System

Abstract

A system is provided.SOLUTION: A system, comprising: means for receiving a customization request from a user; means for generating design data using artificial intelligence based on the received customization request; means for sending the design data to digital production equipment that produces a product using the generated design data; and means for sending a notification to the user after production is complete.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In Japan's traditional craft manufacturing, the number of artisans is decreasing, making it difficult to maintain production efficiency and product quality. Furthermore, producing customized products to meet individual customer needs requires a great deal of labor and time, making it difficult to respond quickly to demand. Furthermore, artisanal techniques are not being passed down, and there is a lack of easy ways for the next generation and people overseas to learn. Therefore, there is a need for a way to efficiently produce high-quality crafts while utilizing the skills of artisans and pass on these techniques to the next generation. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for receiving a customization request from a user, a means for generating design data using artificial intelligence based on the received customization request, a means for transmitting the generated design data to a digital production device that produces a product using the design data, and a means for sending a notification to the user after production is complete. This allows for quick and efficient response to user customization needs and facilitates the transfer of traditional craft production techniques to the next generation and overseas users. The system also includes a means for producing product prototypes using the design data, thereby improving product quality and production efficiency. Furthermore, the system also includes a means for storing production information in a database to facilitate skill transfer. With these features, the present invention effectively solves the challenges of traditional craft production.

[0006] "User" refers to an individual or organization that uses the System to order a customized craft.

[0007] A "customization request" refers to a request that a user inputs and sends to the system specifications such as the desired design, material, size, and color.

[0008] "Means for receiving" refers to the process or function by which the system receives a customization request sent by a user.

[0009] "Artificial intelligence" refers to machine learning models and algorithms that automatically generate design data based on incoming customization requests.

[0010] "Design data" refers to digital files or information generated by artificial intelligence that detail the design of an artefact.

[0011] "Digital production equipment for creating products" refers to digitally controlled equipment, such as CNC machines and 3D printers, that are used to actually create craft products based on design data.

[0012] "Means for sending notifications" refers to various communication methods (e.g., email, push notification, etc.) used to notify users that production has been completed.

[0013] "Means for producing prototypes" refers to the processes and equipment used to create prototype products using design data and verify their quality and design.

[0014] "Database" refers to a storage system that systematically stores production information and design data and makes them accessible in the future. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] This invention is a system that receives customization requests from users, generates design data using artificial intelligence (AI), and produces products using digital production equipment based on the design data. It also includes a means for sending a notification to the user after production is complete. This system improves product quality and production efficiency, and can pass on technology to the next generation.

[0037] Program processing

[0038] 1. User submits customization request

[0039] Through the web or mobile application, users input specifications such as the design, material, size, and color of the craft they want, for example, "a floral design, bamboo, medium size, blue."

[0040] 2. The server receives the request

[0041] When a user submits a request, the server receives the request and analyzes the provided specifications, including details such as design, material, size, and color.

[0042] 3. Generate design data

[0043] The server then calls the AI ​​module based on the received customization request. The AI ​​module generates a digital design based on the specified design and materials. This design data details the design of the craft and is generated as a file in CAD software format, for example.

[0044] 4. Sending design data and prototyping

[0045] The server sends the generated design data to a digital manufacturing device (e.g., a CNC machine or 3D printer) that then produces a prototype based on the specified design. Once the prototype is complete, it is inspected for quality and design.

[0046] 5. Final production of the product

[0047] Based on the design data verified during the prototype stage, digital fabrication equipment creates the final product, enabling efficient and high-quality production of customized crafts.

[0048] 6. Notice to Users

[0049] Once the product is complete, the server sends a notification to the user, including instructions on how to receive the product and other details.

[0050] Specific examples

[0051] For example, consider a user ordering a medium-sized bamboo craft with a blue floral design. The user logs into a web application and submits a customization request by inputting the desired design, material, size, and color. The server receives the request and passes the specified criteria to an AI module, which then generates a digital blueprint for the medium-sized bamboo craft with a blue floral design.

[0052] The server then sends the design data to the factory's CNC machine to create a prototype. After the prototype's quality is confirmed, the final product is produced. Finally, the server notifies the user that the product is complete and provides detailed instructions for receiving it. This system allows users to efficiently receive their desired customized crafts and provides high-quality products.

[0053] In this way, the system of the present invention can respond to the diverse needs of users while preserving traditional techniques and passing them on to the next generation.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] A user accesses a web or mobile application and submits a customization request by filling out a form with specifications such as desired design, material, size, and color, and then clicking the "Submit" button.

[0057] Step 2:

[0058] The server receives customization requests from users via API, analyzes the received requests as JSON data, and extracts detailed information such as design, material, size, and color.

[0059] Step 3:

[0060] The server launches an artificial intelligence (AI) module to generate the design data. Based on the received specifications, the AI ​​module generates the appropriate digital design drawings (e.g., CAD files). The generated design data accurately reflects the design and dimensions according to the specified conditions.

[0061] Step 4:

[0062] The server sends the generated design data to a digital fabrication device, such as a CNC machine or 3D printer, which produces a prototype based on the specified design.

[0063] Step 5:

[0064] The digital production equipment receives the design data and creates a prototype. Once the prototype is complete, its quality and design are verified. Any necessary modifications are made at this stage.

[0065] Step 6:

[0066] The server retransmits the final design data, reflecting any modifications made during the prototype stage, to the digital production device, which then produces the final product.

[0067] Step 7:

[0068] Once production is complete, the server notifies the user that the product is ready. This notification includes details on how to receive the product. The user receives the notification and proceeds with the process of receiving the product.

[0069] Step 8:

[0070] The server stores production information in a database, which allows the technology to be passed on to future generations and for overseas users to easily learn.

[0071] Example 1

[0072] 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."

[0073] In today's customized product manufacturing, there is a need to efficiently produce high-quality products based on user requirements while reducing errors and defects at each stage of the manufacturing process. Prompt and accurate notification to users after manufacturing is also an important issue. Furthermore, in order to pass on technology, it is necessary to systematically store manufacturing information and create an environment in which the next generation and overseas users can easily learn the technology.

[0074] 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.

[0075] In this invention, the server includes: means for receiving a customization request from a user; means for generating design data using artificial intelligence based on the received customization request; means for transmitting the design data to a digital production device that uses the generated design data to produce a product; means for sending a notification to the user after production is complete; a terminal for the user to input the customization request; means for the terminal to transmit the request to the server; means for the server to analyze the request and generate a prompt for the AI ​​module; and a terminal for reporting the production status of the digital production device to the server. This allows for efficient production based on the user's customization request and simplifies management of each process. Furthermore, storing production information in a database enables the transfer of skills, allowing the next generation and overseas users to easily acquire skills.

[0076] "User" refers to an individual or corporation that uses the system to input a customization request and request the production of a product.

[0077] A "customization request" is information including specifications such as product design, material, size, and color specified by the user.

[0078] A "terminal" is a device (such as a smartphone, tablet, or PC) that a user uses to input a customization request and send it to the server.

[0079] The "server" is a central control device that receives customization requests from users, analyzes them, invokes AI modules to generate design data, and transmits the design data to digital production devices.

[0080] "Artificial intelligence (AI)" is a computer system that generates design data based on a user's customization requests.

[0081] "Design data" refers to digital blueprints of products generated by AI, and is expressed as files in CAD software format.

[0082] "Digital production equipment" refers to equipment that produces actual products based on generated design data, including CNC machines and 3D printers.

[0083] A "prompt sentence" is an instruction sentence issued by the server to the AI ​​module, which specifically indicates the content of the customization request.

[0084] The "database" is a system for systematically storing information about product manufacturing, and is used as a reference by the next generation and overseas users when learning the technology.

[0085] "Notification" means information sent to the user in the form of email, SMS, in-app notification, etc. to inform the user that the product has been completed.

[0086] This invention is a system that receives customization requests from users, generates design data using artificial intelligence (AI), and produces products using digital production equipment based on the design data. It also includes a means for sending a notification to the user after production is complete. This system improves product quality and production efficiency, and can pass on technology to the next generation.

[0087] Users access the web or mobile application from their own devices (smartphones, tablets, PCs, etc.) and enter detailed specifications such as the design, material, size, color, etc. of the product they want. For example, if a user wants a "medium-sized bamboo craft with a blue floral pattern," they submit a customization request based on the specified specifications.

[0088] The customization request sent from the device is securely transmitted to the server using the HTTPS protocol. The server analyzes the received request and calls a generative AI model (e.g., OpenAI's GPT-3) based on the analysis results. At this time, the server sends a prompt message to the generative AI model that specifically describes the customization request.

[0089] An example of a prompt is:

[0090] "Generate a digital blueprint for a medium-sized bamboo craft with a blue floral pattern."

[0091] The following content is generated:

[0092] The generative AI model receives this prompt and generates a digital blueprint in CAD software format based on the specified design. The generated design data is sent back to the server, which then sends it to digital fabrication equipment (e.g., a CNC machine or 3D printer).

[0093] The digital production equipment begins producing a prototype based on the received design data. The production equipment's terminal checks the prototype production status and reports log data and progress to the server. The server verifies the quality of the prototype and instructs corrections if necessary. After the quality of the prototype has been confirmed, the server issues instructions for final production to the digital production equipment.

[0094] Once the final product is completed, the server will notify the user of the completion. The notification will be sent via email, SMS, in-app notification, etc., and will include details on how to receive the product. In addition, information about the product's creation will be stored in a database, creating an environment where the next generation and overseas users can easily learn the technology.

[0095] The system allows users to efficiently receive high-quality customized products and ensures that each step of the production process is under control, reducing errors and defects and improving product quality and production efficiency.

[0096] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0097] Step 1:

[0098] A user accesses the web or mobile application from their device and enters detailed specifications for the product they want, such as design, material, size, and color. An example of input data might be "a medium-sized bamboo craft with a blue floral pattern." This data is entered into the form and confirmed by pressing the submit button.

[0099] Step 2:

[0100] The device receives the customization request from the user and sends it to the server using the HTTPS protocol. This communication is encrypted to ensure a secure connection. The input is the customization request in JSON data format, and the output is the data sent to the server.

[0101] Step 3:

[0102] The server receives the customization request and analyzes the data using the analysis module. The input is the received JSON data, and the analysis results are individual elements such as design, material, size, color, etc. The analysis results are sent to the AI ​​module.

[0103] Step 4:

[0104] The server generates a prompt for the generative AI model (e.g., GPT-3) based on the analysis results. An example of a generated prompt is, "Please generate a digital blueprint for a medium-sized craft item made of bamboo with a blue floral pattern." The input is the analysis result data, and the output is the prompt.

[0105] Step 5:

[0106] The server sends prompts to the generative AI model, which then generates design data. The input is the prompt, and the output is a digital design in CAD software format. This design is then returned to the server.

[0107] Step 6:

[0108] The server sends the received design data to the digital production equipment. This communication uses MQTT and HTTP protocols. The input is the digital design drawing, and the output is the data sent to the production equipment.

[0109] Step 7:

[0110] The terminal monitors the progress of the production equipment and reports the prototype production status to the server. The input is progress data from the production equipment, and the output is report data to the server. This allows the server to check the quality of the prototype.

[0111] Step 8:

[0112] The server checks the quality of the prototype and issues instructions for final production to the production equipment. The input is the quality data of the prototype, and the output is the instructions for final production. The production equipment produces the final product based on these instructions.

[0113] Step 9:

[0114] When the final product is completed, the server sends a notification to the user, which can be sent by email, SMS, or in-app notification, and includes details on how to receive the product. The input is the production completion data, and the output is the notification to the user.

[0115] (Application example 1)

[0116] 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."

[0117] Today's consumers want to quickly obtain high-quality, customized products that meet their individual needs and preferences. However, achieving this goal requires significant time and cost. Furthermore, there is a lack of efficient means to execute the production process in physical stores and generate high-quality products on the spot. A system that solves these issues is needed.

[0118] 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.

[0119] In this invention, the server includes means for receiving a customization request from a user, means for generating design data using artificial intelligence based on the received customization request, means for transmitting the generated design data to digital production equipment that produces a product using the design data, means for sending a notification to the user after production is complete, and means for producing the product in real time in cooperation with the digital production equipment located in a physical store. This makes it possible to produce customized products quickly and with high quality to meet the diverse needs of users.

[0120] "Means for receiving customization requests from users" refers to an interface that allows users to input desired product specifications (design, material, size, color, etc.) and send them to the server.

[0121] "Means for generating design data using artificial intelligence based on received customization requests" refers to a system that analyzes specifications received from users and generates design data using machine learning algorithms and deep learning.

[0122] "Means for transmitting the generated design data to a digital fabrication device that uses the generated design data to fabricate a product" means a communications interface that transmits the generated design data in real time to fabrication equipment (e.g., a 3D printer or CNC machine) to initiate fabrication.

[0123] "Means for sending a notification to the user after production is completed" refers to a communication means (notification system or message sending system) for informing the user that the product has been completed.

[0124] "Means for producing products in real time in cooperation with digital production equipment installed in a physical store" refers to a system that works in cooperation with production equipment installed in a physical store to quickly produce products to user specifications on the spot.

[0125] System Program

[0126] The system for realizing the present invention is configured as follows: Basically, a server, a user terminal, and in-store digital production equipment work in cooperation with each other.

[0127] Program processing

[0128] The server first receives a customization request from the user. This request is sent by the user via a smartphone or tablet. For example, the user may enter a specific request such as, "I would like to order a medium-sized, blue craft with a floral design made from bamboo." The server analyzes this request and creates the relevant design data using a generative AI model. Examples of generative AI models used include TensorFlow and PyTorch.

[0129] The generated design data is sent from the server to digital production equipment (e.g., 3D printers and CNC machines) located in the physical store. The production equipment creates products in real time based on this design data. When the product is completed, the server sends a notification to the user, informing them of how to receive the product. Notifications are sent via SMS, email, in-app notifications, and other means.

[0130] Specific examples

[0131] Consider a scenario where a user uses a smartphone app to order a medium-sized blue bamboo craft with a floral design. The user logs into the app and enters the following prompt:

[0132] "I'd like to order a medium-sized blue bamboo craft with a new floral design."

[0133] The server receives this request and calls an AI module to generate the design data, which is then sent to the in-store 3D printer, which then produces the product on-site. Once the product is complete, the server notifies the user.

[0134] This system allows users to quickly and with high quality receive customized products that meet their diverse needs, and by utilizing digital production equipment in physical stores, the production process is made more efficient, improving the user experience.

[0135] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0136] Step 1:

[0137] A user inputs a customization request from a smartphone and sends the request to the server. The input includes information such as the desired product design, material, size, and color. Specifically, the user inputs a prompt such as "A floral design, bamboo material, medium size, blue craft item." The output is the customization request data received by the server.

[0138] Step 2:

[0139] The server receives the customization request and analyzes its contents. This analysis includes extracting information such as design, material, size, and color. The input is the customization request data received in step 1, and the output is the analyzed specification data. Specifically, the server parses the design pattern, material name, and size information into a data structure.

[0140] Step 3:

[0141] The server calls the generative AI model and generates design data based on the analyzed specification data. The data processing and calculations performed here are processes that generate design data using machine learning algorithms. The input is the specification data obtained in step 2, and the output is the generated design data. Specifically, the server generates blueprints and 3D models using TensorFlow, for example.

[0142] Step 4:

[0143] The server transmits the generated design data to the digital production device in the physical store. This transmission process includes sending data using network communication. The input is the design data generated in step 3, and the output is the design data received by the digital production device. Specifically, the server calls a Web API to transmit the data.

[0144] Step 5:

[0145] Digital production equipment in the physical store begins producing the product based on the received design data. The input is the design data received in step 4, and the output is the finished product. Specifically, 3D printers and CNC machines produce the product in real time based on the design data.

[0146] Step 6:

[0147] After the product is completed, the server sends a notification to the user that the product is complete. The notification includes details such as the product being completed and how to receive it. The input is the product completion status information, and the output is the notification sent to the user. Specifically, the server uses means such as SMS, email, or in-app notification.

[0148] 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.

[0149] This invention is a system that receives customization requests from users, generates design data using artificial intelligence (AI), and produces products using digital production equipment based on the design data. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide products that better meet the individual needs of each user by making customization suggestions and design adjustments based on the user's emotions.

[0150] Program processing

[0151] 1. User submits customization request

[0152] Through the web or mobile application, users input specifications such as the design, material, size, and color of the craft they want, for example, "a floral design, bamboo, medium size, blue."

[0153] 2. Emotion engine recognizes user emotions

[0154] The emotion engine recognizes the user's emotions by analyzing subtle facial expressions and tone of voice during input and operation. This information is reflected in the content of customization requests and suggestions.

