System

Abstract

To provide a system.SOLUTION: A system includes: feeling analysis means for recognizing a feeling of a user; means for generating a design pattern on the basis of a prompt sentence for instructing generation of the design pattern on the basis of a feeling analysis result using a generative AI model; means for proposing the generated design pattern to the user; and means for the user to select an optimum design pattern from the generated design patterns.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] "Pattern creation" is a very important act in design production, but doing it manually takes time and effort. In addition, pattern creation by humans depends on the perspective and experience of the person, so there is a possibility that it may lack diversity. [Means for solving the problem]

[0005] As a means of solving this problem, we provide a system that uses AI to generate design patterns. This system includes a means for making a final selection from an infinite number of patterns, and a means for generating these patterns using AI. This makes it possible to significantly reduce time and labor and provide a wide variety of design patterns. [Brief description of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a process flow of the data processing system in the first embodiment of the first form example. [Figure 12] FIG. 11 is a sequence diagram showing a process flow of the data processing system in the application example 1 of the first embodiment. [Figure 13] FIG. 11 is a sequence diagram showing a process flow of the data processing system in the second embodiment of the second embodiment. [Figure 14] FIG. 13 is a sequence diagram showing a process flow of the data processing system in application example 2 of embodiment 2. [Figure 15] FIG. 13 is a sequence diagram showing a process flow of a data processing system in a third embodiment of the present invention. [Figure 16]FIG. 13 is a sequence diagram showing a process flow of the data processing system in the third application example of the third embodiment. [Figure 17] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when combined with an emotion engine. [Figure 19] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system according to the embodiment 2 of the second embodiment in the case where an emotion engine is combined. [Figure 20] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when combined with an emotion engine. [Figure 21] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in the embodiment 3 of the form example 3 in the case where an emotion engine is combined. [Figure 22] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when combined with an emotion engine. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (e.g., voice 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 voice according to instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of main functions of the data processing device 12 and the smart device 14.

[0023] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores a reception output program 60. The reception output program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads out 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 the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] One aspect of the present invention is a system for generating design patterns using AI. This system includes a means for generating patterns in design production, a means for making a final selection from an infinite number of patterns, and a means for generating these patterns using AI. Specifically, the AI ​​first generates design patterns. In this case, the AI ​​generates a variety of patterns by combining various design elements. Next, the final selection is made from the generated patterns. This selection is made based on user input and AI recommendations.

[0029] "Example 2"

[0030] As a concrete example, consider the UI design of an app. First, AI generates various UI design patterns by combining elements such as button shape, color, and placement. Then, the user selects the most suitable one from the generated patterns. This selection is based on the user's preferences and the purpose of the app.

[0031] "Example 3"

[0032] The same is true for web design. AI generates various web design variations by combining elements such as header design, font type, and background color. Users then select the best one from the generated variations. This selection is based on the user's preferences and the purpose of the website.

[0033] The process flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: AI generates design patterns. In this process, the AI ​​combines various design elements to generate diverse patterns.

[0036] Step 2: Make a final selection from the generated patterns. This selection is based on user input and / or AI recommendations.

[0037] "Example 2"

[0038] Step 1: For app UI design, AI will generate diverse UI design patterns by combining elements such as button shape, color, and placement.

[0039] Step 2: The user selects the best one from the generated patterns. This selection is based on the user's preferences and the purpose of the app.

[0040] "Example 3"

[0041] Step 1: For web design, AI will combine elements such as header design, font type, and background color to generate diverse web design patterns.

[0042] Step 2: The user selects the best one from the generated patterns. This selection is based on the user's preferences and the website's objectives.

[0043] Example 1

[0044] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal".

[0045] Conventional design production has the problem that it takes a lot of time and effort to come up with design patterns and make the final selection. In addition, there is a lack of means to efficiently generate a variety of design patterns that meet the user's needs. Furthermore, the process of selecting the most suitable one from the generated design patterns is also complicated, which is a factor that reduces user satisfaction.

[0046] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0047] In this invention, the server includes a means for a user to input a prompt text, a means for the server to receive the prompt text and send it to the generative AI model, a means for the generative AI model to generate a design pattern, a means for the server to save the generated pattern in a database, a means for a user to view the generated patterns on a terminal, a means for a user to select a pattern, and a means for the server to save the selected pattern and make final adjustments as necessary. This allows a user to efficiently generate a variety of design patterns and select the most suitable one from among them.

[0048] "User" means an individual or group that uses the system to generate and select design patterns.

[0049] A "prompt sentence" is text that describes instructions or requirements that a user enters into a system.

[0050] The "server" is a computer system responsible for receiving prompts from the user and sending them to the generative AI model.

[0051] A "generative AI model" is an artificial intelligence algorithm that generates design patterns based on a prompt sentence.

[0052] A "design pattern" is a visual layout or composition that combines various design elements generated by a generative AI model.

[0053] A "database" is an information management system for storing the generated design patterns.

[0054] A "terminal" is a computer or mobile device that a user uses to view and select design patterns.

[0055] "Final adjustment" refers to fine adjustments and modifications made to the design pattern selected by the user.

[0056] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0057] The present invention is a system for efficiently performing pattern selection and final selection in design production. A specific embodiment of this system will be described below.

[0058] System Configuration

[0059] The system includes a user, a server, a terminal, and a generative AI model. The user inputs a prompt sentence using the terminal, and the server receives the prompt sentence and sends it to the generative AI model. The generative AI model generates a design pattern based on the prompt sentence, and the server stores the generated pattern in a database. The user can browse and select the generated pattern using the terminal. The server stores the selected pattern and makes final adjustments if necessary.

[0060] Hardware and Software Used

[0061] Server: A high-performance computer system that receives prompts, sends them to the generative AI model, and stores and finalizes the generated design patterns.

[0062] Terminal: The computer or mobile device used by the user to enter prompts and view and select design patterns.

[0063] Generative AI models: For example, using advanced artificial intelligence algorithms such as OpenAI's GPT-4 to generate design patterns based on a prompt.

[0064] Database: An information management system for storing the generated design patterns.

[0065] Data processing and calculation

[0066] The server analyzes the prompt text sent by the user, converts it into an appropriate format, and sends it to the generative AI model. The generative AI model generates multiple design patterns by combining various design elements (color, shape, layout, etc.) based on the prompt text. The generated patterns are stored in a database by the server, and can be viewed and selected by the user using their terminal.

[0067] Examples

[0068] For example, if a user wants to create a new website design, they can enter a prompt like this:

[0069] "Generate modern website design patterns"

[0070] "Please suggest a simple and clean logo design."

[0071] "Please suggest a color palette that would suit this design."

[0072] The server receives this prompt and sends it to the generative AI model. The generative AI model generates multiple design patterns based on the prompt, and the server stores them in a database. The user can use the terminal to browse the generated patterns and select the design they like. The selected design is stored on the server and final adjustments are made if necessary.

[0073] In this way, users can utilize AI to efficiently generate design patterns and make final selections.

[0074] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0075] Step 1:

[0076] The user enters a prompt sentence.

[0077] The user types "Generate a modern website design pattern" into the input field of the terminal and clicks the submit button.

[0078] Input: The prompt text entered by the user.

[0079] Output: The prompt sent from the terminal to the server.

[0080] Step 2:

[0081] The server receives the prompt text and sends it to the generative AI model.

[0082] The server receives the prompt text sent by the user, parses it, converts it into an appropriate format, and then sends the prompt text to the generative AI model.

[0083] Input: The prompt text sent from the terminal.

[0084] Output: The prompt that is sent to the generative AI model.

[0085] Step 3:

[0086] A generative AI model generates design patterns.

[0087] Based on the received prompt, the generative AI model generates multiple design patterns by combining various design elements (color, shape, layout, etc.).

[0088] Input: The prompt text sent by the server.

[0089] Output: Multiple generated design patterns.

[0090] Step 4:

[0091] The server stores the generated patterns in a database.

[0092] The server stores the design patterns received from the generative AI model in a database, giving each pattern a unique ID.

[0093] Input: Design patterns sent from the generative AI model.

[0094] Output: Design patterns stored in a database.

[0095] Step 5:

[0096] The user views the generated pattern on the terminal.

[0097] A user uses a terminal to browse the design patterns stored in the database. The generated patterns are displayed in thumbnail format on the terminal screen.

[0098] Input: Design patterns stored in a database.

[0099] Output: A thumbnail of the design pattern displayed on the terminal.

[0100] Step 6:

[0101] The user selects a pattern.

[0102] The user clicks on a design pattern they like on the device screen and presses the "Select this design" button. Detailed information about the selected pattern is displayed again, and the selection is confirmed by pressing the Confirm button.

[0103] Input: A thumbnail of the design pattern displayed on the terminal.

[0104] Output: The design pattern selected by the user.

[0105] Step 7:

[0106] The server stores the selected pattern and makes final adjustments if necessary.

[0107] The server receives the design patterns selected by the user, stores them in a database, and, if necessary, makes minor adjustments to the colors and layout based on the user's feedback to finalize the design.

[0108] Input: A design pattern selected by the user.

[0109] Output: The final adjusted design pattern stored in a database.

[0110] (Application example 1)

[0111] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0112] In conventional design production, the process of generating various patterns and selecting the final design was time-consuming and laborious. In addition, it was difficult to propose designs that took into account the user's preferences and past selection history, making efficient design production difficult. Furthermore, there was a lack of means to efficiently generate design patterns for product displays and store layouts in virtual stores and present them to users. In order to solve these problems, the present invention provides a design pattern generation system using AI.

[0113] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0114] In this invention, the server includes a means for generating patterns in design production, a means for making a final selection from an infinite number of patterns, a means for generating these patterns by AI, a means for generating a prompt sentence based on a user's input, a means for presenting the generated design patterns to the user, and a means for the AI ​​to present recommended designs based on the user's preferences and past selection history. This makes it possible to efficiently generate design patterns for product displays and store layouts in a virtual store and present them to the user.

[0115] "Means for generating patterns in design production" refers to a method or device for combining various elements of a design to generate a variety of design patterns.

[0116] A "means for making a final selection from an infinite number of patterns" is a method or device for selecting the most suitable one from the various design patterns generated.

[0117] "Means for generating these patterns using AI" refers to methods or devices for automatically generating design patterns using artificial intelligence.

[0118] A "means for generating a prompt sentence based on user input" refers to a method or device that creates an instruction sentence (prompt) for an AI to generate a design pattern based on information entered by a user.

[0119] "Means for presenting the generated design pattern to a user" refers to a method or device for visually displaying the design pattern generated by the AI ​​to a user.

[0120] "Means for an AI to recommend designs based on a user's preferences and past selection history" refers to a method or device that analyzes a user's past selections and preferred designs and uses them to suggest optimal designs.

[0121] The embodiment of the present invention will be described. First, Python (registered trademark) and OpenAI (registered trademark) API are used to generate the system program. The server includes a means for generating patterns in design production, a means for making a final selection from an infinite number of patterns, a means for generating these patterns by AI, a means for generating a prompt sentence based on a user's input, a means for presenting the generated design pattern to the user, and a means for the AI ​​to present a recommended design based on the user's preferences and past selection history.

[0122] The server receives information input by the user through a smartphone or head-mounted display and generates a prompt sentence based on that information. For example, if a user inputs "virtual store display design," the server generates a prompt sentence based on this information. This prompt sentence is sent to a generative AI model (e.g., GPT-3 (registered trademark)), which generates a design pattern.

[0123] The generated design patterns are sent from the server to the user's device and visually presented to the user. The user can select the most suitable one from the presented design patterns. The server also analyzes the user's preferences and past selection history, and the AI ​​presents recommended designs based on that. This allows the user to efficiently select the most suitable design.

[0124] For example, when a user inputs "Virtual store display design," the AI ​​generates patterns such as "Modern layout," "Classic layout," and "Minimalist layout" and presents them to the user. The user can then select the most suitable one from these patterns.

[0125] The following are examples of prompt sentences:

[0126] Generate design patterns based on the following criteria: Virtual store display design

[0127] In this way, it is possible to efficiently generate design patterns for product displays and store layouts in a virtual store and present them to the user.

[0128] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0129] Step 1:

[0130] The user requests the generation of a design pattern via a smartphone or head-mounted display.

[0131] Input: Design requirements entered by user (e.g. "Virtual store display design").

[0132] Output: User input data that is sent to the server.

[0133] Specific operation: A user launches the application, enters requirements into an input form to request the generation of a design pattern, and presses the submit button.

[0134] Step 2:

[0135] The server generates a prompt based on the user's input.

[0136] Input: The data entered by the user.

[0137] Output: Generated prompt statement (e.g. "Generate a design pattern based on the following criteria: Virtual store display design").

[0138] Specific operation: The server receives the user's input data and performs processing to generate a prompt sentence based on it.

[0139] Step 3:

[0140] The server sends the generated prompt sentence to the generative AI model to generate a design pattern.

[0141] Input: The generated prompt statement.

[0142] Output: The design patterns returned by the generative AI model.

[0143] Specific operation: The server sends the generated prompt sentence to the OpenAI API, and the generative AI model (e.g. GPT-3) generates a design pattern.

[0144] Step 4:

[0145] The server transmits the generated design pattern to the user's terminal and presents it to the user.

[0146] Input: Design patterns returned by a generative AI model.

[0147] Output: The design pattern that is displayed on the user's terminal.

[0148] Specific operation: The server sends the generated design pattern to the user's terminal, and the application displays it visually.

[0149] Step 5:

[0150] The user selects the most suitable design pattern from the presented ones.

[0151] Input: A design pattern presented to the user.

[0152] Output: The design pattern selected by the user.

[0153] Specific behavior: The user operates an interface to review the presented design patterns and select the most suitable one.

[0154] Step 6:

[0155] The server analyzes the user's preferences and past selection history, and the AI ​​presents recommended designs.

[0156] Input: User preferences and past selections.

[0157] Output: AI recommended design patterns.

[0158] Specific operation: The server retrieves the user's preferences and past selection history from the database, and the AI ​​performs processing based on that to recommend the optimal design.

[0159] Example 2

[0160] Next, a second embodiment of the second embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0161] In conventional design production, the process of efficiently generating a variety of patterns and allowing users to select the optimal design was complicated, time-consuming, and labor-intensive. It was also difficult to quickly provide designs that matched the user's preferences and the purpose of the application.

[0162] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0163] In this invention, the server includes a means for generating design patterns using a generative AI model, a means for saving the generated design patterns in a database, a means for a terminal to acquire the design patterns and display them to a user, a means for the user to select an optimal design pattern, a means for transmitting the user's selection to the server, and a means for saving the user's selection in the database and reflecting it in the user interface of the application. This enables the user to efficiently generate a variety of design patterns and quickly select an optimal design.

[0164] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to automatically generate design patterns.

[0165] A "design pattern" is a combination of visual elements such as the shape, color, and placement of buttons in the user interface of an application, website, etc.

[0166] "Database" is an information management system for storing generated design patterns and user selections.

[0167] A "terminal" is a device operated by a user and has the function of obtaining and displaying design patterns from a server.

[0168] A "user" is an individual or group that uses the system to select design patterns.

[0169] A "server" is a computer system that runs a generative AI model and generates, stores, and distributes design patterns.

[0170] A "prompt" is a piece of text that describes the instructions and conditions for the generative AI model to generate a design pattern.

[0171] "User interface" is a general term for the screens and operating elements that allow a user to interact with an application or website.

[0172] The present invention is a system that uses a generative AI model to automatically generate design patterns and allows a user to select an optimal design. A specific embodiment of this system will be described below.

[0173] Hardware and Software Used

[0174] Server: A computer system that executes generative AI models and generates, stores, and distributes design patterns. Specifically, it uses general server hardware and software for executing generative AI models (e.g., Python, TENSORFLOW (registered trademark), PyTorch, etc.).

[0175] Database: An information management system for storing the generated design patterns and user selections. Specifically, a relational database such as MySQL (registered trademark) or PostgreSQL is used.

[0176] Terminal: A device operated by a user that has the function of retrieving and displaying design patterns from a server. Specifically, this applies to PCs, smartphones, tablets, etc.

[0177] Generative AI model: An algorithm or program that uses artificial intelligence technology to automatically generate design patterns. Specifically, we use generative AI models such as OpenAI's GPT-4.

[0178] Data processing and calculation

[0179] 1. The server generates design patterns using a generative AI model. A prompt sentence is input to the generative AI model to generate multiple design patterns.

[0180] 2. The server stores the generated design patterns in a database, and each design pattern is assigned a unique ID.

[0181] 3. The terminal sends a request for a design pattern to the server and displays the design pattern received from the server to the user, using HTML and CSS to make the display visually easy to understand.

[0182] 4. The user selects the most suitable design pattern from the displayed patterns. The selection is made through interactions such as clicking and tapping.

[0183] 5. The terminal sends the ID of the design pattern selected by the user to the server using an HTTP request (e.g., POST request).

[0184] 6. The server saves the user's selection in a database and updates the application's user interface, possibly using a front-end framework (e.g., React or Vue.js).

[0185] Examples

[0186] For example, if a user wants to create a UI design for a new task management app, the system works as follows:

[0187] 1. The server uses the generative AI model to input the prompt, "Generate the UI design for a new task management app. Create 10 design patterns with combinations of round or square button shapes, blue, red, and green colors, and top, center, and bottom placement." and generate 10 UI design patterns.

[0188] 2. The server stores the generated UI design patterns in a MySQL database and gives each design pattern a unique ID.

[0189] 3. The terminal sends a request for a UI design pattern to the server and displays the design pattern received from the server to the user using HTML and CSS.

[0190] 4. Users click to select the design with a blue circular button in the center.

[0191] 5. The terminal sends the ID of the design pattern selected by the user to the server in a POST request.

[0192] 6. The server saves the user's selection to the database and updates the application's user interface using React.

[0193] In this way, users can leverage generative AI models to make efficient UI design choices.

[0194] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0195] Step 1:

[0196] The server generates design patterns using a generative AI model. Specifically, the server inputs a prompt sentence into the generative AI model to generate multiple design patterns. The server uses the prompt sentence "Generate a UI design for a new task management app. Create 10 design patterns with combinations of round or square button shapes, blue, red, and green colors, and top, center, and bottom placements" as input, and obtains 10 design patterns as output.

[0197] Step 2:

[0198] The server saves the generated design patterns in a database. Specifically, the server assigns a unique ID to each design pattern and saves it in a MySQL database. It receives the generated design patterns as input and obtains the design patterns saved in the database as output.

[0199] Step 3:

[0200] A terminal transmits a request for a design pattern to a server. Specifically, the terminal accesses the server using an HTTP request and requests a design pattern. The terminal transmits the request as input and receives the design pattern from the server as output.

[0201] Step 4:

[0202] The terminal displays the received design pattern to the user. Specifically, the terminal visually displays the design pattern using HTML and CSS. It uses the design pattern received from the server as input and obtains the design pattern displayed to the user as output.

[0203] Step 5:

[0204] The user selects the most suitable design pattern from the displayed design patterns. Specifically, the user selects a design pattern through interactions such as clicking or tapping. The displayed design patterns are referred to as input, and the selected design pattern is obtained as output.

[0205] Step 6:

[0206] The terminal sends the ID of the design pattern selected by the user to the server. Specifically, the terminal sends the ID of the selected design pattern to the server using an HTTP POST request. It receives the ID of the selected design pattern as input and obtains the ID sent to the server as output.

[0207] Step 7:

[0208] The server saves the user's selection in a database and reflects it in the application's user interface. Specifically, the server saves the ID of the selected design pattern in a database and updates the application's UI using a front-end framework such as React. It receives the ID of the selected design pattern as input and gets the updated application UI as output.

[0209] (Application example 2)

[0210] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0211] In conventional design production, the generation and selection of design patterns is done manually, which is time-consuming and labor-intensive. It is also difficult to generate designs that reflect the user's preferences, and a lot of trial and error is required to find the optimal design. Furthermore, there is a lack of functionality to preview the selected design in real time, making it difficult to increase user satisfaction.

[0212] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0213] In this invention, the server includes a means for generating patterns in design production, a means for making a final selection from an infinite number of patterns, a means for generating these patterns using AI, a means for reflecting the user's selection history in subsequent design generation, and a means for previewing the selected design in real time. This makes it possible to automatically generate design patterns and select an optimal design that reflects the user's preferences, and further increases user satisfaction with the real-time preview function.

[0214] "Design creation" is the process of combining visual elements to create a design that has a specific purpose or function.

[0215] "Pattern creation" is the act of combining design elements to generate multiple design patterns.

[0216] "Final selection" refers to the act of selecting the optimal design pattern from among the multiple design patterns generated.

[0217] "AI generation" is the process of automatically generating design patterns using artificial intelligence.

[0218] "User selection history" is a record of design patterns that the user has previously selected.

[0219] "Reflecting in subsequent design generation" means taking the user's preferences into consideration when generating subsequent design patterns based on the user's selection history.

[0220] "Real-time preview" means that the user can instantly see and check the design they have selected.

[0221] A system for implementing this invention includes means for generating patterns in design production, means for making a final selection from an infinite number of patterns, means for generating these patterns using AI, means for reflecting the user's selection history in subsequent design generation, and means for previewing the selected design in real time.

[0222] System Programming

[0223] The system is built using Python and TensorFlow. The server generates design elements (button shape, color, placement, etc.) and generates various design patterns using an AI model. Users select the most suitable one from the generated design patterns, and the selection history is reflected in subsequent design generation. The selected design is previewed in real time.

[0224] Processing Description

[0225] The server first generates design elements. Specifically, it randomly combines button shapes (e.g., rectangle, circle, oval), colors (e.g., red, blue, green), and layouts (e.g., top-left, top-right, bottom-left, bottom-right). Next, it uses TensorFlow to define a neural network model and generates various design patterns using the design elements as input.

[0226] The user selects the most suitable design pattern from the generated design patterns. The selection history is stored in a database and is reflected in the next and subsequent design generation. This allows the system to generate design patterns that take the user's preferences into account.

[0227] The real-time preview function allows users to instantly see and confirm the design they have selected, allowing them to instantly see the results of applying their selected design and increasing satisfaction.

[0228] Examples

[0229] For example, if a user selects a design with a "rectangle" shaped, "red" button placed on the "top-left", that selection history will be reflected in the next design generation. In the next design generation, a similar design pattern will be preferentially generated, taking into account the user's preferences.

[0230] Examples of prompt statements

[0231] "Generate 10 different UI design patterns by combining button shapes, colors, and placements."

[0232] In this way, it is possible to automatically generate design patterns using AI and select the optimal design that reflects user preferences, and the real-time preview function can further increase user satisfaction.

[0233] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0234] Step 1:

[0235] The server generates design elements by randomly combining button shapes (e.g. rectangle, circle, oval), colors (e.g. red, blue, green), and alignments (e.g. top-left, top-right, bottom-left, bottom-right). The input is a list of design elements, and the output is a combination of the generated design elements. The server randomly selects these elements to create candidate design patterns.

[0236] Step 2:

[0237] The server uses TensorFlow to define a neural network model. The input is a combination of design elements, and the output is a neural network model. The server takes design elements as input and builds a neural network model. This model is used to generate design patterns.

