

[1531] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1532] Conventional customization of aftermarket parts for automobiles requires advanced specialized knowledge and skills, making it difficult for ordinary users to design parts to suit their preferences. It is also difficult to check the feasibility and progress of manufacturing, so a system that allows users to easily order and receive custom parts was needed.

[1533] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1534] In this invention, the server includes a data input means for a user to input the ideal after-market part requirements, intended use, and lifestyle, a generation model means for analyzing the user's input data and automatically generating the ideal after-market part design, an image recognition means for applying image recognition technology to the generated design to propose and design the optimal part, a manufacturing request means for transmitting the optimized part design to a manufacturer and requesting manufacturing, a transportation means for delivering the manufactured part to the user, a notification means for notifying the user's mobile information terminal in real time of the design generation and manufacturing progress status, a comparison means for comparing with past custom design data and evaluating actual manufacturability, and a recycling processing means for manufacturing parts using recycled materials. This enables users to easily design, manufacture, and receive their preferred after-market parts without specialized knowledge.

[1535] The "data input means" is an interface that allows users to input their ideal aftermarket parts requirements, intended use, and lifestyle.

[1536] The "generative model means" is an AI model that analyzes user input data and automatically generates ideal aftermarket part designs.

[1537] "Image recognition means" is a technology that applies image recognition technology to the generated design to propose and design the optimal parts.

[1538] The "manufacturing request means" is a system for transmitting the optimized part design to a manufacturer and requesting manufacturing.

[1539] "Transportation" means the means for delivering manufactured parts to users.

[1540] "Notification means" refers to a technology for notifying the user of the design creation and manufacturing progress status in real time via their mobile information terminal.

[1541] "Comparison means" refers to a technique for comparing with past custom design data to evaluate actual manufacturability.

[1542] "Recycling means" refers to technology for reusing waste materials to manufacture parts.

[1543] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention provides a system that allows users to easily design, manufacture, and receive aftermarket parts for automobiles.

[1544] System Configuration

[1545] The system consists of the following major components:

[1546] 1. User data entry method

[1547] 2. Server

[1548] 3. Generative AI Model Means

[1549] 4. Image Recognition Methods

[1550] 5. Manufacturing request method

[1551] 6. Means of transportation

[1552] 7. Means of notification

[1553] 8. Means of comparison

[1554] 9. Recycling methods

[1555] Hardware and software used

[1556] 1. Hardware:

[1557] Mobile information terminal (smartphone)

[1558] Servers on the cloud (general cloud service providers)

[1559] 2. Software:

[1560] Mobile app development frameworks (React Native, Swift, Kotlin, etc.)

[1561] Server infrastructure (AWS, Google Cloud, Azure, etc.)

[1562] Generative AI models (GPT models, etc.)

[1563] Image recognition technology (OpenCV, TensorFlow, etc.)

[1564] EC platform system (general API-based EC system)

[1565] Processing flow

[1566] First, the user uses the data input means to input their ideal aftermarket parts requirements, intended use, and lifestyle into the mobile information terminal application. As an example of input, the user might provide a prompt such as, "I use my Toyota Prius for long-distance weekend drives. Please generate a spoiler design that emphasizes aerodynamic performance."

[1567] The server then receives this input data and passes it to a generative AI model, which then generates a design tailored to the user's needs. The generative AI model used in this step is an advanced text generation model such as GPT-4.

[1568] The generated design is then processed by the server using image recognition tools and compared with previous custom design data. The image recognition tools use OpenCV and TensorFlow. The comparison tool evaluates the manufacturability of the generated design and proposes a realistic manufacturing plan.

[1569] The server then sends the optimized design to the manufacturer via a manufacturing request mechanism, which includes CAD data and manufacturing instructions. Once manufacturing is complete, a transportation mechanism is activated to deliver the manufactured parts to the user.

[1570] Furthermore, the server uses a notification means to notify the user's mobile information device of the design creation and manufacturing progress in real time, using push notification technology (e.g., Firebase Cloud Messaging).

[1571] Finally, recycling processes provide a mechanism for reusing waste materials to manufacture parts, resulting in an environmentally friendly and sustainable system.

