System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to input a design idea in a text form; means for a server to receive the inputted text data and analyze the inputted content using a natural-language processing engine; means for the server to call an image generation AI based on the analyzed keyword and generate various design proposals; means for the server to transmit and provide the generated design proposals to user terminals; means for the user to evaluate the provided design proposals and input feedback; and means for the server to re-analyze the user feedback and generate improved design proposals.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] There are issues such as the time and effort required to propose design proposals, and the time required for communication between designers and clients during the design process. This can hinder the improvement of the quality of design proposals and effective discussions. The purpose of this invention is to generate diverse design proposals quickly through collaboration between designers and AI, improve the efficiency of the design process, and promote smooth communication. [Means for solving the problem]

[0005] The present invention is a system that includes a means for users to input design ideas in text format, a server that receives the input text data and analyzes the input content using a natural language processing engine, a server that generates various design proposals by calling an image generation AI based on the keywords analyzed by the server, a server that transmits the generated design proposals to the user's device, a user that evaluates the provided design proposals and inputs feedback, and a server that re-analyzes the user's feedback and generates improved design proposals. This allows for the rapid generation of various design proposals and an objective and efficient design process.

[0006] A "user" is an individual or group that inputs design ideas in text form.

[0007] A "server" is a computer system that receives text data entered by a user, analyzes it, and generates design proposals.

[0008] "Text format" refers to a form of text written in natural language, used to input design ideas.

[0009] A "natural language processing engine" is a software program that analyzes input text data and extracts important keywords and meanings.

[0010] "Image generation AI" is an artificial intelligence technology that generates a variety of design ideas based on analyzed keywords.

[0011] A "design idea" is a visual idea of ​​a product or service provided in the form of an image.

[0012] A "terminal" is a device (computer, smartphone, etc.) that a user accesses to input and evaluate design ideas.

[0013] "Evaluation" is the act of a user expressing their thoughts and opinions on the generated design proposal.

[0014] "Feedback" refers to the act of a user inputting opinions, including specific improvements and requests, during evaluation.

[0015] "Analysis" is the process by which the server uses a natural language processing engine to extract important information from the input text data. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] 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.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] 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.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] 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.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The present invention relates to a system for generating design ideas quickly and diversifiedly and for realizing an effective design process. The system includes components of a user, a server, and a terminal.

[0038] System Overview

[0039] In this system, users input their design ideas in text format using a device, and the server receives and analyzes the ideas and generates design proposals using image generation AI. The generated design proposals are then provided back to the device, where users can evaluate and provide feedback. The feedback is analyzed by the server and used to generate new design proposals.

[0040] Program processing explanation

[0041] Idea input phase

[0042] The user accesses the web interface on the device and enters a design idea in the text input field, for example, "Simple and modern coffee cup design." This input text is sent to the server by clicking the "Submit" button.

[0043] Input data parsing phase

[0044] The server passes the text data received from the user to a natural language processing engine, which extracts and analyzes important keywords, such as "simple," "modern," and "coffee cup."

[0045] Design proposal generation phase

[0046] The server calls an image generation AI based on the analyzed keywords to generate various design proposals. The image generation AI creates multiple design proposals taking into account the specified keywords. These design proposals are sent back to the server in image format.

[0047] Design proposal provision phase

[0048] The server sends the generated design proposals to the user's device and provides them to the user via a web interface, where the user can view them, rate them, and provide feedback.

[0049] Evaluation and Feedback Phase

[0050] The user can provide feedback on the displayed design proposals in the form of star ratings and comments. For example, they can enter specific suggestions for improvement, such as "I'd like the colors to be a little brighter." The feedback is then sent back to the server.

[0051] Feedback analysis and refinement phase

[0052] The server then passes the user's feedback to a natural language processing engine for reanalysis, and then calls an image generation AI to generate an improved design proposal based on the results. This creates an improved design proposal and sends it back to the user's device.

[0053] Specific examples

[0054] Example: Coffee cup design

[0055] The user enters "simple, modern coffee cup design," and the server extracts the keywords "simple," "modern," and "coffee cup." The image generation AI generates multiple coffee cup designs that combine simple shapes with modern colors. These design proposals are provided to the user's device, and the user provides feedback, requesting that the colors be a little brighter. The server analyzes this feedback, and the image generation AI generates improved design proposals again. This process brings the design closer to the user's desired design.

[0056] In this way, the system of the present invention streamlines the design process, enabling the rapid generation and provision of diverse ideas. It also contributes to improving the quality of design proposals by quickly improving the system based on user feedback.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] The user accesses the device's web interface and logs in, at which point the user account is authenticated.

[0060] Step 2:

[0061] The user enters a design idea in the text input field, for example, "simple modern coffee cup design."

[0062] Step 3:

[0063] The user clicks the "Submit" button to send the entered text to the server.

[0064] Step 4:

[0065] The server receives the text data sent by the user and temporarily stores it in an internal database.

[0066] Step 5:

[0067] The server passes the received text to a natural language processing (NLP) engine, which analyzes it for important keywords, such as "simple," "modern," and "coffee cup."

[0068] Step 6:

[0069] The server uses the analyzed keywords to send a request to the image generation AI, which includes the extracted keywords and additional parameters (color, shape, style, etc.).

[0070] Step 7:

[0071] The image generation AI generates various design ideas based on the request it receives, including a coffee cup design with simple shapes and modern colors.

[0072] Step 8:

[0073] The image generation AI sends the generated design proposal to the server as an image file (e.g., JPEG, PNG).

[0074] Step 9:

[0075] The server sends the design proposal received from the image generation AI to the user's device.

[0076] Step 10:

[0077] The terminal updates the web interface to display the design proposal received from the server.

[0078] Step 11:

[0079] The user checks the displayed design proposal and gives an evaluation and feedback, for example, by inputting a comment such as "I wish the colors were a little brighter."

[0080] Step 12:

[0081] The user sends feedback to the server.

[0082] Step 13:

[0083] The server receives the feedback sent by the user and passes it to the natural language processing engine for analysis, extracting important improvements (e.g., "make the colors brighter").

[0084] Step 14:

[0085] Based on the extracted improvements, the server sends a request with new requirements to the image generation AI.

[0086] Step 15:

[0087] The image generation AI generates improved design proposals based on new requests and sends them back to the server.

[0088] Step 16:

[0089] The server then transmits the improved design proposal back to the user's terminal.

[0090] Step 17:

[0091] The terminal displays the improved design proposal for the user to confirm.

[0092] By repeating this process, the user can successively obtain design proposals that are closest to their ideal.

[0093] Example 1

[0094] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0095] In the conventional design process, it takes a lot of time and effort to convert user ideas into concrete design proposals. Furthermore, there is no efficient method to quickly incorporate user feedback and generate improved designs. As a result, it is difficult to improve the quality of designs and quickly generate diverse ideas.

[0096] 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.

[0097] In this invention, the server includes a means for a user to input design ideas in text format, a means for the server to receive the input text data and analyze the input content using a natural language processing engine, and a means for generating various design proposals by calling an image generation AI model based on the keywords analyzed by the server. This makes it possible to quickly materialize user ideas into various design proposals and quickly improve the designs based on user feedback.

[0098] "User" refers to a person who uses the system to input design ideas and provide ratings and feedback.

[0099] "Server" refers to the central control device that receives and analyzes design ideas and generates and provides design proposals using an image generation AI model.

[0100] "Terminal" refers to an electronic device that allows a user to input design ideas, view design proposals from the server, and provide feedback.

[0101] "Design Ideas" refers to design concepts and requests entered by users in text format.

[0102] A "natural language processing engine" refers to a software tool that analyzes input text data and extracts important keywords.

[0103] "Keywords" refer to important words and phrases extracted from design ideas by natural language processing engines.

[0104] An "image generation AI model" refers to an artificial intelligence that generates a variety of design ideas based on specified keywords.

[0105] "Design proposal" refers to a specific design image generated by an image generation AI model.

[0106] "Feedback" refers to comments that users make on the provided design proposal, such as evaluations and suggestions for improvement.

[0107] A "prompt sentence" refers to a text sentence containing keywords that is sent to an image generation AI model.

[0108] This invention is a system that uses an image generation AI model to quickly generate a variety of design proposals based on design ideas input by the user and provides them to the user. To implement this system, a user, a server, and a terminal are required, each of which plays a specific role. A specific implementation method for this system is described below.

[0109] Overall system configuration

[0110] The system of the present invention comprises the following elements:

[0111] 1. A means for users to input their design ideas

[0112] 2. Server input data reception and analysis method

[0113] 3. Method for generating design proposals using image generation AI models

[0114] 4. Means of providing design proposals to devices

[0115] 5. User Rating and Feedback

[0116] 6. Feedback analysis and improved design generation method

[0117] Hardware and software used

[0118] Device: An electronic device (e.g., PC, smartphone, tablet) on which a user inputs design ideas and reviews the generated design proposals.

[0119] Server: A central control unit that receives and analyzes input data from users and generates design proposals using image generation AI models.

[0120] Natural language processing engine: Software for extracting important keywords from input text data (e.g., Google Cloud Natural Language API).

[0121] Image generation AI model: An artificial intelligence that generates various design ideas based on specified keywords (e.g., OpenAI's DALL-E).

[0122] Specific explanation of the system's operation

[0123] Enter your design ideas

[0124] The user accesses the device's web interface, enters a design idea into the text input field, for example, "Simple and modern coffee cup design," and then clicks the "Submit" button, which sends the input text to the server as an HTTP POST request.

[0125] Text data analysis

[0126] The server passes the received text data to a natural language processing engine, which analyzes and extracts important keywords, such as "simple," "modern," and "coffee cup."

[0127] Generate design ideas

[0128] The server generates a prompt sentence based on the analyzed keywords and sends it to the image generation AI model. The image generation AI model generates multiple design proposals based on the specified prompt sentence and returns them to the server as image data. An example of a prompt sentence is a phrase such as "a simple, modern coffee cup."

[0129] Providing design ideas

[0130] The server sends the generated design proposals to the user's terminal and makes them available for viewing on a web interface, allowing the user to check the generated design proposals and provide evaluation and feedback.

[0131] Feedback input and analysis

[0132] The user can then enter specific evaluations and feedback for the displayed design proposals. For example, they can enter comments such as "I'd like the colors to be a little brighter." This feedback data is then sent to the server as an HTTP POST request.

[0133] Design improvements based on feedback

[0134] The server then passes the user feedback data to the natural language processing engine for analysis, extracting new keywords and areas for improvement. Based on the results, it again sends a request to the image generation AI model to generate an improved design proposal. This improved design proposal is then provided back to the user's device.

[0135] Implementing specific examples

[0136] For example, suppose a user enters "simple, modern coffee cup design." The server extracts the keywords "simple," "modern," and "coffee cup" from this text and sends them to an image generation AI model (e.g., OpenAI's DALL-E). This AI model generates various design proposals taking these keywords into account and sends them back to the server. The user reviews these design proposals and provides feedback such as "I wish the colors were a little brighter." The server then reanalyzes this feedback and generates and provides improved design proposals.

[0137] This system not only allows us to respond quickly to user requests, but also makes it possible to efficiently generate and improve a variety of design proposals.

[0138] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0139] Step 1:

[0140] Enter your ideas

[0141] A user accesses the device's web interface, enters a design idea into the text input field, for example, "Simple and modern coffee cup design," and then clicks the "Submit" button, which sends the input text to the server as an HTTP POST request.

[0142] Input: User-entered text design ideas

[0143] Output: The entered design idea is sent to the server as an HTTP POST request.

[0144] Step 2:

[0145] Receiving data

[0146] The server receives an HTTP POST request sent by the user, with the body of the request containing the user's input text design ideas.

[0147] Input: Text data sent as an HTTP POST request

[0148] Output: Text design ideas received by the server

[0149] Step 3:

[0150] Text data analysis

[0151] The server passes the received text design ideas to a natural language processing engine, which analyzes and extracts important keywords. The natural language processing engine (e.g., Google Cloud Natural Language API) extracts keywords such as "simple," "modern," and "coffee cup" from the text.

[0152] Input: Received text design ideas

[0153] Output: Extracted keywords

[0154] Step 4:

[0155] Generate prompt statement

[0156] The server generates a prompt for the image generation AI model based on the extracted keywords, such as "A simple and modern coffee cup."

[0157] Input: Extracted keywords

[0158] Output: Generated prompt statement

[0159] Step 5:

[0160] Generate design ideas

[0161] The server sends the generated prompt to an image generation AI model (e.g., OpenAI's DALL-E) requesting the generation of design proposals. The image generation AI model generates multiple design proposals based on the specified prompt and returns these design proposals to the server as image data.

[0162] Input: Generated prompt text

[0163] Output: Multiple generated design proposals (image data)

[0164] Step 6:

[0165] Providing design ideas

[0166] The server receives the generated design proposals and sends them to the user's device. The server then generates an HTTP response containing image data and sends it to the user's device. The user can view these design proposals through the device's web interface.

[0167] Input: Generated design proposal (image data)

[0168] Output: Providing design proposals to the user's device

[0169] Step 7:

[0170] Enter your rating and feedback

[0171] The user provides feedback on the displayed design proposal in the form of a star rating or a comment. For example, they can enter specific suggestions for improvement, such as "I'd like the colors to be a little brighter." The feedback data is then sent to the server again as an HTTP POST request.

[0172] Input: User feedback (ratings and comments)

[0173] Output: Feedback data is sent to the server as an HTTP POST request

[0174] Step 8:

[0175] Receiving and Analyzing Feedback

[0176] The server receives the feedback data sent by the user and passes the received feedback text to the natural language processing engine again to analyze and extract points for improvement.

[0177] Input: Received feedback (ratings and comments)

[0178] Output: Extracted improvement points

[0179] Step 9:

[0180] Generation of improved design proposals

[0181] The server then sends a prompt to the image generation AI model again based on the improvement points extracted from the feedback, requesting the generation of an improved design proposal. The image generation AI model then generates a new design proposal taking into account the improvement points and sends it back to the server.

[0182] Input: Extracted refinements

[0183] Output: Generated improved design proposal (image data)

[0184] Step 10:

[0185] Providing improved design proposals

[0186] The server transmits the improved design proposal back to the user's terminal, and the user can again evaluate and provide feedback on the improved design.

[0187] Input: Generated improved design proposal (image data)

[0188] Output: Providing improved design proposals to the user's device

[0189] In this way, the system can quickly and efficiently realize users' design ideas and continuously improve them based on user feedback.

[0190] (Application example 1)

[0191] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0192] Conventional design generation and evaluation systems make it difficult for users to quickly and accurately create customized designs with unique styles and functions. Furthermore, especially for electronic payment card designs, users are often restricted by existing templates, making it difficult to create original designs that reflect the user's individuality.

[0193] 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.

[0194] In this invention, the server includes means for a user to input design ideas in text format, means for the server to receive the input text data and analyze the input content using a natural language processing engine, means for calling an image generation AI based on the analyzed keywords to generate various design proposals, means for generating improved design proposals based on feedback, and means for the user to generate their own original electronic payment card design, thereby enabling users to quickly and accurately create customized electronic payment cards that reflect their individuality.

[0195] "User" refers to a person who uses the system to input design ideas and provide feedback.

[0196] "Text format" refers to a format of information expressed in characters that is used by users to input design ideas.

[0197] "Design idea" refers to text information that includes concepts and features related to a design that a user wants to create.

[0198] "Server" refers to a central computer system that receives, analyzes, processes, and transmits data to user terminals.

[0199] "Natural language processing engine" refers to a technical device that analyzes input text and extracts important keywords.

[0200] "Image generation AI" refers to artificial intelligence that generates various design ideas based on input keywords.

[0201] "Design proposal" refers to a specific design image or concept proposal generated by image generation AI.

[0202] "User's device" refers to the device that a user uses to input design ideas and receive and evaluate generated design proposals.

[0203] "Feedback" refers to the evaluation and suggestions for improvement that a user provides for a generated design proposal.

[0204] "Improved design proposal" refers to an improved design generated by the image generation AI based on user feedback.

[0205] An "electronic payment card" refers to a card-type medium used for electronic payments, which is a payment method that includes the user's personal information.

[0206] The following describes an embodiment of the present invention.

[0207] Overall system overview

[0208] The present invention is a system that uses image generation AI to generate design proposals based on design ideas entered by users in text format, and includes a process for evaluating and improving the proposed designs. It is particularly applicable to generating custom designs for electronic payment cards.

[0209] Hardware and software used

[0210] The system includes the following main components:

[0211] User device: A device such as a smartphone or computer on which users input design ideas and receive and evaluate the generated design proposals.

[0212] Server: A central computer system that receives, analyzes, and generates and refines data.

[0213] Natural language processing engine: Analyzes input text data and extracts important keywords (e.g., spaCy).

[0214] Image generation AI model: An artificial intelligence that generates design ideas based on input keywords. (Example: DALL-E 2)

[0215] Program processing explanation

[0216] 1. Design idea input phase

[0217] The user uses the device's interface to input design ideas in text format, for example, "I want a simple and elegant design."

[0218] 2. Input data analysis phase

[0219] The server passes the text data received from the user to a natural language processing engine and extracts keywords, such as "simple" and "elegant."

[0220] 3. Design proposal generation phase

[0221] The server then calls an image generation AI model based on the analyzed keywords to generate multiple design proposals, which are then sent back to the server.

[0222] 4. Design proposal provision phase

[0223] The server transmits the generated design proposal to the user's terminal and provides it to the user, who can view it, evaluate it, and provide feedback.

[0224] 5. Evaluation and feedback phase

[0225] Users evaluate the provided design proposals and enter specific feedback, such as "I would like the background color to be brighter."

[0226] 6. Feedback analysis and refinement phase

[0227] The server then analyzes the user's feedback again using a natural language processing engine, calls the image generation AI model again, and generates an improved design proposal, which is then sent back to the user's device.

[0228] Specific examples

[0229] When a user inputs "I want a simple yet elegant design," the server extracts the keywords "simple" and "elegant," and uses an image generation AI model to generate a design proposal with a pale blue background and an elegant pattern. If the user provides feedback that "I would like the background color to be brighter," an improved design with a pale pink background is provided.

[0230] Prompt Sentence Examples

[0231] Input prompt:

[0232] "I want a simple and elegant design."

[0233] I want the background color to be brighter.

[0234] The system allows users to easily create and quickly refine personalized, customized electronic payment card designs.

[0235] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0236] Step 1:

[0237] Enter your design ideas

[0238] Users input their design ideas in text format on their device, such as a prompt statement like "I want a simple and elegant design" into the interface of their smartphone or computer, and click the "Submit" button.

[0239] Input: "I want a simple and elegant design"

[0240] Output: Text data sent to the server

[0241] Step 2:

[0242] Receiving input data

[0243] The server receives the text data sent from the user terminal, temporarily stores the received text data, and passes it on to the next analysis step.

[0244] Input: Text data submitted by the user

[0245] Output: Text data stored on the server

[0246] Step 3:

[0247] Analyzing input data

[0248] The server passes the received text data to a natural language processing engine (e.g., spaCy) to extract important keywords, such as "simple" and "elegant."

[0249] Input: Text data stored on the server

[0250] Output: Extracted keywords ("simple", "elegant")

[0251] Step 4:

[0252] Generate design ideas

[0253] The server calls an image generation AI model (e.g., DALL-E 2) based on the extracted keywords and generates various design proposals. The image generation AI model creates multiple design proposals taking into account the specified keywords.

[0254] Input: Extracted keywords

[0255] Output: Generated multiple design proposals (image files)

[0256] Step 5:

[0257] Submit your design proposal

[0258] The server sends the generated design proposals to the user's terminal, which receives them and displays them on the interface.

[0259] Input: Generated multiple design proposals (image files)

[0260] Output: Design proposal sent to user device

[0261] Step 6:

[0262] Evaluation and feedback of design proposals

[0263] Users can evaluate the displayed design proposals and enter feedback, such as specific suggestions for improvement like "I'd like the background color to be brighter," and submit the proposal.

[0264] Input: The design proposal displayed by the user

[0265] Output: User feedback (text)

[0266] Step 7:

[0267] Receiving and analyzing feedback

[0268] The server receives the feedback from the user and analyzes it again using a natural language processing engine, which extracts keywords based on the content of the feedback.