[0155] 3. The server receives the request

[0156] When a user submits a request, the server receives the request and analyzes the specified specifications, including detailed items such as design, material, size, and color, as well as emotional information recognized by the emotion engine.

[0157] 4. Generate design data

[0158] The server then calls the AI ​​module based on the received customization request and emotional information. The AI ​​module generates a digital blueprint based on the specified design and materials. The emotional information may also be used to fine-tune the design and color.

[0159] 5. Sending design data and prototyping

[0160] The server then sends the generated design data to digital fabrication equipment, such as CNC machines and 3D printers, which then create prototypes based on the specified design. Once the prototypes are complete, their quality and design are verified.

[0161] 6. Final production of the product

[0162] Based on the design data verified during the prototype stage, digital fabrication equipment creates the final product, enabling efficient and high-quality production of customized crafts.

[0163] 7. Notice to Users

[0164] When the product is completed, the server sends a notification to the user, which includes details on how to receive the product. The user receives the notification and proceeds with the process of receiving the product.

[0165] 8. Storing information in a database

[0166] The server stores production information and the emotional information recognized by the emotion engine in a database, allowing the technology to be passed on to future generations and for overseas users to easily learn.

[0167] Specific examples

[0168] For example, consider the case where a user orders a "medium-sized bamboo craft with a blue floral design." The user logs in to a web application and submits a customization request by entering the desired design, material, size, and color. At this time, the emotion engine analyzes the user's facial expressions and tone of voice to recognize emotions such as satisfaction and expectation. The server receives the request and passes it along with the analyzed emotion information to the AI ​​module. The AI ​​module generates a digital blueprint of the medium-sized bamboo craft with a blue floral design. If necessary, the design is fine-tuned based on the emotion information.

[0169] The server then sends the design data to the factory's CNC machine, which produces a prototype. After the prototype's quality is confirmed, the final product is produced. Finally, the server notifies the user that the product is complete and provides detailed instructions for receiving it. Furthermore, production information and user emotional information from this process are stored in a database for future use in technology transfer and customization optimization. This system allows users to efficiently receive the customized crafts they desire, providing high-quality products and optimal suggestions tailored to their emotions.

[0170] In this way, the system of the present invention can respond to the diverse needs and feelings of users while preserving traditional techniques and passing them on to the next generation.

[0171] The processing flow will be explained below.

[0172] Step 1:

[0173] A user accesses a web or mobile application and inputs specifications for the desired craft, such as design, material, size, and color. For example, a specific request could be "floral design, bamboo, medium size, blue."

[0174] Step 2:

[0175] The emotion engine installed in the device analyzes the user's input, subtle facial expressions during operation, tone of voice, etc. to recognize the user's emotions. For example, it uses facial recognition technology and voice analysis technology to determine whether the user is satisfied or dissatisfied.

[0176] Step 3:

[0177] The server receives the customization request sent by the user via API and analyzes the detailed information on design, material, size, and color contained therein, as well as the emotional information recognized by the emotion engine.

[0178] Step 4:

[0179] The server analyzes the customization request and emotional information, then activates an artificial intelligence (AI) module, which passes this information to the AI ​​module and instructs it to generate an appropriate digital blueprint.

[0180] Step 5:

[0181] The AI ​​module generates a digital blueprint (e.g., a CAD file) based on the user's design and materials. It may also adjust the design and color palette based on emotional information. For example, if the user is excited, it may suggest more vibrant colors.

[0182] Step 6:

[0183] The server then sends the generated digital design to a digital fabrication device (e.g., a CNC machine or a 3D printer), where the design data is updated with the emotion-based adjustments.

[0184] Step 7:

[0185] The digital production equipment creates a prototype based on the received design data. Once the prototype is complete, its quality and design are verified. Any problems discovered during this verification process are corrected.

[0186] Step 8:

[0187] The server retransmits the final design data, reflecting the corrections found during the prototype stage, to the digital production device, which then produces the final product.

[0188] Step 9:

[0189] Once production is complete, the server notifies the user that the product is ready. This notification includes details on how to receive the product. The user receives the notification and proceeds with the process of receiving the product.

[0190] Step 10:

[0191] The server stores production information and the emotional information recognized by the emotion engine in a database. This allows the technology to be passed on to future generations and makes it easy for the next generation and overseas users to learn. The stored emotional information is also used to optimize future customization requests.

[0192] Example 2

[0193] 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."

[0194] Conventional customized product production systems do not take user emotions into consideration, which can lead to low user satisfaction. Furthermore, the lack of prototype production and quality verification processes can lead to variations in the quality of the final product. Furthermore, because production information and emotion information are not stored in a database, it is difficult to transfer skills and optimize customization.

[0195] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a customization request from a user, means for generating design data using artificial intelligence based on the received customization request, means for adjusting the customization request using an emotion engine that recognizes the user's emotions, means for transmitting the design data to a digital production device that produces a product using the generated design data, means for producing a prototype of the product and verifying its quality and design, and means for sending a notification to the user after production is complete. This enables the production of customized products that take the user's emotions into consideration, making it possible to provide high-quality products that provide a high level of satisfaction. Furthermore, storing production information and emotion information in a database facilitates future technology transfer and customization optimization.

[0196] A "customization request" is information input by a user to specify specifications such as desired design, material, size, and color.

[0197] "Artificial intelligence" is a technology that analyzes received customization requests and generates design data.

[0198] The "emotion engine" is a technology that analyzes the user's facial expressions and tone of voice to recognize their emotions.

[0199] "Design data" is a digital blueprint generated by artificial intelligence.

[0200] "Digital production equipment" refers to machines that produce products based on generated design data, and specifically includes CNC machines and 3D printers.

[0201] A "prototype" is a prototype created before the final product is manufactured.

[0202] "Quality and design verification" is the process of checking the quality and design of a prototype after it has been completed.

[0203] "Notification" is information that notifies the user that production of the product has been completed.

[0204] A "database" is a system that stores production-related and emotional information.

[0205] "Technology transfer" is the process of enabling preserved information to be learned and utilized by future generations and users in other countries.

[0206] "Customization optimization" means proposing customization that best suits the user's needs based on past data.

[0207] This invention is a system that receives a user's customization request, generates design data using artificial intelligence (AI), and produces a product using digital production equipment based on the design data. By combining it with an emotion engine that recognizes the user's emotions, it is possible to make customization suggestions and design adjustments based on the user's emotions.

[0208] Users enter their customization request using a web browser or mobile application. The device's camera and microphone are used to capture the user's facial expressions and tone of voice, which are then sent to the emotion engine. For example, a specific request could be, "I want to create a medium-sized craft item made of bamboo with a blue floral pattern."

[0209] The emotion engine analyzes data acquired using the device's camera and microphone to recognize the user's emotions. This emotional information is reflected in adjustments and suggestions for the customization request. The server calls the AI ​​module based on the received customization request and emotional information to generate a prompt.

[0210] The AI ​​module generates a digital blueprint based on the specified design and materials. This process takes emotional information into account and fine-tunes the design and color tone as needed. The generated digital blueprint is then sent by the server to a digital fabrication device (e.g., a CNC machine or 3D printer).

[0211] Digital fabrication equipment creates a prototype based on the generated design. After the prototype is completed, its quality and design are verified. If there are any problems during this process, the design is adjusted and the prototype is repeated.

[0212] Finally, the digital production equipment produces the final product based on the design data confirmed in the prototype stage. When the product is completed, the server sends a notification to the user that production is complete, and the user can then process the product. This notification includes details about how to receive the product.

[0213] Furthermore, production information and emotional information recognized by the emotion engine are stored in a database, enabling future technology transfer and optimization of customization.

[0214] For example, if a user wants to order a "medium-sized bamboo craft with a blue floral pattern," they log in to the web application and submit a customization request by entering the desired design, material, size, and color. The emotion engine analyzes the user's facial expressions and tone of voice to recognize emotions such as satisfaction and expectation. The server receives the request and passes it along with the analyzed emotional information to the AI ​​module, which then creates a prototype and final product based on the generated digital blueprint. The server then notifies the user, who then completes the process by receiving the product.

[0215] In this way, the system of the present invention can efficiently provide high-quality customized products while responding to the diverse needs and emotions of users. Furthermore, by facilitating technology transfer and optimizing customization, it can also accommodate the next generation and users in other countries.

[0216] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0217] Processing flow

[0218] Step 1:

[0219] The user enters a customization request

[0220] A user opens a web or mobile application and enters a customization request, specifically selecting specifications such as design, material, size, and color.

[0221] Input: Design, material, size, color specifications

[0222] Output: Customization request data

[0223] Specific actions: The user enters the required information into the form and presses the submit button.

[0224] Step 2:

[0225] Emotion engine recognizes user emotions

[0226] The device's camera and microphone capture the user's facial expressions and tone of voice and analyze them in real time.

[0227] Input: User's facial expression data, tone of voice

[0228] Output: User's emotional information (satisfaction, expectations, etc.)

[0229] Specific operation: The camera captures the user's face and the microphone collects audio. This data is sent to the emotion engine for analysis.

[0230] Step 3:

[0231] The server receives the request

[0232] The user's input information and emotion information are sent to the server.

[0233] Input: Customization request data, emotion information

[0234] Output: Parsed request data

[0235] Specific operation: The server receives the data and analyzes the design, material, size, color, and emotional information.

[0236] Step 4:

[0237] Generate design data

[0238] The server calls the AI ​​module and generates a prompt, which then generates a digital blueprint based on the specified design and materials.

[0239] Input: Parsed request data, emotion information

[0240] Output: Digital blueprint

[0241] Specific operation: The server sends a prompt to the generative AI model via the API and receives the generated digital blueprint.

[0242] Step 5:

[0243] Sending design data and prototyping

[0244] The server transmits the generated design data to digital production equipment to produce a prototype.

[0245] Input: Digital blueprint

[0246] Output: Prototype

[0247] Specific operation: The server sends the design data to the production equipment, and a prototype is produced.

[0248] Step 6:

[0249] Quality and design verification

[0250] Verify the quality and design of the prototype and make any necessary adjustments if there are any issues.

[0251] Input: Prototype

[0252] Output: Final production data

[0253] Specific Actions: Physically validate prototype and re-adjust as needed.

[0254] Step 7:

[0255] Final production of the product

[0256] Final production is carried out based on the data confirmed at the prototype stage.

[0257] Input: Final production data

[0258] Output: Final product

[0259] Specific Action: Digital production equipment produces the final product.

[0260] Step 8:

[0261] User Notification

[0262] Once the product is completed, the server will send a notification to the user.

[0263] Input: Final product data

[0264] Output: Notification message

[0265] Specific operation: The server sends an email or app notification to the user.

[0266] Step 9:

[0267] Storing information in a database

[0268] Production information and emotional information are stored in a database.

[0269] Input: Production information, emotional information

[0270] Output: Saved data

[0271] Specific operation: The server stores the information in a database for future use.

[0272] (Application example 2)

[0273] 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."

[0274] Conventional customized product production systems only produce products based on user requests, without providing design suggestions or adjustments that reflect the user's emotions and preferences. This can result in designs and functions that do not necessarily satisfy users, leading to a decline in the shopping experience and product satisfaction. Furthermore, this information is not stored in a database, making it difficult for the next generation and overseas users to easily learn the technology.

[0275] 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 means for receiving a customization request from a user, means for recognizing the user's emotion using an emotion engine, means for generating design data using artificial intelligence based on the received customization request and the recognized emotion information, means for transmitting the design data to a digital production device that produces a product using the generated design data, and means for sending a notification to the user after production is complete. This allows for optimal design proposals based on the user's emotions and preferences, enabling the production of customized products that increase user satisfaction. Furthermore, by storing information about product production and user emotion information in a database, the next generation and overseas users can easily acquire the skills.

[0276] - "Means for receiving customization requests from users" refers to an interface through which users input specifications such as desired design, material, size, and color on a web or mobile application and transmit that information to the system.

[0277] "Means for recognizing a user's emotions using an emotion engine" refers to software or a device that analyzes a user's facial expressions and voice data to read a specific emotional state.

[0278] The "means for generating design data using artificial intelligence" is a program for automatically generating digital designs using an algorithm based on a received customization request and recognized emotional information.

[0279] The "means for transmitting the design data to a digital fabrication device that uses the generated design data to fabricate a product" is a communications system for transferring the generated digital design data to fabrication equipment such as a 3D printer or CNC machine.

[0280] The "means for sending a notification to the user after production is completed" refers to a communication means such as email or push notification for notifying the user after production of the product is completed.

[0281] "Means for producing a prototype of a product using design data" refers to physical digital production equipment for producing a prototype based on the generated design data.

[0282] "Means for storing product production information and user emotional information in a database" refers to a digital recording system for long-term storage of production process and user emotional data, making it accessible in the future.

[0283] To implement the present invention, the following system configuration and processing procedure are followed.

[0284] System Configuration

[0285] 1. User Device

[0286] A device such as a smartphone, tablet, or PC that allows users to input customization requests. It is equipped with a camera and microphone to capture the user's facial expressions and voice.

[0287] 2. Server

[0288] It receives customization requests from users and generates design data based on them. Its main software and libraries include an emotion engine (EmotionalRecognition) and an AI design module (AIDesignModule).

[0289] 3. Digital Production Equipment

[0290] The product is manufactured based on the generated design data, specifically using 3D printers and CNC machines.

[0291] 4. Database

[0292] It stores information about product creation and user sentiment, which can be used for future technology transfer and customization optimization.

[0293] Processing Details

[0294] User terminal

[0295] Users input customization requests using a smartphone or PC. Input items include specifications such as design, material, size, and color. The user's facial and voice data is also captured and analyzed by the emotion engine.

[0296] server

[0297] The server receives the request and emotional information sent from the user's device. It also receives emotional data analyzed by the Emotional Recognition engine. Based on the received data, the AI ​​Design Module generates design data. This design data is fine-tuned to reflect not only the user's customization request but also the emotional information.

[0298] Digital Production Equipment

[0299] The generated design data is sent to the manufacturing equipment, which then produces a prototype based on the data (such as a 3D printer or CNC machine). After the prototype's quality is confirmed, the final product is produced.

[0300] Database

[0301] The production process and user emotional information are stored in a database, which will be used for future technology transfer and customization optimization.

[0302] Specific examples

[0303] For example, let's say a user orders a "medium-sized bamboo craft with a blue floral pattern." The user uses their smartphone to input the desired design, material, size, and color. At this time, the Emotional Recognition engine analyzes the user's facial expressions and voice and sends emotional information to the server. The server generates design data using the AI ​​Design Module based on the received customization request and emotional information. The generated data is sent to digital production equipment, which creates a prototype. The final product is then completed and a notification is sent to the user. The production data and the user's emotional information are stored in a database.

[0304] Prompt Sentence Examples

[0305] The user wants a medium-sized design with a bamboo and blue floral pattern. Analysis of the user's facial expression indicates that their current emotion is "joy." Generate a design based on this user's preference.

[0306] In this way, based on the embodiments of the invention, customized products can be produced effectively and user satisfaction can be increased.

[0307] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0308] Step 1:

[0309] Users input customization requests using devices such as smartphones or PCs. Input information includes design, material, size, color, etc. Once input is complete, the device sends the request to the server. The information input here is specific data regarding the user's desired specifications.

[0310] Step 2:

[0311] The device uses a camera and microphone to capture the user's facial expressions and voice. The emotion engine analyzes this data and recognizes the user's emotional state. The emotion engine generates analysis results and sends emotional data containing the results to the server. The input data is digital data of facial expressions and voice, and this is output as emotional information.

[0312] Step 3:

[0313] The server receives customization requests and emotional data sent from the user's device. The received data includes design, material, size, color, and emotional information. The server analyzes this information and generates input data to invoke the AI ​​design module. The data calculation here involves integrating the input information and converting it into the format required for design generation.

[0314] Step 4:

[0315] The server generates design data using an AI design module. The AI ​​design module receives customization requests and emotion information as input and generates design data. This design data reflects fine-tuning based on the user's specifications and emotions. The output data is a detailed blueprint for digital production.

[0316] Step 5:

[0317] The server sends the generated design data to a digital fabrication device (such as a 3D printer or CNC machine) that creates a prototype based on the received design data. The input data is a design drawing, which is then used by the machine to generate the physical prototype.

[0318] Step 6:

[0319] Once the prototype is produced and its quality is confirmed, the digital production equipment moves on to produce the final product. At this stage, monitoring data during production is sent to the server for quality control. The output data is the finished product.

[0320] Step 7:

[0321] Once the product is completed, the server sends a notification to the user that production is complete. The notification includes details about how to receive the product and other information. This notification is sent to the user's device via email or push notification. The input data here is production status information, and the user notification is generated based on that information.

[0322] Step 8:

[0323] Finally, the server stores production information and user emotional information in a database. This information will be used for future technology transfer and service improvement. The input data is all production process and emotional data, which is then stored in the database.

