system

Abstract

We provide the system. [Solution] A data analysis method that performs analysis based on preference information obtained from users, A design generation means that generates multiple design variations based on the aforementioned analysis results, A user interface means that provides the user with the aforementioned design variations and enables selection and fine-tuning, A manufacturing control means for manufacturing a product based on the selected and fine-tuned design data, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the modern consumer market, the demand for individualized products is increasing, but there are problems that custom-made products are generally expensive and time-consuming to manufacture. Therefore, it is difficult for consumers to easily obtain products that meet their individual needs. Also, with conventional methods, it is difficult to fully meet individual preferences, and customer satisfaction may not be sufficiently achieved. Therefore, there is a need to provide a system that enables consumers to obtain customized products that match their preferences in a reasonable and rapid manner.

Means for Solving the Problems

[0005] This invention solves the above problem by providing a data analysis means that performs data analysis based on preference information acquired from users, and a design generation means that generates multiple design variations based on the analysis results. Furthermore, a user interface means is provided to the user with a real-time preview and adjustment function for the design. In addition, a manufacturing control means utilizes a 3D printer to manufacture products quickly and efficiently, and also performs material selection and quality control, making it possible to provide custom products that meet the individual needs of consumers while keeping costs down.

[0006] A "user" refers to a consumer who uses the system to seek custom products based on their preferences.

[0007] "Preference information" refers to information related to a user's personal preferences regarding design, use, color, materials, etc.

[0008] "Data analysis means" refers to technical methods for analyzing preference information obtained from users and reflecting it in the custom design of products.

[0009] "Design variations" refer to multiple different design options generated through data analysis.

[0010] "User interface means" refers to an interface that allows users to view, select, and fine-tune design variations.

[0011] "Manufacturing control means" refers to a system control mechanism for manufacturing products based on a design selected by the user.

[0012] A "3D printer" refers to a device used to print physical products in three dimensions.

[0013] "Material selection" refers to the process of selecting appropriate materials in the manufacturing process.

[0014] "Quality control" refers to the management process that guarantees the quality of manufactured products and ensures they meet certain standards. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. <%

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

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

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is a system in which a server and a terminal work together to execute a series of processes for collecting user preference information and generating custom products based on that information. The system consists of the following steps:

[0037] First, when a user accesses the service through their device, they input information about their preferences, such as desired design, color, intended use, and materials. If applicable, they also provide information about their past purchase history and social media activity. This data is then transmitted from the device to the server.

[0038] The server processes the received data through data analysis tools to identify designs that match user preferences. The analysis draws insights from historical data and trend information, enabling the generation of the most appropriate design variations.

[0039] The multiple design options generated by the analysis are sent back to the user's terminal, where the user can preview them via the user interface and make any necessary adjustments. After selecting a design of interest, the user confirms it and sends it to the server.

[0040] The server transmits the finalized design to the 3D printer via manufacturing control. The 3D printer, under AI-powered automated control, selects appropriate materials and manufactures the product while maintaining quality control. After the manufactured product is verified by the user, it is prepared for delivery and delivered to the designated location.

[0041] This system allows users to obtain personalized products quickly and efficiently. A major advantage of this invention is that it is offered at a reasonable price compared to conventional custom products. Specifically, users can design a watch to suit their tastes and fine-tune their preferred color scheme and strap material. This system accurately meets user needs and provides a mechanism for creating a one-of-a-kind original product.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users access the service using their devices and input preference information such as design, usage, color, and materials. Users also grant permission for the service to use their past purchase history and social media activity data.

[0045] Step 2:

[0046] The device sends user preference information and permitted data to the server. This data is used as information necessary for generating the user's custom design.

[0047] Step 3:

[0048] The server analyzes the received data using data analysis tools, compares it with trend information and historical data, and generates design variations that are best suited to the user's preferences.

[0049] Step 4:

[0050] The server sends the generated design variations to the terminal and provides a user interface that allows the user to preview and fine-tune the design.

[0051] Step 5:

[0052] The user reviews the provided design options, makes adjustments to colors, shapes, etc., as needed, and selects the final design. The selection is then sent back to the server.

[0053] Step 6:

[0054] The server receives the user's final design and sends the design data to the 3D printer using manufacturing control mechanisms. It controls the manufacturing process, including material selection and quality control.

[0055] Step 7:

[0056] A 3D printer manufactures products based on a specified design, and AI is used to perform real-time quality control before producing the finished product.

[0057] Step 8:

[0058] The server confirms the product is complete and notifies the user. The product is ready for delivery and arrangements are made to ship it to the specified address.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] In creating customized products, there is a need to quickly and efficiently provide personalized suggestions that reflect user preferences and past data. However, current technology has limitations in terms of sufficient flexibility to meet diverse user demands and efficiency in automating production processes.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes information analysis means that performs analysis based on preference information and other input information obtained from the user, design generation means that generates a plurality of design options based on the analysis results, and user interface means that provides the user with the design options and enables selection and adjustment. This enables personalized product suggestions for the user and efficient product manufacturing.

[0064] "Information analysis means" refers to a part of a system that analyzes preference information and other related input information obtained from users to provide suggestions tailored to the user's preferences.

[0065] A "design generation means" is a component of a system that has the function of generating multiple design options to be provided to the user based on analyzed data.

[0066] "User interface means" refers to means that provide an interface for users to preview, select, and adjust the generated design options.

[0067] "Manufacturing management means" refers to the part of the system that manages the process of actually manufacturing a product based on the design data selected and adjusted by the user.

[0068] A "generative AI model" is a model that utilizes artificial intelligence to efficiently optimize the process of data analysis and the generation of design options.

[0069] A "prompt message" is a method for efficiently collecting information necessary for the system to provide the optimal result in response to user input.

[0070] A "three-dimensional product output device" is a manufacturing machine used to create three-dimensional objects, and the term primarily refers to a 3D printer.

[0071] To implement this invention, a system is constructed in which a server and a terminal work together. First, the user accesses the service using the terminal and inputs their preference information. This input includes preferred designs, colors, uses, materials, etc. In addition, the system collects the user's purchase history and various activity data and transmits it from the terminal to the server. Data collection at this stage is important for improving the accuracy of the system.

[0072] Next, the server analyzes the received data using a generation AI model. This AI model analyzes historical data and trend information to generate appropriate design options based on the user's preferences. As a result of the analysis, multiple design options are generated to offer to the user. This allows the server to provide personalized suggestions to the user.

[0073] The generated design options are sent to the user's device and previewed through the user interface. The user can visualize the design through the interface and adjust colors and materials as needed. Once the user has completed their adjustments, the selected design is sent to the server and the manufacturing process begins.

[0074] The server sends the finalized design to a 3D printing device (usually a 3D printer), where the product is generated under AI-driven automated control. During the manufacturing process, appropriate materials are selected and strict quality control is implemented to ensure the product is created precisely according to the design specifications.

[0075] A concrete example is the process where users can set a watch design that suits their tastes and then fine-tune the desired color scheme, strap material, and other details. This system makes it easy for users to obtain a unique product that reflects their personal preferences.

[0076] For example, a prompt message such as, "Please suggest a custom watch design that suits my taste. I can specify the color, purpose, and material," allows the system to efficiently respond to user requests.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] Users access the system through a terminal and input their preferences, such as desired design, color, use, and materials. In some cases, they may also input their purchase history and activity data. The terminal then sends this information to the server. This input data forms the basis for the system to understand the user's individual preferences. The preference information is then transferred to the server as output.

[0080] Step 2:

[0081] The server inputs preference information received from the terminal into a generating AI model and performs data analysis. In this process, the model refers to historical data and trend information to generate appropriate design options based on the user's preferences. Specifically, the AI ​​model uses statistical methods and machine learning algorithms to extract the optimal design proposal. As output, multiple generated design options are created.

[0082] Step 3:

[0083] The server sends the generated design options to the terminal and provides them to the user. The user can visualize these designs using the user interface on the terminal and fine-tune the colors, materials, shapes, and other elements. This interface prioritizes usability and visual feedback, allowing users to intuitively improve the designs. The adjusted designs undergo a final design check before being sent back to the server.

[0084] Step 4:

[0085] The server transmits the user-tuned design to the 3D printing equipment. Specifically, the server creates a manufacturing plan and issues instructions to the equipment, such as the 3D printer. AI-powered automated control is employed throughout the manufacturing process, including material selection and precise quality control. The result is a highly accurate product manufactured according to the design.

[0086] Step 5:

[0087] Once manufacturing is complete, the product is checked by the user. The user performs a final check to ensure the product has been manufactured according to specifications. After this check, the product is prepared for shipment and sent to the designated delivery address. The server manages the shipping arrangements and tracking, and the user is notified. As a result, the user receives their customized product.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] In recent years, consumers have increasingly demanded personalized products, creating a need for methods to rapidly generate customized products to meet these needs. However, existing systems are inefficient in design proposals and lack sufficient user interaction. Furthermore, quality control during the manufacturing process is difficult. This invention aims to solve these problems and realize the provision of high-quality customized products tailored to user preferences.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes data processing means that perform analysis based on preference information acquired from the user, design generation means that generate multiple design variations based on the analysis results, and recommendation means that propose designs using a generated AI model. This enables the provision of appropriate and personalized designs to the user and rapid manufacturing.

[0093] "Preference information" refers to information that indicates a user's individual preferences and tastes, and includes data related to design, color, usage, materials, etc.

[0094] "Data processing means" refers to a system or method for analyzing preference information obtained from users and obtaining relevant insights.

[0095] "Design generation means" refers to a system or method for automatically generating optimal design variations for a user based on data analysis.

[0096] "Interaction means" refers to an interface that allows users to select and fine-tune design variations, enabling real-time interaction with the user.

[0097] "Production control means" refers to a system or method for manufacturing goods based on selected and fine-tuned design data, and for controlling the process at each stage of the manufacturing process.

[0098] A "three-dimensional printing device" is a device that creates a physical shape from a design chosen by the user, and is commonly called a 3D printer.

[0099] A "generative AI model" is a model that uses artificial intelligence to propose data-driven designs, and it utilizes machine learning algorithms.

[0100] "Instant design display and adjustment" refers to a process where the design selected by the user is displayed in real time, allowing the user to make adjustments on the spot.

[0101] "3D preview" is a feature that allows users to visually check their chosen design in three dimensions, providing a means to understand the final form of the product more concretely.

[0102] The system that realizes this invention provides a series of processes for collecting preference information from users and manufacturing personalized products based on that information.

[0103] First, users access the system using mobile devices such as smartphones and tablets and input their preferences. This includes specific elements such as design preferences, colors, intended use, and materials. Past purchase history and social media activity data can also be accessed.

[0104] Preference information transmitted from the terminal is received by the server and analyzed using data processing tools. The analysis is performed by a generative AI model utilizing machine learning algorithms, which generates design suggestions based on the user's preferences. This provides the user with multiple appropriate and personalized design variations.

[0105] Users can preview the provided design variations in real time through the interface on their device. Three-dimensional display allows for visual confirmation of the design and fine-tuning as needed. The user's final selected design is then sent to the server.

[0106] The server sends design data selected and adjusted by the user to the 3D printing machine via production control means, and manufactures the item. This machine produces high-quality custom products by selecting materials and performing quality control during manufacturing.

[0107] As a concrete example, consider a scenario where a user designs their own custom tote bag. The user inputs information such as color patterns and materials through the app, and then checks the AI-generated design in a 3D preview. The result is a one-of-a-kind tote bag that faithfully reflects the user's requirements.

[0108] Here's an example of a prompt to intelligently utilize a generative AI model: "Use the AI ​​to create a cool-looking tote bag. I want colorful, easy-to-carry handles."

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] Users access the system using a terminal and input their preference information. This input includes detailed elements such as design preferences, colors, uses, and materials. The terminal then collects the user's preference information and prepares it for transmission to the server. This input information serves as foundational data for data analysis.