[0269] Input: User feedback (text)

[0270] Output: Extracted feedback keywords (e.g., "brighten the background color")

[0271] Step 8:

[0272] Generation of improved design proposals

[0273] The server then calls the image generation AI model again based on the analyzed feedback keywords to generate an improved design proposal, resulting in a new design proposal that reflects the user's feedback.

[0274] Input: Extracted feedback keywords

[0275] Output: Improved design proposal (image file)

[0276] Step 9:

[0277] Providing improved design proposals

[0278] The server then sends the improved design proposal back to the user's device, where the user can receive it and provide feedback again.

[0279] Input: Improved design proposal (image file)

[0280] Output: Improved design proposal provided to user device

[0281] By repeating this process, the user can create a satisfactory electronic payment card design.

[0282] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0283] This invention relates to a system in which designers input design ideas in text format, and an image generation AI generates various design proposals based on those ideas. Furthermore, this system combines an emotion engine that recognizes the user's emotions to provide more appropriate design proposals.

[0284] System Overview

[0285] This system recognizes the user's emotions when they input their design ideas, and can generate design proposals that match the user's intentions and emotions. The design process includes everything from inputting the design idea to analyzing feedback and making improvements, and the user's emotions are also reflected in the process.

[0286] Program processing explanation

[0287] Idea input phase

[0288] The user accesses the web interface on the device and enters a design idea in the text input field, for example, "Simple and modern coffee cup design." This input text is sent to the server by clicking the "Submit" button.

[0289] Emotion Recognition Phase

[0290] The server receives and stores the text data sent by the user. It then analyzes the emotions of the input text using an emotion engine. For example, emotions such as "joy," "expectation," and "excitement" are recognized.

[0291] Input data parsing phase

[0292] The server passes the received text to the emotion engine and the natural language processing engine at the same time, and extracts and analyzes important keywords. For example, keywords such as "simple," "modern," and "coffee cup" are extracted.

[0293] Design proposal generation phase

[0294] The server sends a request to the image generation AI based on the analyzed keywords and emotional information. The request includes the keywords and parameters that reflect the user's emotions. The image generation AI then generates various design proposals based on these.

[0295] Design proposal provision phase

[0296] The server sends the generated design proposals to the user's device and provides them to the user via a web interface, where the user can view them, rate them, and provide feedback.

[0297] Evaluation and Feedback Phase

[0298] Users can provide feedback on the displayed design proposals in the form of star ratings and comments. For example, they can input specific suggestions for improvement, such as "I'd like the colors to be a little brighter" or "I'd like the curves to be smoother." The feedback is then sent back to the server.

[0299] Feedback analysis and refinement phase

[0300] The server passes the feedback sent by the user to the emotion engine, which analyzes the feedback's emotions. It then passes it to the natural language processing engine for analysis and extracts important improvements. Based on the extracted improvements and emotion information, the image generation AI is again invoked to generate an improved design proposal.

[0301] Specific examples

[0302] Example: Emotion Recognition and Design Improvement

[0303] The user inputs a "simple, modern coffee cup design," and simultaneously recognizes "expectations." The server analyzes keywords such as "simple," "modern," and "coffee cup," adds "expectations" as emotional information, and sends it to the image generation AI. The AI ​​generates a modern design that reflects expectations and provides it to the user's device.

[0304] The user looks at the proposed design and gives feedback such as "I'd like the colors to be a little brighter," which recognizes the user's dissatisfaction with the depiction. The server reanalyzes this feedback and emotional information to generate a new improvement proposal. The new design proposal is then provided to the user's device again, and the user continues to evaluate and provide feedback on it.

[0305] In this way, the system of the present invention can quickly provide more satisfying design proposals by reflecting the user's feelings in the design process.

[0306] The processing flow will be explained below.

[0307] Step 1:

[0308] The user accesses the terminal's web interface and logs in. At this point, the user is authenticated.

[0309] Step 2:

[0310] The user enters a design idea, such as "simple and modern coffee cup design," into a text input field.

[0311] Step 3:

[0312] The user clicks the "Submit" button to send the entered text to the server.

[0313] Step 4:

[0314] The server receives the text data sent by the user and temporarily stores it in a database.

[0315] Step 5:

[0316] The server sends the text data to the emotion engine, which analyzes the emotions in the text. As a result, emotions such as "joy" and "expectation" are recognized.

[0317] Step 6:

[0318] The server simultaneously sends the text data to a natural language processing (NLP) engine to extract important keywords, such as "simple," "modern," and "coffee cup."

[0319] Step 7:

[0320] The server creates a request to the image generation AI based on the emotion recognition results and the analyzed keywords. The request includes the extracted keywords and emotion information.

[0321] Step 8:

[0322] The image generation AI receives a request and generates multiple design proposals based on the specified keywords and emotional information, for example, a brightly colored coffee cup design that reflects a sense of anticipation.

[0323] Step 9:

[0324] The image generation AI sends the generated design proposal to the server as an image file.

[0325] Step 10:

[0326] The server sends the design proposal received from the image generation AI to the user's device.

[0327] Step 11:

[0328] The terminal updates the web interface to allow the user to view the received design proposals.

[0329] Step 12:

[0330] The user checks the displayed design proposal and gives an evaluation and feedback. For example, the user can input feedback such as "I would like the colors to be a little brighter."

[0331] Step 13:

[0332] The user sends feedback to the server.

[0333] Step 14:

[0334] The server stores the feedback received from the user and passes it back to the emotion engine for emotion analysis. For example, emotions such as "dissatisfaction" and "request for improvement" are recognized.

[0335] Step 15:

[0336] The server also passes the feedback to a natural language processing (NLP) engine to extract key improvements, such as "make the colors brighter."

[0337] Step 16:

[0338] The server creates a new request based on the extracted improvements and emotion information and sends it to the image generation AI.

[0339] Step 17:

[0340] The image generation AI generates improved design ideas based on new requests, such as a brighter colored coffee cup design.

[0341] Step 18:

[0342] The image generation AI sends the improved design proposal to the server as an image file.

[0343] Step 19:

[0344] The server transmits the improved design proposal to the user's terminal.

[0345] Step 20:

[0346] The terminal displays the improved design proposal for the user to confirm.

[0347] By repeating this cycle, users can arrive at a design proposal that best suits their needs and emotions.

[0348] Example 2

[0349] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0350] Conventional design generation systems have had the problem of difficulty in generating design proposals that fully reflect the user's emotions and intentions. Another issue is the inefficient process of properly analyzing user feedback and reflecting it in design proposals. Our goal is to solve these problems and quickly provide design proposals that will provide greater user satisfaction.

[0351] 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.

[0352] In this invention, the server includes: means for a user to input design ideas in text format; means for receiving the input text data and analyzing the emotion of the input text using an emotion recognition engine; means for analyzing the input content using a natural language processing engine together with the emotion information analyzed by the server; means for calling an image generation model based on the keywords and emotion information analyzed by the server to generate various design proposals; means for transmitting the generated design proposals to the user's terminal and providing them; means for the user to evaluate the provided design proposals and input feedback; and means for the server to reanalyze the user's feedback using the emotion recognition engine and natural language processing engine to generate improved design proposals. This makes it possible to quickly generate design proposals that reflect the user's emotions and intentions and to efficiently analyze user feedback and reflect it in the design proposals.

[0353] "User" refers to a person or organization that inputs design ideas and evaluates and provides feedback on the generated design proposals.

[0354] A "server" refers to a central processing unit that receives information from users and performs processes such as data analysis and image generation.

[0355] "Text data" refers to design ideas in the form of text entered by the user.

[0356] An "emotion recognition engine" is a software engine for analyzing the emotions in input text data.

[0357] A "natural language processing engine" is a software engine for extracting and analyzing keywords from input text data.

[0358] An "image generation model" refers to an artificial intelligence engine that generates design proposals based on input keywords and emotional information.

[0359] "Design proposals" are various design options generated by the image generation model.

[0360] "Feedback" refers to the evaluation and improvement requests made by the user regarding the generated design proposal.

[0361] "Emotion information" refers to the user's emotion data analyzed by an emotion recognition engine.

[0362] "Keywords" refer to words or phrases that are central to a design idea, extracted by a natural language processing engine.

[0363] This invention relates to a system in which a user inputs a design idea in text format, and an image generation model generates various design proposals based on the idea. Furthermore, this system combines an emotion recognition engine that recognizes the user's emotions, providing more appropriate design proposals.

[0364] First, a user accesses the device's web interface and enters a design idea into the text input field. For example, they might enter "Simple and modern coffee cup design." The input text is sent to the server by clicking the "Submit" button. When the user clicks the submit button, the input text is sent to the server using asynchronous communication (Ajax).

[0365] The server receives and stores the text data sent by the user. The stored text data is then passed to an emotion recognition engine (e.g., IBM Watson or Microsoft Text Analytics) to analyze the emotion of the input text. The emotion recognition engine analyzes the text data and extracts emotions such as "happiness," "anticipation," and "excitement." The emotion is also output as a numerical score, which is used to generate design proposals.

[0366] The server then analyzes the text data received from the user using a natural language processing engine (e.g., Google Cloud Natural Language API) while simultaneously recognizing emotions. Specifically, it extracts important keywords (e.g., "simple," "modern," "coffee cup") from the text. This identifies the core elements related to the design.

[0367] Next, the server sends the extracted keywords and emotional information as a request to an image generation model (such as OpenAI's DALL-E or Midjourney). The request includes the user's emotional information and keywords. The image generation model generates various design proposals based on this information. Specifically, the generative model incorporates the emotional information into its parameters and generates several design proposals.

[0368] The generated design proposals are sent from the server to the user's device and displayed on a web interface. The user evaluates the provided design proposals and provides feedback in the form of star ratings and comments. The feedback is often specific, such as "I wish the colors were a little brighter" or "I wish the curves were smoother." The user clicks the "Submit Feedback" button to send the feedback to the server.

[0369] The server passes the feedback received from the user back to the emotion recognition engine, which analyzes the emotions in the feedback. The feedback is then passed to the natural language processing engine for analysis and key improvements are extracted. Based on the extracted improvements and emotion information, the image generation model is invoked again to generate new design proposals. The new design proposals are then provided to the user's device, where the user can continue to reassess and provide feedback.

[0370] Specific examples

[0371] The user inputs a "simple, modern coffee cup design," and simultaneously recognizes "expectation." The server sends the keywords "simple," "modern," and "coffee cup" along with the emotion score for "expectation" to the image generation model. The model generates a modern design that reflects the expectation and provides it to the user's device.

[0372] The user looks at the proposed design and gives feedback such as "I'd like the colors to be a little brighter," recognizing their "dissatisfaction with the depiction." The server reanalyzes this feedback and emotional information, and calls the image generation model again to generate an improved design. The new design is then provided to the user's device, and the user continues to evaluate and provide feedback.

[0373] Prompt Sentence Examples

[0374] "Enter a simple and modern coffee cup design with a sense of anticipation"

[0375] "When providing feedback to brighten the colors of a user's proposed design, dissatisfaction with the depiction is recognized."

[0376] In this way, the system of the present invention is capable of quickly generating design proposals that reflect the user's feelings and intentions, and efficiently analyzing user feedback and reflecting it in the design proposals.

[0377] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0378] Step 1: Idea input phase

[0379] The user accesses the web interface on the device and enters a design idea into the text input field. For example, they enter "Simple and modern coffee cup design." This input text is sent to the server by clicking the "Submit" button.

[0380] Input: Text data of design ideas entered by the user.

[0381] Output: The text data sent to the server.

[0382] Step 2: Text data reception phase

[0383] The server saves the received text data, checks the format of the received data, and performs initial processing to prepare it for passing to the emotion recognition engine.

[0384] Input: Text data sent by the user.

[0385] Output: The saved text data.

[0386] Step 3: Emotion Recognition Phase

[0387] The server passes the saved text data to an emotion recognition engine, which analyzes the emotions in the input text. This analysis extracts emotions such as "happiness," "anticipation," and "excitement" as numerical scores. For example, IBM Watson or Microsoft Text Analytics are used.

[0388] Input: Saved text data.

[0389] Output: Parsed emotion information (emotion type and numeric score).

[0390] Step 4: Natural Language Processing Phase

[0391] The server analyzes the text data using a natural language processing engine (e.g., Google Cloud Natural Language API) while simultaneously recognizing emotions. Through the analysis, important keywords (e.g., "simple," "modern," and "coffee cup") are extracted from the text.

[0392] Input: Saved text data.

[0393] Output: Extracted keywords.

[0394] Step 5: Design proposal generation request phase

[0395] The server sends the analyzed keywords and emotion scores as a request to an image generation model (e.g., OpenAI's DALL-E or Midjourney). The request includes the user's emotion information and keywords. The image generation model generates various design proposals based on this information.

[0396] Input: Extracted keywords and sentiment information.

[0397] Output: Request submission and generated design proposal.

[0398] Step 6: Design proposal phase

[0399] The server sends the generated design proposal to the user's device, where it is displayed on a web interface for easy viewing by the user.

[0400] Input: Generated design proposal.

[0401] Output: A design proposal displayed on the user's device.

[0402] Step 7: Evaluation and feedback phase

[0403] Users provide feedback on the provided design proposals in the form of star ratings and comments, such as "I'd like the colors to be a little brighter" or "I'd like the curves to be smoother." The user then clicks the "Submit" button to send the feedback to the server.

[0404] Input: Feedback entered by the user.

[0405] Output: Feedback data sent to the server.

[0406] Step 8: Feedback analysis phase

[0407] The server then passes the received feedback to the emotion recognition engine to analyze the emotion of the feedback, and then passes the feedback content to the natural language processing engine to extract important improvements.

[0408] Input: Feedback data submitted by the user.

[0409] Output: Emotional information and important refinements.

[0410] Step 9: Improved design generation phase

[0411] The server then calls the image generation model again based on the extracted improvements and emotion information to generate an improved design proposal.

[0412] Input: Extracted refinements and sentiment information.

[0413] Output: Generated improved design proposals.

[0414] Step 10: Providing improved design proposals

[0415] The server sends the improved design proposal to the user's device and continues to receive evaluations and feedback.

[0416] Input: Improved design proposal.

[0417] Output: Improved design proposal displayed on the user's device.

[0418] (Application example 2)

[0419] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0420] Conventional design generation systems only generate design proposals based on text entered by the user, which means they cannot fully reflect the user's emotions and intentions. Furthermore, feedback on the generated design proposals is simply analyzed as information, without considering the user's emotions during refinement, limiting the ability to enhance user satisfaction. This problem is particularly important for brick-and-mortar stores, where the rapid and effective design of attractive displays and posters is essential.

[0421] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a user to input design ideas in text format; means for the server to receive the input text data and analyze the input content using a natural language processing engine; means for recognizing the emotion of the input text using an emotion engine; means for generating various design proposals by calling an image generation AI based on the keywords and emotion information analyzed by the server; means for the server to send and provide the generated design proposals to the user's terminal; means for the user to evaluate the provided design proposals and input feedback; and means for the server to analyze the user's feedback again using the emotion engine to generate improved design proposals. This makes it possible to generate and improve design proposals that reflect the user's emotions and intentions, thereby improving the efficiency of the design creation process and the customer experience, especially in physical stores.

[0422] "Users" refer to individuals and companies who use the system to input design ideas, evaluate the generated design proposals, and provide feedback.

[0423] "Text format" refers to a format in which design ideas are written in text.

[0424] "Design Idea" refers to text information that a user inputs to communicate specific design concepts and requirements.

[0425] "Server" refers to a computer system that receives, stores, analyzes, and processes input data.

[0426] A "natural language processing engine" refers to software or a program that analyzes input text data and extracts important keywords.

[0427] "Emotion engine" refers to software or a program for recognizing and analyzing the emotions of input text.

[0428] "Keywords" refer to important words or phrases extracted from text data.

[0429] "Emotion information" refers to the user's emotion data analyzed by the emotion engine of the input text.

[0430] "Image generation AI" refers to artificial intelligence that generates design proposals based on keywords and emotional information.

[0431] "Design proposal" refers to a specific design proposal generated by image generation AI.

[0432] "Terminal" refers to the device (e.g., smartphone, PC) used by a user to input design ideas and display generated design proposals.

[0433] "Feedback" refers to the evaluation and improvement requests made by the user regarding the generated design proposal.

[0434] This invention relates to a system for generating and improving design proposals for digital signage deployed in brick-and-mortar stores. This system allows users (e.g., store managers or design teams) to input design ideas in text format, and generates and provides a variety of design proposals based on the ideas and the user's emotional information.

[0435] First, a user uses the smartphone app "Store Design Manager" to input a design idea into a text box. For example, a specific idea such as "I want a bright and exciting design created for a new product campaign" can be input. The emotion engine then analyzes the user's emotions from the input text and obtains emotional information such as "expectation" and "excitement."

[0436] The input text data and emotion data are then sent to a server, which uses a natural language processing engine to analyze the input text data and extract key keywords (e.g., "new product campaign," "cheerful," "exciting"). At the same time, the analysis results from the emotion engine are also recorded.

[0437] The server requests the image generation AI to generate design proposals based on the analyzed keywords and emotional information. The image generation AI generates a variety of design proposals based on this input data. This results in multiple design proposals that reflect the user's wishes and emotions. These design proposals are provided to the user's smartphone via the server.

[0438] The user views the provided design proposals and provides evaluation and feedback. For example, they can input specific requests for improvement, such as "I would like the colors to be more vivid." This feedback is also analyzed by the emotion engine, which recognizes the emotion contained in the feedback (for example, "hope for improvement"). Based on the results of this analysis, the server again requests the image generation AI to generate an improved design proposal.

[0439] Through this process, users can efficiently generate and refine design proposals that reflect their own emotions and intentions, which will streamline the design creation process, especially in physical stores, and improve the customer experience.

[0440] Example prompt sentence:

[0441] TXT

[0442] Keywords: new product campaign, bright, exciting

[0443] Emotions: anticipation, excitement

[0444] Generate design proposals.

[0445] To achieve these steps, the following technologies are specifically used:

[0446] Natural language processing engine: A technology for extracting key keywords from text. Examples include open source NLP libraries.

[0447] Emotion engine: A technology for analyzing the emotion of input text. For example, an emotion analysis API can be used.

[0448] Image generation AI: An AI model that generates images using keywords and emotional information. A specific example is an image generation algorithm that uses deep learning.

[0449] This system configuration makes it possible to efficiently generate designs for physical stores and provide attractive design proposals that reflect the user's intentions and emotions.

[0450] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0451] Step 1:

[0452] A user launches a smartphone app and enters a design idea into a text input box. For example, they might enter, "I want you to create a bright and exciting design for a new product campaign." This input is sent to the server, which then receives the text data.

[0453] Step 2:

[0454] The server passes the received text data to a natural language processing engine and analyzes the keywords. For example, keywords such as "new product campaign," "bright," and "exciting" are extracted. The results of this analysis are used as input data for the next step of processing.

[0455] Step 3:

[0456] At the same time, the server calls the emotion engine and analyzes the user's emotions from the input text. For example, emotion information such as "expectation" and "excitement" is obtained. This emotion information is also used as input data for the next process.

[0457] Step 4:

[0458] The server requests the image generation AI to generate design proposals based on the keywords and emotional information obtained in steps 2 and 3. The image generation AI receives the keywords and emotional information as prompts and generates various design proposals. The generated design proposals are returned to the server.

[0459] Step 5:

[0460] The server then sends the generated design proposals to the user's smartphone, where the user can view and evaluate them using the app.

[0461] Step 6:

[0462] The user inputs feedback based on their evaluation, for example, a specific request such as "I want the colors to be more vivid." Along with this feedback, the user's new emotion (e.g., "hope for improvement") is also analyzed by the emotion engine.

[0463] Step 7:

[0464] The server then requests the image generation AI to generate an improved design proposal based on the user's feedback and its analysis. The image generation AI generates an improved design proposal according to the new prompts. The server receives the new design proposal and sends it to the user's device.

[0465] Through specific actions, data processing, and calculations at each step, the system is able to quickly generate design proposals that are in line with the user's intentions and emotions, and continue to improve them.

