System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting a condition or description of an image desired by a user; means for transmitting the input condition to a server; means for analyzing the input condition and converting the input condition into a format suitable for a generation model; means for inputting the converted format to the generation model and generating an original image; and means for transmitting the generated image to a terminal.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] The present invention aims to solve the problem of difficulty in finding suitable copyright-free images when creating materials. Users often spend a lot of time searching for images that match the materials, and in the process are forced to avoid the risk of copyright infringement. There is a need to solve this problem and provide a means to efficiently obtain ideal images. [Means for solving the problem]

[0005] The present invention provides a means for a user to input the conditions and description of a desired image and send them to a server. The server includes a means for analyzing the input conditions and converting them into a format suitable for a generative model. The generative model generates an original image based on the converted format and provides a system including a means for transmitting the generated image to a terminal. The system also includes a means for uploading reference images and a means for allowing users to view and download the generated images. This allows users to quickly generate their ideal copyright-free image and use it efficiently in document creation.

[0006] "User" means an individual or entity that uses the system to generate images.

[0007] "Image conditions and description" refers to input data about the characteristics and content of the image the user wants to generate.

[0008] A "server" is a computer system that analyzes input data and provides the computational resources to run generative models.

[0009] "Transmission means" refers to the mechanism or protocol for transmitting data from the user terminal to the server.

[0010] "Analysis" refers to the process of analyzing the input data and converting it into a form that the generative model can understand.

[0011] A "generative model" is an artificial intelligence (AI) algorithm that generates new images based on input data.

[0012] An "original image" is an image newly created by a generative model based on user input conditions.

[0013] "Terminal" means a device (e.g., PC, smartphone, tablet) used by a user to access the system.

[0014] "Means for uploading" refers to the function by which a user transfers reference images to the system.

[0015] "Means for making the image downloadable" refers to a function that allows a user to save the generated image on their own device. [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] This invention is a system in which a user inputs desired conditions and images, and a generative AI model analyzes them to generate original images that can be used for document creation. The invention consists of three main elements: a server, a terminal, and a user.

[0038] User operations

[0039] First, the user accesses the interface of the self-made image generation tool and enters the desired image conditions and image in the text boxes. At this time, it is also possible to upload a reference image file. Once the input is complete, the user presses the "Start Generation" button, and the input data is sent to the device.

[0040] For example, if a user desires "autumn foliage scenery" for creating a document, the user enters "autumn foliage scenery" in the text box and uploads a reference image if necessary.

[0041] Terminal handling

[0042] The device collects the data entered by the user and creates an API request to send it to the server. Once the API request is complete, it sends it to the server and waits for a response.

[0043] Specifically, this is a process of sending the user's input data, text information "autumn foliage scenery," to the server.

[0044] Server Processing

[0045] The server receives the API request sent from the device and first performs data analysis. Analysis is the process of converting the input conditions into a format that the generative AI model can understand. Once this conversion is complete, the data is input into the generative AI model, and the original image generation process begins.

[0046] The generative AI model uses the analyzed data to generate unique images that match the user's desired criteria. In doing so, the model creates images with visual characteristics that match the criteria, resulting in original content created from scratch.

[0047] When image generation is complete, the server receives the generated image data and prepares to send it to the terminal as a response.

[0048] Finalizing the device

[0049] The device receives the generated image data sent from the server and displays it on the user interface. The user can check the generated image and, if necessary, press the download button to save the image file to their device.

[0050] For example, if a user requests an image of "autumn foliage scenery," the generated image is displayed and the user can download it and use it in their materials.

[0051] Specific examples

[0052] For example, if a user needs an image of a "future city" to create educational materials, they enter "future city" into the interface and press the "Start Generation" button. The device sends the data to the server, which analyzes it and passes it to the generative AI model. The generative AI model generates an image of the "future city" and returns it to the server. The device then displays the image to the user, who then downloads it.

[0053] In this way, users can quickly obtain ideal, copyright-free images with minimal effort and use them in creating materials. The system of the present invention is extremely useful in that it provides high-quality images through a complex process, simply by the user inputting their requirements.

[0054] The processing flow will be explained below.

[0055] Program processing steps

[0056] Step 1:

[0057] The user accesses the interface of the self-made image generation tool and inputs the desired image conditions and image into the text box. For example, they can input "autumn foliage scenery." If they have a reference image, they can upload that image file. Once they have completed the input, they press the "Start Generation" button.

[0058] Step 2:

[0059] The device collects the data entered by the user. The collected data (text information and uploaded images) is organized as an API request. This API request is then prepared for sending to the server.

[0060] Step 3:

[0061] The device sends the organized API request to the server, and then waits for a response from the server.

[0062] Step 4:

[0063] The server receives API requests sent from the device, analyzes the received data, and converts it into a format that the generative AI model can understand. This conversion includes tokenizing text data and preprocessing image data.

[0064] Step 5:

[0065] The server inputs the analyzed data into a generative AI model, which then starts the image generation process based on the input conditions and images.

[0066] Step 6:

[0067] Generative AI models generate original images that fit the criteria, a process that involves running algorithms to create images from scratch with the right visual characteristics.

[0068] Step 7:

[0069] The generated image data is returned from the generative AI model to the server, which temporarily stores the image data and prepares to send it to the device as a response.

[0070] Step 8:

[0071] The server sends the generated image data to the device as a response, which includes metadata and a download link for the generated image.

[0072] Step 9:

[0073] The terminal receives the generated image data sent from the server, displays the received image data on the user interface, and allows the user to confirm it.

[0074] Step 10:

[0075] Users can check the generated image and press the download button to save the image file to their own device, allowing them to freely use the generated image in creating documents.

[0076] Through this series of processes, users can quickly generate original images that meet their desired conditions and use them without copyright issues.

[0077] Example 1

[0078] 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."

[0079] Conventional image generation systems have had difficulty in quickly and easily generating original images that meet the user's desired conditions. They also required complex procedures and settings, which was time-consuming for the user. Furthermore, the generated images were often of low quality, or did not fully meet the user's expectations.

[0080] 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.

[0081] In this invention, the server includes means for a user to input desired image conditions and descriptions, means for transmitting the input conditions to a terminal, means for the terminal to collect the user's input data and transmit it as an API request to the server, means for the server to analyze the received API request and convert it into a format understandable by the generative AI model, means for inputting the converted data into the generative AI model to generate an original image, means for the server to transmit the generated image to the terminal, and means for the terminal to display the image received to the user. This allows a user to quickly generate high-quality original images and easily use them simply by inputting their desired conditions.

[0082] A "user" is a person who uses the system to make a request for image generation.

[0083] The "conditions and description of the desired image" is information indicating the specific characteristics and content of the image that the user wants to generate.

[0084] A "terminal" is a computer device that allows a user to input conditions and explanations and transmit them to a server.

[0085] An "API request" refers to data sent from a device to a server to request specific data or functions.

[0086] A "server" is a computer system that receives and analyzes data sent from a terminal and generates images using a generative AI model.

[0087] A "generative AI model" is an algorithm or program that generates original images from input data based on machine learning and artificial intelligence techniques.

[0088] "Analysis" is the process of converting the conditions and explanations entered by the user into a format that the generative AI model can understand.

[0089] An "original image" is a new, copyright-free image generated from scratch by a generative AI model.

[0090] "Upload" refers to a user sending a reference image or file from their own device to the server.

[0091] "Download" refers to a user saving an image generated from a server or device to their own device.

[0092] A "user interface" is a part of software that provides the screens and operating methods for users to operate a system.

[0093] This invention is a system in which, when a user inputs desired conditions and descriptions, a generative AI model analyzes them and generates original images that can be used for document creation, etc. The invention consists of three main elements: a server, a terminal, and a user.