[0238] Step 3:

[0239] The server generates various design patterns using the neural network model. The input is a combination of the neural network model and the design elements, and the output is the generated design pattern. The server inputs the design elements to the model and generates multiple design patterns.

[0240] Step 4:

[0241] The user selects the most suitable one from the generated design patterns. The input is the generated design pattern, and the output is the design pattern selected by the user. The user checks the displayed design patterns and selects the most suitable one.

[0242] Step 5:

[0243] The server saves the user's selection history in a database. The input is the design pattern selected by the user, and the output is the updated database. The server records the user's selection history and reflects it in subsequent design generation.

[0244] Step 6:

[0245] The server generates design patterns based on the user's selection history for subsequent design generation. The input is the user's selection history and design elements, and the output is a design pattern that reflects the user's preferences. The server takes the selection history into consideration and generates design patterns that match the user's preferences with priority.

[0246] Step 7:

[0247] The terminal previews the selected design in real time. The input is the design pattern selected by the user, and the output is the preview screen. The terminal immediately displays the selected design for the user to confirm.

[0248] Example 3

[0249] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0250] Conventional design production requires time-consuming and labor-intensive pattern selection and final selection, making efficient design production difficult. In addition, it is difficult to quickly generate a variety of design patterns that meet the needs of users, and the flexibility required to provide creative designs is lacking.

[0251] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0252] In this invention, the server includes a means for a user to input a prompt sentence, a means for the server to receive the prompt sentence and send it to the generative AI model, a means for the generative AI model to generate a design pattern, a means for the server to send the generated design pattern to the user, and a means for the user to select a design pattern. This makes it possible to rapidly generate a variety of design patterns according to user requests and to perform design production efficiently and flexibly.

[0253] A "user" is an individual or group that uses the system to generate and select design patterns.

[0254] A "prompt" is text that a user enters into a generative AI model containing design requirements or wishes.

[0255] The "server" is a computer system that receives prompt text from a user, sends it to the generative AI model, and returns the generated design pattern to the user.

[0256] A "generative AI model" is an artificial intelligence model that automatically generates design patterns based on a prompt sentence.

[0257] A "design pattern" is a specific example of a web design that combines elements such as header design, font type, and background color, generated by a generative AI model.

[0258] "User interface design" refers to the design of the operation screen of an application or website, which is designed to allow users to operate it intuitively.

[0259] "Web design" is the design of the visual layout and style of a website to enhance the user's visual experience.

[0260] An "illustration" is an image or picture for the purpose of visual explanation or decoration and is used as part of a design.

[0261] A "banner" is a rectangular image or graphic used to display advertising or information within a web page or application.

[0262] A "key visual" is the central visual element of a design, used to emphasize the message of a brand or campaign.

[0263] An "icon" is a small graphic symbol intended to represent a function or action within an application or website.

[0264] This invention is a system in which a user inputs a prompt sentence, generates various design patterns using a generative AI model, and selects the optimal design. A specific embodiment of this system is described below.

[0265] Hardware and Software Used

[0266] Hardware: Servers, terminals (users' PCs and smartphones)

[0267] Software: Generative AI model (e.g., OpenAI's GPT-4), web browser, design tool (e.g., Adobe® XD)

[0268] System Overview

[0269] A user accesses the system's interface through a web browser and inputs a prompt statement including design requirements and wishes. The server receives the prompt statement and sends it to the generative AI model. The generative AI model generates multiple design patterns based on the prompt statement and sends them back to the server. The server sends the generated design patterns to the user's device, and the user selects the most suitable design from them.

[0270] Examples

[0271] For example, if a user enters a prompt like "I want you to generate a design for a cafe website using warm colors and handwritten fonts," the following design pattern will be generated when this prompt is input to the generative AI model.

[0272] Design variant 1: A handwritten font in the header, a beige background, and brown buttons.

[0273] Design variant 2: A handwritten font in the header, a light green background, and dark green buttons.

[0274] Design variant 3: A handwritten font in the header, a light blue background, and navy buttons.

[0275] Users can choose from these design patterns the one they like best.

[0276] Implementation details

[0277] 1. The user enters a prompt:

[0278] A user opens a web browser, accesses the system's interface, and enters a prompt statement containing the desired web design requirements and wishes into an input field on the interface.

[0279] 2. The server receives the prompt:

[0280] The user presses the send button to send the prompt text, and the server receives the prompt text via an HTTP request.

[0281] 3. The server sends a prompt to the generative AI model:

[0282] The server creates an API request to send the received prompt sentence to the generative AI model. It sends a request including the prompt sentence to the API endpoint of the generative AI model.

[0283] 4. Generative AI models generate design patterns:

[0284] The generative AI model analyzes the prompt text it receives and generates multiple web design variations that combine elements such as header design, font type, and background color.

[0285] 5. The server receives the generated design patterns:

[0286] The generative AI model sends the generated design pattern back to the server. The server receives the response from the generative AI model and obtains the design pattern.

[0287] 6. The server sends the design pattern to the user:

[0288] The server creates a response for sending the acquired design pattern to the user's terminal, and sends the response including the generated design pattern to the user's terminal.

[0289] 7. User chooses design pattern:

[0290] The user's device displays the received design patterns on a web browser. The user selects the design that best suits his or her preferences and the purpose of the website from the multiple design patterns displayed, and presses a button to confirm the selected design pattern.

[0291] In this way, the user can easily generate and select a web design that meets his / her needs by utilizing the generative AI model. The flow of the specification process in the third embodiment will be described with reference to FIG.

[0292] Step 1:

[0293] The user enters a prompt.

[0294] A user opens a web browser and accesses the system's interface. In an input field on the interface, the user enters a prompt statement that includes the requirements and desires of the desired web design. For example, the user may enter "I want you to generate a design for a website that has a modern and simple header." The input data is the prompt statement in text format.

[0295] Step 2:

[0296] The server receives the prompt.

[0297] The user presses the send button to send the prompt text entered by the user. The server receives the prompt text through an HTTP request. The received data is the prompt text entered by the user. The server analyzes the prompt text and passes it to the next processing step.

[0298] Step 3:

[0299] The server sends a prompt to the generative AI model.

[0300] The server creates an API request to send the received prompt sentence to the generative AI model. It sends a request including the prompt sentence to the API endpoint of the generative AI model. The input data is the prompt sentence, and the output data is the request to the generative AI model.

[0301] Step 4:

[0302] A generative AI model generates design patterns.

[0303] The generative AI model analyzes the received prompt text and generates multiple web design patterns that combine elements such as header design, font type, background color, etc. The input data is the prompt text, and the output data is the multiple generated design patterns. For example, the following design patterns are generated.

[0304] Design variant 1: A handwritten font in the header, a beige background, and brown buttons.

[0305] Design variant 2: A handwritten font in the header, a light green background, and dark green buttons.

[0306] Design variant 3: A handwritten font in the header, a light blue background, and navy buttons.

[0307] Step 5:

[0308] A server receives the generated design patterns.

[0309] The generative AI model returns the generated design pattern to the server. The server receives the response from the generative AI model and obtains the design pattern. The input data is the response from the generative AI model, and the output data is the obtained design pattern.

[0310] Step 6:

[0311] The server sends the design pattern to the user.

[0312] The server creates a response for sending the acquired design pattern to the user's terminal. The server sends the response including the generated design pattern to the user's terminal. The input data is the acquired design pattern, and the output data is the response sent to the user's terminal.

[0313] Step 7:

[0314] The user selects the design pattern.

[0315] The user's device displays the received design patterns on a web browser. The user selects the design that best suits his or her preferences and the purpose of the website from the multiple design patterns displayed. The user then presses a button to confirm the selected design pattern. The input data is the displayed design pattern, and the output data is the design pattern selected by the user.

[0316] (Application example 3)

[0317] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0318] In conventional design production, the generation and selection of design patterns is often done manually, which is problematic in terms of time and effort. In addition, when the user tries to select the optimal design, it is difficult to visually confirm it, making efficient design selection difficult. Furthermore, the process of applying the generated design to the actual system is also time-consuming, making it difficult to make rapid design changes. There is a demand for an efficient system to solve these issues.

[0319] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0320] In this invention, the server includes a means for generating patterns in design production, a means for making a final selection from an infinite number of patterns, a means for generating these patterns using a generative AI model, a means for displaying a list of generated design patterns, a means for a user to select an optimal design, a means for previewing the selected design, and a means for saving and applying the selected design. This enables efficient generation and selection of design patterns, and allows a user to select an optimal design while visually checking. It also enables the selected design to be quickly applied to an actual system.

[0321] "Design creation" is the process of combining visual elements to create a design based on a specific purpose or theme.

[0322] "Pattern creation" is the act of combining multiple design elements to generate various design patterns.

[0323] A "generative AI model" is an algorithm or system that uses artificial intelligence to automatically generate design patterns.

[0324] "List display" refers to displaying multiple generated design patterns side-by-side so that they can be checked at a glance.

[0325] "Means for the user to select the optimal design" refers to an interface or function that allows the user to select the most appropriate one from the generated design patterns.

[0326] "Preview" is a display function that allows you to check in advance how the selected design will look in the actual application environment.

[0327] The "means of saving and applying" is a function for saving the selected design in a database or the like and applying it to an actual system or website.

[0328] A system for implementing this invention includes means for generating patterns in design production, means for making a final selection from countless patterns, means for generating these patterns using a generative AI model, means for displaying a list of the generated design patterns, means for a user to select the optimal design, means for previewing the selected design, and means for saving and applying the selected design.

[0329] System Programming

[0330] The server generates design patterns using a generative AI model. Specifically, it uses machine learning libraries such as TensorFlow and Keras to generate various design patterns that combine design elements such as headers, fonts, and background colors. The generated design patterns are stored in a database and sent to the user's device.

[0331] The user's device displays a list of the generated design patterns. The user can visually check the generated design patterns using a device such as a smartphone or tablet and select the most suitable design. The selected design can be previewed to see how it will look in the actual application environment.

[0332] The selected design is sent to the server and stored in the database. The saved design is then applied to the actual system or website, allowing users to change the design quickly and efficiently.

[0333] Hardware and Software Used

[0334] Hardware: Servers, smartphones, tablets

[0335] Software: Python, TensorFlow, Keras, Database Management System

[0336] Examples

[0337] For example, when an online shopping site operator is creating a new product page design, they open the smartphone app and view 10 design patterns generated by AI. The operator selects the most suitable design from among them and uses the preview function to check how it will look when applied to the actual page. The selected design is saved on the server and immediately applied to the online shopping site.

[0338] Examples of prompt statements

[0339] "Create an application that uses AI to generate design variations for product pages and allow users to select the best design. Design elements include headers, fonts, and background colors. Display a list of generated design variations and allow users to preview and save the selected design."

[0340] The flow of the specific process in the application example 3 will be described with reference to FIG.

[0341] Step 1:

[0342] The server generates design patterns using a generative AI model. It receives design elements such as headers, fonts, and background colors as input, and combines them to generate a variety of design patterns. It uses TensorFlow and Keras to process the data, randomly combining elements and generating multiple design patterns as output.

[0343] Step 2:

[0344] The server stores the generated design patterns in a database. As input, it receives the design patterns generated in step 1 and stores them using a database management system. As data operation, it converts the design patterns into an appropriate format and obtains the design patterns stored in the database as output.

[0345] Step 3:

[0346] The server transmits the stored design pattern to the user terminal. As input, the server receives the design pattern stored in the database and transmits it to the user terminal via the network. As data processing, the server converts the design pattern into a transmittable format and obtains the design pattern transmitted to the user terminal as output.

[0347] Step 4:

[0348] The user terminal displays the received design patterns in a list.,As input, the design patterns sent from the server are,received and displayed on the user interface.,As data processing, the design patterns are converted,into a visually confirmable format, and a list of,design patterns is obtained as output.

[0349] Step 5:

[0350] The user selects the optimal design from the listed design patterns. As input, the user visually checks the listed design patterns and selects the optimal design. As data computation, the user's selection is recorded and the selected design pattern is obtained as output.

[0351] Step 6:

[0352] The user terminal previews the selected design pattern. It receives the design pattern selected by the user as input and displays how it will look in the actual application environment. As data processing, it converts the design pattern to suit the application environment and obtains the previewed design pattern as output.

[0353] Step 7:

[0354] The user checks the preview and saves the selected design. As input, the user checks the previewed design pattern and issues an instruction to save it. As data calculation, the instruction to save is recorded, and the design pattern instructed to be saved is obtained as output.

[0355] Step 8:

[0356] The server saves the designated design pattern in a database and applies it to the actual system. As input, the server receives the designated design pattern sent from the user terminal, saves it in a database, and applies it to the actual system. As data processing, the server converts the design pattern into an applicable format, and obtains the design pattern applied to the actual system as output.

[0357] Furthermore, an emotion engine that estimates the emotion of the user may be combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0358] "Example 1"

[0359] In one embodiment of the present invention, AI generates design patterns and makes a final selection from countless patterns. The system includes an emotion engine for recognizing the user's emotions. The emotion engine analyzes the user's emotions from facial expressions, tone of voice, text input, etc., and feeds the results back to the AI. For example, when the user is feeling happy, it recommends design patterns with bright colors and a fun image. Conversely, when the user is feeling depressed, it recommends design patterns with calm colors and a sense of security.

[0360] "Example 2"

[0361] In another embodiment, the emotion engine adjusts the design pattern output based on the user's emotions. Specifically, the emotion engine analyzes the user's emotions, and the AI ​​generates design patterns based on the results. For example, when the user is feeling happy, the AI ​​generates design patterns with bright colors and a fun image. Conversely, when the user is depressed, the AI ​​generates design patterns with calm colors and a sense of security.

[0362] "Example 3"

[0363] In yet another embodiment, the emotion engine supports the final selection based on the user's emotions. Specifically, the emotion engine analyzes the user's emotions, and the AI ​​supports the final selection based on the results. For example, when the user is feeling happy, the AI ​​recommends design patterns with bright colors and fun images. Conversely, when the user is feeling depressed, the AI ​​recommends design patterns with calm colors and a sense of security.

[0364] The process flow of each embodiment will be described below.

[0365] "Example 1"

[0366] Step 1: The user boots up the system and begins patterning the design.

[0367] Step 2: The emotion engine analyzes the user's emotions from facial expressions, tone of voice, text input, etc.

[0368] Step 3: The analysis results of the emotion engine are fed back to the AI, which then creates design patterns.

[0369] Step 4: Present the AI-generated design patterns to the user and make the final selection.

[0370] "Example 2"

[0371] Step 1: The user boots up the system and begins patterning the design.

[0372] Step 2: The emotion engine analyzes the user's emotions.

[0373] Step 3: AI generates design patterns based on the emotion engine analysis results.

[0374] Step 4: Present the AI-generated design patterns to the user and make the final selection.

[0375] "Example 3"

[0376] Step 1: The user boots up the system and begins patterning the design.

[0377] Step 2: The emotion engine analyzes the user's emotions.

[0378] Step 3: AI helps with the final selection based on the emotion engine analysis results.

[0379] Step 4: Present the AI-recommended design patterns to the user and make the final selection.

[0380] Example 1

[0381] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal".

[0382] In conventional pattern-creation systems, it was difficult to generate design patterns that reflected the user's emotions and preferences. In addition, when selecting the optimal design from countless patterns, the user's emotions could not be taken into consideration, resulting in a lack of improvement in the user experience.

[0383] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0384] In this invention, the server includes a means for generating patterns in design production, a means for making a final selection from an infinite number of patterns, a means for generating these patterns by artificial intelligence, a means for analyzing the user's emotions, and a means for feeding back the analysis results to the artificial intelligence. This makes it possible to generate design patterns according to the user's emotions and preferences, and to select the most suitable design.

[0385] "Design creation" is the creative process of combining visual elements to convey a particular purpose or message.

[0386] "Pattern creation" is the process of generating multiple different design variations or layouts.

[0387] "Countless patterns" refers to a huge number of different design variations.

[0388] "Final selection" is the process of selecting the optimal design pattern from among the multiple design patterns generated.

[0389] "Artificial intelligence" is the technology that enables computer systems to mimic human intelligence by learning, reasoning, and self-correcting.

[0390] "User emotion" refers to the psychological response a user has to a particular situation or stimulus.

[0391] "Analysis" is the process of examining data or information in detail to reveal its structure and meaning.

[0392] "Feedback" is the process of providing information to a system to adjust its next action or processing based on the information it has obtained.

[0393] This invention is a system for generating patterns in design production, which uses artificial intelligence to generate design patterns according to the user's emotions and select the optimal design.

[0394] First, the user accesses the system using a terminal and inputs the design requirements. For example, the user may input, "I want you to generate a design pattern with a fun image in bright colors." The server receives this input and sends the prompt sentence, "Please generate a design pattern with a fun image in bright colors," to the generative AI model.

[0395] As a generative AI model, for example, a general-purpose natural language processing model such as OpenAI's GPT-4 is used. The AI ​​model generates multiple design patterns based on the prompt sentence and returns them to the server.

[0396] The server presents the generated design patterns to the user. The user checks these patterns on the screen of the terminal. While the user is checking the presented design patterns, the emotion engine analyzes the user's facial expressions and tone of voice. As the emotion engine, for example, general emotion analysis software is used. Specifically, Azure (registered trademark) Cognitive Services by Microsoft (registered trademark) is one example.

[0397] The emotion engine analyzes the user's emotions and sends the results to the server. For example, if the user is smiling, it detects "joy" and sends the result to the server. The server feeds back the analysis results to the AI, and issues instructions such as, "The user is feeling happy, so please generate more design patterns with bright colors and a fun image."

[0398] Based on this feedback, the AI ​​generates design patterns that further match the user's preferences and sends them back to the server. The server then presents the final design patterns to the user, who can then select the best one from these patterns.

[0399] As a concrete example, if a user inputs "I want a design pattern with a bright color scheme and a fun image to be generated," the server will send the following prompt to the generative AI model:

[0400] "Create design patterns with fun images in bright colors."

[0401] The generative AI model generates multiple design patterns based on this prompt and returns them to the server, which then presents them to the user and analyzes the user's emotions to generate further optimal design patterns.

[0402] In this way, it becomes possible to generate design patterns according to the user's feelings and preferences and to select the most suitable design.

[0403] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0404] Step 1:

[0405] The user inputs the design requirements.

[0406] A user accesses the system using a terminal and inputs design requirements. For example, the user might type, "I want you to generate a design pattern with a bright color scheme and a fun image." The input data is sent to the server in text format.

[0407] Step 2:

[0408] The server receives user input and sends prompts to the AI ​​model.

[0409] The server receives the user's input and converts it into a prompt. For example, the prompt might be "Please generate a design pattern with bright colors and fun images." This prompt is then sent to the generative AI model. The input is the user's requirement text, and the output is the prompt.

[0410] Step 3:

[0411] The AI ​​model generates design patterns based on the prompt.

[0412] A generative AI model (e.g., GPT-4) receives a prompt and generates multiple design patterns. The input is the prompt, and the output is a set of generated design patterns. The AI ​​model internally performs natural language processing and combination of design elements.

[0413] Step 4:

[0414] The server presents the generated design patterns to the user.

[0415] The server presents the design patterns received from the AI ​​model to the user, who then views these patterns on the screen of his or her device. The input is the set of generated design patterns, and the output is the design patterns displayed on the user's screen.

[0416] Step 5:

[0417] The user checks the presented design patterns.

[0418] The user reviews the presented design patterns and decides which one they like best. At this time, the emotion engine analyzes the user's facial expressions and tone of voice. The input is the visual information of the design patterns, and the output is the user's emotional response.

[0419] Step 6:

[0420] The emotion engine analyzes the user's emotions.

[0421] The emotion engine analyzes emotions from the user's facial expressions, tone of voice, text input, etc. For example, if the user is smiling, it detects "happiness." The input is the user's facial expression and voice data, and the output is the analyzed emotion information.

[0422] Step 7:

[0423] The server feeds back the results of the emotion engine's analysis to the AI.

[0424] The server feeds back the analysis results of the emotion engine to the AI. For example, it may give feedback such as, "The user is feeling happy, so please generate more design patterns with bright colors and fun images." The input is the analyzed emotion information, and the output is the feedback instruction text.

[0425] Step 8:

[0426] The AI ​​will generate further design patterns based on the feedback.

[0427] Based on the feedback, the AI ​​generates design patterns that further match the user's preferences. The generated patterns are sent to the server again. The input is the feedback instructions, and the output is a set of newly generated design patterns.

[0428] Step 9:

[0429] The server presents the final design pattern to the user.

[0430] The server presents the final generated design patterns to the user, who can select the best one from these patterns. The input is the set of newly generated design patterns, and the output is the final design pattern displayed on the user's screen.

[0431] (Application example 1)

[0432] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0433] In conventional design production systems, design patterns are generated without considering the user's emotions, making it difficult to provide designs that match the user's emotions. In addition, when selecting the optimal design from countless design patterns, there was a lack of a means to reflect the user's emotions, making it impossible to increase user satisfaction.

[0434] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0435] In this invention, the server includes a means for generating patterns in design production, a means for making a final selection from an infinite number of patterns, a means for generating these patterns by AI, a sentiment analysis means for recognizing the user's sentiment, and a means for recommending a design pattern based on the sentiment analysis result, thereby making it possible to generate and select a design pattern according to the user's sentiment.

[0436] A "means for generating patterns in design production" is a means for combining various elements of design to generate a variety of design patterns.

[0437] "Means for making final selection from an infinite number of patterns" refers to a means for selecting the most suitable one from among the generated design patterns.

[0438] "Means for generating these patterns using AI" refers to means for automatically generating design patterns using artificial intelligence.

[0439] The "emotion analysis means for recognizing a user's emotions" refers to a means for recognizing a user's emotions by analyzing the user's facial expressions, tone of voice, text input, etc.

[0440] The "means for recommending a design pattern based on the result of emotion analysis" is a means for proposing an appropriate design pattern based on the user's emotion obtained by the emotion analysis means.

[0441] A system for implementing this invention includes means for generating patterns in design production, means for making a final selection from an infinite number of patterns, means for generating these patterns using AI, emotion analysis means for recognizing a user's emotions, and means for recommending design patterns based on the results of the emotion analysis.

[0442] System Configuration

[0443] The server uses the following hardware and software:

[0444] Hardware: Camera (to capture the user's facial expressions)

[0445] Software: OpenCV (image processing), Keras (executing emotion recognition models and design pattern generation models), TextBlob (analyzing emotions from text)

[0446] Processing flow

[0447] 1. User sentiment analysis:

[0448] The device's camera captures the user's facial expression and obtains image data.

[0449] The server uses an emotion recognition model (using Keras) to analyze the user's emotions from the captured image data.

[0450] Furthermore, the text entered by the user is parsed using TextBlob and emotions are obtained from the text.

[0451] 2. Design Pattern Generation:

[0452] The server generates design patterns based on the analyzed emotion data using a design pattern generation model (using Keras).

[0453] The generated design pattern corresponds to the user's emotions; for example, if the user is having fun, a bright and colorful design is generated, whereas if the user is depressed, a calm and subdued design is generated.

[0454] 3. Displaying Design Patterns:

[0455] The terminal displays the generated design pattern on the screen.