[0466] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0467] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0468] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0469] [Second embodiment]

[0470] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0471] 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.

[0472] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0473] 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.

[0474] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0475] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0476] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0477] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0478] 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.

[0479] 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.

[0480] 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.

[0481] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0482] The present invention relates to a system for generating design ideas quickly and diversifiedly and for realizing an effective design process. The system includes components of a user, a server, and a terminal.

[0483] System Overview

[0484] In this system, users input their design ideas in text format using a device, and the server receives and analyzes the ideas and generates design proposals using image generation AI. The generated design proposals are then provided back to the device, where users can evaluate and provide feedback. The feedback is analyzed by the server and used to generate new design proposals.

[0485] Program processing explanation

[0486] Idea input phase

[0487] The user accesses the web interface on the device and enters a design idea in the text input field, for example, "Simple and modern coffee cup design." This input text is sent to the server by clicking the "Submit" button.

[0488] Input data parsing phase

[0489] The server passes the text data received from the user to a natural language processing engine, which extracts and analyzes important keywords, such as "simple," "modern," and "coffee cup."

[0490] Design proposal generation phase

[0491] The server calls an image generation AI based on the analyzed keywords to generate various design proposals. The image generation AI creates multiple design proposals taking into account the specified keywords. These design proposals are sent back to the server in image format.

[0492] Design proposal provision phase

[0493] The server sends the generated design proposals to the user's device and provides them to the user via a web interface, where the user can view them, rate them, and provide feedback.

[0494] Evaluation and Feedback Phase

[0495] The user can provide feedback on the displayed design proposals in the form of star ratings and comments. For example, they can enter specific suggestions for improvement, such as "I'd like the colors to be a little brighter." The feedback is then sent back to the server.

[0496] Feedback analysis and refinement phase

[0497] The server then passes the user's feedback to a natural language processing engine for reanalysis, and then calls an image generation AI to generate an improved design proposal based on the results. This creates an improved design proposal and sends it back to the user's device.

[0498] Specific examples

[0499] Example: Coffee cup design

[0500] The user enters "simple, modern coffee cup design," and the server extracts the keywords "simple," "modern," and "coffee cup." The image generation AI generates multiple coffee cup designs that combine simple shapes with modern colors. These design proposals are provided to the user's device, and the user provides feedback, requesting that the colors be a little brighter. The server analyzes this feedback, and the image generation AI generates improved design proposals again. This process brings the design closer to the user's desired design.

[0501] In this way, the system of the present invention streamlines the design process, enabling the rapid generation and provision of diverse ideas. It also contributes to improving the quality of design proposals by quickly improving the system based on user feedback.

[0502] The processing flow will be explained below.

[0503] Step 1:

[0504] The user accesses the device's web interface and logs in, at which point the user account is authenticated.

[0505] Step 2:

[0506] The user enters a design idea in the text input field, for example, "simple modern coffee cup design."

[0507] Step 3:

[0508] The user clicks the "Submit" button to send the entered text to the server.

[0509] Step 4:

[0510] The server receives the text data sent by the user and temporarily stores it in an internal database.

[0511] Step 5:

[0512] The server passes the received text to a natural language processing (NLP) engine, which analyzes it for important keywords, such as "simple," "modern," and "coffee cup."

[0513] Step 6:

[0514] The server uses the analyzed keywords to send a request to the image generation AI, which includes the extracted keywords and additional parameters (color, shape, style, etc.).

[0515] Step 7:

[0516] The image generation AI generates various design ideas based on the request it receives, including a coffee cup design with simple shapes and modern colors.

[0517] Step 8:

[0518] The image generation AI sends the generated design proposal to the server as an image file (e.g., JPEG, PNG).

[0519] Step 9:

[0520] The server sends the design proposal received from the image generation AI to the user's device.

[0521] Step 10:

[0522] The terminal updates the web interface to display the design proposal received from the server.

[0523] Step 11:

[0524] The user checks the displayed design proposal and gives an evaluation and feedback, for example, by inputting a comment such as "I wish the colors were a little brighter."

[0525] Step 12:

[0526] The user sends feedback to the server.

[0527] Step 13:

[0528] The server receives the feedback sent by the user and passes it to the natural language processing engine for analysis, extracting important improvements (e.g., "make the colors brighter").

[0529] Step 14:

[0530] Based on the extracted improvements, the server sends a request with new requirements to the image generation AI.

[0531] Step 15:

[0532] The image generation AI generates improved design proposals based on new requests and sends them back to the server.

[0533] Step 16:

[0534] The server then transmits the improved design proposal back to the user's terminal.

[0535] Step 17:

[0536] The terminal displays the improved design proposal for the user to confirm.

[0537] By repeating this process, the user can successively obtain design proposals that are closest to their ideal.

[0538] Example 1

[0539] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0540] In the conventional design process, it takes a lot of time and effort to convert user ideas into concrete design proposals. Furthermore, there is no efficient method to quickly incorporate user feedback and generate improved designs. As a result, it is difficult to improve the quality of designs and quickly generate diverse ideas.

[0541] 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.

[0542] In this invention, the server includes a means for a user to input design ideas in text format, a means for the server to receive the input text data and analyze the input content using a natural language processing engine, and a means for generating various design proposals by calling an image generation AI model based on the keywords analyzed by the server. This makes it possible to quickly materialize user ideas into various design proposals and quickly improve the designs based on user feedback.

[0543] "User" refers to a person who uses the system to input design ideas and provide ratings and feedback.

[0544] "Server" refers to the central control device that receives and analyzes design ideas and generates and provides design proposals using an image generation AI model.

[0545] "Terminal" refers to an electronic device that allows a user to input design ideas, view design proposals from the server, and provide feedback.

[0546] "Design Ideas" refers to design concepts and requests entered by users in text format.

[0547] A "natural language processing engine" refers to a software tool that analyzes input text data and extracts important keywords.

[0548] "Keywords" refer to important words and phrases extracted from design ideas by natural language processing engines.

[0549] An "image generation AI model" refers to an artificial intelligence that generates a variety of design ideas based on specified keywords.

[0550] "Design proposal" refers to a specific design image generated by an image generation AI model.

[0551] "Feedback" refers to comments that users make on the provided design proposal, such as evaluations and suggestions for improvement.

[0552] A "prompt sentence" refers to a text sentence containing keywords that is sent to an image generation AI model.

[0553] This invention is a system that uses an image generation AI model to quickly generate a variety of design proposals based on design ideas input by the user and provides them to the user. To implement this system, a user, a server, and a terminal are required, each of which plays a specific role. A specific implementation method for this system is described below.

[0554] Overall system configuration

[0555] The system of the present invention comprises the following elements:

[0556] 1. A means for users to input their design ideas

[0557] 2. Server input data reception and analysis method

[0558] 3. Method for generating design proposals using image generation AI models

[0559] 4. Means of providing design proposals to devices

[0560] 5. User Rating and Feedback

[0561] 6. Feedback analysis and improved design generation method

[0562] Hardware and software used

[0563] Device: An electronic device (e.g., PC, smartphone, tablet) on which a user inputs design ideas and reviews the generated design proposals.

[0564] Server: A central control unit that receives and analyzes input data from users and generates design proposals using image generation AI models.

[0565] Natural language processing engine: Software for extracting important keywords from input text data (e.g., Google Cloud Natural Language API).

[0566] Image generation AI model: An artificial intelligence that generates various design ideas based on specified keywords (e.g., OpenAI's DALL-E).

[0567] Specific explanation of the system's operation

[0568] Enter your design ideas

[0569] The user accesses the device's web interface, enters a design idea into the text input field, for example, "Simple and modern coffee cup design," and then clicks the "Submit" button, which sends the input text to the server as an HTTP POST request.

[0570] Text data analysis

[0571] The server passes the received text data to a natural language processing engine, which analyzes and extracts important keywords, such as "simple," "modern," and "coffee cup."

[0572] Generate design ideas

[0573] The server generates a prompt sentence based on the analyzed keywords and sends it to the image generation AI model. The image generation AI model generates multiple design proposals based on the specified prompt sentence and returns them to the server as image data. An example of a prompt sentence is a phrase such as "a simple, modern coffee cup."

[0574] Providing design ideas

[0575] The server sends the generated design proposals to the user's terminal and makes them available for viewing on a web interface, allowing the user to check the generated design proposals and provide evaluation and feedback.

[0576] Feedback input and analysis

[0577] The user can then enter specific evaluations and feedback for the displayed design proposals. For example, they can enter comments such as "I'd like the colors to be a little brighter." This feedback data is then sent to the server as an HTTP POST request.

[0578] Design improvements based on feedback

[0579] The server then passes the user feedback data to the natural language processing engine for analysis, extracting new keywords and areas for improvement. Based on the results, it again sends a request to the image generation AI model to generate an improved design proposal. This improved design proposal is then provided back to the user's device.

[0580] Implementing specific examples

[0581] For example, suppose a user enters "simple, modern coffee cup design." The server extracts the keywords "simple," "modern," and "coffee cup" from this text and sends them to an image generation AI model (e.g., OpenAI's DALL-E). This AI model generates various design proposals taking these keywords into account and sends them back to the server. The user reviews these design proposals and provides feedback such as "I wish the colors were a little brighter." The server then reanalyzes this feedback and generates and provides improved design proposals.

[0582] This system not only allows us to respond quickly to user requests, but also makes it possible to efficiently generate and improve a variety of design proposals.

[0583] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0584] Step 1:

[0585] Enter your ideas

[0586] A user accesses the device's web interface, enters a design idea into the text input field, for example, "Simple and modern coffee cup design," and then clicks the "Submit" button, which sends the input text to the server as an HTTP POST request.

[0587] Input: User-entered text design ideas

[0588] Output: The entered design idea is sent to the server as an HTTP POST request.

[0589] Step 2:

[0590] Receiving data

[0591] The server receives an HTTP POST request sent by the user, with the body of the request containing the user's input text design ideas.

[0592] Input: Text data sent as an HTTP POST request

[0593] Output: Text design ideas received by the server

[0594] Step 3:

[0595] Text data analysis

[0596] The server passes the received text design ideas to a natural language processing engine, which analyzes and extracts important keywords. The natural language processing engine (e.g., Google Cloud Natural Language API) extracts keywords such as "simple," "modern," and "coffee cup" from the text.

[0597] Input: Received text design ideas

[0598] Output: Extracted keywords

[0599] Step 4:

[0600] Generate prompt statement

[0601] The server generates a prompt for the image generation AI model based on the extracted keywords, such as "A simple and modern coffee cup."

[0602] Input: Extracted keywords

[0603] Output: Generated prompt statement

[0604] Step 5:

[0605] Generate design ideas

[0606] The server sends the generated prompt to an image generation AI model (e.g., OpenAI's DALL-E) requesting the generation of design proposals. The image generation AI model generates multiple design proposals based on the specified prompt and returns these design proposals to the server as image data.

[0607] Input: Generated prompt text

[0608] Output: Multiple generated design proposals (image data)

[0609] Step 6:

[0610] Providing design ideas

[0611] The server receives the generated design proposals and sends them to the user's device. The server then generates an HTTP response containing image data and sends it to the user's device. The user can view these design proposals through the device's web interface.

[0612] Input: Generated design proposal (image data)

[0613] Output: Providing design proposals to the user's device

[0614] Step 7:

[0615] Enter your rating and feedback

[0616] The user provides feedback on the displayed design proposal in the form of a star rating or a comment. For example, they can enter specific suggestions for improvement, such as "I'd like the colors to be a little brighter." The feedback data is then sent to the server again as an HTTP POST request.

[0617] Input: User feedback (ratings and comments)

[0618] Output: Feedback data is sent to the server as an HTTP POST request

[0619] Step 8:

[0620] Receiving and Analyzing Feedback

[0621] The server receives the feedback data sent by the user and passes the received feedback text to the natural language processing engine again to analyze and extract points for improvement.

[0622] Input: Received feedback (ratings and comments)

[0623] Output: Extracted improvement points

[0624] Step 9:

[0625] Generation of improved design proposals

[0626] The server then sends a prompt to the image generation AI model again based on the improvement points extracted from the feedback, requesting the generation of an improved design proposal. The image generation AI model then generates a new design proposal taking into account the improvement points and sends it back to the server.

[0627] Input: Extracted refinements

[0628] Output: Generated improved design proposal (image data)

[0629] Step 10:

[0630] Providing improved design proposals

[0631] The server transmits the improved design proposal back to the user's terminal, and the user can again evaluate and provide feedback on the improved design.

[0632] Input: Generated improved design proposal (image data)

[0633] Output: Providing improved design proposals to the user's device

[0634] In this way, the system can quickly and efficiently realize users' design ideas and continuously improve them based on user feedback.

[0635] (Application example 1)

[0636] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0637] Conventional design generation and evaluation systems make it difficult for users to quickly and accurately create customized designs with unique styles and functions. Furthermore, especially for electronic payment card designs, users are often restricted by existing templates, making it difficult to create original designs that reflect the user's individuality.

[0638] 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.

[0639] In this invention, the server includes means for a user to input design ideas in text format, means for the server to receive the input text data and analyze the input content using a natural language processing engine, means for calling an image generation AI based on the analyzed keywords to generate various design proposals, means for generating improved design proposals based on feedback, and means for the user to generate their own original electronic payment card design, thereby enabling users to quickly and accurately create customized electronic payment cards that reflect their individuality.

[0640] "User" refers to a person who uses the system to input design ideas and provide feedback.

[0641] "Text format" refers to a format of information expressed in characters that is used by users to input design ideas.

[0642] "Design idea" refers to text information that includes concepts and features related to a design that a user wants to create.

[0643] "Server" refers to a central computer system that receives, analyzes, processes, and transmits data to user terminals.

[0644] "Natural language processing engine" refers to a technical device that analyzes input text and extracts important keywords.

[0645] "Image generation AI" refers to artificial intelligence that generates various design ideas based on input keywords.

[0646] "Design proposal" refers to a specific design image or concept proposal generated by image generation AI.

[0647] "User's device" refers to the device that a user uses to input design ideas and receive and evaluate generated design proposals.

[0648] "Feedback" refers to the evaluation and suggestions for improvement that a user provides for a generated design proposal.

[0649] "Improved design proposal" refers to an improved design generated by the image generation AI based on user feedback.

[0650] An "electronic payment card" refers to a card-type medium used for electronic payments, which is a payment method that includes the user's personal information.

[0651] The following describes an embodiment of the present invention.

[0652] Overall system overview

[0653] The present invention is a system that uses image generation AI to generate design proposals based on design ideas entered by users in text format, and includes a process for evaluating and improving the proposed designs. It is particularly applicable to generating custom designs for electronic payment cards.

[0654] Hardware and software used

[0655] The system includes the following main components:

[0656] User device: A device such as a smartphone or computer on which users input design ideas and receive and evaluate the generated design proposals.

[0657] Server: A central computer system that receives, analyzes, and generates and refines data.

[0658] Natural language processing engine: Analyzes input text data and extracts important keywords (e.g., spaCy).

[0659] Image generation AI model: An artificial intelligence that generates design ideas based on input keywords. (Example: DALL-E 2)

[0660] Program processing explanation

[0661] 1. Design idea input phase

[0662] The user uses the device's interface to input design ideas in text format, for example, "I want a simple and elegant design."

[0663] 2. Input data analysis phase

[0664] The server passes the text data received from the user to a natural language processing engine and extracts keywords, such as "simple" and "elegant."

[0665] 3. Design proposal generation phase

[0666] The server then calls an image generation AI model based on the analyzed keywords to generate multiple design proposals, which are then sent back to the server.

[0667] 4. Design proposal provision phase

[0668] The server transmits the generated design proposal to the user's terminal and provides it to the user, who can view it, evaluate it, and provide feedback.

[0669] 5. Evaluation and feedback phase

[0670] Users evaluate the provided design proposals and enter specific feedback, such as "I would like the background color to be brighter."

[0671] 6. Feedback analysis and refinement phase

[0672] The server then analyzes the user's feedback again using a natural language processing engine, calls the image generation AI model again, and generates an improved design proposal, which is then sent back to the user's device.

[0673] Specific examples

[0674] When a user inputs "I want a simple yet elegant design," the server extracts the keywords "simple" and "elegant," and uses an image generation AI model to generate a design proposal with a pale blue background and an elegant pattern. If the user provides feedback that "I would like the background color to be brighter," an improved design with a pale pink background is provided.

[0675] Prompt Sentence Examples

[0676] Input prompt:

[0677] "I want a simple and elegant design."

[0678] I want the background color to be brighter.

[0679] The system allows users to easily create and quickly refine personalized, customized electronic payment card designs.

[0680] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0681] Step 1:

[0682] Enter your design ideas

[0683] Users input their design ideas in text format on their device, such as a prompt statement like "I want a simple and elegant design" into the interface of their smartphone or computer, and click the "Submit" button.

[0684] Input: "I want a simple and elegant design"

[0685] Output: Text data sent to the server

[0686] Step 2:

[0687] Receiving input data

[0688] The server receives the text data sent from the user terminal, temporarily stores the received text data, and passes it on to the next analysis step.

[0689] Input: Text data submitted by the user

[0690] Output: Text data stored on the server

[0691] Step 3:

[0692] Analyzing input data

[0693] The server passes the received text data to a natural language processing engine (e.g., spaCy) to extract important keywords, such as "simple" and "elegant."

[0694] Input: Text data stored on the server

[0695] Output: Extracted keywords ("simple", "elegant")

[0696] Step 4:

[0697] Generate design ideas

[0698] The server calls an image generation AI model (e.g., DALL-E 2) based on the extracted keywords and generates various design proposals. The image generation AI model creates multiple design proposals taking into account the specified keywords.

[0699] Input: Extracted keywords

[0700] Output: Generated multiple design proposals (image files)

[0701] Step 5:

[0702] Submit your design proposal

[0703] The server sends the generated design proposals to the user's terminal, which receives them and displays them on the interface.

[0704] Input: Generated multiple design proposals (image files)

[0705] Output: Design proposal sent to user device

[0706] Step 6:

[0707] Evaluation and feedback of design proposals

[0708] Users can evaluate the displayed design proposals and enter feedback, such as specific suggestions for improvement like "I'd like the background color to be brighter," and submit the proposal.

[0709] Input: The design proposal displayed by the user

[0710] Output: User feedback (text)

[0711] Step 7:

[0712] Receiving and analyzing feedback

[0713] The server receives the feedback from the user and analyzes it again using a natural language processing engine, which extracts keywords based on the content of the feedback.

[0714] Input: User feedback (text)

[0715] Output: Extracted feedback keywords (e.g., "brighten the background color")

[0716] Step 8:

[0717] Generation of improved design proposals

[0718] The server then calls the image generation AI model again based on the analyzed feedback keywords to generate an improved design proposal, resulting in a new design proposal that reflects the user's feedback.

[0719] Input: Extracted feedback keywords

[0720] Output: Improved design proposal (image file)

[0721] Step 9:

[0722] Providing improved design proposals

[0723] The server then sends the improved design proposal back to the user's device, where the user can receive it and provide feedback again.

[0724] Input: Improved design proposal (image file)

[0725] Output: Improved design proposal provided to user device

[0726] By repeating this process, the user can create a satisfactory electronic payment card design.

[0727] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0728] This invention relates to a system in which designers input design ideas in text format, and an image generation AI generates various design proposals based on those ideas. Furthermore, this system combines an emotion engine that recognizes the user's emotions to provide more appropriate design proposals.

[0729] System Overview

[0730] This system recognizes the user's emotions when they input their design ideas, and can generate design proposals that match the user's intentions and emotions. The design process includes everything from inputting the design idea to analyzing feedback and making improvements, and the user's emotions are also reflected in the process.

[0731] Program processing explanation

[0732] Idea input phase

[0733] The user accesses the web interface on the device and enters a design idea in the text input field, for example, "Simple and modern coffee cup design." This input text is sent to the server by clicking the "Submit" button.

[0734] Emotion Recognition Phase

[0735] The server receives and stores the text data sent by the user. It then analyzes the emotions of the input text using an emotion engine. For example, emotions such as "joy," "expectation," and "excitement" are recognized.

[0736] Input data parsing phase

[0737] The server passes the received text to the emotion engine and the natural language processing engine at the same time, and extracts and analyzes important keywords. For example, keywords such as "simple," "modern," and "coffee cup" are extracted.