[0112] Step 2:

[0113] The server receives preference information transmitted from the terminal. The server analyzes this information using data processing tools to extract specific preferences and patterns. This process includes matching with a database and analyzing past trends. As a result, it becomes possible to gain insights into the user's preferences.

[0114] Step 3:

[0115] The server utilizes a generative AI model to generate design variations based on user preferences. This process uses data analysis results as input and outputs the optimal design proposed by the AI. Using prompts allows for even more flexible and accurate suggestions.

[0116] Step 4:

[0117] The generated design variations are provided to the user via the terminal. The user can preview the design in real time using interaction tools and make adjustments as needed. The terminal compiles the user's selections and adjustments and sends them to the server as the final design data.

[0118] Step 5:

[0119] The server transmits the final design data to the 3D printing machine via the production control system. The printing machine then manufactures the product based on this design data. During the manufacturing process, selected materials are used and quality control is implemented. This process results in the output of high-quality, user-optimized products.

[0120] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0121] This invention is a system that analyzes user preference and emotional information to efficiently generate personalized designs. A server, terminal, emotion engine, design generation means, user interface means, and manufacturing control means work in conjunction with each other.

[0122] The system's implementation process begins with the user accessing the service using a device and entering preference information such as design, color, usage, and materials. If the user grants permission, the emotion engine acquires real-time emotional information from the user through sensors such as the camera and microphone. This data is then transmitted from the device to the server.

[0123] The server analyzes user preferences and emotional information using data analysis tools. Based on the emotional state provided by the emotion engine, the design generation tool generates further optimized design variations. This process generates designs that respond to the user's temporary emotions and long-term preferences.

[0124] The generated design variations are provided to the user on the device through a user interface. The user can preview and adjust these designs. The system also responds to changes in the user's emotions and dynamically modifies suggestions as needed. Therefore, the user interface can always provide the latest design options.

[0125] Once the user finalizes the design, the server uses manufacturing control to send the design data to the 3D printer, and product manufacturing begins. AI handles material selection and quality control. As a result, customized products tailored to the user's needs are generated in real time.

[0126] For example, if a user expresses joy, the system suggests a design with bright colors; if they express calm emotions, it suggests cool colors and simple shapes. Users can select and fine-tune the design that best suits their emotions, ultimately resulting in a unique product. This embodiment makes it possible to manufacture personalized products that take into account not only preferences but also emotions.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] Users access the service using their devices and input their preferences, such as design, intended use, color, and materials. Simultaneously, if the user grants permission, the emotion engine acquires real-time emotional data from the user's facial expressions and voice via sensors such as the camera and microphone built into the device.

[0130] Step 2:

[0131] The device sends user preference information and emotional data to the server. This data is stored on the server as essential information for generating custom designs.

[0132] Step 3:

[0133] The server uses data analysis tools to analyze the received preference information and emotion data. The results of this analysis are provided to the design generation tools. Based on the emotion information analyzed by the emotion engine, design variations are generated that take into account the user's current emotional state.

[0134] Step 4:

[0135] The server generates design variations and sends them back to the terminal, providing the user with preview and adjustment options through a user interface. Users can adjust colors and shapes while viewing the provided designs. Furthermore, the design suggestions are dynamically updated in response to changes in the user's emotional state.

[0136] Step 5:

[0137] Once the user has selected the final design and completed any necessary adjustments, the design is submitted to the server for final confirmation. At this stage, the user interface prompts confirmation through a final preview screen.

[0138] Step 6:

[0139] The server transmits the finalized design data to the manufacturing control system, and product manufacturing begins on the 3D printer. Products reflecting the optimal design, shaped by the emotion engine, are generated. AI performs material selection and quality control during the manufacturing process, guaranteeing high-quality products.

[0140] Step 7:

[0141] Once manufacturing is complete, the server notifies the user, and the product is prepared for delivery. Finally, the customized product is delivered to the user. The user receives a unique product that perfectly matches their own feelings.

[0142] (Example 2)

[0143] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0144] In modern manufacturing, there is a demand to optimize designs based on individual consumer preferences and real-time emotional states, and to manufacture products quickly and efficiently. However, existing systems struggle to generate personalized designs that take user emotional information into account, and the efficiency of the manufacturing process is far from optimal. Solving this problem is the objective of this invention.

[0145] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0146] In this invention, the server includes information analysis means that performs analysis based on preference information and emotional information acquired from the user; design generation means that generates multiple design variations based on the analysis results and emotional information; and human-machine interface means that provides the user with the design variations and enables selection and fine-tuning. This makes it possible to generate designs tailored to the user's individual preferences and emotions, and to manufacture products quickly and efficiently based on those designs.

[0147] "Information analysis means" refers to a mechanism that analyzes preference and emotional information obtained from users and extracts elements necessary for design generation.

[0148] A "design generation means" is a mechanism that generates multiple design variations to be provided to the user based on data extracted by an information analysis means.

[0149] A "human-machine interface means" is a mechanism that can be operated through the user's device, providing an interface that allows the user to view, select, and fine-tune design variations.

[0150] A "production control system" is a mechanism that directs and supervises the manufacturing of a product based on selected and fine-tuned design data.

[0151] A "three-dimensional additive manufacturing device" is a device that forms products in three dimensions based on instructions from a production control system, and is commonly known as a 3D printer.

[0152] "Material selection and quality control" refers to a series of processes and inspections to ensure the use of appropriate materials in the manufacturing process and to maintain a certain level of product quality.

[0153] This invention is a system that analyzes user preference and emotional information and efficiently generates personalized designs based on that information. The system consists of a server, a terminal, an emotional engine, design generation means, human-machine interface means, and production control means.

[0154] Users can access the system using their devices and input preference information such as design, color, intended use, and materials. If the user consents, emotional information collected through the device's camera and microphone is also used. This information is transmitted from the device to the server, which uses data analysis tools to analyze the user's preferences and emotional information.

[0155] The emotion engine determines the user's emotional state based on emotional information. This data is utilized by the design generation mechanism to generate design variations that match the user's preferences and emotions. A generative AI model is used for design generation, and prompts for constructing new designs are also generated.

[0156] The generated design variations are displayed on the user's device. A human-machine interface allows the user to preview each element of the design and make adjustments as needed. This interface supports real-time adjustments and flexibly responds to changes in the user's emotions.

[0157] Finally, the user's selected design is transmitted via a server to a production control system. This control system then initiates physical product manufacturing using a 3D additive manufacturing device. During the manufacturing process, AI technology performs material selection and quality control to supply the optimal product.

[0158] As a concrete example, by introducing a prompt message on the user's device stating, "Please generate the optimal design variations based on the latest user preferences and sentiment information," the system automatically generates designs according to the set conditions. In this way, it becomes possible to provide customized products that meet the individual needs of the user.

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1:

[0161] Users access the system using a terminal and input preference information such as design, color, intended use, and materials. The input information is initially filtered within the terminal and formatted as basic data for customization. The terminal then sends this formatted data to the server.

[0162] Step 2:

[0163] If the user grants permission, real-time emotional information is collected through the device's camera and microphone. This sensor data is input into an emotion engine to analyze the user's emotional state. The results of this analysis are sent to the server as emotional information.

[0164] Step 3:

[0165] The server receives preference and emotion information transmitted from the terminal. Using information analysis tools, it analyzes this data and sets initial conditions based on the user's preferences and emotions. The analysis results are passed to the design generation tool as guidelines for design generation.

[0166] Step 4:

[0167] The server uses a design generation mechanism to construct design variations based on the analysis results, utilizing a generation AI model. A prompt message is input, and the generated design is sent back to the user interface mechanism.

[0168] Step 5:

[0169] The user previews the generated design variations on their device. Through a human-machine interface, the user can adjust each element of the design in real time, changing colors and shapes. These adjustments are reflected in the data for the final design selection.

[0170] Step 6:

[0171] Once the user has finalized the design, the terminal sends this design data to the server. The server, through production control mechanisms, transmits the determined design to the 3D additive manufacturing machine, thereby initiating product manufacturing. During the manufacturing process, AI is used for material selection and quality control, resulting in the creation of an optimal product.

[0172] (Application Example 2)

[0173] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0174] In modern product design, it is crucial to reflect users' individual preferences and emotions in real time. However, current systems face challenges in flexibly responding to changes in user emotions and applying those designs to their environment. Furthermore, it is not common to verify in real time whether individualized designs are suitable for their environment. Therefore, there is a need to enable more intuitive and personalized design proposals based on emotional information.

[0175] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0176] In this invention, the server includes information analysis means, design generation means, operation interface means, and environmental data acquisition and analysis means. This makes it possible to generate personalized designs based on the user's preference information and emotional information, and to suggest in real time whether the designs harmonize with the environment.

[0177] "Information analysis means" refers to a device or method that analyzes preference information and emotional information obtained from users and provides data that forms the basis for generating personalized designs.

[0178] "Design generation means" refers to a device or method that generates multiple design variations based on analyzed information and proposes them to the user.

[0179] "Operation interface means" refers to an interface that allows the user to select and adjust the generated design variations.

[0180] "Manufacturing control means" refers to a control device or method that issues instructions for manufacturing a product based on the final design selected by the user.

[0181] "Environmental data acquisition and analysis means" refers to a device or method that acquires data on the user's surrounding environment and proposes environmental decorations based on emotional information.

[0182] In order to implement this invention, the coordination of various hardware and software is necessary. The server consists of a computer system equipped with information analysis means, design generation means, operation interface means, and environmental data acquisition and analysis means.

[0183] Users access the system using a terminal, providing preference information and inputting emotional information via camera and microphone. This data is sent from the terminal to the server. The information analysis system on the server uses image processing libraries such as OpenCV to recognize emotions from the user's face and then performs detailed emotional analysis using an emotion analysis API.

[0184] The design generation method generates multiple design variations using CG design software (e.g., Unity) based on the analysis results. These designs reflect the user's emotional state in terms of color and shape.

[0185] These design variations are displayed on the device through an operating interface. Users can preview the provided designs on their smartphones or tablets and make adjustments as needed. Environmental data acquisition and analysis means suggest decorations suitable for the interior environment based on the user's emotional information. For example, if the user is calm, a simple and calming interior design will be suggested.

[0186] Finally, once the user has finalized the design, the design data is transmitted from the server to the 3D printer via the manufacturing control system, and product manufacturing begins. In this way, customized products and interior designs that respond to the user's real-time emotions and preferences can be easily realized.

[0187] An example of a prompt used in a generative AI model is: "Please suggest a room design suitable for when the user is in a calm mood. Specifically, what kind of lighting and furniture arrangement would be appropriate?"

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] Users access the system via a terminal and input preference and emotional information. Preference information includes design preferences, intended use, and color, while emotional information is collected in real time via camera and microphone. The input data is transmitted to the server in digital format.

[0191] Step 2:

[0192] The server processes the received preference and emotion information using information analysis tools. Specifically, to analyze emotion information, it uses OpenCV to detect the user's face from image data and an emotion analysis API to identify the emotional state. The output of this analysis is a dataset showing the user's current emotions.

[0193] Step 3:

[0194] The server generates design variations using design generation tools based on the analyzed data. Using CG design software such as Unity, it creates designs that match the user's preferences and emotions. The generated design variations are then output.

[0195] Step 4:

[0196] The server transmits the generated design variations to the terminal via an operating interface. The user can preview the design on the terminal's display and make adjustments as needed. The output is a set of adjustable designs presented to the user.

[0197] Step 5:

[0198] The server transmits the user's final selected design to the 3D printer via manufacturing control. Specifically, it converts the selected design data into a printer-compatible format and issues instructions to the printer to begin product manufacturing. The output is a custom product based on the user's emotions and preferences.