[0456] The user can select the most suitable design from the displayed designs.

[0457] Examples

[0458] For example, when a user enters a virtual store, a camera captures the user's facial expression and analyzes their emotions. If the user types, "I'm in a happy mood today," the system will generate bright and colorful design patterns and change the design of the store.

[0459] Examples of prompt statements

[0460] "Analyze the user's facial expressions and text input and generate design patterns based on their emotions. If the user is happy, suggest a bright and colorful design, and if the user is depressed, suggest a calm color scheme."

[0461] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0462] Step 1:

[0463] The device's camera captures the user's facial expression and obtains image data.

[0464] Input: User's facial expression

[0465] Output: Image data

[0466] Specific operation: The device's camera takes a picture of the user's face in real time and sends the image data to the server.

[0467] Step 2:

[0468] The server uses an emotion recognition model (using Keras) to analyze the user's emotions from the captured image data.

[0469] Input: Image data

[0470] Output: Emotion data (e.g., happiness, sadness, surprise, etc.)

[0471] Specific operation: The server inputs the received image data into the emotion recognition model, and the model infers the user's emotion from the image. The estimated emotion data is output as a numerical value or a category.

[0472] Step 3:

[0473] The server parses the text entered by the user using TextBlob and extracts the sentiment from the text.

[0474] Input: User text input

[0475] Output: Text sentiment data (e.g. positive, negative, neutral, etc.)

[0476] Specific operation: The server passes the text entered by the user to TextBlob, which performs sentiment analysis on the text. TextBlob outputs the sentiment of the text as a number or category.

[0477] Step 4:

[0478] The server generates design patterns based on the analyzed emotion data using a design pattern generation model (using Keras).

[0479] Input: emotion data, text emotion data

[0480] Output: Design Patterns

[0481] Specific operation: The server inputs emotion data and text emotion data into the design pattern generation model, and the model generates appropriate design patterns based on these data. The generated design patterns are output as images or layouts.

[0482] Step 5:

[0483] The terminal displays the generated design pattern on the screen.

[0484] Input: Design Patterns

[0485] Output: Displayed design pattern

[0486] Specific operation: The terminal displays the design pattern received from the server on the screen, allowing the user to visually confirm it.

[0487] Step 6:

[0488] The user selects the most suitable design pattern from the displayed patterns.

[0489] Input: The displayed design pattern

[0490] Output: User's choice

[0491] Specific operation: The user looks at multiple design patterns displayed on the terminal screen and selects the one he likes best. The selected design pattern is sent from the terminal to the server.

[0492] Example 2

[0493] Next, a second embodiment of the second embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0494] In conventional design production, the generation and selection of design patterns is often done manually, which is time-consuming and labor-intensive. In addition, it is difficult to adjust the design according to the user's emotions, making it difficult to improve the user experience. Furthermore, there is a lack of systems that can efficiently generate a variety of design patterns and select the optimal one.

[0495] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0496] In this invention, the server includes a means for a user to input a prompt sentence, a means for the server to receive the prompt sentence and send it to the generative AI model, a means for the generative AI model to generate a UI design pattern, a means for the server to save the generated design pattern in a database, a means for a user to browse the design patterns using a terminal, a means for the user to select an optimal design pattern, a means for the server to record the selected design pattern, a means for an emotion engine to analyze the emotion of the user, and a means for the server to adjust the design pattern based on the emotion analysis result. This enables efficient generation and selection of design patterns, and further enables adjustment of the design according to the emotion of the user.

[0497] A "prompt sentence" is a sentence containing instructions or requirements that a user inputs to a generative AI model.

[0498] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate user interface designs and other design patterns.

[0499] A "UI design pattern" is a design pattern that combines elements such as button shape, color, and placement in user interface design.

[0500] A "database" is a system for storing and managing generated design patterns and other data.

[0501] A "terminal" is a device that a user uses to view design patterns and enter prompt statements.

[0502] An "emotion engine" is software or a system that analyzes a user's facial expressions and voice to identify the user's emotional state.

[0503] The "server" is a computer system that receives prompts, sends them to the generative AI model, stores and manages design patterns, and processes the results of the emotion engine.

[0504] This invention is a system that allows a user to input a prompt sentence, generates various UI design patterns using a generative AI model, and selects the most suitable design pattern. Furthermore, it can adjust the design according to the user's emotions using an emotion engine.

[0505] The server receives a prompt sentence from the user and sends it to the generative AI model. The generative AI model generates a UI design pattern based on the prompt sentence. The generated design pattern is stored in a database by the server. The user uses a terminal to browse the design patterns and selects the most suitable design pattern. The selected design pattern is recorded by the server.

[0506] The emotion engine analyzes the user's facial expressions and voice to identify the user's emotional state. The server then uses the generative AI model again based on the emotion analysis results to adjust the design pattern. For example, if the user is feeling happy, it generates a design pattern with bright colors, and if the user is feeling depressed, it generates a design pattern with calm colors.

[0507] As a concrete example, let's say a user is thinking about the UI design of a new music app. The user opens a browser on their device and enters the following prompt into a special input form: “Generate a UI design for a new music app. Consider the shape, color, and placement of buttons, and use bright colors when the user is happy and muted colors when the user is sad.”

[0508] The server receives this prompt text and sends a request to the API of the generative AI model. The generative AI model analyzes the prompt text and generates multiple UI design patterns. The generated design patterns are returned to the server in JSON format. The server analyzes the received JSON data and saves the design patterns in a database.

[0509] The user opens the design pattern viewing page in the device's browser and checks the design patterns retrieved from the database. Thumbnail images and detailed information are displayed on the page. The user clicks on the design pattern they like on the viewing page and presses the Select button. The ID of the selected design pattern is sent to the server. The server records the ID of the design pattern selected by the user in the database.

[0510] If the user allows emotion analysis, the device's camera and microphone are activated to capture the user's facial expressions and voice. The emotion engine analyzes the captured data in real time to identify the user's emotional state. The server sends the emotion analysis results received from the emotion engine to the generative AI model to regenerate the design pattern. The new design pattern generated is stored in the database again and presented to the user.

[0511] In this way, efficient generation and selection of design patterns is possible, and further, the design can be adjusted according to the user's emotions.

[0512] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0513] Step 1:

[0514] The user enters a prompt sentence.

[0515] The user opens the browser on the device and inputs a prompt text into a dedicated input form. For example, the user might input, "Generate a UI design for a new music app. Consider the shape, color, and placement of the buttons, and design them in bright colors when the user is feeling happy, and calm colors when the user is feeling depressed." The input prompt text is sent to the server.

[0516] Step 2:

[0517] The server receives the prompt text and sends it to the generative AI model.

[0518] The server receives the prompt text sent by the user. It sends the received prompt text as a request to the API of the generative AI model. The input is the prompt text, and the output is the design pattern data returned from the generative AI model.

[0519] Step 3:

[0520] A generative AI model generates UI design patterns.

[0521] The generative AI model generates multiple UI design patterns based on the received prompt sentence. The generated design patterns are returned to the server in JSON format. The input is the prompt sentence, and the output is the data of the generated design pattern.

[0522] Step 4:

[0523] The server stores the generated design patterns in a database.

[0524] The server parses the JSON data received from the generative AI model and saves the design patterns in a database. The input is the data of the generated design patterns, and the output is the design patterns saved in the database.

[0525] Step 5:

[0526] A user uses a terminal to view the design pattern.

[0527] The user opens the design pattern viewing page in the browser of the terminal and checks the design patterns retrieved from the database. The page displays thumbnail images and detailed information. The input is the design pattern retrieved from the database, and the output is the design pattern displayed on the user's screen.

[0528] Step 6:

[0529] The user selects the most suitable design pattern.

[0530] The user clicks on the design pattern he likes on the browse page and presses the select button. The ID of the selected design pattern is sent to the server. The input is the design pattern selected by the user, and the output is the ID of the design pattern sent to the server.

[0531] Step 7:

[0532] The server records the selected design pattern.

[0533] The server records the ID of the design pattern selected by the user in the database. The record associates the user ID with the design pattern ID. The input is the ID of the selected design pattern, and the output is the ID of the design pattern recorded in the database.

[0534] Step 8:

[0535] The emotion engine analyzes user emotions (optional).

[0536] If the user allows emotion analysis, the device's camera and microphone are activated to capture the user's facial expressions and voice. The emotion engine analyzes the captured data in real time to identify the user's emotional state. The input is the captured facial and voice data, and the output is the analyzed emotional state.

[0537] Step 9:

[0538] The server optionally adjusts design patterns based on sentiment analysis results.

[0539] The server sends the emotion analysis results received from the emotion engine to the generative AI model, which regenerates the design pattern. For example, if the user feels happy, the server instructs the generative AI model to "generate a design pattern with bright colors." The new design pattern generated is saved in the database again and presented to the user. The input is the emotion analysis result, and the output is the adjusted design pattern.

[0540] (Application example 2)

[0541] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0542] Conventional design production systems have difficulty generating design patterns based on user emotions, and are therefore unable to provide optimal designs that reflect the user's emotions and preferences. In addition, there is a problem in that selecting the optimal design from a myriad of design patterns places a heavy burden on the user, making it difficult to make an efficient selection.

[0543] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0544] In this invention, the server includes a means for generating patterns in design production, a means for making a final selection from an infinite number of patterns, a means for generating these patterns by AI, a means for analyzing the user's emotions, a means for adjusting the design patterns based on the analyzed emotions, a means for presenting the generated design patterns to the user, and a means for the user to select the optimal design pattern. This enables the generation and efficient selection of optimal design patterns based on the user's emotions.

[0545] A "means for generating patterns in design production" is a means for generating a variety of design patterns by combining design elements (shape, color, arrangement, etc.).

[0546] "Means for making the final selection from countless patterns" refers to a means for the user to select the most suitable one from the diverse design patterns generated.

[0547] "Means for generating these patterns using AI" refers to means for automatically generating design patterns using artificial intelligence.

[0548] "Means for analyzing user emotions" refers to means for analyzing the user's facial expressions and behavior to identify the user's emotional state.

[0549] The "means for adjusting a design pattern based on the analyzed emotion" refers to a means for adjusting the color tone and shape of a generated design pattern according to the emotional state of a user.

[0550] The "means for presenting the generated design pattern to the user" refers to a means for visually displaying the adjusted design pattern to the user.

[0551] "Means for the user to select the most suitable design pattern" refers to a means for the user to select the most suitable one from the presented design patterns.

[0552] A system for implementing this invention includes means for generating patterns in design production, means for making a final selection from an infinite number of patterns, means for generating these patterns using AI, means for analyzing a user's emotions, means for adjusting the design patterns based on the analyzed emotions, means for presenting the generated design patterns to a user, and means for the user to select the optimal design pattern.

[0553] Hardware and Software Used

[0554] Hardware: Smartphone camera

[0555] Software: OpenCV (image processing library), Keras (deep learning library), Numpy (numerical calculation library)

[0556] System processing overview

[0557] 1. User sentiment analysis:

[0558] An image of the user's face is captured using the smartphone camera.

[0559] Using OpenCV, the facial images are converted to grayscale and input into the emotion analysis model.

[0560] Using Keras, the sentiment analysis model predicts user emotions.

[0561] 2. Design Pattern Generation:

[0562] Based on the predicted emotion, AI determines the color tone, shape, and placement of the design pattern.

[0563] The generated design pattern is presented to the user.

[0564] 3. Your Choices:

[0565] The user selects the most appropriate design pattern from the presented ones.

[0566] Examples

[0567] For example, when a user opens the app, the camera will automatically launch and capture the user's face. If the emotion analysis model predicts the user's emotion as "happy," a bright color scheme will be generated. The user can then choose the one they like best from the generated patterns.

[0568] Examples of prompt statements

[0569] When a user opens the app, the app will launch the camera to capture a facial image and use an emotion analysis model to predict the user's emotion. Based on the predicted emotion, the app will determine the color tone, shape, and layout of the UI design, and present the generated design patterns to the user. The user can then select the most suitable design.

[0570] In this way, it becomes possible to generate and efficiently select optimal design patterns based on user emotions.

[0571] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0572] Step 1:

[0573] When a user opens the app, the device activates the camera and captures the user's facial image. The input is the real-time video from the camera, and the output is the captured facial image, which is used for subsequent emotion analysis.

[0574] Step 2:

[0575] The device converts the captured face image to grayscale using OpenCV. The input is the captured face image, and the output is a grayscale image. This grayscale image is then converted into a format suitable for the emotion analysis model.

[0576] Step 3:

[0577] The device resizes and normalizes the grayscale image for input to the sentiment analysis model. The input is a grayscale image, and the output is normalized image data. This data is converted to a format suitable for the sentiment analysis model.

[0578] Step 4:

[0579] The device inputs normalized image data into an emotion analysis model using Keras to predict the user's emotion. The input is normalized image data, and the output is an emotion label (e.g., happy, sad, neutral). This emotion label is used to generate subsequent design patterns.

[0580] Step 5:

[0581] The server uses AI to determine the color tone, shape, and placement of the design pattern based on the predicted emotion label. The input is the emotion label, and the output is the generated design pattern. This design pattern is presented to the user.

[0582] Step 6:

[0583] The terminal visually presents the generated design pattern to the user. The input is the generated design pattern, and the output is the design pattern displayed on the user's screen. The user visually confirms this design pattern.

[0584] Step 7:

[0585] The user selects the best design pattern from the presented design patterns. The input is the design patterns presented to the user, and the output is the design pattern selected by the user. This selection is fed back to the system and finalized as the design.

[0586] Example 3

[0587] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0588] In conventional design production systems, users had to spend a lot of time and effort selecting the best design from a myriad of design patterns. In addition, there was a lack of a way to optimize the design based on the user's emotions, and the user experience was not sufficiently improved.

[0589] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0590] In this invention, the server includes a means for selecting patterns in design production, a means for making a final selection from an infinite number of patterns, a means for generating these patterns by artificial intelligence, a means for analyzing the user's emotions, and a means for recommending an optimal design pattern based on the analysis results. This allows the user to efficiently select an optimal design pattern, and further enables optimization of the design based on the user's emotions.

[0591] "Means for generating patterns in design production" refers to a function for combining design elements to generate multiple design patterns.

[0592] "Means for making the final selection from countless patterns" is a function that enables the user to select the optimal one from the multiple design patterns that have been generated.

[0593] "Means for generating using artificial intelligence" refers to a function for automatically generating design patterns using machine learning models and algorithms.

[0594] The "means for analyzing the user's emotions" refers to a function for analyzing the user's emotional state, and may use, for example, facial expression recognition or text analysis.

[0595] The "means for recommending the optimal design pattern based on the analysis results" is a function for suggesting the optimal design pattern to a user based on the results of the user's sentiment analysis.

[0596] This invention is a system for generating patterns in design production and making a final selection from an infinite number of patterns. The system uses artificial intelligence to generate design patterns and includes a function for analyzing user sentiment to recommend the most suitable design pattern.

[0597] Hardware and Software Used

[0598] Hardware: Servers, user devices (PCs, smartphones, etc.)

[0599] Software: Generative AI models (e.g. GPT-4), sentiment analysis engines (e.g. IBM Watson(R) Tone Analyzer)

[0600] Details of data processing and calculation

[0601] 1. Generating Web Design Patterns

[0602] The server uses a generative AI model to generate a variety of web design variations by combining elements such as header design, font type, and background color.

[0603] Example: A server sends a prompt to a generative AI model: "Generate a web design pattern with a blue header, Arial font, and white background."

[0604] 2. Your Choices

[0605] Users use their device to select the most suitable one from the generated web design patterns.

[0606] Example: A user selects "Blue header and Arial font design."

[0607] 3. Supporting the final choice with an emotion engine

[0608] The server uses a sentiment analysis engine to analyze the user's sentiment.

[0609] Based on the analysis results, the generative AI model recommends design patterns that support the final selection.

[0610] Example: If the user is feeling happy, the server might recommend "design patterns with bright colors and fun images."

[0611] Examples of prompt statements

[0612] "Generate a web design pattern with a blue header, Arial font, and a white background."

[0613] "Recommend web design patterns that are appropriate when users experience delight."

[0614] This system aims to improve the user experience by providing an optimal web design based on the user's preferences and emotions. The flow of the specification process in the third embodiment will be described with reference to FIG.

[0615] Step 1:

[0616] A user requests the generation of a web design.

[0617] Specific actions: A user uses a terminal to open a web browser, access the system's web page, and click the "Generate Design" button.

[0618] Input: User request

[0619] Output: A request to generate the design is sent to the server.

[0620] Step 2:

[0621] The server generates web design patterns using the generative AI model.

[0622] Specific operation: The server sends a prompt to the generative AI model: "Generate a web design pattern that uses blue for the header, Arial font, and white for the background."

[0623] Input: User request, prompt

[0624] Output: Generated multiple web design patterns

[0625] Step 3:

[0626] The server presents the generated design patterns to the user.

[0627] Specific operation: The server displays the generated design pattern in HTML format in the user's web browser.

[0628] Input: Generated web design patterns

[0629] Output: Design patterns that are displayed on the user's device

[0630] Step 4:

[0631] The user selects the design pattern.

[0632] Specific behavior: User clicks to select the design pattern they like.

[0633] Input: User's choice

[0634] Output: Information about the selected design pattern is sent to the server.

[0635] Step 5:

[0636] The server uses a sentiment analysis engine to analyze the user's sentiment.

[0637] Specific operation: The server sends the user's facial expression at the time of selection and the entered text to the emotion analysis engine and receives the analysis results.

[0638] Input: User facial expression data and text data

[0639] Output: User sentiment analysis results

[0640] Step 6:

[0641] The server recommends optimal design patterns based on the results of sentiment analysis.

[0642] Specific behavior: The server again presents an appropriate design pattern to the user based on the logic that "if the user is feeling happy, then a design pattern with bright colors and a fun image is recommended."

[0643] Input: User sentiment analysis results

[0644] Output: Suggested design patterns

[0645] (Application example 3)

[0646] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0647] Conventional design production systems lack the functionality to recommend optimal designs based on the user's emotions, making it difficult for users to select designs that correspond to their psychological state. In addition, there is a problem that the burden on users is heavy when selecting the optimal design from a myriad of design patterns.

[0648] The specification process by the specification processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means. In this invention, the server includes a means for selecting patterns in design production, a means for making a final selection from an infinite number of patterns, a means for generating these patterns by artificial intelligence, a means for analyzing the user's emotions, and a means for recommending an optimal design based on the analyzed emotions. This makes it possible to recommend an optimal design based on the user's emotions, and enables the user to select a design according to their psychological state while reducing the burden on the user.

[0649] "Pattern creation in design production" is the process of combining design elements to generate a variety of design patterns.

[0650] "Making the final selection from countless patterns" refers to the process of selecting the optimal one from the diverse design patterns generated.

[0651] "Artificial intelligence" is the technology that enables computer systems to mimic human intelligence by learning, reasoning, and self-correcting.

[0652] "Analyzing user emotions" is the process of identifying a user's psychological state from their input and behavior.

[0653] "Recommending optimal design based on analyzed emotions" is the process of proposing the most suitable design pattern based on the results of user emotion analysis.

[0654] "User interface design" is the design of the visual and functional elements that allow a user to interact with an application or website.

[0655] "Web design" is the design of the visual and functional elements of a website.

[0656] "Illustration" is the creation of images or pictures to communicate a visual story or concept.

[0657] A "banner" is a rectangular image or graphic used to display advertising or information within a web page or application.

[0658] A "key visual" is an image or graphic used as the main visual element of a brand or campaign.

[0659] An "icon" is a small image or symbol that visually represents a function or information within an application or website.

[0660] A system for implementing this invention includes a means for generating patterns in design production, a means for making a final selection from an infinite number of patterns, a means for generating these patterns using artificial intelligence, a means for analyzing a user's emotions, and a means for recommending an optimal design based on the analyzed emotions.

[0661] The server uses artificial intelligence to generate various design patterns for design production. Specifically, it generates multiple design patterns by combining elements such as header design, font type, and background color. For this, a generative AI model using Python and other programming languages ​​is used.

[0662] Next, an emotion engine is used to analyze the user's emotions. The emotion engine analyzes the user's input text and behavioral data to identify their psychological state. For example, if a user enters "I'm feeling very happy today!", the emotion engine detects "joy." The EmotionEngine library is used for this emotion analysis.

[0663] Based on the analyzed emotions, the server recommends the optimal design. From the generated design patterns, the design that best suits the user's emotions is selected and recommended. For example, if the user feels "joy", designs with bright colors and fun images are recommended. The DesignGenerator library is used in this recommendation process.

[0664] For example, if a user types "I'm feeling very happy today!", the emotion engine will detect "joy" and recommend designs with bright colors. On the other hand, if a user types "I'm feeling a bit down," the emotion engine will detect "sadness" and recommend designs with calm colors.

[0665] An example of a prompt is as follows:

[0666] User Input: "I'm feeling very happy today!"

[0667] Emotion engine output: "Joy"

[0668] Recommended design: Light color design

[0669] In this way, it becomes possible to recommend optimal designs based on the user's emotions, reducing the burden on the user and enabling them to select designs that suit their psychological state.

[0670] The flow of the specific process in the application example 3 will be described with reference to FIG.

[0671] Step 1:

[0672] The user inputs their emotions. The user inputs their emotions or mood in text format through the interface of their smartphone or computer. For example, they input "I'm feeling very happy today!"

[0673] Step 2:

[0674] The server receives user input and sends it to the emotion engine. The input text data is passed to the EmotionEngine library for emotion analysis. The input is the user's text data, and the output is the analyzed emotion (e.g. "joy").

[0675] Step 3:

[0676] The server receives the analysis results from the emotion engine and sends them to the design generation engine. Based on the analyzed emotion data, multiple design patterns are generated using the DesignGenerator library. The input is the analyzed emotion data, and the output is a list of generated design patterns.

[0677] Step 4:

[0678] The server receives the list of generated design patterns and selects the optimal design based on the sentiment. It uses the DesignGenerator library to select the design pattern that best matches the analyzed sentiment. The input is the list of design patterns and sentiment data, and the output is the optimal design pattern.

[0679] Step 5:

[0680] The server recommends the optimal design pattern to the user. The selected design pattern is sent to the user's terminal and displayed to the user. The input is the optimal design pattern, and the output is the design displayed on the user's terminal.

[0681] Step 6:

[0682] The user reviews the recommended designs and makes a final selection. The user selects the one he likes best from the displayed design patterns and makes a decision. The input is the recommended design pattern, and the output is the user's final selection.

[0683] In this way, optimal design recommendations and selections are made based on the user's emotions.

[0684] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0685] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0686] Another example of generative AI is Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0687] In the above embodiment, an example was given in which the specific process was performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0688] [Second embodiment]

[0689] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0690] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0691] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0692] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0693] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0694] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0695] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0696] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0697] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0698] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0699] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0700] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0701] "Example 1"

[0702] One aspect of the present invention is a system for generating design patterns using AI. This system includes a means for generating patterns in design production, a means for making a final selection from an infinite number of patterns, and a means for generating these patterns using AI. Specifically, the AI ​​first generates design patterns. In this case, the AI ​​generates a variety of patterns by combining various design elements. Next, the final selection is made from the generated patterns. This selection is made based on user input and AI recommendations.