[0738] Design proposal generation phase

[0739] The server sends a request to the image generation AI based on the analyzed keywords and emotional information. The request includes the keywords and parameters that reflect the user's emotions. The image generation AI then generates various design proposals based on these.

[0740] Design proposal provision phase

[0741] The server sends the generated design proposals to the user's device and provides them to the user via a web interface, where the user can view them, rate them, and provide feedback.

[0742] Evaluation and Feedback Phase

[0743] Users can provide feedback on the displayed design proposals in the form of star ratings and comments. For example, they can input specific suggestions for improvement, such as "I'd like the colors to be a little brighter" or "I'd like the curves to be smoother." The feedback is then sent back to the server.

[0744] Feedback analysis and refinement phase

[0745] The server passes the feedback sent by the user to the emotion engine, which analyzes the feedback's emotions. It then passes it to the natural language processing engine for analysis and extracts important improvements. Based on the extracted improvements and emotion information, the image generation AI is again invoked to generate an improved design proposal.

[0746] Specific examples

[0747] Example: Emotion Recognition and Design Improvement

[0748] The user inputs a "simple, modern coffee cup design," and simultaneously recognizes "expectations." The server analyzes keywords such as "simple," "modern," and "coffee cup," adds "expectations" as emotional information, and sends it to the image generation AI. The AI ​​generates a modern design that reflects expectations and provides it to the user's device.

[0749] The user looks at the proposed design and gives feedback such as "I'd like the colors to be a little brighter," which recognizes the user's dissatisfaction with the depiction. The server reanalyzes this feedback and emotional information to generate a new improvement proposal. The new design proposal is then provided to the user's device again, and the user continues to evaluate and provide feedback on it.

[0750] In this way, the system of the present invention can quickly provide more satisfying design proposals by reflecting the user's feelings in the design process.

[0751] The processing flow will be explained below.

[0752] Step 1:

[0753] The user accesses the terminal's web interface and logs in. At this point, the user is authenticated.

[0754] Step 2:

[0755] The user enters a design idea, such as "simple and modern coffee cup design," into a text input field.

[0756] Step 3:

[0757] The user clicks the "Submit" button to send the entered text to the server.

[0758] Step 4:

[0759] The server receives the text data sent by the user and temporarily stores it in a database.

[0760] Step 5:

[0761] The server sends the text data to the emotion engine, which analyzes the emotions in the text. As a result, emotions such as "joy" and "expectation" are recognized.

[0762] Step 6:

[0763] The server simultaneously sends the text data to a natural language processing (NLP) engine to extract important keywords, such as "simple," "modern," and "coffee cup."

[0764] Step 7:

[0765] The server creates a request to the image generation AI based on the emotion recognition results and the analyzed keywords. The request includes the extracted keywords and emotion information.

[0766] Step 8:

[0767] The image generation AI receives a request and generates multiple design proposals based on the specified keywords and emotional information, for example, a brightly colored coffee cup design that reflects a sense of anticipation.

[0768] Step 9:

[0769] The image generation AI sends the generated design proposal to the server as an image file.

[0770] Step 10:

[0771] The server sends the design proposal received from the image generation AI to the user's device.

[0772] Step 11:

[0773] The terminal updates the web interface to allow the user to view the received design proposals.

[0774] Step 12:

[0775] The user checks the displayed design proposal and gives an evaluation and feedback. For example, the user can input feedback such as "I would like the colors to be a little brighter."

[0776] Step 13:

[0777] The user sends feedback to the server.

[0778] Step 14:

[0779] The server stores the feedback received from the user and passes it back to the emotion engine for emotion analysis. For example, emotions such as "dissatisfaction" and "request for improvement" are recognized.

[0780] Step 15:

[0781] The server also passes the feedback to a natural language processing (NLP) engine to extract key improvements, such as "make the colors brighter."

[0782] Step 16:

[0783] The server creates a new request based on the extracted improvements and emotion information and sends it to the image generation AI.

[0784] Step 17:

[0785] The image generation AI generates improved design ideas based on new requests, such as a brighter colored coffee cup design.

[0786] Step 18:

[0787] The image generation AI sends the improved design proposal to the server as an image file.

[0788] Step 19:

[0789] The server transmits the improved design proposal to the user's terminal.

[0790] Step 20:

[0791] The terminal displays the improved design proposal for the user to confirm.

[0792] By repeating this cycle, users can arrive at a design proposal that best suits their needs and emotions.

[0793] Example 2

[0794] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0795] Conventional design generation systems have had the problem of difficulty in generating design proposals that fully reflect the user's emotions and intentions. Another issue is the inefficient process of properly analyzing user feedback and reflecting it in design proposals. Our goal is to solve these problems and quickly provide design proposals that will provide greater user satisfaction.

[0796] 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.

[0797] In this invention, the server includes: means for a user to input design ideas in text format; means for receiving the input text data and analyzing the emotion of the input text using an emotion recognition engine; means for analyzing the input content using a natural language processing engine together with the emotion information analyzed by the server; means for calling an image generation model based on the keywords and emotion information analyzed by the server to generate various design proposals; means for transmitting the generated design proposals to the user's terminal and providing them; means for the user to evaluate the provided design proposals and input feedback; and means for the server to reanalyze the user's feedback using the emotion recognition engine and natural language processing engine to generate improved design proposals. This makes it possible to quickly generate design proposals that reflect the user's emotions and intentions and to efficiently analyze user feedback and reflect it in the design proposals.

[0798] "User" refers to a person or organization that inputs design ideas and evaluates and provides feedback on the generated design proposals.

[0799] A "server" refers to a central processing unit that receives information from users and performs processes such as data analysis and image generation.

[0800] "Text data" refers to design ideas in the form of text entered by the user.

[0801] An "emotion recognition engine" is a software engine for analyzing the emotions in input text data.

[0802] A "natural language processing engine" is a software engine for extracting and analyzing keywords from input text data.

[0803] An "image generation model" refers to an artificial intelligence engine that generates design proposals based on input keywords and emotional information.

[0804] "Design proposals" are various design options generated by the image generation model.

[0805] "Feedback" refers to the evaluation and improvement requests made by the user regarding the generated design proposal.

[0806] "Emotion information" refers to the user's emotion data analyzed by an emotion recognition engine.

[0807] "Keywords" refer to words or phrases that are central to a design idea, extracted by a natural language processing engine.

[0808] This invention relates to a system in which a user inputs a design idea in text format, and an image generation model generates various design proposals based on the idea. Furthermore, this system combines an emotion recognition engine that recognizes the user's emotions, providing more appropriate design proposals.

[0809] First, a user accesses the device's web interface and enters a design idea into the text input field. For example, they might enter "Simple and modern coffee cup design." The input text is sent to the server by clicking the "Submit" button. When the user clicks the submit button, the input text is sent to the server using asynchronous communication (Ajax).

[0810] The server receives and stores the text data sent by the user. The stored text data is then passed to an emotion recognition engine (e.g., IBM Watson or Microsoft Text Analytics) to analyze the emotion of the input text. The emotion recognition engine analyzes the text data and extracts emotions such as "happiness," "anticipation," and "excitement." The emotion is also output as a numerical score, which is used to generate design proposals.

[0811] The server then analyzes the text data received from the user using a natural language processing engine (e.g., Google Cloud Natural Language API) while simultaneously recognizing emotions. Specifically, it extracts important keywords (e.g., "simple," "modern," "coffee cup") from the text. This identifies the core elements related to the design.

[0812] Next, the server sends the extracted keywords and emotional information as a request to an image generation model (such as OpenAI's DALL-E or Midjourney). The request includes the user's emotional information and keywords. The image generation model generates various design proposals based on this information. Specifically, the generative model incorporates the emotional information into its parameters and generates several design proposals.

[0813] The generated design proposals are sent from the server to the user's device and displayed on a web interface. The user evaluates the provided design proposals and provides feedback in the form of star ratings and comments. The feedback is often specific, such as "I wish the colors were a little brighter" or "I wish the curves were smoother." The user clicks the "Submit Feedback" button to send the feedback to the server.

[0814] The server passes the feedback received from the user back to the emotion recognition engine, which analyzes the emotions in the feedback. The feedback is then passed to the natural language processing engine for analysis and key improvements are extracted. Based on the extracted improvements and emotion information, the image generation model is invoked again to generate new design proposals. The new design proposals are then provided to the user's device, where the user can continue to reassess and provide feedback.

[0815] Specific examples

[0816] The user inputs a "simple, modern coffee cup design," and simultaneously recognizes "expectation." The server sends the keywords "simple," "modern," and "coffee cup" along with the emotion score for "expectation" to the image generation model. The model generates a modern design that reflects the expectation and provides it to the user's device.

[0817] The user looks at the proposed design and gives feedback such as "I'd like the colors to be a little brighter," recognizing their "dissatisfaction with the depiction." The server reanalyzes this feedback and emotional information, and calls the image generation model again to generate an improved design. The new design is then provided to the user's device, and the user continues to evaluate and provide feedback.

[0818] Prompt Sentence Examples

[0819] "Enter a simple and modern coffee cup design with a sense of anticipation"

[0820] "When providing feedback to brighten the colors of a user's proposed design, dissatisfaction with the depiction is recognized."

[0821] In this way, the system of the present invention is capable of quickly generating design proposals that reflect the user's feelings and intentions, and efficiently analyzing user feedback and reflecting it in the design proposals.

[0822] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0823] Step 1: Idea input phase

[0824] The user accesses the web interface on the device and enters a design idea into the text input field. For example, they enter "Simple and modern coffee cup design." This input text is sent to the server by clicking the "Submit" button.

[0825] Input: Text data of design ideas entered by the user.

[0826] Output: The text data sent to the server.

[0827] Step 2: Text data reception phase

[0828] The server saves the received text data, checks the format of the received data, and performs initial processing to prepare it for passing to the emotion recognition engine.

[0829] Input: Text data sent by the user.

[0830] Output: The saved text data.

[0831] Step 3: Emotion Recognition Phase

[0832] The server passes the saved text data to an emotion recognition engine, which analyzes the emotions in the input text. This analysis extracts emotions such as "happiness," "anticipation," and "excitement" as numerical scores. For example, IBM Watson or Microsoft Text Analytics are used.

[0833] Input: Saved text data.

[0834] Output: Parsed emotion information (emotion type and numeric score).

[0835] Step 4: Natural Language Processing Phase

[0836] The server analyzes the text data using a natural language processing engine (e.g., Google Cloud Natural Language API) while simultaneously recognizing emotions. Through the analysis, important keywords (e.g., "simple," "modern," and "coffee cup") are extracted from the text.

[0837] Input: Saved text data.

[0838] Output: Extracted keywords.

[0839] Step 5: Design proposal generation request phase

[0840] The server sends the analyzed keywords and emotion scores as a request to an image generation model (e.g., OpenAI's DALL-E or Midjourney). The request includes the user's emotion information and keywords. The image generation model generates various design proposals based on this information.

[0841] Input: Extracted keywords and sentiment information.

[0842] Output: Request submission and generated design proposal.

[0843] Step 6: Design proposal phase

[0844] The server sends the generated design proposal to the user's device, where it is displayed on a web interface for easy viewing by the user.

[0845] Input: Generated design proposal.

[0846] Output: A design proposal displayed on the user's device.

[0847] Step 7: Evaluation and feedback phase

[0848] Users provide feedback on the provided design proposals in the form of star ratings and comments, such as "I'd like the colors to be a little brighter" or "I'd like the curves to be smoother." The user then clicks the "Submit" button to send the feedback to the server.

[0849] Input: Feedback entered by the user.

[0850] Output: Feedback data sent to the server.

[0851] Step 8: Feedback analysis phase

[0852] The server then passes the received feedback to the emotion recognition engine to analyze the emotion of the feedback, and then passes the feedback content to the natural language processing engine to extract important improvements.

[0853] Input: Feedback data submitted by the user.

[0854] Output: Emotional information and important refinements.

[0855] Step 9: Improved design generation phase

[0856] The server then calls the image generation model again based on the extracted improvements and emotion information to generate an improved design proposal.

[0857] Input: Extracted refinements and sentiment information.

[0858] Output: Generated improved design proposals.

[0859] Step 10: Providing improved design proposals

[0860] The server sends the improved design proposal to the user's device and continues to receive evaluations and feedback.

[0861] Input: Improved design proposal.

[0862] Output: Improved design proposal displayed on the user's device.

[0863] (Application example 2)

[0864] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0865] Conventional design generation systems only generate design proposals based on text entered by the user, which means they cannot fully reflect the user's emotions and intentions. Furthermore, feedback on the generated design proposals is simply analyzed as information, without considering the user's emotions during refinement, limiting the ability to enhance user satisfaction. This problem is particularly important for brick-and-mortar stores, where the rapid and effective design of attractive displays and posters is essential.

[0866] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a user to input design ideas in text format; means for the server to receive the input text data and analyze the input content using a natural language processing engine; means for recognizing the emotion of the input text using an emotion engine; means for generating various design proposals by calling an image generation AI based on the keywords and emotion information analyzed by the server; means for the server to send and provide the generated design proposals to the user's terminal; means for the user to evaluate the provided design proposals and input feedback; and means for the server to analyze the user's feedback again using the emotion engine to generate improved design proposals. This makes it possible to generate and improve design proposals that reflect the user's emotions and intentions, thereby improving the efficiency of the design creation process and the customer experience, especially in physical stores.

[0867] "Users" refer to individuals and companies who use the system to input design ideas, evaluate the generated design proposals, and provide feedback.

[0868] "Text format" refers to a format in which design ideas are written in text.

[0869] "Design Idea" refers to text information that a user inputs to communicate specific design concepts and requirements.

[0870] "Server" refers to a computer system that receives, stores, analyzes, and processes input data.

[0871] A "natural language processing engine" refers to software or a program that analyzes input text data and extracts important keywords.

[0872] "Emotion engine" refers to software or a program for recognizing and analyzing the emotions of input text.

[0873] "Keywords" refer to important words or phrases extracted from text data.

[0874] "Emotion information" refers to the user's emotion data analyzed by the emotion engine of the input text.

[0875] "Image generation AI" refers to artificial intelligence that generates design proposals based on keywords and emotional information.

[0876] "Design proposal" refers to a specific design proposal generated by image generation AI.

[0877] "Terminal" refers to the device (e.g., smartphone, PC) used by a user to input design ideas and display generated design proposals.

[0878] "Feedback" refers to the evaluation and improvement requests made by the user regarding the generated design proposal.

[0879] This invention relates to a system for generating and improving design proposals for digital signage deployed in brick-and-mortar stores. This system allows users (e.g., store managers or design teams) to input design ideas in text format, and generates and provides a variety of design proposals based on the ideas and the user's emotional information.

[0880] First, a user uses the smartphone app "Store Design Manager" to input a design idea into a text box. For example, a specific idea such as "I want a bright and exciting design created for a new product campaign" can be input. The emotion engine then analyzes the user's emotions from the input text and obtains emotional information such as "expectation" and "excitement."

[0881] The input text data and emotion data are then sent to a server, which uses a natural language processing engine to analyze the input text data and extract key keywords (e.g., "new product campaign," "cheerful," "exciting"). At the same time, the analysis results from the emotion engine are also recorded.

[0882] The server requests the image generation AI to generate design proposals based on the analyzed keywords and emotional information. The image generation AI generates a variety of design proposals based on this input data. This results in multiple design proposals that reflect the user's wishes and emotions. These design proposals are provided to the user's smartphone via the server.

[0883] The user views the provided design proposals and provides evaluation and feedback. For example, they can input specific requests for improvement, such as "I would like the colors to be more vivid." This feedback is also analyzed by the emotion engine, which recognizes the emotion contained in the feedback (for example, "hope for improvement"). Based on the results of this analysis, the server again requests the image generation AI to generate an improved design proposal.

[0884] Through this process, users can efficiently generate and refine design proposals that reflect their own emotions and intentions, which will streamline the design creation process, especially in physical stores, and improve the customer experience.

[0885] Example prompt sentence:

[0886] TXT

[0887] Keywords: new product campaign, bright, exciting

[0888] Emotions: anticipation, excitement

[0889] Generate design proposals.

[0890] To achieve these steps, the following technologies are specifically used:

[0891] Natural language processing engine: A technology for extracting key keywords from text. Examples include open source NLP libraries.

[0892] Emotion engine: A technology for analyzing the emotion of input text. For example, an emotion analysis API can be used.

[0893] Image generation AI: An AI model that generates images using keywords and emotional information. A specific example is an image generation algorithm that uses deep learning.

[0894] This system configuration makes it possible to efficiently generate designs for physical stores and provide attractive design proposals that reflect the user's intentions and emotions.

[0895] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0896] Step 1:

[0897] A user launches a smartphone app and enters a design idea into a text input box. For example, they might enter, "I want you to create a bright and exciting design for a new product campaign." This input is sent to the server, which then receives the text data.

[0898] Step 2:

[0899] The server passes the received text data to a natural language processing engine and analyzes the keywords. For example, keywords such as "new product campaign," "bright," and "exciting" are extracted. The results of this analysis are used as input data for the next step of processing.

[0900] Step 3:

[0901] At the same time, the server calls the emotion engine and analyzes the user's emotions from the input text. For example, emotion information such as "expectation" and "excitement" is obtained. This emotion information is also used as input data for the next process.

[0902] Step 4:

[0903] The server requests the image generation AI to generate design proposals based on the keywords and emotional information obtained in steps 2 and 3. The image generation AI receives the keywords and emotional information as prompts and generates various design proposals. The generated design proposals are returned to the server.

[0904] Step 5:

[0905] The server then sends the generated design proposals to the user's smartphone, where the user can view and evaluate them using the app.

[0906] Step 6:

[0907] The user inputs feedback based on their evaluation, for example, a specific request such as "I want the colors to be more vivid." Along with this feedback, the user's new emotion (e.g., "hope for improvement") is also analyzed by the emotion engine.

[0908] Step 7:

[0909] The server then requests the image generation AI to generate an improved design proposal based on the user's feedback and its analysis. The image generation AI generates an improved design proposal according to the new prompts. The server receives the new design proposal and sends it to the user's device.

[0910] Through specific actions, data processing, and calculations at each step, the system is able to quickly generate design proposals that are in line with the user's intentions and emotions, and continue to improve them.

[0911] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0912] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0913] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0914] [Third embodiment]

[0915] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0916] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0917] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0918] 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.

[0919] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0920] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0921] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0922] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0923] 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.

[0924] 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.

[0925] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0926] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0927] The present invention relates to a system for generating design ideas quickly and diversifiedly and for realizing an effective design process. The system includes components of a user, a server, and a terminal.

[0928] System Overview

[0929] In this system, users input their design ideas in text format using a device, and the server receives and analyzes the ideas and generates design proposals using image generation AI. The generated design proposals are then provided back to the device, where users can evaluate and provide feedback. The feedback is analyzed by the server and used to generate new design proposals.

[0930] Program processing explanation

[0931] Idea input phase

[0932] The user accesses the web interface on the device and enters a design idea in the text input field, for example, "Simple and modern coffee cup design." This input text is sent to the server by clicking the "Submit" button.

[0933] Input data parsing phase

[0934] The server passes the text data received from the user to a natural language processing engine, which extracts and analyzes important keywords, such as "simple," "modern," and "coffee cup."

[0935] Design proposal generation phase

[0936] The server calls an image generation AI based on the analyzed keywords to generate various design proposals. The image generation AI creates multiple design proposals taking into account the specified keywords. These design proposals are sent back to the server in image format.

[0937] Design proposal provision phase

[0938] The server sends the generated design proposals to the user's device and provides them to the user via a web interface, where the user can view them, rate them, and provide feedback.

[0939] Evaluation and Feedback Phase

[0940] The user can provide feedback on the displayed design proposals in the form of star ratings and comments. For example, they can enter specific suggestions for improvement, such as "I'd like the colors to be a little brighter." The feedback is then sent back to the server.

[0941] Feedback analysis and refinement phase

[0942] The server then passes the user's feedback to a natural language processing engine for reanalysis, and then calls an image generation AI to generate an improved design proposal based on the results. This creates an improved design proposal and sends it back to the user's device.