[0094] User operations

[0095] First, the user accesses the interface of the self-made image generation tool and enters the desired image conditions and description in the text box. At this time, it is also possible to upload a reference image file. Once the input is complete, the user presses the "Start Generation" button, and the input data is sent to the device.

[0096] For example, if a user wants an image of a "future city" for creating a document, they can enter "future city" in the text box and upload a reference image if necessary.

[0097] Terminal handling

[0098] The device collects the data entered by the user and creates an API request to send it to the server. The device converts the text data entered by the user into JSON format and saves the uploaded image in a temporary folder. Once this API request is complete, the device sends the data to the server and waits for a response.

[0099] Server Processing

[0100] The server receives the API request sent from the device and first performs data analysis. Analysis is the process of converting the input conditions into a format that the generative AI model can understand. The server then analyzes the received JSON data and converts it into a format that the generative AI model can understand (for example, tokenized text). Once this conversion is complete, the server inputs the analyzed data into the generative AI model and begins the original image generation process.

[0101] Image generation using generative AI models

[0102] The generative AI model generates unique images that match the user's desired criteria based on the analysis data provided by the server. For example, based on the image of a "future city," an image featuring skyscrapers and futuristic infrastructure is generated. In this generation process, the generative AI model uses past data and learning to create original images that meet the user's requirements.

[0103] Finalizing the device

[0104] The generated image is received by the server and sent to the terminal as a response. The terminal receives the generated image data sent from the server and displays it on the user interface. The user can check the generated image and, if necessary, press the download button to save the image file to their terminal.

[0105] As a specific example, if a user requests an image of a "future city," a generated image is displayed and the user can download it and use it in their materials.

[0106] Using this system, users can quickly obtain ideal, copyright-free images with little effort and use them in document creation. Examples of prompts include: "Generate an image of a city of the future" or "Generate an image of autumn foliage."

[0107] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0108] Step 1: User Input

[0109] Users access the interface of the self-made image generation tool and enter the desired image conditions and description in the text box. They can also upload reference image files. For example, they can enter "future city" in the text box and upload the reference image by dragging and dropping. This input data includes the conditions in text format and the image file.

[0110] input:

[0111] Text image condition "City of the Future"

[0112] Uploaded reference images (if needed)

[0113] output:

[0114] Send user input data to the terminal

[0115] Specific behavior:

[0116] When the user clicks the "Start Generation" button, the text conditions and reference images are sent to the terminal.

[0117] Step 2: Collecting device data and making API requests

[0118] The device collects the data entered by the user. The collected data is the text information "Future City" and (optionally) the uploaded reference image file. Based on this data, the device creates an API request to send to the server. The device converts the input data to JSON format and saves the uploaded image in a temporary folder.

[0119] input:

[0120] User-submitted text information and reference images

[0121] output:

[0122] JSON formatted API request

[0123] Specific behavior:

[0124] The device encodes the text information into JSON format, saves the image to a temporary folder on the local file system, and combines this data into a single API request.

[0125] Step 3: Receiving and analyzing data on the server

[0126] The server receives the API request sent from the device. The received data includes the text information "future city" in JSON format and the path to the reference image. The server then analyzes this data and converts it into a format that the generative AI model can understand (for example, tokenized text). This allows the generative AI model to accurately understand the user's request.

[0127] input:

[0128] API request from the device (JSON format text information and image path)

[0129] output:

[0130] Data after conversion into a format that can be understood by the generative AI model

[0131] Specific behavior:

[0132] The server parses the JSON data, tokenizes the text information, and, if reference images are available, preprocesses the images to convert them into a format suitable for the generative AI model.

[0133] Step 4: Image generation using a generative AI model

[0134] The generative AI model generates unique images that match the user's desired conditions based on the analysis data provided by the server. For example, based on the image of a "future city," it generates an image featuring skyscrapers and futuristic infrastructure. This process is carried out by the generative AI model based on past data and learnings, in accordance with the user's wishes.

[0135] input:

[0136] Converted data from the server

[0137] output:

[0138] Generated original image

[0139] Specific behavior:

[0140] The generative AI model runs its internal algorithms based on the provided text data and reference images to generate images that meet the user's desired criteria.

[0141] Step 5: Server receives image and prepares response

[0142] The generated image is received by the server, which encodes it into an appropriate format and creates a response to send to the device.

[0143] input:

[0144] Generated images from generative AI models

[0145] output:

[0146] A response containing the encoded image data.

[0147] Specific behavior:

[0148] The server encodes the generated image in PNG format and adds it to the body of the HTTP response.

[0149] Step 6: Receive and display images on your device

[0150] The device decodes the image data received from the server and displays it on the user interface. The user can check the displayed image and, if satisfied, press the download button to save the image.

[0151] input:

[0152] Encoded image data from the server

[0153] output:

[0154] The decoded image displayed in the user interface

[0155] Specific behavior:

[0156] The device extracts the image data from the HTTP response and displays it in the user interface using an image viewer.

[0157] (Application example 1)

[0158] 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."

[0159] In the past, when a user requested image generation, they only had to input the desired image conditions and a description, and subsequent usage was limited. In particular, there was a lack of means for users to visually check items without actually trying them on in virtual stores, so there is a need to improve the user experience.

[0160] 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.

[0161] In this invention, the server includes means for a user to input desired image conditions and descriptions, means for transmitting the input conditions to the server, means for analyzing the input conditions and converting them into a format suitable for a generative model, means for inputting the converted format into a generative model to generate an original image, means for transmitting the generated image to a terminal, and means for a user to try on an avatar using the generated image in a virtual store, thereby enabling a user to visually check items without actually picking them up.

[0162] The "means for the user to input the conditions and description of the desired image" is an interface that allows the user to input the content of the desired image by text or other means.

[0163] The "means for transmitting the input conditions to the server" refers to a communication means for transmitting the conditions input by the user to the server via the Internet or the like.

[0164] The "means for analyzing the input conditions and converting them into a format suitable for the generative model" refers to a data analysis and conversion means for receiving the user's transmitted conditions and converting them into a data format that is easy for the generative AI model to understand.

[0165] "Means for inputting the converted format into a generative model and generating an original image" refers to means for inputting the converted data into a generative AI model, which then creates a new original image.

[0166] The "means for transmitting the generated image to the terminal" refers to a communication means for sending the generated original image to the user's terminal.

[0167] The "means for allowing a user to try on a generated image on an avatar" refers to a means for applying a generated image to a user's avatar and displaying the avatar as if it were trying on the image.

[0168] This invention relates to a system that generates original images using a generative AI model based on user input of desired image conditions and descriptions. In particular, it provides a system that allows users to try on generated images on an avatar in a virtual store.

[0169] Hardware and Software Configuration

[0170] To implement the system, the following hardware and software are required:

[0171] Hardware:

[0172] Smartphone (iOS / Android)

[0173] Server (cloud-based)

[0174] software:

[0175] Mobile app development framework (React Native)

[0176] API management server (Express.js)

[0177] Generative AI model (Stable Diffusion, DALL-E 2)

[0178] Explanation of program processing

[0179] 1. User Action:

[0180] Users access the smartphone app interface and enter the desired image criteria and description into a text box, for example, "business casual style attire." They can also upload reference images if needed.

[0181] 2. Terminal processing:

[0182] It collects the data entered by the user and creates an API request to send it to the server. Specifically, it sends the input text "Business casual style attire" to the server and waits for a response from the server.

[0183] 3. Server processing:

[0184] The server receives the API request, analyzes the input data, and converts it into a format suitable for the generative AI model. This converted data is passed to the generative AI model (e.g., Stable Diffusion) to generate the original image. The server receives the generated image data and prepares it to be sent to the device as a response.