[1572] Specific examples

[1573] The user inputs the following into the application: "I drive a Toyota Prius on long weekend drives. Please generate a spoiler design that emphasizes aerodynamic performance." Based on this prompt, the generative AI model generates a design, which is then evaluated for manufacturability using image recognition. Once the evaluation is complete, a manufacturing request is sent, and once completed, the user receives a real-time notification and the product is delivered.

[1574] This provides a system that allows users to easily design, manufacture, and receive aftermarket parts to their liking, even without specialized knowledge.

[1575] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1576] Step 1:

[1577] The user starts the application on the mobile information terminal and inputs the ideal aftermarket parts requirements, usage, and lifestyle. As an input example, a prompt sentence is provided: "I use a Toyota Prius for long-distance weekend drives. Please generate a spoiler design that emphasizes aerodynamic performance." This input data is sent from the terminal to the server.

[1578] Step 2:

[1579] The server passes the input data received from the terminal to the generative AI model means. The generative AI model (e.g., a GPT-based model) uses this data to generate aftermarket part designs tailored to the user's needs. Here, the model performs analysis and data generation based on the prompt statement, and outputs the design data.

[1580] Step 3:

[1581] The generated design is returned to the server, which then runs it through an image recognition tool (using OpenCV or TensorFlow) to compare the generated design with past custom design data and evaluate its manufacturability. The tool analyzes the image data, performs comparison calculations, and outputs the manufacturability evaluation results.

[1582] Step 4:

[1583] If the server confirms that the design proposed by the generative AI model is manufacturable based on the optimized part design and the manufacturability evaluation results, it sends a manufacturing request to the manufacturer via the manufacturing request means. This request includes CAD data and manufacturing instructions. The manufacturing request data is sent.

[1584] Step 5:

[1585] The manufacturer receives the manufacturing request and manufactures the parts. Once manufacturing is complete, the manufacturer sends a manufacturing completion notification to the server. The server receives this notification and arranges for the parts to be delivered to the user using a transportation means. The delivery procedure data is sent to the transportation service.

[1586] Step 6:

[1587] The server uses the notification means along with the transportation data to notify the user's device of the design creation, manufacturing progress, and delivery status in real time, allowing the user to always be aware of the status of their order. Notification data is sent to the device using push notification technology (e.g., Firebase Cloud Messaging).

[1588] Step 7:

[1589] The recycling process implements a process to promote reuse of waste materials in the manufacturing process. The server collects recycling data and optimizes the recycling process. The recycling data is analyzed and reported.

[1590] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1591] The present invention provides a system that allows users to easily design, manufacture, and receive aftermarket parts for automobiles, and further has a function for recognizing the emotions of users.

[1592] System Configuration

[1593] The system consists of the following main components:

[1594] 1. User Device

[1595] 2. Server

[1596] 3. Generative AI Models

[1597] 4. Image Recognition Technology

[1598] 5. Manufacturer Interface

[1599] 6. EC Service Interface

[1600] 7. Emotion Engine

[1601] Input Method

[1602] The user terminal provides an interface for the user to input their ideal aftermarket parts requirements, usage, and lifestyle. This interface includes text input, drop-down menus, sliders, etc.

[1603] Examples:

[1604] The user enters their vehicle type, daily driving habits, and preferred design tastes into the input screen.

[1605] Emotion Engine

[1606] It is equipped with an emotion engine that analyzes user input and emotional data from the device, allowing it to analyze the user's emotional state in real time, and adjust the output of generative AI models and image recognition technology based on the emotional data.

[1607] Examples:

[1608] If the user is feeling stressed while typing, the emotion engine will detect this and suggest easier options.

[1609] Generative AI Models

[1610] The server receives input data sent from the user's device and the analysis results of the emotion engine, and passes them to the generative AI model for analysis. The generative AI model generates the ideal aftermarket part design based on the user's input data and emotional state.

[1611] Examples:

[1612] Based on user input and emotional data, the generative AI model designs a spoiler that is "highly safe but also takes aesthetics into consideration."

[1613] Image Recognition Method

[1614] The generated design is then run through image recognition technology by the server, which compares it to previous custom photos to match it to realistic manufacturability.