[0943] Specific examples

[0944] Example: Coffee cup design

[0945] The user enters "simple, modern coffee cup design," and the server extracts the keywords "simple," "modern," and "coffee cup." The image generation AI generates multiple coffee cup designs that combine simple shapes with modern colors. These design proposals are provided to the user's device, and the user provides feedback, requesting that the colors be a little brighter. The server analyzes this feedback, and the image generation AI generates improved design proposals again. This process brings the design closer to the user's desired design.

[0946] In this way, the system of the present invention streamlines the design process, enabling the rapid generation and provision of diverse ideas. It also contributes to improving the quality of design proposals by quickly improving the system based on user feedback.

[0947] The processing flow will be explained below.

[0948] Step 1:

[0949] The user accesses the device's web interface and logs in, at which point the user account is authenticated.

[0950] Step 2:

[0951] The user enters a design idea in the text input field, for example, "simple modern coffee cup design."

[0952] Step 3:

[0953] The user clicks the "Submit" button to send the entered text to the server.

[0954] Step 4:

[0955] The server receives the text data sent by the user and temporarily stores it in an internal database.

[0956] Step 5:

[0957] The server passes the received text to a natural language processing (NLP) engine, which analyzes it for important keywords, such as "simple," "modern," and "coffee cup."

[0958] Step 6:

[0959] The server uses the analyzed keywords to send a request to the image generation AI, which includes the extracted keywords and additional parameters (color, shape, style, etc.).

[0960] Step 7:

[0961] The image generation AI generates various design ideas based on the request it receives, including a coffee cup design with simple shapes and modern colors.

[0962] Step 8:

[0963] The image generation AI sends the generated design proposal to the server as an image file (e.g., JPEG, PNG).

[0964] Step 9:

[0965] The server sends the design proposal received from the image generation AI to the user's device.

[0966] Step 10:

[0967] The terminal updates the web interface to display the design proposal received from the server.

[0968] Step 11:

[0969] The user checks the displayed design proposal and gives an evaluation and feedback, for example, by inputting a comment such as "I wish the colors were a little brighter."

[0970] Step 12:

[0971] The user sends feedback to the server.

[0972] Step 13:

[0973] The server receives the feedback sent by the user and passes it to the natural language processing engine for analysis, extracting important improvements (e.g., "make the colors brighter").

[0974] Step 14:

[0975] Based on the extracted improvements, the server sends a request with new requirements to the image generation AI.

[0976] Step 15:

[0977] The image generation AI generates improved design proposals based on new requests and sends them back to the server.

[0978] Step 16:

[0979] The server then transmits the improved design proposal back to the user's terminal.

[0980] Step 17:

[0981] The terminal displays the improved design proposal for the user to confirm.

[0982] By repeating this process, the user can successively obtain design proposals that are closest to their ideal.

[0983] Example 1

[0984] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0985] In the conventional design process, it takes a lot of time and effort to convert user ideas into concrete design proposals. Furthermore, there is no efficient method to quickly incorporate user feedback and generate improved designs. As a result, it is difficult to improve the quality of designs and quickly generate diverse ideas.

[0986] 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.

[0987] In this invention, the server includes a means for a user to input design ideas in text format, a means for the server to receive the input text data and analyze the input content using a natural language processing engine, and a means for generating various design proposals by calling an image generation AI model based on the keywords analyzed by the server. This makes it possible to quickly materialize user ideas into various design proposals and quickly improve the designs based on user feedback.

[0988] "User" refers to a person who uses the system to input design ideas and provide ratings and feedback.

[0989] "Server" refers to the central control device that receives and analyzes design ideas and generates and provides design proposals using an image generation AI model.

[0990] "Terminal" refers to an electronic device that allows a user to input design ideas, view design proposals from the server, and provide feedback.

[0991] "Design Ideas" refers to design concepts and requests entered by users in text format.

[0992] A "natural language processing engine" refers to a software tool that analyzes input text data and extracts important keywords.

[0993] "Keywords" refer to important words and phrases extracted from design ideas by natural language processing engines.

[0994] An "image generation AI model" refers to an artificial intelligence that generates a variety of design ideas based on specified keywords.

[0995] "Design proposal" refers to a specific design image generated by an image generation AI model.

[0996] "Feedback" refers to comments that users make on the provided design proposal, such as evaluations and suggestions for improvement.

[0997] A "prompt sentence" refers to a text sentence containing keywords that is sent to an image generation AI model.

[0998] This invention is a system that uses an image generation AI model to quickly generate a variety of design proposals based on design ideas input by the user and provides them to the user. To implement this system, a user, a server, and a terminal are required, each of which plays a specific role. A specific implementation method for this system is described below.

[0999] Overall system configuration

[1000] The system of the present invention comprises the following elements:

[1001] 1. A means for users to input their design ideas

[1002] 2. Server input data reception and analysis method

[1003] 3. Method for generating design proposals using image generation AI models

[1004] 4. Means of providing design proposals to devices

[1005] 5. User Rating and Feedback

[1006] 6. Feedback analysis and improved design generation method

[1007] Hardware and software used

[1008] Device: An electronic device (e.g., PC, smartphone, tablet) on which a user inputs design ideas and reviews the generated design proposals.

[1009] Server: A central control unit that receives and analyzes input data from users and generates design proposals using image generation AI models.

[1010] Natural language processing engine: Software for extracting important keywords from input text data (e.g., Google Cloud Natural Language API).

[1011] Image generation AI model: An artificial intelligence that generates various design ideas based on specified keywords (e.g., OpenAI's DALL-E).

[1012] Specific explanation of the system's operation

[1013] Enter your design ideas

[1014] The user accesses the device's web interface, enters a design idea into the text input field, for example, "Simple and modern coffee cup design," and then clicks the "Submit" button, which sends the input text to the server as an HTTP POST request.

[1015] Text data analysis

[1016] The server passes the received text data to a natural language processing engine, which analyzes and extracts important keywords, such as "simple," "modern," and "coffee cup."

[1017] Generate design ideas

[1018] The server generates a prompt sentence based on the analyzed keywords and sends it to the image generation AI model. The image generation AI model generates multiple design proposals based on the specified prompt sentence and returns them to the server as image data. An example of a prompt sentence is a phrase such as "a simple, modern coffee cup."

[1019] Providing design ideas

[1020] The server sends the generated design proposals to the user's terminal and makes them available for viewing on a web interface, allowing the user to check the generated design proposals and provide evaluation and feedback.

[1021] Feedback input and analysis

[1022] The user can then enter specific evaluations and feedback for the displayed design proposals. For example, they can enter comments such as "I'd like the colors to be a little brighter." This feedback data is then sent to the server as an HTTP POST request.

[1023] Design improvements based on feedback

[1024] The server then passes the user feedback data to the natural language processing engine for analysis, extracting new keywords and areas for improvement. Based on the results, it again sends a request to the image generation AI model to generate an improved design proposal. This improved design proposal is then provided back to the user's device.

[1025] Implementing specific examples

[1026] For example, suppose a user enters "simple, modern coffee cup design." The server extracts the keywords "simple," "modern," and "coffee cup" from this text and sends them to an image generation AI model (e.g., OpenAI's DALL-E). This AI model generates various design proposals taking these keywords into account and sends them back to the server. The user reviews these design proposals and provides feedback such as "I wish the colors were a little brighter." The server then reanalyzes this feedback and generates and provides improved design proposals.

[1027] This system not only allows us to respond quickly to user requests, but also makes it possible to efficiently generate and improve a variety of design proposals.

[1028] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1029] Step 1:

[1030] Enter your ideas

[1031] A user accesses the device's web interface, enters a design idea into the text input field, for example, "Simple and modern coffee cup design," and then clicks the "Submit" button, which sends the input text to the server as an HTTP POST request.

[1032] Input: User-entered text design ideas

[1033] Output: The entered design idea is sent to the server as an HTTP POST request.

[1034] Step 2:

[1035] Receiving data

[1036] The server receives an HTTP POST request sent by the user, with the body of the request containing the user's input text design ideas.

[1037] Input: Text data sent as an HTTP POST request

[1038] Output: Text design ideas received by the server

[1039] Step 3:

[1040] Text data analysis

[1041] The server passes the received text design ideas to a natural language processing engine, which analyzes and extracts important keywords. The natural language processing engine (e.g., Google Cloud Natural Language API) extracts keywords such as "simple," "modern," and "coffee cup" from the text.

[1042] Input: Received text design ideas

[1043] Output: Extracted keywords

[1044] Step 4:

[1045] Generate prompt statement

[1046] The server generates a prompt for the image generation AI model based on the extracted keywords, such as "A simple and modern coffee cup."

[1047] Input: Extracted keywords

[1048] Output: Generated prompt statement

[1049] Step 5:

[1050] Generate design ideas

[1051] The server sends the generated prompt to an image generation AI model (e.g., OpenAI's DALL-E) requesting the generation of design proposals. The image generation AI model generates multiple design proposals based on the specified prompt and returns these design proposals to the server as image data.

[1052] Input: Generated prompt text

[1053] Output: Multiple generated design proposals (image data)

[1054] Step 6:

[1055] Providing design ideas

[1056] The server receives the generated design proposals and sends them to the user's device. The server then generates an HTTP response containing image data and sends it to the user's device. The user can view these design proposals through the device's web interface.

[1057] Input: Generated design proposal (image data)

[1058] Output: Providing design proposals to the user's device

[1059] Step 7:

[1060] Enter your rating and feedback

[1061] The user provides feedback on the displayed design proposal in the form of a star rating or a comment. For example, they can enter specific suggestions for improvement, such as "I'd like the colors to be a little brighter." The feedback data is then sent to the server again as an HTTP POST request.

[1062] Input: User feedback (ratings and comments)

[1063] Output: Feedback data is sent to the server as an HTTP POST request

[1064] Step 8:

[1065] Receiving and Analyzing Feedback

[1066] The server receives the feedback data sent by the user and passes the received feedback text to the natural language processing engine again to analyze and extract points for improvement.

[1067] Input: Received feedback (ratings and comments)

[1068] Output: Extracted improvement points

[1069] Step 9:

[1070] Generation of improved design proposals

[1071] The server then sends a prompt to the image generation AI model again based on the improvement points extracted from the feedback, requesting the generation of an improved design proposal. The image generation AI model then generates a new design proposal taking into account the improvement points and sends it back to the server.

[1072] Input: Extracted refinements

[1073] Output: Generated improved design proposal (image data)

[1074] Step 10:

[1075] Providing improved design proposals

[1076] The server transmits the improved design proposal back to the user's terminal, and the user can again evaluate and provide feedback on the improved design.

[1077] Input: Generated improved design proposal (image data)

[1078] Output: Providing improved design proposals to the user's device

[1079] In this way, the system can quickly and efficiently realize users' design ideas and continuously improve them based on user feedback.

[1080] (Application example 1)

[1081] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1082] Conventional design generation and evaluation systems make it difficult for users to quickly and accurately create customized designs with unique styles and functions. Furthermore, especially for electronic payment card designs, users are often restricted by existing templates, making it difficult to create original designs that reflect the user's individuality.

[1083] 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.

[1084] In this invention, the server includes means for a user to input design ideas in text format, means for the server to receive the input text data and analyze the input content using a natural language processing engine, means for calling an image generation AI based on the analyzed keywords to generate various design proposals, means for generating improved design proposals based on feedback, and means for the user to generate their own original electronic payment card design, thereby enabling users to quickly and accurately create customized electronic payment cards that reflect their individuality.

[1085] "User" refers to a person who uses the system to input design ideas and provide feedback.

[1086] "Text format" refers to a format of information expressed in characters that is used by users to input design ideas.

[1087] "Design idea" refers to text information that includes concepts and features related to a design that a user wants to create.

[1088] "Server" refers to a central computer system that receives, analyzes, processes, and transmits data to user terminals.

[1089] "Natural language processing engine" refers to a technical device that analyzes input text and extracts important keywords.

[1090] "Image generation AI" refers to artificial intelligence that generates various design ideas based on input keywords.

[1091] "Design proposal" refers to a specific design image or concept proposal generated by image generation AI.

[1092] "User's device" refers to the device that a user uses to input design ideas and receive and evaluate generated design proposals.

[1093] "Feedback" refers to the evaluation and suggestions for improvement that a user provides for a generated design proposal.

[1094] "Improved design proposal" refers to an improved design generated by the image generation AI based on user feedback.

[1095] An "electronic payment card" refers to a card-type medium used for electronic payments, which is a payment method that includes the user's personal information.

[1096] The following describes an embodiment of the present invention.

[1097] Overall system overview

[1098] The present invention is a system that uses image generation AI to generate design proposals based on design ideas entered by users in text format, and includes a process for evaluating and improving the proposed designs. It is particularly applicable to generating custom designs for electronic payment cards.

[1099] Hardware and software used

[1100] The system includes the following main components:

[1101] User device: A device such as a smartphone or computer on which users input design ideas and receive and evaluate the generated design proposals.

[1102] Server: A central computer system that receives, analyzes, and generates and refines data.

[1103] Natural language processing engine: Analyzes input text data and extracts important keywords (e.g., spaCy).

[1104] Image generation AI model: An artificial intelligence that generates design ideas based on input keywords. (Example: DALL-E 2)

[1105] Program processing explanation

[1106] 1. Design idea input phase

[1107] The user uses the device's interface to input design ideas in text format, for example, "I want a simple and elegant design."

[1108] 2. Input data analysis phase

[1109] The server passes the text data received from the user to a natural language processing engine and extracts keywords, such as "simple" and "elegant."

[1110] 3. Design proposal generation phase

[1111] The server then calls an image generation AI model based on the analyzed keywords to generate multiple design proposals, which are then sent back to the server.

[1112] 4. Design proposal provision phase

[1113] The server transmits the generated design proposal to the user's terminal and provides it to the user, who can view it, evaluate it, and provide feedback.

[1114] 5. Evaluation and feedback phase

[1115] Users evaluate the provided design proposals and enter specific feedback, such as "I would like the background color to be brighter."

[1116] 6. Feedback analysis and refinement phase

[1117] The server then analyzes the user's feedback again using a natural language processing engine, calls the image generation AI model again, and generates an improved design proposal, which is then sent back to the user's device.

[1118] Specific examples

[1119] When a user inputs "I want a simple yet elegant design," the server extracts the keywords "simple" and "elegant," and uses an image generation AI model to generate a design proposal with a pale blue background and an elegant pattern. If the user provides feedback that "I would like the background color to be brighter," an improved design with a pale pink background is provided.

[1120] Prompt Sentence Examples

[1121] Input prompt:

[1122] "I want a simple and elegant design."

[1123] I want the background color to be brighter.

[1124] The system allows users to easily create and quickly refine personalized, customized electronic payment card designs.

[1125] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1126] Step 1:

[1127] Enter your design ideas

[1128] Users input their design ideas in text format on their device, such as a prompt statement like "I want a simple and elegant design" into the interface of their smartphone or computer, and click the "Submit" button.

[1129] Input: "I want a simple and elegant design"

[1130] Output: Text data sent to the server

[1131] Step 2:

[1132] Receiving input data

[1133] The server receives the text data sent from the user terminal, temporarily stores the received text data, and passes it on to the next analysis step.

[1134] Input: Text data submitted by the user

[1135] Output: Text data stored on the server

[1136] Step 3:

[1137] Analyzing input data

[1138] The server passes the received text data to a natural language processing engine (e.g., spaCy) to extract important keywords, such as "simple" and "elegant."

[1139] Input: Text data stored on the server

[1140] Output: Extracted keywords ("simple", "elegant")

[1141] Step 4:

[1142] Generate design ideas

[1143] The server calls an image generation AI model (e.g., DALL-E 2) based on the extracted keywords and generates various design proposals. The image generation AI model creates multiple design proposals taking into account the specified keywords.

[1144] Input: Extracted keywords

[1145] Output: Generated multiple design proposals (image files)

[1146] Step 5:

[1147] Submit your design proposal

[1148] The server sends the generated design proposals to the user's terminal, which receives them and displays them on the interface.

[1149] Input: Generated multiple design proposals (image files)

[1150] Output: Design proposal sent to user device

[1151] Step 6:

[1152] Evaluation and feedback of design proposals

[1153] Users can evaluate the displayed design proposals and enter feedback, such as specific suggestions for improvement like "I'd like the background color to be brighter," and submit the proposal.

[1154] Input: The design proposal displayed by the user

[1155] Output: User feedback (text)

[1156] Step 7:

[1157] Receiving and analyzing feedback

[1158] The server receives the feedback from the user and analyzes it again using a natural language processing engine, which extracts keywords based on the content of the feedback.

[1159] Input: User feedback (text)

[1160] Output: Extracted feedback keywords (e.g., "brighten the background color")

[1161] Step 8:

[1162] Generation of improved design proposals

[1163] The server then calls the image generation AI model again based on the analyzed feedback keywords to generate an improved design proposal, resulting in a new design proposal that reflects the user's feedback.

[1164] Input: Extracted feedback keywords

[1165] Output: Improved design proposal (image file)

[1166] Step 9:

[1167] Providing improved design proposals

[1168] The server then sends the improved design proposal back to the user's device, where the user can receive it and provide feedback again.

[1169] Input: Improved design proposal (image file)

[1170] Output: Improved design proposal provided to user device

[1171] By repeating this process, the user can create a satisfactory electronic payment card design.

[1172] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1173] This invention relates to a system in which designers input design ideas in text format, and an image generation AI generates various design proposals based on those ideas. Furthermore, this system combines an emotion engine that recognizes the user's emotions to provide more appropriate design proposals.

[1174] System Overview

[1175] This system recognizes the user's emotions when they input their design ideas, and can generate design proposals that match the user's intentions and emotions. The design process includes everything from inputting the design idea to analyzing feedback and making improvements, and the user's emotions are also reflected in the process.

[1176] Program processing explanation

[1177] Idea input phase

[1178] The user accesses the web interface on the device and enters a design idea in the text input field, for example, "Simple and modern coffee cup design." This input text is sent to the server by clicking the "Submit" button.

[1179] Emotion Recognition Phase

[1180] The server receives and stores the text data sent by the user. It then analyzes the emotions of the input text using an emotion engine. For example, emotions such as "joy," "expectation," and "excitement" are recognized.

[1181] Input data parsing phase

[1182] The server passes the received text to the emotion engine and the natural language processing engine at the same time, and extracts and analyzes important keywords. For example, keywords such as "simple," "modern," and "coffee cup" are extracted.

[1183] Design proposal generation phase

[1184] The server sends a request to the image generation AI based on the analyzed keywords and emotional information. The request includes the keywords and parameters that reflect the user's emotions. The image generation AI then generates various design proposals based on these.

[1185] Design proposal provision phase

[1186] The server sends the generated design proposals to the user's device and provides them to the user via a web interface, where the user can view them, rate them, and provide feedback.

[1187] Evaluation and Feedback Phase

[1188] Users can provide feedback on the displayed design proposals in the form of star ratings and comments. For example, they can input specific suggestions for improvement, such as "I'd like the colors to be a little brighter" or "I'd like the curves to be smoother." The feedback is then sent back to the server.

[1189] Feedback analysis and refinement phase

[1190] The server passes the feedback sent by the user to the emotion engine, which analyzes the feedback's emotions. It then passes it to the natural language processing engine for analysis and extracts important improvements. Based on the extracted improvements and emotion information, the image generation AI is again invoked to generate an improved design proposal.

[1191] Specific examples

[1192] Example: Emotion Recognition and Design Improvement

[1193] The user inputs a "simple, modern coffee cup design," and simultaneously recognizes "expectations." The server analyzes keywords such as "simple," "modern," and "coffee cup," adds "expectations" as emotional information, and sends it to the image generation AI. The AI ​​generates a modern design that reflects expectations and provides it to the user's device.

[1194] The user looks at the proposed design and gives feedback such as "I'd like the colors to be a little brighter," which recognizes the user's dissatisfaction with the depiction. The server reanalyzes this feedback and emotional information to generate a new improvement proposal. The new design proposal is then provided to the user's device again, and the user continues to evaluate and provide feedback on it.

[1195] In this way, the system of the present invention can quickly provide more satisfying design proposals by reflecting the user's feelings in the design process.