[0185] 4. Finalize your device:

[0186] The terminal receives the generated image data sent from the server and displays it on the user interface. The user can check the generated image and try it on their avatar if necessary.

[0187] Specific examples

[0188] For example, consider the case where a user inputs "I want to try on casual summer clothes." The user inputs "casual summer clothes" into the interface of a smartphone app and presses the start generation button. The device collects the input data and sends it to the server. The server analyzes the received data and passes it to the generative AI model. The generative AI model generates an image of "casual summer clothes" and returns it to the server. The device receives the image and displays it on the user interface. The user can review the image and try it on their avatar. The user can also download the image.

[0189] Prompt Sentence Examples

[0190] "Generate business casual style outfits and have your avatar try them on."

[0191] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0192] Step 1:

[0193] The user accesses the smartphone app interface and enters the desired image criteria and description in the text boxes. The user can also upload reference images if necessary. The input data is "business casual style clothing," and the user presses the "Start Generation" button. The output is the criteria data entered by the user.

[0194] Step 2:

[0195] The device collects the user's input data and generates an API request to send it to the server. Specifically, it converts the input text data into an API request format (such as JSON) and sends it to the server address. The input is the user's condition data, and the output is the data sent to the server as an API request.

[0196] Step 3:

[0197] The server receives the API request and analyzes the input data (user condition data). This analysis is the process of converting the conditions entered by the user into a data format that the generative AI model can understand. Specifically, it converts text data into a model input format (e.g., tokenized text). The input is the API request data, and the output is the data in the converted format.

[0198] Step 4:

[0199] The server inputs the converted data into a generative AI model (e.g., Stable Diffusion) to generate the original image. The model generates an image based on the input data and returns the image data to the server. The input is the converted data, and the output is the generated image data.

[0200] Step 5:

[0201] The server receives the generated image data and prepares to send it to the device. It generates an API response for sending the image data to the device and sends it to the device. The input is the generated image data, and the output is the data sent to the device as an API response.

[0202] Step 6:

[0203] The terminal receives the generated image data sent from the server and displays it on the user interface. The user checks the generated image and selects an option to try it on the avatar. The input is the received generated image data, and the output is the user's visual confirmation and the result of trying it on the avatar.

[0204] 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.

[0205] This invention is a system in which a generative AI model uses an emotion engine to generate original images based on the user's desired conditions and images. This system consists of three main elements: a server, a terminal, and a user, and is combined with an emotion engine that senses the user's emotions and reflects them in the image generation.

[0206] User operations

[0207] The user accesses the interface of the self-made image generation tool and enters the desired image conditions and image into the text box. For example, they can enter "autumn foliage scenery." They can also upload a reference image file. Once the input is complete, the user presses the "Start Generation" button.

[0208] Furthermore, the system also recognizes the user's emotional state. The emotion engine detects emotions from the user's facial expressions, voice, or text, and adds them as complementary conditions for image generation.

[0209] For example, if the user has a tired expression, the emotion engine will modify the conditions to something like "a relaxing autumn scene with autumn leaves."

[0210] Terminal handling

[0211] The device collects the data entered by the user and the output of the emotion engine, creates an API request to send it to the server, and then waits for a response.

[0212] For example, data such as "autumn foliage scenery" and "user needs relaxation" are collected and sent to the server.

[0213] Server Processing

[0214] The server receives the API request sent from the device and first analyzes the data. Analysis is the process of converting the user's desired conditions and the output of the emotion engine into a format that the generative AI model can understand. This conversion includes tokenizing text data and preprocessing image data.

[0215] The analyzed data is then fed into a generative AI model, which then begins the process of generating an original image. The generative AI model then generates the optimal image based on the user's desired conditions and emotional state. This process creates an image with the appropriate visual characteristics, color tone, and style.

[0216] For example, the generative AI model generates "relaxing autumn foliage scenes."

[0217] The generated image data is returned from the generation AI model to the server, which temporarily stores the image data and prepares to send it to the device as a response.

[0218] Finalizing the device

[0219] The device receives the generated image data sent from the server and displays it on the user interface. The user can check the generated image and, if necessary, press the download button to save the image file to their device.

[0220] For example, a user may check out "Relaxing Autumn Foliage Scenery" and download it to use in their materials.

[0221] Specific examples

[0222] For example, if an image of a "future city" is needed to create educational materials, the user enters "future city" into the interface and presses the generation start button. If the emotion engine detects the user's elevated emotions, it will supplement the criteria with an "energetic future city." The device sends the data to the server, which analyzes and converts the data and passes it to the generative AI model. The generative AI model generates an image of the "energetic future city" and returns it to the server. The device then displays the image to the user, who then downloads it.

[0223] In this way, users can quickly generate original images that meet the conditions that take into account their emotional state, and use them without copyright issues. The system of the present invention is extremely useful in that it not only allows users to input conditions, but also reflects their emotional state, thereby providing more suitable, high-quality images.

[0224] The processing flow will be explained below.

[0225] Program processing steps

[0226] Step 1:

[0227] The user accesses the interface of the self-made image generation tool and inputs the desired image conditions and image into the text box. For example, they can input "autumn foliage scenery." They can also upload reference image files. Once the input is complete, the user presses the "Start Generation" button.

[0228] Step 2:

[0229] The device collects data entered by the user, including text information and uploaded image files, and then makes an API request to send the collected data to the server.

[0230] Step 3:

[0231] The device sends the created API request to the server and then waits for a response from the server.

[0232] Step 4:

[0233] The server receives API requests sent from the device, then parses the received data and converts it into a format that the generative AI model can understand. Parsing includes tokenizing text data and preprocessing uploaded images.

[0234] Step 5:

[0235] The server inputs the analyzed data into the generative AI model, which then initiates the process of generating an original image based on the user's input conditions.

[0236] Step 6:

[0237] At the same time, the emotion engine recognizes the user's emotions. The emotion engine detects emotions from the user's facial expressions, voice, and text input. This emotion data is provided as supplementary data to the generative AI model.

[0238] Step 7:

[0239] The generative AI model generates the optimal original image based on the input conditions and emotional data from the emotion engine. For example, if the emotion engine detects "relaxation," the generated "autumn foliage scene" will be adjusted to have a more calming atmosphere.

[0240] Step 8:

[0241] The generated image data is returned from the generative AI model to the server, which temporarily stores the image data and prepares to send it to the device as a response.

[0242] Step 9:

[0243] The server sends the generated image data to the device, and the response includes metadata and a download link for the generated image.

[0244] Step 10:

[0245] The terminal receives the generated image data sent from the server, displays the received image data on the user interface, and allows the user to confirm it.

[0246] Step 11:

[0247] Users can check the generated images and, if necessary, press the download button to save the image files to their own devices, allowing them to freely use the generated images in creating materials.

[0248] As a concrete example, consider a case where a user needs an image of a "future city" for educational materials. The user enters "future city" in the text box and presses the start generation button. If the emotion engine detects the user's excited emotion, the generative AI model will generate an image of an "energetic future city." Through this image generation process, the user can quickly obtain a high-quality original image to use in creating materials.

[0249] Example 2

[0250] 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."

[0251] While modern image generation technology can generate original images based on specific user requirements and descriptions, technology for generating more personalized images by reflecting the user's emotional state is still immature. This is because it is difficult for users to clearly communicate their emotions through text, facial expressions, or voice, and as a result, generated images do not necessarily match the user's emotions or requirements. Furthermore, if the generated image does not meet the user's expectations, it is time-consuming to reset the conditions, making efficient image generation difficult.

[0252] 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.