[0199] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0200] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0201] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0202] [Second Embodiment]

[0203] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0204] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0205] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0206] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0207] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0208] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0209] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0210] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0211] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0212] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0213] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0214] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0215] This invention is a system in which a server and a terminal work together to execute a series of processes for collecting user preference information and generating custom products based on that information. The system consists of the following steps:

[0216] First, when a user accesses the service through their device, they input information about their preferences, such as desired design, color, intended use, and materials. If applicable, they also provide information about their past purchase history and social media activity. This data is then transmitted from the device to the server.

[0217] The server processes the received data through data analysis tools to identify designs that match user preferences. The analysis draws insights from historical data and trend information, enabling the generation of the most appropriate design variations.

[0218] The multiple design options generated by the analysis are sent back to the user's terminal, where the user can preview them via the user interface and make any necessary adjustments. After selecting a design of interest, the user confirms it and sends it to the server.

[0219] The server transmits the finalized design to the 3D printer via manufacturing control. The 3D printer, under AI-powered automated control, selects appropriate materials and manufactures the product while maintaining quality control. After the manufactured product is verified by the user, it is prepared for delivery and delivered to the designated location.

[0220] This system allows users to obtain personalized products quickly and efficiently. A major advantage of this invention is that it is offered at a reasonable price compared to conventional custom products. Specifically, users can design a watch to suit their tastes and fine-tune their preferred color scheme and strap material. This system accurately meets user needs and provides a mechanism for creating a one-of-a-kind original product.

[0221] The following describes the processing flow.

[0222] Step 1:

[0223] Users access the service using their devices and input preference information such as design, usage, color, and materials. Users also grant permission for the service to use their past purchase history and social media activity data.

[0224] Step 2:

[0225] The device sends user preference information and permitted data to the server. This data is used as information necessary for generating the user's custom design.

[0226] Step 3:

[0227] The server analyzes the received data using data analysis tools, compares it with trend information and historical data, and generates design variations that are best suited to the user's preferences.

[0228] Step 4:

[0229] The server sends the generated design variations to the terminal and provides a user interface that allows the user to preview and fine-tune the design.

[0230] Step 5:

[0231] The user reviews the provided design options, makes adjustments to colors, shapes, etc., as needed, and selects the final design. The selection is then sent back to the server.

[0232] Step 6:

[0233] The server receives the user's final design and sends the design data to the 3D printer using manufacturing control mechanisms. It controls the manufacturing process, including material selection and quality control.

[0234] Step 7:

[0235] A 3D printer manufactures products based on a specified design, and AI is used to perform real-time quality control before producing the finished product.

[0236] Step 8:

[0237] The server confirms the product is complete and notifies the user. The product is ready for delivery and arrangements are made to ship it to the specified address.

[0238] (Example 1)

[0239] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0240] In creating customized products, there is a need to quickly and efficiently provide personalized suggestions that reflect user preferences and past data. However, current technology has limitations in terms of sufficient flexibility to meet diverse user demands and efficiency in automating production processes.

[0241] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0242] In this invention, the server includes information analysis means that performs analysis based on preference information and other input information obtained from the user, design generation means that generates a plurality of design options based on the analysis results, and user interface means that provides the user with the design options and enables selection and adjustment. This enables personalized product suggestions for the user and efficient product manufacturing.

[0243] "Information analysis means" refers to a part of a system that analyzes preference information and other related input information obtained from users to provide suggestions tailored to the user's preferences.

[0244] A "design generation means" is a component of a system that has the function of generating multiple design options to be provided to the user based on analyzed data.

[0245] "User interface means" refers to means that provide an interface for users to preview, select, and adjust the generated design options.

[0246] "Manufacturing management means" refers to the part of the system that manages the process of actually manufacturing a product based on the design data selected and adjusted by the user.

[0247] A "generative AI model" is a model that utilizes artificial intelligence to efficiently optimize the process of data analysis and the generation of design options.

[0248] A "prompt message" is a method for efficiently collecting information necessary for the system to provide the optimal result in response to user input.

[0249] A "three-dimensional product output device" is a manufacturing machine used to create three-dimensional objects, and the term primarily refers to a 3D printer.

[0250] To implement this invention, a system is constructed in which a server and a terminal work together. First, the user accesses the service using the terminal and inputs their preference information. This input includes preferred designs, colors, uses, materials, etc. In addition, the system collects the user's purchase history and various activity data and transmits it from the terminal to the server. Data collection at this stage is important for improving the accuracy of the system.

[0251] Next, the server analyzes the received data using a generation AI model. This AI model analyzes historical data and trend information to generate appropriate design options based on the user's preferences. As a result of the analysis, multiple design options are generated to offer to the user. This allows the server to provide personalized suggestions to the user.

[0252] The generated design options are sent to the user's device and previewed through the user interface. The user can visualize the design through the interface and adjust colors and materials as needed. Once the user has completed their adjustments, the selected design is sent to the server and the manufacturing process begins.

[0253] The server sends the finalized design to a 3D printing device (usually a 3D printer), where the product is generated under AI-driven automated control. During the manufacturing process, appropriate materials are selected and strict quality control is implemented to ensure the product is created precisely according to the design specifications.

[0254] A concrete example is the process where users can set a watch design that suits their tastes and then fine-tune the desired color scheme, strap material, and other details. This system makes it easy for users to obtain a unique product that reflects their personal preferences.

[0255] For example, a prompt message such as, "Please suggest a custom watch design that suits my taste. I can specify the color, purpose, and material," allows the system to efficiently respond to user requests.

[0256] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0257] Step 1:

[0258] Users access the system through a terminal and input their preferences, such as desired design, color, use, and materials. In some cases, they may also input their purchase history and activity data. The terminal then sends this information to the server. This input data forms the basis for the system to understand the user's individual preferences. The preference information is then transferred to the server as output.

[0259] Step 2:

[0260] The server inputs preference information received from the terminal into a generating AI model and performs data analysis. In this process, the model refers to historical data and trend information to generate appropriate design options based on the user's preferences. Specifically, the AI ​​model uses statistical methods and machine learning algorithms to extract the optimal design proposal. As output, multiple generated design options are created.

[0261] Step 3:

[0262] The server sends the generated design options to the terminal and provides them to the user. The user can visualize these designs using the user interface on the terminal and fine-tune the colors, materials, shapes, and other elements. This interface prioritizes usability and visual feedback, allowing users to intuitively improve the designs. The adjusted designs undergo a final design check before being sent back to the server.

[0263] Step 4:

[0264] The server transmits the user-tuned design to the 3D printing equipment. Specifically, the server creates a manufacturing plan and issues instructions to the equipment, such as the 3D printer. AI-powered automated control is employed throughout the manufacturing process, including material selection and precise quality control. The result is a highly accurate product manufactured according to the design.

[0265] Step 5:

[0266] Once manufacturing is complete, the product is checked by the user. The user performs a final check to ensure the product has been manufactured according to specifications. After this check, the product is prepared for shipment and sent to the designated delivery address. The server manages the shipping arrangements and tracking, and the user is notified. As a result, the user receives their customized product.

[0267] (Application Example 1)

[0268] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0269] In recent years, consumers have increasingly demanded personalized products, creating a need for methods to rapidly generate customized products to meet these needs. However, existing systems are inefficient in design proposals and lack sufficient user interaction. Furthermore, quality control during the manufacturing process is difficult. This invention aims to solve these problems and realize the provision of high-quality customized products tailored to user preferences.

[0270] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0271] In this invention, the server includes data processing means that perform analysis based on preference information acquired from the user, design generation means that generate multiple design variations based on the analysis results, and recommendation means that propose designs using a generated AI model. This enables the provision of appropriate and personalized designs to the user and rapid manufacturing.

[0272] "Preference information" refers to information that indicates a user's individual preferences and tastes, and includes data related to design, color, usage, materials, etc.

[0273] "Data processing means" refers to a system or method for analyzing preference information obtained from users and obtaining relevant insights.

[0274] "Design generation means" refers to a system or method for automatically generating optimal design variations for a user based on data analysis.

[0275] "Interaction means" refers to an interface that allows users to select and fine-tune design variations, enabling real-time interaction with the user.

[0276] "Production control means" refers to a system or method for manufacturing goods based on selected and fine-tuned design data, and for controlling the process at each stage of the manufacturing process.

[0277] A "three-dimensional printing device" is a device that creates a physical shape from a design chosen by the user, and is commonly called a 3D printer.

[0278] A "generative AI model" is a model that uses artificial intelligence to propose data-driven designs, and it utilizes machine learning algorithms.

[0279] "Instant design display and adjustment" refers to a process where the design selected by the user is displayed in real time, allowing the user to make adjustments on the spot.

[0280] "3D preview" is a feature that allows users to visually check their chosen design in three dimensions, providing a means to understand the final form of the product more concretely.

[0281] The system that realizes this invention provides a series of processes for collecting preference information from users and manufacturing personalized products based on that information.

[0282] First, users access the system using mobile devices such as smartphones and tablets and input their preferences. This includes specific elements such as design preferences, colors, intended use, and materials. Past purchase history and social media activity data can also be accessed.

[0283] Preference information transmitted from the terminal is received by the server and analyzed using data processing tools. The analysis is performed by a generative AI model utilizing machine learning algorithms, which generates design suggestions based on the user's preferences. This provides the user with multiple appropriate and personalized design variations.

[0284] The user can preview the provided design variations in real time through the interface on the terminal. With three-dimensional display, it is possible to visually confirm the design and make fine adjustments as needed. The design finally selected by the user is sent to the server.

[0285] The server sends the design data selected and adjusted by the user to the three-dimensional printing device through the production control means to manufacture the article. This device realizes high-quality custom products by selecting materials and manufacturing the article while performing quality control.

[0286] As a specific example, consider the case where the user designs a tote bag for their own use. The user inputs information on color patterns and materials through the app and views the AI-generated design in a 3D preview. As a result, a one-of-a-kind tote bag that faithfully reflects the user's requirements is obtained.

[0287] Examples of prompt sentences for making wise use of the generative AI model are shown. "Please create a cool-designed tote bag using AI. I want a colorful and easy-to-carry handle."

[0288] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0289] Step 1:

[0290] The user accesses the system using the terminal and inputs their preference information. The input information includes detailed elements such as design preferences, colors, uses, and materials. Thereby, the terminal collects the user's preference information and prepares to send it to the server. This input information serves as basic data for data analysis.

[0291] Step 2:

[0292] The server receives preference information transmitted from the terminal. The server analyzes this information using data processing tools to extract specific preferences and patterns. This process includes matching with a database and analyzing past trends. As a result, it becomes possible to gain insights into the user's preferences.

[0293] Step 3:

[0294] The server utilizes a generative AI model to generate design variations based on user preferences. This process uses data analysis results as input and outputs the optimal design proposed by the AI. Using prompts allows for even more flexible and accurate suggestions.

[0295] Step 4:

[0296] The generated design variations are provided to the user via the terminal. The user can preview the design in real time using interaction tools and make adjustments as needed. The terminal compiles the user's selections and adjustments and sends them to the server as the final design data.

[0297] Step 5:

[0298] The server transmits the final design data to the 3D printing machine via the production control system. The printing machine then manufactures the product based on this design data. During the manufacturing process, selected materials are used and quality control is implemented. This process results in the output of high-quality, user-optimized products.

[0299] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0300] This invention is a system that analyzes user preference and emotional information to efficiently generate personalized designs. A server, terminal, emotion engine, design generation means, user interface means, and manufacturing control means work in conjunction with each other.

[0301] The system's implementation process begins with the user accessing the service using a device and entering preference information such as design, color, usage, and materials. If the user grants permission, the emotion engine acquires real-time emotional information from the user through sensors such as the camera and microphone. This data is then transmitted from the device to the server.

[0302] The server analyzes user preferences and emotional information using data analysis tools. Based on the emotional state provided by the emotion engine, the design generation tool generates further optimized design variations. This process generates designs that respond to the user's temporary emotions and long-term preferences.