[0324] 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.

[0325] 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.

[0326] 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.

[0327] [Second embodiment]

[0328] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0329] 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.

[0330] 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).

[0331] 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.

[0332] 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.

[0333] 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).

[0334] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0335] 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.

[0336] 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.

[0337] 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.

[0338] 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.

[0339] 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."

[0340] This invention is a system that receives customization requests from users, generates design data using artificial intelligence (AI), and produces products using digital production equipment based on the design data. It also includes a means for sending a notification to the user after production is complete. This system improves product quality and production efficiency, and can pass on technology to the next generation.

[0341] Program processing

[0342] 1. User submits customization request

[0343] Through the web or mobile application, users input specifications such as the design, material, size, and color of the craft they want, for example, "a floral design, bamboo, medium size, blue."

[0344] 2. The server receives the request

[0345] When a user submits a request, the server receives the request and analyzes the provided specifications, including details such as design, material, size, and color.

[0346] 3. Generate design data

[0347] The server then calls the AI ​​module based on the received customization request. The AI ​​module generates a digital design based on the specified design and materials. This design data details the design of the craft and is generated as a file in CAD software format, for example.

[0348] 4. Sending design data and prototyping

[0349] The server sends the generated design data to a digital manufacturing device (e.g., a CNC machine or 3D printer) that then produces a prototype based on the specified design. Once the prototype is complete, it is inspected for quality and design.

[0350] 5. Final production of the product

[0351] Based on the design data verified during the prototype stage, digital fabrication equipment creates the final product, enabling efficient and high-quality production of customized crafts.

[0352] 6. Notice to Users

[0353] Once the product is complete, the server sends a notification to the user, including instructions on how to receive the product and other details.

[0354] Specific examples

[0355] For example, consider a user ordering a medium-sized bamboo craft with a blue floral design. The user logs into a web application and submits a customization request by inputting the desired design, material, size, and color. The server receives the request and passes the specified criteria to an AI module, which then generates a digital blueprint for the medium-sized bamboo craft with a blue floral design.

[0356] The server then sends the design data to the factory's CNC machine to create a prototype. After the prototype's quality is confirmed, the final product is produced. Finally, the server notifies the user that the product is complete and provides detailed instructions for receiving it. This system allows users to efficiently receive their desired customized crafts and provides high-quality products.

[0357] In this way, the system of the present invention can respond to the diverse needs of users while preserving traditional techniques and passing them on to the next generation.

[0358] The processing flow will be explained below.

[0359] Step 1:

[0360] A user accesses a web or mobile application and submits a customization request by filling out a form with specifications such as desired design, material, size, and color, and then clicking the "Submit" button.

[0361] Step 2:

[0362] The server receives customization requests from users via API, analyzes the received requests as JSON data, and extracts detailed information such as design, material, size, and color.

[0363] Step 3:

[0364] The server launches an artificial intelligence (AI) module to generate the design data. Based on the received specifications, the AI ​​module generates the appropriate digital design drawings (e.g., CAD files). The generated design data accurately reflects the design and dimensions according to the specified conditions.

[0365] Step 4:

[0366] The server sends the generated design data to a digital fabrication device, such as a CNC machine or 3D printer, which produces a prototype based on the specified design.

[0367] Step 5:

[0368] The digital production equipment receives the design data and creates a prototype. Once the prototype is complete, its quality and design are verified. Any necessary modifications are made at this stage.

[0369] Step 6:

[0370] The server retransmits the final design data, reflecting any modifications made during the prototype stage, to the digital production device, which then produces the final product.

[0371] Step 7:

[0372] Once production is complete, the server notifies the user that the product is ready. This notification includes details on how to receive the product. The user receives the notification and proceeds with the process of receiving the product.

[0373] Step 8:

[0374] The server stores production information in a database, which allows the technology to be passed on to future generations and for overseas users to easily learn.

[0375] Example 1

[0376] 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."

[0377] In today's customized product manufacturing, there is a need to efficiently produce high-quality products based on user requirements while reducing errors and defects at each stage of the manufacturing process. Prompt and accurate notification to users after manufacturing is also an important issue. Furthermore, in order to pass on technology, it is necessary to systematically store manufacturing information and create an environment in which the next generation and overseas users can easily learn the technology.

[0378] 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.

[0379] In this invention, the server includes: means for receiving a customization request from a user; means for generating design data using artificial intelligence based on the received customization request; means for transmitting the design data to a digital production device that uses the generated design data to produce a product; means for sending a notification to the user after production is complete; a terminal for the user to input the customization request; means for the terminal to transmit the request to the server; means for the server to analyze the request and generate a prompt for the AI ​​module; and a terminal for reporting the production status of the digital production device to the server. This allows for efficient production based on the user's customization request and simplifies management of each process. Furthermore, storing production information in a database enables the transfer of skills, allowing the next generation and overseas users to easily acquire skills.

[0380] "User" refers to an individual or corporation that uses the system to input a customization request and request the production of a product.

[0381] A "customization request" is information including specifications such as product design, material, size, and color specified by the user.

[0382] A "terminal" is a device (such as a smartphone, tablet, or PC) that a user uses to input a customization request and send it to the server.

[0383] The "server" is a central control device that receives customization requests from users, analyzes them, invokes AI modules to generate design data, and transmits the design data to digital production devices.

[0384] "Artificial intelligence (AI)" is a computer system that generates design data based on a user's customization requests.

[0385] "Design data" refers to digital blueprints of products generated by AI, and is expressed as files in CAD software format.

[0386] "Digital production equipment" refers to equipment that produces actual products based on generated design data, including CNC machines and 3D printers.

[0387] A "prompt sentence" is an instruction sentence issued by the server to the AI ​​module, which specifically indicates the content of the customization request.

[0388] The "database" is a system for systematically storing information about product manufacturing, and is used as a reference by the next generation and overseas users when learning the technology.

[0389] "Notification" means information sent to the user in the form of email, SMS, in-app notification, etc. to inform the user that the product has been completed.

[0390] This invention is a system that receives customization requests from users, generates design data using artificial intelligence (AI), and produces products using digital production equipment based on the design data. It also includes a means for sending a notification to the user after production is complete. This system improves product quality and production efficiency, and can pass on technology to the next generation.

[0391] Users access the web or mobile application from their own devices (smartphones, tablets, PCs, etc.) and enter detailed specifications such as the design, material, size, color, etc. of the product they want. For example, if a user wants a "medium-sized bamboo craft with a blue floral pattern," they submit a customization request based on the specified specifications.

[0392] The customization request sent from the device is securely transmitted to the server using the HTTPS protocol. The server analyzes the received request and calls a generative AI model (e.g., OpenAI's GPT-3) based on the analysis results. At this time, the server sends a prompt message to the generative AI model that specifically describes the customization request.

[0393] An example of a prompt is:

[0394] "Generate a digital blueprint for a medium-sized bamboo craft with a blue floral pattern."

[0395] The following content is generated:

[0396] The generative AI model receives this prompt and generates a digital blueprint in CAD software format based on the specified design. The generated design data is sent back to the server, which then sends it to digital fabrication equipment (e.g., a CNC machine or 3D printer).

[0397] The digital production equipment begins producing a prototype based on the received design data. The production equipment's terminal checks the prototype production status and reports log data and progress to the server. The server verifies the quality of the prototype and instructs corrections if necessary. After the quality of the prototype has been confirmed, the server issues instructions for final production to the digital production equipment.

[0398] Once the final product is completed, the server will notify the user of the completion. The notification will be sent via email, SMS, in-app notification, etc., and will include details on how to receive the product. In addition, information about the product's creation will be stored in a database, creating an environment where the next generation and overseas users can easily learn the technology.

[0399] The system allows users to efficiently receive high-quality customized products and ensures that each step of the production process is under control, reducing errors and defects and improving product quality and production efficiency.

[0400] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0401] Step 1:

[0402] A user accesses the web or mobile application from their device and enters detailed specifications for the product they want, such as design, material, size, and color. An example of input data might be "a medium-sized bamboo craft with a blue floral pattern." This data is entered into the form and confirmed by pressing the submit button.

[0403] Step 2:

[0404] The device receives the customization request from the user and sends it to the server using the HTTPS protocol. This communication is encrypted to ensure a secure connection. The input is the customization request in JSON data format, and the output is the data sent to the server.

[0405] Step 3:

[0406] The server receives the customization request and analyzes the data using the analysis module. The input is the received JSON data, and the analysis results are individual elements such as design, material, size, color, etc. The analysis results are sent to the AI ​​module.

[0407] Step 4:

[0408] The server generates a prompt for the generative AI model (e.g., GPT-3) based on the analysis results. An example of a generated prompt is, "Please generate a digital blueprint for a medium-sized craft item made of bamboo with a blue floral pattern." The input is the analysis result data, and the output is the prompt.

[0409] Step 5:

[0410] The server sends prompts to the generative AI model, which then generates design data. The input is the prompt, and the output is a digital design in CAD software format. This design is then returned to the server.

[0411] Step 6:

[0412] The server sends the received design data to the digital production equipment. This communication uses MQTT and HTTP protocols. The input is the digital design drawing, and the output is the data sent to the production equipment.

[0413] Step 7:

[0414] The terminal monitors the progress of the production equipment and reports the prototype production status to the server. The input is progress data from the production equipment, and the output is report data to the server. This allows the server to check the quality of the prototype.

[0415] Step 8:

[0416] The server checks the quality of the prototype and issues instructions for final production to the production equipment. The input is the quality data of the prototype, and the output is the instructions for final production. The production equipment produces the final product based on these instructions.

[0417] Step 9:

[0418] When the final product is completed, the server sends a notification to the user, which can be sent by email, SMS, or in-app notification, and includes details on how to receive the product. The input is the production completion data, and the output is the notification to the user.

[0419] (Application example 1)

[0420] 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."

[0421] Today's consumers want to quickly obtain high-quality, customized products that meet their individual needs and preferences. However, achieving this goal requires significant time and cost. Furthermore, there is a lack of efficient means to execute the production process in physical stores and generate high-quality products on the spot. A system that solves these issues is needed.

[0422] 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.

[0423] In this invention, the server includes means for receiving a customization request from a user, means for generating design data using artificial intelligence based on the received customization request, means for transmitting the generated design data to digital production equipment that produces a product using the design data, means for sending a notification to the user after production is complete, and means for producing the product in real time in cooperation with the digital production equipment located in a physical store. This makes it possible to produce customized products quickly and with high quality to meet the diverse needs of users.

[0424] "Means for receiving customization requests from users" refers to an interface that allows users to input desired product specifications (design, material, size, color, etc.) and send them to the server.

[0425] "Means for generating design data using artificial intelligence based on received customization requests" refers to a system that analyzes specifications received from users and generates design data using machine learning algorithms and deep learning.

[0426] "Means for transmitting the generated design data to a digital fabrication device that uses the generated design data to fabricate a product" means a communications interface that transmits the generated design data in real time to fabrication equipment (e.g., a 3D printer or CNC machine) to initiate fabrication.

[0427] "Means for sending a notification to the user after production is completed" refers to a communication means (notification system or message sending system) for informing the user that the product has been completed.

[0428] "Means for producing products in real time in cooperation with digital production equipment installed in a physical store" refers to a system that works in cooperation with production equipment installed in a physical store to quickly produce products to user specifications on the spot.

[0429] System Program

[0430] The system for realizing the present invention is configured as follows: Basically, a server, a user terminal, and in-store digital production equipment work in cooperation with each other.

[0431] Program processing

[0432] The server first receives a customization request from the user. This request is sent by the user via a smartphone or tablet. For example, the user may enter a specific request such as, "I would like to order a medium-sized, blue craft with a floral design made from bamboo." The server analyzes this request and creates the relevant design data using a generative AI model. Examples of generative AI models used include TensorFlow and PyTorch.

[0433] The generated design data is sent from the server to digital production equipment (e.g., 3D printers and CNC machines) located in the physical store. The production equipment creates products in real time based on this design data. When the product is completed, the server sends a notification to the user, informing them of how to receive the product. Notifications are sent via SMS, email, in-app notifications, and other means.

[0434] Specific examples

[0435] Consider a scenario where a user uses a smartphone app to order a medium-sized blue bamboo craft with a floral design. The user logs into the app and enters the following prompt:

[0436] "I'd like to order a medium-sized blue bamboo craft with a new floral design."

[0437] The server receives this request and calls an AI module to generate the design data, which is then sent to the in-store 3D printer, which then produces the product on-site. Once the product is complete, the server notifies the user.

[0438] This system allows users to quickly and with high quality receive customized products that meet their diverse needs, and by utilizing digital production equipment in physical stores, the production process is made more efficient, improving the user experience.

[0439] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0440] Step 1:

[0441] A user inputs a customization request from a smartphone and sends the request to the server. The input includes information such as the desired product design, material, size, and color. Specifically, the user inputs a prompt such as "A floral design, bamboo material, medium size, blue craft item." The output is the customization request data received by the server.

[0442] Step 2:

[0443] The server receives the customization request and analyzes its contents. This analysis includes extracting information such as design, material, size, and color. The input is the customization request data received in step 1, and the output is the analyzed specification data. Specifically, the server parses the design pattern, material name, and size information into a data structure.

[0444] Step 3:

[0445] The server calls the generative AI model and generates design data based on the analyzed specification data. The data processing and calculations performed here are processes that generate design data using machine learning algorithms. The input is the specification data obtained in step 2, and the output is the generated design data. Specifically, the server generates blueprints and 3D models using TensorFlow, for example.

[0446] Step 4:

[0447] The server transmits the generated design data to the digital production device in the physical store. This transmission process includes sending data using network communication. The input is the design data generated in step 3, and the output is the design data received by the digital production device. Specifically, the server calls a Web API to transmit the data.

[0448] Step 5:

[0449] Digital production equipment in the physical store begins producing the product based on the received design data. The input is the design data received in step 4, and the output is the finished product. Specifically, 3D printers and CNC machines produce the product in real time based on the design data.

[0450] Step 6:

[0451] After the product is completed, the server sends a notification to the user that the product is complete. The notification includes details such as the product being completed and how to receive it. The input is the product completion status information, and the output is the notification sent to the user. Specifically, the server uses means such as SMS, email, or in-app notification.

[0452] 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.

[0453] This invention is a system that receives customization requests from users, generates design data using artificial intelligence (AI), and produces products using digital production equipment based on the design data. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide products that better meet the individual needs of each user by making customization suggestions and design adjustments based on the user's emotions.

[0454] Program processing

[0455] 1. User submits customization request

[0456] Through the web or mobile application, users input specifications such as the design, material, size, and color of the craft they want, for example, "a floral design, bamboo, medium size, blue."

[0457] 2. Emotion engine recognizes user emotions

[0458] The emotion engine recognizes the user's emotions by analyzing subtle facial expressions and tone of voice during input and operation. This information is reflected in the content of customization requests and suggestions.

[0459] 3. The server receives the request

[0460] When a user submits a request, the server receives the request and analyzes the specified specifications, including detailed items such as design, material, size, and color, as well as emotional information recognized by the emotion engine.

[0461] 4. Generate design data

[0462] The server then calls the AI ​​module based on the received customization request and emotional information. The AI ​​module generates a digital blueprint based on the specified design and materials. The emotional information may also be used to fine-tune the design and color.

[0463] 5. Sending design data and prototyping

[0464] The server then sends the generated design data to digital fabrication equipment, such as CNC machines and 3D printers, which then create prototypes based on the specified design. Once the prototypes are complete, their quality and design are verified.

[0465] 6. Final production of the product

[0466] Based on the design data verified during the prototype stage, digital fabrication equipment creates the final product, enabling efficient and high-quality production of customized crafts.

[0467] 7. Notice to Users

[0468] When the product is completed, the server sends a notification to the user, which includes details on how to receive the product. The user receives the notification and proceeds with the process of receiving the product.

[0469] 8. Storing information in a database

[0470] The server stores production information and the emotional information recognized by the emotion engine in a database, allowing the technology to be passed on to future generations and for overseas users to easily learn.

[0471] Specific examples

[0472] For example, consider the case where a user orders a "medium-sized bamboo craft with a blue floral design." The user logs in to a web application and submits a customization request by entering the desired design, material, size, and color. At this time, the emotion engine analyzes the user's facial expressions and tone of voice to recognize emotions such as satisfaction and expectation. The server receives the request and passes it along with the analyzed emotion information to the AI ​​module. The AI ​​module generates a digital blueprint of the medium-sized bamboo craft with a blue floral design. If necessary, the design is fine-tuned based on the emotion information.