[0253] In this invention, the server includes a means for a user to input desired image conditions and descriptions, a means for transmitting the input conditions to the server, a means for analyzing the input conditions and converting them into a format suitable for a generative AI model, a means for inputting the converted format into the generative AI model to generate an original image, a means for transmitting the generated image to a terminal, a means including an emotion engine that detects the user's emotions and adds them as complementary conditions for image generation, and a means for integrating the output of the emotion engine into analysis data. This makes it possible to generate images that reflect the specific conditions and emotional state desired by the user.

[0254] A "user" is an individual or entity that utilizes the system to make an image generation request.

[0255] "Conditions" are information for specifying the characteristics and content of the image desired by the user.

[0256] "Description" is text information that allows a user to specify the detailed content and context of a desired image.

[0257] "Means" are hardware and software components used within a system to achieve a particular purpose.

[0258] A "server" is a computing device on a network that receives data sent by a user, analyzes and processes it, and sends the final results to a terminal.

[0259] "Terminal" refers to the device through which a user accesses the system, inputs conditions, and checks and downloads generated images.

[0260] "Input conditions" refers to the characteristics and descriptions specified by the user for image generation.

[0261] "Means for sending" refers to the process or function for sending the conditions entered by the user to the server.

[0262] "Means of analysis" refers to the process or function for converting the input conditions into a format that the generative AI model can understand.

[0263] A "generative AI model" is an algorithm or program that uses artificial intelligence to generate original images based on user input.

[0264] "Means for converting" refers to the process or function for analyzing the input conditions and converting them into a format suitable for the generative AI model.

[0265] "Means of generation" refers to the process or function for inputting converted data into a generative AI model and creating an original image.

[0266] "Means for generating an image" refers to the process or function for inputting the converted format into a generative AI model to generate the original image.

[0267] "Transmitting means" refers to the process or function for transmitting the generated image to the terminal.

[0268] An "emotion engine" is software or hardware that has the function of detecting emotions from a user's facial expressions, voice, text, etc., and adding them as complementary conditions for image generation.

[0269] "Means of integration" refers to the processes and functions for integrating the output of the emotion engine into the analytical data.

[0270] This invention is a system that generates original images based on the user's desired conditions and emotions. The system consists of three main elements: the user, the terminal, and the server. It also includes an emotion engine that detects the user's emotions and reflects them in the image generation.

[0271] User operations

[0272] Users access the interface of the self-made image generation tool and enter the conditions and description of the desired image in the text box. Specifically, they can enter a prompt such as "Autumn foliage scenery." They can also upload a reference image. Once the input is complete, the user presses the "Start Generation" button.

[0273] Emotion detection

[0274] The system is equipped with an emotion engine that recognizes the user's emotional state in real time. The emotion engine detects emotions from the user's facial expressions, voice, or input text and adds them as complementary conditions for image generation. For example, if the user has a tired expression, the emotion engine will modify the conditions to something like "relaxing autumn foliage scenery."

[0275] Device Role

[0276] The device collects the data entered by the user and the output of the emotion engine, and creates an API request to send it to the server. It generates a JSON-formatted request containing the user-entered emotion data "Autumn foliage scenery" and "Relaxed," and sends it to the server. The device waits for a response from the server and displays the generated image on the user interface.

[0277] Server Processing

[0278] The server receives the API request sent from the device and first analyzes the data. This analysis is the process of converting the user's desired conditions and the output of the emotion engine into a format that the generative AI model can understand. The analysis includes tokenizing the text data and converting the emotion data into numbers. The analyzed data is input into the generative AI model, which starts the original image generation process. For example, an image of a "relaxing autumn foliage scene" is generated. The generated image data is then sent from the server to the device.

[0279] Specific examples

[0280] For example, if an image of a "future city" is needed to create educational materials, the user enters "future city" into the interface and presses the generation start button. If the emotion engine detects the user's elevated emotions, it will supplement the criteria with an "energetic future city." The device sends the data to the server, which analyzes and converts the data and passes it to the generative AI model. The generative AI model generates an image of the "energetic future city" and returns it to the server. The device then displays the image to the user, who then downloads it.

[0281] Prompt Sentence Examples

[0282] "Generate an image of autumn foliage. The user wants a relaxing image."

[0283] In this way, users can quickly generate original images that match their emotional state and use them without copyright issues. This system is extremely useful in that it not only allows users to input conditions, but also reflects their emotional state, providing more suitable, high-quality images.

[0284] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0285] Step 1:

[0286] The user accesses the interface of the self-made image generation tool and enters the conditions and description of the desired image in the text box. For example, they can enter a prompt such as "Autumn foliage scenery." Once they have completed the input, they press the "Start generation" button. The input data is the text data "Autumn foliage scenery," and this is passed to the next processing step.

[0287] Step 2:

[0288] The device receives the text data entered by the user and activates the emotion engine. The emotion engine detects emotions from the user's facial expressions, voice, and entered text. This analysis generates emotion data such as "The user wants to relax." Specifically, the device uses a camera and microphone to read the user's emotions in real time and obtains the analysis results as text data.

[0289] Step 3:

[0290] The device collects the text data entered by the user and the emotion engine's output (emotion data) and creates an API request to send it to the server. Specifically, the device generates a JSON-formatted request containing the conditions "autumn foliage scenery" and "relaxation." Once the API request is complete, the device sends the data to the server. This sent data is passed on to the next processing step.

[0291] Step 4:

[0292] The server receives the API request sent from the device and first analyzes the data. Analysis is the process of converting the user's desired conditions and the output of the emotion engine into a format that the generative AI model can understand. Specifically, the server tokenizes the sent JSON data, converts text data into character vectors, and quantifies emotion data. This generates a data format suitable for input to the generative AI model.

[0293] Step 5:

[0294] The server inputs the analyzed data into a generative AI model and begins the original image generation process. The generative AI model uses specific algorithms to generate the optimal image based on the user's desired conditions and emotional state. This process creates an image with appropriate visual features, color tone, and style. Specifically, the server inputs tokenized text data and quantified emotional data for "Autumn foliage scenery with a relaxing effect" into the generative AI model and waits for the image to be generated. The generated image data is then passed to the next processing step.

[0295] Step 6:

[0296] The generated image data is returned from the generative AI model to the server, which temporarily stores the generated image data. The server then prepares to send the generated image data to the device as a response. Specifically, the server stores the generated image in temporary storage and encodes it as part of the JSON response.

[0297] Step 7:

[0298] The device receives the generated image data sent from the server. The received image data is displayed on the user interface, allowing the user to confirm the generated image. Specifically, the device extracts the image data from the received JSON response and displays it on the interface. At this point, the user can view the generated image and, if necessary, press the download button to save the image file to their device.

[0299] (Application example 2)

[0300] 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."

[0301] Conventional advertising image generation systems have difficulty generating images that take the user's emotional state into account, making it impossible to generate images that best fit the target or theme of the advertisement. Even when users input desired conditions, the system does not generate images that reflect the emotional elements based on those conditions, limiting their effectiveness as advertising. Furthermore, images generated solely based on user-input conditions and reference images often lack originality and creativity.

[0302] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0303] In this invention, the server includes: a means for a user to input desired image conditions and descriptions; a means for transmitting the input conditions and the user's emotional state to the server; a means for analyzing the input conditions and emotional state and converting them into a format suitable for a generative model; a means for inputting the converted format into the generative model to generate an original image based on the user's emotional state; and a means for transmitting the generated image to a terminal and displaying it. This enables advertising creators and marketers to easily generate high-quality original images that reflect emotional elements and are best suited to their target or theme.

[0304] The "means for the user to input the conditions and description of the image desired" is a function for the user to input the specific conditions and description of the image he or she wants to generate using an input interface such as a text box.

[0305] The "means for transmitting the input conditions and the user's emotional state to the server" is a function for transmitting the conditions and explanations input by the user and data on the user's emotional state detected by the emotion engine to the server via a network.