[1196] The processing flow will be explained below.

[1197] Step 1:

[1198] The user accesses the terminal's web interface and logs in. At this point, the user is authenticated.

[1199] Step 2:

[1200] The user enters a design idea, such as "simple and modern coffee cup design," into a text input field.

[1201] Step 3:

[1202] The user clicks the "Submit" button to send the entered text to the server.

[1203] Step 4:

[1204] The server receives the text data sent by the user and temporarily stores it in a database.

[1205] Step 5:

[1206] The server sends the text data to the emotion engine, which analyzes the emotions in the text. As a result, emotions such as "joy" and "expectation" are recognized.

[1207] Step 6:

[1208] The server simultaneously sends the text data to a natural language processing (NLP) engine to extract important keywords, such as "simple," "modern," and "coffee cup."

[1209] Step 7:

[1210] The server creates a request to the image generation AI based on the emotion recognition results and the analyzed keywords. The request includes the extracted keywords and emotion information.

[1211] Step 8:

[1212] The image generation AI receives a request and generates multiple design proposals based on the specified keywords and emotional information, for example, a brightly colored coffee cup design that reflects a sense of anticipation.

[1213] Step 9:

[1214] The image generation AI sends the generated design proposal to the server as an image file.

[1215] Step 10:

[1216] The server sends the design proposal received from the image generation AI to the user's device.

[1217] Step 11:

[1218] The terminal updates the web interface to allow the user to view the received design proposals.

[1219] Step 12:

[1220] The user checks the displayed design proposal and gives an evaluation and feedback. For example, the user can input feedback such as "I would like the colors to be a little brighter."

[1221] Step 13:

[1222] The user sends feedback to the server.

[1223] Step 14:

[1224] The server stores the feedback received from the user and passes it back to the emotion engine for emotion analysis. For example, emotions such as "dissatisfaction" and "request for improvement" are recognized.

[1225] Step 15:

[1226] The server also passes the feedback to a natural language processing (NLP) engine to extract key improvements, such as "make the colors brighter."

[1227] Step 16:

[1228] The server creates a new request based on the extracted improvements and emotion information and sends it to the image generation AI.

[1229] Step 17:

[1230] The image generation AI generates improved design ideas based on new requests, such as a brighter colored coffee cup design.

[1231] Step 18:

[1232] The image generation AI sends the improved design proposal to the server as an image file.

[1233] Step 19:

[1234] The server transmits the improved design proposal to the user's terminal.

[1235] Step 20:

[1236] The terminal displays the improved design proposal for the user to confirm.

[1237] By repeating this cycle, users can arrive at a design proposal that best suits their needs and emotions.

[1238] Example 2

[1239] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1240] Conventional design generation systems have had the problem of difficulty in generating design proposals that fully reflect the user's emotions and intentions. Another issue is the inefficient process of properly analyzing user feedback and reflecting it in design proposals. Our goal is to solve these problems and quickly provide design proposals that will provide greater user satisfaction.

[1241] 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.

[1242] In this invention, the server includes: means for a user to input design ideas in text format; means for receiving the input text data and analyzing the emotion of the input text using an emotion recognition engine; means for analyzing the input content using a natural language processing engine together with the emotion information analyzed by the server; means for calling an image generation model based on the keywords and emotion information analyzed by the server to generate various design proposals; means for transmitting the generated design proposals to the user's terminal and providing them; means for the user to evaluate the provided design proposals and input feedback; and means for the server to reanalyze the user's feedback using the emotion recognition engine and natural language processing engine to generate improved design proposals. This makes it possible to quickly generate design proposals that reflect the user's emotions and intentions and to efficiently analyze user feedback and reflect it in the design proposals.

[1243] "User" refers to a person or organization that inputs design ideas and evaluates and provides feedback on the generated design proposals.

[1244] A "server" refers to a central processing unit that receives information from users and performs processes such as data analysis and image generation.

[1245] "Text data" refers to design ideas in the form of text entered by the user.

[1246] An "emotion recognition engine" is a software engine for analyzing the emotions in input text data.

[1247] A "natural language processing engine" is a software engine for extracting and analyzing keywords from input text data.

[1248] An "image generation model" refers to an artificial intelligence engine that generates design proposals based on input keywords and emotional information.

[1249] "Design proposals" are various design options generated by the image generation model.

[1250] "Feedback" refers to the evaluation and improvement requests made by the user regarding the generated design proposal.

[1251] "Emotion information" refers to the user's emotion data analyzed by an emotion recognition engine.

[1252] "Keywords" refer to words or phrases that are central to a design idea, extracted by a natural language processing engine.

[1253] This invention relates to a system in which a user inputs a design idea in text format, and an image generation model generates various design proposals based on the idea. Furthermore, this system combines an emotion recognition engine that recognizes the user's emotions, providing more appropriate design proposals.

[1254] First, a user accesses the device's web interface and enters a design idea into the text input field. For example, they might enter "Simple and modern coffee cup design." The input text is sent to the server by clicking the "Submit" button. When the user clicks the submit button, the input text is sent to the server using asynchronous communication (Ajax).

[1255] The server receives and stores the text data sent by the user. The stored text data is then passed to an emotion recognition engine (e.g., IBM Watson or Microsoft Text Analytics) to analyze the emotion of the input text. The emotion recognition engine analyzes the text data and extracts emotions such as "happiness," "anticipation," and "excitement." The emotion is also output as a numerical score, which is used to generate design proposals.

[1256] The server then analyzes the text data received from the user using a natural language processing engine (e.g., Google Cloud Natural Language API) while simultaneously recognizing emotions. Specifically, it extracts important keywords (e.g., "simple," "modern," "coffee cup") from the text. This identifies the core elements related to the design.

[1257] Next, the server sends the extracted keywords and emotional information as a request to an image generation model (such as OpenAI's DALL-E or Midjourney). The request includes the user's emotional information and keywords. The image generation model generates various design proposals based on this information. Specifically, the generative model incorporates the emotional information into its parameters and generates several design proposals.

[1258] The generated design proposals are sent from the server to the user's device and displayed on a web interface. The user evaluates the provided design proposals and provides feedback in the form of star ratings and comments. The feedback is often specific, such as "I wish the colors were a little brighter" or "I wish the curves were smoother." The user clicks the "Submit Feedback" button to send the feedback to the server.

[1259] The server passes the feedback received from the user back to the emotion recognition engine, which analyzes the emotions in the feedback. The feedback is then passed to the natural language processing engine for analysis and key improvements are extracted. Based on the extracted improvements and emotion information, the image generation model is invoked again to generate new design proposals. The new design proposals are then provided to the user's device, where the user can continue to reassess and provide feedback.

[1260] Specific examples

[1261] The user inputs a "simple, modern coffee cup design," and simultaneously recognizes "expectation." The server sends the keywords "simple," "modern," and "coffee cup" along with the emotion score for "expectation" to the image generation model. The model generates a modern design that reflects the expectation and provides it to the user's device.

[1262] The user looks at the proposed design and gives feedback such as "I'd like the colors to be a little brighter," recognizing their "dissatisfaction with the depiction." The server reanalyzes this feedback and emotional information, and calls the image generation model again to generate an improved design. The new design is then provided to the user's device, and the user continues to evaluate and provide feedback.

[1263] Prompt Sentence Examples

[1264] "Enter a simple and modern coffee cup design with a sense of anticipation"

[1265] "When providing feedback to brighten the colors of a user's proposed design, dissatisfaction with the depiction is recognized."

[1266] In this way, the system of the present invention is capable of quickly generating design proposals that reflect the user's feelings and intentions, and efficiently analyzing user feedback and reflecting it in the design proposals.

[1267] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1268] Step 1: Idea input phase

[1269] The user accesses the web interface on the device and enters a design idea into the text input field. For example, they enter "Simple and modern coffee cup design." This input text is sent to the server by clicking the "Submit" button.

[1270] Input: Text data of design ideas entered by the user.

[1271] Output: The text data sent to the server.

[1272] Step 2: Text data reception phase

[1273] The server saves the received text data, checks the format of the received data, and performs initial processing to prepare it for passing to the emotion recognition engine.

[1274] Input: Text data sent by the user.

[1275] Output: The saved text data.

[1276] Step 3: Emotion Recognition Phase

[1277] The server passes the saved text data to an emotion recognition engine, which analyzes the emotions in the input text. This analysis extracts emotions such as "happiness," "anticipation," and "excitement" as numerical scores. For example, IBM Watson or Microsoft Text Analytics are used.

[1278] Input: Saved text data.

[1279] Output: Parsed emotion information (emotion type and numeric score).

[1280] Step 4: Natural Language Processing Phase

[1281] The server analyzes the text data using a natural language processing engine (e.g., Google Cloud Natural Language API) while simultaneously recognizing emotions. Through the analysis, important keywords (e.g., "simple," "modern," and "coffee cup") are extracted from the text.

[1282] Input: Saved text data.

[1283] Output: Extracted keywords.

[1284] Step 5: Design proposal generation request phase

[1285] The server sends the analyzed keywords and emotion scores as a request to an image generation model (e.g., OpenAI's DALL-E or Midjourney). The request includes the user's emotion information and keywords. The image generation model generates various design proposals based on this information.

[1286] Input: Extracted keywords and sentiment information.

[1287] Output: Request submission and generated design proposal.

[1288] Step 6: Design proposal phase

[1289] The server sends the generated design proposal to the user's device, where it is displayed on a web interface for easy viewing by the user.

[1290] Input: Generated design proposal.

[1291] Output: A design proposal displayed on the user's device.

[1292] Step 7: Evaluation and feedback phase

[1293] Users provide feedback on the provided design proposals in the form of star ratings and comments, such as "I'd like the colors to be a little brighter" or "I'd like the curves to be smoother." The user then clicks the "Submit" button to send the feedback to the server.

[1294] Input: Feedback entered by the user.

[1295] Output: Feedback data sent to the server.

[1296] Step 8: Feedback analysis phase

[1297] The server then passes the received feedback to the emotion recognition engine to analyze the emotion of the feedback, and then passes the feedback content to the natural language processing engine to extract important improvements.

[1298] Input: Feedback data submitted by the user.

[1299] Output: Emotional information and important refinements.

[1300] Step 9: Improved design generation phase

[1301] The server then calls the image generation model again based on the extracted improvements and emotion information to generate an improved design proposal.

[1302] Input: Extracted refinements and sentiment information.

[1303] Output: Generated improved design proposals.

[1304] Step 10: Providing improved design proposals

[1305] The server sends the improved design proposal to the user's device and continues to receive evaluations and feedback.

[1306] Input: Improved design proposal.

[1307] Output: Improved design proposal displayed on the user's device.

[1308] (Application example 2)

[1309] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1310] Conventional design generation systems only generate design proposals based on text entered by the user, which means they cannot fully reflect the user's emotions and intentions. Furthermore, feedback on the generated design proposals is simply analyzed as information, without considering the user's emotions during refinement, limiting the ability to enhance user satisfaction. This problem is particularly important for brick-and-mortar stores, where the rapid and effective design of attractive displays and posters is essential.

[1311] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a user to input design ideas in text format; means for the server to receive the input text data and analyze the input content using a natural language processing engine; means for recognizing the emotion of the input text using an emotion engine; means for generating various design proposals by calling an image generation AI based on the keywords and emotion information analyzed by the server; means for the server to send and provide the generated design proposals to the user's terminal; means for the user to evaluate the provided design proposals and input feedback; and means for the server to analyze the user's feedback again using the emotion engine to generate improved design proposals. This makes it possible to generate and improve design proposals that reflect the user's emotions and intentions, thereby improving the efficiency of the design creation process and the customer experience, especially in physical stores.

[1312] "Users" refer to individuals and companies who use the system to input design ideas, evaluate the generated design proposals, and provide feedback.

[1313] "Text format" refers to a format in which design ideas are written in text.

[1314] "Design Idea" refers to text information that a user inputs to communicate specific design concepts and requirements.

[1315] "Server" refers to a computer system that receives, stores, analyzes, and processes input data.

[1316] A "natural language processing engine" refers to software or a program that analyzes input text data and extracts important keywords.

[1317] "Emotion engine" refers to software or a program for recognizing and analyzing the emotions of input text.

[1318] "Keywords" refer to important words or phrases extracted from text data.

[1319] "Emotion information" refers to the user's emotion data analyzed by the emotion engine of the input text.

[1320] "Image generation AI" refers to artificial intelligence that generates design proposals based on keywords and emotional information.

[1321] "Design proposal" refers to a specific design proposal generated by image generation AI.

[1322] "Terminal" refers to the device (e.g., smartphone, PC) used by a user to input design ideas and display generated design proposals.

[1323] "Feedback" refers to the evaluation and improvement requests made by the user regarding the generated design proposal.

[1324] This invention relates to a system for generating and improving design proposals for digital signage deployed in brick-and-mortar stores. This system allows users (e.g., store managers or design teams) to input design ideas in text format, and generates and provides a variety of design proposals based on the ideas and the user's emotional information.

[1325] First, a user uses the smartphone app "Store Design Manager" to input a design idea into a text box. For example, a specific idea such as "I want a bright and exciting design created for a new product campaign" can be input. The emotion engine then analyzes the user's emotions from the input text and obtains emotional information such as "expectation" and "excitement."

[1326] The input text data and emotion data are then sent to a server, which uses a natural language processing engine to analyze the input text data and extract key keywords (e.g., "new product campaign," "cheerful," "exciting"). At the same time, the analysis results from the emotion engine are also recorded.

[1327] The server requests the image generation AI to generate design proposals based on the analyzed keywords and emotional information. The image generation AI generates a variety of design proposals based on this input data. This results in multiple design proposals that reflect the user's wishes and emotions. These design proposals are provided to the user's smartphone via the server.

[1328] The user views the provided design proposals and provides evaluation and feedback. For example, they can input specific requests for improvement, such as "I would like the colors to be more vivid." This feedback is also analyzed by the emotion engine, which recognizes the emotion contained in the feedback (for example, "hope for improvement"). Based on the results of this analysis, the server again requests the image generation AI to generate an improved design proposal.

[1329] Through this process, users can efficiently generate and refine design proposals that reflect their own emotions and intentions, which will streamline the design creation process, especially in physical stores, and improve the customer experience.

[1330] Example prompt sentence:

[1331] TXT

[1332] Keywords: new product campaign, bright, exciting

[1333] Emotions: anticipation, excitement

[1334] Generate design proposals.

[1335] To achieve these steps, the following technologies are specifically used:

[1336] Natural language processing engine: A technology for extracting key keywords from text. Examples include open source NLP libraries.

[1337] Emotion engine: A technology for analyzing the emotion of input text. For example, an emotion analysis API can be used.

[1338] Image generation AI: An AI model that generates images using keywords and emotional information. A specific example is an image generation algorithm that uses deep learning.

[1339] This system configuration makes it possible to efficiently generate designs for physical stores and provide attractive design proposals that reflect the user's intentions and emotions.

[1340] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1341] Step 1:

[1342] A user launches a smartphone app and enters a design idea into a text input box. For example, they might enter, "I want you to create a bright and exciting design for a new product campaign." This input is sent to the server, which then receives the text data.

[1343] Step 2:

[1344] The server passes the received text data to a natural language processing engine and analyzes the keywords. For example, keywords such as "new product campaign," "bright," and "exciting" are extracted. The results of this analysis are used as input data for the next step of processing.

[1345] Step 3:

[1346] At the same time, the server calls the emotion engine and analyzes the user's emotions from the input text. For example, emotion information such as "expectation" and "excitement" is obtained. This emotion information is also used as input data for the next process.

[1347] Step 4:

[1348] The server requests the image generation AI to generate design proposals based on the keywords and emotional information obtained in steps 2 and 3. The image generation AI receives the keywords and emotional information as prompts and generates various design proposals. The generated design proposals are returned to the server.

[1349] Step 5:

[1350] The server then sends the generated design proposals to the user's smartphone, where the user can view and evaluate them using the app.

[1351] Step 6:

[1352] The user inputs feedback based on their evaluation, for example, a specific request such as "I want the colors to be more vivid." Along with this feedback, the user's new emotion (e.g., "hope for improvement") is also analyzed by the emotion engine.

[1353] Step 7:

[1354] The server then requests the image generation AI to generate an improved design proposal based on the user's feedback and its analysis. The image generation AI generates an improved design proposal according to the new prompts. The server receives the new design proposal and sends it to the user's device.

[1355] Through specific actions, data processing, and calculations at each step, the system is able to quickly generate design proposals that are in line with the user's intentions and emotions, and continue to improve them.

[1356] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1357] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1358] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1359] [Fourth embodiment]

[1360] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1361] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1362] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1363] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1364] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1365] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1366] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1367] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1368] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1369] 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.

[1370] 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.

[1371] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1372] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1373] The present invention relates to a system for generating design ideas quickly and diversifiedly and for realizing an effective design process. The system includes components of a user, a server, and a terminal.

[1374] System Overview

[1375] In this system, users input their design ideas in text format using a device, and the server receives and analyzes the ideas and generates design proposals using image generation AI. The generated design proposals are then provided back to the device, where users can evaluate and provide feedback. The feedback is analyzed by the server and used to generate new design proposals.

[1376] Program processing explanation

[1377] Idea input phase

[1378] The user accesses the web interface on the device and enters a design idea in the text input field, for example, "Simple and modern coffee cup design." This input text is sent to the server by clicking the "Submit" button.

[1379] Input data parsing phase

[1380] The server passes the text data received from the user to a natural language processing engine, which extracts and analyzes important keywords, such as "simple," "modern," and "coffee cup."

[1381] Design proposal generation phase

[1382] The server calls an image generation AI based on the analyzed keywords to generate various design proposals. The image generation AI creates multiple design proposals taking into account the specified keywords. These design proposals are sent back to the server in image format.

[1383] Design proposal provision phase

[1384] The server sends the generated design proposals to the user's device and provides them to the user via a web interface, where the user can view them, rate them, and provide feedback.

[1385] Evaluation and Feedback Phase

[1386] The user can provide feedback on the displayed design proposals in the form of star ratings and comments. For example, they can enter specific suggestions for improvement, such as "I'd like the colors to be a little brighter." The feedback is then sent back to the server.

[1387] Feedback analysis and refinement phase

[1388] The server then passes the user's feedback to a natural language processing engine for reanalysis, and then calls an image generation AI to generate an improved design proposal based on the results. This creates an improved design proposal and sends it back to the user's device.

[1389] Specific examples

[1390] Example: Coffee cup design

[1391] The user enters "simple, modern coffee cup design," and the server extracts the keywords "simple," "modern," and "coffee cup." The image generation AI generates multiple coffee cup designs that combine simple shapes with modern colors. These design proposals are provided to the user's device, and the user provides feedback, requesting that the colors be a little brighter. The server analyzes this feedback, and the image generation AI generates improved design proposals again. This process brings the design closer to the user's desired design.

[1392] In this way, the system of the present invention streamlines the design process, enabling the rapid generation and provision of diverse ideas. It also contributes to improving the quality of design proposals by quickly improving the system based on user feedback.

[1393] The processing flow will be explained below.

[1394] Step 1:

[1395] The user accesses the device's web interface and logs in, at which point the user account is authenticated.

[1396] Step 2:

[1397] The user enters a design idea in the text input field, for example, "simple modern coffee cup design."

[1398] Step 3:

[1399] The user clicks the "Submit" button to send the entered text to the server.

[1400] Step 4:

[1401] The server receives the text data sent by the user and temporarily stores it in an internal database.

[1402] Step 5:

[1403] The server passes the received text to a natural language processing (NLP) engine, which analyzes it for important keywords, such as "simple," "modern," and "coffee cup."

[1404] Step 6:

[1405] The server uses the analyzed keywords to send a request to the image generation AI, which includes the extracted keywords and additional parameters (color, shape, style, etc.).

[1406] Step 7:

[1407] The image generation AI generates various design ideas based on the request it receives, including a coffee cup design with simple shapes and modern colors.

[1408] Step 8:

[1409] The image generation AI sends the generated design proposal to the server as an image file (e.g., JPEG, PNG).

[1410] Step 9:

[1411] The server sends the design proposal received from the image generation AI to the user's device.

