system

Abstract

We provide the system. [Solution] A method for generating multiple design proposals using generative artificial intelligence, A means for providing the aforementioned design proposals to the user's terminal, enabling selection and adjustment, A means for calculating detailed design information and manufacturing costs based on the selected design, A means of updating generative artificial intelligence by accumulating generated designs and user selection information, 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 and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The apparel industry and the traditional handicraft industry both face common problems such as market shrinkage, issues in grasping demand, and a lack of appeal for uniqueness. Therefore, both industries need to enhance their competitiveness and find new market opportunities. In addition, there is a need for product development means to improve international evaluation while reducing labor and costs in design development. There is also a problem that it is difficult to quickly and effectively generate designs that utilize the uniqueness of traditional handicrafts with conventional methods.

Means for Solving the Problems

[0005] This invention solves these problems by providing a design proposal generation system using generative artificial intelligence. Generative AI rapidly generates multiple design proposals, providing them to the user's terminal for selection and adjustment. Subsequently, detailed design information and manufacturing costs are calculated based on the selected design, supporting efficient product development. Furthermore, by accumulating the generated designs and user selection information and continuously updating the generative AI, the accuracy of subsequent design generation is improved. This enhances the competitiveness of the apparel and traditional crafts industries and creates new markets.

[0006] "Generative artificial intelligence" is an algorithm that learns from past design data and specific patterns to automatically generate new design proposals.

[0007] A "design proposal" refers to information that describes a visual plan or concept in product development, including specific shapes, colors, patterns, and other details.

[0008] A "terminal" is an electronic device used by users to access a design generation system and generate, select, and adjust designs.

[0009] "Detailed design information" refers to technical documents that include specific drawings, dimensions, material information, and other details necessary for commercializing the selected design proposal.

[0010] "Manufacturing cost" refers to the total cost, including material costs, labor costs, and equipment costs, necessary to actually manufacture the selected design as a product.

[0011] A "design request" is input information that allows users to specify their desired design style, colors, patterns, etc., to the system. [Brief explanation of the drawing]

[0012] [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] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] 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.

[0016] 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.

[0017] 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.

[0018] In the following embodiments, a numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention relates to a design generation system that efficiently generates design proposals using generative artificial intelligence, enabling the customization and commercialization of designs according to user needs.

[0034] Users access the design generation system using a terminal and input their desired design requirements through a dedicated interface. These requirements can include multiple design parameters such as color, style, and elements of traditional craftsmanship.

[0035] The server receives requests from users and activates generative artificial intelligence to generate multiple design options. This allows users to consider various options to find the design that best suits them. The generated design options are sent to the terminal as images or 3D models for visual review by the user.

[0036] Users can select their favorite design from several options presented on their device and make fine adjustments using the provided tools. For example, with a jacket design, they can change the sleeve length and color, adjust the pattern placement, and more in real time.

[0037] The server generates detailed design information necessary for product development based on the design ultimately selected by the user. This information includes specific dimensions, materials to be used, and manufacturing steps. Cost calculations related to manufacturing are also performed simultaneously and provided to the user.

[0038] Furthermore, the selected design proposals and user adjustment information are stored on the server as feedback data. This data is used to update the generative artificial intelligence model and improve the accuracy of future design generation.

[0039] Thus, the design generation system according to the present invention supports the development of highly innovative products while meeting the needs of the apparel and traditional crafts industries. This will enhance the competitiveness of both industries and enable the creation of new market opportunities.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] Users access the design generation system via their device and input requirements for their desired design. These requirements include specific design parameters such as color, style, materials, and elements of traditional craftsmanship.

[0043] Step 2:

[0044] The terminal collects the design requirements entered by the user as data and sends it to the server.

[0045] Step 3:

[0046] The server analyzes the received design requirements and runs a generative artificial intelligence based on them. The AI ​​model generates multiple design options and constructs them as images or 3D models.

[0047] Step 4:

[0048] The server sends the generated design proposals back to the terminal, allowing the user to visually review the design proposals on the terminal.

[0049] Step 5:

[0050] Users select their preferred design from the provided options using the interface on their device. They can then fine-tune the colors and shapes in real time as needed.

[0051] Step 6:

[0052] The device then resends the user's selections and adjustments to the server.

[0053] Step 7:

[0054] The server generates detailed design information based on the selected final design. This includes dimensions required for product development, material selection, and suggested manufacturing processes. Furthermore, it calculates the manufacturing cost and provides it to the terminal.

[0055] Step 8:

[0056] The server stores user feedback and the final design in a database, which is used to update and improve the generative artificial intelligence. This aims to improve the accuracy and efficiency of future design generation.

[0057] (Example 1)

[0058] 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."

[0059] To efficiently generate designs that meet the diverse needs of today's world, advanced customization capabilities and optimized production processes are required. However, conventional systems have limited functionality for quickly generating multiple design options based on specific user requests, and for selecting and adjusting them. Furthermore, calculating the detailed design information necessary for product development is time-consuming.

[0060] 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.

[0061] In this invention, the server includes means for generating a large number of design proposals using generative artificial intelligence, means for providing the design proposals to the user's information processing device and enabling selection and modification, and means for calculating detailed design data and manufacturing costs based on the selected design. This enables flexible design generation that can immediately respond to the demands of diverse users and streamlines the productization process.

[0062] "Generative artificial intelligence" is an artificial intelligence technology used to automatically generate designs and ideas.

[0063] A "design proposal" is a design suggestion generated based on user requirements.

[0064] An "information processing device" is an electronic device used for inputting, processing, and outputting data.

[0065] "Detailed design data" refers to information regarding specific dimensions and materials necessary for product development.

[0066] "Manufacturing costs" refer to the total cost required to produce a product.

[0067] A "prompt sentence" is an input sentence used to give instructions to a generative artificial intelligence.

[0068] "Recording" refers to the act of storing generated information and user choices for later analysis and processing.

[0069] This invention is a system that utilizes generative artificial intelligence to efficiently generate designs that meet user needs, and facilitates customization and product development. This system primarily operates through the collaborative efforts of a server, terminals, and users, with each component fulfilling the following roles.

[0070] The server plays a central role in generating numerous design proposals using generative artificial intelligence. Based on design requirements submitted by the user, the AI ​​model generates design proposals based on prompts. The software used is an advanced computing platform to run the AI ​​model. Furthermore, based on the design selected and adjusted by the user from the generated designs, the system calculates the detailed design data and manufacturing costs necessary for product development. CAD software is used at this stage to create specific dimension drawings and material lists.

[0071] The terminal functions as an interface connecting the user and the server. It sends the design requirements entered by the user to the server and visually presents the design proposals received from the server. It also provides tools on the terminal for the user to select a design and make fine adjustments.

[0072] Users access the design generation system through their terminal and input their individual design requirements. For example, if a user wants a new Japanese-style tablecloth design, they can use a prompt such as, "A modern style tablecloth that incorporates the uniquely Japanese elements of traditional crafts." The server then uses a generation AI model to generate multiple design options, which the user can select and customize, thereby streamlining product development.

[0073] This invention enables the development of innovative products that combine traditional and modern elements, allowing us to quickly respond to the diverse needs of our users.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The user accesses the design generation system using a terminal. First, the user enters design requirements into a dedicated interface. These requirements include design parameters such as desired color, style, and theme. For example, the user might enter requirements such as "Color: Blue, Style: Modern, Traditional Element: Japanese Pattern." This clarifies the user's preferences.

[0077] Step 2:

[0078] The terminal sends the entered design requirements to the server. It converts the input data into a structured format and outputs it as a request to the server. Specifically, it generates prompt statements and sends them to the server.

[0079] Step 3:

[0080] The server launches a generative AI model based on the received design requirements. The server inputs prompt messages into the generative AI model and generates multiple design options. As part of the data calculation, the AI ​​model explores design possibilities, and candidate design options are generated as output.

[0081] Step 4:

[0082] The server converts the generated design proposals into image data and 3D model data, and sends them to the terminal. Specifically, it compresses the image data and converts it into a format that the user can view. This allows the user to review the options.

[0083] Step 5:

[0084] The user checks the design proposals presented on the device interface and selects the one they like best. Specifically, the user can use zoom and simulation functions to examine the design details and make adjustments. The user's selection is entered as selection data.

[0085] Step 6:

[0086] The user makes fine adjustments to the selected design. The input here is specific adjustment information using sliders and color palettes, and the output is the final adjusted design. Specific actions include adjusting sleeve length with sliders and changing color tones with a palette.

[0087] Step 7:

[0088] The server calculates detailed design data and manufacturing costs based on the final selected design. Specifically, it uses CAD software to generate detailed dimensions and material lists, and then performs cost calculations. This provides all the information necessary for product development.

[0089] Step 8:

[0090] The server records user selection information and adjustment data and stores it as feedback data. This data is used to update the generative artificial intelligence model, contributing to improved accuracy in future design generation. Specifically, this involves entering data into the database and updating the training set for the next generation.

[0091] (Application Example 1)

[0092] 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."

[0093] Current design generation systems have the challenge of making it difficult for users to immediately understand how a design will look on an actual product when reviewing and customizing design proposals. Furthermore, there is a need for methods that expand design options without physical constraints.

[0094] 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.

[0095] In this invention, the server includes means for generating multiple design proposals using generative artificial intelligence, means for implementing the design generation system on a visual device to enable users to visually adjust and select design proposals in real space, and means for accumulating the generated designs and selection information and updating the generative artificial intelligence. This makes it possible for users to check and customize design proposals in real time in their real space.

[0096] "Generative artificial intelligence" is an artificial intelligence technology that automatically generates design proposals based on diverse design requirements from users.

[0097] A "design proposal" is a set of multiple design suggestions created by a generative artificial intelligence system according to the user's specified requirements, and these proposals are customizable.

[0098] A "user information processing device" is a device that allows users to access a design generation system and select and adjust designs.

[0099] "Detailed design information" includes specific dimensions, materials used, and manufacturing process information necessary to produce a product based on the selected design.

[0100] "Manufacturing costs" refer to the estimated and calculated cost of bringing the selected design to market.

[0101] A "visual device" is a device used by users to visually confirm and customize design proposals in real space.

[0102] "Real space" refers to the physical environment surrounding a user in their normal living environment.

[0103] This invention relates to a design generation system using generative artificial intelligence, in which the user accesses the design generation system through their information processing device. The server utilizes generative artificial intelligence to generate multiple design proposals based on the input design requirements. The generated design proposals are provided to the user's information processing device and visual device, through which the user can visually confirm the design proposals and make adjustments and selections in real space.

[0104] Smart glasses and similar devices are used as visual aids. This allows users to customize designs in real time and visually confirm the changes on the spot. The server also generates detailed design information based on the user's selected design and calculates the manufacturing costs. This allows users to intuitively understand the process of how their customized design will actually be turned into a product.

[0105] For example, if a user wants to design the interior of their home, they can use smart glasses to customize the position and style of furniture and decorations on the spot and create a new design. The design proposals generated in this way are saved and used as feedback for the server to make more refined suggestions in future design generation.

[0106] By using a generative AI model, prompts such as "a red casual jacket, with a traditional pattern, and longer sleeves" are generated and used as input information for creating design proposals. This allows the system to efficiently understand the design the user wants to realize and immediately reflect the customizations.

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The user accesses the design generation system using their information processing device. Basic design requirements, such as color and style, are entered as prompt messages. This input is then sent to the server for data processing.

[0110] Step 2:

[0111] The server activates a generative AI model based on the received prompt message and generates multiple design proposals. The generative AI model interprets the design requirements and proposes the most suitable design. The output design proposals are sent back to the user's information processing device as image data or a 3D model.

[0112] Step 3:

[0113] The terminal displays design proposals sent from the server to the user. The user uses visual devices to visualize and adjust the design proposals in real time in the real world. During this process, the user makes fine adjustments such as changing sleeve length or color, and this adjustment information is sent to the server via the terminal.

[0114] Step 4:

[0115] The server generates detailed design information from the final design selected and adjusted by the user. Specifically, dimensions, materials to be used, and manufacturing steps are determined based on the selected design proposal. At the same time, manufacturing costs are calculated, and the output information is sent to the user's information processing device.

[0116] Step 5:

[0117] Users review the detailed design information and manufacturing costs of the final design proposal and use this information to determine the feasibility of the design. Further adjustments are made as needed before the final decision is made. During this process, the generated design proposal and user adjustment information are stored on the server and used to improve the generated AI model.