[0306] "Means for analyzing the input conditions and emotional state and converting them into a format suitable for the generative model" refers to a function that executes a process of analyzing the user's conditions, explanations, and emotional state data and converting them into a format that can be understood by the generative AI model.

[0307] "Means for inputting the converted format into a generative model and generating an original image based on the user's emotional state" refers to a function for inputting the converted data into a generative AI model and generating an original image that reflects emotional elements based on this.

[0308] The "means for transmitting the generated image to the terminal and displaying it" is a function for transmitting the generated original image data from the server to the user's terminal and displaying it on the user interface.

[0309] The "means for uploading reference images" is a function for uploading to the system an image file that serves as a reference for the image that the user wants to generate.

[0310] A "generative model" is an artificial intelligence model that automatically generates original images based on the user's input conditions and emotional state.

[0311] The system embodying the present invention functions as a tool that enables advertising creators and marketers to easily generate high-quality images that reflect emotional elements. Specific embodiments of the system are described below.

[0312] User operations

[0313] Using a smartphone app, users enter the theme and conditions of their ad into a text box. They can also upload a reference image. The system then uses a camera and microphone to recognize facial expressions and voice to detect emotions in real time. For example, a user who enters "Christmas sale ad image" and has a smiling expression may use the app.

[0314] Terminal handling

[0315] It collects the conditions and emotional state entered by the user and generates an API request to send this to the server. For example, it collects data such as "Christmas sale advertising image" and "user is smiling" and sends it to the server.

[0316] Server Processing

[0317] The server analyzes the received API request and converts it into a format that the generative AI model can understand. This conversion process includes tokenizing the text data and tagging the emotional state. The analyzed data is input into the generative AI model, which generates an original image that takes into account the user's emotional state. Examples of generative AI models used are OpenAI's DALL·E and Google AI's Imagen. The generated image will look something like a "Smiling Happy Christmas Sale Advertising Image."

[0318] Finalizing the device

[0319] The generated image data sent from the server is received and displayed on the user interface. The user can check the generated image and, if necessary, press the download button to save the image file to their device.

[0320] The hardware used is a smartphone, and real-time emotion recognition is performed using a camera and microphone. The software used is an emotion engine (e.g., Amazon Rekognition or Microsoft Azure Face API), and the generative AI model is OpenAI's DALL·E or Google AI's Imagen.

[0321] Examples and prompts

[0322] Specifically, if the user desires a "happy Christmas sale advertising image" and has a smiling expression, the system generates a "smiling happy Christmas sale advertising image."

[0323] Example prompt sentence:

[0324] "Christmas sale advertising image"

[0325] "We detected that the user is smiling"

[0326] "Generate smiling, happy Christmas sale advertising images"

[0327] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0328] Step 1:

[0329] The user launches the smartphone app and enters the advertising theme and conditions into a text box. For example, they might enter "Christmas sale advertising image." They can also upload a reference image. Furthermore, facial expressions and voice are recognized using a camera and microphone. The inputs for this step are the text conditions entered by the user, the uploaded reference image, and the emotional state obtained in real time. The output is a combination of the input data and emotional data.

[0330] Step 2:

[0331] The device organizes the collected data and generates an API request to send to the server. Specifically, it combines the text condition entered by the user, "Christmas sale advertising image," and the emotion data, "the user is smiling," into an API request. The input is the text information, reference image, and emotion data provided by the user. The output is a formatted API request to send to the server.

[0332] Step 3:

[0333] The server receives the API request and first tokenizes the text data and tags it with emotion data. This analysis process converts the data into a format that the generative AI model can understand. For example, the terms "Christmas sale advertising image" and "smile" are tokenized. The input is the data received from the API request, and the output is the analyzed data to be input into the generative AI model.

[0334] Step 4:

[0335] The server inputs the analyzed data into a generative AI model to generate an original image. The generative AI model generates an image based on the user's conditions and emotional state. In this case, the model generates a "smiling, happy Christmas sale advertising image." The input is the analyzed data, and the output is the generated image data.

[0336] Step 5:

[0337] The server temporarily stores the generated image data and prepares it for transmission to the terminal. The input is the generated image data, and the output is the prepared data to be transmitted to the terminal.

[0338] Step 6:

[0339] The terminal receives the image data sent from the server and displays it on the user interface. The user can check the generated image and, if necessary, press the download button to save the image file to their terminal. The input is the image data received from the server, and the output is the generated image displayed to the user.

[0340] keyword

[0341] Generative AI model, prompt sentence

[0342] 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.

[0343] 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.

[0344] 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.

[0345] [Second embodiment]

[0346] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0347] 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.

[0348] 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).

[0349] 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.

[0350] 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.

[0351] 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).

[0352] 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.

[0353] 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.

[0354] 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.

[0355] 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.

[0356] 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.

[0357] 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."

[0358] This invention is a system in which a user inputs desired conditions and images, and a generative AI model analyzes them to generate original images that can be used for document creation. The invention consists of three main elements: a server, a terminal, and a user.

[0359] User operations

[0360] First, the user accesses the interface of the self-made image generation tool and enters the desired image conditions and image in the text boxes. At this time, it is also possible to upload a reference image file. Once the input is complete, the user presses the "Start Generation" button, and the input data is sent to the device.

[0361] For example, if a user desires "autumn foliage scenery" for creating a document, the user enters "autumn foliage scenery" in the text box and uploads a reference image if necessary.

[0362] Terminal handling

[0363] The device collects the data entered by the user and creates an API request to send it to the server. Once the API request is complete, it sends it to the server and waits for a response.

[0364] Specifically, this is a process of sending the user's input data, text information "autumn foliage scenery," to the server.

[0365] Server Processing

[0366] The server receives the API request sent from the device and first performs data analysis. Analysis is the process of converting the input conditions into a format that the generative AI model can understand. Once this conversion is complete, the data is input into the generative AI model, and the original image generation process begins.

[0367] The generative AI model uses the analyzed data to generate unique images that match the user's desired criteria. In doing so, the model creates images with visual characteristics that match the criteria, resulting in original content created from scratch.

[0368] When image generation is complete, the server receives the generated image data and prepares to send it to the terminal as a response.

[0369] Finalizing the device

[0370] The device receives the generated image data sent from the server and displays it on the user interface. The user can check the generated image and, if necessary, press the download button to save the image file to their device.

[0371] For example, if a user requests an image of "autumn foliage scenery," the generated image is displayed and the user can download it and use it in their materials.

[0372] Specific examples

[0373] For example, if a user needs an image of a "future city" to create educational materials, they enter "future city" into the interface and press the "Start Generation" button. The device sends the data to the server, which analyzes it and passes it to the generative AI model. The generative AI model generates an image of the "future city" and returns it to the server. The device then displays the image to the user, who then downloads it.

[0374] In this way, users can quickly obtain ideal, copyright-free images with minimal effort and use them in creating materials. The system of the present invention is extremely useful in that it provides high-quality images through a complex process, simply by the user inputting their requirements.

[0375] The processing flow will be explained below.

[0376] Program processing steps

[0377] Step 1:

[0378] The user accesses the interface of the self-made image generation tool and inputs the desired image conditions and image into the text box. For example, they can input "autumn foliage scenery." If they have a reference image, they can upload that image file. Once they have completed the input, they press the "Start Generation" button.

[0379] Step 2:

[0380] The device collects the data entered by the user. The collected data (text information and uploaded images) is organized as an API request. This API request is then prepared for sending to the server.

[0381] Step 3:

[0382] The device sends the organized API request to the server, and then waits for a response from the server.

[0383] Step 4:

[0384] The server receives API requests sent from the device, analyzes the received data, and converts it into a format that the generative AI model can understand. This conversion includes tokenizing text data and preprocessing image data.