[0473] The server then sends the design data to the factory's CNC machine, which produces a prototype. After the prototype's quality is confirmed, the final product is produced. Finally, the server notifies the user that the product is complete and provides detailed instructions for receiving it. Furthermore, production information and user emotional information from this process are stored in a database for future use in technology transfer and customization optimization. This system allows users to efficiently receive the customized crafts they desire, providing high-quality products and optimal suggestions tailored to their emotions.

[0474] In this way, the system of the present invention can respond to the diverse needs and feelings of users while preserving traditional techniques and passing them on to the next generation.

[0475] The processing flow will be explained below.

[0476] Step 1:

[0477] A user accesses a web or mobile application and inputs specifications for the desired craft, such as design, material, size, and color. For example, a specific request could be "floral design, bamboo, medium size, blue."

[0478] Step 2:

[0479] The emotion engine installed in the device analyzes the user's input, subtle facial expressions during operation, tone of voice, etc. to recognize the user's emotions. For example, it uses facial recognition technology and voice analysis technology to determine whether the user is satisfied or dissatisfied.

[0480] Step 3:

[0481] The server receives the customization request sent by the user via API and analyzes the detailed information on design, material, size, and color contained therein, as well as the emotional information recognized by the emotion engine.

[0482] Step 4:

[0483] The server analyzes the customization request and emotional information, then activates an artificial intelligence (AI) module, which passes this information to the AI ​​module and instructs it to generate an appropriate digital blueprint.

[0484] Step 5:

[0485] The AI ​​module generates a digital blueprint (e.g., a CAD file) based on the user's design and materials. It may also adjust the design and color palette based on emotional information. For example, if the user is excited, it may suggest more vibrant colors.

[0486] Step 6:

[0487] The server then sends the generated digital design to a digital fabrication device (e.g., a CNC machine or a 3D printer), where the design data is updated with the emotion-based adjustments.

[0488] Step 7:

[0489] The digital production equipment creates a prototype based on the received design data. Once the prototype is complete, its quality and design are verified. Any problems discovered during this verification process are corrected.

[0490] Step 8:

[0491] The server retransmits the final design data, reflecting the corrections found during the prototype stage, to the digital production device, which then produces the final product.

[0492] Step 9:

[0493] Once production is complete, the server notifies the user that the product is ready. This notification includes details on how to receive the product. The user receives the notification and proceeds with the process of receiving the product.

[0494] Step 10:

[0495] The server stores production information and the emotional information recognized by the emotion engine in a database. This allows the technology to be passed on to future generations and makes it easy for the next generation and overseas users to learn. The stored emotional information is also used to optimize future customization requests.

[0496] Example 2

[0497] 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."

[0498] Conventional customized product production systems do not take user emotions into consideration, which can lead to low user satisfaction. Furthermore, the lack of prototype production and quality verification processes can lead to variations in the quality of the final product. Furthermore, because production information and emotion information are not stored in a database, it is difficult to transfer skills and optimize customization.

[0499] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a customization request from a user, means for generating design data using artificial intelligence based on the received customization request, means for adjusting the customization request using an emotion engine that recognizes the user's emotions, means for transmitting the design data to a digital production device that produces a product using the generated design data, means for producing a prototype of the product and verifying its quality and design, and means for sending a notification to the user after production is complete. This enables the production of customized products that take the user's emotions into consideration, making it possible to provide high-quality products that provide a high level of satisfaction. Furthermore, storing production information and emotion information in a database facilitates future technology transfer and customization optimization.

[0500] A "customization request" is information input by a user to specify specifications such as desired design, material, size, and color.

[0501] "Artificial intelligence" is a technology that analyzes received customization requests and generates design data.

[0502] The "emotion engine" is a technology that analyzes the user's facial expressions and tone of voice to recognize their emotions.

[0503] "Design data" is a digital blueprint generated by artificial intelligence.

[0504] "Digital production equipment" refers to machines that produce products based on generated design data, and specifically includes CNC machines and 3D printers.

[0505] A "prototype" is a prototype created before the final product is manufactured.

[0506] "Quality and design verification" is the process of checking the quality and design of a prototype after it has been completed.

[0507] "Notification" is information that notifies the user that production of the product has been completed.

[0508] A "database" is a system that stores production-related and emotional information.

[0509] "Technology transfer" is the process of enabling preserved information to be learned and utilized by future generations and users in other countries.

[0510] "Customization optimization" means proposing customization that best suits the user's needs based on past data.

[0511] This invention is a system that receives a user's customization request, generates design data using artificial intelligence (AI), and produces a product using digital production equipment based on the design data. By combining it with an emotion engine that recognizes the user's emotions, it is possible to make customization suggestions and design adjustments based on the user's emotions.

[0512] Users enter their customization request using a web browser or mobile application. The device's camera and microphone are used to capture the user's facial expressions and tone of voice, which are then sent to the emotion engine. For example, a specific request could be, "I want to create a medium-sized craft item made of bamboo with a blue floral pattern."

[0513] The emotion engine analyzes data acquired using the device's camera and microphone to recognize the user's emotions. This emotional information is reflected in adjustments and suggestions for the customization request. The server calls the AI ​​module based on the received customization request and emotional information to generate a prompt.

[0514] The AI ​​module generates a digital blueprint based on the specified design and materials. This process takes emotional information into account and fine-tunes the design and color tone as needed. The generated digital blueprint is then sent by the server to a digital fabrication device (e.g., a CNC machine or 3D printer).

[0515] Digital fabrication equipment creates a prototype based on the generated design. After the prototype is completed, its quality and design are verified. If there are any problems during this process, the design is adjusted and the prototype is repeated.

[0516] Finally, the digital production equipment produces the final product based on the design data confirmed in the prototype stage. When the product is completed, the server sends a notification to the user that production is complete, and the user can then process the product. This notification includes details about how to receive the product.

[0517] Furthermore, production information and emotional information recognized by the emotion engine are stored in a database, enabling future technology transfer and optimization of customization.

[0518] For example, if a user wants to order a "medium-sized bamboo craft with a blue floral pattern," they log in to the web application and submit a customization request by entering the desired design, material, size, and color. The emotion engine analyzes the user's facial expressions and tone of voice to recognize emotions such as satisfaction and expectation. The server receives the request and passes it along with the analyzed emotional information to the AI ​​module, which then creates a prototype and final product based on the generated digital blueprint. The server then notifies the user, who then completes the process by receiving the product.

[0519] In this way, the system of the present invention can efficiently provide high-quality customized products while responding to the diverse needs and emotions of users. Furthermore, by facilitating technology transfer and optimizing customization, it can also accommodate the next generation and users in other countries.

[0520] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0521] Processing flow

[0522] Step 1:

[0523] The user enters a customization request

[0524] A user opens a web or mobile application and enters a customization request, specifically selecting specifications such as design, material, size, and color.

[0525] Input: Design, material, size, color specifications

[0526] Output: Customization request data

[0527] Specific actions: The user enters the required information into the form and presses the submit button.

[0528] Step 2:

[0529] Emotion engine recognizes user emotions

[0530] The device's camera and microphone capture the user's facial expressions and tone of voice and analyze them in real time.

[0531] Input: User's facial expression data, tone of voice

[0532] Output: User's emotional information (satisfaction, expectations, etc.)

[0533] Specific operation: The camera captures the user's face and the microphone collects audio. This data is sent to the emotion engine for analysis.

[0534] Step 3:

[0535] The server receives the request

[0536] The user's input information and emotion information are sent to the server.

[0537] Input: Customization request data, emotion information

[0538] Output: Parsed request data

[0539] Specific operation: The server receives the data and analyzes the design, material, size, color, and emotional information.

[0540] Step 4:

[0541] Generate design data

[0542] The server calls the AI ​​module and generates a prompt, which then generates a digital blueprint based on the specified design and materials.

[0543] Input: Parsed request data, emotion information

[0544] Output: Digital blueprint

[0545] Specific operation: The server sends a prompt to the generative AI model via the API and receives the generated digital blueprint.

[0546] Step 5:

[0547] Sending design data and prototyping

[0548] The server transmits the generated design data to digital production equipment to produce a prototype.

[0549] Input: Digital blueprint

[0550] Output: Prototype

[0551] Specific operation: The server sends the design data to the production equipment, and a prototype is produced.

[0552] Step 6:

[0553] Quality and design verification

[0554] Verify the quality and design of the prototype and make any necessary adjustments if there are any issues.

[0555] Input: Prototype

[0556] Output: Final production data

[0557] Specific Actions: Physically validate prototype and re-adjust as needed.

[0558] Step 7:

[0559] Final production of the product

[0560] Final production is carried out based on the data confirmed at the prototype stage.

[0561] Input: Final production data

[0562] Output: Final product

[0563] Specific Action: Digital production equipment produces the final product.

[0564] Step 8:

[0565] User Notification

[0566] Once the product is completed, the server will send a notification to the user.

[0567] Input: Final product data

[0568] Output: Notification message

[0569] Specific operation: The server sends an email or app notification to the user.

[0570] Step 9:

[0571] Storing information in a database

[0572] Production information and emotional information are stored in a database.

[0573] Input: Production information, emotional information

[0574] Output: Saved data

[0575] Specific operation: The server stores the information in a database for future use.

[0576] (Application example 2)

[0577] 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."

[0578] Conventional customized product production systems only produce products based on user requests, without providing design suggestions or adjustments that reflect the user's emotions and preferences. This can result in designs and functions that do not necessarily satisfy users, leading to a decline in the shopping experience and product satisfaction. Furthermore, this information is not stored in a database, making it difficult for the next generation and overseas users to easily learn the technology.

[0579] 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 means for receiving a customization request from a user, means for recognizing the user's emotion using an emotion engine, means for generating design data using artificial intelligence based on the received customization request and the recognized emotion information, means for transmitting the design data to a digital production device that produces a product using the generated design data, and means for sending a notification to the user after production is complete. This allows for optimal design proposals based on the user's emotions and preferences, enabling the production of customized products that increase user satisfaction. Furthermore, by storing information about product production and user emotion information in a database, the next generation and overseas users can easily acquire the skills.

[0580] - "Means for receiving customization requests from users" refers to an interface through which users input specifications such as desired design, material, size, and color on a web or mobile application and transmit that information to the system.

[0581] "Means for recognizing a user's emotions using an emotion engine" refers to software or a device that analyzes a user's facial expressions and voice data to read a specific emotional state.

[0582] The "means for generating design data using artificial intelligence" is a program for automatically generating digital designs using an algorithm based on a received customization request and recognized emotional information.

[0583] The "means for transmitting the design data to a digital fabrication device that uses the generated design data to fabricate a product" is a communications system for transferring the generated digital design data to fabrication equipment such as a 3D printer or CNC machine.

[0584] The "means for sending a notification to the user after production is completed" refers to a communication means such as email or push notification for notifying the user after production of the product is completed.

[0585] "Means for producing a prototype of a product using design data" refers to physical digital production equipment for producing a prototype based on the generated design data.

[0586] "Means for storing product production information and user emotional information in a database" refers to a digital recording system for long-term storage of production process and user emotional data, making it accessible in the future.

[0587] To implement the present invention, the following system configuration and processing procedure are followed.

[0588] System Configuration

[0589] 1. User Device

[0590] A device such as a smartphone, tablet, or PC that allows users to input customization requests. It is equipped with a camera and microphone to capture the user's facial expressions and voice.

[0591] 2. Server

[0592] It receives customization requests from users and generates design data based on them. Its main software and libraries include an emotion engine (EmotionalRecognition) and an AI design module (AIDesignModule).

[0593] 3. Digital Production Equipment

[0594] The product is manufactured based on the generated design data, specifically using 3D printers and CNC machines.

[0595] 4. Database

[0596] It stores information about product creation and user sentiment, which can be used for future technology transfer and customization optimization.

[0597] Processing Details

[0598] User terminal

[0599] Users input customization requests using a smartphone or PC. Input items include specifications such as design, material, size, and color. The user's facial and voice data is also captured and analyzed by the emotion engine.

[0600] server

[0601] The server receives the request and emotional information sent from the user's device. It also receives emotional data analyzed by the Emotional Recognition engine. Based on the received data, the AI ​​Design Module generates design data. This design data is fine-tuned to reflect not only the user's customization request but also the emotional information.

[0602] Digital Production Equipment

[0603] The generated design data is sent to the manufacturing equipment, which then produces a prototype based on the data (such as a 3D printer or CNC machine). After the prototype's quality is confirmed, the final product is produced.

[0604] Database

[0605] The production process and user emotional information are stored in a database, which will be used for future technology transfer and customization optimization.

[0606] Specific examples

[0607] For example, let's say a user orders a "medium-sized bamboo craft with a blue floral pattern." The user uses their smartphone to input the desired design, material, size, and color. At this time, the Emotional Recognition engine analyzes the user's facial expressions and voice and sends emotional information to the server. The server generates design data using the AI ​​Design Module based on the received customization request and emotional information. The generated data is sent to digital production equipment, which creates a prototype. The final product is then completed and a notification is sent to the user. The production data and the user's emotional information are stored in a database.

[0608] Prompt Sentence Examples

[0609] The user wants a medium-sized design with a bamboo and blue floral pattern. Analysis of the user's facial expression indicates that their current emotion is "joy." Generate a design based on this user's preference.

[0610] In this way, based on the embodiments of the invention, customized products can be produced effectively and user satisfaction can be increased.

[0611] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0612] Step 1:

[0613] Users input customization requests using devices such as smartphones or PCs. Input information includes design, material, size, color, etc. Once input is complete, the device sends the request to the server. The information input here is specific data regarding the user's desired specifications.

[0614] Step 2:

[0615] The device uses a camera and microphone to capture the user's facial expressions and voice. The emotion engine analyzes this data and recognizes the user's emotional state. The emotion engine generates analysis results and sends emotional data containing the results to the server. The input data is digital data of facial expressions and voice, and this is output as emotional information.

[0616] Step 3:

[0617] The server receives customization requests and emotional data sent from the user's device. The received data includes design, material, size, color, and emotional information. The server analyzes this information and generates input data to invoke the AI ​​design module. The data calculation here involves integrating the input information and converting it into the format required for design generation.

[0618] Step 4:

[0619] The server generates design data using an AI design module. The AI ​​design module receives customization requests and emotion information as input and generates design data. This design data reflects fine-tuning based on the user's specifications and emotions. The output data is a detailed blueprint for digital production.

[0620] Step 5:

[0621] The server sends the generated design data to a digital fabrication device (such as a 3D printer or CNC machine) that creates a prototype based on the received design data. The input data is a design drawing, which is then used by the machine to generate the physical prototype.

[0622] Step 6:

[0623] Once the prototype is produced and its quality is confirmed, the digital production equipment moves on to produce the final product. At this stage, monitoring data during production is sent to the server for quality control. The output data is the finished product.

[0624] Step 7:

[0625] Once the product is completed, the server sends a notification to the user that production is complete. The notification includes details about how to receive the product and other information. This notification is sent to the user's device via email or push notification. The input data here is production status information, and the user notification is generated based on that information.

[0626] Step 8:

[0627] Finally, the server stores production information and user emotional information in a database. This information will be used for future technology transfer and service improvement. The input data is all production process and emotional data, which is then stored in the database.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] [Third embodiment]

[0632] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0633] 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.

[0634] 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).

[0635] 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.

[0636] 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.

[0637] 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).

[0638] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] 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."

[0644] This invention is a system that receives customization requests from users, generates design data using artificial intelligence (AI), and produces products using digital production equipment based on the design data. It also includes a means for sending a notification to the user after production is complete. This system improves product quality and production efficiency, and can pass on technology to the next generation.

[0645] Program processing

[0646] 1. User submits customization request

[0647] Through the web or mobile application, users input specifications such as the design, material, size, and color of the craft they want, for example, "a floral design, bamboo, medium size, blue."

[0648] 2. The server receives the request

[0649] When a user submits a request, the server receives the request and analyzes the provided specifications, including details such as design, material, size, and color.

[0650] 3. Generate design data

[0651] The server then calls the AI ​​module based on the received customization request. The AI ​​module generates a digital design based on the specified design and materials. This design data details the design of the craft and is generated as a file in CAD software format, for example.

[0652] 4. Sending design data and prototyping

[0653] The server sends the generated design data to a digital manufacturing device (e.g., a CNC machine or 3D printer) that then produces a prototype based on the specified design. Once the prototype is complete, it is inspected for quality and design.

[0654] 5. Final production of the product

[0655] Based on the design data verified during the prototype stage, digital fabrication equipment creates the final product, enabling efficient and high-quality production of customized crafts.

[0656] 6. Notice to Users

[0657] Once the product is complete, the server sends a notification to the user, including instructions on how to receive the product and other details.

[0658] Specific examples

[0659] For example, consider a user ordering a medium-sized bamboo craft with a blue floral design. The user logs into a web application and submits a customization request by inputting the desired design, material, size, and color. The server receives the request and passes the specified criteria to an AI module, which then generates a digital blueprint for the medium-sized bamboo craft with a blue floral design.