[0118] 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.

[0119] This invention combines a generative artificial intelligence-based design generation system with an emotion engine to generate design proposals that take into account the user's emotional state. This system enhances user satisfaction when customizing designs using a device and enables more personalized suggestions.

[0120] The user accesses the device and enters a design request into the design generation system. The device has a built-in emotion engine that analyzes the user's facial expressions, voice tone, and other emotional indicators to detect the user's emotional state.

[0121] The server receives data from the emotion engine in conjunction with the user's design request. Based on this data, it operates a generative artificial intelligence to generate multiple design options that harmonize with the user's emotional state. In this process, for example, if the user is expressing joy, it can suggest a colorful and optimistic design, while if they are calm, it can suggest a chic and simple design.

[0122] The server sends the generated design proposals to the terminal, where the user visually reviews them. Each presented design is tailored based on the user's emotional state, providing a personalized experience.

[0123] Users can use their devices to select design proposals and make further fine adjustments. The emotion engine remains active throughout this process, and the suggested designs change in response to the user's emotional responses as they make their selections.

[0124] The final selected design is resubmitted to the server, where detailed design information for product development is generated. This information includes specific dimensions, materials, manufacturing processes, and cost details. The generated design proposals and selection process are also stored in a database and used for future updates to the generative artificial intelligence.

[0125] Thus, the system according to the present invention provides a highly personalized user experience through design generation that reflects the user's emotions. This makes it possible to support the improvement of competitiveness and the creation of new market opportunities in the apparel and traditional crafts industries.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The user accesses the terminal and enters their design requirements into the design generation system interface. These requirements include the selection of colors, styles, and traditional craft elements.

[0129] Step 2:

[0130] The device receives user input information, simultaneously analyzes the user's facial expressions and voice through its built-in camera and microphone, and sends emotional data to the emotion engine.

[0131] Step 3:

[0132] The device's emotion engine determines the user's current emotional state based on the analysis results. For example, if the user is excited, it will be recorded as a positive emotion.

[0133] Step 4:

[0134] The server integrates design requests and emotional state data received from the terminal and uses generative artificial intelligence to generate design proposals that are in harmony with the emotional state. In the case of a positive emotional state, it selects designs with bright colors and innovative styles.

[0135] Step 5:

[0136] The server sends multiple generated design options to the terminal. This allows the user to review design choices that match their specified emotion.

[0137] Step 6:

[0138] The user reviews the presented design proposals on their device, and is then presented with design options that incorporate feedback from the emotion engine. They select their preferred design and adjust the details as needed. Their emotional state is also considered during this process, providing optimal adjustment support.

[0139] Step 7:

[0140] The terminal sends the final selected design and its adjustment information to the server.

[0141] Step 8:

[0142] The server creates detailed product design information based on the selected design. This includes specific dimensions, materials to be used, and suggested manufacturing processes. It also calculates manufacturing costs and provides this information to the terminal.

[0143] Step 9:

[0144] The server stores generated design proposals, selection processes, and user sentiment data to help improve future generative artificial intelligence learning and sentiment engines.

[0145] This allows users to generate more satisfying designs through personalized, emotion-based experiences.

[0146] (Example 2)

[0147] 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".

[0148] Conventional design generation systems struggle to propose designs that take into account the individual emotional states of users, resulting in uniform design proposals that fail to improve user satisfaction. Furthermore, they cannot respond to changes in users' emotions during the design selection process, making it impossible to provide proposals that better meet individual needs.

[0149] 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.

[0150] In this invention, the server includes means for generating multiple design proposals using generative artificial intelligence, means for analyzing the user's emotional state and adjusting the provided design proposals based on that analysis, and means for accumulating the generated designs and user selection information and updating the generative artificial intelligence. This enables personalized design suggestions that respond to the user's emotions, thereby improving user satisfaction.

[0151] "Generative artificial intelligence" is a general term for artificial intelligence technologies used to automatically generate design proposals. It uses machine learning algorithms to create new designs based on user requests and data.

[0152] A "design proposal" refers to a specific design option presented to the user from among several candidate designs proposed by generative artificial intelligence.

[0153] "Emotional state" refers to the psychological and emotional conditions that users exhibit when selecting designs or interacting with them, and this is analyzed by the emotion engine.

[0154] "Detailed design information" refers to a collection of design data that is concretized based on the selected design proposal, and includes important elements such as dimensions, materials, manufacturing processes, and costs.

[0155] An "emotion engine" is a technology that analyzes a user's facial expressions, voice tone, and other emotional indicators, and plays a role in detecting the user's emotional state.

[0156] This invention is a system that takes into account the user's emotional state and provides highly personalized design suggestions. The system mainly consists of terminals and servers, and utilizes generative artificial intelligence and an emotion engine to provide users with a unique design experience.

[0157] First, the user accesses the design generation system using a terminal and enters a design request. At this time, the user can freely enter an outline of the desired design and specific requirements. The terminal has a built-in emotion engine that analyzes the user's facial expressions and voice tone in real time to detect the user's emotional state. This information is sent to the server along with the design request.

[0158] The server activates generative artificial intelligence based on the received emotion data and design request. The generative AI utilizes the accumulated database and past design selection information to generate multiple design options. In this process, a design that reflects the user's emotional state is selected; for example, if the user is expressing calm emotions, a design with soft colors will be suggested.

[0159] The generated design proposals are sent from the server to the terminal, where the user can visually review them. The user selects their preferred design from several options and makes further adjustments as needed. The emotion engine continues to work throughout this adjustment process, optimizing the suggestions based on the user's changing emotions.

[0160] Once the final design is decided, the proposal is resubmitted to the server, and detailed design information is generated. This information includes specific dimensions, materials, manufacturing processes, and cost information. Furthermore, all generated design proposals and selection processes are stored in a database and used to train subsequent generative artificial intelligence models.

[0161] For example, if a user wants a modern interior design for a home party, they would request "modern and simple interior design" at the terminal. If the emotion engine detects a relaxed emotion, the generated design proposal is expected to include furniture arrangements in soft colors and minimalist decorations.

[0162] Examples of prompt statements include:

[0163] "I'd like a modern and simple interior design. Please suggest furniture and decorations in soft colors to create a relaxed atmosphere."

[0164] The following sentences are possible.

[0165] This system aims to improve user satisfaction by generating designs that incorporate individual user emotions, and to support enhanced competitiveness and the creation of new markets in the apparel industry and traditional crafts sector.

[0166] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0167] Step 1:

[0168] The user accesses the terminal and enters a design request into the design generation system. The user enters specific themes and preferences as prompts. Based on this input, the system sets the initial conditions for design generation and begins extracting the necessary requirements. The output is the initial conditions set based on the user's requests.

[0169] Step 2:

[0170] The device uses its built-in emotion engine to analyze the user's facial expressions and voice tone. Input is real-time data from the device's camera and microphone, and output is the detection of the user's emotional state. This emotional state is sent to the server as data to be considered when generating design proposals.

[0171] Step 3:

[0172] The server receives design requests and emotion data from the terminal. Using the received data as input, it activates a generative AI model, accesses a database, and refers to similar cases. Based on this, multiple design options are automatically generated. The output consists of multiple design options that harmonize with the user's emotions.

[0173] Step 4:

[0174] The server sends the generated design proposals to the terminal, allowing the user to visually review them. The output here is the design proposals converted into a viewable format. Users can view and select from these on the screen. Specifically, the user reviews the design proposals by scrolling through them within the user interface.

[0175] Step 5:

[0176] The user selects one of the presented design options and makes minor adjustments as needed. During the adjustment process, the user's emotional state is analyzed again, and the system modifies the suggestions in real time. The input is the user's selection and the emotional data at that time, and the output is the adjusted design option.

[0177] Step 6:

[0178] Based on the final design proposal resubmitted to the server, detailed design information is generated. This step outputs design data that includes specific dimensions, materials, manufacturing processes, and cost information. The generated design proposals and selection process data are also stored in a database and used to improve the AI ​​model later.

[0179] (Application Example 2)

[0180] 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 device 14 will be referred to as the "terminal."

[0181] Modern users desire more personalized design suggestions based on their emotions. However, existing design generation systems fail to adequately consider the user's emotional state, resulting in a limited quality of user experience. There is a need to provide a more intuitive and satisfying customized experience by automatically generating designs that align with the user's emotions.

[0182] 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.

[0183] In this invention, the server includes means for generating multiple design proposals using generative artificial intelligence, means for analyzing emotional indicators such as the user's facial expression data to identify their emotional state, and means for generating design proposals that are in harmony with the user's emotions based on the emotional state. This makes it possible to propose designs that take the user's emotions into consideration.

[0184] "Generative artificial intelligence" is an artificial intelligence technology that can generate new designs and content based on given conditions and information.

[0185] A "design proposal" is a specific design suggestion that reflects the user's requirements and feelings, and is subject to selection and adjustment.

[0186] "Emotional indicators" are physiological or psychological data used to indicate an emotional state, such as a user's facial expressions or voice tone.

[0187] "Emotional state" refers to the emotional state of a user at a specific moment and is information that has a significant impact on design generation.

[0188] A "communication terminal" is a device used by a user to access a design generation system, and includes computing devices and personal information terminals.

[0189] "Detailed design information" refers to technical information such as dimensions, materials, manufacturing processes, and costs necessary to realize the selected design proposal.

[0190] "Visual information" refers to information that includes images and visual elements presented to the user on a screen or display.

[0191] "Three-dimensional structure" refers to data formats and models used to represent design proposals in three dimensions, provided in a way that users can understand spatially.

[0192] The system for realizing this invention mainly consists of a server, a user's communication terminal, generative artificial intelligence, and an emotion engine.

[0193] The server provides a platform for generating multiple design options using generative artificial intelligence. Based on a pre-trained model, the generative AI generates design options that match the user's requirements and emotions. This AI model is designed using the latest deep learning technology.

[0194] The user's communication terminal is equipped with input devices such as a camera and microphone. Through these input devices, emotional indicators (such as facial expressions and tone of voice) are transmitted to the emotion engine. The emotion engine uses software such as the Emotion SDK to analyze the user's emotional state in real time. The analyzed emotional data is sent to a server. This data is input as a prompt to generative artificial intelligence, which generates design proposals appropriate to the user's emotional state.

[0195] Users can review the design proposals generated on their communication terminal and select and adjust designs from those displayed as visual information. During this process, changes in the user's emotions are also analyzed by the emotion engine, and the final proposal is continuously refined.

[0196] As a concrete example, when a user uses the application for online shopping, their facial expression is detected by the camera. If they appear cheerful, the application suggests colorful and vibrant clothing designs. Conversely, if they appear calm, it presents simple and elegant designs. By using a prompt such as, "The user's emotional state indicates joy. Please suggest a vibrant and colorful dress design for a party," it becomes possible to make suggestions that match their emotions.

[0197] In this way, the system of the present invention enables personalized design suggestions that take user emotions into consideration, thereby contributing to an improved user experience.

[0198] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0199] Step 1:

[0200] The user inputs their emotional data through the camera and microphone on the device. The device uses these devices to acquire emotional indicators such as facial expressions and tone of voice, and sends this data to the emotion engine.

[0201] Step 2:

[0202] The emotion engine analyzes emotional data received from the device to identify the user's emotional state. Using the Emotion SDK, it analyzes facial expression features and voice frequency patterns to output specific emotion tags such as joy, calmness, and surprise.

[0203] Step 3:

[0204] The server receives emotional state data obtained from the emotion engine. This is converted into prompt text for the generative AI model and used as input to generate design suggestions based on the user's emotions. The prompt used is: "The user's emotional state indicates joy. Please suggest a design for a glamorous and colorful dress for a party."

[0205] Step 4:

[0206] Generative artificial intelligence generates design proposals based on prompt text, creating multiple design options. The generated design proposals reflect the user's emotional state in terms of visual elements such as color, shape, and style.

[0207] Step 5:

[0208] The server sends the generated design proposals to the terminal, allowing the user to review the design visually. The user reviews the proposed design on the terminal, selects their preferred options, and makes adjustments as needed.

[0209] Step 6:

[0210] The design selected by the user is sent back to the server from the terminal. The server generates detailed design information based on the selected design and calculates the final manufacturing cost. This includes details such as dimensions, materials, and manufacturing process.