[0303] The generated design variations are provided to the user on the device through a user interface. The user can preview and adjust these designs. The system also responds to changes in the user's emotions and dynamically modifies suggestions as needed. Therefore, the user interface can always provide the latest design options.

[0304] Once the user finalizes the design, the server uses manufacturing control to send the design data to the 3D printer, and product manufacturing begins. AI handles material selection and quality control. As a result, customized products tailored to the user's needs are generated in real time.

[0305] As a specific example, when the user shows a happy emotion, the system proposes a design with bright colors, and when the user shows a calm emotion, it proposes cool colors or simple shapes. The user can select and fine-tune the design that best suits their emotion, and ultimately obtain an original product. With this embodiment, it is possible to realize the manufacture of individualized products that take into account not only simple preference information but also emotions.

[0306] The processing flow will be described below.

[0307] Step 1:

[0308] The user accesses the service using the terminal and enters preference information such as their design, usage, color, and material. At the same time, if the user gives permission, the emotion engine obtains real-time emotion data from the user's expression and voice via sensors such as the camera and microphone mounted on the terminal.

[0309] Step 2:

[0310] The terminal sends the user's preference information and emotion data to the server. This data is stored in the server as essential information for custom design generation.

[0311] Step 3:

[0312] The server uses data analysis means to analyze the received preference information and emotion data. The analysis result is provided to the design generation means. Based on the emotion information analyzed by the emotion engine, design variations that take into account the user's current emotional state are generated.

[0313] Step 4:

[0314] The server generates design variations and sends them back to the terminal, providing the user with preview and adjustment options through a user interface. Users can adjust colors and shapes while viewing the provided designs. Furthermore, the design suggestions are dynamically updated in response to changes in the user's emotional state.

[0315] Step 5:

[0316] Once the user has selected the final design and completed any necessary adjustments, the design is submitted to the server for final confirmation. At this stage, the user interface prompts confirmation through a final preview screen.

[0317] Step 6:

[0318] The server transmits the finalized design data to the manufacturing control system, and product manufacturing begins on the 3D printer. Products reflecting the optimal design, shaped by the emotion engine, are generated. AI handles material selection and quality control during the manufacturing process, guaranteeing high-quality products.

[0319] Step 7:

[0320] Once manufacturing is complete, the server notifies the user, and the product is prepared for delivery. Finally, the customized product is delivered to the user. The user receives a unique product that perfectly matches their own feelings.

[0321] (Example 2)

[0322] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0323] In modern manufacturing, there is a demand to optimize designs based on individual consumer preferences and real-time emotional states, and to manufacture products quickly and efficiently. However, existing systems struggle to generate personalized designs that take user emotional information into account, and the efficiency of the manufacturing process is far from optimal. Solving this problem is the objective of this invention.

[0324] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0325] In this invention, the server includes information analysis means that performs analysis based on preference information and emotional information acquired from the user; design generation means that generates multiple design variations based on the analysis results and emotional information; and human-machine interface means that provides the user with the design variations and enables selection and fine-tuning. This makes it possible to generate designs tailored to the user's individual preferences and emotions, and to manufacture products quickly and efficiently based on those designs.

[0326] "Information analysis means" refers to a mechanism that analyzes preference and emotional information obtained from users and extracts elements necessary for design generation.

[0327] A "design generation means" is a mechanism that generates multiple design variations to be provided to the user based on data extracted by an information analysis means.

[0328] A "human-machine interface means" is a mechanism that can be operated through the user's device, providing an interface that allows the user to view, select, and fine-tune design variations.

[0329] A "production control system" is a mechanism that directs and supervises the manufacturing of a product based on selected and fine-tuned design data.

[0330] A "three-dimensional additive manufacturing device" is a device that forms products in three dimensions based on instructions from a production control system, and is commonly known as a 3D printer.

[0331] "Material selection and quality control" refers to a series of processes and inspections to ensure the use of appropriate materials in the manufacturing process and to maintain a certain level of product quality.

[0332] This invention is a system that analyzes user preference and emotional information and efficiently generates personalized designs based on that information. The system consists of a server, a terminal, an emotional engine, design generation means, human-machine interface means, and production control means.

[0333] Users can access the system using their devices and input preference information such as design, color, intended use, and materials. If the user consents, emotional information collected through the device's camera and microphone is also used. This information is transmitted from the device to the server, which uses data analysis tools to analyze the user's preferences and emotional information.

[0334] The emotion engine determines the user's emotional state based on emotional information. This data is utilized by the design generation mechanism to generate design variations that match the user's preferences and emotions. A generative AI model is used for design generation, and prompts for constructing new designs are also generated.

[0335] The generated design variations are displayed on the user's device. A human-machine interface allows the user to preview each element of the design and make adjustments as needed. This interface supports real-time adjustments and flexibly responds to changes in the user's emotions.

[0336] Finally, the user's selected design is transmitted via a server to a production control system. This control system then initiates physical product manufacturing using a 3D additive manufacturing device. During the manufacturing process, AI technology performs material selection and quality control to supply the optimal product.

[0337] As a concrete example, by introducing a prompt message on the user's device stating, "Please generate the optimal design variations based on the latest user preferences and sentiment information," the system automatically generates designs according to the set conditions. In this way, it becomes possible to provide customized products that meet the individual needs of the user.

[0338] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0339] Step 1:

[0340] Users access the system using a terminal and input preference information such as design, color, intended use, and materials. The input information is initially filtered within the terminal and formatted as basic data for customization. The terminal then sends this formatted data to the server.

[0341] Step 2:

[0342] If the user grants permission, real-time emotional information is collected through the device's camera and microphone. This sensor data is input into an emotion engine to analyze the user's emotional state. The results of this analysis are sent to the server as emotional information.

[0343] Step 3:

[0344] The server receives preference and emotion information transmitted from the terminal. Using information analysis tools, it analyzes this data and sets initial conditions based on the user's preferences and emotions. The analysis results are passed to the design generation tool as guidelines for design generation.

[0345] Step 4:

[0346] The server uses a design generation mechanism to construct design variations based on the analysis results, utilizing a generation AI model. A prompt message is input, and the generated design is sent back to the user interface mechanism.

[0347] Step 5:

[0348] The user previews the generated design variations on their device. Through a human-machine interface, the user can adjust each element of the design in real time, changing colors and shapes. These adjustments are reflected in the data for the final design selection.

[0349] Step 6:

[0350] Once the user has finalized the design, the terminal sends this design data to the server. The server, through production control mechanisms, transmits the determined design to the 3D additive manufacturing machine, thereby initiating product manufacturing. During the manufacturing process, AI is used for material selection and quality control, resulting in the creation of an optimal product.

[0351] (Application Example 2)

[0352] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0353] In modern product design, it is crucial to reflect users' individual preferences and emotions in real time. However, current systems face challenges in flexibly responding to changes in user emotions and applying those designs to their environment. Furthermore, it is not common to verify in real time whether individualized designs are suitable for their environment. Therefore, there is a need to enable more intuitive and personalized design proposals based on emotional information.

[0354] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0355] In this invention, the server includes information analysis means, design generation means, operation interface means, and environmental data acquisition and analysis means. This makes it possible to generate personalized designs based on the user's preference information and emotional information, and to suggest in real time whether the designs harmonize with the environment.

[0356] "Information analysis means" refers to a device or method that analyzes preference information and emotional information obtained from users and provides data that forms the basis for generating personalized designs.

[0357] "Design generation means" refers to a device or method that generates multiple design variations based on analyzed information and proposes them to the user.

[0358] "Operation interface means" refers to an interface that allows the user to select and adjust the generated design variations.

[0359] "Manufacturing control means" refers to a control device or method that issues instructions for manufacturing a product based on the final design selected by the user.

[0360] "Environmental data acquisition and analysis means" refers to a device or method that acquires data on the user's surrounding environment and proposes environmental decorations based on emotional information.

[0361] In order to implement this invention, the coordination of various hardware and software is necessary. The server consists of a computer system equipped with information analysis means, design generation means, operation interface means, and environmental data acquisition and analysis means.

[0362] Users access the system using a terminal, providing preference information and inputting emotional information via camera and microphone. This data is sent from the terminal to the server. The information analysis system on the server uses image processing libraries such as OpenCV to recognize emotions from the user's face and then performs detailed emotional analysis using an emotion analysis API.

[0363] The design generation method generates multiple design variations using CG design software (e.g., Unity) based on the analysis results. These designs reflect the user's emotional state in terms of color and shape.

[0364] These design variations are displayed on the device through an operating interface. Users can preview the provided designs on their smartphones or tablets and make adjustments as needed. Environmental data acquisition and analysis means suggest decorations suitable for the interior environment based on the user's emotional information. For example, if the user is calm, a simple and calming interior design will be suggested.

[0365] Finally, once the user has finalized the design, the design data is transmitted from the server to the 3D printer via the manufacturing control system, and product manufacturing begins. In this way, customized products and interior designs that respond to the user's real-time emotions and preferences can be easily realized.

[0366] An example of a prompt used in a generative AI model is: "Please suggest a room design suitable for when the user is in a calm mood. Specifically, what kind of lighting and furniture arrangement would be appropriate?"

[0367] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0368] Step 1:

[0369] Users access the system via a terminal and input preference and emotional information. Preference information includes design preferences, intended use, and color, while emotional information is collected in real time via camera and microphone. The input data is transmitted to the server in digital format.

[0370] Step 2:

[0371] The server processes the received preference and emotion information using information analysis tools. Specifically, to analyze emotion information, it uses OpenCV to detect the user's face from image data and an emotion analysis API to identify the emotional state. The output of this analysis is a dataset showing the user's current emotions.

[0372] Step 3:

[0373] The server generates design variations using design generation tools based on the analyzed data. Using CG design software such as Unity, it creates designs that match the user's preferences and emotions. The generated design variations are then output.

[0374] Step 4:

[0375] The server transmits the generated design variations to the terminal via an operating interface. The user can preview the design on the terminal's display and make adjustments as needed. The output is a set of adjustable designs presented to the user.

[0376] Step 5:

[0377] The server transmits the user's final selected design to the 3D printer via manufacturing control. Specifically, it converts the selected design data into a printer-compatible format and issues instructions to the printer to begin product manufacturing. The output is a custom product based on the user's emotions and preferences.

[0378] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0379] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0380] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0381] [Third Embodiment]

[0382] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0383] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0384] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0385] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0386] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0387] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0388] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0389] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0390] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0391] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0392] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0393] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0394] This invention is a system in which a server and a terminal work together to execute a series of processes for collecting user preference information and generating custom products based on that information. The system consists of the following steps:

[0395] First, when a user accesses the service through their device, they input information about their preferences, such as desired design, color, intended use, and materials. If applicable, they also provide information about their past purchase history and social media activity. This data is then transmitted from the device to the server.

[0396] The server processes the received data through data analysis tools to identify designs that match user preferences. The analysis draws insights from historical data and trend information, enabling the generation of the most appropriate design variations.

[0397] The multiple design options generated by the analysis are sent back to the user's terminal, where the user can preview them via the user interface and make any necessary adjustments. After selecting a design of interest, the user confirms it and sends it to the server.

[0398] The server transmits the finalized design to the 3D printer via manufacturing control. The 3D printer, under AI-powered automated control, selects appropriate materials and manufactures the product while maintaining quality control. After the manufactured product is verified by the user, it is prepared for delivery and delivered to the designated location.

[0399] This system allows users to obtain personalized products quickly and efficiently. A major advantage of this invention is that it is offered at a reasonable price compared to conventional custom products. Specifically, users can design a watch to suit their tastes and fine-tune their preferred color scheme and strap material. This system accurately meets user needs and provides a mechanism for creating a one-of-a-kind original product.

[0400] The following describes the processing flow.

[0401] Step 1:

[0402] Users access the service using their devices and input preference information such as design, usage, color, and materials. Users also grant permission for the service to use their past purchase history and social media activity data.