[0660] The server then sends the design data to the factory's CNC machine to create a prototype. After the prototype's quality is confirmed, the final product is produced. Finally, the server notifies the user that the product is complete and provides detailed instructions for receiving it. This system allows users to efficiently receive their desired customized crafts and provides high-quality products.

[0661] In this way, the system of the present invention can respond to the diverse needs of users while preserving traditional techniques and passing them on to the next generation.

[0662] The processing flow will be explained below.

[0663] Step 1:

[0664] A user accesses a web or mobile application and submits a customization request by filling out a form with specifications such as desired design, material, size, and color, and then clicking the "Submit" button.

[0665] Step 2:

[0666] The server receives customization requests from users via API, analyzes the received requests as JSON data, and extracts detailed information such as design, material, size, and color.

[0667] Step 3:

[0668] The server launches an artificial intelligence (AI) module to generate the design data. Based on the received specifications, the AI ​​module generates the appropriate digital design drawings (e.g., CAD files). The generated design data accurately reflects the design and dimensions according to the specified conditions.

[0669] Step 4:

[0670] The server sends the generated design data to a digital fabrication device, such as a CNC machine or 3D printer, which produces a prototype based on the specified design.

[0671] Step 5:

[0672] The digital production equipment receives the design data and creates a prototype. Once the prototype is complete, its quality and design are verified. Any necessary modifications are made at this stage.

[0673] Step 6:

[0674] The server retransmits the final design data, reflecting any modifications made during the prototype stage, to the digital production device, which then produces the final product.

[0675] Step 7:

[0676] Once production is complete, the server notifies the user that the product is ready. This notification includes details on how to receive the product. The user receives the notification and proceeds with the process of receiving the product.

[0677] Step 8:

[0678] The server stores production information in a database, which allows the technology to be passed on to future generations and for overseas users to easily learn.

[0679] Example 1

[0680] 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."

[0681] In today's customized product manufacturing, there is a need to efficiently produce high-quality products based on user requirements while reducing errors and defects at each stage of the manufacturing process. Prompt and accurate notification to users after manufacturing is also an important issue. Furthermore, in order to pass on technology, it is necessary to systematically store manufacturing information and create an environment in which the next generation and overseas users can easily learn the technology.

[0682] 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.

[0683] In this invention, the server includes: means for receiving a customization request from a user; means for generating design data using artificial intelligence based on the received customization request; means for transmitting the design data to a digital production device that uses the generated design data to produce a product; means for sending a notification to the user after production is complete; a terminal for the user to input the customization request; means for the terminal to transmit the request to the server; means for the server to analyze the request and generate a prompt for the AI ​​module; and a terminal for reporting the production status of the digital production device to the server. This allows for efficient production based on the user's customization request and simplifies management of each process. Furthermore, storing production information in a database enables the transfer of skills, allowing the next generation and overseas users to easily acquire skills.

[0684] "User" refers to an individual or corporation that uses the system to input a customization request and request the production of a product.

[0685] A "customization request" is information including specifications such as product design, material, size, and color specified by the user.

[0686] A "terminal" is a device (such as a smartphone, tablet, or PC) that a user uses to input a customization request and send it to the server.

[0687] The "server" is a central control device that receives customization requests from users, analyzes them, invokes AI modules to generate design data, and transmits the design data to digital production devices.

[0688] "Artificial intelligence (AI)" is a computer system that generates design data based on a user's customization requests.

[0689] "Design data" refers to digital blueprints of products generated by AI, and is expressed as files in CAD software format.

[0690] "Digital production equipment" refers to equipment that produces actual products based on generated design data, including CNC machines and 3D printers.

[0691] A "prompt sentence" is an instruction sentence issued by the server to the AI ​​module, which specifically indicates the content of the customization request.

[0692] The "database" is a system for systematically storing information about product manufacturing, and is used as a reference by the next generation and overseas users when learning the technology.

[0693] "Notification" means information sent to the user in the form of email, SMS, in-app notification, etc. to inform the user that the product has been completed.

[0694] This invention is a system that receives customization requests from users, generates design data using artificial intelligence (AI), and produces products using digital production equipment based on the design data. It also includes a means for sending a notification to the user after production is complete. This system improves product quality and production efficiency, and can pass on technology to the next generation.

[0695] Users access the web or mobile application from their own devices (smartphones, tablets, PCs, etc.) and enter detailed specifications such as the design, material, size, color, etc. of the product they want. For example, if a user wants a "medium-sized bamboo craft with a blue floral pattern," they submit a customization request based on the specified specifications.

[0696] The customization request sent from the device is securely transmitted to the server using the HTTPS protocol. The server analyzes the received request and calls a generative AI model (e.g., OpenAI's GPT-3) based on the analysis results. At this time, the server sends a prompt message to the generative AI model that specifically describes the customization request.

[0697] An example of a prompt is:

[0698] "Generate a digital blueprint for a medium-sized bamboo craft with a blue floral pattern."

[0699] The following content is generated:

[0700] The generative AI model receives this prompt and generates a digital blueprint in CAD software format based on the specified design. The generated design data is sent back to the server, which then sends it to digital fabrication equipment (e.g., a CNC machine or 3D printer).

[0701] The digital production equipment begins producing a prototype based on the received design data. The production equipment's terminal checks the prototype production status and reports log data and progress to the server. The server verifies the quality of the prototype and instructs corrections if necessary. After the quality of the prototype has been confirmed, the server issues instructions for final production to the digital production equipment.

[0702] Once the final product is completed, the server will notify the user of the completion. The notification will be sent via email, SMS, in-app notification, etc., and will include details on how to receive the product. In addition, information about the product's creation will be stored in a database, creating an environment where the next generation and overseas users can easily learn the technology.

[0703] The system allows users to efficiently receive high-quality customized products and ensures that each step of the production process is under control, reducing errors and defects and improving product quality and production efficiency.

[0704] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0705] Step 1:

[0706] A user accesses the web or mobile application from their device and enters detailed specifications for the product they want, such as design, material, size, and color. An example of input data might be "a medium-sized bamboo craft with a blue floral pattern." This data is entered into the form and confirmed by pressing the submit button.

[0707] Step 2:

[0708] The device receives the customization request from the user and sends it to the server using the HTTPS protocol. This communication is encrypted to ensure a secure connection. The input is the customization request in JSON data format, and the output is the data sent to the server.

[0709] Step 3:

[0710] The server receives the customization request and analyzes the data using the analysis module. The input is the received JSON data, and the analysis results are individual elements such as design, material, size, color, etc. The analysis results are sent to the AI ​​module.

[0711] Step 4:

[0712] The server generates a prompt for the generative AI model (e.g., GPT-3) based on the analysis results. An example of a generated prompt is, "Please generate a digital blueprint for a medium-sized craft item made of bamboo with a blue floral pattern." The input is the analysis result data, and the output is the prompt.

[0713] Step 5:

[0714] The server sends prompts to the generative AI model, which then generates design data. The input is the prompt, and the output is a digital design in CAD software format. This design is then returned to the server.

[0715] Step 6:

[0716] The server sends the received design data to the digital production equipment. This communication uses MQTT and HTTP protocols. The input is the digital design drawing, and the output is the data sent to the production equipment.

[0717] Step 7:

[0718] The terminal monitors the progress of the production equipment and reports the prototype production status to the server. The input is progress data from the production equipment, and the output is report data to the server. This allows the server to check the quality of the prototype.

[0719] Step 8:

[0720] The server checks the quality of the prototype and issues instructions for final production to the production equipment. The input is the quality data of the prototype, and the output is the instructions for final production. The production equipment produces the final product based on these instructions.

[0721] Step 9:

[0722] When the final product is completed, the server sends a notification to the user, which can be sent by email, SMS, or in-app notification, and includes details on how to receive the product. The input is the production completion data, and the output is the notification to the user.

[0723] (Application example 1)

[0724] 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."

[0725] Today's consumers want to quickly obtain high-quality, customized products that meet their individual needs and preferences. However, achieving this goal requires significant time and cost. Furthermore, there is a lack of efficient means to execute the production process in physical stores and generate high-quality products on the spot. A system that solves these issues is needed.

[0726] 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.

[0727] In this invention, the server includes means for receiving a customization request from a user, means for generating design data using artificial intelligence based on the received customization request, means for transmitting the generated design data to digital production equipment that produces a product using the design data, means for sending a notification to the user after production is complete, and means for producing the product in real time in cooperation with the digital production equipment located in a physical store. This makes it possible to produce customized products quickly and with high quality to meet the diverse needs of users.

[0728] "Means for receiving customization requests from users" refers to an interface that allows users to input desired product specifications (design, material, size, color, etc.) and send them to the server.

[0729] "Means for generating design data using artificial intelligence based on received customization requests" refers to a system that analyzes specifications received from users and generates design data using machine learning algorithms and deep learning.

[0730] "Means for transmitting the generated design data to a digital fabrication device that uses the generated design data to fabricate a product" means a communications interface that transmits the generated design data in real time to fabrication equipment (e.g., a 3D printer or CNC machine) to initiate fabrication.

[0731] "Means for sending a notification to the user after production is completed" refers to a communication means (notification system or message sending system) for informing the user that the product has been completed.

[0732] "Means for producing products in real time in cooperation with digital production equipment installed in a physical store" refers to a system that works in cooperation with production equipment installed in a physical store to quickly produce products to user specifications on the spot.

[0733] System Program

[0734] The system for realizing the present invention is configured as follows: Basically, a server, a user terminal, and in-store digital production equipment work in cooperation with each other.

[0735] Program processing

[0736] The server first receives a customization request from the user. This request is sent by the user via a smartphone or tablet. For example, the user may enter a specific request such as, "I would like to order a medium-sized, blue craft with a floral design made from bamboo." The server analyzes this request and creates the relevant design data using a generative AI model. Examples of generative AI models used include TensorFlow and PyTorch.

[0737] The generated design data is sent from the server to digital production equipment (e.g., 3D printers and CNC machines) located in the physical store. The production equipment creates products in real time based on this design data. When the product is completed, the server sends a notification to the user, informing them of how to receive the product. Notifications are sent via SMS, email, in-app notifications, and other means.

[0738] Specific examples

[0739] Consider a scenario where a user uses a smartphone app to order a medium-sized blue bamboo craft with a floral design. The user logs into the app and enters the following prompt:

[0740] "I'd like to order a medium-sized blue bamboo craft with a new floral design."

[0741] The server receives this request and calls an AI module to generate the design data, which is then sent to the in-store 3D printer, which then produces the product on-site. Once the product is complete, the server notifies the user.

[0742] This system allows users to quickly and with high quality receive customized products that meet their diverse needs, and by utilizing digital production equipment in physical stores, the production process is made more efficient, improving the user experience.

[0743] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0744] Step 1:

[0745] A user inputs a customization request from a smartphone and sends the request to the server. The input includes information such as the desired product design, material, size, and color. Specifically, the user inputs a prompt such as "A floral design, bamboo material, medium size, blue craft item." The output is the customization request data received by the server.

[0746] Step 2:

[0747] The server receives the customization request and analyzes its contents. This analysis includes extracting information such as design, material, size, and color. The input is the customization request data received in step 1, and the output is the analyzed specification data. Specifically, the server parses the design pattern, material name, and size information into a data structure.

[0748] Step 3:

[0749] The server calls the generative AI model and generates design data based on the analyzed specification data. The data processing and calculations performed here are processes that generate design data using machine learning algorithms. The input is the specification data obtained in step 2, and the output is the generated design data. Specifically, the server generates blueprints and 3D models using TensorFlow, for example.

[0750] Step 4:

[0751] The server transmits the generated design data to the digital production device in the physical store. This transmission process includes sending data using network communication. The input is the design data generated in step 3, and the output is the design data received by the digital production device. Specifically, the server calls a Web API to transmit the data.

[0752] Step 5:

[0753] Digital production equipment in the physical store begins producing the product based on the received design data. The input is the design data received in step 4, and the output is the finished product. Specifically, 3D printers and CNC machines produce the product in real time based on the design data.

[0754] Step 6:

[0755] After the product is completed, the server sends a notification to the user that the product is complete. The notification includes details such as the product being completed and how to receive it. The input is the product completion status information, and the output is the notification sent to the user. Specifically, the server uses means such as SMS, email, or in-app notification.

[0756] 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.

[0757] This invention is a system that receives customization requests from users, generates design data using artificial intelligence (AI), and produces products using digital production equipment based on the design data. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide products that better meet the individual needs of each user by making customization suggestions and design adjustments based on the user's emotions.

[0758] Program processing

[0759] 1. User submits customization request

[0760] Through the web or mobile application, users input specifications such as the design, material, size, and color of the craft they want, for example, "a floral design, bamboo, medium size, blue."

[0761] 2. Emotion engine recognizes user emotions

[0762] The emotion engine recognizes the user's emotions by analyzing subtle facial expressions and tone of voice during input and operation. This information is reflected in the content of customization requests and suggestions.

[0763] 3. The server receives the request

[0764] When a user submits a request, the server receives the request and analyzes the specified specifications, including detailed items such as design, material, size, and color, as well as emotional information recognized by the emotion engine.

[0765] 4. Generate design data

[0766] The server then calls the AI ​​module based on the received customization request and emotional information. The AI ​​module generates a digital blueprint based on the specified design and materials. The emotional information may also be used to fine-tune the design and color.

[0767] 5. Sending design data and prototyping

[0768] The server then sends the generated design data to digital fabrication equipment, such as CNC machines and 3D printers, which then create prototypes based on the specified design. Once the prototypes are complete, their quality and design are verified.

[0769] 6. Final production of the product

[0770] Based on the design data verified during the prototype stage, digital fabrication equipment creates the final product, enabling efficient and high-quality production of customized crafts.

[0771] 7. Notice to Users

[0772] When the product is completed, the server sends a notification to the user, which includes details on how to receive the product. The user receives the notification and proceeds with the process of receiving the product.

[0773] 8. Storing information in a database

[0774] The server stores production information and the emotional information recognized by the emotion engine in a database, allowing the technology to be passed on to future generations and for overseas users to easily learn.

[0775] Specific examples

[0776] For example, consider the case where a user orders a "medium-sized bamboo craft with a blue floral design." The user logs in to a web application and submits a customization request by entering the desired design, material, size, and color. At this time, the emotion engine analyzes the user's facial expressions and tone of voice to recognize emotions such as satisfaction and expectation. The server receives the request and passes it along with the analyzed emotion information to the AI ​​module. The AI ​​module generates a digital blueprint of the medium-sized bamboo craft with a blue floral design. If necessary, the design is fine-tuned based on the emotion information.

[0777] The server then sends the design data to the factory's CNC machine, which produces a prototype. After the prototype's quality is confirmed, the final product is produced. Finally, the server notifies the user that the product is complete and provides detailed instructions for receiving it. Furthermore, production information and user emotional information from this process are stored in a database for future use in technology transfer and customization optimization. This system allows users to efficiently receive the customized crafts they desire, providing high-quality products and optimal suggestions tailored to their emotions.

[0778] In this way, the system of the present invention can respond to the diverse needs and feelings of users while preserving traditional techniques and passing them on to the next generation.

[0779] The processing flow will be explained below.

[0780] Step 1:

[0781] A user accesses a web or mobile application and inputs specifications for the desired craft, such as design, material, size, and color. For example, a specific request could be "floral design, bamboo, medium size, blue."

[0782] Step 2:

[0783] The emotion engine installed in the device analyzes the user's input, subtle facial expressions during operation, tone of voice, etc. to recognize the user's emotions. For example, it uses facial recognition technology and voice analysis technology to determine whether the user is satisfied or dissatisfied.

[0784] Step 3:

[0785] The server receives the customization request sent by the user via API and analyzes the detailed information on design, material, size, and color contained therein, as well as the emotional information recognized by the emotion engine.

[0786] Step 4:

[0787] The server analyzes the customization request and emotional information, then activates an artificial intelligence (AI) module, which passes this information to the AI ​​module and instructs it to generate an appropriate digital blueprint.

[0788] Step 5:

[0789] The AI ​​module generates a digital blueprint (e.g., a CAD file) based on the user's design and materials. It may also adjust the design and color palette based on emotional information. For example, if the user is excited, it may suggest more vibrant colors.

[0790] Step 6:

[0791] The server then sends the generated digital design to a digital fabrication device (e.g., a CNC machine or a 3D printer), where the design data is updated with the emotion-based adjustments.

[0792] Step 7:

[0793] The digital production equipment creates a prototype based on the received design data. Once the prototype is complete, its quality and design are verified. Any problems discovered during this verification process are corrected.

[0794] Step 8:

[0795] The server retransmits the final design data, reflecting the corrections found during the prototype stage, to the digital production device, which then produces the final product.