[0385] Step 5:

[0386] The server inputs the analyzed data into a generative AI model, which then starts the image generation process based on the input conditions and images.

[0387] Step 6:

[0388] Generative AI models generate original images that fit the criteria, a process that involves running algorithms to create images from scratch with the right visual characteristics.

[0389] Step 7:

[0390] The generated image data is returned from the generative AI model to the server, which temporarily stores the image data and prepares to send it to the device as a response.

[0391] Step 8:

[0392] The server sends the generated image data to the device as a response, which includes metadata and a download link for the generated image.

[0393] Step 9:

[0394] The terminal receives the generated image data sent from the server, displays the received image data on the user interface, and allows the user to confirm it.

[0395] Step 10:

[0396] Users can check the generated image and press the download button to save the image file to their own device, allowing them to freely use the generated image in creating documents.

[0397] Through this series of processes, users can quickly generate original images that meet their desired conditions and use them without copyright issues.

[0398] Example 1

[0399] 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."

[0400] Conventional image generation systems have had difficulty in quickly and easily generating original images that meet the user's desired conditions. They also required complex procedures and settings, which was time-consuming for the user. Furthermore, the generated images were often of low quality, or did not fully meet the user's expectations.

[0401] 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.

[0402] In this invention, the server includes means for a user to input desired image conditions and descriptions, means for transmitting the input conditions to a terminal, means for the terminal to collect the user's input data and transmit it as an API request to the server, means for the server to analyze the received API request and convert it into a format understandable by the generative AI model, means for inputting the converted data into the generative AI model to generate an original image, means for the server to transmit the generated image to the terminal, and means for the terminal to display the image received to the user. This allows a user to quickly generate high-quality original images and easily use them simply by inputting their desired conditions.

[0403] A "user" is a person who uses the system to make a request for image generation.

[0404] The "conditions and description of the desired image" is information indicating the specific characteristics and content of the image that the user wants to generate.

[0405] A "terminal" is a computer device that allows a user to input conditions and explanations and transmit them to a server.

[0406] An "API request" refers to data sent from a device to a server to request specific data or functions.

[0407] A "server" is a computer system that receives and analyzes data sent from a terminal and generates images using a generative AI model.

[0408] A "generative AI model" is an algorithm or program that generates original images from input data based on machine learning and artificial intelligence techniques.

[0409] "Analysis" is the process of converting the conditions and explanations entered by the user into a format that the generative AI model can understand.

[0410] An "original image" is a new, copyright-free image generated from scratch by a generative AI model.

[0411] "Upload" refers to a user sending a reference image or file from their own device to the server.

[0412] "Download" refers to a user saving an image generated from a server or device to their own device.

[0413] A "user interface" is a part of software that provides the screens and operating methods for users to operate a system.

[0414] This invention is a system in which, when a user inputs desired conditions and descriptions, a generative AI model analyzes them and generates original images that can be used for document creation, etc. The invention consists of three main elements: a server, a terminal, and a user.

[0415] User operations

[0416] First, the user accesses the interface of the self-made image generation tool and enters the desired image conditions and description in the text box. At this time, it is also possible to upload a reference image file. Once the input is complete, the user presses the "Start Generation" button, and the input data is sent to the device.

[0417] For example, if a user wants an image of a "future city" for creating a document, they can enter "future city" in the text box and upload a reference image if necessary.

[0418] Terminal handling

[0419] The device collects the data entered by the user and creates an API request to send it to the server. The device converts the text data entered by the user into JSON format and saves the uploaded image in a temporary folder. Once this API request is complete, the device sends the data to the server and waits for a response.

[0420] Server Processing

[0421] The server receives the API request sent from the device and first performs data analysis. Analysis is the process of converting the input conditions into a format that the generative AI model can understand. The server then analyzes the received JSON data and converts it into a format that the generative AI model can understand (for example, tokenized text). Once this conversion is complete, the server inputs the analyzed data into the generative AI model and begins the original image generation process.

[0422] Image generation using generative AI models

[0423] The generative AI model generates unique images that match the user's desired criteria based on the analysis data provided by the server. For example, based on the image of a "future city," an image featuring skyscrapers and futuristic infrastructure is generated. In this generation process, the generative AI model uses past data and learning to create original images that meet the user's requirements.

[0424] Finalizing the device

[0425] The generated image is received by the server and sent to the terminal as a response. The terminal receives the generated image data sent from the server and displays it on the user interface. The user can check the generated image and, if necessary, press the download button to save the image file to their terminal.

[0426] As a specific example, if a user requests an image of a "future city," a generated image is displayed and the user can download it and use it in their materials.

[0427] Using this system, users can quickly obtain ideal, copyright-free images with little effort and use them in document creation. Examples of prompts include: "Generate an image of a city of the future" or "Generate an image of autumn foliage."

[0428] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0429] Step 1: User Input

[0430] Users access the interface of the self-made image generation tool and enter the desired image conditions and description in the text box. They can also upload reference image files. For example, they can enter "future city" in the text box and upload the reference image by dragging and dropping. This input data includes the conditions in text format and the image file.

[0431] input:

[0432] Text image condition "City of the Future"

[0433] Uploaded reference images (if needed)

[0434] output:

[0435] Send user input data to the terminal

[0436] Specific behavior:

[0437] When the user clicks the "Start Generation" button, the text conditions and reference images are sent to the terminal.

[0438] Step 2: Collecting device data and making API requests

[0439] The device collects the data entered by the user. The collected data is the text information "Future City" and (optionally) the uploaded reference image file. Based on this data, the device creates an API request to send to the server. The device converts the input data to JSON format and saves the uploaded image in a temporary folder.

[0440] input:

[0441] User-submitted text information and reference images

[0442] output:

[0443] JSON formatted API request

[0444] Specific behavior:

[0445] The device encodes the text information into JSON format, saves the image to a temporary folder on the local file system, and combines this data into a single API request.

[0446] Step 3: Receiving and analyzing data on the server

[0447] The server receives the API request sent from the device. The received data includes the text information "future city" in JSON format and the path to the reference image. The server then analyzes this data and converts it into a format that the generative AI model can understand (for example, tokenized text). This allows the generative AI model to accurately understand the user's request.

[0448] input:

[0449] API request from the device (JSON format text information and image path)

[0450] output:

[0451] Data after conversion into a format that can be understood by the generative AI model

[0452] Specific behavior:

[0453] The server parses the JSON data, tokenizes the text information, and, if reference images are available, preprocesses the images to convert them into a format suitable for the generative AI model.

[0454] Step 4: Image generation using a generative AI model

[0455] The generative AI model generates unique images that match the user's desired conditions based on the analysis data provided by the server. For example, based on the image of a "future city," it generates an image featuring skyscrapers and futuristic infrastructure. This process is carried out by the generative AI model based on past data and learnings, in accordance with the user's wishes.

[0456] input:

[0457] Converted data from the server

[0458] output:

[0459] Generated original image

[0460] Specific behavior:

[0461] The generative AI model runs its internal algorithms based on the provided text data and reference images to generate images that meet the user's desired criteria.

[0462] Step 5: Server receives image and prepares response

[0463] The generated image is received by the server, which encodes it into an appropriate format and creates a response to send to the device.

[0464] input:

[0465] Generated images from generative AI models

[0466] output:

[0467] A response containing the encoded image data.

[0468] Specific behavior:

[0469] The server encodes the generated image in PNG format and adds it to the body of the HTTP response.

[0470] Step 6: Receive and display images on your device

[0471] The device decodes the image data received from the server and displays it on the user interface. The user can check the displayed image and, if satisfied, press the download button to save the image.