[0703] "Example 2"

[0704] As a concrete example, consider the UI design of an app. First, AI generates various UI design patterns by combining elements such as button shape, color, and placement. Then, the user selects the most suitable one from the generated patterns. This selection is based on the user's preferences and the purpose of the app.

[0705] "Example 3"

[0706] The same is true for web design. AI generates various web design variations by combining elements such as header design, font type, and background color. Users then select the best one from the generated variations. This selection is based on the user's preferences and the purpose of the website.

[0707] The process flow of each embodiment will be described below.

[0708] "Example 1"

[0709] Step 1: AI generates design patterns. In this process, the AI ​​combines various design elements to generate diverse patterns.

[0710] Step 2: Make a final selection from the generated patterns. This selection is based on user input and / or AI recommendations.

[0711] "Example 2"

[0712] Step 1: For app UI design, AI will generate diverse UI design patterns by combining elements such as button shape, color, and placement.

[0713] Step 2: The user selects the best one from the generated patterns. This selection is based on the user's preferences and the purpose of the app.

[0714] "Example 3"

[0715] Step 1: For web design, AI will combine elements such as header design, font type, and background color to generate diverse web design patterns.

[0716] Step 2: The user selects the best one from the generated patterns. This selection is based on the user's preferences and the website's objectives.

[0717] Example 1

[0718] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0719] In conventional design production, there was a problem that it took a lot of time and effort to come up with design patterns and make a final selection. In addition, there was a lack of means for efficiently generating a variety of design patterns according to user requests. Furthermore, the process of selecting the most suitable one from the generated design patterns was also complicated, which was a factor in reducing user satisfaction. The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0720] In this invention, the server includes a means for a user to input a prompt text, a means for the server to receive the prompt text and send it to the generative AI model, a means for the generative AI model to generate a design pattern, a means for the server to save the generated pattern in a database, a means for a user to view the generated patterns on a terminal, a means for a user to select a pattern, and a means for the server to save the selected pattern and make final adjustments as necessary. This allows a user to efficiently generate a variety of design patterns and select the most suitable one from among them.

[0721] "User" means an individual or group that uses the system to generate and select design patterns.

[0722] A "prompt sentence" is text that describes instructions or requirements that a user enters into a system.

[0723] The "server" is a computer system responsible for receiving prompts from the user and sending them to the generative AI model.

[0724] A "generative AI model" is an artificial intelligence algorithm that generates design patterns based on a prompt sentence.

[0725] A "design pattern" is a visual layout or composition that combines various design elements generated by a generative AI model.

[0726] A "database" is an information management system for storing the generated design patterns.

[0727] A "terminal" is a computer or mobile device that a user uses to view and select design patterns.

[0728] "Final adjustment" refers to fine adjustments and modifications made to the design pattern selected by the user.

[0729] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0730] The present invention is a system for efficiently performing pattern selection and final selection in design production. A specific embodiment of this system will be described below.

[0731] System Configuration

[0732] The system includes a user, a server, a terminal, and a generative AI model. The user inputs a prompt sentence using the terminal, and the server receives the prompt sentence and sends it to the generative AI model. The generative AI model generates a design pattern based on the prompt sentence, and the server stores the generated pattern in a database. The user can browse and select the generated pattern using the terminal. The server stores the selected pattern and makes final adjustments if necessary.

[0733] Hardware and Software Used

[0734] Server: A high-performance computer system that receives prompts, sends them to the generative AI model, and stores and finalizes the generated design patterns.

[0735] Terminal: The computer or mobile device used by the user to enter prompts and view and select design patterns.

[0736] Generative AI models: For example, using advanced artificial intelligence algorithms such as OpenAI's GPT-4 to generate design patterns based on a prompt.

[0737] Database: An information management system for storing the generated design patterns.

[0738] Data processing and calculation

[0739] The server analyzes the prompt text sent by the user, converts it into an appropriate format, and sends it to the generative AI model. The generative AI model generates multiple design patterns by combining various design elements (color, shape, layout, etc.) based on the prompt text. The generated patterns are stored in a database by the server, and can be viewed and selected by the user using their terminal.

[0740] Examples

[0741] For example, if a user wants to create a new website design, they can enter a prompt like this:

[0742] "Generate modern website design patterns"

[0743] "Please suggest a simple and clean logo design."

[0744] "Please suggest a color palette that would suit this design."

[0745] The server receives this prompt and sends it to the generative AI model. The generative AI model generates multiple design patterns based on the prompt, and the server stores them in a database. The user can use the terminal to browse the generated patterns and select the design they like. The selected design is stored on the server and final adjustments are made if necessary.

[0746] In this way, users can utilize AI to efficiently generate design patterns and make final selections.

[0747] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0748] Step 1:

[0749] The user enters a prompt sentence.

[0750] The user types "Generate a modern website design pattern" into the input field of the terminal and clicks the submit button.

[0751] Input: The prompt text entered by the user.

[0752] Output: The prompt sent from the terminal to the server.

[0753] Step 2:

[0754] The server receives the prompt text and sends it to the generative AI model.

[0755] The server receives the prompt text sent by the user, parses it, converts it into an appropriate format, and then sends the prompt text to the generative AI model.

[0756] Input: The prompt text sent from the terminal.

[0757] Output: The prompt that is sent to the generative AI model.

[0758] Step 3:

[0759] A generative AI model generates design patterns.

[0760] Based on the received prompt, the generative AI model generates multiple design patterns by combining various design elements (color, shape, layout, etc.).

[0761] Input: The prompt text sent by the server.

[0762] Output: Multiple generated design patterns.

[0763] Step 4:

[0764] The server stores the generated patterns in a database.

[0765] The server stores the design patterns received from the generative AI model in a database, giving each pattern a unique ID.

[0766] Input: Design patterns sent from the generative AI model.

[0767] Output: Design patterns stored in a database.

[0768] Step 5:

[0769] The user views the generated pattern on the terminal.

[0770] A user uses a terminal to browse the design patterns stored in the database. The generated patterns are displayed in thumbnail format on the terminal screen.

[0771] Input: Design patterns stored in a database.

[0772] Output: A thumbnail of the design pattern displayed on the terminal.

[0773] Step 6:

[0774] The user selects a pattern.

[0775] The user clicks on a design pattern they like on the device screen and presses the "Select this design" button. Detailed information about the selected pattern is displayed again, and the selection is confirmed by pressing the Confirm button.

[0776] Input: A thumbnail of the design pattern displayed on the terminal.

[0777] Output: The design pattern selected by the user.

[0778] Step 7:

[0779] The server stores the selected pattern and makes final adjustments if necessary.

[0780] The server receives the design patterns selected by the user, stores them in a database, and, if necessary, makes minor adjustments to the colors and layout based on the user's feedback to finalize the design.

[0781] Input: A design pattern selected by the user.

[0782] Output: The final adjusted design pattern stored in a database.

[0783] (Application example 1)

[0784] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0785] In conventional design production, the process of generating various patterns and selecting the final design was time-consuming and laborious. In addition, it was difficult to propose designs that took into account the user's preferences and past selection history, making efficient design production difficult. Furthermore, there was a lack of means to efficiently generate design patterns for product displays and store layouts in virtual stores and present them to users. In order to solve these problems, the present invention provides a design pattern generation system using AI.

[0786] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0787] In this invention, the server includes a means for generating patterns in design production, a means for making a final selection from an infinite number of patterns, a means for generating these patterns by AI, a means for generating a prompt sentence based on a user's input, a means for presenting the generated design patterns to the user, and a means for the AI ​​to present recommended designs based on the user's preferences and past selection history. This makes it possible to efficiently generate design patterns for product displays and store layouts in a virtual store and present them to the user.

[0788] "Means for generating patterns in design production" refers to a method or device for combining various elements of a design to generate a variety of design patterns.

[0789] A "means for making a final selection from an infinite number of patterns" is a method or device for selecting the most suitable one from the various design patterns generated.

[0790] "Means for generating these patterns using AI" refers to methods or devices for automatically generating design patterns using artificial intelligence.

[0791] A "means for generating a prompt sentence based on user input" refers to a method or device that creates an instruction sentence (prompt) for an AI to generate a design pattern based on information entered by a user.

[0792] "Means for presenting the generated design pattern to a user" refers to a method or device for visually displaying the design pattern generated by the AI ​​to a user.

[0793] "Means for an AI to recommend designs based on a user's preferences and past selection history" refers to a method or device that analyzes a user's past selections and preferred designs and uses them to suggest optimal designs.

[0794] The embodiment of the present invention will be described. First, Python and OpenAI API are used to generate the system program. The server includes a means for generating patterns in design production, a means for making a final selection from an infinite number of patterns, a means for generating these patterns by AI, a means for generating a prompt sentence based on a user's input, a means for presenting the generated design pattern to the user, and a means for the AI ​​to present a recommended design based on the user's preferences and past selection history.

[0795] The server receives information input by the user through a smartphone or head-mounted display and generates a prompt sentence based on that information. For example, if a user inputs "virtual store display design," the server generates a prompt sentence based on this information. This prompt sentence is sent to a generative AI model (e.g., GPT-3), which generates a design pattern.

[0796] The generated design patterns are sent from the server to the user's device and visually presented to the user. The user can select the most suitable one from the presented design patterns. The server also analyzes the user's preferences and past selection history, and the AI ​​presents recommended designs based on that. This allows the user to efficiently select the most suitable design.

[0797] For example, when a user inputs "Virtual store display design," the AI ​​generates patterns such as "Modern layout," "Classic layout," and "Minimalist layout" and presents them to the user. The user can then select the most suitable one from these patterns.

[0798] The following are examples of prompt sentences:

[0799] Generate design patterns based on the following criteria: Virtual store display design

[0800] In this way, it is possible to efficiently generate design patterns for product displays and store layouts in a virtual store and present them to the user.

[0801] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0802] Step 1:

[0803] The user requests the generation of a design pattern via a smartphone or head-mounted display.

[0804] Input: Design requirements entered by user (e.g. "Virtual store display design").

[0805] Output: User input data that is sent to the server.

[0806] Specific operation: A user launches the application, enters requirements into an input form to request the generation of a design pattern, and presses the submit button.

[0807] Step 2:

[0808] The server generates a prompt based on the user's input.

[0809] Input: The data entered by the user.

[0810] Output: Generated prompt statement (e.g. "Generate a design pattern based on the following criteria: Virtual store display design").

[0811] Specific operation: The server receives the user's input data and performs processing to generate a prompt sentence based on it.

[0812] Step 3:

[0813] The server sends the generated prompt sentence to the generative AI model to generate a design pattern.

[0814] Input: The generated prompt statement.

[0815] Output: The design patterns returned by the generative AI model.

[0816] Specific operation: The server sends the generated prompt sentence to the OpenAI API, and the generative AI model (e.g. GPT-3) generates a design pattern.

[0817] Step 4:

[0818] The server transmits the generated design pattern to the user's terminal and presents it to the user.

[0819] Input: Design patterns returned by a generative AI model.

[0820] Output: The design pattern that is displayed on the user's terminal.

[0821] Specific operation: The server sends the generated design pattern to the user's terminal, and the application displays it visually.

[0822] Step 5:

[0823] The user selects the most suitable design pattern from the presented ones.

[0824] Input: A design pattern presented to the user.

[0825] Output: The design pattern selected by the user.

[0826] Specific behavior: The user operates an interface to review the presented design patterns and select the most suitable one.

[0827] Step 6:

[0828] The server analyzes the user's preferences and past selection history, and the AI ​​presents recommended designs.

[0829] Input: User preferences and past selections.

[0830] Output: AI recommended design patterns.

[0831] Specific operation: The server retrieves the user's preferences and past selection history from the database, and the AI ​​performs processing based on that to recommend the optimal design.

[0832] Example 2

[0833] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[0834] In conventional design production, the process of efficiently generating a variety of patterns and allowing users to select the optimal design was complicated, time-consuming, and labor-intensive. It was also difficult to quickly provide designs that matched the user's preferences and the purpose of the application.

[0835] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0836] In this invention, the server includes a means for generating design patterns using a generative AI model, a means for saving the generated design patterns in a database, a means for a terminal to acquire the design patterns and display them to a user, a means for the user to select an optimal design pattern, a means for transmitting the user's selection to the server, and a means for saving the user's selection in the database and reflecting it in the user interface of the application. This enables the user to efficiently generate a variety of design patterns and quickly select an optimal design.

[0837] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to automatically generate design patterns.

[0838] A "design pattern" is a combination of visual elements such as the shape, color, and placement of buttons in the user interface of an application, website, etc.

[0839] "Database" is an information management system for storing generated design patterns and user selections.

[0840] A "terminal" is a device operated by a user and has the function of obtaining and displaying design patterns from a server.

[0841] A "user" is an individual or group that uses the system to select design patterns.

[0842] A "server" is a computer system that runs a generative AI model and generates, stores, and distributes design patterns.

[0843] A "prompt" is a piece of text that describes the instructions and conditions for the generative AI model to generate a design pattern.

[0844] "User interface" is a general term for the screens and operating elements that allow a user to interact with an application or website.

[0845] The present invention is a system that uses a generative AI model to automatically generate design patterns and allows a user to select an optimal design. A specific embodiment of this system will be described below.

[0846] Hardware and Software Used

[0847] Server: A computer system that runs generative AI models and generates, stores, and distributes design patterns. Specifically, it uses common server hardware and software for running generative AI models (e.g., Python, TensorFlow, PyTorch, etc.).

[0848] Database: An information management system for storing the generated design patterns and user selections. Specifically, a relational database such as MySQL or PostgreSQL is used.

[0849] Terminal: A device operated by a user that has the function of retrieving and displaying design patterns from a server. Specifically, this applies to PCs, smartphones, tablets, etc.

[0850] Generative AI model: An algorithm or program that uses artificial intelligence technology to automatically generate design patterns. Specifically, we use generative AI models such as OpenAI's GPT-4.

[0851] Data processing and calculation

[0852] 1. The server generates design patterns using a generative AI model. A prompt sentence is input to the generative AI model to generate multiple design patterns.

[0853] 2. The server stores the generated design patterns in a database, and each design pattern is assigned a unique ID.

[0854] 3. The terminal sends a request for a design pattern to the server and displays the design pattern received from the server to the user, using HTML and CSS to make the display visually easy to understand.

[0855] 4. The user selects the most suitable design pattern from the displayed patterns. The selection is made through interactions such as clicking and tapping.

[0856] 5. The terminal sends the ID of the design pattern selected by the user to the server using an HTTP request (e.g., POST request).

[0857] 6. The server saves the user's selection in a database and updates the application's user interface, possibly using a front-end framework (e.g., React or Vue.js).

[0858] Examples

[0859] For example, if a user wants to create a UI design for a new task management app, the system works as follows:

[0860] 1. The server uses the generative AI model to input the prompt, "Generate the UI design for a new task management app. Create 10 design patterns with combinations of round or square button shapes, blue, red, and green colors, and top, center, and bottom placement." and generate 10 UI design patterns.

[0861] 2. The server stores the generated UI design patterns in a MySQL database and gives each design pattern a unique ID.

[0862] 3. The terminal sends a request for a UI design pattern to the server and displays the design pattern received from the server to the user using HTML and CSS.

[0863] 4. Users click to select the design with a blue circular button in the center.

[0864] 5. The terminal sends the ID of the design pattern selected by the user to the server in a POST request.

[0865] 6. The server saves the user's selection to the database and updates the application's user interface using React.

[0866] In this way, users can leverage generative AI models to make efficient UI design choices.

[0867] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0868] Step 1:

[0869] The server generates design patterns using a generative AI model. Specifically, the server inputs a prompt sentence into the generative AI model to generate multiple design patterns. The server uses the prompt sentence "Generate a UI design for a new task management app. Create 10 design patterns with combinations of round or square button shapes, blue, red, and green colors, and top, center, and bottom placements" as input, and obtains 10 design patterns as output.

[0870] Step 2:

[0871] The server saves the generated design patterns in a database. Specifically, the server assigns a unique ID to each design pattern and saves it in a MySQL database. It receives the generated design patterns as input and obtains the design patterns saved in the database as output.

[0872] Step 3:

[0873] A terminal transmits a request for a design pattern to a server. Specifically, the terminal accesses the server using an HTTP request and requests a design pattern. The terminal transmits the request as input and receives the design pattern from the server as output.

[0874] Step 4:

[0875] The terminal displays the received design pattern to the user. Specifically, the terminal visually displays the design pattern using HTML and CSS. It uses the design pattern received from the server as input and obtains the design pattern displayed to the user as output.

[0876] Step 5:

[0877] The user selects the most suitable design pattern from the displayed design patterns. Specifically, the user selects a design pattern through interactions such as clicking or tapping. The displayed design patterns are referred to as input, and the selected design pattern is obtained as output.

[0878] Step 6:

[0879] The terminal sends the ID of the design pattern selected by the user to the server. Specifically, the terminal sends the ID of the selected design pattern to the server using an HTTP POST request. It receives the ID of the selected design pattern as input and obtains the ID sent to the server as output.

[0880] Step 7:

[0881] The server saves the user's selection in a database and reflects it in the application's user interface. Specifically, the server saves the ID of the selected design pattern in a database and updates the application's UI using a front-end framework such as React. It receives the ID of the selected design pattern as input and gets the updated application UI as output.

[0882] (Application example 2)

[0883] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the smart glasses 214 will be referred to as a "terminal".

[0884] In conventional design production, the generation and selection of design patterns is done manually, which is time-consuming and labor-intensive. It is also difficult to generate designs that reflect the user's preferences, and a lot of trial and error is required to find the optimal design. Furthermore, there is a lack of functionality to preview the selected design in real time, making it difficult to increase user satisfaction.

[0885] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0886] In this invention, the server includes a means for generating patterns in design production, a means for making a final selection from an infinite number of patterns, a means for generating these patterns using AI, a means for reflecting the user's selection history in subsequent design generation, and a means for previewing the selected design in real time. This makes it possible to automatically generate design patterns and select an optimal design that reflects the user's preferences, and further increases user satisfaction with the real-time preview function.

[0887] "Design creation" is the process of combining visual elements to create a design that has a specific purpose or function.

[0888] "Pattern creation" is the act of combining design elements to generate multiple design patterns.

[0889] "Final selection" refers to the act of selecting the optimal design pattern from among the multiple design patterns generated.

[0890] "AI generation" is the process of automatically generating design patterns using artificial intelligence.

[0891] "User selection history" is a record of design patterns that the user has previously selected.

[0892] "Reflecting in subsequent design generation" means taking the user's preferences into consideration when generating subsequent design patterns based on the user's selection history.

[0893] "Real-time preview" means that the user can instantly see and check the design they have selected.

[0894] A system for implementing this invention includes means for generating patterns in design production, means for making a final selection from an infinite number of patterns, means for generating these patterns using AI, means for reflecting the user's selection history in subsequent design generation, and means for previewing the selected design in real time.

[0895] System Programming

[0896] The system is built using Python and TensorFlow. The server generates design elements (button shape, color, placement, etc.) and generates various design patterns using an AI model. Users select the most suitable one from the generated design patterns, and the selection history is reflected in subsequent design generation. The selected design is previewed in real time.

[0897] Processing Description

[0898] The server first generates design elements. Specifically, it randomly combines button shapes (e.g., rectangle, circle, oval), colors (e.g., red, blue, green), and layouts (e.g., top-left, top-right, bottom-left, bottom-right). Next, it uses TensorFlow to define a neural network model and generates various design patterns using the design elements as input.

[0899] The user selects the most suitable design pattern from the generated design patterns. The selection history is stored in a database and is reflected in the next and subsequent design generation. This allows the system to generate design patterns that take the user's preferences into account.

[0900] The real-time preview function allows users to instantly see and confirm the design they have selected, allowing them to instantly see the results of applying their selected design and increasing satisfaction.

[0901] Examples

[0902] For example, if a user selects a design with a "rectangle" shaped, "red" button placed on the "top-left", that selection history will be reflected in the next design generation. In the next design generation, a similar design pattern will be preferentially generated, taking into account the user's preferences.

[0903] Examples of prompt statements

[0904] "Generate 10 different UI design patterns by combining button shapes, colors, and placements."

[0905] In this way, it is possible to automatically generate design patterns using AI and select the optimal design that reflects user preferences, and the real-time preview function can further increase user satisfaction.

[0906] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0907] Step 1:

[0908] The server generates design elements by randomly combining button shapes (e.g. rectangle, circle, oval), colors (e.g. red, blue, green), and alignments (e.g. top-left, top-right, bottom-left, bottom-right). The input is a list of design elements, and the output is a combination of the generated design elements. The server randomly selects these elements to create candidate design patterns.

[0909] Step 2:

[0910] The server uses TensorFlow to define a neural network model. The input is a combination of design elements, and the output is a neural network model. The server takes design elements as input and builds a neural network model. This model is used to generate design patterns.

[0911] Step 3:

[0912] The server generates various design patterns using the neural network model. The input is a combination of the neural network model and the design elements, and the output is the generated design pattern. The server inputs the design elements to the model and generates multiple design patterns.

[0913] Step 4:

[0914] The user selects the most suitable one from the generated design patterns. The input is the generated design pattern, and the output is the design pattern selected by the user. The user checks the displayed design patterns and selects the most suitable one.

[0915] Step 5:

[0916] The server saves the user's selection history in a database. The input is the design pattern selected by the user, and the output is the updated database. The server records the user's selection history and reflects it in subsequent design generation.

[0917] Step 6:

[0918] The server generates design patterns based on the user's selection history for subsequent design generation. The input is the user's selection history and design elements, and the output is a design pattern that reflects the user's preferences. The server takes the selection history into consideration and generates design patterns that match the user's preferences with priority.

[0919] Step 7:

[0920] The terminal previews the selected design in real time. The input is the design pattern selected by the user, and the output is the preview screen. The terminal immediately displays the selected design for the user to confirm.

[0921] Example 3

[0922] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0923] Conventional design production requires time-consuming and labor-intensive pattern selection and final selection, making efficient design production difficult. In addition, it is difficult to quickly generate a variety of design patterns that meet the needs of users, and the flexibility required to provide creative designs is lacking.

[0924] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0925] In this invention, the server includes a means for a user to input a prompt sentence, a means for the server to receive the prompt sentence and send it to the generative AI model, a means for the generative AI model to generate a design pattern, a means for the server to send the generated design pattern to the user, and a means for the user to select a design pattern. This makes it possible to rapidly generate a variety of design patterns according to user requests and to perform design production efficiently and flexibly.

[0926] A "user" is an individual or group that uses the system to generate and select design patterns.

[0927] A "prompt" is text that a user enters into a generative AI model containing design requirements or wishes.

[0928] The "server" is a computer system that receives prompt text from a user, sends it to the generative AI model, and returns the generated design pattern to the user.

[0929] A "generative AI model" is an artificial intelligence model that automatically generates design patterns based on a prompt sentence.

[0930] A "design pattern" is a specific example of a web design that combines elements such as header design, font type, and background color, generated by a generative AI model.

[0931] "User interface design" refers to the design of the operation screen of an application or website, which is designed to allow users to operate it intuitively.