[1615] Examples:

[1616] Image recognition technology has determined that "this spoiler design is similar to past successful designs and has a high manufacturing success rate."

[1617] Manufacturing request method

[1618] After the design is optimized, the server sends a manufacturing request through the manufacturer's interface, which includes CAD data and manufacturing instructions.

[1619] Examples:

[1620] The server transmits request data to the manufacturer, such as "Please manufacture a spoiler with this design."

[1621] Delivery method

[1622] Once the parts are manufactured, they are sent via the EC service interface, and the server notifies the user of the delivery tracking number.

[1623] Examples:

[1624] The server, upon receiving the completion notification from the manufacturer, simultaneously requests delivery from the EC service and notifies the user that "the spoiler is currently being delivered."

[1625] Monitoring Methods

[1626] The server monitors the manufacturing progress and notifies users in real time of the manufacturing status, allowing them to see the current stage of their order.

[1627] Examples:

[1628] The server automatically receives status updates from the manufacturer and notifies the user that "production is currently underway."

[1629] This invention allows users to easily design, manufacture, and receive aftermarket parts that suit their needs, eliminating the high costs and time involved. Emotion recognition capabilities further personalize the user experience, increasing satisfaction. Furthermore, recycling waste materials and monitoring manufacturing processes promote diversity and sustainability.

[1630] The processing flow will be explained below.

[1631] Program processing steps

[1632] Step 1: User Input

[1633] User: Fill out the form with requirements for custom car parts, intended use, and lifestyle.

[1634] Terminal: Collects the data entered by the user and sends it to the server.

[1635] Step 2: Collecting Emotional Data

[1636] Device: Collects emotional data based on sensors on the device and user interactions.

[1637] Device: Sends emotion data to the server.

[1638] Step 3: Data reception and analysis

[1639] Server: Receives input data and emotion data sent by the user and checks the integrity of the data.

[1640] Step 4: Sending data to the generative AI model

[1641] Server: Passes the received input data and emotion data to the generative AI model engine and requests analysis and design generation.

[1642] Step 5: Aftermarket parts design generation

[1643] Generative AI model: Generates optimal aftermarket part designs based on user input and sentiment data.

[1644] Server: Receives the generated design data.

[1645] Step 6: Image recognition of the design

[1646] Server: The generated design is analyzed using image recognition technology and compared with previous custom photos to assess realistic manufacturability.

[1647] Step 7: Optimize and suggest

[1648] Image recognition technology: Evaluate whether a design is realistically manufacturable and adjust the design if necessary.

[1649] Server: Prepare the tuned and optimized design.

[1650] Step 8: Prepare the manufacturing order

[1651] Server: Converts the optimized design into data to send to the manufacturer and formats manufacturing instructions.

[1652] Step 9: Submitting a manufacturing request

[1653] Server: Sends manufacturing requests through the manufacturer's interface.

[1654] Step 10: Production monitoring

[1655] Server: Monitors progress and receives status updates from the manufacturer.

[1656] Step 11: Receive notification of manufacturing completion

[1657] Server: Receives "Manufacturing Completion Notification" from the manufacturer and confirms that the part is complete.

[1658] Step 12: Prepare for shipping

[1659] Server: Passes data to the e-commerce service to process the shipping of the completed parts.

[1660] Step 13: Submit a shipping request

[1661] Server: Calls the API of the e-commerce service and sends a delivery request to send the parts to the user.

[1662] Step 14: Notification of shipping tracking information

[1663] Server: Receives the shipping tracking number from the e-commerce service and notifies the user.

[1664] Step 15: User Receipt

[1665] User: Waits for the part to be delivered and then receives it.

[1666] The above are the specific process steps for implementing the present invention. These detailed process steps allow users to easily and efficiently design, manufacture, and receive sophisticated custom parts. Furthermore, emotion recognition capabilities further personalize the user experience, increasing satisfaction.

[1667] Example 2

[1668] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1669] Modern consumers are increasingly seeking customizable products that fit their personal tastes. However, traditional product design and manufacturing systems have struggled to generate personalized designs that take into account the user's emotional state. Furthermore, efficient systems are needed to confirm manufacturability and reuse waste materials. This has hindered efforts to improve user experience and reduce environmental impact.