[0403] Step 2:

[0404] The device sends user preference information and permitted data to the server. This data is used as information necessary for generating the user's custom design.

[0405] Step 3:

[0406] The server analyzes the received data using data analysis tools, compares it with trend information and historical data, and generates design variations that are best suited to the user's preferences.

[0407] Step 4:

[0408] The server sends the generated design variations to the terminal and provides a user interface that allows the user to preview and fine-tune the design.

[0409] Step 5:

[0410] The user reviews the provided design options, makes adjustments to colors, shapes, etc., as needed, and selects the final design. The selection is then sent back to the server.

[0411] Step 6:

[0412] The server receives the user's final design and sends the design data to the 3D printer using manufacturing control mechanisms. It controls the manufacturing process, including material selection and quality control.

[0413] Step 7:

[0414] A 3D printer manufactures products based on a specified design, and AI is used to perform real-time quality control before producing the finished product.

[0415] Step 8:

[0416] The server confirms the product is complete and notifies the user. The product is ready for delivery and arrangements are made to ship it to the specified address.

[0417] (Example 1)

[0418] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0419] In creating customized products, there is a need to quickly and efficiently provide personalized suggestions that reflect user preferences and past data. However, current technology has limitations in terms of sufficient flexibility to meet diverse user demands and efficiency in automating production processes.

[0420] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0421] In this invention, the server includes information analysis means that performs analysis based on preference information and other input information obtained from the user, design generation means that generates a plurality of design options based on the analysis results, and user interface means that provides the user with the design options and enables selection and adjustment. This enables personalized product suggestions for the user and efficient product manufacturing.

[0422] "Information analysis means" refers to a part of a system that analyzes preference information and other related input information obtained from users to provide suggestions tailored to the user's preferences.

[0423] A "design generation means" is a component of a system that has the function of generating multiple design options to be provided to the user based on analyzed data.

[0424] "User interface means" refers to means that provide an interface for users to preview, select, and adjust the generated design options.

[0425] "Manufacturing management means" refers to the part of the system that manages the process of actually manufacturing a product based on the design data selected and adjusted by the user.

[0426] A "generative AI model" is a model that utilizes artificial intelligence to efficiently optimize the process of data analysis and the generation of design options.

[0427] A "prompt message" is a method for efficiently collecting information necessary for the system to provide the optimal result in response to user input.

[0428] A "three-dimensional product output device" is a manufacturing machine used to create three-dimensional objects, and the term primarily refers to a 3D printer.

[0429] To implement this invention, a system is constructed in which a server and a terminal work together. First, the user accesses the service using the terminal and inputs their preference information. This input includes preferred designs, colors, uses, materials, etc. In addition, the system collects the user's purchase history and various activity data and transmits it from the terminal to the server. Data collection at this stage is important for improving the accuracy of the system.

[0430] Next, the server analyzes the received data using a generation AI model. This AI model analyzes historical data and trend information to generate appropriate design options based on the user's preferences. As a result of the analysis, multiple design options are generated to offer to the user. This allows the server to provide personalized suggestions to the user.

[0431] The generated design options are sent to the user's device and previewed through the user interface. The user can visualize the design through the interface and adjust colors and materials as needed. Once the user has completed their adjustments, the selected design is sent to the server and the manufacturing process begins.

[0432] The server sends the finalized design to a 3D printing device (usually a 3D printer), where the product is generated under AI-driven automated control. During the manufacturing process, appropriate materials are selected and strict quality control is implemented to ensure the product is created precisely according to the design specifications.

[0433] A concrete example is the process where users can set a watch design that suits their tastes and then fine-tune the desired color scheme, strap material, and other details. This system makes it easy for users to obtain a unique product that reflects their personal preferences.

[0434] For example, a prompt message such as, "Please suggest a custom watch design that suits my taste. I can specify the color, purpose, and material," allows the system to efficiently respond to user requests.

[0435] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0436] Step 1:

[0437] Users access the system through a terminal and input their preferences, such as desired design, color, use, and materials. In some cases, they may also input their purchase history and activity data. The terminal then sends this information to the server. This input data forms the basis for the system to understand the user's individual preferences. The preference information is then transferred to the server as output.

[0438] Step 2:

[0439] The server inputs preference information received from the terminal into a generating AI model and performs data analysis. In this process, the model refers to historical data and trend information to generate appropriate design options based on the user's preferences. Specifically, the AI ​​model uses statistical methods and machine learning algorithms to extract the optimal design proposal. As output, multiple generated design options are created.

[0440] Step 3:

[0441] The server sends the generated design options to the terminal and provides them to the user. The user can visualize these designs using the user interface on the terminal and fine-tune the colors, materials, shapes, and other elements. This interface prioritizes usability and visual feedback, allowing users to intuitively improve the designs. The adjusted designs undergo a final design check before being sent back to the server.

[0442] Step 4:

[0443] The server transmits the user-tuned design to the 3D printing equipment. Specifically, the server creates a manufacturing plan and issues instructions to the equipment, such as the 3D printer. AI-powered automated control is employed throughout the manufacturing process, including material selection and precise quality control. The result is a highly accurate product manufactured according to the design.

[0444] Step 5:

[0445] Once manufacturing is complete, the product is checked by the user. The user performs a final check to ensure the product has been manufactured according to specifications. After this check, the product is prepared for shipment and sent to the designated delivery address. The server manages the shipping arrangements and tracking, and the user is notified. As a result, the user receives their customized product.

[0446] (Application Example 1)

[0447] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0448] In recent years, consumers have increasingly demanded personalized products, creating a need for methods to rapidly generate customized products to meet these needs. However, existing systems are inefficient in design proposals and lack sufficient user interaction. Furthermore, quality control during the manufacturing process is difficult. This invention aims to solve these problems and realize the provision of high-quality customized products tailored to user preferences.

[0449] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0450] In this invention, the server includes data processing means that perform analysis based on preference information acquired from the user, design generation means that generate multiple design variations based on the analysis results, and recommendation means that propose designs using a generated AI model. This enables the provision of appropriate and personalized designs to the user and rapid manufacturing.

[0451] "Preference information" refers to information that indicates a user's individual preferences and tastes, and includes data related to design, color, usage, materials, etc.

[0452] "Data processing means" refers to a system or method for analyzing preference information obtained from users and obtaining relevant insights.

[0453] "Design generation means" refers to a system or method for automatically generating optimal design variations for a user based on data analysis.

[0454] "Interaction means" refers to an interface that allows users to select and fine-tune design variations, enabling real-time interaction with the user.

[0455] "Production control means" refers to a system or method for manufacturing goods based on selected and fine-tuned design data, and for controlling the process at each stage of the manufacturing process.

[0456] A "three-dimensional printing device" is a device that creates a physical shape from a design chosen by the user, and is commonly called a 3D printer.

[0457] A "generative AI model" is a model that uses artificial intelligence to propose data-driven designs, and it utilizes machine learning algorithms.

[0458] "Instant design display and adjustment" refers to a process where the design selected by the user is displayed in real time, allowing the user to make adjustments on the spot.

[0459] "3D preview" is a feature that allows users to visually check their chosen design in three dimensions, providing a means to understand the final form of the product more concretely.

[0460] The system that realizes this invention provides a series of processes for collecting preference information from users and manufacturing personalized products based on that information.

[0461] First, users access the system using mobile devices such as smartphones and tablets and input their preferences. This includes specific elements such as design preferences, colors, intended use, and materials. Past purchase history and social media activity data can also be accessed.

[0462] Preference information transmitted from the terminal is received by the server and analyzed using data processing tools. The analysis is performed by a generative AI model utilizing machine learning algorithms, which generates design suggestions based on the user's preferences. This provides the user with multiple appropriate and personalized design variations.

[0463] Users can preview the provided design variations in real time through the interface on their device. Three-dimensional display allows for visual confirmation of the design and fine-tuning as needed. The user's final selected design is then sent to the server.

[0464] The server sends design data selected and adjusted by the user to the 3D printing machine via production control means, and manufactures the item. This machine produces high-quality custom products by selecting materials and performing quality control during manufacturing.

[0465] As a concrete example, consider a scenario where a user designs their own custom tote bag. The user inputs information such as color patterns and materials through the app, and then checks the AI-generated design in a 3D preview. The result is a one-of-a-kind tote bag that faithfully reflects the user's requirements.

[0466] Here's an example of a prompt to intelligently utilize a generative AI model: "Use the AI ​​to create a cool-looking tote bag. I want colorful, easy-to-carry handles."

[0467] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0468] Step 1:

[0469] Users access the system using a terminal and input their preference information. This input includes detailed elements such as design preferences, colors, uses, and materials. The terminal then collects the user's preference information and prepares it for transmission to the server. This input information serves as foundational data for data analysis.

[0470] Step 2:

[0471] The server receives preference information transmitted from the terminal. The server analyzes this information using data processing tools to extract specific preferences and patterns. This process includes matching with a database and analyzing past trends. As a result, it becomes possible to gain insights into the user's preferences.

[0472] Step 3:

[0473] The server utilizes a generative AI model to generate design variations based on user preferences. This process uses data analysis results as input and outputs the optimal design proposed by the AI. Using prompts allows for even more flexible and accurate suggestions.

[0474] Step 4:

[0475] The generated design variations are provided to the user via the terminal. The user can preview the design in real time using interaction tools and make adjustments as needed. The terminal compiles the user's selections and adjustments and sends them to the server as the final design data.

[0476] Step 5:

[0477] The server transmits the final design data to the 3D printing machine via the production control system. The printing machine then manufactures the product based on this design data. During the manufacturing process, selected materials are used and quality control is implemented. This process results in the output of high-quality, user-optimized products.

[0478] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0479] This invention is a system that analyzes user preference and emotional information to efficiently generate personalized designs. A server, terminal, emotion engine, design generation means, user interface means, and manufacturing control means work in conjunction with each other.

[0480] The system's implementation process begins with the user accessing the service using a device and entering preference information such as design, color, usage, and materials. If the user grants permission, the emotion engine acquires real-time emotional information from the user through sensors such as the camera and microphone. This data is then transmitted from the device to the server.

[0481] The server analyzes user preferences and emotional information using data analysis tools. Based on the emotional state provided by the emotion engine, the design generation tool generates further optimized design variations. This process generates designs that respond to the user's temporary emotions and long-term preferences.

[0482] The generated design variations are provided to the user on the device through a user interface. The user can preview and adjust these designs. The system also responds to changes in the user's emotions and dynamically modifies suggestions as needed. Therefore, the user interface can always provide the latest design options.

[0483] Once the user finalizes the design, the server uses manufacturing control to send the design data to the 3D printer, and product manufacturing begins. AI handles material selection and quality control. As a result, customized products tailored to the user's needs are generated in real time.

[0484] For example, if a user expresses joy, the system suggests a design with bright colors; if they express calm emotions, it suggests cool colors and simple shapes. Users can select and fine-tune the design that best suits their emotions, ultimately resulting in a unique product. This embodiment makes it possible to manufacture personalized products that take into account not only preferences but also emotions.

[0485] The following describes the processing flow.

[0486] Step 1:

[0487] Users access the service using their devices and input their preferences, such as design, intended use, color, and materials. Simultaneously, if the user grants permission, the emotion engine acquires real-time emotional data from the user's facial expressions and voice via sensors such as the camera and microphone built into the device.

[0488] Step 2:

[0489] The device sends user preference information and emotional data to the server. This data is stored on the server as essential information for generating custom designs.

[0490] Step 3:

[0491] The server uses data analysis tools to analyze the received preference information and emotion data. The results of this analysis are provided to the design generation tools. Based on the emotion information analyzed by the emotion engine, design variations are generated that take into account the user's current emotional state.

[0492] Step 4:

[0493] The server generates design variations and sends them back to the terminal, providing the user with preview and adjustment options through a user interface. Users can adjust colors and shapes while viewing the provided designs. Furthermore, the design suggestions are dynamically updated in response to changes in the user's emotional state.