[1412] Step 10:

[1413] The terminal updates the web interface to display the design proposal received from the server.

[1414] Step 11:

[1415] The user checks the displayed design proposal and gives an evaluation and feedback, for example, by inputting a comment such as "I wish the colors were a little brighter."

[1416] Step 12:

[1417] The user sends feedback to the server.

[1418] Step 13:

[1419] The server receives the feedback sent by the user and passes it to the natural language processing engine for analysis, extracting important improvements (e.g., "make the colors brighter").

[1420] Step 14:

[1421] Based on the extracted improvements, the server sends a request with new requirements to the image generation AI.

[1422] Step 15:

[1423] The image generation AI generates improved design proposals based on new requests and sends them back to the server.

[1424] Step 16:

[1425] The server then transmits the improved design proposal back to the user's terminal.

[1426] Step 17:

[1427] The terminal displays the improved design proposal for the user to confirm.

[1428] By repeating this process, the user can successively obtain design proposals that are closest to their ideal.

[1429] Example 1

[1430] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1431] In the conventional design process, it takes a lot of time and effort to convert user ideas into concrete design proposals. Furthermore, there is no efficient method to quickly incorporate user feedback and generate improved designs. As a result, it is difficult to improve the quality of designs and quickly generate diverse ideas.

[1432] 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.

[1433] In this invention, the server includes a means for a user to input design ideas in text format, a means for the server to receive the input text data and analyze the input content using a natural language processing engine, and a means for generating various design proposals by calling an image generation AI model based on the keywords analyzed by the server. This makes it possible to quickly materialize user ideas into various design proposals and quickly improve the designs based on user feedback.

[1434] "User" refers to a person who uses the system to input design ideas and provide ratings and feedback.

[1435] "Server" refers to the central control device that receives and analyzes design ideas and generates and provides design proposals using an image generation AI model.

[1436] "Terminal" refers to an electronic device that allows a user to input design ideas, view design proposals from the server, and provide feedback.

[1437] "Design Ideas" refers to design concepts and requests entered by users in text format.

[1438] A "natural language processing engine" refers to a software tool that analyzes input text data and extracts important keywords.

[1439] "Keywords" refer to important words and phrases extracted from design ideas by natural language processing engines.

[1440] An "image generation AI model" refers to an artificial intelligence that generates a variety of design ideas based on specified keywords.

[1441] "Design proposal" refers to a specific design image generated by an image generation AI model.

[1442] "Feedback" refers to comments that users make on the provided design proposal, such as evaluations and suggestions for improvement.

[1443] A "prompt sentence" refers to a text sentence containing keywords that is sent to an image generation AI model.

[1444] This invention is a system that uses an image generation AI model to quickly generate a variety of design proposals based on design ideas input by the user and provides them to the user. To implement this system, a user, a server, and a terminal are required, each of which plays a specific role. A specific implementation method for this system is described below.

[1445] Overall system configuration

[1446] The system of the present invention comprises the following elements:

[1447] 1. A means for users to input their design ideas

[1448] 2. Server input data reception and analysis method

[1449] 3. Method for generating design proposals using image generation AI models

[1450] 4. Means of providing design proposals to devices

[1451] 5. User Rating and Feedback

[1452] 6. Feedback analysis and improved design generation method

[1453] Hardware and software used

[1454] Device: An electronic device (e.g., PC, smartphone, tablet) on which a user inputs design ideas and reviews the generated design proposals.

[1455] Server: A central control unit that receives and analyzes input data from users and generates design proposals using image generation AI models.

[1456] Natural language processing engine: Software for extracting important keywords from input text data (e.g., Google Cloud Natural Language API).

[1457] Image generation AI model: An artificial intelligence that generates various design ideas based on specified keywords (e.g., OpenAI's DALL-E).

[1458] Specific explanation of the system's operation

[1459] Enter your design ideas

[1460] The user accesses the device's web interface, enters a design idea into the text input field, for example, "Simple and modern coffee cup design," and then clicks the "Submit" button, which sends the input text to the server as an HTTP POST request.

[1461] Text data analysis

[1462] The server passes the received text data to a natural language processing engine, which analyzes and extracts important keywords, such as "simple," "modern," and "coffee cup."

[1463] Generate design ideas

[1464] The server generates a prompt sentence based on the analyzed keywords and sends it to the image generation AI model. The image generation AI model generates multiple design proposals based on the specified prompt sentence and returns them to the server as image data. An example of a prompt sentence is a phrase such as "a simple, modern coffee cup."

[1465] Providing design ideas

[1466] The server sends the generated design proposals to the user's terminal and makes them available for viewing on a web interface, allowing the user to check the generated design proposals and provide evaluation and feedback.

[1467] Feedback input and analysis

[1468] The user can then enter specific evaluations and feedback for the displayed design proposals. For example, they can enter comments such as "I'd like the colors to be a little brighter." This feedback data is then sent to the server as an HTTP POST request.

[1469] Design improvements based on feedback

[1470] The server then passes the user feedback data to the natural language processing engine for analysis, extracting new keywords and areas for improvement. Based on the results, it again sends a request to the image generation AI model to generate an improved design proposal. This improved design proposal is then provided back to the user's device.

[1471] Implementing specific examples

[1472] For example, suppose a user enters "simple, modern coffee cup design." The server extracts the keywords "simple," "modern," and "coffee cup" from this text and sends them to an image generation AI model (e.g., OpenAI's DALL-E). This AI model generates various design proposals taking these keywords into account and sends them back to the server. The user reviews these design proposals and provides feedback such as "I wish the colors were a little brighter." The server then reanalyzes this feedback and generates and provides improved design proposals.

[1473] This system not only allows us to respond quickly to user requests, but also makes it possible to efficiently generate and improve a variety of design proposals.

[1474] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1475] Step 1:

[1476] Enter your ideas

[1477] A user accesses the device's web interface, enters a design idea into the text input field, for example, "Simple and modern coffee cup design," and then clicks the "Submit" button, which sends the input text to the server as an HTTP POST request.

[1478] Input: User-entered text design ideas

[1479] Output: The entered design idea is sent to the server as an HTTP POST request.

[1480] Step 2:

[1481] Receiving data

[1482] The server receives an HTTP POST request sent by the user, with the body of the request containing the user's input text design ideas.

[1483] Input: Text data sent as an HTTP POST request

[1484] Output: Text design ideas received by the server

[1485] Step 3:

[1486] Text data analysis

[1487] The server passes the received text design ideas to a natural language processing engine, which analyzes and extracts important keywords. The natural language processing engine (e.g., Google Cloud Natural Language API) extracts keywords such as "simple," "modern," and "coffee cup" from the text.

[1488] Input: Received text design ideas

[1489] Output: Extracted keywords

[1490] Step 4:

[1491] Generate prompt statement

[1492] The server generates a prompt for the image generation AI model based on the extracted keywords, such as "A simple and modern coffee cup."

[1493] Input: Extracted keywords

[1494] Output: Generated prompt statement

[1495] Step 5:

[1496] Generate design ideas

[1497] The server sends the generated prompt to an image generation AI model (e.g., OpenAI's DALL-E) requesting the generation of design proposals. The image generation AI model generates multiple design proposals based on the specified prompt and returns these design proposals to the server as image data.

[1498] Input: Generated prompt text

[1499] Output: Multiple generated design proposals (image data)

[1500] Step 6:

[1501] Providing design ideas

[1502] The server receives the generated design proposals and sends them to the user's device. The server then generates an HTTP response containing image data and sends it to the user's device. The user can view these design proposals through the device's web interface.

[1503] Input: Generated design proposal (image data)

[1504] Output: Providing design proposals to the user's device

[1505] Step 7:

[1506] Enter your rating and feedback

[1507] The user provides feedback on the displayed design proposal in the form of a star rating or a comment. For example, they can enter specific suggestions for improvement, such as "I'd like the colors to be a little brighter." The feedback data is then sent to the server again as an HTTP POST request.

[1508] Input: User feedback (ratings and comments)

[1509] Output: Feedback data is sent to the server as an HTTP POST request

[1510] Step 8:

[1511] Receiving and Analyzing Feedback

[1512] The server receives the feedback data sent by the user and passes the received feedback text to the natural language processing engine again to analyze and extract points for improvement.

[1513] Input: Received feedback (ratings and comments)

[1514] Output: Extracted improvement points

[1515] Step 9:

[1516] Generation of improved design proposals

[1517] The server then sends a prompt to the image generation AI model again based on the improvement points extracted from the feedback, requesting the generation of an improved design proposal. The image generation AI model then generates a new design proposal taking into account the improvement points and sends it back to the server.

[1518] Input: Extracted refinements

[1519] Output: Generated improved design proposal (image data)

[1520] Step 10:

[1521] Providing improved design proposals

[1522] The server transmits the improved design proposal back to the user's terminal, and the user can again evaluate and provide feedback on the improved design.

[1523] Input: Generated improved design proposal (image data)

[1524] Output: Providing improved design proposals to the user's device

[1525] In this way, the system can quickly and efficiently realize users' design ideas and continuously improve them based on user feedback.

[1526] (Application example 1)

[1527] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1528] Conventional design generation and evaluation systems make it difficult for users to quickly and accurately create customized designs with unique styles and functions. Furthermore, especially for electronic payment card designs, users are often restricted by existing templates, making it difficult to create original designs that reflect the user's individuality.

[1529] 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.

[1530] In this invention, the server includes means for a user to input design ideas in text format, means for the server to receive the input text data and analyze the input content using a natural language processing engine, means for calling an image generation AI based on the analyzed keywords to generate various design proposals, means for generating improved design proposals based on feedback, and means for the user to generate their own original electronic payment card design, thereby enabling users to quickly and accurately create customized electronic payment cards that reflect their individuality.

[1531] "User" refers to a person who uses the system to input design ideas and provide feedback.

[1532] "Text format" refers to a format of information expressed in characters that is used by users to input design ideas.

[1533] "Design idea" refers to text information that includes concepts and features related to a design that a user wants to create.

[1534] "Server" refers to a central computer system that receives, analyzes, processes, and transmits data to user terminals.

[1535] "Natural language processing engine" refers to a technical device that analyzes input text and extracts important keywords.

[1536] "Image generation AI" refers to artificial intelligence that generates various design ideas based on input keywords.

[1537] "Design proposal" refers to a specific design image or concept proposal generated by image generation AI.

[1538] "User's device" refers to the device that a user uses to input design ideas and receive and evaluate generated design proposals.

[1539] "Feedback" refers to the evaluation and suggestions for improvement that a user provides for a generated design proposal.

[1540] "Improved design proposal" refers to an improved design generated by the image generation AI based on user feedback.

[1541] An "electronic payment card" refers to a card-type medium used for electronic payments, which is a payment method that includes the user's personal information.

[1542] The following describes an embodiment of the present invention.

[1543] Overall system overview

[1544] The present invention is a system that uses image generation AI to generate design proposals based on design ideas entered by users in text format, and includes a process for evaluating and improving the proposed designs. It is particularly applicable to generating custom designs for electronic payment cards.

[1545] Hardware and software used

[1546] The system includes the following main components:

[1547] User device: A device such as a smartphone or computer on which users input design ideas and receive and evaluate the generated design proposals.

[1548] Server: A central computer system that receives, analyzes, and generates and refines data.

[1549] Natural language processing engine: Analyzes input text data and extracts important keywords (e.g., spaCy).

[1550] Image generation AI model: An artificial intelligence that generates design ideas based on input keywords. (Example: DALL-E 2)

[1551] Program processing explanation

[1552] 1. Design idea input phase

[1553] The user uses the device's interface to input design ideas in text format, for example, "I want a simple and elegant design."

[1554] 2. Input data analysis phase

[1555] The server passes the text data received from the user to a natural language processing engine and extracts keywords, such as "simple" and "elegant."

[1556] 3. Design proposal generation phase

[1557] The server then calls an image generation AI model based on the analyzed keywords to generate multiple design proposals, which are then sent back to the server.

[1558] 4. Design proposal provision phase

[1559] The server transmits the generated design proposal to the user's terminal and provides it to the user, who can view it, evaluate it, and provide feedback.

[1560] 5. Evaluation and feedback phase

[1561] Users evaluate the provided design proposals and enter specific feedback, such as "I would like the background color to be brighter."

[1562] 6. Feedback analysis and refinement phase

[1563] The server then analyzes the user's feedback again using a natural language processing engine, calls the image generation AI model again, and generates an improved design proposal, which is then sent back to the user's device.

[1564] Specific examples

[1565] When a user inputs "I want a simple yet elegant design," the server extracts the keywords "simple" and "elegant," and uses an image generation AI model to generate a design proposal with a pale blue background and an elegant pattern. If the user provides feedback that "I would like the background color to be brighter," an improved design with a pale pink background is provided.

[1566] Prompt Sentence Examples

[1567] Input prompt:

[1568] "I want a simple and elegant design."

[1569] I want the background color to be brighter.

[1570] The system allows users to easily create and quickly refine personalized, customized electronic payment card designs.

[1571] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1572] Step 1:

[1573] Enter your design ideas

[1574] Users input their design ideas in text format on their device, such as a prompt statement like "I want a simple and elegant design" into the interface of their smartphone or computer, and click the "Submit" button.

[1575] Input: "I want a simple and elegant design"

[1576] Output: Text data sent to the server

[1577] Step 2:

[1578] Receiving input data

[1579] The server receives the text data sent from the user terminal, temporarily stores the received text data, and passes it on to the next analysis step.

[1580] Input: Text data submitted by the user

[1581] Output: Text data stored on the server

[1582] Step 3:

[1583] Analyzing input data

[1584] The server passes the received text data to a natural language processing engine (e.g., spaCy) to extract important keywords, such as "simple" and "elegant."

[1585] Input: Text data stored on the server

[1586] Output: Extracted keywords ("simple", "elegant")

[1587] Step 4:

[1588] Generate design ideas

[1589] The server calls an image generation AI model (e.g., DALL-E 2) based on the extracted keywords and generates various design proposals. The image generation AI model creates multiple design proposals taking into account the specified keywords.

[1590] Input: Extracted keywords

[1591] Output: Generated multiple design proposals (image files)

[1592] Step 5:

[1593] Submit your design proposal

[1594] The server sends the generated design proposals to the user's terminal, which receives them and displays them on the interface.

[1595] Input: Generated multiple design proposals (image files)

[1596] Output: Design proposal sent to user device

[1597] Step 6:

[1598] Evaluation and feedback of design proposals

[1599] Users can evaluate the displayed design proposals and enter feedback, such as specific suggestions for improvement like "I'd like the background color to be brighter," and submit the proposal.

[1600] Input: The design proposal displayed by the user

[1601] Output: User feedback (text)

[1602] Step 7:

[1603] Receiving and analyzing feedback

[1604] The server receives the feedback from the user and analyzes it again using a natural language processing engine, which extracts keywords based on the content of the feedback.

[1605] Input: User feedback (text)

[1606] Output: Extracted feedback keywords (e.g., "brighten the background color")

[1607] Step 8:

[1608] Generation of improved design proposals

[1609] The server then calls the image generation AI model again based on the analyzed feedback keywords to generate an improved design proposal, resulting in a new design proposal that reflects the user's feedback.

[1610] Input: Extracted feedback keywords

[1611] Output: Improved design proposal (image file)

[1612] Step 9:

[1613] Providing improved design proposals

[1614] The server then sends the improved design proposal back to the user's device, where the user can receive it and provide feedback again.

[1615] Input: Improved design proposal (image file)

[1616] Output: Improved design proposal provided to user device

[1617] By repeating this process, the user can create a satisfactory electronic payment card design.

[1618] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1619] This invention relates to a system in which designers input design ideas in text format, and an image generation AI generates various design proposals based on those ideas. Furthermore, this system combines an emotion engine that recognizes the user's emotions to provide more appropriate design proposals.

[1620] System Overview

[1621] This system recognizes the user's emotions when they input their design ideas, and can generate design proposals that match the user's intentions and emotions. The design process includes everything from inputting the design idea to analyzing feedback and making improvements, and the user's emotions are also reflected in the process.

[1622] Program processing explanation

[1623] Idea input phase

[1624] The user accesses the web interface on the device and enters a design idea in the text input field, for example, "Simple and modern coffee cup design." This input text is sent to the server by clicking the "Submit" button.

[1625] Emotion Recognition Phase

[1626] The server receives and stores the text data sent by the user. It then analyzes the emotions of the input text using an emotion engine. For example, emotions such as "joy," "expectation," and "excitement" are recognized.

[1627] Input data parsing phase

[1628] The server passes the received text to the emotion engine and the natural language processing engine at the same time, and extracts and analyzes important keywords. For example, keywords such as "simple," "modern," and "coffee cup" are extracted.

[1629] Design proposal generation phase

[1630] The server sends a request to the image generation AI based on the analyzed keywords and emotional information. The request includes the keywords and parameters that reflect the user's emotions. The image generation AI then generates various design proposals based on these.

[1631] Design proposal provision phase

[1632] The server sends the generated design proposals to the user's device and provides them to the user via a web interface, where the user can view them, rate them, and provide feedback.

[1633] Evaluation and Feedback Phase

[1634] Users can provide feedback on the displayed design proposals in the form of star ratings and comments. For example, they can input specific suggestions for improvement, such as "I'd like the colors to be a little brighter" or "I'd like the curves to be smoother." The feedback is then sent back to the server.

[1635] Feedback analysis and refinement phase

[1636] The server passes the feedback sent by the user to the emotion engine, which analyzes the feedback's emotions. It then passes it to the natural language processing engine for analysis and extracts important improvements. Based on the extracted improvements and emotion information, the image generation AI is again invoked to generate an improved design proposal.

[1637] Specific examples

[1638] Example: Emotion Recognition and Design Improvement

[1639] The user inputs a "simple, modern coffee cup design," and simultaneously recognizes "expectations." The server analyzes keywords such as "simple," "modern," and "coffee cup," adds "expectations" as emotional information, and sends it to the image generation AI. The AI ​​generates a modern design that reflects expectations and provides it to the user's device.

[1640] The user looks at the proposed design and gives feedback such as "I'd like the colors to be a little brighter," which recognizes the user's dissatisfaction with the depiction. The server reanalyzes this feedback and emotional information to generate a new improvement proposal. The new design proposal is then provided to the user's device again, and the user continues to evaluate and provide feedback on it.

[1641] In this way, the system of the present invention can quickly provide more satisfying design proposals by reflecting the user's feelings in the design process.

[1642] The processing flow will be explained below.

[1643] Step 1:

[1644] The user accesses the terminal's web interface and logs in. At this point, the user is authenticated.

[1645] Step 2:

[1646] The user enters a design idea, such as "simple and modern coffee cup design," into a text input field.

[1647] Step 3:

[1648] The user clicks the "Submit" button to send the entered text to the server.

[1649] Step 4:

[1650] The server receives the text data sent by the user and temporarily stores it in a database.

[1651] Step 5:

[1652] The server sends the text data to the emotion engine, which analyzes the emotions in the text. As a result, emotions such as "joy" and "expectation" are recognized.

[1653] Step 6:

[1654] The server simultaneously sends the text data to a natural language processing (NLP) engine to extract important keywords, such as "simple," "modern," and "coffee cup."

[1655] Step 7:

[1656] The server creates a request to the image generation AI based on the emotion recognition results and the analyzed keywords. The request includes the extracted keywords and emotion information.

[1657] Step 8:

[1658] The image generation AI receives a request and generates multiple design proposals based on the specified keywords and emotional information, for example, a brightly colored coffee cup design that reflects a sense of anticipation.

[1659] Step 9:

[1660] The image generation AI sends the generated design proposal to the server as an image file.

[1661] Step 10:

[1662] The server sends the design proposal received from the image generation AI to the user's device.

[1663] Step 11:

[1664] The terminal updates the web interface to allow the user to view the received design proposals.

[1665] Step 12:

[1666] The user checks the displayed design proposal and gives an evaluation and feedback. For example, the user can input feedback such as "I would like the colors to be a little brighter."

[1667] Step 13:

[1668] The user sends feedback to the server.

[1669] Step 14:

[1670] The server stores the feedback received from the user and passes it back to the emotion engine for emotion analysis. For example, emotions such as "dissatisfaction" and "request for improvement" are recognized.