[1670] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an input means for the user to input ideal product conditions, usage purpose, and lifestyle; a generative model means for analyzing the user's input data and emotional data and automatically generating an ideal product design; and an image recognition means for applying image recognition technology to the generated design to propose and design an optimal product. This enables the generation of personalized product designs that correspond to the user's preferences and emotional state. Furthermore, by supporting evaluation of the manufacturability of the generated design and the reuse of waste materials during the manufacturing stage, it is possible to simultaneously achieve an improved user experience and a reduced environmental impact.

[1671] "Ideal product conditions" refer to the specific product features and requirements desired by users.

[1672] "Usage" refers to information about how a user will use the product.

[1673] "Lifestyle" refers to information that indicates the user's lifestyle and preferences.

[1674] "Input means" refers to an interface for a user to input data.

[1675] "Input data" refers to information provided by the user, such as product conditions, intended use, and lifestyle.

[1676] "Emotion data" refers to information for analyzing the user's emotional state.

[1677] "Generative model means" refers to a means for analyzing input data and emotional data and automatically generating product designs.

[1678] "Image recognition technology" refers to the technology that analyzes and evaluates the generated design.

[1679] "Image recognition means" refers to a means for optimizing and evaluating designs generated using image recognition technology.

[1680] "Manufacturer Interface" refers to the means by which the generated design is transmitted to the manufacturer.

[1681] "Manufacturing request means" refers to a means for requesting a product design from a manufacturer.

[1682] "Delivery means" refers to a means for delivering manufactured products to users.

[1683] "Comparison means" refers to a means for comparing historical product data with the generated design to evaluate manufacturability.

[1684] "Recycling means" refers to means of reusing waste materials to manufacture products.

[1685] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention provides a system that allows users to easily design, manufacture, and receive products, and further has a function for recognizing user emotions.

[1686] System Configuration

[1687] The system consists of the following main components:

[1688] 1. User Device

[1689] 2. Server

[1690] 3. Generative AI Models

[1691] 4. Image Recognition Technology

[1692] 5. Manufacturer Interface

[1693] 6. Delivery method

[1694] 7. Emotion Engine

[1695] 8. Means of comparison

[1696] 9. Recycling methods

[1697] Input Method

[1698] The user terminal provides an interface for the user to input ideal product requirements, usage purposes, and lifestyles, including text input, drop-down menus, and sliders.

[1699] Examples:

[1700] Based on their needs, users select the product type ("Custom Furniture"), intended use ("Living Room"), and preferred design style ("Modern and Minimal") on the input screen.

[1701] Emotion Engine

[1702] It is equipped with an emotion engine that analyzes user input and emotional data from the device, allowing it to analyze the user's emotional state in real time and adjust the output of generative AI models and image recognition technology.

[1703] Examples:

[1704] If the user is stressed while typing, the emotion engine will detect this and offer easier options.

[1705] Generative AI Models

[1706] The server receives input data sent from the user's device and the analysis results of the emotion engine, and passes them to the generative AI model for analysis. The generative AI model generates an ideal product design based on the user's input data and emotional state.

[1707] Examples:

[1708] Based on user input and emotional data, the generative AI model designs "modern furniture that is highly functional and well-designed."

[1709] Image Recognition Method

[1710] The generated design is then run through image recognition technology by the server, which compares it with historical product data to ensure realistic manufacturability.

[1711] Examples:

[1712] Image recognition technology determines that "this furniture design is similar to past successful designs and has a high manufacturing success rate."

[1713] Manufacturing request method

[1714] After the design is optimized, the server sends a manufacturing request through the manufacturer's interface, which includes the CAD data and manufacturing instructions.

[1715] Examples:

[1716] The server sends request data to the manufacturer saying, "Please manufacture furniture with this design."

[1717] Delivery method

[1718] Once the manufacturing is complete, the server will process the delivery of the product and notify the user of the delivery tracking number.

[1719] Examples:

[1720] The server receives a completion notification from the manufacturer and simultaneously requests delivery, notifying the user that "The furniture is currently being delivered. The tracking number is XXXXXXX."