[0472] input:

[0473] Encoded image data from the server

[0474] output:

[0475] The decoded image displayed in the user interface

[0476] Specific behavior:

[0477] The device extracts the image data from the HTTP response and displays it in the user interface using an image viewer.

[0478] (Application example 1)

[0479] 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."

[0480] In the past, when a user requested image generation, they only had to input the desired image conditions and a description, and subsequent usage was limited. In particular, there was a lack of means for users to visually check items without actually trying them on in virtual stores, so there is a need to improve the user experience.

[0481] 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.

[0482] In this invention, the server includes means for a user to input desired image conditions and descriptions, means for transmitting the input conditions to the server, means for analyzing the input conditions and converting them into a format suitable for a generative model, means for inputting the converted format into a generative model to generate an original image, means for transmitting the generated image to a terminal, and means for a user to try on an avatar using the generated image in a virtual store, thereby enabling a user to visually check items without actually picking them up.

[0483] The "means for the user to input the conditions and description of the desired image" is an interface that allows the user to input the content of the desired image by text or other means.

[0484] The "means for transmitting the input conditions to the server" refers to a communication means for transmitting the conditions input by the user to the server via the Internet or the like.

[0485] The "means for analyzing the input conditions and converting them into a format suitable for the generative model" refers to a data analysis and conversion means for receiving the user's transmitted conditions and converting them into a data format that is easy for the generative AI model to understand.

[0486] "Means for inputting the converted format into a generative model and generating an original image" refers to means for inputting the converted data into a generative AI model, which then creates a new original image.

[0487] The "means for transmitting the generated image to the terminal" refers to a communication means for sending the generated original image to the user's terminal.

[0488] The "means for allowing a user to try on a generated image on an avatar" refers to a means for applying a generated image to a user's avatar and displaying the avatar as if it were trying on the image.

[0489] This invention relates to a system that generates original images using a generative AI model based on user input of desired image conditions and descriptions. In particular, it provides a system that allows users to try on generated images on an avatar in a virtual store.

[0490] Hardware and Software Configuration

[0491] To implement the system, the following hardware and software are required:

[0492] Hardware:

[0493] Smartphone (iOS / Android)

[0494] Server (cloud-based)

[0495] software:

[0496] Mobile app development framework (React Native)

[0497] API management server (Express.js)

[0498] Generative AI model (Stable Diffusion, DALL-E 2)

[0499] Explanation of program processing

[0500] 1. User Action:

[0501] Users access the smartphone app interface and enter the desired image criteria and description into a text box, for example, "business casual style attire." They can also upload reference images if needed.

[0502] 2. Terminal processing:

[0503] It collects the data entered by the user and creates an API request to send it to the server. Specifically, it sends the input text "Business casual style attire" to the server and waits for a response from the server.

[0504] 3. Server processing:

[0505] The server receives the API request, analyzes the input data, and converts it into a format suitable for the generative AI model. This converted data is passed to the generative AI model (e.g., Stable Diffusion) to generate the original image. The server receives the generated image data and prepares it to be sent to the device as a response.

[0506] 4. Finalize your device:

[0507] The terminal receives the generated image data sent from the server and displays it on the user interface. The user can check the generated image and try it on their avatar if necessary.

[0508] Specific examples

[0509] For example, consider the case where a user inputs "I want to try on casual summer clothes." The user inputs "casual summer clothes" into the interface of a smartphone app and presses the start generation button. The device collects the input data and sends it to the server. The server analyzes the received data and passes it to the generative AI model. The generative AI model generates an image of "casual summer clothes" and returns it to the server. The device receives the image and displays it on the user interface. The user can review the image and try it on their avatar. The user can also download the image.

[0510] Prompt Sentence Examples

[0511] "Generate business casual style outfits and have your avatar try them on."

[0512] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0513] Step 1:

[0514] The user accesses the smartphone app interface and enters the desired image criteria and description in the text boxes. The user can also upload reference images if necessary. The input data is "business casual style clothing," and the user presses the "Start Generation" button. The output is the criteria data entered by the user.

[0515] Step 2:

[0516] The device collects the user's input data and generates an API request to send it to the server. Specifically, it converts the input text data into an API request format (such as JSON) and sends it to the server address. The input is the user's condition data, and the output is the data sent to the server as an API request.

[0517] Step 3:

[0518] The server receives the API request and analyzes the input data (user condition data). This analysis is the process of converting the conditions entered by the user into a data format that the generative AI model can understand. Specifically, it converts text data into a model input format (e.g., tokenized text). The input is the API request data, and the output is the data in the converted format.

[0519] Step 4:

[0520] The server inputs the converted data into a generative AI model (e.g., Stable Diffusion) to generate the original image. The model generates an image based on the input data and returns the image data to the server. The input is the converted data, and the output is the generated image data.

[0521] Step 5:

[0522] The server receives the generated image data and prepares to send it to the device. It generates an API response for sending the image data to the device and sends it to the device. The input is the generated image data, and the output is the data sent to the device as an API response.

[0523] Step 6:

[0524] The terminal receives the generated image data sent from the server and displays it on the user interface. The user checks the generated image and selects an option to try it on the avatar. The input is the received generated image data, and the output is the user's visual confirmation and the result of trying it on the avatar.

[0525] 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.

[0526] This invention is a system in which a generative AI model uses an emotion engine to generate original images based on the user's desired conditions and images. This system consists of three main elements: a server, a terminal, and a user, and is combined with an emotion engine that senses the user's emotions and reflects them in the image generation.

[0527] User operations

[0528] The user accesses the interface of the self-made image generation tool and enters the desired image conditions and image into the text box. For example, they can enter "autumn foliage scenery." They can also upload a reference image file. Once the input is complete, the user presses the "Start Generation" button.

[0529] Furthermore, the system also recognizes the user's emotional state. The emotion engine detects emotions from the user's facial expressions, voice, or text, and adds them as complementary conditions for image generation.

[0530] For example, if the user has a tired expression, the emotion engine will modify the conditions to something like "a relaxing autumn scene with autumn leaves."

[0531] Terminal handling

[0532] The device collects the data entered by the user and the output of the emotion engine, creates an API request to send it to the server, and then waits for a response.

[0533] For example, data such as "autumn foliage scenery" and "user needs relaxation" are collected and sent to the server.

[0534] Server Processing

[0535] The server receives the API request sent from the device and first analyzes the data. Analysis is the process of converting the user's desired conditions and the output of the emotion engine into a format that the generative AI model can understand. This conversion includes tokenizing text data and preprocessing image data.

[0536] The analyzed data is then fed into a generative AI model, which then begins the process of generating an original image. The generative AI model then generates the optimal image based on the user's desired conditions and emotional state. This process creates an image with the appropriate visual characteristics, color tone, and style.

[0537] For example, the generative AI model generates "relaxing autumn foliage scenes."

[0538] The generated image data is returned from the generation AI model to the server, which temporarily stores the image data and prepares to send it to the device as a response.

[0539] Finalizing the device

[0540] The device receives the generated image data sent from the server and displays it on the user interface. The user can check the generated image and, if necessary, press the download button to save the image file to their device.

[0541] For example, a user may check out "Relaxing Autumn Foliage Scenery" and download it to use in their materials.

[0542] Specific examples

[0543] For example, if an image of a "future city" is needed to create educational materials, the user enters "future city" into the interface and presses the generation start button. If the emotion engine detects the user's elevated emotions, it will supplement the criteria with an "energetic future city." The device sends the data to the server, which analyzes and converts the data and passes it to the generative AI model. The generative AI model generates an image of the "energetic future city" and returns it to the server. The device then displays the image to the user, who then downloads it.