[0211] Step 7:

[0212] The server stores the generated designs and user selection information in a database, which is then used as training data for the next generation of generative artificial intelligence. The stored information is used to improve the accuracy of the generative AI model.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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".

[0229] This invention relates to a design generation system that efficiently generates design proposals using generative artificial intelligence, enabling the customization and commercialization of designs according to user needs.

[0230] Users access the design generation system using a terminal and input their desired design requirements through a dedicated interface. These requirements can include multiple design parameters such as color, style, and elements of traditional craftsmanship.

[0231] The server receives requests from users and activates generative artificial intelligence to generate multiple design options. This allows users to consider various options to find the design that best suits them. The generated design options are sent to the terminal as images or 3D models for visual review by the user.

[0232] Users can select their favorite design from several options presented on their device and make fine adjustments using the provided tools. For example, with a jacket design, they can change the sleeve length and color, adjust the pattern placement, and more in real time.

[0233] The server generates detailed design information necessary for product development based on the design ultimately selected by the user. This information includes specific dimensions, materials to be used, and manufacturing steps. Cost calculations related to manufacturing are also performed simultaneously and provided to the user.

[0234] Furthermore, the selected design proposals and user adjustment information are stored on the server as feedback data. This data is used to update the generative artificial intelligence model and improve the accuracy of future design generation.

[0235] Thus, the design generation system according to the present invention supports the development of highly innovative products while meeting the needs of the apparel and traditional crafts industries. This will enhance the competitiveness of both industries and enable the creation of new market opportunities.

[0236] The following describes the processing flow.

[0237] Step 1:

[0238] Users access the design generation system via their device and input requirements for their desired design. These requirements include specific design parameters such as color, style, materials, and elements of traditional craftsmanship.

[0239] Step 2:

[0240] The terminal collects the design requirements entered by the user as data and sends it to the server.

[0241] Step 3:

[0242] The server analyzes the received design requirements and runs a generative artificial intelligence based on them. The AI ​​model generates multiple design options and constructs them as images or 3D models.

[0243] Step 4:

[0244] The server sends the generated design proposals back to the terminal, allowing the user to visually review the design proposals on the terminal.

[0245] Step 5:

[0246] Users select their preferred design from the provided options using the interface on their device. They can then fine-tune the colors and shapes in real time as needed.

[0247] Step 6:

[0248] The device then resends the user's selections and adjustments to the server.

[0249] Step 7:

[0250] The server generates detailed design information based on the selected final design. This includes dimensions required for product development, material selection, and suggested manufacturing processes. Furthermore, it calculates the manufacturing cost and provides it to the terminal.

[0251] Step 8:

[0252] The server stores user feedback and the final design in a database, which is used to update and improve the generative artificial intelligence. This aims to improve the accuracy and efficiency of future design generation.

[0253] (Example 1)

[0254] 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."

[0255] To efficiently generate designs that meet the diverse needs of today's world, advanced customization capabilities and optimized production processes are required. However, conventional systems have limited functionality for quickly generating multiple design options based on specific user requests, and for selecting and adjusting them. Furthermore, calculating the detailed design information necessary for product development is time-consuming.

[0256] 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.

[0257] In this invention, the server includes means for generating a large number of design proposals using generative artificial intelligence, means for providing the design proposals to the user's information processing device and enabling selection and modification, and means for calculating detailed design data and manufacturing costs based on the selected design. This enables flexible design generation that can immediately respond to the demands of diverse users and streamlines the productization process.

[0258] "Generative artificial intelligence" is an artificial intelligence technology used to automatically generate designs and ideas.

[0259] A "design proposal" is a design suggestion generated based on user requirements.

[0260] An "information processing device" is an electronic device used for inputting, processing, and outputting data.

[0261] "Detailed design data" refers to information regarding specific dimensions and materials necessary for product development.

[0262] "Manufacturing costs" refer to the total cost required to produce a product.

[0263] A "prompt sentence" is an input sentence used to give instructions to a generative artificial intelligence.

[0264] "Recording" refers to the act of storing generated information and user choices for later analysis and processing.

[0265] This invention is a system that utilizes generative artificial intelligence to efficiently generate designs that meet user needs, and facilitates customization and product development. This system primarily operates through the collaborative efforts of a server, terminals, and users, with each component fulfilling the following roles.

[0266] The server plays a central role in generating numerous design proposals using generative artificial intelligence. Based on design requirements submitted by the user, the AI ​​model generates design proposals based on prompts. The software used is an advanced computing platform to run the AI ​​model. Furthermore, based on the design selected and adjusted by the user from the generated designs, the system calculates the detailed design data and manufacturing costs necessary for product development. CAD software is used at this stage to create specific dimension drawings and material lists.

[0267] The terminal functions as an interface connecting the user and the server. It sends the design requirements entered by the user to the server and visually presents the design proposals received from the server. It also provides tools on the terminal for the user to select a design and make fine adjustments.

[0268] Users access the design generation system through their terminal and input their individual design requirements. For example, if a user wants a new Japanese-style tablecloth design, they can use a prompt such as, "A modern style tablecloth that incorporates the uniquely Japanese elements of traditional crafts." The server then uses a generation AI model to generate multiple design options, which the user can select and customize, thereby streamlining product development.

[0269] This invention enables the development of innovative products that combine traditional and modern elements, allowing us to quickly respond to the diverse needs of our users.

[0270] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0271] Step 1:

[0272] The user accesses the design generation system using a terminal. First, the user enters design requirements into a dedicated interface. These requirements include design parameters such as desired color, style, and theme. For example, the user might enter requirements such as "Color: Blue, Style: Modern, Traditional Element: Japanese Pattern." This clarifies the user's preferences.

[0273] Step 2:

[0274] The terminal sends the entered design requirements to the server. It converts the input data into a structured format and outputs it as a request to the server. Specifically, it generates prompt statements and sends them to the server.

[0275] Step 3:

[0276] The server activates the generative AI model based on the received design requirements. The server inputs the prompt text into the generative AI model to generate multiple design proposals. As a data operation, the exploration of design possibilities by the AI model is carried out, and candidate design proposals are generated as the output.

[0277] Step 4:

[0278] The server converts the generated design proposals into image data or three-dimensional model data and transmits them to the terminal. The specific operation is a process of compressing the image data and converting it into a format that can be browsed by the user. Thereby, the user can check the options.

[0279] Step 5:

[0280] The user checks the design proposals presented on the terminal interface and selects the most liked proposal. As a specific operation, it is possible for the user to check the details of the design and make adjustments using the zoom-in or simulation functions. The user's selection is input as selection data.

[0281] Step 6:

[0282] The user makes fine adjustments to the selected design. The input here is specific adjustment information using sliders or color palettes, and the final adjusted design is generated as the output. The specific operation is an operation of adjusting the sleeve length with a slider or changing the color tone with a palette.

[0283] Step 7:

[0284] The server calculates the detailed design data and manufacturing costs based on the finally selected design. Specifically, detailed dimensions and a material list are generated using CAD software, and further cost calculations are performed. Thereby, all the information required for productization is obtained.

[0285] Step 8:

[0286] The server records the user's selection information and adjustment data and stores it as feedback data. This data is used to update the model of generative artificial intelligence and contributes to improving the accuracy of the next design generation. The specific operations are data entry into the database and updating the learning set for the next use.

[0287] (Application Example 1)

[0288] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0289] In the current design generation system, when the user checks and customizes the design proposal, there is a problem that it is difficult to immediately understand how the actual product will look. Also, a method for expanding the design options without physical constraints is required.

[0290] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0291] In this invention, the server includes means for generating a plurality of design proposals using generative artificial intelligence, means for implementing the design generation system on a visual device so that the user can visually adjust and select the design proposal in the real space, and means for accumulating the generated design and selection information and updating the generative artificial intelligence. As a result, it becomes possible for the user to confirm and customize the design proposal in real time in the user's real space.

[0292] "Generative artificial intelligence" is an artificial intelligence technology that automatically generates design proposals based on various design requirements from users.

[0293] The "design proposal" is a proposal of a plurality of designs created by the generative artificial intelligence according to the requirements specified by the user and can be customized.

[0294] A "user information processing device" is a device that allows users to access a design generation system and select and adjust designs.

[0295] "Detailed design information" includes specific dimensions, materials used, and manufacturing process information necessary to produce a product based on the selected design.

[0296] "Manufacturing costs" refer to the estimated and calculated cost of bringing the selected design to market.

[0297] A "visual device" is a device used by users to visually confirm and customize design proposals in real space.

[0298] "Real space" refers to the physical environment surrounding a user in their normal living environment.

[0299] This invention relates to a design generation system using generative artificial intelligence, in which the user accesses the design generation system through their information processing device. The server utilizes generative artificial intelligence to generate multiple design proposals based on the input design requirements. The generated design proposals are provided to the user's information processing device and visual device, through which the user can visually confirm the design proposals and make adjustments and selections in real space.

[0300] Smart glasses and similar devices are used as visual aids. This allows users to customize designs in real time and visually confirm the changes on the spot. The server also generates detailed design information based on the user's selected design and calculates the manufacturing costs. This allows users to intuitively understand the process of how their customized design will actually be turned into a product.

[0301] As a specific example, when a user desires to design the interior of a house, it is possible to use smart glasses to customize the position and style of furniture and decorations on-site and create a new design. The design plan thus generated is saved and utilized as feedback for the server to make more refined proposals in future design generation.

[0302] Using a generative AI model, a prompt sentence such as "a red casual jacket with traditional patterns and long sleeves" is generated and utilized as input information for creating a design plan. This enables the system to efficiently understand the design that the user wishes to materialize and immediately reflect the customization.

[0303] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0304] Step 1:

[0305] The user accesses the design generation system using the user's information processing device. As basic requirements for the design, colors, styles, etc. are input as a prompt sentence. This input is sent to the server for data processing.

[0306] Step 2:

[0307] The server activates the generative AI model based on the received prompt sentence and generates multiple design plans. The generative AI model interprets the design requirements and proposes the most suitable design. The output design plans are sent back to the user's information processing device as image data or a three-dimensional model.

[0308] Step 3:

[0309] The terminal displays design proposals sent from the server to the user. The user uses visual devices to visualize and adjust the design proposals in real time in the real world. During this process, the user makes fine adjustments such as changing sleeve length or color, and this adjustment information is sent to the server via the terminal.

[0310] Step 4:

[0311] The server generates detailed design information from the final design selected and adjusted by the user. Specifically, dimensions, materials to be used, and manufacturing steps are determined based on the selected design proposal. At the same time, manufacturing costs are calculated, and the output information is sent to the user's information processing device.

[0312] Step 5:

[0313] Users review the detailed design information and manufacturing costs of the final design proposal and use this information to determine the feasibility of the design. Further adjustments are made as needed before the final decision is made. During this process, the generated design proposal and user adjustment information are stored on the server and used to improve the generated AI model.

[0314] 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.

[0315] This invention combines a generative artificial intelligence-based design generation system with an emotion engine to generate design proposals that take into account the user's emotional state. This system enhances user satisfaction when customizing designs using a device and enables more personalized suggestions.

[0316] The user accesses the device and enters a design request into the design generation system. The device has a built-in emotion engine that analyzes the user's facial expressions, voice tone, and other emotional indicators to detect the user's emotional state.

[0317] The server receives data from the emotion engine in conjunction with the user's design request. Based on this data, it operates a generative artificial intelligence to generate multiple design options that harmonize with the user's emotional state. In this process, for example, if the user is expressing joy, it can suggest a colorful and optimistic design, while if they are calm, it can suggest a chic and simple design.

[0318] The server sends the generated design proposals to the terminal, where the user visually reviews them. Each presented design is tailored based on the user's emotional state, providing a personalized experience.

[0319] Users can use their devices to select design proposals and make further fine adjustments. The emotion engine remains active throughout this process, and the suggested designs change in response to the user's emotional responses as they make their selections.

[0320] The final selected design is resubmitted to the server, where detailed design information for product development is generated. This information includes specific dimensions, materials, manufacturing processes, and cost details. The generated design proposals and selection process are also stored in a database and used for future updates to the generative artificial intelligence.

[0321] Thus, the system according to the present invention provides a highly personalized user experience through design generation that reflects the user's emotions. This makes it possible to support the improvement of competitiveness and the creation of new market opportunities in the apparel and traditional crafts industries.

[0322] The following describes the processing flow.