[1721] Monitoring Methods

[1722] The server monitors the manufacturing progress and notifies users in real time of the manufacturing status, allowing them to see the current stage of their order.

[1723] Examples:

[1724] The server automatically receives status updates from the manufacturer and notifies the user, "Currently in production."

[1725] This allows users to easily design, manufacture, and receive their own personalized products, eliminating the high costs and time required. Emotion recognition capabilities further personalize the user experience, increasing satisfaction. Furthermore, recycling waste materials and monitoring the manufacturing process promote diversity and sustainability.

[1726] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1727] Step 1:

[1728] The user inputs their ideal product requirements, intended use, and lifestyle. Input methods include text entry, drop-down menus, and sliders. When the user selects one of these, the device collects the input data.

[1729] Specific behavior:

[1730] The user inputs the car model "Custom Car," the intended use "daily use," and the design taste "sporty and elegant" into the input screen. The input data includes the car model, intended use, and design taste.

[1731] Input: Data provided by a user through an input interface.

[1732] Output: The user device is ready to send the collected data to the server.

[1733] Step 2:

[1734] The user terminal sends the collected input data to the server using a secure data transfer protocol (e.g., HTTPS).

[1735] Specific behavior:

[1736] After the input data is entered, the terminal encrypts the data and sends it to the server. The transmitted data includes the vehicle model, intended use, and design taste.

[1737] Input: Input data collected by the user terminal.

[1738] Output: The data is encrypted and sent to the server.

[1739] Step 3:

[1740] The server receives the input data along with the user's emotional data, which is then analyzed by the emotion engine. Emotional data is collected from the user's typing speed and other biometric information.

[1741] Specific behavior:

[1742] The server uses an emotion engine to detect emotions such as "stress" or "relaxation" from the user's input speed and operation patterns. The emotion engine analyzes real-time data and uses an algorithm to determine the user's emotional state.

[1743] Input: Input data and emotion data sent from the user device.

[1744] Output: Parsed emotional state (e.g. "High Stress").

[1745] Step 4:

[1746] Based on the analysis results, the server passes the data to a generative AI model for further analysis, which then generates an optimal product design based on the user's input data and emotional state.

[1747] Specific behavior:

[1748] The server sends the emotion analysis results and input data to the generative AI model, which then sets a "relaxing design suitable for users who are feeling stressed" as the generation prompt. The generative AI model then generates the optimal design, taking the user's emotions into consideration.

[1749] Input: Input data and sentiment analysis results.

[1750] Output: Generated product design (e.g. "Relaxed and sporty design").

[1751] Step 5:

[1752] The server then runs the generated design through image recognition technology, which compares it with past successful designs to assess its manufacturability.

[1753] Specific behavior:

[1754] The server processes the generated design using image recognition technology and compares it with a database of past products. An algorithm is then run to obtain an evaluation such as "This design is likely to be manufactured."

[1755] Input: Product design from a generative AI model.

[1756] Output: Manufacturability assessment result (e.g., "Highly manufacturable").

[1757] Step 6:

[1758] The server sends the optimized design to the manufacturer interface and issues a manufacturing request, which includes CAD data and manufacturing instructions.

[1759] Specific behavior:

[1760] After receiving the evaluation results, the server prepares CAD data for the generated design and requests the manufacturer to "manufacture a product with this design."

[1761] Input: Design assessed for manufacturability, CAD data.

[1762] Output: The manufacturing request data is sent to the manufacturer.

[1763] Step 7:

[1764] While the manufacturing is in progress, the server monitors the manufacturing progress and notifies the user in real time.

[1765] Specific behavior:

[1766] The server automatically receives status updates from the manufacturer and notifies the user as soon as a new situation is confirmed, e.g., a message such as "Currently in production."

[1767] Input: Progress updates from the manufacturer.

[1768] Output: Real-time manufacturing progress notification.

[1769] Step 8:

[1770] Once the manufacturing is complete, the server will process the delivery of the product and notify the user of the delivery tracking number.

[1771] Specific behavior:

[1772] The server receives a completion notification from the manufacturer, makes a request to the delivery company, and notifies the user that "The product is on its way. The tracking number is XXXXXXX."

[1773] Input: Notice of completion from manufacturer.

[1774] Output: Notification of shipping process and tracking information.