[0796] Step 9:

[0797] Once production is complete, the server notifies the user that the product is ready. This notification includes details on how to receive the product. The user receives the notification and proceeds with the process of receiving the product.

[0798] Step 10:

[0799] The server stores production information and the emotional information recognized by the emotion engine in a database. This allows the technology to be passed on to future generations and makes it easy for the next generation and overseas users to learn. The stored emotional information is also used to optimize future customization requests.

[0800] Example 2

[0801] 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."

[0802] Conventional customized product production systems do not take user emotions into consideration, which can lead to low user satisfaction. Furthermore, the lack of prototype production and quality verification processes can lead to variations in the quality of the final product. Furthermore, because production information and emotion information are not stored in a database, it is difficult to transfer skills and optimize customization.

[0803] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a customization request from a user, means for generating design data using artificial intelligence based on the received customization request, means for adjusting the customization request using an emotion engine that recognizes the user's emotions, means for transmitting the design data to a digital production device that produces a product using the generated design data, means for producing a prototype of the product and verifying its quality and design, and means for sending a notification to the user after production is complete. This enables the production of customized products that take the user's emotions into consideration, making it possible to provide high-quality products that provide a high level of satisfaction. Furthermore, storing production information and emotion information in a database facilitates future technology transfer and customization optimization.

[0804] A "customization request" is information input by a user to specify specifications such as desired design, material, size, and color.

[0805] "Artificial intelligence" is a technology that analyzes received customization requests and generates design data.

[0806] The "emotion engine" is a technology that analyzes the user's facial expressions and tone of voice to recognize their emotions.

[0807] "Design data" is a digital blueprint generated by artificial intelligence.

[0808] "Digital production equipment" refers to machines that produce products based on generated design data, and specifically includes CNC machines and 3D printers.

[0809] A "prototype" is a prototype created before the final product is manufactured.

[0810] "Quality and design verification" is the process of checking the quality and design of a prototype after it has been completed.

[0811] "Notification" is information that notifies the user that production of the product has been completed.

[0812] A "database" is a system that stores production-related and emotional information.

[0813] "Technology transfer" is the process of enabling preserved information to be learned and utilized by future generations and users in other countries.

[0814] "Customization optimization" means proposing customization that best suits the user's needs based on past data.

[0815] This invention is a system that receives a user's customization request, generates design data using artificial intelligence (AI), and produces a product using digital production equipment based on the design data. By combining it with an emotion engine that recognizes the user's emotions, it is possible to make customization suggestions and design adjustments based on the user's emotions.

[0816] Users enter their customization request using a web browser or mobile application. The device's camera and microphone are used to capture the user's facial expressions and tone of voice, which are then sent to the emotion engine. For example, a specific request could be, "I want to create a medium-sized craft item made of bamboo with a blue floral pattern."

[0817] The emotion engine analyzes data acquired using the device's camera and microphone to recognize the user's emotions. This emotional information is reflected in adjustments and suggestions for the customization request. The server calls the AI ​​module based on the received customization request and emotional information to generate a prompt.

[0818] The AI ​​module generates a digital blueprint based on the specified design and materials. This process takes emotional information into account and fine-tunes the design and color tone as needed. The generated digital blueprint is then sent by the server to a digital fabrication device (e.g., a CNC machine or 3D printer).

[0819] Digital fabrication equipment creates a prototype based on the generated design. After the prototype is completed, its quality and design are verified. If there are any problems during this process, the design is adjusted and the prototype is repeated.

[0820] Finally, the digital production equipment produces the final product based on the design data confirmed in the prototype stage. When the product is completed, the server sends a notification to the user that production is complete, and the user can then process the product. This notification includes details about how to receive the product.

[0821] Furthermore, production information and emotional information recognized by the emotion engine are stored in a database, enabling future technology transfer and optimization of customization.

[0822] For example, if a user wants to order a "medium-sized bamboo craft with a blue floral pattern," they log in to the web application and submit a customization request by entering the desired design, material, size, and color. The emotion engine analyzes the user's facial expressions and tone of voice to recognize emotions such as satisfaction and expectation. The server receives the request and passes it along with the analyzed emotional information to the AI ​​module, which then creates a prototype and final product based on the generated digital blueprint. The server then notifies the user, who then completes the process by receiving the product.

[0823] In this way, the system of the present invention can efficiently provide high-quality customized products while responding to the diverse needs and emotions of users. Furthermore, by facilitating technology transfer and optimizing customization, it can also accommodate the next generation and users in other countries.

[0824] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0825] Processing flow

[0826] Step 1:

[0827] The user enters a customization request

[0828] A user opens a web or mobile application and enters a customization request, specifically selecting specifications such as design, material, size, and color.

[0829] Input: Design, material, size, color specifications

[0830] Output: Customization request data

[0831] Specific actions: The user enters the required information into the form and presses the submit button.

[0832] Step 2:

[0833] Emotion engine recognizes user emotions

[0834] The device's camera and microphone capture the user's facial expressions and tone of voice and analyze them in real time.

[0835] Input: User's facial expression data, tone of voice

[0836] Output: User's emotional information (satisfaction, expectations, etc.)

[0837] Specific operation: The camera captures the user's face and the microphone collects audio. This data is sent to the emotion engine for analysis.

[0838] Step 3:

[0839] The server receives the request

[0840] The user's input information and emotion information are sent to the server.

[0841] Input: Customization request data, emotion information

[0842] Output: Parsed request data

[0843] Specific operation: The server receives the data and analyzes the design, material, size, color, and emotional information.

[0844] Step 4:

[0845] Generate design data

[0846] The server calls the AI ​​module and generates a prompt, which then generates a digital blueprint based on the specified design and materials.

[0847] Input: Parsed request data, emotion information

[0848] Output: Digital blueprint

[0849] Specific operation: The server sends a prompt to the generative AI model via the API and receives the generated digital blueprint.

[0850] Step 5:

[0851] Sending design data and prototyping

[0852] The server transmits the generated design data to digital production equipment to produce a prototype.

[0853] Input: Digital blueprint

[0854] Output: Prototype

[0855] Specific operation: The server sends the design data to the production equipment, and a prototype is produced.

[0856] Step 6:

[0857] Quality and design verification

[0858] Verify the quality and design of the prototype and make any necessary adjustments if there are any issues.

[0859] Input: Prototype

[0860] Output: Final production data

[0861] Specific Actions: Physically validate prototype and re-adjust as needed.

[0862] Step 7:

[0863] Final production of the product

[0864] Final production is carried out based on the data confirmed at the prototype stage.

[0865] Input: Final production data

[0866] Output: Final product

[0867] Specific Action: Digital production equipment produces the final product.

[0868] Step 8:

[0869] User Notification

[0870] Once the product is completed, the server will send a notification to the user.

[0871] Input: Final product data

[0872] Output: Notification message

[0873] Specific operation: The server sends an email or app notification to the user.

[0874] Step 9:

[0875] Storing information in a database

[0876] Production information and emotional information are stored in a database.

[0877] Input: Production information, emotional information

[0878] Output: Saved data

[0879] Specific operation: The server stores the information in a database for future use.

[0880] (Application example 2)

[0881] 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."

[0882] Conventional customized product production systems only produce products based on user requests, without providing design suggestions or adjustments that reflect the user's emotions and preferences. This can result in designs and functions that do not necessarily satisfy users, leading to a decline in the shopping experience and product satisfaction. Furthermore, this information is not stored in a database, making it difficult for the next generation and overseas users to easily learn the technology.

[0883] 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 means for receiving a customization request from a user, means for recognizing the user's emotion using an emotion engine, means for generating design data using artificial intelligence based on the received customization request and the recognized emotion information, means for transmitting the design data to a digital production device that produces a product using the generated design data, and means for sending a notification to the user after production is complete. This allows for optimal design proposals based on the user's emotions and preferences, enabling the production of customized products that increase user satisfaction. Furthermore, by storing information about product production and user emotion information in a database, the next generation and overseas users can easily acquire the skills.

[0884] - "Means for receiving customization requests from users" refers to an interface through which users input specifications such as desired design, material, size, and color on a web or mobile application and transmit that information to the system.

[0885] "Means for recognizing a user's emotions using an emotion engine" refers to software or a device that analyzes a user's facial expressions and voice data to read a specific emotional state.

[0886] The "means for generating design data using artificial intelligence" is a program for automatically generating digital designs using an algorithm based on a received customization request and recognized emotional information.

[0887] The "means for transmitting the design data to a digital fabrication device that uses the generated design data to fabricate a product" is a communications system for transferring the generated digital design data to fabrication equipment such as a 3D printer or CNC machine.

[0888] The "means for sending a notification to the user after production is completed" refers to a communication means such as email or push notification for notifying the user after production of the product is completed.

[0889] "Means for producing a prototype of a product using design data" refers to physical digital production equipment for producing a prototype based on the generated design data.

[0890] "Means for storing product production information and user emotional information in a database" refers to a digital recording system for long-term storage of production process and user emotional data, making it accessible in the future.

[0891] To implement the present invention, the following system configuration and processing procedure are followed.

[0892] System Configuration

[0893] 1. User Device

[0894] A device such as a smartphone, tablet, or PC that allows users to input customization requests. It is equipped with a camera and microphone to capture the user's facial expressions and voice.

[0895] 2. Server

[0896] It receives customization requests from users and generates design data based on them. Its main software and libraries include an emotion engine (EmotionalRecognition) and an AI design module (AIDesignModule).

[0897] 3. Digital Production Equipment

[0898] The product is manufactured based on the generated design data, specifically using 3D printers and CNC machines.

[0899] 4. Database

[0900] It stores information about product creation and user sentiment, which can be used for future technology transfer and customization optimization.

[0901] Processing Details

[0902] User terminal

[0903] Users input customization requests using a smartphone or PC. Input items include specifications such as design, material, size, and color. The user's facial and voice data is also captured and analyzed by the emotion engine.

[0904] server

[0905] The server receives the request and emotional information sent from the user's device. It also receives emotional data analyzed by the Emotional Recognition engine. Based on the received data, the AI ​​Design Module generates design data. This design data is fine-tuned to reflect not only the user's customization request but also the emotional information.

[0906] Digital Production Equipment

[0907] The generated design data is sent to the manufacturing equipment, which then produces a prototype based on the data (such as a 3D printer or CNC machine). After the prototype's quality is confirmed, the final product is produced.

[0908] Database

[0909] The production process and user emotional information are stored in a database, which will be used for future technology transfer and customization optimization.

[0910] Specific examples

[0911] For example, let's say a user orders a "medium-sized bamboo craft with a blue floral pattern." The user uses their smartphone to input the desired design, material, size, and color. At this time, the Emotional Recognition engine analyzes the user's facial expressions and voice and sends emotional information to the server. The server generates design data using the AI ​​Design Module based on the received customization request and emotional information. The generated data is sent to digital production equipment, which creates a prototype. The final product is then completed and a notification is sent to the user. The production data and the user's emotional information are stored in a database.

[0912] Prompt Sentence Examples

[0913] The user wants a medium-sized design with a bamboo and blue floral pattern. Analysis of the user's facial expression indicates that their current emotion is "joy." Generate a design based on this user's preference.

[0914] In this way, based on the embodiments of the invention, customized products can be produced effectively and user satisfaction can be increased.

[0915] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0916] Step 1:

[0917] Users input customization requests using devices such as smartphones or PCs. Input information includes design, material, size, color, etc. Once input is complete, the device sends the request to the server. The information input here is specific data regarding the user's desired specifications.

[0918] Step 2:

[0919] The device uses a camera and microphone to capture the user's facial expressions and voice. The emotion engine analyzes this data and recognizes the user's emotional state. The emotion engine generates analysis results and sends emotional data containing the results to the server. The input data is digital data of facial expressions and voice, and this is output as emotional information.

[0920] Step 3:

[0921] The server receives customization requests and emotional data sent from the user's device. The received data includes design, material, size, color, and emotional information. The server analyzes this information and generates input data to invoke the AI ​​design module. The data calculation here involves integrating the input information and converting it into the format required for design generation.

[0922] Step 4:

[0923] The server generates design data using an AI design module. The AI ​​design module receives customization requests and emotion information as input and generates design data. This design data reflects fine-tuning based on the user's specifications and emotions. The output data is a detailed blueprint for digital production.

[0924] Step 5:

[0925] The server sends the generated design data to a digital fabrication device (such as a 3D printer or CNC machine) that creates a prototype based on the received design data. The input data is a design drawing, which is then used by the machine to generate the physical prototype.

[0926] Step 6:

[0927] Once the prototype is produced and its quality is confirmed, the digital production equipment moves on to produce the final product. At this stage, monitoring data during production is sent to the server for quality control. The output data is the finished product.

[0928] Step 7:

[0929] Once the product is completed, the server sends a notification to the user that production is complete. The notification includes details about how to receive the product and other information. This notification is sent to the user's device via email or push notification. The input data here is production status information, and the user notification is generated based on that information.

[0930] Step 8:

[0931] Finally, the server stores production information and user emotional information in a database. This information will be used for future technology transfer and service improvement. The input data is all production process and emotional data, which is then stored in the database.

[0932] 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.

[0933] 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.

[0934] 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.

[0935] [Fourth embodiment]

[0936] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0937] 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.

[0938] 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).

[0939] 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.

[0940] 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.

[0941] 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).

[0942] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0943] 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.

[0944] 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.

[0945] 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.

[0946] 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.

[0947] 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.

[0948] 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."

[0949] This invention is a system that receives customization requests from users, generates design data using artificial intelligence (AI), and produces products using digital production equipment based on the design data. It also includes a means for sending a notification to the user after production is complete. This system improves product quality and production efficiency, and can pass on technology to the next generation.

[0950] Program processing

[0951] 1. User submits customization request

[0952] Through the web or mobile application, users input specifications such as the design, material, size, and color of the craft they want, for example, "a floral design, bamboo, medium size, blue."

[0953] 2. The server receives the request

[0954] When a user submits a request, the server receives the request and analyzes the provided specifications, including details such as design, material, size, and color.

[0955] 3. Generate design data

[0956] The server then calls the AI ​​module based on the received customization request. The AI ​​module generates a digital design based on the specified design and materials. This design data details the design of the craft and is generated as a file in CAD software format, for example.

[0957] 4. Sending design data and prototyping

[0958] The server sends the generated design data to a digital manufacturing device (e.g., a CNC machine or 3D printer) that then produces a prototype based on the specified design. Once the prototype is complete, it is inspected for quality and design.

[0959] 5. Final production of the product

[0960] Based on the design data verified during the prototype stage, digital fabrication equipment creates the final product, enabling efficient and high-quality production of customized crafts.

[0961] 6. Notice to Users

[0962] Once the product is complete, the server sends a notification to the user, including instructions on how to receive the product and other details.

[0963] Specific examples

[0964] For example, consider a user ordering a medium-sized bamboo craft with a blue floral design. The user logs into a web application and submits a customization request by inputting the desired design, material, size, and color. The server receives the request and passes the specified criteria to an AI module, which then generates a digital blueprint for the medium-sized bamboo craft with a blue floral design.

[0965] The server then sends the design data to the factory's CNC machine to create a prototype. After the prototype's quality is confirmed, the final product is produced. Finally, the server notifies the user that the product is complete and provides detailed instructions for receiving it. This system allows users to efficiently receive their desired customized crafts and provides high-quality products.

[0966] In this way, the system of the present invention can respond to the diverse needs of users while preserving traditional techniques and passing them on to the next generation.

[0967] The processing flow will be explained below.

[0968] Step 1:

[0969] A user accesses a web or mobile application and submits a customization request by filling out a form with specifications such as desired design, material, size, and color, and then clicking the "Submit" button.

[0970] Step 2:

[0971] The server receives customization requests from users via API, analyzes the received requests as JSON data, and extracts detailed information such as design, material, size, and color.

[0972] Step 3:

[0973] The server launches an artificial intelligence (AI) module to generate the design data. Based on the received specifications, the AI ​​module generates the appropriate digital design drawings (e.g., CAD files). The generated design data accurately reflects the design and dimensions according to the specified conditions.

[0974] Step 4:

[0975] The server sends the generated design data to a digital fabrication device, such as a CNC machine or 3D printer, which produces a prototype based on the specified design.

[0976] Step 5:

[0977] The digital production equipment receives the design data and creates a prototype. Once the prototype is complete, its quality and design are verified. Any necessary modifications are made at this stage.

[0978] Step 6:

[0979] The server retransmits the final design data, reflecting any modifications made during the prototype stage, to the digital production device, which then produces the final product.

[0980] Step 7:

[0981] Once production is complete, the server notifies the user that the product is ready. This notification includes details on how to receive the product. The user receives the notification and proceeds with the process of receiving the product.

[0982] Step 8:

[0983] The server stores production information in a database, which allows the technology to be passed on to future generations and for overseas users to easily learn.