[0494] Step 5:

[0495] Once the user has selected the final design and completed any necessary adjustments, the design is submitted to the server for final confirmation. At this stage, the user interface prompts confirmation through a final preview screen.

[0496] Step 6:

[0497] The server transmits the finalized design data to the manufacturing control system, and product manufacturing begins on the 3D printer. Products reflecting the optimal design, shaped by the emotion engine, are generated. AI handles material selection and quality control during the manufacturing process, guaranteeing high-quality products.

[0498] Step 7:

[0499] Once manufacturing is complete, the server notifies the user, and the product is prepared for delivery. Finally, the customized product is delivered to the user. The user receives a unique product that perfectly matches their own feelings.

[0500] (Example 2)

[0501] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0502] In modern manufacturing, there is a demand to optimize designs based on individual consumer preferences and real-time emotional states, and to manufacture products quickly and efficiently. However, existing systems struggle to generate personalized designs that take user emotional information into account, and the efficiency of the manufacturing process is far from optimal. Solving this problem is the objective of this invention.

[0503] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0504] In this invention, the server includes information analysis means that performs analysis based on preference information and emotional information acquired from the user; design generation means that generates multiple design variations based on the analysis results and emotional information; and human-machine interface means that provides the user with the design variations and enables selection and fine-tuning. This makes it possible to generate designs tailored to the user's individual preferences and emotions, and to manufacture products quickly and efficiently based on those designs.

[0505] "Information analysis means" refers to a mechanism that analyzes preference and emotional information obtained from users and extracts elements necessary for design generation.

[0506] A "design generation means" is a mechanism that generates multiple design variations to be provided to the user based on data extracted by an information analysis means.

[0507] A "human-machine interface means" is a mechanism that can be operated through the user's device, providing an interface that allows the user to view, select, and fine-tune design variations.

[0508] A "production control system" is a mechanism that directs and supervises the manufacturing of a product based on selected and fine-tuned design data.

[0509] A "three-dimensional additive manufacturing device" is a device that forms products in three dimensions based on instructions from a production control system, and is commonly known as a 3D printer.

[0510] "Material selection and quality control" refers to a series of processes and inspections to ensure the use of appropriate materials in the manufacturing process and to maintain a certain level of product quality.

[0511] This invention is a system that analyzes user preference and emotional information and efficiently generates personalized designs based on that information. The system consists of a server, a terminal, an emotional engine, design generation means, human-machine interface means, and production control means.

[0512] Users can access the system using their devices and input preference information such as design, color, intended use, and materials. If the user consents, emotional information collected through the device's camera and microphone is also used. This information is transmitted from the device to the server, which uses data analysis tools to analyze the user's preferences and emotional information.

[0513] The emotion engine determines the user's emotional state based on emotional information. This data is utilized by the design generation mechanism to generate design variations that match the user's preferences and emotions. A generative AI model is used for design generation, and prompts for constructing new designs are also generated.

[0514] The generated design variations are displayed on the user's device. A human-machine interface allows the user to preview each element of the design and make adjustments as needed. This interface supports real-time adjustments and flexibly responds to changes in the user's emotions.

[0515] Finally, the user's selected design is transmitted via a server to a production control system. This control system then initiates physical product manufacturing using a 3D additive manufacturing device. During the manufacturing process, AI technology performs material selection and quality control to supply the optimal product.

[0516] As a concrete example, by introducing a prompt message on the user's device stating, "Please generate the optimal design variations based on the latest user preferences and sentiment information," the system automatically generates designs according to the set conditions. In this way, it becomes possible to provide customized products that meet the individual needs of the user.

[0517] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0518] Step 1:

[0519] Users access the system using a terminal and input preference information such as design, color, intended use, and materials. The input information is initially filtered within the terminal and formatted as basic data for customization. The terminal then sends this formatted data to the server.

[0520] Step 2:

[0521] If the user grants permission, real-time emotional information is collected through the device's camera and microphone. This sensor data is input into an emotion engine to analyze the user's emotional state. The results of this analysis are sent to the server as emotional information.

[0522] Step 3:

[0523] The server receives preference and emotion information transmitted from the terminal. Using information analysis tools, it analyzes this data and sets initial conditions based on the user's preferences and emotions. The analysis results are passed to the design generation tool as guidelines for design generation.

[0524] Step 4:

[0525] The server uses a design generation mechanism to construct design variations based on the analysis results, utilizing a generation AI model. A prompt message is input, and the generated design is sent back to the user interface mechanism.

[0526] Step 5:

[0527] The user previews the generated design variations on their device. Through a human-machine interface, the user can adjust each element of the design in real time, changing colors and shapes. These adjustments are reflected in the data for the final design selection.

[0528] Step 6:

[0529] Once the user has finalized the design, the terminal sends this design data to the server. The server, through production control mechanisms, transmits the determined design to the 3D additive manufacturing machine, thereby initiating product manufacturing. During the manufacturing process, AI is used for material selection and quality control, resulting in the creation of an optimal product.

[0530] (Application Example 2)

[0531] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0532] In modern product design, it is crucial to reflect users' individual preferences and emotions in real time. However, current systems face challenges in flexibly responding to changes in user emotions and applying those designs to their environment. Furthermore, it is not common to verify in real time whether individualized designs are suitable for their environment. Therefore, there is a need to enable more intuitive and personalized design proposals based on emotional information.

[0533] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0534] In this invention, the server includes information analysis means, design generation means, operation interface means, and environmental data acquisition and analysis means. This makes it possible to generate personalized designs based on the user's preference information and emotional information, and to suggest in real time whether the designs harmonize with the environment.

[0535] "Information analysis means" refers to a device or method that analyzes preference information and emotional information obtained from users and provides data that forms the basis for generating personalized designs.

[0536] "Design generation means" refers to a device or method that generates multiple design variations based on analyzed information and proposes them to the user.

[0537] "Operation interface means" refers to an interface that allows the user to select and adjust the generated design variations.

[0538] "Manufacturing control means" refers to a control device or method that issues instructions for manufacturing a product based on the final design selected by the user.

[0539] "Environmental data acquisition and analysis means" refers to a device or method that acquires data on the user's surrounding environment and proposes environmental decorations based on emotional information.

[0540] In order to implement this invention, the coordination of various hardware and software is necessary. The server consists of a computer system equipped with information analysis means, design generation means, operation interface means, and environmental data acquisition and analysis means.

[0541] Users access the system using a terminal, providing preference information and inputting emotional information via camera and microphone. This data is sent from the terminal to the server. The information analysis system on the server uses image processing libraries such as OpenCV to recognize emotions from the user's face and then performs detailed emotional analysis using an emotion analysis API.

[0542] The design generation method generates multiple design variations using CG design software (e.g., Unity) based on the analysis results. These designs reflect the user's emotional state in terms of color and shape.

[0543] These design variations are displayed on the device through an operating interface. Users can preview the provided designs on their smartphones or tablets and make adjustments as needed. Environmental data acquisition and analysis means suggest decorations suitable for the interior environment based on the user's emotional information. For example, if the user is calm, a simple and calming interior design will be suggested.

[0544] Finally, once the user has finalized the design, the design data is transmitted from the server to the 3D printer via the manufacturing control system, and product manufacturing begins. In this way, customized products and interior designs that respond to the user's real-time emotions and preferences can be easily realized.

[0545] An example of a prompt used in a generative AI model is: "Please suggest a room design suitable for when the user is in a calm mood. Specifically, what kind of lighting and furniture arrangement would be appropriate?"

[0546] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0547] Step 1:

[0548] Users access the system via a terminal and input preference and emotional information. Preference information includes design preferences, intended use, and color, while emotional information is collected in real time via camera and microphone. The input data is transmitted to the server in digital format.

[0549] Step 2:

[0550] The server processes the received preference and emotion information using information analysis tools. Specifically, to analyze emotion information, it uses OpenCV to detect the user's face from image data and an emotion analysis API to identify the emotional state. The output of this analysis is a dataset showing the user's current emotions.

[0551] Step 3:

[0552] The server generates design variations using design generation tools based on the analyzed data. Using CG design software such as Unity, it creates designs that match the user's preferences and emotions. The generated design variations are then output.

[0553] Step 4:

[0554] The server transmits the generated design variations to the terminal via an operating interface. The user can preview the design on the terminal's display and make adjustments as needed. The output is a set of adjustable designs presented to the user.

[0555] Step 5:

[0556] The server transmits the user's final selected design to the 3D printer via manufacturing control. Specifically, it converts the selected design data into a printer-compatible format and issues instructions to the printer to begin product manufacturing. The output is a custom product based on the user's emotions and preferences.

[0557] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0558] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0559] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0560] [Fourth Embodiment]

[0561] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0562] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0563] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0564] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0565] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0566] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0567] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0568] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0569] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0570] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0571] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0572] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0573] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0574] This invention is a system in which a server and a terminal work together to execute a series of processes for collecting user preference information and generating custom products based on that information. The system consists of the following steps:

[0575] First, when a user accesses the service through their device, they input information about their preferences, such as desired design, color, intended use, and materials. If applicable, they also provide information about their past purchase history and social media activity. This data is then transmitted from the device to the server.

[0576] The server processes the received data through data analysis tools to identify designs that match user preferences. The analysis draws insights from historical data and trend information, enabling the generation of the most appropriate design variations.

[0577] The multiple design options generated by the analysis are sent back to the user's terminal, where the user can preview them via the user interface and make any necessary adjustments. After selecting a design of interest, the user confirms it and sends it to the server.

[0578] The server transmits the finalized design to the 3D printer via manufacturing control. The 3D printer, under AI-powered automated control, selects appropriate materials and manufactures the product while maintaining quality control. After the manufactured product is verified by the user, it is prepared for delivery and delivered to the designated location.

[0579] This system allows users to obtain personalized products quickly and efficiently. A major advantage of this invention is that it is offered at a reasonable price compared to conventional custom products. Specifically, users can design a watch to suit their tastes and fine-tune their preferred color scheme and strap material. This system accurately meets user needs and provides a mechanism for creating a one-of-a-kind original product.

[0580] The following describes the processing flow.

[0581] Step 1:

[0582] Users access the service using their devices and input preference information such as design, usage, color, and materials. Users also grant permission for the service to use their past purchase history and social media activity data.

[0583] Step 2:

[0584] The device sends user preference information and permitted data to the server. This data is used as information necessary for generating the user's custom design.

[0585] Step 3:

[0586] The server analyzes the received data using data analysis tools, compares it with trend information and historical data, and generates design variations that are best suited to the user's preferences.

[0587] Step 4:

[0588] The server sends the generated design variations to the terminal and provides a user interface that allows the user to preview and fine-tune the design.

[0589] Step 5:

[0590] The user reviews the provided design options, makes adjustments to colors, shapes, etc., as needed, and selects the final design. The selection is then sent back to the server.

[0591] Step 6:

[0592] The server receives the user's final design and sends the design data to the 3D printer using manufacturing control mechanisms. It controls the manufacturing process, including material selection and quality control.

[0593] Step 7:

[0594] A 3D printer manufactures products based on a specified design, and AI is used to perform real-time quality control before producing the finished product.

[0595] Step 8:

[0596] The server confirms the product is complete and notifies the user. The product is ready for delivery and arrangements are made to ship it to the specified address.

[0597] (Example 1)

[0598] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0599] In creating customized products, there is a need to quickly and efficiently provide personalized suggestions that reflect user preferences and past data. However, current technology has limitations in terms of sufficient flexibility to meet diverse user demands and efficiency in automating production processes.

[0600] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0601] In this invention, the server includes information analysis means that performs analysis based on preference information and other input information obtained from the user, design generation means that generates a plurality of design options based on the analysis results, and user interface means that provides the user with the design options and enables selection and adjustment. This enables personalized product suggestions for the user and efficient product manufacturing.