[0932] "Web design" is the design of the visual layout and style of a website to enhance the user's visual experience.

[0933] An "illustration" is an image or picture for the purpose of visual explanation or decoration and is used as part of a design.

[0934] A "banner" is a rectangular image or graphic used to display advertising or information within a web page or application.

[0935] A "key visual" is the central visual element of a design, used to emphasize the message of a brand or campaign.

[0936] An "icon" is a small graphic symbol intended to represent a function or action within an application or website.

[0937] This invention is a system in which a user inputs a prompt sentence, generates various design patterns using a generative AI model, and selects the optimal design. A specific embodiment of this system is described below.

[0938] Hardware and Software Used

[0939] Hardware: Servers, terminals (users' PCs and smartphones)

[0940] Software: A generative AI model (e.g., OpenAI's GPT-4), a web browser, and a design tool (e.g., Adobe XD).

[0941] System Overview

[0942] A user accesses the system's interface through a web browser and inputs a prompt statement including design requirements and wishes. The server receives the prompt statement and sends it to the generative AI model. The generative AI model generates multiple design patterns based on the prompt statement and sends them back to the server. The server sends the generated design patterns to the user's device, and the user selects the most suitable design from them.

[0943] Examples

[0944] For example, if a user enters a prompt like "I want you to generate a design for a cafe website using warm colors and handwritten fonts," the following design pattern will be generated when this prompt is input to the generative AI model.

[0945] Design variant 1: A handwritten font in the header, a beige background, and brown buttons.

[0946] Design variant 2: A handwritten font in the header, a light green background, and dark green buttons.

[0947] Design variant 3: A handwritten font in the header, a light blue background, and navy buttons.

[0948] Users can choose from these design patterns the one they like best.

[0949] Implementation details

[0950] 1. The user enters a prompt:

[0951] A user opens a web browser, accesses the system's interface, and enters a prompt statement containing the desired web design requirements and wishes into an input field on the interface.

[0952] 2. The server receives the prompt:

[0953] The user presses the send button to send the prompt text, and the server receives the prompt text via an HTTP request.

[0954] 3. The server sends a prompt to the generative AI model:

[0955] The server creates an API request to send the received prompt sentence to the generative AI model. It sends a request including the prompt sentence to the API endpoint of the generative AI model.

[0956] 4. Generative AI models generate design patterns:

[0957] The generative AI model analyzes the prompt text it receives and generates multiple web design variations that combine elements such as header design, font type, and background color.

[0958] 5. The server receives the generated design patterns:

[0959] The generative AI model sends the generated design pattern back to the server. The server receives the response from the generative AI model and obtains the design pattern.

[0960] 6. The server sends the design pattern to the user:

[0961] The server creates a response for sending the acquired design pattern to the user's terminal, and sends the response including the generated design pattern to the user's terminal.

[0962] 7. User chooses design pattern:

[0963] The user's device displays the received design patterns on a web browser. The user selects the design that best suits his or her preferences and the purpose of the website from the multiple design patterns displayed, and presses a button to confirm the selected design pattern.

[0964] In this way, the user can easily generate and select a web design that meets his / her needs by utilizing the generative AI model. The flow of the specification process in the third embodiment will be described with reference to FIG.

[0965] Step 1:

[0966] The user enters a prompt.

[0967] A user opens a web browser and accesses the system's interface. In an input field on the interface, the user enters a prompt statement that includes the requirements and desires of the desired web design. For example, the user may enter "I want you to generate a design for a website that has a modern and simple header." The input data is the prompt statement in text format.

[0968] Step 2:

[0969] The server receives the prompt.

[0970] The user presses the send button to send the prompt text entered by the user. The server receives the prompt text through an HTTP request. The received data is the prompt text entered by the user. The server analyzes the prompt text and passes it to the next processing step.

[0971] Step 3:

[0972] The server sends a prompt to the generative AI model.

[0973] The server creates an API request to send the received prompt sentence to the generative AI model. It sends a request including the prompt sentence to the API endpoint of the generative AI model. The input data is the prompt sentence, and the output data is the request to the generative AI model.

[0974] Step 4:

[0975] A generative AI model generates design patterns.

[0976] The generative AI model analyzes the received prompt text and generates multiple web design patterns that combine elements such as header design, font type, background color, etc. The input data is the prompt text, and the output data is the multiple generated design patterns. For example, the following design patterns are generated.

[0977] Design variant 1: A handwritten font in the header, a beige background, and brown buttons.

[0978] Design variant 2: A handwritten font in the header, a light green background, and dark green buttons.

[0979] Design variant 3: A handwritten font in the header, a light blue background, and navy buttons.

[0980] Step 5:

[0981] A server receives the generated design patterns.

[0982] The generative AI model returns the generated design pattern to the server. The server receives the response from the generative AI model and obtains the design pattern. The input data is the response from the generative AI model, and the output data is the obtained design pattern.

[0983] Step 6:

[0984] The server sends the design pattern to the user.

[0985] The server creates a response for sending the acquired design pattern to the user's terminal. The server sends the response including the generated design pattern to the user's terminal. The input data is the acquired design pattern, and the output data is the response sent to the user's terminal.

[0986] Step 7:

[0987] The user selects the design pattern.

[0988] The user's device displays the received design patterns on a web browser. The user selects the design that best suits his or her preferences and the purpose of the website from the multiple design patterns displayed. The user then presses a button to confirm the selected design pattern. The input data is the displayed design pattern, and the output data is the design pattern selected by the user.

[0989] (Application example 3)

[0990] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server", and the smart glasses 214 will be referred to as a "terminal".

[0991] In conventional design production, the generation and selection of design patterns is often done manually, which is problematic in terms of time and effort. In addition, when the user tries to select the optimal design, it is difficult to visually confirm it, making efficient design selection difficult. Furthermore, the process of applying the generated design to the actual system is also time-consuming, making it difficult to make rapid design changes. There is a demand for an efficient system to solve these issues.

[0992] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0993] In this invention, the server includes a means for generating patterns in design production, a means for making a final selection from an infinite number of patterns, a means for generating these patterns using a generative AI model, a means for displaying a list of generated design patterns, a means for a user to select an optimal design, a means for previewing the selected design, and a means for saving and applying the selected design. This enables efficient generation and selection of design patterns, and allows a user to select an optimal design while visually checking. It also enables the selected design to be quickly applied to an actual system.

[0994] "Design creation" is the process of combining visual elements to create a design based on a specific purpose or theme.

[0995] "Pattern creation" is the act of combining multiple design elements to generate various design patterns.

[0996] A "generative AI model" is an algorithm or system that uses artificial intelligence to automatically generate design patterns.

[0997] "List display" refers to displaying multiple generated design patterns side-by-side so that they can be checked at a glance.

[0998] "Means for the user to select the optimal design" refers to an interface or function that allows the user to select the most appropriate one from the generated design patterns.

[0999] "Preview" is a display function that allows you to check in advance how the selected design will look in the actual application environment.

[1000] The "means of saving and applying" is a function for saving the selected design in a database or the like and applying it to an actual system or website.

[1001] A system for implementing this invention includes means for generating patterns in design production, means for making a final selection from countless patterns, means for generating these patterns using a generative AI model, means for displaying a list of the generated design patterns, means for a user to select the optimal design, means for previewing the selected design, and means for saving and applying the selected design.

[1002] System Programming

[1003] The server generates design patterns using a generative AI model. Specifically, it uses machine learning libraries such as TensorFlow and Keras to generate various design patterns that combine design elements such as headers, fonts, and background colors. The generated design patterns are stored in a database and sent to the user's device.

[1004] The user's device displays a list of the generated design patterns. The user can visually check the generated design patterns using a device such as a smartphone or tablet and select the most suitable design. The selected design can be previewed to see how it will look in the actual application environment.

[1005] The selected design is sent to the server and stored in the database. The saved design is then applied to the actual system or website, allowing users to change the design quickly and efficiently.

[1006] Hardware and Software Used

[1007] Hardware: Servers, smartphones, tablets

[1008] Software: Python, TensorFlow, Keras, Database Management System

[1009] Examples

[1010] For example, when an online shopping site operator is creating a new product page design, they open the smartphone app and view 10 design patterns generated by AI. The operator selects the most suitable design from among them and uses the preview function to check how it will look when applied to the actual page. The selected design is saved on the server and immediately applied to the online shopping site.

[1011] Examples of prompt statements

[1012] "Create an application that uses AI to generate design variations for product pages and allow users to select the best design. Design elements include headers, fonts, and background colors. Display a list of generated design variations and allow users to preview and save the selected design."

[1013] The flow of the specific process in the application example 3 will be described with reference to FIG.

[1014] Step 1:

[1015] The server generates design patterns using a generative AI model. It receives design elements such as headers, fonts, and background colors as input, and combines them to generate a variety of design patterns. It uses TensorFlow and Keras to process the data, randomly combining elements and generating multiple design patterns as output.

[1016] Step 2:

[1017] The server stores the generated design patterns in a database. As input, it receives the design patterns generated in step 1 and stores them using a database management system. As data operation, it converts the design patterns into an appropriate format and obtains the design patterns stored in the database as output.

[1018] Step 3:

[1019] The server transmits the stored design pattern to the user terminal. As input, the server receives the design pattern stored in the database and transmits it to the user terminal via the network. As data processing, the server converts the design pattern into a transmittable format and obtains the design pattern transmitted to the user terminal as output.

[1020] Step 4:

[1021] The user terminal displays the received design patterns in a list.,As input, the design patterns sent from the server are,received and displayed on the user interface.,As data processing, the design patterns are converted,into a visually confirmable format, and a list of,design patterns is obtained as output.

[1022] Step 5:

[1023] The user selects the optimal design from the listed design patterns. As input, the user visually checks the listed design patterns and selects the optimal design. As data computation, the user's selection is recorded and the selected design pattern is obtained as output.

[1024] Step 6:

[1025] The user terminal previews the selected design pattern. It receives the design pattern selected by the user as input and displays how it will look in the actual application environment. As data processing, it converts the design pattern to suit the application environment and obtains the previewed design pattern as output.

[1026] Step 7:

[1027] The user checks the preview and saves the selected design. As input, the user checks the previewed design pattern and issues an instruction to save it. As data calculation, the instruction to save is recorded, and the design pattern instructed to be saved is obtained as output.

[1028] Step 8:

[1029] The server saves the designated design pattern in a database and applies it to the actual system. As input, the server receives the designated design pattern sent from the user terminal, saves it in a database, and applies it to the actual system. As data processing, the server converts the design pattern into an applicable format, and obtains the design pattern applied to the actual system as output.

[1030] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1031] "Example 1"

[1032] In one embodiment of the present invention, AI generates design patterns and makes a final selection from countless patterns. The system includes an emotion engine for recognizing the user's emotions. The emotion engine analyzes the user's emotions from facial expressions, tone of voice, text input, etc., and feeds the results back to the AI. For example, when the user is feeling happy, it recommends design patterns with bright colors and a fun image. Conversely, when the user is feeling depressed, it recommends design patterns with calm colors and a sense of security.

[1033] "Example 2"

[1034] In another embodiment, the emotion engine adjusts the design pattern output based on the user's emotions. Specifically, the emotion engine analyzes the user's emotions, and the AI ​​generates design patterns based on the results. For example, when the user is feeling happy, the AI ​​generates design patterns with bright colors and a fun image. Conversely, when the user is depressed, the AI ​​generates design patterns with calm colors and a sense of security.

[1035] "Example 3"

[1036] In yet another embodiment, the emotion engine supports the final selection based on the user's emotions. Specifically, the emotion engine analyzes the user's emotions, and the AI ​​supports the final selection based on the results. For example, when the user is feeling happy, the AI ​​recommends design patterns with bright colors and fun images. Conversely, when the user is feeling depressed, the AI ​​recommends design patterns with calm colors and a sense of security.

[1037] The process flow of each embodiment will be described below.

[1038] "Example 1"

[1039] Step 1: The user boots up the system and begins patterning the design.

[1040] Step 2: The emotion engine analyzes the user's emotions from facial expressions, tone of voice, text input, etc.

[1041] Step 3: The analysis results of the emotion engine are fed back to the AI, which then creates design patterns.

[1042] Step 4: Present the AI-generated design patterns to the user and make the final selection.

[1043] "Example 2"

[1044] Step 1: The user boots up the system and begins patterning the design.

[1045] Step 2: The emotion engine analyzes the user's emotions.

[1046] Step 3: AI generates design patterns based on the analysis results of the emotion engine.

[1047] Step 4: Present the AI-generated design patterns to the user and make the final selection.

[1048] "Example 3"

[1049] Step 1: The user boots up the system and begins patterning the design.

[1050] Step 2: The emotion engine analyzes the user's emotions.

[1051] Step 3: AI helps make the final choice based on the emotion engine analysis results.

[1052] Step 4: Present the AI-recommended design patterns to the user and make the final selection.

[1053] Example 1

[1054] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[1055] In conventional pattern-creation systems, it was difficult to generate design patterns that reflected the user's emotions and preferences. In addition, when selecting the optimal design from countless patterns, the user's emotions could not be taken into consideration, resulting in a lack of improvement in the user experience.

[1056] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1057] In this invention, the server includes a means for generating patterns in design production, a means for making a final selection from an infinite number of patterns, a means for generating these patterns by artificial intelligence, a means for analyzing the user's emotions, and a means for feeding back the analysis results to the artificial intelligence. This makes it possible to generate design patterns according to the user's emotions and preferences, and to select the most suitable design.

[1058] "Design creation" is the creative process of combining visual elements to convey a particular purpose or message.

[1059] "Pattern creation" is the process of generating multiple different design variations or layouts.

[1060] "Countless patterns" refers to a huge number of different design variations.

[1061] "Final selection" is the process of selecting the optimal design pattern from among the multiple design patterns generated.

[1062] "Artificial intelligence" is the technology that enables computer systems to mimic human intelligence by learning, reasoning, and self-correcting.

[1063] "User emotion" refers to the psychological response a user has to a particular situation or stimulus.

[1064] "Analysis" is the process of examining data or information in detail to reveal its structure and meaning.

[1065] "Feedback" is the process of providing information to a system to adjust its next action or processing based on the information it has obtained.

[1066] This invention is a system for generating patterns in design production, which uses artificial intelligence to generate design patterns according to the user's emotions and select the optimal design.

[1067] First, the user accesses the system using a terminal and inputs the design requirements. For example, the user may input, "I want you to generate a design pattern with a fun image in bright colors." The server receives this input and sends the prompt sentence, "Please generate a design pattern with a fun image in bright colors," to the generative AI model.

[1068] As a generative AI model, for example, a general-purpose natural language processing model such as OpenAI's GPT-4 is used. The AI ​​model generates multiple design patterns based on the prompt sentence and returns them to the server.

[1069] The server presents the generated design patterns to the user. The user checks these patterns on the screen of the terminal. While the user is checking the presented design patterns, the emotion engine analyzes the user's facial expressions and tone of voice. As the emotion engine, for example, general emotion analysis software is used. Specifically, Microsoft's Azure Cognitive Services is one such example.

[1070] The emotion engine analyzes the user's emotions and sends the results to the server. For example, if the user is smiling, it detects "joy" and sends the result to the server. The server feeds back the analysis results to the AI, and issues instructions such as, "The user is feeling happy, so please generate more design patterns with bright colors and a fun image."

[1071] Based on this feedback, the AI ​​generates design patterns that further match the user's preferences and sends them back to the server. The server then presents the final design patterns to the user, who can then select the best one from these patterns.

[1072] As a concrete example, if a user inputs "I want a design pattern with a bright color scheme and a fun image to be generated," the server will send the following prompt to the generative AI model:

[1073] "Create design patterns with fun images in bright colors."

[1074] The generative AI model generates multiple design patterns based on this prompt and returns them to the server, which then presents them to the user and analyzes the user's emotions to generate further optimal design patterns.

[1075] In this way, it becomes possible to generate design patterns according to the user's feelings and preferences and to select the most suitable design.

[1076] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1077] Step 1:

[1078] The user inputs the design requirements.

[1079] A user accesses the system using a terminal and inputs design requirements. For example, the user might type, "I want you to generate a design pattern with a bright color scheme and a fun image." The input data is sent to the server in text format.

[1080] Step 2:

[1081] The server receives user input and sends prompts to the AI ​​model.

[1082] The server receives the user's input and converts it into a prompt. For example, the prompt might be "Please generate a design pattern with bright colors and fun images." This prompt is then sent to the generative AI model. The input is the user's requirement text, and the output is the prompt.

[1083] Step 3:

[1084] The AI ​​model generates design patterns based on the prompt.

[1085] A generative AI model (e.g., GPT-4) receives a prompt and generates multiple design patterns. The input is the prompt, and the output is a set of generated design patterns. The AI ​​model internally performs natural language processing and combination of design elements.

[1086] Step 4:

[1087] The server presents the generated design patterns to the user.

[1088] The server presents the design patterns received from the AI ​​model to the user, who then views these patterns on the screen of his or her device. The input is the set of generated design patterns, and the output is the design patterns displayed on the user's screen.

[1089] Step 5:

[1090] The user checks the presented design patterns.

[1091] The user reviews the presented design patterns and decides which one they like best. At this time, the emotion engine analyzes the user's facial expressions and tone of voice. The input is the visual information of the design patterns, and the output is the user's emotional response.

[1092] Step 6:

[1093] The emotion engine analyzes the user's emotions.

[1094] The emotion engine analyzes emotions from the user's facial expressions, tone of voice, text input, etc. For example, if the user is smiling, it detects "happiness." The input is the user's facial expression and voice data, and the output is the analyzed emotion information.

[1095] Step 7:

[1096] The server feeds back the results of the emotion engine's analysis to the AI.

[1097] The server feeds back the analysis results of the emotion engine to the AI. For example, it may give feedback such as, "The user is feeling happy, so please generate more design patterns with bright colors and fun images." The input is the analyzed emotion information, and the output is the feedback instruction text.

[1098] Step 8:

[1099] The AI ​​will generate further design patterns based on the feedback.

[1100] Based on the feedback, the AI ​​generates design patterns that further match the user's preferences. The generated patterns are sent to the server again. The input is the feedback instructions, and the output is a set of newly generated design patterns.

[1101] Step 9:

[1102] The server presents the final design pattern to the user.

[1103] The server presents the final generated design patterns to the user, who can select the best one from these patterns. The input is the set of newly generated design patterns, and the output is the final design pattern displayed on the user's screen.

[1104] (Application example 1)

[1105] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[1106] In conventional design production systems, design patterns are generated without considering the user's emotions, making it difficult to provide designs that match the user's emotions. In addition, when selecting the optimal design from countless design patterns, there was a lack of a means to reflect the user's emotions, making it impossible to increase user satisfaction.

[1107] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1108] In this invention, the server includes a means for generating patterns in design production, a means for making a final selection from an infinite number of patterns, a means for generating these patterns by AI, a sentiment analysis means for recognizing the user's sentiment, and a means for recommending a design pattern based on the sentiment analysis result, thereby making it possible to generate and select a design pattern according to the user's sentiment.

[1109] A "means for generating patterns in design production" is a means for combining various elements of design to generate a variety of design patterns.

[1110] "Means for making final selection from an infinite number of patterns" refers to a means for selecting the most suitable one from among the generated design patterns.

[1111] "Means for generating these patterns using AI" refers to means for automatically generating design patterns using artificial intelligence.

[1112] The "emotion analysis means for recognizing a user's emotions" refers to a means for recognizing a user's emotions by analyzing the user's facial expressions, tone of voice, text input, etc.

[1113] The "means for recommending a design pattern based on the result of emotion analysis" is a means for proposing an appropriate design pattern based on the user's emotion obtained by the emotion analysis means.

[1114] A system for implementing this invention includes means for generating patterns in design production, means for making a final selection from an infinite number of patterns, means for generating these patterns using AI, emotion analysis means for recognizing a user's emotions, and means for recommending design patterns based on the results of the emotion analysis.

[1115] System Configuration

[1116] The server uses the following hardware and software:

[1117] Hardware: Camera (to capture the user's facial expressions)

[1118] Software: OpenCV (image processing), Keras (executing emotion recognition models and design pattern generation models), TextBlob (analyzing emotions from text)

[1119] Processing flow

[1120] 1. User sentiment analysis:

[1121] The device's camera captures the user's facial expression and obtains image data.

[1122] The server uses an emotion recognition model (using Keras) to analyze the user's emotions from the captured image data.

[1123] Furthermore, the text entered by the user is parsed using TextBlob and emotions are obtained from the text.

[1124] 2. Design Pattern Generation:

[1125] The server generates design patterns based on the analyzed emotion data using a design pattern generation model (using Keras).

[1126] The generated design pattern corresponds to the user's emotions; for example, if the user is having fun, a bright and colorful design is generated, whereas if the user is depressed, a calm and subdued design is generated.

[1127] 3. Displaying Design Patterns:

[1128] The terminal displays the generated design pattern on the screen.

[1129] The user can select the most suitable design from the displayed designs.

[1130] Examples

[1131] For example, when a user enters a virtual store, a camera captures the user's facial expression and analyzes their emotions. If the user types, "I'm in a happy mood today," the system will generate bright and colorful design patterns and change the design of the store.

[1132] Examples of prompt statements

[1133] "Analyze the user's facial expressions and text input and generate design patterns based on their emotions. If the user is happy, suggest a bright and colorful design, and if the user is depressed, suggest a calm color scheme."

[1134] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1135] Step 1:

[1136] The device's camera captures the user's facial expression and obtains image data.

[1137] Input: User's facial expression

[1138] Output: Image data

[1139] Specific operation: The device's camera takes a picture of the user's face in real time and sends the image data to the server.

[1140] Step 2:

[1141] The server uses an emotion recognition model (using Keras) to analyze the user's emotions from the captured image data.

[1142] Input: Image data

[1143] Output: Emotion data (e.g., happiness, sadness, surprise, etc.)

[1144] Specific operation: The server inputs the received image data into the emotion recognition model, and the model infers the user's emotion from the image. The estimated emotion data is output as a numerical value or a category.

[1145] Step 3:

[1146] The server parses the text entered by the user using TextBlob and extracts the sentiment from the text.

[1147] Input: User text input

[1148] Output: Text sentiment data (e.g. positive, negative, neutral, etc.)

[1149] Specific operation: The server passes the text entered by the user to TextBlob, which performs sentiment analysis on the text. TextBlob outputs the sentiment of the text as a number or category.

[1150] Step 4:

[1151] The server generates design patterns based on the analyzed emotion data using a design pattern generation model (using Keras).

[1152] Input: emotion data, text emotion data

[1153] Output: Design Patterns

[1154] Specific operation: The server inputs emotion data and text emotion data into the design pattern generation model, and the model generates appropriate design patterns based on these data. The generated design patterns are output as images or layouts.

[1155] Step 5:

[1156] The terminal displays the generated design pattern on the screen.

[1157] Input: Design Patterns

[1158] Output: Displayed design pattern

[1159] Specific operation: The terminal displays the design pattern received from the server on the screen, allowing the user to visually confirm it.

[1160] Step 6:

[1161] The user selects the most suitable design pattern from the displayed patterns.