[0984] Example 1

[0985] 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."

[0986] In today's customized product manufacturing, there is a need to efficiently produce high-quality products based on user requirements while reducing errors and defects at each stage of the manufacturing process. Prompt and accurate notification to users after manufacturing is also an important issue. Furthermore, in order to pass on technology, it is necessary to systematically store manufacturing information and create an environment in which the next generation and overseas users can easily learn the technology.

[0987] 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.

[0988] In this invention, the server includes: means for receiving a customization request from a user; means for generating design data using artificial intelligence based on the received customization request; means for transmitting the design data to a digital production device that uses the generated design data to produce a product; means for sending a notification to the user after production is complete; a terminal for the user to input the customization request; means for the terminal to transmit the request to the server; means for the server to analyze the request and generate a prompt for the AI ​​module; and a terminal for reporting the production status of the digital production device to the server. This allows for efficient production based on the user's customization request and simplifies management of each process. Furthermore, storing production information in a database enables the transfer of skills, allowing the next generation and overseas users to easily acquire skills.

[0989] "User" refers to an individual or corporation that uses the system to input a customization request and request the production of a product.

[0990] A "customization request" is information including specifications such as product design, material, size, and color specified by the user.

[0991] A "terminal" is a device (such as a smartphone, tablet, or PC) that a user uses to input a customization request and send it to the server.

[0992] The "server" is a central control device that receives customization requests from users, analyzes them, invokes AI modules to generate design data, and transmits the design data to digital production devices.

[0993] "Artificial intelligence (AI)" is a computer system that generates design data based on a user's customization requests.

[0994] "Design data" refers to digital blueprints of products generated by AI, and is expressed as files in CAD software format.

[0995] "Digital production equipment" refers to equipment that produces actual products based on generated design data, including CNC machines and 3D printers.

[0996] A "prompt sentence" is an instruction sentence issued by the server to the AI ​​module, which specifically indicates the content of the customization request.

[0997] The "database" is a system for systematically storing information about product manufacturing, and is used as a reference by the next generation and overseas users when learning the technology.

[0998] "Notification" means information sent to the user in the form of email, SMS, in-app notification, etc. to inform the user that the product has been completed.

[0999] This invention is a system that receives customization requests from users, generates design data using artificial intelligence (AI), and produces products using digital production equipment based on the design data. It also includes a means for sending a notification to the user after production is complete. This system improves product quality and production efficiency, and can pass on technology to the next generation.

[1000] Users access the web or mobile application from their own devices (smartphones, tablets, PCs, etc.) and enter detailed specifications such as the design, material, size, color, etc. of the product they want. For example, if a user wants a "medium-sized bamboo craft with a blue floral pattern," they submit a customization request based on the specified specifications.

[1001] The customization request sent from the device is securely transmitted to the server using the HTTPS protocol. The server analyzes the received request and calls a generative AI model (e.g., OpenAI's GPT-3) based on the analysis results. At this time, the server sends a prompt message to the generative AI model that specifically describes the customization request.

[1002] An example of a prompt is:

[1003] "Generate a digital blueprint for a medium-sized bamboo craft with a blue floral pattern."

[1004] The following content is generated:

[1005] The generative AI model receives this prompt and generates a digital blueprint in CAD software format based on the specified design. The generated design data is sent back to the server, which then sends it to digital fabrication equipment (e.g., a CNC machine or 3D printer).

[1006] The digital production equipment begins producing a prototype based on the received design data. The production equipment's terminal checks the prototype production status and reports log data and progress to the server. The server verifies the quality of the prototype and instructs corrections if necessary. After the quality of the prototype has been confirmed, the server issues instructions for final production to the digital production equipment.

[1007] Once the final product is completed, the server will notify the user of the completion. The notification will be sent via email, SMS, in-app notification, etc., and will include details on how to receive the product. In addition, information about the product's creation will be stored in a database, creating an environment where the next generation and overseas users can easily learn the technology.

[1008] The system allows users to efficiently receive high-quality customized products and ensures that each step of the production process is under control, reducing errors and defects and improving product quality and production efficiency.

[1009] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1010] Step 1:

[1011] A user accesses the web or mobile application from their device and enters detailed specifications for the product they want, such as design, material, size, and color. An example of input data might be "a medium-sized bamboo craft with a blue floral pattern." This data is entered into the form and confirmed by pressing the submit button.

[1012] Step 2:

[1013] The device receives the customization request from the user and sends it to the server using the HTTPS protocol. This communication is encrypted to ensure a secure connection. The input is the customization request in JSON data format, and the output is the data sent to the server.

[1014] Step 3:

[1015] The server receives the customization request and analyzes the data using the analysis module. The input is the received JSON data, and the analysis results are individual elements such as design, material, size, color, etc. The analysis results are sent to the AI ​​module.

[1016] Step 4:

[1017] The server generates a prompt for the generative AI model (e.g., GPT-3) based on the analysis results. An example of a generated prompt is, "Please generate a digital blueprint for a medium-sized craft item made of bamboo with a blue floral pattern." The input is the analysis result data, and the output is the prompt.

[1018] Step 5:

[1019] The server sends prompts to the generative AI model, which then generates design data. The input is the prompt, and the output is a digital design in CAD software format. This design is then returned to the server.

[1020] Step 6:

[1021] The server sends the received design data to the digital production equipment. This communication uses MQTT and HTTP protocols. The input is the digital design drawing, and the output is the data sent to the production equipment.

[1022] Step 7:

[1023] The terminal monitors the progress of the production equipment and reports the prototype production status to the server. The input is progress data from the production equipment, and the output is report data to the server. This allows the server to check the quality of the prototype.

[1024] Step 8:

[1025] The server checks the quality of the prototype and issues instructions for final production to the production equipment. The input is the quality data of the prototype, and the output is the instructions for final production. The production equipment produces the final product based on these instructions.

[1026] Step 9:

[1027] When the final product is completed, the server sends a notification to the user, which can be sent by email, SMS, or in-app notification, and includes details on how to receive the product. The input is the production completion data, and the output is the notification to the user.

[1028] (Application example 1)

[1029] 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."

[1030] Today's consumers want to quickly obtain high-quality, customized products that meet their individual needs and preferences. However, achieving this goal requires significant time and cost. Furthermore, there is a lack of efficient means to execute the production process in physical stores and generate high-quality products on the spot. A system that solves these issues is needed.

[1031] 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.

[1032] In this invention, the server includes means for receiving a customization request from a user, means for generating design data using artificial intelligence based on the received customization request, means for transmitting the generated design data to digital production equipment that produces a product using the design data, means for sending a notification to the user after production is complete, and means for producing the product in real time in cooperation with the digital production equipment located in a physical store. This makes it possible to produce customized products quickly and with high quality to meet the diverse needs of users.

[1033] "Means for receiving customization requests from users" refers to an interface that allows users to input desired product specifications (design, material, size, color, etc.) and send them to the server.

[1034] "Means for generating design data using artificial intelligence based on received customization requests" refers to a system that analyzes specifications received from users and generates design data using machine learning algorithms and deep learning.

[1035] "Means for transmitting the generated design data to a digital fabrication device that uses the generated design data to fabricate a product" means a communications interface that transmits the generated design data in real time to fabrication equipment (e.g., a 3D printer or CNC machine) to initiate fabrication.

[1036] "Means for sending a notification to the user after production is completed" refers to a communication means (notification system or message sending system) for informing the user that the product has been completed.

[1037] "Means for producing products in real time in cooperation with digital production equipment installed in a physical store" refers to a system that works in cooperation with production equipment installed in a physical store to quickly produce products to user specifications on the spot.

[1038] System Program

[1039] The system for realizing the present invention is configured as follows: Basically, a server, a user terminal, and in-store digital production equipment work in cooperation with each other.

[1040] Program processing

[1041] The server first receives a customization request from the user. This request is sent by the user via a smartphone or tablet. For example, the user may enter a specific request such as, "I would like to order a medium-sized, blue craft with a floral design made from bamboo." The server analyzes this request and creates the relevant design data using a generative AI model. Examples of generative AI models used include TensorFlow and PyTorch.

[1042] The generated design data is sent from the server to digital production equipment (e.g., 3D printers and CNC machines) located in the physical store. The production equipment creates products in real time based on this design data. When the product is completed, the server sends a notification to the user, informing them of how to receive the product. Notifications are sent via SMS, email, in-app notifications, and other means.

[1043] Specific examples

[1044] Consider a scenario where a user uses a smartphone app to order a medium-sized blue bamboo craft with a floral design. The user logs into the app and enters the following prompt:

[1045] "I'd like to order a medium-sized blue bamboo craft with a new floral design."

[1046] The server receives this request and calls an AI module to generate the design data, which is then sent to the in-store 3D printer, which then produces the product on-site. Once the product is complete, the server notifies the user.

[1047] This system allows users to quickly and with high quality receive customized products that meet their diverse needs, and by utilizing digital production equipment in physical stores, the production process is made more efficient, improving the user experience.

[1048] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1049] Step 1:

[1050] A user inputs a customization request from a smartphone and sends the request to the server. The input includes information such as the desired product design, material, size, and color. Specifically, the user inputs a prompt such as "A floral design, bamboo material, medium size, blue craft item." The output is the customization request data received by the server.

[1051] Step 2:

[1052] The server receives the customization request and analyzes its contents. This analysis includes extracting information such as design, material, size, and color. The input is the customization request data received in step 1, and the output is the analyzed specification data. Specifically, the server parses the design pattern, material name, and size information into a data structure.

[1053] Step 3:

[1054] The server calls the generative AI model and generates design data based on the analyzed specification data. The data processing and calculations performed here are processes that generate design data using machine learning algorithms. The input is the specification data obtained in step 2, and the output is the generated design data. Specifically, the server generates blueprints and 3D models using TensorFlow, for example.

[1055] Step 4:

[1056] The server transmits the generated design data to the digital production device in the physical store. This transmission process includes sending data using network communication. The input is the design data generated in step 3, and the output is the design data received by the digital production device. Specifically, the server calls a Web API to transmit the data.

[1057] Step 5:

[1058] Digital production equipment in the physical store begins producing the product based on the received design data. The input is the design data received in step 4, and the output is the finished product. Specifically, 3D printers and CNC machines produce the product in real time based on the design data.

[1059] Step 6:

[1060] After the product is completed, the server sends a notification to the user that the product is complete. The notification includes details such as the product being completed and how to receive it. The input is the product completion status information, and the output is the notification sent to the user. Specifically, the server uses means such as SMS, email, or in-app notification.

[1061] 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.

[1062] This invention is a system that receives customization requests from users, generates design data using artificial intelligence (AI), and produces products using digital production equipment based on the design data. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide products that better meet the individual needs of each user by making customization suggestions and design adjustments based on the user's emotions.

[1063] Program processing

[1064] 1. User submits customization request

[1065] Through the web or mobile application, users input specifications such as the design, material, size, and color of the craft they want, for example, "a floral design, bamboo, medium size, blue."

[1066] 2. Emotion engine recognizes user emotions

[1067] The emotion engine recognizes the user's emotions by analyzing subtle facial expressions and tone of voice during input and operation. This information is reflected in the content of customization requests and suggestions.

[1068] 3. The server receives the request

[1069] When a user submits a request, the server receives the request and analyzes the specified specifications, including detailed items such as design, material, size, and color, as well as emotional information recognized by the emotion engine.

[1070] 4. Generate design data

[1071] The server then calls the AI ​​module based on the received customization request and emotional information. The AI ​​module generates a digital blueprint based on the specified design and materials. The emotional information may also be used to fine-tune the design and color.

[1072] 5. Sending design data and prototyping

[1073] The server then sends the generated design data to digital fabrication equipment, such as CNC machines and 3D printers, which then create prototypes based on the specified design. Once the prototypes are complete, their quality and design are verified.

[1074] 6. Final production of the product

[1075] Based on the design data verified during the prototype stage, digital fabrication equipment creates the final product, enabling efficient and high-quality production of customized crafts.

[1076] 7. Notice to Users

[1077] When the product is completed, the server sends a notification to the user, which includes details on how to receive the product. The user receives the notification and proceeds with the process of receiving the product.

[1078] 8. Storing information in a database

[1079] The server stores production information and the emotional information recognized by the emotion engine in a database, allowing the technology to be passed on to future generations and for overseas users to easily learn.

[1080] Specific examples

[1081] For example, consider the case where a user orders a "medium-sized bamboo craft with a blue floral design." The user logs in to a web application and submits a customization request by entering the desired design, material, size, and color. At this time, the emotion engine analyzes the user's facial expressions and tone of voice to recognize emotions such as satisfaction and expectation. The server receives the request and passes it along with the analyzed emotion information to the AI ​​module. The AI ​​module generates a digital blueprint of the medium-sized bamboo craft with a blue floral design. If necessary, the design is fine-tuned based on the emotion information.

[1082] The server then sends the design data to the factory's CNC machine, which produces a prototype. After the prototype's quality is confirmed, the final product is produced. Finally, the server notifies the user that the product is complete and provides detailed instructions for receiving it. Furthermore, production information and user emotional information from this process are stored in a database for future use in technology transfer and customization optimization. This system allows users to efficiently receive the customized crafts they desire, providing high-quality products and optimal suggestions tailored to their emotions.

[1083] In this way, the system of the present invention can respond to the diverse needs and feelings of users while preserving traditional techniques and passing them on to the next generation.

[1084] The processing flow will be explained below.

[1085] Step 1:

[1086] A user accesses a web or mobile application and inputs specifications for the desired craft, such as design, material, size, and color. For example, a specific request could be "floral design, bamboo, medium size, blue."

[1087] Step 2:

[1088] The emotion engine installed in the device analyzes the user's input, subtle facial expressions during operation, tone of voice, etc. to recognize the user's emotions. For example, it uses facial recognition technology and voice analysis technology to determine whether the user is satisfied or dissatisfied.

[1089] Step 3:

[1090] The server receives the customization request sent by the user via API and analyzes the detailed information on design, material, size, and color contained therein, as well as the emotional information recognized by the emotion engine.

[1091] Step 4:

[1092] The server analyzes the customization request and emotional information, then activates an artificial intelligence (AI) module, which passes this information to the AI ​​module and instructs it to generate an appropriate digital blueprint.

[1093] Step 5:

[1094] The AI ​​module generates a digital blueprint (e.g., a CAD file) based on the user's design and materials. It may also adjust the design and color palette based on emotional information. For example, if the user is excited, it may suggest more vibrant colors.

[1095] Step 6:

[1096] The server then sends the generated digital design to a digital fabrication device (e.g., a CNC machine or a 3D printer), where the design data is updated with the emotion-based adjustments.

[1097] Step 7:

[1098] The digital production equipment creates a prototype based on the received design data. Once the prototype is complete, its quality and design are verified. Any problems discovered during this verification process are corrected.

[1099] Step 8:

[1100] The server retransmits the final design data, reflecting the corrections found during the prototype stage, to the digital production device, which then produces the final product.

[1101] Step 9:

[1102] Once production is complete, the server notifies the user that the product is ready. This notification includes details on how to receive the product. The user receives the notification and proceeds with the process of receiving the product.

[1103] Step 10:

[1104] The server stores production information and the emotional information recognized by the emotion engine in a database. This allows the technology to be passed on to future generations and makes it easy for the next generation and overseas users to learn. The stored emotional information is also used to optimize future customization requests.

[1105] Example 2

[1106] 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."

[1107] Conventional customized product production systems do not take user emotions into consideration, which can lead to low user satisfaction. Furthermore, the lack of prototype production and quality verification processes can lead to variations in the quality of the final product. Furthermore, because production information and emotion information are not stored in a database, it is difficult to transfer skills and optimize customization.

[1108] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a customization request from a user, means for generating design data using artificial intelligence based on the received customization request, means for adjusting the customization request using an emotion engine that recognizes the user's emotions, means for transmitting the design data to a digital production device that produces a product using the generated design data, means for producing a prototype of the product and verifying its quality and design, and means for sending a notification to the user after production is complete. This enables the production of customized products that take the user's emotions into consideration, making it possible to provide high-quality products that provide a high level of satisfaction. Furthermore, storing production information and emotion information in a database facilitates future technology transfer and customization optimization.

[1109] A "customization request" is information input by a user to specify specifications such as desired design, material, size, and color.

[1110] "Artificial intelligence" is a technology that analyzes received customization requests and generates design data.

[1111] The "emotion engine" is a technology that analyzes the user's facial expressions and tone of voice to recognize their emotions.

[1112] "Design data" is a digital blueprint generated by artificial intelligence.

[1113] "Digital production equipment" refers to machines that produce products based on generated design data, and specifically includes CNC machines and 3D printers.

[1114] A "prototype" is a prototype created before the final product is manufactured.