[0602] "Information analysis means" refers to a part of a system that analyzes preference information and other related input information obtained from users to provide suggestions tailored to the user's preferences.

[0603] A "design generation means" is a component of a system that has the function of generating multiple design options to be provided to the user based on analyzed data.

[0604] "User interface means" refers to means that provide an interface for users to preview, select, and adjust the generated design options.

[0605] "Manufacturing management means" refers to the part of the system that manages the process of actually manufacturing a product based on the design data selected and adjusted by the user.

[0606] A "generative AI model" is a model that utilizes artificial intelligence to efficiently optimize the process of data analysis and the generation of design options.

[0607] A "prompt message" is a method for efficiently collecting information necessary for the system to provide the optimal result in response to user input.

[0608] A "three-dimensional product output device" is a manufacturing machine used to create three-dimensional objects, and the term primarily refers to a 3D printer.

[0609] To implement this invention, a system is constructed in which a server and a terminal work together. First, the user accesses the service using the terminal and inputs their preference information. This input includes preferred designs, colors, uses, materials, etc. In addition, the system collects the user's purchase history and various activity data and transmits it from the terminal to the server. Data collection at this stage is important for improving the accuracy of the system.

[0610] Next, the server analyzes the received data using a generation AI model. This AI model analyzes historical data and trend information to generate appropriate design options based on the user's preferences. As a result of the analysis, multiple design options are generated to offer to the user. This allows the server to provide personalized suggestions to the user.

[0611] The generated design options are sent to the user's device and previewed through the user interface. The user can visualize the design through the interface and adjust colors and materials as needed. Once the user has completed their adjustments, the selected design is sent to the server and the manufacturing process begins.

[0612] The server sends the finalized design to a 3D printing device (usually a 3D printer), where the product is generated under AI-driven automated control. During the manufacturing process, appropriate materials are selected and strict quality control is implemented to ensure the product is created precisely according to the design specifications.

[0613] A concrete example is the process where users can set a watch design that suits their tastes and then fine-tune the desired color scheme, strap material, and other details. This system makes it easy for users to obtain a unique product that reflects their personal preferences.

[0614] For example, a prompt message such as, "Please suggest a custom watch design that suits my taste. I can specify the color, purpose, and material," allows the system to efficiently respond to user requests.

[0615] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0616] Step 1:

[0617] Users access the system through a terminal and input their preferences, such as desired design, color, use, and materials. In some cases, they may also input their purchase history and activity data. The terminal then sends this information to the server. This input data forms the basis for the system to understand the user's individual preferences. The preference information is then transferred to the server as output.

[0618] Step 2:

[0619] The server inputs preference information received from the terminal into a generating AI model and performs data analysis. In this process, the model refers to historical data and trend information to generate appropriate design options based on the user's preferences. Specifically, the AI ​​model uses statistical methods and machine learning algorithms to extract the optimal design proposal. As output, multiple generated design options are created.

[0620] Step 3:

[0621] The server sends the generated design options to the terminal and provides them to the user. The user can visualize these designs using the user interface on the terminal and fine-tune the colors, materials, shapes, and other elements. This interface prioritizes usability and visual feedback, allowing users to intuitively improve the designs. The adjusted designs undergo a final design check before being sent back to the server.

[0622] Step 4:

[0623] The server transmits the user-tuned design to the 3D printing equipment. Specifically, the server creates a manufacturing plan and issues instructions to the equipment, such as the 3D printer. AI-powered automated control is employed throughout the manufacturing process, including material selection and precise quality control. The result is a highly accurate product manufactured according to the design.

[0624] Step 5:

[0625] Once manufacturing is complete, the product is checked by the user. The user performs a final check to ensure the product has been manufactured according to specifications. After this check, the product is prepared for shipment and sent to the designated delivery address. The server manages the shipping arrangements and tracking, and the user is notified. As a result, the user receives their customized product.

[0626] (Application Example 1)

[0627] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0628] In recent years, consumers have increasingly demanded personalized products, creating a need for methods to rapidly generate customized products to meet these needs. However, existing systems are inefficient in design proposals and lack sufficient user interaction. Furthermore, quality control during the manufacturing process is difficult. This invention aims to solve these problems and realize the provision of high-quality customized products tailored to user preferences.

[0629] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0630] In this invention, the server includes data processing means that perform analysis based on preference information acquired from the user, design generation means that generate multiple design variations based on the analysis results, and recommendation means that propose designs using a generated AI model. This enables the provision of appropriate and personalized designs to the user and rapid manufacturing.

[0631] "Preference information" refers to information that indicates a user's individual preferences and tastes, and includes data related to design, color, usage, materials, etc.

[0632] "Data processing means" refers to a system or method for analyzing preference information obtained from users and obtaining relevant insights.

[0633] "Design generation means" refers to a system or method for automatically generating optimal design variations for a user based on data analysis.

[0634] "Interaction means" refers to an interface that allows users to select and fine-tune design variations, enabling real-time interaction with the user.

[0635] "Production control means" refers to a system or method for manufacturing goods based on selected and fine-tuned design data, and for controlling the process at each stage of the manufacturing process.

[0636] A "three-dimensional printing device" is a device that creates a physical shape from a design chosen by the user, and is commonly called a 3D printer.

[0637] A "generative AI model" is a model that uses artificial intelligence to propose data-driven designs, and it utilizes machine learning algorithms.

[0638] "Instant design display and adjustment" refers to a process where the design selected by the user is displayed in real time, allowing the user to make adjustments on the spot.

[0639] "3D preview" is a feature that allows users to visually check their chosen design in three dimensions, providing a means to understand the final form of the product more concretely.

[0640] The system that realizes this invention provides a series of processes for collecting preference information from users and manufacturing personalized products based on that information.

[0641] First, users access the system using mobile devices such as smartphones and tablets and input their preferences. This includes specific elements such as design preferences, colors, intended use, and materials. Past purchase history and social media activity data can also be accessed.

[0642] Preference information transmitted from the terminal is received by the server and analyzed using data processing tools. The analysis is performed by a generative AI model utilizing machine learning algorithms, which generates design suggestions based on the user's preferences. This provides the user with multiple appropriate and personalized design variations.

[0643] Users can preview the provided design variations in real time through the interface on their device. Three-dimensional display allows for visual confirmation of the design and fine-tuning as needed. The user's final selected design is then sent to the server.

[0644] The server sends design data selected and adjusted by the user to the 3D printing machine via production control means, and manufactures the item. This machine produces high-quality custom products by selecting materials and performing quality control during manufacturing.

[0645] As a concrete example, consider a scenario where a user designs their own custom tote bag. The user inputs information such as color patterns and materials through the app, and then checks the AI-generated design in a 3D preview. The result is a one-of-a-kind tote bag that faithfully reflects the user's requirements.

[0646] Here's an example of a prompt to intelligently utilize a generative AI model: "Use the AI ​​to create a cool-looking tote bag. I want colorful, easy-to-carry handles."

[0647] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0648] Step 1:

[0649] Users access the system using a terminal and input their preference information. This input includes detailed elements such as design preferences, colors, uses, and materials. The terminal then collects the user's preference information and prepares it for transmission to the server. This input information serves as foundational data for data analysis.

[0650] Step 2:

[0651] The server receives preference information transmitted from the terminal. The server analyzes this information using data processing tools to extract specific preferences and patterns. This process includes matching with a database and analyzing past trends. As a result, it becomes possible to gain insights into the user's preferences.

[0652] Step 3:

[0653] The server utilizes a generative AI model to generate design variations based on user preferences. This process uses data analysis results as input and outputs the optimal design proposed by the AI. Using prompts allows for even more flexible and accurate suggestions.

[0654] Step 4:

[0655] The generated design variations are provided to the user via the terminal. The user can preview the design in real time using interaction tools and make adjustments as needed. The terminal compiles the user's selections and adjustments and sends them to the server as the final design data.

[0656] Step 5:

[0657] The server transmits the final design data to the 3D printing machine via the production control system. The printing machine then manufactures the product based on this design data. During the manufacturing process, selected materials are used and quality control is implemented. This process results in the output of high-quality, user-optimized products.

[0658] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0659] This invention is a system that analyzes user preference and emotional information to efficiently generate personalized designs. A server, terminal, emotion engine, design generation means, user interface means, and manufacturing control means work in conjunction with each other.

[0660] The system's implementation process begins with the user accessing the service using a device and entering preference information such as design, color, usage, and materials. If the user grants permission, the emotion engine acquires real-time emotional information from the user through sensors such as the camera and microphone. This data is then transmitted from the device to the server.

[0661] The server analyzes user preferences and emotional information using data analysis tools. Based on the emotional state provided by the emotion engine, the design generation tool generates further optimized design variations. This process generates designs that respond to the user's temporary emotions and long-term preferences.

[0662] The generated design variations are provided to the user on the device through a user interface. The user can preview and adjust these designs. The system also responds to changes in the user's emotions and dynamically modifies suggestions as needed. Therefore, the user interface can always provide the latest design options.

[0663] Once the user finalizes the design, the server uses manufacturing control to send the design data to the 3D printer, and product manufacturing begins. AI handles material selection and quality control. As a result, customized products tailored to the user's needs are generated in real time.

[0664] For example, if a user expresses joy, the system suggests a design with bright colors; if they express calm emotions, it suggests cool colors and simple shapes. Users can select and fine-tune the design that best suits their emotions, ultimately resulting in a unique product. This embodiment makes it possible to manufacture personalized products that take into account not only preferences but also emotions.

[0665] The following describes the processing flow.

[0666] Step 1:

[0667] Users access the service using their devices and input their preferences, such as design, intended use, color, and materials. Simultaneously, if the user grants permission, the emotion engine acquires real-time emotional data from the user's facial expressions and voice via sensors such as the camera and microphone built into the device.

[0668] Step 2:

[0669] The device sends user preference information and emotional data to the server. This data is stored on the server as essential information for generating custom designs.

[0670] Step 3:

[0671] The server uses data analysis tools to analyze the received preference information and emotion data. The results of this analysis are provided to the design generation tools. Based on the emotion information analyzed by the emotion engine, design variations are generated that take into account the user's current emotional state.

[0672] Step 4:

[0673] The server generates design variations and sends them back to the terminal, providing the user with preview and adjustment options through a user interface. Users can adjust colors and shapes while viewing the provided designs. Furthermore, the design suggestions are dynamically updated in response to changes in the user's emotional state.

[0674] Step 5:

[0675] Once the user has selected the final design and completed any necessary adjustments, the design is submitted to the server for final confirmation. At this stage, the user interface prompts confirmation through a final preview screen.

[0676] Step 6:

[0677] The server transmits the finalized design data to the manufacturing control system, and product manufacturing begins on the 3D printer. Products reflecting the optimal design, shaped by the emotion engine, are generated. AI handles material selection and quality control during the manufacturing process, guaranteeing high-quality products.

[0678] Step 7:

[0679] Once manufacturing is complete, the server notifies the user, and the product is prepared for delivery. Finally, the customized product is delivered to the user. The user receives a unique product that perfectly matches their own feelings.

[0680] (Example 2)

[0681] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0682] In modern manufacturing, there is a demand to optimize designs based on individual consumer preferences and real-time emotional states, and to manufacture products quickly and efficiently. However, existing systems struggle to generate personalized designs that take user emotional information into account, and the efficiency of the manufacturing process is far from optimal. Solving this problem is the objective of this invention.

[0683] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0684] In this invention, the server includes information analysis means that performs analysis based on preference information and emotional information acquired from the user; design generation means that generates multiple design variations based on the analysis results and emotional information; and human-machine interface means that provides the user with the design variations and enables selection and fine-tuning. This makes it possible to generate designs tailored to the user's individual preferences and emotions, and to manufacture products quickly and efficiently based on those designs.