[1162] Input: The displayed design pattern

[1163] Output: User's choice

[1164] Specific operation: The user looks at multiple design patterns displayed on the terminal screen and selects the one he likes best. The selected design pattern is sent from the terminal to the server.

[1165] Example 2

[1166] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[1167] In conventional design production, the generation and selection of design patterns is often done manually, which is time-consuming and labor-intensive. In addition, it is difficult to adjust the design according to the user's emotions, making it difficult to improve the user experience. Furthermore, there is a lack of systems that can efficiently generate a variety of design patterns and select the optimal one.

[1168] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1169] In this invention, the server includes a means for a user to input a prompt sentence, a means for the server to receive the prompt sentence and send it to the generative AI model, a means for the generative AI model to generate a UI design pattern, a means for the server to save the generated design pattern in a database, a means for a user to browse the design patterns using a terminal, a means for the user to select an optimal design pattern, a means for the server to record the selected design pattern, a means for an emotion engine to analyze the emotion of the user, and a means for the server to adjust the design pattern based on the emotion analysis result. This enables efficient generation and selection of design patterns, and further enables adjustment of the design according to the emotion of the user.

[1170] A "prompt sentence" is a sentence containing instructions or requirements that a user inputs to a generative AI model.

[1171] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate user interface designs and other design patterns.

[1172] A "UI design pattern" is a design pattern that combines elements such as button shape, color, and placement in user interface design.

[1173] A "database" is a system for storing and managing generated design patterns and other data.

[1174] A "terminal" is a device that a user uses to view design patterns and enter prompt statements.

[1175] An "emotion engine" is software or a system that analyzes a user's facial expressions and voice to identify the user's emotional state.

[1176] The "server" is a computer system that receives prompts, sends them to the generative AI model, stores and manages design patterns, and processes the results of the emotion engine.

[1177] This invention is a system that allows a user to input a prompt sentence, generates various UI design patterns using a generative AI model, and selects the most suitable design pattern. Furthermore, it can adjust the design according to the user's emotions using an emotion engine.

[1178] The server receives a prompt sentence from the user and sends it to the generative AI model. The generative AI model generates a UI design pattern based on the prompt sentence. The generated design pattern is stored in a database by the server. The user uses a terminal to browse the design patterns and selects the most suitable design pattern. The selected design pattern is recorded by the server.

[1179] The emotion engine analyzes the user's facial expressions and voice to identify the user's emotional state. The server then uses the generative AI model again based on the emotion analysis results to adjust the design pattern. For example, if the user is feeling happy, it generates a design pattern with bright colors, and if the user is feeling depressed, it generates a design pattern with calm colors.

[1180] As a concrete example, let's say a user is thinking about the UI design of a new music app. The user opens a browser on their device and enters the following prompt into a special input form: “Generate a UI design for a new music app. Consider the shape, color, and placement of buttons, and use bright colors when the user is happy and muted colors when the user is sad.”

[1181] The server receives this prompt text and sends a request to the API of the generative AI model. The generative AI model analyzes the prompt text and generates multiple UI design patterns. The generated design patterns are returned to the server in JSON format. The server analyzes the received JSON data and saves the design patterns in a database.

[1182] The user opens the design pattern viewing page in the device's browser and checks the design patterns retrieved from the database. Thumbnail images and detailed information are displayed on the page. The user clicks on the design pattern they like on the viewing page and presses the Select button. The ID of the selected design pattern is sent to the server. The server records the ID of the design pattern selected by the user in the database.

[1183] If the user allows emotion analysis, the device's camera and microphone are activated to capture the user's facial expressions and voice. The emotion engine analyzes the captured data in real time to identify the user's emotional state. The server sends the emotion analysis results received from the emotion engine to the generative AI model to regenerate the design pattern. The new design pattern generated is stored in the database again and presented to the user.

[1184] In this way, efficient generation and selection of design patterns is possible, and further, the design can be adjusted according to the user's emotions.

[1185] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1186] Step 1:

[1187] The user enters a prompt sentence.

[1188] The user opens the browser on the device and inputs a prompt text into a dedicated input form. For example, the user might input, "Generate a UI design for a new music app. Consider the shape, color, and placement of the buttons, and design them in bright colors when the user is feeling happy, and calm colors when the user is feeling depressed." The input prompt text is sent to the server.

[1189] Step 2:

[1190] The server receives the prompt text and sends it to the generative AI model.

[1191] The server receives the prompt text sent by the user. It sends the received prompt text as a request to the API of the generative AI model. The input is the prompt text, and the output is the design pattern data returned from the generative AI model.

[1192] Step 3:

[1193] A generative AI model generates UI design patterns.

[1194] The generative AI model generates multiple UI design patterns based on the received prompt sentence. The generated design patterns are returned to the server in JSON format. The input is the prompt sentence, and the output is the data of the generated design pattern.

[1195] Step 4:

[1196] The server stores the generated design patterns in a database.

[1197] The server parses the JSON data received from the generative AI model and saves the design patterns in a database. The input is the data of the generated design patterns, and the output is the design patterns saved in the database.

[1198] Step 5:

[1199] A user uses a terminal to view the design pattern.

[1200] The user opens the design pattern viewing page in the browser of the terminal and checks the design patterns retrieved from the database. The page displays thumbnail images and detailed information. The input is the design pattern retrieved from the database, and the output is the design pattern displayed on the user's screen.

[1201] Step 6:

[1202] The user selects the most suitable design pattern.

[1203] The user clicks on the design pattern he likes on the browse page and presses the select button. The ID of the selected design pattern is sent to the server. The input is the design pattern selected by the user, and the output is the ID of the design pattern sent to the server.

[1204] Step 7:

[1205] The server records the selected design pattern.

[1206] The server records the ID of the design pattern selected by the user in the database. The record associates the user ID with the design pattern ID. The input is the ID of the selected design pattern, and the output is the ID of the design pattern recorded in the database.

[1207] Step 8:

[1208] The emotion engine analyzes user emotions (optional).

[1209] If the user allows emotion analysis, the device's camera and microphone are activated to capture the user's facial expressions and voice. The emotion engine analyzes the captured data in real time to identify the user's emotional state. The input is the captured facial and voice data, and the output is the analyzed emotional state.

[1210] Step 9:

[1211] The server optionally adjusts design patterns based on sentiment analysis results.

[1212] The server sends the emotion analysis results received from the emotion engine to the generative AI model, which regenerates the design pattern. For example, if the user feels happy, the server instructs the generative AI model to "generate a design pattern with bright colors." The new design pattern generated is saved in the database again and presented to the user. The input is the emotion analysis result, and the output is the adjusted design pattern.

[1213] (Application example 2)

[1214] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the smart glasses 214 will be referred to as a "terminal".

[1215] Conventional design production systems have difficulty generating design patterns based on user emotions, and are therefore unable to provide optimal designs that reflect the user's emotions and preferences. In addition, there is a problem in that selecting the optimal design from a myriad of design patterns places a heavy burden on the user, making it difficult to make an efficient selection.

[1216] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1217] In this invention, the server includes a means for generating patterns in design production, a means for making a final selection from an infinite number of patterns, a means for generating these patterns by AI, a means for analyzing the user's emotions, a means for adjusting the design patterns based on the analyzed emotions, a means for presenting the generated design patterns to the user, and a means for the user to select the optimal design pattern. This enables the generation and efficient selection of optimal design patterns based on the user's emotions.

[1218] A "means for generating patterns in design production" is a means for generating a variety of design patterns by combining design elements (shape, color, arrangement, etc.).

[1219] "Means for making the final selection from countless patterns" refers to a means for the user to select the most suitable one from the diverse design patterns generated.

[1220] "Means for generating these patterns using AI" refers to means for automatically generating design patterns using artificial intelligence.

[1221] "Means for analyzing user emotions" refers to means for analyzing the user's facial expressions and behavior to identify the user's emotional state.

[1222] The "means for adjusting a design pattern based on the analyzed emotion" refers to a means for adjusting the color tone and shape of a generated design pattern according to the emotional state of a user.

[1223] The "means for presenting the generated design pattern to the user" refers to a means for visually displaying the adjusted design pattern to the user.

[1224] "Means for the user to select the most suitable design pattern" refers to a means for the user to select the most suitable one from the presented design patterns.

[1225] A system for implementing this invention includes means for generating patterns in design production, means for making a final selection from an infinite number of patterns, means for generating these patterns using AI, means for analyzing a user's emotions, means for adjusting the design patterns based on the analyzed emotions, means for presenting the generated design patterns to a user, and means for the user to select the optimal design pattern.

[1226] Hardware and Software Used

[1227] Hardware: Smartphone camera

[1228] Software: OpenCV (image processing library), Keras (deep learning library), Numpy (numerical calculation library)

[1229] System processing overview

[1230] 1. User sentiment analysis:

[1231] An image of the user's face is captured using the smartphone camera.

[1232] Using OpenCV, the facial images are converted to grayscale and input into the emotion analysis model.

[1233] Using Keras, the sentiment analysis model predicts user emotions.

[1234] 2. Design Pattern Generation:

[1235] Based on the predicted emotion, AI determines the color tone, shape, and placement of the design pattern.

[1236] The generated design pattern is presented to the user.

[1237] 3. Your Choices:

[1238] The user selects the most appropriate design pattern from the presented ones.

[1239] Examples

[1240] For example, when a user opens the app, the camera will automatically launch and capture the user's face. If the emotion analysis model predicts the user's emotion as "happy," a bright color scheme will be generated. The user can then choose the one they like best from the generated patterns.

[1241] Examples of prompt statements

[1242] When a user opens the app, the app will launch the camera to capture a facial image and use an emotion analysis model to predict the user's emotion. Based on the predicted emotion, the app will determine the color tone, shape, and layout of the UI design, and present the generated design patterns to the user. The user can then select the most suitable design.

[1243] In this way, it becomes possible to generate and efficiently select optimal design patterns based on user emotions.

[1244] The flow of the identification process in the application example 2 will be described with reference to FIG.

[1245] Step 1:

[1246] When a user opens the app, the device activates the camera and captures the user's facial image. The input is the real-time video from the camera, and the output is the captured facial image, which is used for subsequent emotion analysis.

[1247] Step 2:

[1248] The device converts the captured face image to grayscale using OpenCV. The input is the captured face image, and the output is a grayscale image. This grayscale image is then converted into a format suitable for the emotion analysis model.

[1249] Step 3:

[1250] The device resizes and normalizes the grayscale image for input to the sentiment analysis model. The input is a grayscale image, and the output is normalized image data. This data is converted to a format suitable for the sentiment analysis model.

[1251] Step 4:

[1252] The device inputs normalized image data into an emotion analysis model using Keras to predict the user's emotion. The input is normalized image data, and the output is an emotion label (e.g., happy, sad, neutral). This emotion label is used to generate subsequent design patterns.

[1253] Step 5:

[1254] The server uses AI to determine the color tone, shape, and placement of the design pattern based on the predicted emotion label. The input is the emotion label, and the output is the generated design pattern. This design pattern is presented to the user.

[1255] Step 6:

[1256] The terminal visually presents the generated design pattern to the user. The input is the generated design pattern, and the output is the design pattern displayed on the user's screen. The user visually confirms this design pattern.

[1257] Step 7:

[1258] The user selects the best design pattern from the presented design patterns. The input is the design patterns presented to the user, and the output is the design pattern selected by the user. This selection is fed back to the system and finalized as the design.

[1259] Example 3

[1260] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[1261] In conventional design production systems, users had to spend a lot of time and effort selecting the best design from a myriad of design patterns. In addition, there was a lack of a way to optimize the design based on the user's emotions, and the user experience was not sufficiently improved.

[1262] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1263] In this invention, the server includes a means for selecting patterns in design production, a means for making a final selection from an infinite number of patterns, a means for generating these patterns by artificial intelligence, a means for analyzing the user's emotions, and a means for recommending an optimal design pattern based on the analysis results. This allows the user to efficiently select an optimal design pattern, and further enables optimization of the design based on the user's emotions.

[1264] "Means for generating patterns in design production" refers to a function for combining design elements to generate multiple design patterns.

[1265] "Means for making the final selection from countless patterns" is a function that enables the user to select the optimal one from the multiple design patterns that have been generated.

[1266] "Means for generating using artificial intelligence" refers to a function for automatically generating design patterns using machine learning models and algorithms.

[1267] The "means for analyzing the user's emotions" refers to a function for analyzing the user's emotional state, and may use, for example, facial expression recognition or text analysis.

[1268] The "means for recommending the optimal design pattern based on the analysis results" is a function for suggesting the optimal design pattern to a user based on the results of the user's sentiment analysis.

[1269] This invention is a system for generating patterns in design production and making a final selection from an infinite number of patterns. The system uses artificial intelligence to generate design patterns and includes a function for analyzing user sentiment to recommend the most suitable design pattern.

[1270] Hardware and Software Used

[1271] Hardware: Servers, user devices (PCs, smartphones, etc.)

[1272] Software: Generative AI models (e.g. GPT-4), sentiment analysis engines (e.g. IBM Watson Tone Analyzer)

[1273] Details of data processing and calculation

[1274] 1. Generating Web Design Patterns

[1275] The server uses a generative AI model to generate a variety of web design variations by combining elements such as header design, font type, and background color.

[1276] Example: A server sends a prompt to a generative AI model: "Generate a web design pattern with a blue header, Arial font, and white background."

[1277] 2. Your Choices

[1278] Users use their device to select the most suitable one from the generated web design patterns.

[1279] Example: A user selects "Blue header and Arial font design."

[1280] 3. Supporting the final choice with an emotion engine

[1281] The server uses a sentiment analysis engine to analyze the user's sentiment.

[1282] Based on the analysis results, the generative AI model recommends design patterns that support the final selection.

[1283] Example: If the user is feeling happy, the server might recommend "design patterns with bright colors and fun images."

[1284] Examples of prompt statements

[1285] "Generate a web design pattern with a blue header, Arial font, and a white background."

[1286] "Recommend web design patterns that are appropriate when users experience delight."

[1287] This system aims to improve the user experience by providing an optimal web design based on the user's preferences and emotions. The flow of the specification process in the third embodiment will be described with reference to FIG.

[1288] Step 1:

[1289] A user requests the generation of a web design.

[1290] Specific actions: A user uses a terminal to open a web browser, access the system's web page, and click the "Generate Design" button.

[1291] Input: User request

[1292] Output: A request to generate the design is sent to the server.

[1293] Step 2:

[1294] The server generates web design patterns using the generative AI model.

[1295] Specific operation: The server sends a prompt to the generative AI model: "Generate a web design pattern that uses blue for the header, Arial font, and white for the background."

[1296] Input: User request, prompt

[1297] Output: Generated multiple web design patterns

[1298] Step 3:

[1299] The server presents the generated design patterns to the user.

[1300] Specific operation: The server displays the generated design pattern in HTML format in the user's web browser.

[1301] Input: Generated web design patterns

[1302] Output: Design patterns that are displayed on the user's device

[1303] Step 4:

[1304] The user selects the design pattern.

[1305] Specific behavior: User clicks to select the design pattern they like.

[1306] Input: User's choice

[1307] Output: Information about the selected design pattern is sent to the server.

[1308] Step 5:

[1309] The server uses a sentiment analysis engine to analyze the user's sentiment.

[1310] Specific operation: The server sends the user's facial expression at the time of selection and the entered text to the emotion analysis engine and receives the analysis results.

[1311] Input: User facial expression data and text data

[1312] Output: User sentiment analysis results

[1313] Step 6:

[1314] The server recommends optimal design patterns based on the results of sentiment analysis.

[1315] Specific behavior: The server again presents an appropriate design pattern to the user based on the logic that "if the user is feeling happy, then a design pattern with bright colors and a fun image is recommended."

[1316] Input: User sentiment analysis results

[1317] Output: Suggested design patterns

[1318] (Application example 3)

[1319] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server", and the smart glasses 214 will be referred to as a "terminal".

[1320] Conventional design production systems lack the functionality to recommend optimal designs based on the user's emotions, making it difficult for users to select designs that correspond to their psychological state. In addition, there is a problem that the burden on users is heavy when selecting the optimal design from a myriad of design patterns.

[1321] The specification process by the specification processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means. In this invention, the server includes a means for selecting patterns in design production, a means for making a final selection from an infinite number of patterns, a means for generating these patterns by artificial intelligence, a means for analyzing the user's emotions, and a means for recommending an optimal design based on the analyzed emotions. This makes it possible to recommend an optimal design based on the user's emotions, and enables the user to select a design according to their psychological state while reducing the burden on the user.

[1322] "Pattern creation in design production" is the process of combining design elements to generate a variety of design patterns.

[1323] "Making the final selection from countless patterns" refers to the process of selecting the optimal one from the diverse design patterns generated.

[1324] "Artificial intelligence" is the technology that enables computer systems to mimic human intelligence by learning, reasoning, and self-correcting.

[1325] "Analyzing user emotions" is the process of identifying a user's psychological state from their input and behavior.

[1326] "Recommending optimal design based on analyzed emotions" is the process of proposing the most suitable design pattern based on the results of user emotion analysis.

[1327] "User interface design" is the design of the visual and functional elements that allow a user to interact with an application or website.

[1328] "Web design" is the design of the visual and functional elements of a website.

[1329] "Illustration" is the creation of images or pictures to communicate a visual story or concept.

[1330] A "banner" is a rectangular image or graphic used to display advertising or information within a web page or application.

[1331] A "key visual" is an image or graphic used as the main visual element of a brand or campaign.

[1332] An "icon" is a small image or symbol that visually represents a function or information within an application or website.

[1333] A system for implementing this invention includes a means for generating patterns in design production, a means for making a final selection from an infinite number of patterns, a means for generating these patterns using artificial intelligence, a means for analyzing a user's emotions, and a means for recommending an optimal design based on the analyzed emotions.

[1334] The server uses artificial intelligence to generate various design patterns for design production. Specifically, it generates multiple design patterns by combining elements such as header design, font type, and background color. For this, a generative AI model using Python and other programming languages ​​is used.

[1335] Next, an emotion engine is used to analyze the user's emotions. The emotion engine analyzes the user's input text and behavioral data to identify their psychological state. For example, if a user enters "I'm feeling very happy today!", the emotion engine detects "joy." The EmotionEngine library is used for this emotion analysis.

[1336] Based on the analyzed emotions, the server recommends the optimal design. From the generated design patterns, the design that best suits the user's emotions is selected and recommended. For example, if the user feels "joy", designs with bright colors and fun images are recommended. The DesignGenerator library is used in this recommendation process.

[1337] For example, if a user types "I'm feeling very happy today!", the emotion engine will detect "joy" and recommend designs with bright colors. On the other hand, if a user types "I'm feeling a bit down," the emotion engine will detect "sadness" and recommend designs with calm colors.

[1338] An example of a prompt is as follows:

[1339] User Input: "I'm feeling very happy today!"

[1340] Emotion engine output: "Joy"

[1341] Recommended design: Light color design

[1342] In this way, it becomes possible to recommend optimal designs based on the user's emotions, reducing the burden on the user and enabling them to select designs that suit their psychological state.

[1343] The flow of the specific process in the application example 3 will be described with reference to FIG.

[1344] Step 1:

[1345] The user inputs their emotions. The user inputs their emotions or mood in text format through the interface of their smartphone or computer. For example, they input "I'm feeling very happy today!"

[1346] Step 2:

[1347] The server receives user input and sends it to the emotion engine. The input text data is passed to the EmotionEngine library for emotion analysis. The input is the user's text data, and the output is the analyzed emotion (e.g. "joy").

[1348] Step 3:

[1349] The server receives the analysis results from the emotion engine and sends them to the design generation engine. Based on the analyzed emotion data, multiple design patterns are generated using the DesignGenerator library. The input is the analyzed emotion data, and the output is a list of generated design patterns.

[1350] Step 4:

[1351] The server receives the list of generated design patterns and selects the optimal design based on the sentiment. It uses the DesignGenerator library to select the design pattern that best matches the analyzed sentiment. The input is the list of design patterns and sentiment data, and the output is the optimal design pattern.

[1352] Step 5:

[1353] The server recommends the optimal design pattern to the user. The selected design pattern is sent to the user's terminal and displayed to the user. The input is the optimal design pattern, and the output is the design displayed on the user's terminal.

[1354] Step 6:

[1355] The user reviews the recommended designs and makes a final selection. The user selects the one he likes best from the displayed design patterns and makes a decision. The input is the recommended design pattern, and the output is the user's final selection.

[1356] In this way, optimal design recommendations and selections are made based on the user's emotions.

[1357] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1358] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1359] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1360] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1361] [Third embodiment]

[1362] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1363] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1364] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[1365] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1366] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[1367] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[1368] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[1369] Fig. 6 shows an example of main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1370] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1371] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1372] In the headset type terminal 314, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1373] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1374] "Example 1"

[1375] One aspect of the present invention is a system for generating design patterns using AI. This system includes a means for generating patterns in design production, a means for making a final selection from an infinite number of patterns, and a means for generating these patterns using AI. Specifically, the AI ​​first generates design patterns. In this case, the AI ​​generates a variety of patterns by combining various design elements. Next, the final selection is made from the generated patterns. This selection is made based on user input and AI recommendations.

[1376] "Example 2"

[1377] As a concrete example, consider the UI design of an app. First, AI generates various UI design patterns by combining elements such as button shape, color, and placement. Then, the user selects the most suitable one from the generated patterns. This selection is based on the user's preferences and the purpose of the app.

[1378] "Example 3"

[1379] The same is true for web design. AI generates various web design variations by combining elements such as header design, font type, and background color. Users then select the best one from the generated variations. This selection is based on the user's preferences and the purpose of the website.

[1380] The process flow of each embodiment will be described below.

[1381] "Example 1"

[1382] Step 1: AI generates design patterns. In this process, the AI ​​combines various design elements to generate diverse patterns.

[1383] Step 2: Make a final selection from the generated patterns. This selection is based on user input and / or AI recommendations.

[1384] "Example 2"

[1385] Step 1: For app UI design, AI will generate diverse UI design patterns by combining elements such as button shape, color, and placement.

[1386] Step 2: The user selects the best one from the generated patterns. This selection is based on the user's preferences and the purpose of the app.

[1387] "Example 3"

[1388] Step 1: For web design, AI will combine elements such as header design, font type, and background color to generate diverse web design patterns.

[1389] Step 2: The user selects the best one from the generated patterns. This selection is based on the user's preferences and the website's objectives.

[1390] Example 1

[1391] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1392] In conventional design production, there was a problem that it took a lot of time and effort to come up with design patterns and make a final selection. In addition, there was a lack of means for efficiently generating a variety of design patterns according to user requests. Furthermore, the process of selecting the most suitable one from the generated design patterns was also complicated, which was a factor in reducing user satisfaction. The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1393] In this invention, the server includes a means for a user to input a prompt text, a means for the server to receive the prompt text and send it to the generative AI model, a means for the generative AI model to generate a design pattern, a means for the server to save the generated pattern in a database, a means for a user to view the generated patterns on a terminal, a means for a user to select a pattern, and a means for the server to save the selected pattern and make final adjustments as necessary. This allows a user to efficiently generate a variety of design patterns and select the most suitable one from among them.

[1394] "User" means an individual or group that uses the system to generate and select design patterns.