[0323] Step 1:

[0324] The user accesses the terminal and enters their design requirements into the design generation system interface. These requirements include the selection of colors, styles, and traditional craft elements.

[0325] Step 2:

[0326] The device receives user input information, simultaneously analyzes the user's facial expressions and voice through its built-in camera and microphone, and sends emotional data to the emotion engine.

[0327] Step 3:

[0328] The device's emotion engine determines the user's current emotional state based on the analysis results. For example, if the user is excited, it will be recorded as a positive emotion.

[0329] Step 4:

[0330] The server integrates design requests and emotional state data received from the terminal and uses generative artificial intelligence to generate design proposals that are in harmony with the emotional state. In the case of a positive emotional state, it selects designs with bright colors and innovative styles.

[0331] Step 5:

[0332] The server sends multiple generated design options to the terminal. This allows the user to review design choices that match their specified emotion.

[0333] Step 6:

[0334] The user reviews the presented design proposals on their device, and is then presented with design options that incorporate feedback from the emotion engine. They select their preferred design and adjust the details as needed. Their emotional state is also considered during this process, providing optimal adjustment support.

[0335] Step 7:

[0336] The terminal sends the final selected design and its adjustment information to the server.

[0337] Step 8:

[0338] The server creates detailed product design information based on the selected design. This includes specific dimensions, materials to be used, and suggested manufacturing processes. It also calculates manufacturing costs and provides this information to the terminal.

[0339] Step 9:

[0340] The server stores generated design proposals, selection processes, and user sentiment data to help improve future generative artificial intelligence learning and sentiment engines.

[0341] This allows users to generate more satisfying designs through personalized, emotion-based experiences.

[0342] (Example 2)

[0343] 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".

[0344] Conventional design generation systems struggle to propose designs that take into account the individual emotional states of users, resulting in uniform design proposals that fail to improve user satisfaction. Furthermore, they cannot respond to changes in users' emotions during the design selection process, making it impossible to provide proposals that better meet individual needs.

[0345] 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.

[0346] In this invention, the server includes means for generating multiple design proposals using generative artificial intelligence, means for analyzing the user's emotional state and adjusting the provided design proposals based on that analysis, and means for accumulating the generated designs and user selection information and updating the generative artificial intelligence. This enables personalized design suggestions that respond to the user's emotions, thereby improving user satisfaction.

[0347] "Generative artificial intelligence" is a general term for artificial intelligence technologies used to automatically generate design proposals. It uses machine learning algorithms to create new designs based on user requests and data.

[0348] A "design proposal" refers to a specific design option presented to the user from among several candidate designs proposed by generative artificial intelligence.

[0349] "Emotional state" refers to the psychological and emotional conditions that users exhibit when selecting designs or interacting with them, and this is analyzed by the emotion engine.

[0350] "Detailed design information" refers to a collection of design data that is concretized based on the selected design proposal, and includes important elements such as dimensions, materials, manufacturing processes, and costs.

[0351] An "emotion engine" is a technology that analyzes a user's facial expressions, voice tone, and other emotional indicators, and plays a role in detecting the user's emotional state.

[0352] This invention is a system that takes into account the user's emotional state and provides highly personalized design suggestions. The system mainly consists of terminals and servers, and utilizes generative artificial intelligence and an emotion engine to provide users with a unique design experience.

[0353] First, the user accesses the design generation system using a terminal and enters a design request. At this time, the user can freely enter an outline of the desired design and specific requirements. The terminal has a built-in emotion engine that analyzes the user's facial expressions and voice tone in real time to detect the user's emotional state. This information is sent to the server along with the design request.

[0354] The server activates generative artificial intelligence based on the received emotion data and design request. The generative AI utilizes the accumulated database and past design selection information to generate multiple design options. In this process, a design that reflects the user's emotional state is selected; for example, if the user is expressing calm emotions, a design with soft colors will be suggested.

[0355] The generated design proposals are sent from the server to the terminal, where the user can visually review them. The user selects their preferred design from several options and makes further adjustments as needed. The emotion engine continues to work throughout this adjustment process, optimizing the suggestions based on the user's changing emotions.

[0356] Once the final design is decided, the proposal is resubmitted to the server, and detailed design information is generated. This information includes specific dimensions, materials, manufacturing processes, and cost information. Furthermore, all generated design proposals and selection processes are stored in a database and used to train subsequent generative artificial intelligence models.

[0357] For example, if a user wants a modern interior design for a home party, they would request "modern and simple interior design" at the terminal. If the emotion engine detects a relaxed emotion, the generated design proposal is expected to include furniture arrangements in soft colors and minimalist decorations.

[0358] Examples of prompt statements include:

[0359] "I'd like a modern and simple interior design. Please suggest furniture and decorations in soft colors to create a relaxed atmosphere."

[0360] The following sentences are possible.

[0361] This system aims to improve user satisfaction by generating designs that incorporate individual user emotions, and to support enhanced competitiveness and the creation of new markets in the apparel industry and traditional crafts sector.

[0362] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0363] Step 1:

[0364] The user accesses the terminal and enters a design request into the design generation system. The user enters specific themes and preferences as prompts. Based on this input, the system sets the initial conditions for design generation and begins extracting the necessary requirements. The output is the initial conditions set based on the user's requests.

[0365] Step 2:

[0366] The device uses its built-in emotion engine to analyze the user's facial expressions and voice tone. Input is real-time data from the device's camera and microphone, and output is the detection of the user's emotional state. This emotional state is sent to the server as data to be considered when generating design proposals.

[0367] Step 3:

[0368] The server receives design requests and emotion data from the terminal. Using the received data as input, it activates a generative AI model, accesses a database, and refers to similar cases. Based on this, multiple design options are automatically generated. The output consists of multiple design options that harmonize with the user's emotions.

[0369] Step 4:

[0370] The server sends the generated design proposals to the terminal, allowing the user to visually review them. The output here is the design proposals converted into a viewable format. Users can view and select from these on the screen. Specifically, the user reviews the design proposals by scrolling through them within the user interface.

[0371] Step 5:

[0372] The user selects one of the presented design options and makes minor adjustments as needed. During the adjustment process, the user's emotional state is analyzed again, and the system modifies the suggestions in real time. The input is the user's selection and the emotional data at that time, and the output is the adjusted design option.

[0373] Step 6:

[0374] Based on the final design proposal resubmitted to the server, detailed design information is generated. This step outputs design data that includes specific dimensions, materials, manufacturing processes, and cost information. The generated design proposals and selection process data are also stored in a database and used to improve the AI ​​model later.

[0375] (Application Example 2)

[0376] 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."

[0377] Modern users desire more personalized design suggestions based on their emotions. However, existing design generation systems fail to adequately consider the user's emotional state, resulting in a limited quality of user experience. There is a need to provide a more intuitive and satisfying customized experience by automatically generating designs that align with the user's emotions.

[0378] 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.

[0379] In this invention, the server includes means for generating multiple design proposals using generative artificial intelligence, means for analyzing emotional indicators such as the user's facial expression data to identify their emotional state, and means for generating design proposals that are in harmony with the user's emotions based on the emotional state. This makes it possible to propose designs that take the user's emotions into consideration.

[0380] "Generative artificial intelligence" is an artificial intelligence technology that can generate new designs and content based on given conditions and information.

[0381] A "design proposal" is a specific design suggestion that reflects the user's requirements and feelings, and is subject to selection and adjustment.

[0382] "Emotional indicators" are physiological or psychological data used to indicate an emotional state, such as a user's facial expressions or voice tone.

[0383] "Emotional state" refers to the emotional state of a user at a specific moment and is information that has a significant impact on design generation.

[0384] A "communication terminal" is a device used by a user to access a design generation system, and includes computing devices and personal information terminals.

[0385] "Detailed design information" refers to technical information such as dimensions, materials, manufacturing processes, and costs necessary to realize the selected design proposal.

[0386] "Visual information" refers to information that includes images and visual elements presented to the user on a screen or display.

[0387] "Three-dimensional structure" refers to data formats and models used to represent design proposals in three dimensions, provided in a way that users can understand spatially.

[0388] The system for realizing this invention mainly consists of a server, a user's communication terminal, generative artificial intelligence, and an emotion engine.

[0389] The server provides a platform for generating multiple design options using generative artificial intelligence. Based on a pre-trained model, the generative AI generates design options that match the user's requirements and emotions. This AI model is designed using the latest deep learning technology.

[0390] The user's communication terminal is equipped with input devices such as a camera and microphone. Through these input devices, emotional indicators (such as facial expressions and tone of voice) are transmitted to the emotion engine. The emotion engine uses software such as the Emotion SDK to analyze the user's emotional state in real time. The analyzed emotional data is sent to a server. This data is input as a prompt to generative artificial intelligence, which generates design proposals appropriate to the user's emotional state.

[0391] Users can review the design proposals generated on their communication terminal and select and adjust designs from those displayed as visual information. During this process, changes in the user's emotions are also analyzed by the emotion engine, and the final proposal is continuously refined.

[0392] As a concrete example, when a user uses the application for online shopping, their facial expression is detected by the camera. If they appear cheerful, the application suggests colorful and vibrant clothing designs. Conversely, if they appear calm, it presents simple and elegant designs. By using a prompt such as, "The user's emotional state indicates joy. Please suggest a vibrant and colorful dress design for a party," it becomes possible to make suggestions that match their emotions.

[0393] In this way, the system of the present invention enables personalized design suggestions that take user emotions into consideration, thereby contributing to an improved user experience.

[0394] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0395] Step 1:

[0396] The user inputs their emotional data through the camera and microphone on the device. The device uses these devices to acquire emotional indicators such as facial expressions and tone of voice, and sends this data to the emotion engine.

[0397] Step 2:

[0398] The emotion engine analyzes emotional data received from the device to identify the user's emotional state. Using the Emotion SDK, it analyzes facial expression features and voice frequency patterns to output specific emotion tags such as joy, calmness, and surprise.

[0399] Step 3:

[0400] The server receives emotional state data obtained from the emotion engine. This is converted into prompt text for the generative AI model and used as input to generate design suggestions based on the user's emotions. The prompt used is: "The user's emotional state indicates joy. Please suggest a design for a glamorous and colorful dress for a party."

[0401] Step 4:

[0402] Generative artificial intelligence generates design proposals based on prompt text, creating multiple design options. The generated design proposals reflect the user's emotional state in terms of visual elements such as color, shape, and style.

[0403] Step 5:

[0404] The server sends the generated design proposals to the terminal, allowing the user to review the design visually. The user reviews the proposed design on the terminal, selects their preferred options, and makes adjustments as needed.

[0405] Step 6:

[0406] The design selected by the user is sent back to the server from the terminal. The server generates detailed design information based on the selected design and calculates the final manufacturing cost. This includes details such as dimensions, materials, and manufacturing process.

[0407] Step 7:

[0408] The server stores the generated designs and user selection information in a database, which is then used as training data for the next generation of generative artificial intelligence. The stored information is used to improve the accuracy of the generative AI model.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] [Third Embodiment]

[0413] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0414] 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.

[0415] 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).

[0416] 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.

[0417] 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.

[0418] 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).

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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".

[0425] This invention relates to a design generation system that efficiently generates design proposals using generative artificial intelligence, enabling the customization and commercialization of designs according to user needs.

[0426] Users access the design generation system using a terminal and input their desired design requirements through a dedicated interface. These requirements can include multiple design parameters such as color, style, and elements of traditional craftsmanship.

[0427] The server receives requests from users and activates generative artificial intelligence to generate multiple design options. This allows users to consider various options to find the design that best suits them. The generated design options are sent to the terminal as images or 3D models for visual review by the user.

[0428] Users can select their favorite design from several options presented on their device and make fine adjustments using the provided tools. For example, with a jacket design, they can change the sleeve length and color, adjust the pattern placement, and more in real time.

[0429] The server generates detailed design information necessary for product development based on the design ultimately selected by the user. This information includes specific dimensions, materials to be used, and manufacturing steps. Cost calculations related to manufacturing are also performed simultaneously and provided to the user.

[0430] Furthermore, the selected design proposals and user adjustment information are stored on the server as feedback data. This data is used to update the generative artificial intelligence model and improve the accuracy of future design generation.

[0431] Thus, the design generation system according to the present invention supports the development of highly innovative products while meeting the needs of the apparel and traditional crafts industries. This will enhance the competitiveness of both industries and enable the creation of new market opportunities.