[1115] "Quality and design verification" is the process of checking the quality and design of a prototype after it has been completed.

[1116] "Notification" is information that notifies the user that production of the product has been completed.

[1117] A "database" is a system that stores production-related and emotional information.

[1118] "Technology transfer" is the process of enabling preserved information to be learned and utilized by future generations and users in other countries.

[1119] "Customization optimization" means proposing customization that best suits the user's needs based on past data.

[1120] This invention is a system that receives a user's customization request, generates design data using artificial intelligence (AI), and produces a product using digital production equipment based on the design data. By combining it with an emotion engine that recognizes the user's emotions, it is possible to make customization suggestions and design adjustments based on the user's emotions.

[1121] Users enter their customization request using a web browser or mobile application. The device's camera and microphone are used to capture the user's facial expressions and tone of voice, which are then sent to the emotion engine. For example, a specific request could be, "I want to create a medium-sized craft item made of bamboo with a blue floral pattern."

[1122] The emotion engine analyzes data acquired using the device's camera and microphone to recognize the user's emotions. This emotional information is reflected in adjustments and suggestions for the customization request. The server calls the AI ​​module based on the received customization request and emotional information to generate a prompt.

[1123] The AI ​​module generates a digital blueprint based on the specified design and materials. This process takes emotional information into account and fine-tunes the design and color tone as needed. The generated digital blueprint is then sent by the server to a digital fabrication device (e.g., a CNC machine or 3D printer).

[1124] Digital fabrication equipment creates a prototype based on the generated design. After the prototype is completed, its quality and design are verified. If there are any problems during this process, the design is adjusted and the prototype is repeated.

[1125] Finally, the digital production equipment produces the final product based on the design data confirmed in the prototype stage. When the product is completed, the server sends a notification to the user that production is complete, and the user can then process the product. This notification includes details about how to receive the product.

[1126] Furthermore, production information and emotional information recognized by the emotion engine are stored in a database, enabling future technology transfer and optimization of customization.

[1127] For example, if a user wants to order a "medium-sized bamboo craft with a blue floral pattern," they log in to the web application and submit a customization request by entering the desired design, material, size, and color. The emotion engine analyzes the user's facial expressions and tone of voice to recognize emotions such as satisfaction and expectation. The server receives the request and passes it along with the analyzed emotional information to the AI ​​module, which then creates a prototype and final product based on the generated digital blueprint. The server then notifies the user, who then completes the process by receiving the product.

[1128] In this way, the system of the present invention can efficiently provide high-quality customized products while responding to the diverse needs and emotions of users. Furthermore, by facilitating technology transfer and optimizing customization, it can also accommodate the next generation and users in other countries.

[1129] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1130] Processing flow

[1131] Step 1:

[1132] The user enters a customization request

[1133] A user opens a web or mobile application and enters a customization request, specifically selecting specifications such as design, material, size, and color.

[1134] Input: Design, material, size, color specifications

[1135] Output: Customization request data

[1136] Specific actions: The user enters the required information into the form and presses the submit button.

[1137] Step 2:

[1138] Emotion engine recognizes user emotions

[1139] The device's camera and microphone capture the user's facial expressions and tone of voice and analyze them in real time.

[1140] Input: User's facial expression data, tone of voice

[1141] Output: User's emotional information (satisfaction, expectations, etc.)

[1142] Specific operation: The camera captures the user's face and the microphone collects audio. This data is sent to the emotion engine for analysis.

[1143] Step 3:

[1144] The server receives the request

[1145] The user's input information and emotion information are sent to the server.

[1146] Input: Customization request data, emotion information

[1147] Output: Parsed request data

[1148] Specific operation: The server receives the data and analyzes the design, material, size, color, and emotional information.

[1149] Step 4:

[1150] Generate design data

[1151] The server calls the AI ​​module and generates a prompt, which then generates a digital blueprint based on the specified design and materials.

[1152] Input: Parsed request data, emotion information

[1153] Output: Digital blueprint

[1154] Specific operation: The server sends a prompt to the generative AI model via the API and receives the generated digital blueprint.

[1155] Step 5:

[1156] Sending design data and prototyping

[1157] The server transmits the generated design data to digital production equipment to produce a prototype.

[1158] Input: Digital blueprint

[1159] Output: Prototype

[1160] Specific operation: The server sends the design data to the production equipment, and a prototype is produced.

[1161] Step 6:

[1162] Quality and design verification

[1163] Verify the quality and design of the prototype and make any necessary adjustments if there are any issues.

[1164] Input: Prototype

[1165] Output: Final production data

[1166] Specific Actions: Physically validate prototype and re-adjust as needed.

[1167] Step 7:

[1168] Final production of the product

[1169] Final production is carried out based on the data confirmed at the prototype stage.

[1170] Input: Final production data

[1171] Output: Final product

[1172] Specific Action: Digital production equipment produces the final product.

[1173] Step 8:

[1174] User Notification

[1175] Once the product is completed, the server will send a notification to the user.

[1176] Input: Final product data

[1177] Output: Notification message

[1178] Specific operation: The server sends an email or app notification to the user.

[1179] Step 9:

[1180] Storing information in a database

[1181] Production information and emotional information are stored in a database.

[1182] Input: Production information, emotional information

[1183] Output: Saved data

[1184] Specific operation: The server stores the information in a database for future use.

[1185] (Application example 2)

[1186] 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."

[1187] Conventional customized product production systems only produce products based on user requests, without providing design suggestions or adjustments that reflect the user's emotions and preferences. This can result in designs and functions that do not necessarily satisfy users, leading to a decline in the shopping experience and product satisfaction. Furthermore, this information is not stored in a database, making it difficult for the next generation and overseas users to easily learn the technology.

[1188] 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 means for receiving a customization request from a user, means for recognizing the user's emotion using an emotion engine, means for generating design data using artificial intelligence based on the received customization request and the recognized emotion information, means for transmitting the design data to a digital production device that produces a product using the generated design data, and means for sending a notification to the user after production is complete. This allows for optimal design proposals based on the user's emotions and preferences, enabling the production of customized products that increase user satisfaction. Furthermore, by storing information about product production and user emotion information in a database, the next generation and overseas users can easily acquire the skills.

[1189] - "Means for receiving customization requests from users" refers to an interface through which users input specifications such as desired design, material, size, and color on a web or mobile application and transmit that information to the system.

[1190] "Means for recognizing a user's emotions using an emotion engine" refers to software or a device that analyzes a user's facial expressions and voice data to read a specific emotional state.

[1191] The "means for generating design data using artificial intelligence" is a program for automatically generating digital designs using an algorithm based on a received customization request and recognized emotional information.

[1192] The "means for transmitting the design data to a digital fabrication device that uses the generated design data to fabricate a product" is a communications system for transferring the generated digital design data to fabrication equipment such as a 3D printer or CNC machine.

[1193] The "means for sending a notification to the user after production is completed" refers to a communication means such as email or push notification for notifying the user after production of the product is completed.

[1194] "Means for producing a prototype of a product using design data" refers to physical digital production equipment for producing a prototype based on the generated design data.

[1195] "Means for storing product production information and user emotional information in a database" refers to a digital recording system for long-term storage of production process and user emotional data, making it accessible in the future.

[1196] To implement the present invention, the following system configuration and processing procedure are followed.

[1197] System Configuration

[1198] 1. User Device

[1199] A device such as a smartphone, tablet, or PC that allows users to input customization requests. It is equipped with a camera and microphone to capture the user's facial expressions and voice.

[1200] 2. Server

[1201] It receives customization requests from users and generates design data based on them. Its main software and libraries include an emotion engine (EmotionalRecognition) and an AI design module (AIDesignModule).

[1202] 3. Digital Production Equipment

[1203] The product is manufactured based on the generated design data, specifically using 3D printers and CNC machines.

[1204] 4. Database

[1205] It stores information about product creation and user sentiment, which can be used for future technology transfer and customization optimization.

[1206] Processing Details

[1207] User terminal

[1208] Users input customization requests using a smartphone or PC. Input items include specifications such as design, material, size, and color. The user's facial and voice data is also captured and analyzed by the emotion engine.

[1209] server

[1210] The server receives the request and emotional information sent from the user's device. It also receives emotional data analyzed by the Emotional Recognition engine. Based on the received data, the AI ​​Design Module generates design data. This design data is fine-tuned to reflect not only the user's customization request but also the emotional information.

[1211] Digital Production Equipment

[1212] The generated design data is sent to the manufacturing equipment, which then produces a prototype based on the data (such as a 3D printer or CNC machine). After the prototype's quality is confirmed, the final product is produced.

[1213] Database

[1214] The production process and user emotional information are stored in a database, which will be used for future technology transfer and customization optimization.

[1215] Specific examples

[1216] For example, let's say a user orders a "medium-sized bamboo craft with a blue floral pattern." The user uses their smartphone to input the desired design, material, size, and color. At this time, the Emotional Recognition engine analyzes the user's facial expressions and voice and sends emotional information to the server. The server generates design data using the AI ​​Design Module based on the received customization request and emotional information. The generated data is sent to digital production equipment, which creates a prototype. The final product is then completed and a notification is sent to the user. The production data and the user's emotional information are stored in a database.

[1217] Prompt Sentence Examples

[1218] The user wants a medium-sized design with a bamboo and blue floral pattern. Analysis of the user's facial expression indicates that their current emotion is "joy." Generate a design based on this user's preference.

[1219] In this way, based on the embodiments of the invention, customized products can be produced effectively and user satisfaction can be increased.

[1220] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1221] Step 1:

[1222] Users input customization requests using devices such as smartphones or PCs. Input information includes design, material, size, color, etc. Once input is complete, the device sends the request to the server. The information input here is specific data regarding the user's desired specifications.

[1223] Step 2:

[1224] The device uses a camera and microphone to capture the user's facial expressions and voice. The emotion engine analyzes this data and recognizes the user's emotional state. The emotion engine generates analysis results and sends emotional data containing the results to the server. The input data is digital data of facial expressions and voice, and this is output as emotional information.

[1225] Step 3:

[1226] The server receives customization requests and emotional data sent from the user's device. The received data includes design, material, size, color, and emotional information. The server analyzes this information and generates input data to invoke the AI ​​design module. The data calculation here involves integrating the input information and converting it into the format required for design generation.

[1227] Step 4:

[1228] The server generates design data using an AI design module. The AI ​​design module receives customization requests and emotion information as input and generates design data. This design data reflects fine-tuning based on the user's specifications and emotions. The output data is a detailed blueprint for digital production.

[1229] Step 5:

[1230] The server sends the generated design data to a digital fabrication device (such as a 3D printer or CNC machine) that creates a prototype based on the received design data. The input data is a design drawing, which is then used by the machine to generate the physical prototype.

[1231] Step 6:

[1232] Once the prototype is produced and its quality is confirmed, the digital production equipment moves on to produce the final product. At this stage, monitoring data during production is sent to the server for quality control. The output data is the finished product.

[1233] Step 7:

[1234] Once the product is completed, the server sends a notification to the user that production is complete. The notification includes details about how to receive the product and other information. This notification is sent to the user's device via email or push notification. The input data here is production status information, and the user notification is generated based on that information.

[1235] Step 8:

[1236] Finally, the server stores production information and user emotional information in a database. This information will be used for future technology transfer and service improvement. The input data is all production process and emotional data, which is then stored in the database.

[1237] 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.

[1238] 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.

[1239] 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.

[1240] 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.

[1241] 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.

[1242] 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.

[1243] 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).

[1244] 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.

[1245] 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."

[1246] 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.

[1247] 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).

[1248] 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.

[1249] 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.

[1250] 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.

[1251] 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.

[1252] 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.

[1253] 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.

[1254] 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.

[1255] 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.

[1256] 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.

[1257] 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.

[1258] The following is further disclosed regarding the above embodiment.

[1259] (Claim 1)

[1260] means for receiving a customization request from a user;

[1261] means for generating design data using artificial intelligence based on the received customization request;

[1262] means for transmitting the generated design data to a digital fabrication device that fabricates a product using the design data;

[1263] means for sending a notification to the user upon completion of production;

[1264] A system including:

[1265] (Claim 2)

[1266] 10. The system of claim 1, further comprising means for utilizing the design data to produce a prototype of the product.

[1267] (Claim 3)

[1268] 2. The system according to claim 1, further comprising means for storing information about the manufacture of the product in a database, so that the next generation and overseas users can easily learn the technology.

[1269] "Example 1"

[1270] (Claim 1)

[1271] means for receiving a customization request from a user;

[1272] means for generating design data using artificial intelligence based on the received customization request;

[1273] means for transmitting the generated design data to a digital fabrication device that fabricates a product using the design data;

[1274] means for sending a notification to the user upon completion of production;

[1275] a terminal for a user to input a customization request;

[1276] A means for the terminal to send a request to the server;

[1277] a means for the server to analyze the request and generate a prompt for the AI ​​module;

[1278] a terminal that reports the production status of the digital production device to a server;

[1279] Including system.

[1280] (Claim 2)

[1281] 2. The system according to claim 1, further comprising means for producing a prototype of the product using the design data, reporting the production status of the prototype to the server, and the server confirming the quality of the prototype.

[1282] (Claim 3)

[1283] 2. The system according to claim 1, further comprising means for storing information about the manufacture of the product in a database, so that the next generation and overseas users can easily learn the technology.

[1284] "Application Example 1"

[1285] Original Claims

[1286] (Claim 1)

[1287] means for receiving a customization request from a user;

[1288] means for generating design data using artificial intelligence based on the received customization request;

[1289] means for transmitting the generated design data to a digital fabrication device that fabricates a product using the design data;

[1290] means for sending a notification to the user upon completion of production;

[1291] A system including:

[1292] (Claim 2)

[1293] 10. The system of claim 1, further comprising means for utilizing the design data to produce a prototype of the product.

[1294] (Claim 3)

[1295] 2. The system according to claim 1, further comprising means for storing information about the manufacture of the product in a database, so that the next generation and overseas users can easily learn the technology.

[1296] New Claims

[1297] (Claim 1)

[1298] means for receiving a customization request from a user;

[1299] means for generating design data using artificial intelligence based on the received customization request;

[1300] means for transmitting the generated design data to a digital fabrication device that fabricates a product using the design data;

[1301] means for sending a notification to the user upon completion of production;

[1302] A means to create products in real time in conjunction with digital production equipment located in physical stores,

[1303] A system including:

[1304] (Claim 2)

[1305] The system according to claim 1, further comprising means for transmitting the generated design data to a production device located in a physical store and producing a prototype of the product on the spot.

[1306] (Claim 3)

[1307] The system according to claim 1, further comprising means for storing information on product production in a database and enabling the next generation and users in remote locations to easily learn the techniques.

[1308] "Example 2: Combining Emotion Engines"

[1309] (Claim 1)

[1310] means for receiving a customization request from a user;

[1311] means for generating design data using artificial intelligence based on the received customization request;

[1312] means for adjusting the customization request using an emotion engine that recognizes the emotion of the user;

[1313] means for transmitting the generated design data to a digital fabrication device that fabricates a product using the design data;

[1314] A means to produce product prototypes and verify quality and design;

[1315] means for sending a notification to the user upon completion of production;

[1316] A system including:

[1317] (Claim 2)

[1318] 2. The system according to claim 1, further comprising means for storing information about the production of the product and the emotion information recognized by the emotion engine in a database, thereby enabling the next generation and users in other countries to easily learn the technology.

[1319] (Claim 3)

[1320] 10. The system of claim 1, further comprising means for adjusting design and color based on the emotion information.

[1321] "Application example 2 when combining emotion engines"

[1322] (Claim 1)

[1323] means for receiving a customization request from a user;

[1324] means for recognizing a user's emotion using an emotion engine;

[1325] means for generating design data using artificial intelligence based on the received customization request and the recognized emotion information;

[1326] means for transmitting the generated design data to a digital fabrication device that fabricates a product using the design data;

[1327] means for sending a notification to the user upon completion of production;

[1328] A system including:

[1329] (Claim 2)

[1330] 10. The system of claim 1, further comprising means for utilizing the design data to produce a prototype of the product.

[1331] (Claim 3)

[1332] The system according to claim 1, further comprising a means for storing information about product production and user emotional information in a database, thereby enabling the next generation and overseas users to easily learn the technology. [Explanation of symbols]

[1333] 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. means for receiving a customization request from a user; means for generating design data using artificial intelligence based on the received customization request; means for transmitting the generated design data to a digital fabrication device that fabricates a product using the design data; means for sending a notification to the user upon completion of production; A system including:

2. The system of claim 1 further comprising means for utilizing the design data to produce a prototype of the product.

3. 2. The system according to claim 1, further comprising means for storing information on the manufacture of the product in a database, so that the next generation and overseas users can easily learn the technology.

Images