[1395] A "prompt sentence" is text that describes instructions or requirements that a user enters into a system.

[1396] The "server" is a computer system responsible for receiving prompts from the user and sending them to the generative AI model.

[1397] A "generative AI model" is an artificial intelligence algorithm that generates design patterns based on a prompt sentence.

[1398] A "design pattern" is a visual layout or composition that combines various design elements generated by a generative AI model.

[1399] A "database" is an information management system for storing the generated design patterns.

[1400] A "terminal" is a computer or mobile device that a user uses to view and select design patterns.

[1401] "Final adjustment" refers to fine adjustments and modifications made to the design pattern selected by the user.

[1402] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[1403] The present invention is a system for efficiently performing pattern selection and final selection in design production. A specific embodiment of this system will be described below.

[1404] System Configuration

[1405] The system includes a user, a server, a terminal, and a generative AI model. The user inputs a prompt sentence using the terminal, and the server receives the prompt sentence and sends it to the generative AI model. The generative AI model generates a design pattern based on the prompt sentence, and the server stores the generated pattern in a database. The user can browse and select the generated pattern using the terminal. The server stores the selected pattern and makes final adjustments if necessary.

[1406] Hardware and Software Used

[1407] Server: A high-performance computer system that receives prompts, sends them to the generative AI model, and stores and finalizes the generated design patterns.

[1408] Terminal: The computer or mobile device used by the user to enter prompts and view and select design patterns.

[1409] Generative AI models: For example, using advanced artificial intelligence algorithms such as OpenAI's GPT-4 to generate design patterns based on a prompt.

[1410] Database: An information management system for storing the generated design patterns.

[1411] Data processing and calculation

[1412] The server analyzes the prompt text sent by the user, converts it into an appropriate format, and sends it to the generative AI model. The generative AI model generates multiple design patterns by combining various design elements (color, shape, layout, etc.) based on the prompt text. The generated patterns are stored in a database by the server, and can be viewed and selected by the user using their terminal.

[1413] Examples

[1414] For example, if a user wants to create a new website design, they can enter a prompt like this:

[1415] "Generate modern website design patterns"

[1416] "Please suggest a simple and clean logo design."

[1417] "Please suggest a color palette that would suit this design."

[1418] The server receives this prompt and sends it to the generative AI model. The generative AI model generates multiple design patterns based on the prompt, and the server stores them in a database. The user can use the terminal to browse the generated patterns and select the design they like. The selected design is stored on the server and final adjustments are made if necessary.

[1419] In this way, users can utilize AI to efficiently generate design patterns and make final selections.

[1420] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1421] Step 1:

[1422] The user enters a prompt sentence.

[1423] The user types "Generate a modern website design pattern" into the input field of the terminal and clicks the submit button.

[1424] Input: The prompt text entered by the user.

[1425] Output: The prompt sent from the terminal to the server.

[1426] Step 2:

[1427] The server receives the prompt text and sends it to the generative AI model.

[1428] The server receives the prompt text sent by the user, parses it, converts it into an appropriate format, and then sends the prompt text to the generative AI model.

[1429] Input: The prompt text sent from the terminal.

[1430] Output: The prompt that is sent to the generative AI model.

[1431] Step 3:

[1432] A generative AI model generates design patterns.

[1433] Based on the received prompt, the generative AI model generates multiple design patterns by combining various design elements (color, shape, layout, etc.).

[1434] Input: The prompt text sent by the server.

[1435] Output: Multiple generated design patterns.

[1436] Step 4:

[1437] The server stores the generated patterns in a database.

[1438] The server stores the design patterns received from the generative AI model in a database, giving each pattern a unique ID.

[1439] Input: Design patterns sent from the generative AI model.

[1440] Output: Design patterns stored in a database.

[1441] Step 5:

[1442] The user views the generated pattern on the terminal.

[1443] A user uses a terminal to browse the design patterns stored in the database. The generated patterns are displayed in thumbnail format on the terminal screen.

[1444] Input: Design patterns stored in a database.

[1445] Output: A thumbnail of the design pattern displayed on the terminal.

[1446] Step 6:

[1447] The user selects a pattern.

[1448] The user clicks on a design pattern they like on the device screen and presses the "Select this design" button. Detailed information about the selected pattern is displayed again, and the selection is confirmed by pressing the Confirm button.

[1449] Input: A thumbnail of the design pattern displayed on the terminal.

[1450] Output: The design pattern selected by the user.

[1451] Step 7:

[1452] The server stores the selected pattern and makes final adjustments if necessary.

[1453] The server receives the design patterns selected by the user, stores them in a database, and, if necessary, makes minor adjustments to the colors and layout based on the user's feedback to finalize the design.

[1454] Input: A design pattern selected by the user.

[1455] Output: The final adjusted design pattern stored in a database.

[1456] (Application example 1)

[1457] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[1458] In conventional design production, the process of generating various patterns and selecting the final design was time-consuming and laborious. In addition, it was difficult to propose designs that took into account the user's preferences and past selection history, making efficient design production difficult. Furthermore, there was a lack of means to efficiently generate design patterns for product displays and store layouts in virtual stores and present them to users. In order to solve these problems, the present invention provides a design pattern generation system using AI.

[1459] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1460] In this invention, the server includes a means for generating patterns in design production, a means for making a final selection from an infinite number of patterns, a means for generating these patterns by AI, a means for generating a prompt sentence based on a user's input, a means for presenting the generated design patterns to the user, and a means for the AI ​​to present recommended designs based on the user's preferences and past selection history. This makes it possible to efficiently generate design patterns for product displays and store layouts in a virtual store and present them to the user.

[1461] "Means for generating patterns in design production" refers to a method or device for combining various elements of a design to generate a variety of design patterns.

[1462] A "means for making a final selection from an infinite number of patterns" is a method or device for selecting the most suitable one from the various design patterns generated.

[1463] "Means for generating these patterns using AI" refers to methods or devices for automatically generating design patterns using artificial intelligence.

[1464] A "means for generating a prompt sentence based on user input" refers to a method or device that creates an instruction sentence (prompt) for an AI to generate a design pattern based on information entered by a user.

[1465] "Means for presenting the generated design pattern to a user" refers to a method or device for visually displaying the design pattern generated by the AI ​​to a user.

[1466] "Means for an AI to recommend designs based on a user's preferences and past selection history" refers to a method or device that analyzes a user's past selections and preferred designs and uses them to suggest optimal designs.

[1467] The embodiment of the present invention will be described. First, Python and OpenAI API are used to generate the system program. The server includes a means for generating patterns in design production, a means for making a final selection from an infinite number of patterns, a means for generating these patterns by AI, a means for generating a prompt sentence based on a user's input, a means for presenting the generated design pattern to the user, and a means for the AI ​​to present a recommended design based on the user's preferences and past selection history.

[1468] The server receives information input by the user through a smartphone or head-mounted display and generates a prompt sentence based on that information. For example, if a user inputs "virtual store display design," the server generates a prompt sentence based on this information. This prompt sentence is sent to a generative AI model (e.g., GPT-3), which generates a design pattern.

[1469] The generated design patterns are sent from the server to the user's device and visually presented to the user. The user can select the most suitable one from the presented design patterns. The server also analyzes the user's preferences and past selection history, and the AI ​​presents recommended designs based on that. This allows the user to efficiently select the most suitable design.

[1470] For example, when a user inputs "Virtual store display design," the AI ​​generates patterns such as "Modern layout," "Classic layout," and "Minimalist layout" and presents them to the user. The user can then select the most suitable one from these patterns.

[1471] The following are examples of prompt sentences:

[1472] Generate design patterns based on the following criteria: Virtual store display design

[1473] In this way, it is possible to efficiently generate design patterns for product displays and store layouts in a virtual store and present them to the user.

[1474] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1475] Step 1:

[1476] The user requests the generation of a design pattern via a smartphone or head-mounted display.

[1477] Input: Design requirements entered by user (e.g. "Virtual store display design").

[1478] Output: User input data that is sent to the server.

[1479] Specific operation: A user launches the application, enters requirements into an input form to request the generation of a design pattern, and presses the submit button.

[1480] Step 2:

[1481] The server generates a prompt based on the user's input.

[1482] Input: The data entered by the user.

[1483] Output: Generated prompt statement (e.g. "Generate a design pattern based on the following criteria: Virtual store display design").

[1484] Specific operation: The server receives the user's input data and performs processing to generate a prompt sentence based on it.

[1485] Step 3:

[1486] The server sends the generated prompt sentence to the generative AI model to generate a design pattern.

[1487] Input: The generated prompt statement.

[1488] Output: The design patterns returned by the generative AI model.

[1489] Specific operation: The server sends the generated prompt sentence to the OpenAI API, and the generative AI model (e.g. GPT-3) generates a design pattern.

[1490] Step 4:

[1491] The server transmits the generated design pattern to the user's terminal and presents it to the user.

[1492] Input: Design patterns returned by a generative AI model.

[1493] Output: The design pattern that is displayed on the user's terminal.

[1494] Specific operation: The server sends the generated design pattern to the user's terminal, and the application displays it visually.

[1495] Step 5:

[1496] The user selects the most suitable design pattern from the presented ones.

[1497] Input: A design pattern presented to the user.

[1498] Output: The design pattern selected by the user.

[1499] Specific behavior: The user operates an interface to review the presented design patterns and select the most suitable one.

[1500] Step 6:

[1501] The server analyzes the user's preferences and past selection history, and the AI ​​presents recommended designs.

[1502] Input: User preferences and past selections.

[1503] Output: AI recommended design patterns.

[1504] Specific operation: The server retrieves the user's preferences and past selection history from the database, and the AI ​​performs processing based on that to recommend the optimal design.

[1505] Example 2

[1506] Next, a second embodiment of the second form will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1507] In conventional design production, the process of efficiently generating a variety of patterns and allowing users to select the optimal design was complicated, time-consuming, and labor-intensive. It was also difficult to quickly provide designs that matched the user's preferences and the purpose of the application.

[1508] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1509] In this invention, the server includes a means for generating design patterns using a generative AI model, a means for saving the generated design patterns in a database, a means for a terminal to acquire the design patterns and display them to a user, a means for the user to select an optimal design pattern, a means for transmitting the user's selection to the server, and a means for saving the user's selection in the database and reflecting it in the user interface of the application. This enables the user to efficiently generate a variety of design patterns and quickly select an optimal design.

[1510] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to automatically generate design patterns.

[1511] A "design pattern" is a combination of visual elements such as the shape, color, and placement of buttons in the user interface of an application, website, etc.

[1512] "Database" is an information management system for storing generated design patterns and user selections.

[1513] A "terminal" is a device operated by a user and has the function of obtaining and displaying design patterns from a server.

[1514] A "user" is an individual or group that uses the system to select design patterns.

[1515] A "server" is a computer system that runs a generative AI model and generates, stores, and distributes design patterns.

[1516] A "prompt" is a piece of text that describes the instructions and conditions for the generative AI model to generate a design pattern.

[1517] "User interface" is a general term for the screens and operating elements that allow a user to interact with an application or website.

[1518] The present invention is a system that uses a generative AI model to automatically generate design patterns and allows a user to select an optimal design. A specific embodiment of this system will be described below.

[1519] Hardware and Software Used

[1520] Server: A computer system that runs generative AI models and generates, stores, and distributes design patterns. Specifically, it uses common server hardware and software for running generative AI models (e.g., Python, TensorFlow, PyTorch, etc.).

[1521] Database: An information management system for storing the generated design patterns and user selections. Specifically, a relational database such as MySQL or PostgreSQL is used.

[1522] Terminal: A device operated by a user that has the function of retrieving and displaying design patterns from a server. Specifically, this applies to PCs, smartphones, tablets, etc.

[1523] Generative AI model: An algorithm or program that uses artificial intelligence technology to automatically generate design patterns. Specifically, we use generative AI models such as OpenAI's GPT-4.

[1524] Data processing and calculation

[1525] 1. The server generates design patterns using a generative AI model. A prompt sentence is input to the generative AI model to generate multiple design patterns.

[1526] 2. The server stores the generated design patterns in a database, and each design pattern is assigned a unique ID.

[1527] 3. The terminal sends a request for a design pattern to the server and displays the design pattern received from the server to the user, using HTML and CSS to make the display visually easy to understand.

[1528] 4. The user selects the most suitable design pattern from the displayed patterns. The selection is made through interactions such as clicking and tapping.

[1529] 5. The terminal sends the ID of the design pattern selected by the user to the server using an HTTP request (e.g., POST request).

[1530] 6. The server saves the user's selection in a database and updates the application's user interface, possibly using a front-end framework (e.g., React or Vue.js).

[1531] Examples

[1532] For example, if a user wants to create a UI design for a new task management app, the system works as follows:

[1533] 1. The server uses the generative AI model to input the prompt, "Generate the UI design for a new task management app. Create 10 design patterns with combinations of round or square button shapes, blue, red, and green colors, and top, center, and bottom placement." and generate 10 UI design patterns.

[1534] 2. The server stores the generated UI design patterns in a MySQL database and gives each design pattern a unique ID.

[1535] 3. The terminal sends a request for a UI design pattern to the server and displays the design pattern received from the server to the user using HTML and CSS.

[1536] 4. Users click to select the design with a blue circular button in the center.

[1537] 5. The terminal sends the ID of the design pattern selected by the user to the server in a POST request.

[1538] 6. The server saves the user's selection to the database and updates the application's user interface using React.

[1539] In this way, users can leverage generative AI models to make efficient UI design choices.

[1540] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1541] Step 1:

[1542] The server generates design patterns using a generative AI model. Specifically, the server inputs a prompt sentence into the generative AI model to generate multiple design patterns. The server uses the prompt sentence "Generate a UI design for a new task management app. Create 10 design patterns with combinations of round or square button shapes, blue, red, and green colors, and top, center, and bottom placements" as input, and obtains 10 design patterns as output.

[1543] Step 2:

[1544] The server saves the generated design patterns in a database. Specifically, the server assigns a unique ID to each design pattern and saves it in a MySQL database. It receives the generated design patterns as input and obtains the design patterns saved in the database as output.

[1545] Step 3:

[1546] A terminal transmits a request for a design pattern to a server. Specifically, the terminal accesses the server using an HTTP request and requests a design pattern. The terminal transmits the request as input and receives the design pattern from the server as output.

[1547] Step 4:

[1548] The terminal displays the received design pattern to the user. Specifically, the terminal visually displays the design pattern using HTML and CSS. It uses the design pattern received from the server as input and obtains the design pattern displayed to the user as output.

[1549] Step 5:

[1550] The user selects the most suitable design pattern from the displayed design patterns. Specifically, the user selects a design pattern through interactions such as clicking or tapping. The displayed design patterns are referred to as input, and the selected design pattern is obtained as output.

[1551] Step 6:

[1552] The terminal sends the ID of the design pattern selected by the user to the server. Specifically, the terminal sends the ID of the selected design pattern to the server using an HTTP POST request. It receives the ID of the selected design pattern as input and obtains the ID sent to the server as output.

[1553] Step 7:

[1554] The server saves the user's selection in a database and reflects it in the application's user interface. Specifically, the server saves the ID of the selected design pattern in a database and updates the application's UI using a front-end framework such as React. It receives the ID of the selected design pattern as input and gets the updated application UI as output.

[1555] (Application example 2)

[1556] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[1557] In conventional design production, the generation and selection of design patterns is done manually, which is time-consuming and labor-intensive. It is also difficult to generate designs that reflect the user's preferences, and a lot of trial and error is required to find the optimal design. Furthermore, there is a lack of functionality to preview the selected design in real time, making it difficult to increase user satisfaction.

[1558] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1559] In this invention, the server includes a means for generating patterns in design production, a means for making a final selection from an infinite number of patterns, a means for generating these patterns using AI, a means for reflecting the user's selection history in subsequent design generation, and a means for previewing the selected design in real time. This makes it possible to automatically generate design patterns and select an optimal design that reflects the user's preferences, and further increases user satisfaction with the real-time preview function.

[1560] "Design creation" is the process of combining visual elements to create a design that has a specific purpose or function.

[1561] "Pattern creation" is the act of combining design elements to generate multiple design patterns.

[1562] "Final selection" refers to the act of selecting the optimal design pattern from among the multiple design patterns generated.

[1563] "AI generation" is the process of automatically generating design patterns using artificial intelligence.

[1564] "User selection history" is a record of design patterns that the user has previously selected.

[1565] "Reflecting in subsequent design generation" means taking the user's preferences into consideration when generating subsequent design patterns based on the user's selection history.

[1566] "Real-time preview" means that the user can instantly see and check the design they have selected.

[1567] A system for implementing this invention includes means for generating patterns in design production, means for making a final selection from an infinite number of patterns, means for generating these patterns using AI, means for reflecting the user's selection history in subsequent design generation, and means for previewing the selected design in real time.

[1568] System Programming

[1569] The system is built using Python and TensorFlow. The server generates design elements (button shape, color, placement, etc.) and generates various design patterns using an AI model. Users select the most suitable one from the generated design patterns, and the selection history is reflected in subsequent design generation. The selected design is previewed in real time.

[1570] Processing Description

[1571] The server first generates design elements. Specifically, it randomly combines button shapes (e.g., rectangle, circle, oval), colors (e.g., red, blue, green), and layouts (e.g., top-left, top-right, bottom-left, bottom-right). Next, it uses TensorFlow to define a neural network model and generates various design patterns using the design elements as input.

[1572] The user selects the most suitable design pattern from the generated design patterns. The selection history is stored in a database and is reflected in the next and subsequent design generation. This allows the system to generate design patterns that take the user's preferences into account.

[1573] The real-time preview function allows users to instantly see and confirm the design they have selected, allowing them to instantly see the results of applying their selected design and increasing satisfaction.

[1574] Examples

[1575] For example, if a user selects a design with a "rectangle" shaped, "red" button placed on the "top-left", that selection history will be reflected in the next design generation. In the next design generation, a similar design pattern will be preferentially generated, taking into account the user's preferences.

[1576] Examples of prompt statements

[1577] "Generate 10 different UI design patterns by combining button shapes, colors, and placements."

[1578] In this way, it is possible to automatically generate design patterns using AI and select the optimal design that reflects user preferences, and the real-time preview function can further increase user satisfaction.

[1579] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1580] Step 1:

[1581] The server generates design elements by randomly combining button shapes (e.g. rectangle, circle, oval), colors (e.g. red, blue, green), and alignments (e.g. top-left, top-right, bottom-left, bottom-right). The input is a list of design elements, and the output is a combination of the generated design elements. The server randomly selects these elements to create candidate design patterns.

[1582] Step 2:

[1583] The server uses TensorFlow to define a neural network model. The input is a combination of design elements, and the output is a neural network model. The server takes design elements as input and builds a neural network model. This model is used to generate design patterns.

[1584] Step 3:

[1585] The server generates various design patterns using the neural network model. The input is a combination of the neural network model and the design elements, and the output is the generated design pattern. The server inputs the design elements to the model and generates multiple design patterns.

[1586] Step 4:

[1587] The user selects the most suitable one from the generated design patterns. The input is the generated design pattern, and the output is the design pattern selected by the user. The user checks the displayed design patterns and selects the most suitable one.

[1588] Step 5:

[1589] The server saves the user's selection history in a database. The input is the design pattern selected by the user, and the output is the updated database. The server records the user's selection history and reflects it in subsequent design generation.

[1590] Step 6:

[1591] The server generates design patterns based on the user's selection history for subsequent design generation. The input is the user's selection history and design elements, and the output is a design pattern that reflects the user's preferences. The server takes the selection history into consideration and generates design patterns that match the user's preferences with priority.

[1592] Step 7:

[1593] The terminal previews the selected design in real time. The input is the design pattern selected by the user, and the output is the preview screen. The terminal immediately displays the selected design for the user to confirm.

[1594] Example 3

[1595] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1596] Conventional design production requires time-consuming and labor-intensive pattern selection and final selection, making efficient design production difficult. In addition, it is difficult to quickly generate a variety of design patterns that meet the needs of users, and the flexibility required to provide creative designs is lacking.

[1597] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1598] In this invention, the server includes a means for a user to input a prompt sentence, a means for the server to receive the prompt sentence and send it to the generative AI model, a means for the generative AI model to generate a design pattern, a means for the server to send the generated design pattern to the user, and a means for the user to select a design pattern. This makes it possible to rapidly generate a variety of design patterns according to user requests and to perform design production efficiently and flexibly.

[1599] A "user" is an individual or group that uses the system to generate and select design patterns.

[1600] A "prompt" is text that a user enters into a generative AI model containing design requirements or wishes.

[1601] The "server" is a computer system that receives prompt text from a user, sends it to the generative AI model, and returns the generated design pattern to the user.

[1602] A "generative AI model" is an artificial intelligence model that automatically generates design patterns based on a prompt sentence.

[1603] A "design pattern" is a specific example of a web design that combines elements such as header design, font type, and background color, generated by a generative AI model.

[1604] "User interface design" refers to the design of the operation screen of an application or website, which is designed to allow users to operate it intuitively.

[1605] "Web design" is the design of the visual layout and style of a website to enhance the user's visual experience.

[1606] An "illustration" is an image or picture for the purpose of visual explanation or decoration and is used as part of a design.

[1607] A "banner" is a rectangular image or graphic used to display advertising or information within a web page or application.

[1608] A "key visual" is the central visual element of a design, used to emphasize the message of a brand or campaign.

[1609] An "icon" is a small graphic symbol intended to represent a function or action within an application or website.

[1610] This invention is a system in which a user inputs a prompt sentence, generates various design patterns using a generative AI model, and selects the optimal design. A specific embodiment of this system is described below.

[1611] Hardware and Software Used

[1612] Hardware: Servers, terminals (users' PCs and smartphones)

[1613] Software: A generative AI model (e.g., OpenAI's GPT-4), a web browser, and a design tool (e.g., Adobe XD).

[1614] System Overview

[1615] A user accesses the system's interface through a web browser and inputs a prompt statement including design requirements and wishes. The server receives the prompt statement and sends it to the generative AI model. The generative AI model generates multiple design patterns based on the prompt statement and sends them back to the server. The server sends the generated design patterns to the user's device, and the user selects the most suitable design from them.

[1616] Examples

[1617] For example, if a user enters a prompt like "I want you to generate a design for a cafe website using warm colors and handwritten fonts," the following design pattern will be generated when this prompt is input to the generative AI model.

[1618] Design variant 1: A handwritten font in the header, a beige background, and brown buttons.

[1619] Design variant 2: A handwritten font in the header, a light green background, and dark green buttons.

[1620] Design variant 3: A handwritten font in the header, a light blue background, and navy buttons.

[1621] Users can choose from these design patterns the one they like best.

[1622] Implementation details

[1623] 1. The user enters a prompt:

[1624] A user opens a web browser, accesses the system's interface, and enters a prompt statement containing the desired web design requirements and wishes into an input field on the interface.

[1625] 2. The server receives the prompt:

[1626] The user presses the send button to send the prompt text, and the server receives the prompt text via an HTTP request.

[1627] 3. The server sends a prompt to the generative AI model:

[1628] The server creates an API request to send the received prompt sentence to the generative AI model. It sends a request including the prompt sentence to the API endpoint of the generative AI model.