[0432] The following describes the processing flow.

[0433] Step 1:

[0434] Users access the design generation system via their device and input requirements for their desired design. These requirements include specific design parameters such as color, style, materials, and elements of traditional craftsmanship.

[0435] Step 2:

[0436] The terminal collects the design requirements entered by the user as data and sends it to the server.

[0437] Step 3:

[0438] The server analyzes the received design requirements and runs a generative artificial intelligence based on them. The AI ​​model generates multiple design options and constructs them as images or 3D models.

[0439] Step 4:

[0440] The server sends the generated design proposals back to the terminal, allowing the user to visually review the design proposals on the terminal.

[0441] Step 5:

[0442] Users select their preferred design from the provided options using the interface on their device. They can then fine-tune the colors and shapes in real time as needed.

[0443] Step 6:

[0444] The device then resends the user's selections and adjustments to the server.

[0445] Step 7:

[0446] The server generates detailed design information based on the selected final design. This includes dimensions required for product development, material selection, and suggested manufacturing processes. Furthermore, it calculates the manufacturing cost and provides it to the terminal.

[0447] Step 8:

[0448] The server stores user feedback and the final design in a database, which is used to update and improve the generative artificial intelligence. This aims to improve the accuracy and efficiency of future design generation.

[0449] (Example 1)

[0450] 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."

[0451] To efficiently generate designs that meet the diverse needs of today's world, advanced customization capabilities and optimized production processes are required. However, conventional systems have limited functionality for quickly generating multiple design options based on specific user requests, and for selecting and adjusting them. Furthermore, calculating the detailed design information necessary for product development is time-consuming.

[0452] 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.

[0453] In this invention, the server includes means for generating a large number of design proposals using generative artificial intelligence, means for providing the design proposals to the user's information processing device and enabling selection and modification, and means for calculating detailed design data and manufacturing costs based on the selected design. This enables flexible design generation that can immediately respond to the demands of diverse users and streamlines the productization process.

[0454] "Generative artificial intelligence" is an artificial intelligence technology used to automatically generate designs and ideas.

[0455] A "design proposal" is a design suggestion generated based on user requirements.

[0456] An "information processing device" is an electronic device used for inputting, processing, and outputting data.

[0457] "Detailed design data" refers to information regarding specific dimensions and materials necessary for product development.

[0458] "Manufacturing costs" refer to the total cost required to produce a product.

[0459] A "prompt sentence" is an input sentence used to give instructions to a generative artificial intelligence.

[0460] "Recording" refers to the act of storing generated information and user choices for later analysis and processing.

[0461] This invention is a system that utilizes generative artificial intelligence to efficiently generate designs that meet user needs, and facilitates customization and product development. This system primarily operates through the collaborative efforts of a server, terminals, and users, with each component fulfilling the following roles.

[0462] The server plays a central role in generating numerous design proposals using generative artificial intelligence. Based on design requirements submitted by the user, the AI ​​model generates design proposals based on prompts. The software used is an advanced computing platform to run the AI ​​model. Furthermore, based on the design selected and adjusted by the user from the generated designs, the system calculates the detailed design data and manufacturing costs necessary for product development. CAD software is used at this stage to create specific dimension drawings and material lists.

[0463] The terminal functions as an interface connecting the user and the server. It sends the design requirements entered by the user to the server and visually presents the design proposals received from the server. It also provides tools on the terminal for the user to select a design and make fine adjustments.

[0464] Users access the design generation system through their terminal and input their individual design requirements. For example, if a user wants a new Japanese-style tablecloth design, they can use a prompt such as, "A modern style tablecloth that incorporates the uniquely Japanese elements of traditional crafts." The server then uses a generation AI model to generate multiple design options, which the user can select and customize, thereby streamlining product development.

[0465] This invention enables the development of innovative products that combine traditional and modern elements, allowing us to quickly respond to the diverse needs of our users.

[0466] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0467] Step 1:

[0468] The user accesses the design generation system using a terminal. First, the user enters design requirements into a dedicated interface. These requirements include design parameters such as desired color, style, and theme. For example, the user might enter requirements such as "Color: Blue, Style: Modern, Traditional Element: Japanese Pattern." This clarifies the user's preferences.

[0469] Step 2:

[0470] The terminal sends the entered design requirements to the server. It converts the input data into a structured format and outputs it as a request to the server. Specifically, it generates prompt statements and sends them to the server.

[0471] Step 3:

[0472] The server launches a generative AI model based on the received design requirements. The server inputs prompt messages into the generative AI model and generates multiple design options. As part of the data calculation, the AI ​​model explores design possibilities, and candidate design options are generated as output.

[0473] Step 4:

[0474] The server converts the generated design proposals into image data and 3D model data, and sends them to the terminal. Specifically, it compresses the image data and converts it into a format that the user can view. This allows the user to review the options.

[0475] Step 5:

[0476] The user checks the design proposals presented on the device interface and selects the one they like best. Specifically, the user can use zoom and simulation functions to examine the design details and make adjustments. The user's selection is entered as selection data.

[0477] Step 6:

[0478] The user makes fine adjustments to the selected design. The input here is specific adjustment information using sliders and color palettes, and the output is the final adjusted design. Specific actions include adjusting sleeve length with sliders and changing color tones with a palette.

[0479] Step 7:

[0480] The server calculates detailed design data and manufacturing costs based on the final selected design. Specifically, it uses CAD software to generate detailed dimensions and material lists, and then performs cost calculations. This provides all the information necessary for product development.

[0481] Step 8:

[0482] The server records user selection information and adjustment data and stores it as feedback data. This data is used to update the generative artificial intelligence model, contributing to improved accuracy in future design generation. Specifically, this involves entering data into the database and updating the training set for the next generation.

[0483] (Application Example 1)

[0484] 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."

[0485] Current design generation systems have the challenge of making it difficult for users to immediately understand how a design will look on an actual product when reviewing and customizing design proposals. Furthermore, there is a need for methods that expand design options without physical constraints.

[0486] 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.

[0487] In this invention, the server includes means for generating multiple design proposals using generative artificial intelligence, means for implementing the design generation system on a visual device to enable users to visually adjust and select design proposals in real space, and means for accumulating the generated designs and selection information and updating the generative artificial intelligence. This makes it possible for users to check and customize design proposals in real time in their real space.

[0488] "Generative artificial intelligence" is an artificial intelligence technology that automatically generates design proposals based on diverse design requirements from users.

[0489] A "design proposal" is a set of multiple design suggestions created by a generative artificial intelligence system according to the user's specified requirements, and these proposals are customizable.

[0490] A "user information processing device" is a device that allows users to access a design generation system and select and adjust designs.

[0491] "Detailed design information" includes specific dimensions, materials used, and manufacturing process information necessary to produce a product based on the selected design.

[0492] "Manufacturing costs" refer to the estimated and calculated cost of bringing the selected design to market.

[0493] A "visual device" is a device used by users to visually confirm and customize design proposals in real space.

[0494] "Real space" refers to the physical environment surrounding a user in their normal living environment.

[0495] This invention relates to a design generation system using generative artificial intelligence, in which the user accesses the design generation system through their information processing device. The server utilizes generative artificial intelligence to generate multiple design proposals based on the input design requirements. The generated design proposals are provided to the user's information processing device and visual device, through which the user can visually confirm the design proposals and make adjustments and selections in real space.

[0496] Smart glasses and similar devices are used as visual aids. This allows users to customize designs in real time and visually confirm the changes on the spot. The server also generates detailed design information based on the user's selected design and calculates the manufacturing costs. This allows users to intuitively understand the process of how their customized design will actually be turned into a product.

[0497] For example, if a user wants to design the interior of their home, they can use smart glasses to customize the position and style of furniture and decorations on the spot and create a new design. The design proposals generated in this way are saved and used as feedback for the server to make more refined suggestions in future design generation.

[0498] By using a generative AI model, prompts such as "a red casual jacket, with a traditional pattern, and longer sleeves" are generated and used as input information for creating design proposals. This allows the system to efficiently understand the design the user wants to realize and immediately reflect the customizations.

[0499] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0500] Step 1:

[0501] The user accesses the design generation system using their information processing device. Basic design requirements, such as color and style, are entered as prompt messages. This input is then sent to the server for data processing.

[0502] Step 2:

[0503] The server activates a generative AI model based on the received prompt message and generates multiple design proposals. The generative AI model interprets the design requirements and proposes the most suitable design. The output design proposals are sent back to the user's information processing device as image data or a 3D model.

[0504] Step 3:

[0505] The terminal displays design proposals sent from the server to the user. The user uses visual devices to visualize and adjust the design proposals in real time in the real world. During this process, the user makes fine adjustments such as changing sleeve length or color, and this adjustment information is sent to the server via the terminal.

[0506] Step 4:

[0507] The server generates detailed design information from the final design selected and adjusted by the user. Specifically, dimensions, materials to be used, and manufacturing steps are determined based on the selected design proposal. At the same time, manufacturing costs are calculated, and the output information is sent to the user's information processing device.

[0508] Step 5:

[0509] Users review the detailed design information and manufacturing costs of the final design proposal and use this information to determine the feasibility of the design. Further adjustments are made as needed before the final decision is made. During this process, the generated design proposal and user adjustment information are stored on the server and used to improve the generated AI model.

[0510] 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.

[0511] This invention combines a generative artificial intelligence-based design generation system with an emotion engine to generate design proposals that take into account the user's emotional state. This system enhances user satisfaction when customizing designs using a device and enables more personalized suggestions.

[0512] The user accesses the device and enters a design request into the design generation system. The device has a built-in emotion engine that analyzes the user's facial expressions, voice tone, and other emotional indicators to detect the user's emotional state.

[0513] The server receives data from the emotion engine in conjunction with the user's design request. Based on this data, it operates a generative artificial intelligence to generate multiple design options that harmonize with the user's emotional state. In this process, for example, if the user is expressing joy, it can suggest a colorful and optimistic design, while if they are calm, it can suggest a chic and simple design.

[0514] The server sends the generated design proposals to the terminal, where the user visually reviews them. Each presented design is tailored based on the user's emotional state, providing a personalized experience.

[0515] Users can use their devices to select design proposals and make further fine adjustments. The emotion engine remains active throughout this process, and the suggested designs change in response to the user's emotional responses as they make their selections.

[0516] The final selected design is resubmitted to the server, where detailed design information for product development is generated. This information includes specific dimensions, materials, manufacturing processes, and cost details. The generated design proposals and selection process are also stored in a database and used for future updates to the generative artificial intelligence.

[0517] Thus, the system according to the present invention provides a highly personalized user experience through design generation that reflects the user's emotions. This makes it possible to support the improvement of competitiveness and the creation of new market opportunities in the apparel and traditional crafts industries.

[0518] The following describes the processing flow.

[0519] Step 1:

[0520] The user accesses the terminal and enters their design requirements into the design generation system interface. These requirements include the selection of colors, styles, and traditional craft elements.

[0521] Step 2:

[0522] The device receives user input information, simultaneously analyzes the user's facial expressions and voice through its built-in camera and microphone, and sends emotional data to the emotion engine.

[0523] Step 3:

[0524] The device's emotion engine determines the user's current emotional state based on the analysis results. For example, if the user is excited, it will be recorded as a positive emotion.

[0525] Step 4:

[0526] The server integrates design requests and emotional state data received from the terminal and uses generative artificial intelligence to generate design proposals that are in harmony with the emotional state. In the case of a positive emotional state, it selects designs with bright colors and innovative styles.

[0527] Step 5:

[0528] The server sends multiple generated design options to the terminal. This allows the user to review design choices that match their specified emotion.

[0529] Step 6:

[0530] The user reviews the presented design proposals on their device, and is then presented with design options that incorporate feedback from the emotion engine. They select their preferred design and adjust the details as needed. Their emotional state is also considered during this process, providing optimal adjustment support.

[0531] Step 7:

[0532] The terminal sends the final selected design and its adjustment information to the server.

[0533] Step 8:

[0534] The server creates detailed product design information based on the selected design. This includes specific dimensions, materials to be used, and suggested manufacturing processes. It also calculates manufacturing costs and provides this information to the terminal.

[0535] Step 9:

[0536] The server stores generated design proposals, selection processes, and user sentiment data to help improve future generative artificial intelligence learning and sentiment engines.