[0685] "Information analysis means" refers to a mechanism that analyzes preference and emotional information obtained from users and extracts elements necessary for design generation.

[0686] A "design generation means" is a mechanism that generates multiple design variations to be provided to the user based on data extracted by an information analysis means.

[0687] A "human-machine interface means" is a mechanism that can be operated through the user's device, providing an interface that allows the user to view, select, and fine-tune design variations.

[0688] A "production control system" is a mechanism that directs and supervises the manufacturing of a product based on selected and fine-tuned design data.

[0689] A "three-dimensional additive manufacturing device" is a device that forms products in three dimensions based on instructions from a production control system, and is commonly known as a 3D printer.

[0690] "Material selection and quality control" refers to a series of processes and inspections to ensure the use of appropriate materials in the manufacturing process and to maintain a certain level of product quality.

[0691] This invention is a system that analyzes user preference and emotional information and efficiently generates personalized designs based on that information. The system consists of a server, a terminal, an emotional engine, design generation means, human-machine interface means, and production control means.

[0692] Users can access the system using their devices and input preference information such as design, color, intended use, and materials. If the user consents, emotional information collected through the device's camera and microphone is also used. This information is transmitted from the device to the server, which uses data analysis tools to analyze the user's preferences and emotional information.

[0693] The emotion engine determines the user's emotional state based on emotional information. This data is utilized by the design generation mechanism to generate design variations that match the user's preferences and emotions. A generative AI model is used for design generation, and prompts for constructing new designs are also generated.

[0694] The generated design variations are displayed on the user's device. A human-machine interface allows the user to preview each element of the design and make adjustments as needed. This interface supports real-time adjustments and flexibly responds to changes in the user's emotions.

[0695] Finally, the user's selected design is transmitted via a server to a production control system. This control system then initiates physical product manufacturing using a 3D additive manufacturing device. During the manufacturing process, AI technology performs material selection and quality control to supply the optimal product.

[0696] As a concrete example, by introducing a prompt message on the user's device stating, "Please generate the optimal design variations based on the latest user preferences and sentiment information," the system automatically generates designs according to the set conditions. In this way, it becomes possible to provide customized products that meet the individual needs of the user.

[0697] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0698] Step 1:

[0699] Users access the system using a terminal and input preference information such as design, color, intended use, and materials. The input information is initially filtered within the terminal and formatted as basic data for customization. The terminal then sends this formatted data to the server.

[0700] Step 2:

[0701] If the user grants permission, real-time emotional information is collected through the device's camera and microphone. This sensor data is input into an emotion engine to analyze the user's emotional state. The results of this analysis are sent to the server as emotional information.

[0702] Step 3:

[0703] The server receives preference and emotion information transmitted from the terminal. Using information analysis tools, it analyzes this data and sets initial conditions based on the user's preferences and emotions. The analysis results are passed to the design generation tool as guidelines for design generation.

[0704] Step 4:

[0705] The server uses a design generation mechanism to construct design variations based on the analysis results, utilizing a generation AI model. A prompt message is input, and the generated design is sent back to the user interface mechanism.

[0706] Step 5:

[0707] The user previews the generated design variations on their device. Through a human-machine interface, the user can adjust each element of the design in real time, changing colors and shapes. These adjustments are reflected in the data for the final design selection.

[0708] Step 6:

[0709] Once the user has finalized the design, the terminal sends this design data to the server. The server, through production control mechanisms, transmits the determined design to the 3D additive manufacturing machine, thereby initiating product manufacturing. During the manufacturing process, AI is used for material selection and quality control, resulting in the creation of an optimal product.

[0710] (Application Example 2)

[0711] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0712] In modern product design, it is crucial to reflect users' individual preferences and emotions in real time. However, current systems face challenges in flexibly responding to changes in user emotions and applying those designs to their environment. Furthermore, it is not common to verify in real time whether individualized designs are suitable for their environment. Therefore, there is a need to enable more intuitive and personalized design proposals based on emotional information.

[0713] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0714] In this invention, the server includes information analysis means, design generation means, operation interface means, and environmental data acquisition and analysis means. This makes it possible to generate personalized designs based on the user's preference information and emotional information, and to suggest in real time whether the designs harmonize with the environment.

[0715] "Information analysis means" refers to a device or method that analyzes preference information and emotional information obtained from users and provides data that forms the basis for generating personalized designs.

[0716] "Design generation means" refers to a device or method that generates multiple design variations based on analyzed information and proposes them to the user.

[0717] "Operation interface means" refers to an interface that allows the user to select and adjust the generated design variations.

[0718] "Manufacturing control means" refers to a control device or method that issues instructions for manufacturing a product based on the final design selected by the user.

[0719] "Environmental data acquisition and analysis means" refers to a device or method that acquires data on the user's surrounding environment and proposes environmental decorations based on emotional information.

[0720] In order to implement this invention, the coordination of various hardware and software is necessary. The server consists of a computer system equipped with information analysis means, design generation means, operation interface means, and environmental data acquisition and analysis means.

[0721] Users access the system using a terminal, providing preference information and inputting emotional information via camera and microphone. This data is sent from the terminal to the server. The information analysis system on the server uses image processing libraries such as OpenCV to recognize emotions from the user's face and then performs detailed emotional analysis using an emotion analysis API.

[0722] The design generation method generates multiple design variations using CG design software (e.g., Unity) based on the analysis results. These designs reflect the user's emotional state in terms of color and shape.

[0723] These design variations are displayed on the device through an operating interface. Users can preview the provided designs on their smartphones or tablets and make adjustments as needed. Environmental data acquisition and analysis means suggest decorations suitable for the interior environment based on the user's emotional information. For example, if the user is calm, a simple and calming interior design will be suggested.

[0724] Finally, once the user has finalized the design, the design data is transmitted from the server to the 3D printer via the manufacturing control system, and product manufacturing begins. In this way, customized products and interior designs that respond to the user's real-time emotions and preferences can be easily realized.

[0725] An example of a prompt used in a generative AI model is: "Please suggest a room design suitable for when the user is in a calm mood. Specifically, what kind of lighting and furniture arrangement would be appropriate?"

[0726] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0727] Step 1:

[0728] Users access the system via a terminal and input preference and emotional information. Preference information includes design preferences, intended use, and color, while emotional information is collected in real time via camera and microphone. The input data is transmitted to the server in digital format.

[0729] Step 2:

[0730] The server processes the received preference and emotion information using information analysis tools. Specifically, to analyze emotion information, it uses OpenCV to detect the user's face from image data and an emotion analysis API to identify the emotional state. The output of this analysis is a dataset showing the user's current emotions.

[0731] Step 3:

[0732] The server generates design variations using design generation tools based on the analyzed data. Using CG design software such as Unity, it creates designs that match the user's preferences and emotions. The generated design variations are then output.

[0733] Step 4:

[0734] The server transmits the generated design variations to the terminal via an operating interface. The user can preview the design on the terminal's display and make adjustments as needed. The output is a set of adjustable designs presented to the user.

[0735] Step 5:

[0736] The server transmits the user's final selected design to the 3D printer via manufacturing control. Specifically, it converts the selected design data into a printer-compatible format and issues instructions to the printer to begin product manufacturing. The output is a custom product based on the user's emotions and preferences.

[0737] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0738] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0739] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0740] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0741] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0742] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0743] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0744] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0745] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0746] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0747] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0748] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0749] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0750] 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.

[0751] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0752] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0753] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0754] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0755] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0756] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0757] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0758] The following is further disclosed regarding the embodiments described above.

[0759] (Claim 1)

[0760] A data analysis method that performs analysis based on preference information obtained from users,

[0761] A design generation means that generates multiple design variations based on the aforementioned analysis results,

[0762] A user interface means that provides the user with the aforementioned design variations and enables selection and fine-tuning,

[0763] A manufacturing control means for manufacturing a product based on the selected and fine-tuned design data,

[0764] A system that includes this.

[0765] (Claim 2)

[0766] The system according to claim 1, wherein the manufacturing control means manufactures products using a 3D printer and performs material selection and quality control during the manufacturing process.

[0767] (Claim 3)

[0768] The system according to claim 1, wherein the user interface means enables real-time preview and adjustment of the design through the user's device.

[0769] "Example 1"

[0770] (Claim 1)

[0771] An information analysis means that performs analysis based on preference information and other input information obtained from the user,

[0772] A design generation means that generates multiple design options based on the analysis results,

[0773] A user interface means that provides the user with the aforementioned design options and enables selection and adjustment,

[0774] A manufacturing control means for manufacturing a product based on the selected and adjusted design data,

[0775] Technical means for performing analysis and generation using generative AI models,

[0776] An input optimization means that optimizes user input using prompt statements,

[0777] A system that includes this.

[0778] (Claim 2)

[0779] The system according to claim 1, wherein the manufacturing control means manufactures products using a three-dimensional product output device and performs material selection and quality control during the manufacturing process.

[0780] (Claim 3)

[0781] The system according to claim 1, wherein the user interface means enables real-time visualization and adjustment of the design through the user's device.

[0782] "Application Example 1"

[0783] (Claim 1)

[0784] A data processing method that performs analysis based on preference information obtained from users,

[0785] A design generation means that generates multiple design variations based on the analysis results,

[0786] Interaction means that provides the user with the aforementioned design variations and enables selection and fine-tuning,

[0787] A production control means for manufacturing an article based on the selected and fine-tuned design data,

[0788] Recommended methods for proposing designs using generative AI models,

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1, wherein the production control means manufactures articles using a three-dimensional printing device and performs raw material selection and quality control during the manufacturing process.

[0792] (Claim 3)

[0793] The system according to claim 1, wherein the interaction means enables immediate display and adjustment of the design through the user's information terminal, and also provides a preview using three-dimensional display.

[0794] "Example 2 of combining an emotion engine"

[0795] (Claim 1)

[0796] An information analysis method that performs analysis based on preference information and emotional information obtained from users,

[0797] A design generation means that generates multiple design variations based on the analysis results and emotional information,

[0798] A human-machine interface means that provides the user with the aforementioned design variations and enables selection and fine-tuning,

[0799] A production control means for manufacturing a product based on the selected and fine-tuned design data,

[0800] A system that includes this.

[0801] (Claim 2)

[0802] The system according to claim 1, wherein the production control means manufactures products using a three-dimensional additive manufacturing apparatus and performs material selection and quality control during the manufacturing process.

[0803] (Claim 3)

[0804] The system according to claim 1, wherein the human-machine interface means enables real-time previewing and adjustment of the design through the user's device.

[0805] "Application example 2 when combining with an emotional engine"

[0806] (Claim 1)

[0807] An information analysis method that performs analysis based on preference information and emotional information obtained from users,

[0808] A design generation means that generates multiple design variations based on the aforementioned analysis results,

[0809] An operating interface means that provides the user with the aforementioned design variations and enables selection and fine-tuning,

[0810] A manufacturing control means for manufacturing a product based on the selected and fine-tuned design data,

[0811] A means for acquiring and analyzing environmental data to propose environmental decorations using user sentiment information,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, wherein the manufacturing control means manufactures products using a three-dimensional printer and performs material selection and quality control during the manufacturing process.

[0815] (Claim 3)

[0816] The system according to claim 1, wherein the operating interface means enables real-time preview and adjustment of the design through the user's device, and further integrates suggestions for environmental decoration. [Explanation of Symbols]

[0817] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A data analysis method that performs analysis based on preference information obtained from users, A design generation means that generates multiple design variations based on the aforementioned analysis results, A user interface means that provides the user with the aforementioned design variations and enables selection and fine-tuning, A manufacturing control means for manufacturing a product based on the selected and fine-tuned design data, A system that includes this.

2. The system according to claim 1, wherein the manufacturing control means manufactures products using a 3D printer and performs material selection and quality control during the manufacturing process.

3. The system according to claim 1, wherein the user interface means enables real-time preview and adjustment of the design through the user's device.

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