[1671] Step 15:

[1672] The server also passes the feedback to a natural language processing (NLP) engine to extract key improvements, such as "make the colors brighter."

[1673] Step 16:

[1674] The server creates a new request based on the extracted improvements and emotion information and sends it to the image generation AI.

[1675] Step 17:

[1676] The image generation AI generates improved design ideas based on new requests, such as a brighter colored coffee cup design.

[1677] Step 18:

[1678] The image generation AI sends the improved design proposal to the server as an image file.

[1679] Step 19:

[1680] The server transmits the improved design proposal to the user's terminal.

[1681] Step 20:

[1682] The terminal displays the improved design proposal for the user to confirm.

[1683] By repeating this cycle, users can arrive at a design proposal that best suits their needs and emotions.

[1684] Example 2

[1685] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1686] Conventional design generation systems have had the problem of difficulty in generating design proposals that fully reflect the user's emotions and intentions. Another issue is the inefficient process of properly analyzing user feedback and reflecting it in design proposals. Our goal is to solve these problems and quickly provide design proposals that will provide greater user satisfaction.

[1687] 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.

[1688] In this invention, the server includes: means for a user to input design ideas in text format; means for receiving the input text data and analyzing the emotion of the input text using an emotion recognition engine; means for analyzing the input content using a natural language processing engine together with the emotion information analyzed by the server; means for calling an image generation model based on the keywords and emotion information analyzed by the server to generate various design proposals; means for transmitting the generated design proposals to the user's terminal and providing them; means for the user to evaluate the provided design proposals and input feedback; and means for the server to reanalyze the user's feedback using the emotion recognition engine and natural language processing engine to generate improved design proposals. This makes it possible to quickly generate design proposals that reflect the user's emotions and intentions and to efficiently analyze user feedback and reflect it in the design proposals.

[1689] "User" refers to a person or organization that inputs design ideas and evaluates and provides feedback on the generated design proposals.

[1690] A "server" refers to a central processing unit that receives information from users and performs processes such as data analysis and image generation.

[1691] "Text data" refers to design ideas in the form of text entered by the user.

[1692] An "emotion recognition engine" is a software engine for analyzing the emotions in input text data.

[1693] A "natural language processing engine" is a software engine for extracting and analyzing keywords from input text data.

[1694] An "image generation model" refers to an artificial intelligence engine that generates design proposals based on input keywords and emotional information.

[1695] "Design proposals" are various design options generated by the image generation model.

[1696] "Feedback" refers to the evaluation and improvement requests made by the user regarding the generated design proposal.

[1697] "Emotion information" refers to the user's emotion data analyzed by an emotion recognition engine.

[1698] "Keywords" refer to words or phrases that are central to a design idea, extracted by a natural language processing engine.

[1699] This invention relates to a system in which a user inputs a design idea in text format, and an image generation model generates various design proposals based on the idea. Furthermore, this system combines an emotion recognition engine that recognizes the user's emotions, providing more appropriate design proposals.

[1700] First, a user accesses the device's web interface and enters a design idea into the text input field. For example, they might enter "Simple and modern coffee cup design." The input text is sent to the server by clicking the "Submit" button. When the user clicks the submit button, the input text is sent to the server using asynchronous communication (Ajax).

[1701] The server receives and stores the text data sent by the user. The stored text data is then passed to an emotion recognition engine (e.g., IBM Watson or Microsoft Text Analytics) to analyze the emotion of the input text. The emotion recognition engine analyzes the text data and extracts emotions such as "happiness," "anticipation," and "excitement." The emotion is also output as a numerical score, which is used to generate design proposals.

[1702] The server then analyzes the text data received from the user using a natural language processing engine (e.g., Google Cloud Natural Language API) while simultaneously recognizing emotions. Specifically, it extracts important keywords (e.g., "simple," "modern," "coffee cup") from the text. This identifies the core elements related to the design.

[1703] Next, the server sends the extracted keywords and emotional information as a request to an image generation model (such as OpenAI's DALL-E or Midjourney). The request includes the user's emotional information and keywords. The image generation model generates various design proposals based on this information. Specifically, the generative model incorporates the emotional information into its parameters and generates several design proposals.

[1704] The generated design proposals are sent from the server to the user's device and displayed on a web interface. The user evaluates the provided design proposals and provides feedback in the form of star ratings and comments. The feedback is often specific, such as "I wish the colors were a little brighter" or "I wish the curves were smoother." The user clicks the "Submit Feedback" button to send the feedback to the server.

[1705] The server passes the feedback received from the user back to the emotion recognition engine, which analyzes the emotions in the feedback. The feedback is then passed to the natural language processing engine for analysis and key improvements are extracted. Based on the extracted improvements and emotion information, the image generation model is invoked again to generate new design proposals. The new design proposals are then provided to the user's device, where the user can continue to reassess and provide feedback.

[1706] Specific examples

[1707] The user inputs a "simple, modern coffee cup design," and simultaneously recognizes "expectation." The server sends the keywords "simple," "modern," and "coffee cup" along with the emotion score for "expectation" to the image generation model. The model generates a modern design that reflects the expectation and provides it to the user's device.

[1708] The user looks at the proposed design and gives feedback such as "I'd like the colors to be a little brighter," recognizing their "dissatisfaction with the depiction." The server reanalyzes this feedback and emotional information, and calls the image generation model again to generate an improved design. The new design is then provided to the user's device, and the user continues to evaluate and provide feedback.

[1709] Prompt Sentence Examples

[1710] "Enter a simple and modern coffee cup design with a sense of anticipation"

[1711] "When providing feedback to brighten the colors of a user's proposed design, dissatisfaction with the depiction is recognized."

[1712] In this way, the system of the present invention is capable of quickly generating design proposals that reflect the user's feelings and intentions, and efficiently analyzing user feedback and reflecting it in the design proposals.

[1713] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1714] Step 1: Idea input phase

[1715] The user accesses the web interface on the device and enters a design idea into the text input field. For example, they enter "Simple and modern coffee cup design." This input text is sent to the server by clicking the "Submit" button.

[1716] Input: Text data of design ideas entered by the user.

[1717] Output: The text data sent to the server.

[1718] Step 2: Text data reception phase

[1719] The server saves the received text data, checks the format of the received data, and performs initial processing to prepare it for passing to the emotion recognition engine.

[1720] Input: Text data sent by the user.

[1721] Output: The saved text data.

[1722] Step 3: Emotion Recognition Phase

[1723] The server passes the saved text data to an emotion recognition engine, which analyzes the emotions in the input text. This analysis extracts emotions such as "happiness," "anticipation," and "excitement" as numerical scores. For example, IBM Watson or Microsoft Text Analytics are used.

[1724] Input: Saved text data.

[1725] Output: Parsed emotion information (emotion type and numeric score).

[1726] Step 4: Natural Language Processing Phase

[1727] The server analyzes the text data using a natural language processing engine (e.g., Google Cloud Natural Language API) while simultaneously recognizing emotions. Through the analysis, important keywords (e.g., "simple," "modern," and "coffee cup") are extracted from the text.

[1728] Input: Saved text data.

[1729] Output: Extracted keywords.

[1730] Step 5: Design proposal generation request phase

[1731] The server sends the analyzed keywords and emotion scores as a request to an image generation model (e.g., OpenAI's DALL-E or Midjourney). The request includes the user's emotion information and keywords. The image generation model generates various design proposals based on this information.

[1732] Input: Extracted keywords and sentiment information.

[1733] Output: Request submission and generated design proposal.

[1734] Step 6: Design proposal phase

[1735] The server sends the generated design proposal to the user's device, where it is displayed on a web interface for easy viewing by the user.

[1736] Input: Generated design proposal.

[1737] Output: A design proposal displayed on the user's device.

[1738] Step 7: Evaluation and feedback phase

[1739] Users provide feedback on the provided design proposals in the form of star ratings and comments, such as "I'd like the colors to be a little brighter" or "I'd like the curves to be smoother." The user then clicks the "Submit" button to send the feedback to the server.

[1740] Input: Feedback entered by the user.

[1741] Output: Feedback data sent to the server.

[1742] Step 8: Feedback analysis phase

[1743] The server then passes the received feedback to the emotion recognition engine to analyze the emotion of the feedback, and then passes the feedback content to the natural language processing engine to extract important improvements.

[1744] Input: Feedback data submitted by the user.

[1745] Output: Emotional information and important refinements.

[1746] Step 9: Improved design generation phase

[1747] The server then calls the image generation model again based on the extracted improvements and emotion information to generate an improved design proposal.

[1748] Input: Extracted refinements and sentiment information.

[1749] Output: Generated improved design proposals.

[1750] Step 10: Providing improved design proposals

[1751] The server sends the improved design proposal to the user's device and continues to receive evaluations and feedback.

[1752] Input: Improved design proposal.

[1753] Output: Improved design proposal displayed on the user's device.

[1754] (Application example 2)

[1755] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1756] Conventional design generation systems only generate design proposals based on text entered by the user, which means they cannot fully reflect the user's emotions and intentions. Furthermore, feedback on the generated design proposals is simply analyzed as information, without considering the user's emotions during refinement, limiting the ability to enhance user satisfaction. This problem is particularly important for brick-and-mortar stores, where the rapid and effective design of attractive displays and posters is essential.

[1757] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a user to input design ideas in text format; means for the server to receive the input text data and analyze the input content using a natural language processing engine; means for recognizing the emotion of the input text using an emotion engine; means for generating various design proposals by calling an image generation AI based on the keywords and emotion information analyzed by the server; means for the server to send and provide the generated design proposals to the user's terminal; means for the user to evaluate the provided design proposals and input feedback; and means for the server to analyze the user's feedback again using the emotion engine to generate improved design proposals. This makes it possible to generate and improve design proposals that reflect the user's emotions and intentions, thereby improving the efficiency of the design creation process and the customer experience, especially in physical stores.

[1758] "Users" refer to individuals and companies who use the system to input design ideas, evaluate the generated design proposals, and provide feedback.

[1759] "Text format" refers to a format in which design ideas are written in text.

[1760] "Design Idea" refers to text information that a user inputs to communicate specific design concepts and requirements.

[1761] "Server" refers to a computer system that receives, stores, analyzes, and processes input data.

[1762] A "natural language processing engine" refers to software or a program that analyzes input text data and extracts important keywords.

[1763] "Emotion engine" refers to software or a program for recognizing and analyzing the emotions of input text.

[1764] "Keywords" refer to important words or phrases extracted from text data.

[1765] "Emotion information" refers to the user's emotion data analyzed by the emotion engine of the input text.

[1766] "Image generation AI" refers to artificial intelligence that generates design proposals based on keywords and emotional information.

[1767] "Design proposal" refers to a specific design proposal generated by image generation AI.

[1768] "Terminal" refers to the device (e.g., smartphone, PC) used by a user to input design ideas and display generated design proposals.

[1769] "Feedback" refers to the evaluation and improvement requests made by the user regarding the generated design proposal.

[1770] This invention relates to a system for generating and improving design proposals for digital signage deployed in brick-and-mortar stores. This system allows users (e.g., store managers or design teams) to input design ideas in text format, and generates and provides a variety of design proposals based on the ideas and the user's emotional information.

[1771] First, a user uses the smartphone app "Store Design Manager" to input a design idea into a text box. For example, a specific idea such as "I want a bright and exciting design created for a new product campaign" can be input. The emotion engine then analyzes the user's emotions from the input text and obtains emotional information such as "expectation" and "excitement."

[1772] The input text data and emotion data are then sent to a server, which uses a natural language processing engine to analyze the input text data and extract key keywords (e.g., "new product campaign," "cheerful," "exciting"). At the same time, the analysis results from the emotion engine are also recorded.

[1773] The server requests the image generation AI to generate design proposals based on the analyzed keywords and emotional information. The image generation AI generates a variety of design proposals based on this input data. This results in multiple design proposals that reflect the user's wishes and emotions. These design proposals are provided to the user's smartphone via the server.

[1774] The user views the provided design proposals and provides evaluation and feedback. For example, they can input specific requests for improvement, such as "I would like the colors to be more vivid." This feedback is also analyzed by the emotion engine, which recognizes the emotion contained in the feedback (for example, "hope for improvement"). Based on the results of this analysis, the server again requests the image generation AI to generate an improved design proposal.

[1775] Through this process, users can efficiently generate and refine design proposals that reflect their own emotions and intentions, which will streamline the design creation process, especially in physical stores, and improve the customer experience.

[1776] Example prompt sentence:

[1777] TXT

[1778] Keywords: new product campaign, bright, exciting

[1779] Emotions: anticipation, excitement

[1780] Generate design proposals.

[1781] To achieve these steps, the following technologies are specifically used:

[1782] Natural language processing engine: A technology for extracting key keywords from text. Examples include open source NLP libraries.

[1783] Emotion engine: A technology for analyzing the emotion of input text. For example, an emotion analysis API can be used.

[1784] Image generation AI: An AI model that generates images using keywords and emotional information. A specific example is an image generation algorithm that uses deep learning.

[1785] This system configuration makes it possible to efficiently generate designs for physical stores and provide attractive design proposals that reflect the user's intentions and emotions.

[1786] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1787] Step 1:

[1788] A user launches a smartphone app and enters a design idea into a text input box. For example, they might enter, "I want you to create a bright and exciting design for a new product campaign." This input is sent to the server, which then receives the text data.

[1789] Step 2:

[1790] The server passes the received text data to a natural language processing engine and analyzes the keywords. For example, keywords such as "new product campaign," "bright," and "exciting" are extracted. The results of this analysis are used as input data for the next step of processing.

[1791] Step 3:

[1792] At the same time, the server calls the emotion engine and analyzes the user's emotions from the input text. For example, emotion information such as "expectation" and "excitement" is obtained. This emotion information is also used as input data for the next process.

[1793] Step 4:

[1794] The server requests the image generation AI to generate design proposals based on the keywords and emotional information obtained in steps 2 and 3. The image generation AI receives the keywords and emotional information as prompts and generates various design proposals. The generated design proposals are returned to the server.

[1795] Step 5:

[1796] The server then sends the generated design proposals to the user's smartphone, where the user can view and evaluate them using the app.

[1797] Step 6:

[1798] The user inputs feedback based on their evaluation, for example, a specific request such as "I want the colors to be more vivid." Along with this feedback, the user's new emotion (e.g., "hope for improvement") is also analyzed by the emotion engine.

[1799] Step 7:

[1800] The server then requests the image generation AI to generate an improved design proposal based on the user's feedback and its analysis. The image generation AI generates an improved design proposal according to the new prompts. The server receives the new design proposal and sends it to the user's device.

[1801] Through specific actions, data processing, and calculations at each step, the system is able to quickly generate design proposals that are in line with the user's intentions and emotions, and continue to improve them.

[1802] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1803] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1804] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1805] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1806] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1807] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1808] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1809] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1810] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1811] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1812] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1813] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1814] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1815] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1816] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1817] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1818] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1819] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1820] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1821] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1822] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1823] The following is further disclosed regarding the above embodiment.

[1824] (Claim 1)

[1825] a means for a user to input design ideas in text form;

[1826] A server receives the input text data and analyzes the input content using a natural language processing engine;

[1827] A method to generate various design proposals by calling image generation AI based on keywords analyzed by the server, and

[1828] A means for the server to transmit the generated design proposal to a user's terminal and provide it;

[1829] a means for users to evaluate the provided design proposals and enter feedback;

[1830] means for the server to re-analyze the user's feedback and generate an improved design proposal;

[1831] A system including:

[1832] (Claim 2)

[1833] 10. The system of claim 1, further comprising means for providing a natural language processing engine for extracting keywords from text entered by a user.

[1834] (Claim 3)

[1835] The system of claim 1, further comprising means for adjusting the parameters of the design proposal generated by the image generation AI based on multiple elements such as color, shape, and style.

[1836] "Example 1"

[1837] (Claim 1)

[1838] a means for a user to input design ideas in text form;

[1839] A server receives the input text data and analyzes the input content using a natural language processing engine;

[1840] A method to generate various design proposals by calling an image generation AI model based on keywords analyzed by the server, and

[1841] A means for the server to transmit the generated design proposal to a user's terminal and provide it;

[1842] a means for users to evaluate the provided design proposals and enter feedback;

[1843] means for the server to re-analyze the user's feedback and generate an improved design proposal;

[1844] A system including:

[1845] (Claim 2)

[1846] 10. The system of claim 1, further comprising a natural language processing engine that extracts keywords from text entered by a user.

[1847] (Claim 3)

[1848] The system of claim 1, further comprising means for adjusting parameters of the design proposal generated by the image generation AI model based on multiple elements such as color, shape, and style.

[1849] "Application Example 1"

[1850] (Claim 1)

[1851] a means for a user to input design ideas in text form;

[1852] A server receives the input text data and analyzes the input content using a natural language processing engine;

[1853] A method to generate various design proposals by calling image generation AI based on keywords analyzed by the server, and

[1854] A means for the server to transmit the generated design proposal to a user's terminal and provide it;

[1855] a means for users to evaluate the provided design proposals and enter feedback;

[1856] means for the server to re-analyze the user's feedback and generate an improved design proposal;

[1857] means by which a user generates his or her own custom electronic payment card design;

[1858] A system including:

[1859] (Claim 2)

[1860] 10. The system of claim 1, further comprising means for providing a natural language processing engine for extracting keywords from text entered by a user.

[1861] (Claim 3)

[1862] The system of claim 1, further comprising means for adjusting the parameters of the design proposal generated by the image generation AI based on multiple elements such as color, shape, and style.

[1863] "Example 2: Combining Emotion Engines"

[1864] (Claim 1)

[1865] a means for a user to input design ideas in text form;

[1866] A server receives input text data and analyzes the emotion of the input text using an emotion recognition engine;

[1867] means for analyzing the input content using a natural language processing engine together with the emotion information analyzed by the server;

[1868] A method for generating various design proposals by calling an image generation model based on keywords and emotion information analyzed by the server;

[1869] A means for the server to transmit the generated design proposal to a user's terminal and provide it;

[1870] a means for users to evaluate the provided design proposals and enter feedback;

[1871] a means for the server to re-analyze the user's feedback using an emotion recognition engine and a natural language processing engine to generate an improved design proposal;

[1872] A system including:

[1873] (Claim 2)

[1874] 10. The system of claim 1, further comprising means for providing a natural language processing engine for extracting keywords from text entered by a user.

[1875] (Claim 3)

[1876] The system according to claim 1, further comprising means for adjusting parameters of the design proposal generated by the image generation model based on a plurality of elements such as emotional information, color, shape, and style.

[1877] "Application example 2 when combining emotion engines"

[1878] (Claim 1)

[1879] a means for a user to input design ideas in text form;

[1880] A server receives the input text data and analyzes the input content using a natural language processing engine;

[1881] means for recognizing sentiment in input text using an emotion engine;

[1882] A method to generate various design proposals by calling image generation AI based on keywords and emotional information analyzed by the server, and

[1883] A means for the server to transmit the generated design proposal to a user's terminal and provide it;

[1884] a means for users to evaluate the provided design proposals and enter feedback;

[1885] A means for the server to analyze the user feedback again using the emotion engine and generate an improved design proposal;

[1886] A system including:

[1887] (Claim 2)

[1888] 10. The system of claim 1, further comprising means for providing a natural language processing engine for extracting keywords from text entered by a user and an emotion engine for analyzing emotions.

[1889] (Claim 3)

[1890] The system of claim 1, further comprising a means for adjusting the parameters of the design proposal generated by the image generation AI based on multiple elements such as color, shape, style, and emotional information. [Explanation of symbols]

[1891] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for a user to input design ideas in text form; A server receives the input text data and analyzes the input content using a natural language processing engine; A method to generate various design proposals by calling image generation AI based on keywords analyzed by the server, and A means for the server to transmit the generated design proposal to a user's terminal and provide it; a means for users to evaluate the provided design proposals and enter feedback; means for the server to re-analyze the user's feedback and generate an improved design proposal; A system including:

2. 10. The system of claim 1, further comprising means for providing a natural language processing engine for extracting keywords from text entered by a user.

3. The system according to claim 1, further comprising means for adjusting parameters of the design proposal generated by the image generation AI based on multiple elements such as color, shape, and style.

Images