[0537] This allows users to generate more satisfying designs through personalized, emotion-based experiences.

[0538] (Example 2)

[0539] 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."

[0540] Conventional design generation systems struggle to propose designs that take into account the individual emotional states of users, resulting in uniform design proposals that fail to improve user satisfaction. Furthermore, they cannot respond to changes in users' emotions during the design selection process, making it impossible to provide proposals that better meet individual needs.

[0541] 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.

[0542] In this invention, the server includes means for generating multiple design proposals using generative artificial intelligence, means for analyzing the user's emotional state and adjusting the provided design proposals based on that analysis, and means for accumulating the generated designs and user selection information and updating the generative artificial intelligence. This enables personalized design suggestions that respond to the user's emotions, thereby improving user satisfaction.

[0543] "Generative artificial intelligence" is a general term for artificial intelligence technologies used to automatically generate design proposals. It uses machine learning algorithms to create new designs based on user requests and data.

[0544] A "design proposal" refers to a specific design option presented to the user from among several candidate designs proposed by generative artificial intelligence.

[0545] "Emotional state" refers to the psychological and emotional conditions that users exhibit when selecting designs or interacting with them, and this is analyzed by the emotion engine.

[0546] "Detailed design information" refers to a collection of design data that is concretized based on the selected design proposal, and includes important elements such as dimensions, materials, manufacturing processes, and costs.

[0547] An "emotion engine" is a technology that analyzes a user's facial expressions, voice tone, and other emotional indicators, and plays a role in detecting the user's emotional state.

[0548] This invention is a system that takes into account the user's emotional state and provides highly personalized design suggestions. The system mainly consists of terminals and servers, and utilizes generative artificial intelligence and an emotion engine to provide users with a unique design experience.

[0549] First, the user accesses the design generation system using a terminal and enters a design request. At this time, the user can freely enter an outline of the desired design and specific requirements. The terminal has a built-in emotion engine that analyzes the user's facial expressions and voice tone in real time to detect the user's emotional state. This information is sent to the server along with the design request.

[0550] The server activates generative artificial intelligence based on the received emotion data and design request. The generative AI utilizes the accumulated database and past design selection information to generate multiple design options. In this process, a design that reflects the user's emotional state is selected; for example, if the user is expressing calm emotions, a design with soft colors will be suggested.

[0551] The generated design proposals are sent from the server to the terminal, where the user can visually review them. The user selects their preferred design from several options and makes further adjustments as needed. The emotion engine continues to work throughout this adjustment process, optimizing the suggestions based on the user's changing emotions.

[0552] Once the final design is decided, the proposal is resubmitted to the server, and detailed design information is generated. This information includes specific dimensions, materials, manufacturing processes, and cost information. Furthermore, all generated design proposals and selection processes are stored in a database and used to train subsequent generative artificial intelligence models.

[0553] For example, if a user wants a modern interior design for a home party, they would request "modern and simple interior design" at the terminal. If the emotion engine detects a relaxed emotion, the generated design proposal is expected to include furniture arrangements in soft colors and minimalist decorations.

[0554] Examples of prompt statements include:

[0555] "I'd like a modern and simple interior design. Please suggest furniture and decorations in soft colors to create a relaxed atmosphere."

[0556] The following sentences are possible.

[0557] This system aims to improve user satisfaction by generating designs that incorporate individual user emotions, and to support enhanced competitiveness and the creation of new markets in the apparel industry and traditional crafts sector.

[0558] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0559] Step 1:

[0560] The user accesses the terminal and enters a design request into the design generation system. The user enters specific themes and preferences as prompts. Based on this input, the system sets the initial conditions for design generation and begins extracting the necessary requirements. The output is the initial conditions set based on the user's requests.

[0561] Step 2:

[0562] The device uses its built-in emotion engine to analyze the user's facial expressions and voice tone. Input is real-time data from the device's camera and microphone, and output is the detection of the user's emotional state. This emotional state is sent to the server as data to be considered when generating design proposals.

[0563] Step 3:

[0564] The server receives design requests and emotion data from the terminal. Using the received data as input, it activates a generative AI model, accesses a database, and refers to similar cases. Based on this, multiple design options are automatically generated. The output consists of multiple design options that harmonize with the user's emotions.

[0565] Step 4:

[0566] The server sends the generated design proposals to the terminal, allowing the user to visually review them. The output here is the design proposals converted into a viewable format. Users can view and select from these on the screen. Specifically, the user reviews the design proposals by scrolling through them within the user interface.

[0567] Step 5:

[0568] The user selects one of the presented design options and makes minor adjustments as needed. During the adjustment process, the user's emotional state is analyzed again, and the system modifies the suggestions in real time. The input is the user's selection and the emotional data at that time, and the output is the adjusted design option.

[0569] Step 6:

[0570] Based on the final design proposal resubmitted to the server, detailed design information is generated. This step outputs design data that includes specific dimensions, materials, manufacturing processes, and cost information. The generated design proposals and selection process data are also stored in a database and used to improve the AI ​​model later.

[0571] (Application Example 2)

[0572] 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."

[0573] Modern users desire more personalized design suggestions based on their emotions. However, existing design generation systems fail to adequately consider the user's emotional state, resulting in a limited quality of user experience. There is a need to provide a more intuitive and satisfying customized experience by automatically generating designs that align with the user's emotions.

[0574] 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.

[0575] In this invention, the server includes means for generating multiple design proposals using generative artificial intelligence, means for analyzing emotional indicators such as the user's facial expression data to identify their emotional state, and means for generating design proposals that are in harmony with the user's emotions based on the emotional state. This makes it possible to propose designs that take the user's emotions into consideration.

[0576] "Generative artificial intelligence" is an artificial intelligence technology that can generate new designs and content based on given conditions and information.

[0577] A "design proposal" is a specific design suggestion that reflects the user's requirements and feelings, and is subject to selection and adjustment.

[0578] "Emotional indicators" are physiological or psychological data used to indicate an emotional state, such as a user's facial expressions or voice tone.

[0579] "Emotional state" refers to the emotional state of a user at a specific moment and is information that has a significant impact on design generation.

[0580] A "communication terminal" is a device used by a user to access a design generation system, and includes computing devices and personal information terminals.

[0581] "Detailed design information" refers to technical information such as dimensions, materials, manufacturing processes, and costs necessary to realize the selected design proposal.

[0582] "Visual information" refers to information that includes images and visual elements presented to the user on a screen or display.

[0583] "Three-dimensional structure" refers to data formats and models used to represent design proposals in three dimensions, provided in a way that users can understand spatially.

[0584] The system for realizing this invention mainly consists of a server, a user's communication terminal, generative artificial intelligence, and an emotion engine.

[0585] The server provides a platform for generating multiple design options using generative artificial intelligence. Based on a pre-trained model, the generative AI generates design options that match the user's requirements and emotions. This AI model is designed using the latest deep learning technology.

[0586] The user's communication terminal is equipped with input devices such as a camera and microphone. Through these input devices, emotional indicators (such as facial expressions and tone of voice) are transmitted to the emotion engine. The emotion engine uses software such as the Emotion SDK to analyze the user's emotional state in real time. The analyzed emotional data is sent to a server. This data is input as a prompt to generative artificial intelligence, which generates design proposals appropriate to the user's emotional state.

[0587] Users can review the design proposals generated on their communication terminal and select and adjust designs from those displayed as visual information. During this process, changes in the user's emotions are also analyzed by the emotion engine, and the final proposal is continuously refined.

[0588] As a concrete example, when a user uses the application for online shopping, their facial expression is detected by the camera. If they appear cheerful, the application suggests colorful and vibrant clothing designs. Conversely, if they appear calm, it presents simple and elegant designs. By using a prompt such as, "The user's emotional state indicates joy. Please suggest a vibrant and colorful dress design for a party," it becomes possible to make suggestions that match their emotions.

[0589] In this way, the system of the present invention enables personalized design suggestions that take user emotions into consideration, thereby contributing to an improved user experience.

[0590] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0591] Step 1:

[0592] The user inputs their emotional data through the camera and microphone on the device. The device uses these devices to acquire emotional indicators such as facial expressions and tone of voice, and sends this data to the emotion engine.

[0593] Step 2:

[0594] The emotion engine analyzes emotional data received from the device to identify the user's emotional state. Using the Emotion SDK, it analyzes facial expression features and voice frequency patterns to output specific emotion tags such as joy, calmness, and surprise.

[0595] Step 3:

[0596] The server receives emotional state data obtained from the emotion engine. This is converted into prompt text for the generative AI model and used as input to generate design suggestions based on the user's emotions. The prompt used is: "The user's emotional state indicates joy. Please suggest a design for a glamorous and colorful dress for a party."

[0597] Step 4:

[0598] Generative artificial intelligence generates design proposals based on prompt text, creating multiple design options. The generated design proposals reflect the user's emotional state in terms of visual elements such as color, shape, and style.

[0599] Step 5:

[0600] The server sends the generated design proposals to the terminal, allowing the user to review the design visually. The user reviews the proposed design on the terminal, selects their preferred options, and makes adjustments as needed.

[0601] Step 6:

[0602] The design selected by the user is sent back to the server from the terminal. The server generates detailed design information based on the selected design and calculates the final manufacturing cost. This includes details such as dimensions, materials, and manufacturing process.

[0603] Step 7:

[0604] The server stores the generated designs and user selection information in a database, which is then used as training data for the next generation of generative artificial intelligence. The stored information is used to improve the accuracy of the generative AI model.

[0605] 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.

[0606] 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.

[0607] 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.

[0608] [Fourth Embodiment]

[0609] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0610] 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.

[0611] 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).

[0612] 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.

[0613] 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.

[0614] 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).

[0615] 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.

[0616] 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.

[0617] 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.

[0618] 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.

[0619] 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.

[0620] 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.

[0621] 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".

[0622] This invention relates to a design generation system that efficiently generates design proposals using generative artificial intelligence, enabling the customization and commercialization of designs according to user needs.

[0623] Users access the design generation system using a terminal and input their desired design requirements through a dedicated interface. These requirements can include multiple design parameters such as color, style, and elements of traditional craftsmanship.

[0624] The server receives requests from users and activates generative artificial intelligence to generate multiple design options. This allows users to consider various options to find the design that best suits them. The generated design options are sent to the terminal as images or 3D models for visual review by the user.

[0625] Users can select their favorite design from several options presented on their device and make fine adjustments using the provided tools. For example, with a jacket design, they can change the sleeve length and color, adjust the pattern placement, and more in real time.

[0626] The server generates detailed design information necessary for product development based on the design ultimately selected by the user. This information includes specific dimensions, materials to be used, and manufacturing steps. Cost calculations related to manufacturing are also performed simultaneously and provided to the user.

[0627] Furthermore, the selected design proposals and user adjustment information are stored on the server as feedback data. This data is used to update the generative artificial intelligence model and improve the accuracy of future design generation.

[0628] Thus, the design generation system according to the present invention supports the development of highly innovative products while meeting the needs of the apparel and traditional crafts industries. This will enhance the competitiveness of both industries and enable the creation of new market opportunities.

[0629] The following describes the processing flow.

[0630] Step 1:

[0631] Users access the design generation system via their device and input requirements for their desired design. These requirements include specific design parameters such as color, style, materials, and elements of traditional craftsmanship.

[0632] Step 2:

[0633] The terminal collects the design requirements entered by the user as data and sends it to the server.

[0634] Step 3:

[0635] The server analyzes the received design requirements and runs a generative artificial intelligence based on them. The AI ​​model generates multiple design options and constructs them as images or 3D models.

[0636] Step 4:

[0637] The server sends the generated design proposals back to the terminal, allowing the user to visually review the design proposals on the terminal.

[0638] Step 5:

[0639] Users select their preferred design from the provided options using the interface on their device. They can then fine-tune the colors and shapes in real time as needed.

[0640] Step 6:

[0641] The device then resends the user's selections and adjustments to the server.

[0642] Step 7:

[0643] The server generates detailed design information based on the selected final design. This includes dimensions required for product development, material selection, and suggested manufacturing processes. Furthermore, it calculates the manufacturing cost and provides it to the terminal.

[0644] Step 8:

[0645] The server stores user feedback and the final design in a database, which is used to update and improve the generative artificial intelligence. This aims to improve the accuracy and efficiency of future design generation.

[0646] (Example 1)

[0647] 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".