[1775] (Application example 2)

[1776] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1777] Conventional aftermarket parts design systems generate designs based on static input data without considering the user's emotional state, which prevents them from fully enhancing user satisfaction. Furthermore, they lack the ability to notify users of the manufacturing progress in real time, making it difficult for users to check the current stage of their order. This can result in a poor user experience and an increase in order cancellations and complaints.

[1778] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an input means for the user to input the ideal aftermarket parts conditions, usage purpose, and lifestyle; a generative model means for analyzing the user's input data and emotional state and automatically generating the ideal aftermarket parts design; an image recognition means for applying image recognition technology to the generated design to propose and design the optimal part; a manufacturing request means for sending the optimized part design to a manufacturer and requesting manufacturing; a monitoring means for monitoring the delivery status of the manufactured part and notifying the user in real time; and a delivery means for delivering the manufactured part to the user. This enables the generation of a personalized design based on the user's emotions and the user to be notified of the manufacturing progress in real time.

[1779] The "input means" is a means for providing an interface through which the user can input the desired aftermarket parts requirements, intended use, lifestyle, and the like.

[1780] The "generative model means" is a means for analyzing the user's input data and emotional state, and automatically generating an ideal aftermarket part design.

[1781] "Image recognition means" is a means of applying image recognition technology to the generated design to propose and design the optimal parts.

[1782] The "manufacturing request means" is a means for transmitting the optimized part design to a manufacturer and requesting manufacturing.

[1783] The "monitoring means" is a means for monitoring the delivery status of manufactured parts and notifying the user in real time.

[1784] "Delivery means" refers to a means for delivering manufactured parts to users.

[1785] "Emotional state" is data that indicates the state of a user's emotions as analyzed in real time.

[1786] The system of the present invention is a multi-functional platform for users to design, manufacture, and receive aftermarket automotive parts. The system consists of the following major components:

[1787] System Configuration

[1788] 1. User Input Method

[1789] The user uses the terminal to input the desired aftermarket parts requirements (e.g., vehicle type, driving habits, design preferences), usage, lifestyle, etc. The terminal interface includes text input, drop-down menus, sliders, etc.

[1790] 2. Emotion analysis method

[1791] The system analyzes the user's emotional state in real time from the user's input data and camera footage. This analysis is performed using facial recognition technology and an emotion analysis model. The software used includes OpenCV (facial recognition) and Keras (emotion analysis model).

[1792] 3. Generative Modeling Methods

[1793] The server receives the user's input data and emotional state and inputs it into a generative AI model. This model generates the ideal aftermarket part design incorporating the user's emotional state to increase user satisfaction. The tool used is Keras.

[1794] 4. Image Recognition Methods

[1795] The server then subjects the generated design to image recognition technology to assess its real-world manufacturability. Image recognition technology compares it with past custom photos to determine whether the generated design is manufacturable.

[1796] 5. Manufacturing request method

[1797] The optimized part design is then sent from the server to the manufacturer for manufacturing, along with the CAD data and manufacturing instructions. The manufacturing request is made via an HTTP request using the requests library.

[1798] 6. Monitoring Methods

[1799] The server monitors the manufacturing progress and notifies the user of the status in real time. It automatically receives status updates from the manufacturer and notifies the user every time, improving the user experience.

[1800] 7. Delivery method

[1801] Once manufactured, the parts are delivered to the user via the EC service interface. The server monitors the delivery status in real time and notifies the user of the delivery tracking number.

[1802] Specific examples

[1803] This example shows a user using a smartphone to design an aftermarket car part. The user inputs their car model (Toyota Prius), driving habits (daily short distance driving), and design preferences (modern and simple). At the same time, the user's emotional state (relaxed) is analyzed from camera footage, and a generative AI model uses this data to generate the ideal part design. The generated design is then verified for real-world manufacturability using image recognition technology, and the optimized design is finally sent to the manufacturer. The manufacturing progress is notified to the user in real time via the server, and the completed part is delivered to the user via delivery means.

[1804] Prompt Sentence Examples

[1805] Create the optimal aftermarket part design based on the following information: The user's car model is "Toyota Prius," their driving habit is "daily short distance driving," and their design preference is "modern and simple." Furthermore, the user's emotional state has been detected as "relaxed." Based on all this information, generate the ideal aftermarket part design.