[1629] 4. Generative AI models generate design patterns:

[1630] The generative AI model analyzes the prompt text it receives and generates multiple web design variations that combine elements such as header design, font type, and background color.

[1631] 5. The server receives the generated design patterns:

[1632] The generative AI model sends the generated design pattern back to the server. The server receives the response from the generative AI model and obtains the design pattern.

[1633] 6. The server sends the design pattern to the user:

[1634] The server creates a response for sending the acquired design pattern to the user's terminal, and sends the response including the generated design pattern to the user's terminal.

[1635] 7. User chooses design pattern:

[1636] The user's device displays the received design patterns on a web browser. The user selects the design that best suits his or her preferences and the purpose of the website from the multiple design patterns displayed, and presses a button to confirm the selected design pattern.

[1637] In this way, the user can easily generate and select a web design that meets his / her needs by utilizing the generative AI model. The flow of the specification process in the third embodiment will be described with reference to FIG.

[1638] Step 1:

[1639] The user enters a prompt.

[1640] A user opens a web browser and accesses the system's interface. In an input field on the interface, the user enters a prompt statement that includes the requirements and desires of the desired web design. For example, the user may enter "I want you to generate a design for a website that has a modern and simple header." The input data is the prompt statement in text format.

[1641] Step 2:

[1642] The server receives the prompt.

[1643] The user presses the send button to send the prompt text entered by the user. The server receives the prompt text through an HTTP request. The received data is the prompt text entered by the user. The server analyzes the prompt text and passes it to the next processing step.

[1644] Step 3:

[1645] The server sends a prompt to the generative AI model.

[1646] The server creates an API request to send the received prompt sentence to the generative AI model. It sends a request including the prompt sentence to the API endpoint of the generative AI model. The input data is the prompt sentence, and the output data is the request to the generative AI model.

[1647] Step 4:

[1648] A generative AI model generates design patterns.

[1649] The generative AI model analyzes the received prompt text and generates multiple web design patterns that combine elements such as header design, font type, background color, etc. The input data is the prompt text, and the output data is the multiple generated design patterns. For example, the following design patterns are generated.

[1650] Design variant 1: A handwritten font in the header, a beige background, and brown buttons.

[1651] Design variant 2: A handwritten font in the header, a light green background, and dark green buttons.

[1652] Design variant 3: A handwritten font in the header, a light blue background, and navy buttons.

[1653] Step 5:

[1654] A server receives the generated design patterns.

[1655] The generative AI model returns the generated design pattern to the server. The server receives the response from the generative AI model and obtains the design pattern. The input data is the response from the generative AI model, and the output data is the obtained design pattern.

[1656] Step 6:

[1657] The server sends the design pattern to the user.

[1658] The server creates a response for sending the acquired design pattern to the user's terminal. The server sends the response including the generated design pattern to the user's terminal. The input data is the acquired design pattern, and the output data is the response sent to the user's terminal.

[1659] Step 7:

[1660] The user selects the design pattern.

[1661] The user's device displays the received design patterns on a web browser. The user selects the design that best suits his or her preferences and the purpose of the website from the multiple design patterns displayed. The user then presses a button to confirm the selected design pattern. The input data is the displayed design pattern, and the output data is the design pattern selected by the user.

[1662] (Application example 3)

[1663] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[1664] In conventional design production, the generation and selection of design patterns is often done manually, which is problematic in terms of time and effort. In addition, when the user tries to select the optimal design, it is difficult to visually confirm it, making efficient design selection difficult. Furthermore, the process of applying the generated design to the actual system is also time-consuming, making it difficult to make rapid design changes. There is a demand for an efficient system to solve these issues.

[1665] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1666] In this invention, the server includes a means for generating patterns in design production, a means for making a final selection from an infinite number of patterns, a means for generating these patterns using a generative AI model, a means for displaying a list of generated design patterns, a means for a user to select an optimal design, a means for previewing the selected design, and a means for saving and applying the selected design. This enables efficient generation and selection of design patterns, and allows a user to select an optimal design while visually checking. It also enables the selected design to be quickly applied to an actual system.

[1667] "Design creation" is the process of combining visual elements to create a design based on a specific purpose or theme.

[1668] "Pattern creation" is the act of combining multiple design elements to generate various design patterns.

[1669] A "generative AI model" is an algorithm or system that uses artificial intelligence to automatically generate design patterns.

[1670] "List display" refers to displaying multiple generated design patterns side-by-side so that they can be checked at a glance.

[1671] "Means for the user to select the optimal design" refers to an interface or function that allows the user to select the most appropriate one from the generated design patterns.

[1672] "Preview" is a display function that allows you to check in advance how the selected design will look in the actual application environment.

[1673] The "means of saving and applying" is a function for saving the selected design in a database or the like and applying it to an actual system or website.

[1674] A system for implementing this invention includes means for generating patterns in design production, means for making a final selection from countless patterns, means for generating these patterns using a generative AI model, means for displaying a list of the generated design patterns, means for a user to select the optimal design, means for previewing the selected design, and means for saving and applying the selected design.

[1675] System Programming

[1676] The server generates design patterns using a generative AI model. Specifically, it uses machine learning libraries such as TensorFlow and Keras to generate various design patterns that combine design elements such as headers, fonts, and background colors. The generated design patterns are stored in a database and sent to the user's device.

[1677] The user's device displays a list of the generated design patterns. The user can visually check the generated design patterns using a device such as a smartphone or tablet and select the most suitable design. The selected design can be previewed to see how it will look in the actual application environment.

[1678] The selected design is sent to the server and stored in the database. The saved design is then applied to the actual system or website, allowing users to change the design quickly and efficiently.

[1679] Hardware and Software Used

[1680] Hardware: Servers, smartphones, tablets

[1681] Software: Python, TensorFlow, Keras, Database Management System

[1682] Examples

[1683] For example, when an online shopping site operator is creating a new product page design, they open the smartphone app and view 10 design patterns generated by AI. The operator selects the most suitable design from among them and uses the preview function to check how it will look when applied to the actual page. The selected design is saved on the server and immediately applied to the online shopping site.

[1684] Examples of prompt statements

[1685] "Create an application that uses AI to generate design variations for product pages and allow users to select the best design. Design elements include headers, fonts, and background colors. Display a list of generated design variations and allow users to preview and save the selected design."

[1686] The flow of the specific process in the application example 3 will be described with reference to FIG.

[1687] Step 1:

[1688] The server generates design patterns using a generative AI model. It receives design elements such as headers, fonts, and background colors as input, and combines them to generate a variety of design patterns. It uses TensorFlow and Keras to process the data, randomly combining elements and generating multiple design patterns as output.

[1689] Step 2:

[1690] The server stores the generated design patterns in a database. As input, it receives the design patterns generated in step 1 and stores them using a database management system. As data operation, it converts the design patterns into an appropriate format and obtains the design patterns stored in the database as output.

[1691] Step 3:

[1692] The server transmits the stored design pattern to the user terminal. As input, the server receives the design pattern stored in the database and transmits it to the user terminal via the network. As data processing, the server converts the design pattern into a transmittable format and obtains the design pattern transmitted to the user terminal as output.

[1693] Step 4:

[1694] The user terminal displays the received design patterns in a list.,As input, the design patterns sent from the server are,received and displayed on the user interface.,As data processing, the design patterns are converted,into a visually confirmable format, and a list of,design patterns is obtained as output.

[1695] Step 5:

[1696] The user selects the optimal design from the listed design patterns. As input, the user visually checks the listed design patterns and selects the optimal design. As data computation, the user's selection is recorded and the selected design pattern is obtained as output.

[1697] Step 6:

[1698] The user terminal previews the selected design pattern. It receives the design pattern selected by the user as input and displays how it will look in the actual application environment. As data processing, it converts the design pattern to suit the application environment and obtains the previewed design pattern as output.

[1699] Step 7:

[1700] The user checks the preview and saves the selected design. As input, the user checks the previewed design pattern and issues an instruction to save it. As data calculation, the instruction to save is recorded, and the design pattern instructed to be saved is obtained as output.

[1701] Step 8:

[1702] The server saves the designated design pattern in a database and applies it to the actual system. As input, the server receives the designated design pattern sent from the user terminal, saves it in a database, and applies it to the actual system. As data processing, the server converts the design pattern into an applicable format, and obtains the design pattern applied to the actual system as output.

[1703] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1704] "Example 1"

[1705] In one embodiment of the present invention, AI generates design patterns and makes a final selection from countless patterns. The system includes an emotion engine for recognizing the user's emotions. The emotion engine analyzes the user's emotions from facial expressions, tone of voice, text input, etc., and feeds the results back to the AI. For example, when the user is feeling happy, it recommends design patterns with bright colors and a fun image. Conversely, when the user is feeling depressed, it recommends design patterns with calm colors and a sense of security.

[1706] "Example 2"

[1707] In another embodiment, the emotion engine adjusts the design pattern output based on the user's emotions. Specifically, the emotion engine analyzes the user's emotions, and the AI ​​generates design patterns based on the results. For example, when the user is feeling happy, the AI ​​generates design patterns with bright colors and a fun image. Conversely, when the user is depressed, the AI ​​generates design patterns with calm colors and a sense of security.

[1708] "Example 3"

[1709] In yet another embodiment, the emotion engine supports the final selection based on the user's emotions. Specifically, the emotion engine analyzes the user's emotions, and the AI ​​supports the final selection based on the results. For example, when the user is feeling happy, the AI ​​recommends design patterns with bright colors and fun images. Conversely, when the user is feeling depressed, the AI ​​recommends design patterns with calm colors and a sense of security.

[1710] The process flow of each embodiment will be described below.

[1711] "Example 1"

[1712] Step 1: The user boots up the system and begins patterning the design.

[1713] Step 2: The emotion engine analyzes the user's emotions from facial expressions, tone of voice, text input, etc.

[1714] Step 3: The analysis results of the emotion engine are fed back to the AI, which then creates design patterns.

[1715] Step 4: Present the AI-generated design patterns to the user and make the final selection.

[1716] "Example 2"

[1717] Step 1: The user boots up the system and begins patterning the design.

[1718] Step 2: The emotion engine analyzes the user's emotions.

[1719] Step 3: AI generates design patterns based on the analysis results of the emotion engine.

[1720] Step 4: Present the AI-generated design patterns to the user and make the final selection.

[1721] "Example 3"

[1722] Step 1: The user boots up the system and begins patterning the design.

[1723] Step 2: The emotion engine analyzes the user's emotions.

[1724] Step 3: AI helps make the final choice based on the emotion engine analysis results.

[1725] Step 4: Present the AI-recommended design patterns to the user and make the final selection.

[1726] Example 1

[1727] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1728] In conventional pattern-creation systems, it was difficult to generate design patterns that reflected the user's emotions and preferences. In addition, when selecting the optimal design from countless patterns, the user's emotions could not be taken into consideration, resulting in a lack of improvement in the user experience.

[1729] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1730] In this invention, the server includes a means for generating patterns in design production, a means for making a final selection from an infinite number of patterns, a means for generating these patterns by artificial intelligence, a means for analyzing the user's emotions, and a means for feeding back the analysis results to the artificial intelligence. This makes it possible to generate design patterns according to the user's emotions and preferences, and to select the most suitable design.

[1731] "Design creation" is the creative process of combining visual elements to convey a particular purpose or message.

[1732] "Pattern creation" is the process of generating multiple different design variations or layouts.

[1733] "Countless patterns" refers to a huge number of different design variations.

[1734] "Final selection" is the process of selecting the optimal design pattern from among the multiple design patterns generated.

[1735] "Artificial intelligence" is the technology that enables computer systems to mimic human intelligence by learning, reasoning, and self-correcting.

[1736] "User emotion" refers to the psychological response a user has to a particular situation or stimulus.

[1737] "Analysis" is the process of examining data or information in detail to reveal its structure and meaning.

[1738] "Feedback" is the process of providing information to a system to adjust its next action or processing based on the information it has obtained.

[1739] This invention is a system for generating patterns in design production, which uses artificial intelligence to generate design patterns according to the user's emotions and select the optimal design.

[1740] First, the user accesses the system using a terminal and inputs the design requirements. For example, the user may input, "I want you to generate a design pattern with a fun image in bright colors." The server receives this input and sends the prompt sentence, "Please generate a design pattern with a fun image in bright colors," to the generative AI model.

[1741] As a generative AI model, for example, a general-purpose natural language processing model such as OpenAI's GPT-4 is used. The AI ​​model generates multiple design patterns based on the prompt sentence and returns them to the server.

[1742] The server presents the generated design patterns to the user. The user checks these patterns on the screen of the terminal. While the user is checking the presented design patterns, the emotion engine analyzes the user's facial expressions and tone of voice. As the emotion engine, for example, general emotion analysis software is used. Specifically, Microsoft's Azure Cognitive Services is one such example.

[1743] The emotion engine analyzes the user's emotions and sends the results to the server. For example, if the user is smiling, it detects "joy" and sends the result to the server. The server feeds back the analysis results to the AI, and issues instructions such as, "The user is feeling happy, so please generate more design patterns with bright colors and a fun image."

[1744] Based on this feedback, the AI ​​generates design patterns that further match the user's preferences and sends them back to the server. The server then presents the final design patterns to the user, who can then select the best one from these patterns.

[1745] As a concrete example, if a user inputs "I want a design pattern with a bright color scheme and a fun image to be generated," the server will send the following prompt to the generative AI model:

[1746] "Create design patterns with fun images in bright colors."

[1747] The generative AI model generates multiple design patterns based on this prompt and returns them to the server, which then presents them to the user and analyzes the user's emotions to generate further optimal design patterns.

[1748] In this way, it becomes possible to generate design patterns according to the user's feelings and preferences and to select the most suitable design.

[1749] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1750] Step 1:

[1751] The user inputs the design requirements.

[1752] A user accesses the system using a terminal and inputs design requirements. For example, the user might type, "I want you to generate a design pattern with a bright color scheme and a fun image." The input data is sent to the server in text format.

[1753] Step 2:

[1754] The server receives user input and sends prompts to the AI ​​model.

[1755] The server receives the user's input and converts it into a prompt. For example, the prompt might be "Please generate a design pattern with bright colors and fun images." This prompt is then sent to the generative AI model. The input is the user's requirement text, and the output is the prompt.

[1756] Step 3:

[1757] The AI ​​model generates design patterns based on the prompt.

[1758] A generative AI model (e.g., GPT-4) receives a prompt and generates multiple design patterns. The input is the prompt, and the output is a set of generated design patterns. The AI ​​model internally performs natural language processing and combination of design elements.

[1759] Step 4:

[1760] The server presents the generated design patterns to the user.

[1761] The server presents the design patterns received from the AI ​​model to the user, who then views these patterns on the screen of his or her device. The input is the set of generated design patterns, and the output is the design patterns displayed on the user's screen.

[1762] Step 5:

[1763] The user checks the presented design patterns.

[1764] The user reviews the presented design patterns and decides which one they like best. At this time, the emotion engine analyzes the user's facial expressions and tone of voice. The input is the visual information of the design patterns, and the output is the user's emotional response.

[1765] Step 6:

[1766] The emotion engine analyzes the user's emotions.

[1767] The emotion engine analyzes emotions from the user's facial expressions, tone of voice, text input, etc. For example, if the user is smiling, it detects "happiness." The input is the user's facial expression and voice data, and the output is the analyzed emotion information.

[1768] Step 7:

[1769] The server feeds back the results of the emotion engine's analysis to the AI.

[1770] The server feeds back the analysis results of the emotion engine to the AI. For example, it may give feedback such as, "The user is feeling happy, so please generate more design patterns with bright colors and fun images." The input is the analyzed emotion information, and the output is the feedback instruction text.

[1771] Step 8:

[1772] The AI ​​will generate further design patterns based on the feedback.

[1773] Based on the feedback, the AI ​​generates design patterns that further match the user's preferences. The generated patterns are sent to the server again. The input is the feedback instructions, and the output is a set of newly generated design patterns.

[1774] Step 9:

[1775] The server presents the final design pattern to the user.

[1776] The server presents the final generated design patterns to the user, who can select the best one from these patterns. The input is the set of newly generated design patterns, and the output is the final design pattern displayed on the user's screen.

[1777] (Application example 1)

[1778] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[1779] In conventional design production systems, design patterns are generated without considering the user's emotions, making it difficult to provide designs that match the user's emotions. In addition, when selecting the optimal design from countless design patterns, there was a lack of a means to reflect the user's emotions, making it impossible to increase user satisfaction.

[1780] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1781] In this invention, the server includes a means for generating patterns in design production, a means for making a final selection from an infinite number of patterns, a means for generating these patterns by AI, a sentiment analysis means for recognizing the user's sentiment, and a means for recommending a design pattern based on the sentiment analysis result, thereby making it possible to generate and select a design pattern according to the user's sentiment.

[1782] A "means for generating patterns in design production" is a means for combining various elements of design to generate a variety of design patterns.

[1783] "Means for making final selection from an infinite number of patterns" refers to a means for selecting the most suitable one from among the generated design patterns.

[1784] "Means for generating these patterns using AI" refers to means for automatically generating design patterns using artificial intelligence.

[1785] The "emotion analysis means for recognizing a user's emotions" refers to a means for recognizing a user's emotions by analyzing the user's facial expressions, tone of voice, text input, etc.

[1786] The "means for recommending a design pattern based on the result of emotion analysis" is a means for proposing an appropriate design pattern based on the user's emotion obtained by the emotion analysis means.

[1787] A system for implementing this invention includes means for generating patterns in design production, means for making a final selection from an infinite number of patterns, means for generating these patterns using AI, emotion analysis means for recognizing a user's emotions, and means for recommending design patterns based on the results of the emotion analysis.

[1788] System Configuration

[1789] The server uses the following hardware and software:

[1790] Hardware: Camera (to capture the user's facial expressions)

[1791] Software: OpenCV (image processing), Keras (executing emotion recognition models and design pattern generation models), TextBlob (analyzing emotions from text)

[1792] Processing flow

[1793] 1. User sentiment analysis:

[1794] The device's camera captures the user's facial expression and obtains image data.

[1795] The server uses an emotion recognition model (using Keras) to analyze the user's emotions from the captured image data.

[1796] Furthermore, the text entered by the user is parsed using TextBlob and emotions are obtained from the text.

[1797] 2. Design Pattern Generation:

[1798] The server generates design patterns based on the analyzed emotion data using a design pattern generation model (using Keras).

[1799] The generated design pattern corresponds to the user's emotions; for example, if the user is having fun, a bright and colorful design is generated, whereas if the user is depressed, a calm and subdued design is generated.

[1800] 3. Displaying Design Patterns:

[1801] The terminal displays the generated design pattern on the screen.

[1802] The user can select the most suitable design from the displayed designs.

[1803] Examples

[1804] For example, when a user enters a virtual store, a camera captures the user's facial expression and analyzes their emotions. If the user types, "I'm in a happy mood today," the system will generate bright and colorful design patterns and change the design of the store.

[1805] Examples of prompt statements

[1806] "Analyze the user's facial expressions and text input and generate design patterns based on their emotions. If the user is happy, suggest a bright and colorful design, and if the user is depressed, suggest a calm color scheme."

[1807] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1808] Step 1:

[1809] The device's camera captures the user's facial expression and obtains image data.

[1810] Input: User's facial expression

[1811] Output: Image data

[1812] Specific operation: The device's camera takes a picture of the user's face in real time and sends the image data to the server.

[1813] Step 2:

[1814] The server uses an emotion recognition model (using Keras) to analyze the user's emotions from the captured image data.

[1815] Input: Image data

[1816] Output: Emotion data (e.g., happiness, sadness, surprise, etc.)

[1817] Specific operation: The server inputs the received image data into the emotion recognition model, and the model infers the user's emotion from the image. The estimated emotion data is output as a numerical value or a category.

[1818] Step 3:

[1819] The server parses the text entered by the user using TextBlob and extracts the sentiment from the text.

[1820] Input: User text input

[1821] Output: Text sentiment data (e.g. positive, negative, neutral, etc.)

[1822] Specific operation: The server passes the text entered by the user to TextBlob, which performs sentiment analysis on the text. TextBlob outputs the sentiment of the text as a number or category.

[1823] Step 4:

[1824] The server generates design patterns based on the analyzed emotion data using a design pattern generation model (using Keras).

[1825] Input: emotion data, text emotion data

[1826] Output: Design Patterns

[1827] Specific operation: The server inputs emotion data and text emotion data into the design pattern generation model, and the model generates appropriate design patterns based on these data. The generated design patterns are output as images or layouts.

[1828] Step 5:

[1829] The terminal displays the generated design pattern on the screen.

[1830] Input: Design Patterns

[1831] Output: Displayed design pattern

[1832] Specific operation: The terminal displays the design pattern received from the server on the screen, allowing the user to visually confirm it.

[1833] Step 6:

[1834] The user selects the most suitable design pattern from the displayed patterns.

[1835] Input: The displayed design pattern

[1836] Output: User's choice

[1837] Specific operation: The user looks at multiple design patterns displayed on the terminal screen and selects the one he likes best. The selected design pattern is sent from the terminal to the server.

[1838] Example 2

[1839] Next, a second embodiment of the second form will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1840] In conventional design production, the generation and selection of design patterns is often done manually, which is time-consuming and labor-intensive. In addition, it is difficult to adjust the design according to the user's emotions, making it difficult to improve the user experience. Furthermore, there is a lack of systems that can efficiently generate a variety of design patterns and select the optimal one.

[1841] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1842] In this invention, the server includes a means for a user to input a prompt sentence, a means for the server to receive the prompt sentence and send it to the generative AI model, a means for the generative AI model to generate a UI design pattern, a means for the server to save the generated design pattern in a database, a means for a user to browse the design patterns using a terminal, a means for the user to select an optimal design pattern, a means for the server to record the selected design pattern, a means for an emotion engine to analyze the emotion of the user, and a means for the server to adjust the design pattern based on the emotion analysis result. This enables efficient generation and selection of design patterns, and further enables adjustment of the design according to the emotion of the user.

[1843] A "prompt sentence" is a sentence containing instructions or requirements that a user inputs to a generative AI model.

[1844] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate user interface designs and other design patterns.

[1845] A "UI design pattern" is a design pattern that combines elements such as button shape, color, and placement in user interface design.

[1846] A "database" is a system for storing and managing generated design patterns and other data.

[1847] A "terminal" is a device that a user uses to view design patterns and enter prompt statements.

[1848] An "emotion engine" is software or a system that analyzes a user's facial expressions and voice to identify the user's emotional state.

[1849] The "server" is a computer system that rec...

Claims

1. A sentiment analysis means for recognizing a user's sentiment; A means for generating a design pattern using a generative AI model based on a prompt sentence instructing to generate a design pattern based on a sentiment analysis result; A means for proposing the generated design pattern to the user; A means for allowing the user to select an optimal one from among the generated design patterns; A system including:

2. The system of claim 1 , wherein the design patterns generated using the generative AI model include design patterns for app UI designs, web designs, illustrations, banners, key visuals, and icons.

3. The system of claim 1 , wherein the emotion analysis means analyzes the user's facial expression, tone of voice, and text entered by the user.

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