[0648] To efficiently generate designs that meet the diverse needs of today's world, advanced customization capabilities and optimized production processes are required. However, conventional systems have limited functionality for quickly generating multiple design options based on specific user requests, and for selecting and adjusting them. Furthermore, calculating the detailed design information necessary for product development is time-consuming.

[0649] 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.

[0650] In this invention, the server includes means for generating a large number of design proposals using generative artificial intelligence, means for providing the design proposals to the user's information processing device and enabling selection and modification, and means for calculating detailed design data and manufacturing costs based on the selected design. This enables flexible design generation that can immediately respond to the demands of diverse users and streamlines the productization process.

[0651] "Generative artificial intelligence" is an artificial intelligence technology used to automatically generate designs and ideas.

[0652] A "design proposal" is a design suggestion generated based on user requirements.

[0653] An "information processing device" is an electronic device used for inputting, processing, and outputting data.

[0654] "Detailed design data" refers to information regarding specific dimensions and materials necessary for product development.

[0655] "Manufacturing costs" refer to the total cost required to produce a product.

[0656] A "prompt sentence" is an input sentence used to give instructions to a generative artificial intelligence.

[0657] "Recording" refers to the act of storing generated information and user choices for later analysis and processing.

[0658] This invention is a system that utilizes generative artificial intelligence to efficiently generate designs that meet user needs, and facilitates customization and product development. This system primarily operates through the collaborative efforts of a server, terminals, and users, with each component fulfilling the following roles.

[0659] The server plays a central role in generating numerous design proposals using generative artificial intelligence. Based on design requirements submitted by the user, the AI ​​model generates design proposals based on prompts. The software used is an advanced computing platform to run the AI ​​model. Furthermore, based on the design selected and adjusted by the user from the generated designs, the system calculates the detailed design data and manufacturing costs necessary for product development. CAD software is used at this stage to create specific dimension drawings and material lists.

[0660] The terminal functions as an interface connecting the user and the server. It sends the design requirements entered by the user to the server and visually presents the design proposals received from the server. It also provides tools on the terminal for the user to select a design and make fine adjustments.

[0661] Users access the design generation system through their terminal and input their individual design requirements. For example, if a user wants a new Japanese-style tablecloth design, they can use a prompt such as, "A modern style tablecloth that incorporates the uniquely Japanese elements of traditional crafts." The server then uses a generation AI model to generate multiple design options, which the user can select and customize, thereby streamlining product development.

[0662] This invention enables the development of innovative products that combine traditional and modern elements, allowing us to quickly respond to the diverse needs of our users.

[0663] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0664] Step 1:

[0665] The user accesses the design generation system using a terminal. First, the user enters design requirements into a dedicated interface. These requirements include design parameters such as desired color, style, and theme. For example, the user might enter requirements such as "Color: Blue, Style: Modern, Traditional Element: Japanese Pattern." This clarifies the user's preferences.

[0666] Step 2:

[0667] The terminal sends the entered design requirements to the server. It converts the input data into a structured format and outputs it as a request to the server. Specifically, it generates prompt statements and sends them to the server.

[0668] Step 3:

[0669] The server launches a generative AI model based on the received design requirements. The server inputs prompt messages into the generative AI model and generates multiple design options. As part of the data calculation, the AI ​​model explores design possibilities, and candidate design options are generated as output.

[0670] Step 4:

[0671] The server converts the generated design proposals into image data and 3D model data, and sends them to the terminal. Specifically, it compresses the image data and converts it into a format that the user can view. This allows the user to review the options.

[0672] Step 5:

[0673] The user checks the design proposals presented on the device interface and selects the one they like best. Specifically, the user can use zoom and simulation functions to examine the design details and make adjustments. The user's selection is entered as selection data.

[0674] Step 6:

[0675] The user makes fine adjustments to the selected design. The input here is specific adjustment information using sliders and color palettes, and the output is the final adjusted design. Specific actions include adjusting sleeve length with sliders and changing color tones with a palette.

[0676] Step 7:

[0677] The server calculates detailed design data and manufacturing costs based on the final selected design. Specifically, it uses CAD software to generate detailed dimensions and material lists, and then performs cost calculations. This provides all the information necessary for product development.

[0678] Step 8:

[0679] The server records user selection information and adjustment data and stores it as feedback data. This data is used to update the generative artificial intelligence model, contributing to improved accuracy in future design generation. Specifically, this involves entering data into the database and updating the training set for the next generation.

[0680] (Application Example 1)

[0681] 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".

[0682] Current design generation systems have the challenge of making it difficult for users to immediately understand how a design will look on an actual product when reviewing and customizing design proposals. Furthermore, there is a need for methods that expand design options without physical constraints.

[0683] 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.

[0684] In this invention, the server includes means for generating multiple design proposals using generative artificial intelligence, means for implementing the design generation system on a visual device to enable users to visually adjust and select design proposals in real space, and means for accumulating the generated designs and selection information and updating the generative artificial intelligence. This makes it possible for users to check and customize design proposals in real time in their real space.

[0685] "Generative artificial intelligence" is an artificial intelligence technology that automatically generates design proposals based on diverse design requirements from users.

[0686] A "design proposal" is a set of multiple design suggestions created by a generative artificial intelligence system according to the user's specified requirements, and these proposals are customizable.

[0687] A "user information processing device" is a device that allows users to access a design generation system and select and adjust designs.

[0688] "Detailed design information" includes specific dimensions, materials used, and manufacturing process information necessary to produce a product based on the selected design.

[0689] "Manufacturing costs" refer to the estimated and calculated cost of bringing the selected design to market.

[0690] A "visual device" is a device used by users to visually confirm and customize design proposals in real space.

[0691] "Real space" refers to the physical environment surrounding a user in their normal living environment.

[0692] This invention relates to a design generation system using generative artificial intelligence, in which the user accesses the design generation system through their information processing device. The server utilizes generative artificial intelligence to generate multiple design proposals based on the input design requirements. The generated design proposals are provided to the user's information processing device and visual device, through which the user can visually confirm the design proposals and make adjustments and selections in real space.

[0693] Smart glasses and similar devices are used as visual aids. This allows users to customize designs in real time and visually confirm the changes on the spot. The server also generates detailed design information based on the user's selected design and calculates the manufacturing costs. This allows users to intuitively understand the process of how their customized design will actually be turned into a product.

[0694] For example, if a user wants to design the interior of their home, they can use smart glasses to customize the position and style of furniture and decorations on the spot and create a new design. The design proposals generated in this way are saved and used as feedback for the server to make more refined suggestions in future design generation.

[0695] By using a generative AI model, prompts such as "a red casual jacket, with a traditional pattern, and longer sleeves" are generated and used as input information for creating design proposals. This allows the system to efficiently understand the design the user wants to realize and immediately reflect the customizations.

[0696] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0697] Step 1:

[0698] The user accesses the design generation system using their information processing device. Basic design requirements, such as color and style, are entered as prompt messages. This input is then sent to the server for data processing.

[0699] Step 2:

[0700] The server activates a generative AI model based on the received prompt message and generates multiple design proposals. The generative AI model interprets the design requirements and proposes the most suitable design. The output design proposals are sent back to the user's information processing device as image data or a 3D model.

[0701] Step 3:

[0702] The terminal displays design proposals sent from the server to the user. The user uses visual devices to visualize and adjust the design proposals in real time in the real world. During this process, the user makes fine adjustments such as changing sleeve length or color, and this adjustment information is sent to the server via the terminal.

[0703] Step 4:

[0704] The server generates detailed design information from the final design selected and adjusted by the user. Specifically, dimensions, materials to be used, and manufacturing steps are determined based on the selected design proposal. At the same time, manufacturing costs are calculated, and the output information is sent to the user's information processing device.

[0705] Step 5:

[0706] Users review the detailed design information and manufacturing costs of the final design proposal and use this information to determine the feasibility of the design. Further adjustments are made as needed before the final decision is made. During this process, the generated design proposal and user adjustment information are stored on the server and used to improve the generated AI model.

[0707] 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.

[0708] This invention combines a generative artificial intelligence-based design generation system with an emotion engine to generate design proposals that take into account the user's emotional state. This system enhances user satisfaction when customizing designs using a device and enables more personalized suggestions.

[0709] The user accesses the device and enters a design request into the design generation system. The device has a built-in emotion engine that analyzes the user's facial expressions, voice tone, and other emotional indicators to detect the user's emotional state.

[0710] The server receives data from the emotion engine in conjunction with the user's design request. Based on this data, it operates a generative artificial intelligence to generate multiple design options that harmonize with the user's emotional state. In this process, for example, if the user is expressing joy, it can suggest a colorful and optimistic design, while if they are calm, it can suggest a chic and simple design.

[0711] The server sends the generated design proposals to the terminal, where the user visually reviews them. Each presented design is tailored based on the user's emotional state, providing a personalized experience.

[0712] Users can use their devices to select design proposals and make further fine adjustments. The emotion engine remains active throughout this process, and the suggested designs change in response to the user's emotional responses as they make their selections.

[0713] The final selected design is resubmitted to the server, where detailed design information for product development is generated. This information includes specific dimensions, materials, manufacturing processes, and cost details. The generated design proposals and selection process are also stored in a database and used for future updates to the generative artificial intelligence.

[0714] Thus, the system according to the present invention provides a highly personalized user experience through design generation that reflects the user's emotions. This makes it possible to support the improvement of competitiveness and the creation of new market opportunities in the apparel and traditional crafts industries.

[0715] The following describes the processing flow.

[0716] Step 1:

[0717] The user accesses the terminal and enters their design requirements into the design generation system interface. These requirements include the selection of colors, styles, and traditional craft elements.

[0718] Step 2:

[0719] The device receives user input information, simultaneously analyzes the user's facial expressions and voice through its built-in camera and microphone, and sends emotional data to the emotion engine.

[0720] Step 3:

[0721] The device's emotion engine determines the user's current emotional state based on the analysis results. For example, if the user is excited, it will be recorded as a positive emotion.

[0722] Step 4:

[0723] The server integrates design requests and emotional state data received from the terminal and uses generative artificial intelligence to generate design proposals that are in harmony with the emotional state. In the case of a positive emotional state, it selects designs with bright colors and innovative styles.

[0724] Step 5:

[0725] The server sends multiple generated design options to the terminal. This allows the user to review design choices that match their specified emotion.

[0726] Step 6:

[0727] The user reviews the presented design proposals on their device, and is then presented with design options that incorporate feedback from the emotion engine. They select their preferred design and adjust the details as needed. Their emotional state is also considered during this process, providing optimal adjustment support.

[0728] Step 7:

[0729] The terminal sends the final selected design and its adjustment information to the server.

[0730] Step 8:

[0731] The server creates detailed product design information based on the selected design. This includes specific dimensions, materials to be used, and suggested manufacturing processes. It also calculates manufacturing costs and provides this information to the terminal.

[0732] Step 9:

[0733] The server stores generated design proposals, selection processes, and user sentiment data to help improve future generative artificial intelligence learning and sentiment engines.

[0734] This allows users to generate more satisfying designs through personalized, emotion-based experiences.

[0735] (Example 2)

[0736] 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".

[0737] Conventional design generation systems struggle to propose designs that take into account the individual emotional states of users, resulting in uniform design proposals that fail to improve user satisfaction. Furthermore, they cannot respond to changes in users' emotions during the design selection process, making it impossible to provide proposals that better meet individual needs.

[0738] 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.

[0739] In this invention, the server includes means for generating multiple design proposals using generative artificial intelligence, means for analyzing the user's emotional state and adjusting the provided design proposals based on that analysis, and means for accumulating the generated designs and user selection information and updating the generative artificial intelligence. This enables personalized design suggestions that respond to the user's emotions, thereby improving user satisfaction.

[0740] "Generative artificial intelligence" is a general term for artificial intelligence technologies used to automatically generate design proposals. It uses machine learning algorithms to create new designs based on user requests and data.

[0741] A "design proposal" refers to a specific design option presented to the user from among several candidate designs proposed by generative artificial intelligence.

[0742] "Emotional state" refers to the psychological and emotional conditions that users exhibit when selecting designs or interacting with them, and this is analyzed by the emotion engine.

[0743] "Detailed design information" refers to a collection of design data that is concretized based on the selected design proposal, and includes important elements such as dimensions, materials, manufacturing processes, and costs.