[1806] This invention enables personalized design generation based on the user's emotions and allows the user to be notified of the manufacturing progress in real time, thereby significantly improving user satisfaction.

[1807] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1808] Step 1:

[1809] The user uses a terminal to input their ideal aftermarket parts requirements, intended use, and lifestyle. Input methods include text input, drop-down menus, sliders, etc. The input data is sent from the terminal to the server.

[1810] Input: User data such as car model, driving habits, design preferences, etc.

[1811] Output: User data sent to the server

[1812] Step 2:

[1813] The device captures the user's facial image using a camera and transmits the image in real time to the emotion analysis means, which uses face recognition technology (OpenCV) and emotion analysis model (Keras) to analyze the user's emotional state.

[1814] Input: User's face image

[1815] Output: Parsed emotional state data

[1816] Step 3:

[1817] The server combines the user data received in step 1 with the emotional state data received in step 2 and inputs this into a generative AI model (Keras). The generative AI model generates the ideal aftermarket part design based on the data.

[1818] Input: Integrated user data and emotional state data

[1819] Output: Generated aftermarket parts design

[1820] Step 4:

[1821] The server runs the generated design through image recognition and compares it with past custom photos to assess manufacturability. Image recognition technology is used to compare the realistic manufacturability of the generated design with past success stories.

[1822] Input: Generated aftermarket parts design

[1823] Output: Designs that are evaluated as manufacturable

[1824] Step 5:

[1825] The server sends the optimized design that is evaluated as manufacturable to the manufacturer through the manufacturing request means, including the necessary CAD data and manufacturing instructions, using an HTTP request (requests library).

[1826] Input: Designs that are assessed as manufacturable

[1827] Output: Manufacturing request data sent to the manufacturer

[1828] Step 6:

[1829] The server monitors the manufacturing progress through a monitoring means and automatically receives status updates from the manufacturer, keeping the user informed of the manufacturing progress in real time.

[1830] Input: Production progress data from the manufacturer

[1831] Output: Notify the user of the manufacturing progress

[1832] Step 7:

[1833] Once manufactured, the parts are delivered via the server's EC service interface. The delivery status is monitored in real time, and the user is notified of the delivery tracking number.

[1834] Input: Manufacturing completion notification and delivery request

[1835] Output: Delivery tracking number notification to user

[1836] This processing step allows users to easily create and receive personalized aftermarket part designs based on their emotions.

[1837] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1838] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1839] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1840] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1841] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1842] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1843] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1844] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1845] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1846] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1847] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1848] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1849] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1850] 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.

[1851] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1852] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1853] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1854] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1855] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1856] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1857] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1858] The following is further disclosed regarding the above embodiment.

[1859] (Claim 1)

[1860] An input means for the user to input the ideal aftermarket parts requirements, usage purpose, and lifestyle;

[1861] A generative modeling means for analyzing user input data and automatically generating an ideal aftermarket part design;

[1862] The generated design is then subjected to image recognition technology to propose and design the optimal parts.

[1863] a manufacturing request means for transmitting the optimized part design to a manufacturer and requesting manufacturing;

[1864] A system including a delivery means for delivering the manufactured parts to a user.

[1865] (Claim 2)

[1866] 10. The system of claim 1, further comprising a comparison means for comparing with past custom photographs to assess actual manufacturability.

[1867] (Claim 3)

[1868] 10. The system of claim 1, further comprising recycling means for reusing waste materials to manufacture parts.

[1869] (Claim 4)

[1870] 2. The system of claim 1, wherein the generative model means includes a function for dynamically adjusting an algorithm for generating a design based on user input data.

[1871] (Claim 5)

[1872] 10. The system of claim 1, further comprising monitoring means for informing a user of the progress of production.

[1873] "Example 1"

[1874] (Claim 1)

[1875] an input means for a user to input ideal conditions, usage purposes, and lifestyles regarding aftermarket parts for automobiles;

[1876] A generation AI model means for analyzing input data sent from a user terminal and automatically generating an ideal aftermarket part design;

[1877] Image recognition means for subjecting the generated design to image recognition technology and comparing it with past custom examples to assess manufacturability;

[1878] a manufacturing request means for transmitting the evaluated part design to a manufacturer and requesting manufacturing;

[1879] A system including a delivery means for delivering the manufactured parts to a user.