[0744] An "emotion engine" is a technology that analyzes a user's facial expressions, voice tone, and other emotional indicators, and plays a role in detecting the user's emotional state.

[0745] This invention is a system that takes into account the user's emotional state and provides highly personalized design suggestions. The system mainly consists of terminals and servers, and utilizes generative artificial intelligence and an emotion engine to provide users with a unique design experience.

[0746] First, the user accesses the design generation system using a terminal and enters a design request. At this time, the user can freely enter an outline of the desired design and specific requirements. The terminal has a built-in emotion engine that analyzes the user's facial expressions and voice tone in real time to detect the user's emotional state. This information is sent to the server along with the design request.

[0747] The server activates generative artificial intelligence based on the received emotion data and design request. The generative AI utilizes the accumulated database and past design selection information to generate multiple design options. In this process, a design that reflects the user's emotional state is selected; for example, if the user is expressing calm emotions, a design with soft colors will be suggested.

[0748] The generated design proposals are sent from the server to the terminal, where the user can visually review them. The user selects their preferred design from several options and makes further adjustments as needed. The emotion engine continues to work throughout this adjustment process, optimizing the suggestions based on the user's changing emotions.

[0749] Once the final design is decided, the proposal is resubmitted to the server, and detailed design information is generated. This information includes specific dimensions, materials, manufacturing processes, and cost information. Furthermore, all generated design proposals and selection processes are stored in a database and used to train subsequent generative artificial intelligence models.

[0750] For example, if a user wants a modern interior design for a home party, they would request "modern and simple interior design" at the terminal. If the emotion engine detects a relaxed emotion, the generated design proposal is expected to include furniture arrangements in soft colors and minimalist decorations.

[0751] Examples of prompt statements include:

[0752] "I'd like a modern and simple interior design. Please suggest furniture and decorations in soft colors to create a relaxed atmosphere."

[0753] The following sentences are possible.

[0754] This system aims to improve user satisfaction by generating designs that incorporate individual user emotions, and to support enhanced competitiveness and the creation of new markets in the apparel industry and traditional crafts sector.

[0755] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0756] Step 1:

[0757] The user accesses the terminal and enters a design request into the design generation system. The user enters specific themes and preferences as prompts. Based on this input, the system sets the initial conditions for design generation and begins extracting the necessary requirements. The output is the initial conditions set based on the user's requests.

[0758] Step 2:

[0759] The device uses its built-in emotion engine to analyze the user's facial expressions and voice tone. Input is real-time data from the device's camera and microphone, and output is the detection of the user's emotional state. This emotional state is sent to the server as data to be considered when generating design proposals.

[0760] Step 3:

[0761] The server receives design requests and emotion data from the terminal. Using the received data as input, it activates a generative AI model, accesses a database, and refers to similar cases. Based on this, multiple design options are automatically generated. The output consists of multiple design options that harmonize with the user's emotions.

[0762] Step 4:

[0763] The server sends the generated design proposals to the terminal, allowing the user to visually review them. The output here is the design proposals converted into a viewable format. Users can view and select from these on the screen. Specifically, the user reviews the design proposals by scrolling through them within the user interface.

[0764] Step 5:

[0765] The user selects one of the presented design options and makes minor adjustments as needed. During the adjustment process, the user's emotional state is analyzed again, and the system modifies the suggestions in real time. The input is the user's selection and the emotional data at that time, and the output is the adjusted design option.

[0766] Step 6:

[0767] Based on the final design proposal resubmitted to the server, detailed design information is generated. This step outputs design data that includes specific dimensions, materials, manufacturing processes, and cost information. The generated design proposals and selection process data are also stored in a database and used to improve the AI ​​model later.

[0768] (Application Example 2)

[0769] 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".

[0770] Modern users desire more personalized design suggestions based on their emotions. However, existing design generation systems fail to adequately consider the user's emotional state, resulting in a limited quality of user experience. There is a need to provide a more intuitive and satisfying customized experience by automatically generating designs that align with the user's emotions.

[0771] 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.

[0772] In this invention, the server includes means for generating multiple design proposals using generative artificial intelligence, means for analyzing emotional indicators such as the user's facial expression data to identify their emotional state, and means for generating design proposals that are in harmony with the user's emotions based on the emotional state. This makes it possible to propose designs that take the user's emotions into consideration.

[0773] "Generative artificial intelligence" is an artificial intelligence technology that can generate new designs and content based on given conditions and information.

[0774] A "design proposal" is a specific design suggestion that reflects the user's requirements and feelings, and is subject to selection and adjustment.

[0775] "Emotional indicators" are physiological or psychological data used to indicate an emotional state, such as a user's facial expressions or voice tone.

[0776] "Emotional state" refers to the emotional state of a user at a specific moment and is information that has a significant impact on design generation.

[0777] A "communication terminal" is a device used by a user to access a design generation system, and includes computing devices and personal information terminals.

[0778] "Detailed design information" refers to technical information such as dimensions, materials, manufacturing processes, and costs necessary to realize the selected design proposal.

[0779] "Visual information" refers to information that includes images and visual elements presented to the user on a screen or display.

[0780] "Three-dimensional structure" refers to data formats and models used to represent design proposals in three dimensions, provided in a way that users can understand spatially.

[0781] The system for realizing this invention mainly consists of a server, a user's communication terminal, generative artificial intelligence, and an emotion engine.

[0782] The server provides a platform for generating multiple design options using generative artificial intelligence. Based on a pre-trained model, the generative AI generates design options that match the user's requirements and emotions. This AI model is designed using the latest deep learning technology.

[0783] The user's communication terminal is equipped with input devices such as a camera and microphone. Through these input devices, emotional indicators (such as facial expressions and tone of voice) are transmitted to the emotion engine. The emotion engine uses software such as the Emotion SDK to analyze the user's emotional state in real time. The analyzed emotional data is sent to a server. This data is input as a prompt to generative artificial intelligence, which generates design proposals appropriate to the user's emotional state.

[0784] Users can review the design proposals generated on their communication terminal and select and adjust designs from those displayed as visual information. During this process, changes in the user's emotions are also analyzed by the emotion engine, and the final proposal is continuously refined.

[0785] As a concrete example, when a user uses the application for online shopping, their facial expression is detected by the camera. If they appear cheerful, the application suggests colorful and vibrant clothing designs. Conversely, if they appear calm, it presents simple and elegant designs. By using a prompt such as, "The user's emotional state indicates joy. Please suggest a vibrant and colorful dress design for a party," it becomes possible to make suggestions that match their emotions.

[0786] In this way, the system of the present invention enables personalized design suggestions that take user emotions into consideration, thereby contributing to an improved user experience.

[0787] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0788] Step 1:

[0789] The user inputs their emotional data through the camera and microphone on the device. The device uses these devices to acquire emotional indicators such as facial expressions and tone of voice, and sends this data to the emotion engine.

[0790] Step 2:

[0791] The emotion engine analyzes emotional data received from the device to identify the user's emotional state. Using the Emotion SDK, it analyzes facial expression features and voice frequency patterns to output specific emotion tags such as joy, calmness, and surprise.

[0792] Step 3:

[0793] The server receives emotional state data obtained from the emotion engine. This is converted into prompt text for the generative AI model and used as input to generate design suggestions based on the user's emotions. The prompt used is: "The user's emotional state indicates joy. Please suggest a design for a glamorous and colorful dress for a party."

[0794] Step 4:

[0795] Generative artificial intelligence generates design proposals based on prompt text, creating multiple design options. The generated design proposals reflect the user's emotional state in terms of visual elements such as color, shape, and style.

[0796] Step 5:

[0797] The server sends the generated design proposals to the terminal, allowing the user to review the design visually. The user reviews the proposed design on the terminal, selects their preferred options, and makes adjustments as needed.

[0798] Step 6:

[0799] The design selected by the user is sent back to the server from the terminal. The server generates detailed design information based on the selected design and calculates the final manufacturing cost. This includes details such as dimensions, materials, and manufacturing process.

[0800] Step 7:

[0801] The server stores the generated designs and user selection information in a database, which is then used as training data for the next generation of generative artificial intelligence. The stored information is used to improve the accuracy of the generative AI model.

[0802] 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.

[0803] 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.

[0804] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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."

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] The following is further disclosed regarding the embodiments described above.

[0824] (Claim 1)

[0825] A method for generating multiple design proposals using generative artificial intelligence,

[0826] A means for providing the aforementioned design proposals to the user's terminal, enabling selection and adjustment,

[0827] A means for calculating detailed design information and manufacturing costs based on the selected design,

[0828] A means of updating generative artificial intelligence by accumulating generated designs and user selection information,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, characterized in that it has means for receiving design requests from users and extracting specific requirements.

[0832] (Claim 3)

[0833] The system according to claim 1, characterized by having means for outputting the generated design proposal as an image or a three-dimensional model.

[0834] "Example 1"

[0835] (Claim 1)

[0836] A method for generating numerous design proposals using generative artificial intelligence,

[0837] The aforementioned design proposal is provided to the information processing device of the constructor, and means are provided to enable selection and modification.

[0838] A means of calculating detailed design data and manufacturing costs based on the selected design,

[0839] A means of recording the generated design and the selection information of the builder, and using it to train a generative artificial intelligence,

[0840] A means of receiving design requirements from the user and generating prompt sentences using generative artificial intelligence,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, characterized in that it has means for receiving design requests from users and developing specific conditions.

[0844] (Claim 3)

[0845] The system according to claim 1, characterized in that it has means for outputting the generated design proposal as visualization data or a three-dimensional model.

[0846] "Application Example 1"

[0847] (Claim 1)

[0848] A method for generating multiple design proposals using generative artificial intelligence,

[0849] A means for providing the aforementioned design proposal to the user's information processing device, enabling selection and adjustment,

[0850] A means of calculating detailed design information and manufacturing costs based on the selected design,

[0851] A means of updating generative artificial intelligence by accumulating generated designs and user selection information,

[0852] The aforementioned design generation system is implemented on a visual device, providing a means to enable users to visually adjust and select design proposals in real space.

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, which has means for receiving design requests from users and extracting specific requirements.

[0856] (Claim 3)

[0857] The system according to claim 1, further comprising means for outputting the generated design proposal as an image or a three-dimensional model.

[0858] "Example 2 of combining an emotion engine"

[0859] (Claim 1)

[0860] A method for generating multiple design proposals using generative artificial intelligence,

[0861] A means for providing the aforementioned design proposals to the user's terminal, enabling selection and adjustment,

[0862] A means of analyzing the user's emotional state and adjusting the provided design proposals based on that analysis,

[0863] A means for calculating detailed design information and manufacturing costs based on the selected design,

[0864] A means of updating generative artificial intelligence by accumulating generated designs and user selection information,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, characterized in that it has means for receiving design requests from users, extracting specific requirements, and further considering the analyzed emotional state.

[0868] (Claim 3)

[0869] The system according to claim 1, characterized in that it has means for outputting the generated design proposal as an image or a three-dimensional model and optimizing its display based on the user's emotional state.

[0870] "Application example 2 when combining with an emotional engine"

[0871] (Claim 1)

[0872] A method for generating multiple design proposals using generative artificial intelligence,

[0873] A means of analyzing emotional indicators such as user facial expression data to identify emotional states,

[0874] A means for generating a design proposal that harmonizes with the user's emotions based on the aforementioned emotional state,

[0875] A means for providing the aforementioned design proposals to the user's communication terminal, enabling selection and adjustment,

[0876] A means for calculating detailed design information and manufacturing costs based on the selected design,

[0877] A means of updating generative artificial intelligence by accumulating generated designs and user selection information,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, characterized in that it has means for capturing emotional indicators such as the user's facial expressions and reflecting the analysis results in the prompt text of a generative artificial intelligence.

[0881] (Claim 3)

[0882] The system according to claim 1, characterized in that it has means for outputting the generated design proposal as visual information or a three-dimensional structure. [Explanation of symbols]

[0883] 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 method for generating multiple design proposals using generative artificial intelligence, A means for providing the aforementioned design proposals to the user's terminal, enabling selection and adjustment, A means for calculating detailed design information and manufacturing costs based on the selected design, A means of updating generative artificial intelligence by accumulating generated designs and user selection information, A system that includes this.

2. The system according to claim 1, characterized in that it has means for receiving design requests from users and extracting specific requirements.

3. The system according to claim 1, characterized in that it has means for outputting the generated design proposal as an image or a three-dimensional model.

Images