[1880] (Claim 2)

[1881] 10. The system of claim 1, further comprising a monitoring means for monitoring the manufacturing progress in real time and notifying a user of the manufacturing status.

[1882] (Claim 3)

[1883] 10. The system of claim 1, further comprising recycling means for reusing waste materials to manufacture parts.

[1884] "Application Example 1"

[1885] (Claim 1)

[1886] A data input method for users to input their ideal aftermarket parts requirements, usage, and lifestyle.

[1887] A generative modeling method for analyzing user input data and automatically generating ideal aftermarket part designs;

[1888] The generated design is then subjected to image recognition technology to propose and design the optimal parts.

[1889] a manufacturing request means for transmitting the optimized part design to a manufacturer and requesting manufacturing;

[1890] a means of transportation for delivering the manufactured parts to the user;

[1891] The system includes a notification means for notifying the user of the design generation and manufacturing progress status in real time via a mobile information terminal.

[1892] (Claim 2)

[1893] 10. The system of claim 1, further comprising a comparison means for comparing with past custom design data to assess actual manufacturability.

[1894] (Claim 3)

[1895] 10. The system of claim 1, further comprising a recycling processing means for reusing waste materials to manufacture parts.

[1896] "Example 2: Combining Emotion Engines"

[1897] (Claim 1)

[1898] an input means for the user to input ideal product conditions, usage purposes, and lifestyle;

[1899] a generative modeling means for analyzing user input data and emotion data and automatically generating an ideal product design;

[1900] The generated design is then subjected to image recognition technology to propose and design the optimal product.

[1901] a manufacturing request means for transmitting the optimized product design to a manufacturer and requesting manufacturing;

[1902] A system including a delivery means for delivering the manufactured products to a user.

[1903] (Claim 2)

[1904] 10. The system of claim 1, further comprising a comparison means for comparing with historical product photographs to assess actual manufacturability.

[1905] (Claim 3)

[1906] 10. The system of claim 1, further comprising recycling means for reusing waste materials to manufacture products.

[1907] "Application example 2 when combining emotion engines"

[1908] (Claim 1)

[1909] An input means for the user to input the ideal aftermarket parts requirements, usage purpose, and lifestyle;

[1910] A generative modeling means for analyzing input data and emotional state of a user and automatically generating an ideal aftermarket part design;

[1911] The generated design is then subjected to image recognition technology to propose and design the optimal parts.

[1912] a manufacturing request means for transmitting the optimized part design to a manufacturer and requesting manufacturing;

[1913] a monitoring means for monitoring the delivery status of manufactured parts and notifying the user in real time;

[1914] A system including a delivery means for delivering the manufactured parts to a user.

[1915] (Claim 2)

[1916] 10. The system of claim 1, further comprising a comparison means for comparing with past custom photographs to assess actual manufacturability.

[1917] (Claim 3)

[1918] 2. The system according to claim 1, wherein the system utilizes emotion analysis means to analyze the user's emotional state in real time, adjusts input data based on the analysis, and suggests optimal options. [Explanation of symbols]

[1919] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. An input means for the user to input the ideal aftermarket parts requirements, usage purpose, and lifestyle; A generative modeling means for analyzing user input data and automatically generating an ideal aftermarket part design; The generated design is then subjected to image recognition technology to propose and design the optimal parts. a manufacturing request means for transmitting the optimized part design to a manufacturer and requesting manufacturing; A system including a delivery means for delivering the manufactured parts to a user.

2. 10. The system of claim 1, further comprising a comparison means for comparing with past custom photographs to assess actual manufacturability.

3. 10. The system of claim 1, further comprising recycling means for reusing waste materials to manufacture parts.

4. 2. The system of claim 1, wherein the generative model means includes a function for dynamically adjusting an algorithm for generating the design based on user input data.

5. 2. The system of claim 1, further comprising monitoring means for informing a user of the progress of production.

Images