[0544] In this way, users can quickly generate original images that meet the conditions that take into account their emotional state, and use them without copyright issues. The system of the present invention is extremely useful in that it not only allows users to input conditions, but also reflects their emotional state, thereby providing more suitable, high-quality images.

[0545] The processing flow will be explained below.

[0546] Program processing steps

[0547] Step 1:

[0548] The user accesses the interface of the self-made image generation tool and inputs the desired image conditions and image into the text box. For example, they can input "autumn foliage scenery." They can also upload reference image files. Once the input is complete, the user presses the "Start Generation" button.

[0549] Step 2:

[0550] The device collects data entered by the user, including text information and uploaded image files, and then makes an API request to send the collected data to the server.

[0551] Step 3:

[0552] The device sends the created API request to the server and then waits for a response from the server.

[0553] Step 4:

[0554] The server receives API requests sent from the device, then parses the received data and converts it into a format that the generative AI model can understand. Parsing includes tokenizing text data and preprocessing uploaded images.

[0555] Step 5:

[0556] The server inputs the analyzed data into the generative AI model, which then initiates the process of generating an original image based on the user's input conditions.

[0557] Step 6:

[0558] At the same time, the emotion engine recognizes the user's emotions. The emotion engine detects emotions from the user's facial expressions, voice, and text input. This emotion data is provided as supplementary data to the generative AI model.

[0559] Step 7:

[0560] The generative AI model generates the optimal original image based on the input conditions and emotional data from the emotion engine. For example, if the emotion engine detects "relaxation," the generated "autumn foliage scene" will be adjusted to have a more calming atmosphere.

[0561] Step 8:

[0562] The generated image data is returned from the generative AI model to the server, which temporarily stores the image data and prepares to send it to the device as a response.

[0563] Step 9:

[0564] The server sends the generated image data to the device, and the response includes metadata and a download link for the generated image.

[0565] Step 10:

[0566] The terminal receives the generated image data sent from the server, displays the received image data on the user interface, and allows the user to confirm it.

[0567] Step 11:

[0568] Users can check the generated images and, if necessary, press the download button to save the image files to their own devices, allowing them to freely use the generated images in creating materials.

[0569] As a concrete example, consider a case where a user needs an image of a "future city" for educational materials. The user enters "future city" in the text box and presses the start generation button. If the emotion engine detects the user's excited emotion, the generative AI model will generate an image of an "energetic future city." Through this image generation process, the user can quickly obtain a high-quality original image to use in creating materials.

[0570] Example 2

[0571] 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."

[0572] While modern image generation technology can generate original images based on specific user requirements and descriptions, technology for generating more personalized images by reflecting the user's emotional state is still immature. This is because it is difficult for users to clearly communicate their emotions through text, facial expressions, or voice, and as a result, generated images do not necessarily match the user's emotions or requirements. Furthermore, if the generated image does not meet the user's expectations, it is time-consuming to reset the conditions, making efficient image generation difficult.

[0573] 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.

[0574] In this invention, the server includes a means for a user to input desired image conditions and descriptions, a means for transmitting the input conditions to the server, a means for analyzing the input conditions and converting them into a format suitable for a generative AI model, a means for inputting the converted format into the generative AI model to generate an original image, a means for transmitting the generated image to a terminal, a means including an emotion engine that detects the user's emotions and adds them as complementary conditions for image generation, and a means for integrating the output of the emotion engine into analysis data. This makes it possible to generate images that reflect the specific conditions and emotional state desired by the user.

[0575] A "user" is an individual or entity that utilizes the system to make an image generation request.

[0576] "Conditions" are information for specifying the characteristics and content of the image desired by the user.

[0577] "Description" is text information that allows a user to specify the detailed content and context of a desired image.

[0578] "Means" are hardware and software components used within a system to achieve a particular purpose.

[0579] A "server" is a computing device on a network that receives data sent by a user, analyzes and processes it, and sends the final results to a terminal.

[0580] "Terminal" refers to the device through which a user accesses the system, inputs conditions, and checks and downloads generated images.

[0581] "Input conditions" refers to the characteristics and descriptions specified by the user for image generation.

[0582] "Means for sending" refers to the process or function for sending the conditions entered by the user to the server.

[0583] "Means of analysis" refers to the process or function for converting the input conditions into a format that the generative AI model can understand.

[0584] A "generative AI model" is an algorithm or program that uses artificial intelligence to generate original images based on user input.

[0585] "Means for converting" refers to the process or function for analyzing the input conditions and converting them into a format suitable for the generative AI model.

[0586] "Means of generation" refers to the process or function for inputting converted data into a generative AI model and creating an original image.

[0587] "Means for generating an image" refers to the process or function for inputting the converted format into a generative AI model to generate the original image.

[0588] "Transmitting means" refers to the process or function for transmitting the generated image to the terminal.

[0589] An "emotion engine" is software or hardware that has the function of detecting emotions from a user's facial expressions, voice, text, etc., and adding them as complementary conditions for image generation.

[0590] "Means of integration" refers to the processes and functions for integrating the output of the emotion engine into the analytical data.

[0591] This invention is a system that generates original images based on the user's desired conditions and emotions. The system consists of three main elements: the user, the terminal, and the server. It also includes an emotion engine that detects the user's emotions and reflects them in the image generation.

[0592] User operations

[0593] Users access the interface of the self-made image generation tool and enter the conditions and description of the desired image in the text box. Specifically, they can enter a prompt such as "Autumn foliage scenery." They can also upload a reference image. Once the input is complete, the user presses the "Start Generation" button.

[0594] Emotion detection

[0595] The system is equipped with an emotion engine that recognizes the user's emotional state in real time. The emotion engine detects emotions from the user's facial expressions, voice, or input text and adds them as complementary conditions for image generation. For example, if the user has a tired expression, the emotion engine will modify the conditions to something like "relaxing autumn foliage scenery."

[0596] Device Role

[0597] The device collects the data entered by the user and the output of the emotion engine, and creates an API request to send it to the server. It generates a JSON-formatted request containing the user-entered emotion data "Autumn foliage scenery" and "Relaxed," and sends it to the server. The device waits for a response from the server and displays the generated image on the user interface.

[0598] Server Processing

[0599] The server receives the API request sent from the device and first analyzes the data. This analysis is the process of converting the user's desired conditions and the output of the emotion engine into a format that the generative AI model can understand. The analysis includes tokenizing the text data and converting the emotion data into numbers. The analyzed data is input into the generative AI model, which starts the original image generation process. For example, an image of a "relaxing autumn foliage scene" is generated. The generated image data is then sent from the server to the device.

[0600] Specific examples

[0601] For example, if an image of a "future city" is needed to create educational materials, the user enters "future city" into the interface and presses the generation start button. If the emotion engine detects the user's elevated emotions, it will supplement the criteria with an "energetic future city." The device sends the data to the server, which analyzes and converts the data and passes it to the generative AI model. The generative AI model generates an image of the "energetic future city" and returns it to the server. The device then displays the image to the user, who then downloads it.

[0602] Prompt Sentence Examples

[0603] "Generate an image of autumn foliage. The user wants a relaxing image."

[0604] In this way, users can quickly generate original images that match their emotional state and use them without copyright issues. This system is extremely useful in that it not only allows users to input conditions, but also reflects their emotional state, providing more suitable, high-quality images.

[0605] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0606] Step 1:

[0607] The user accesses the interface of the self-made image generation tool and enters the conditions and description of the desired image in the text box. For example, they can enter a prompt such as "Autumn foliage scenery." Once they have completed the input, they press the "Start generation" button. The input data is the text data "Autumn foliage scenery," and this is passed to the next processing step.

[0608] Step 2:

[0609] The device receives the text data entered by the user and activates the emotion engine. The emotion engine detects emotions from the user's facial expressions, voice, and entered text. This analysis generates emotion data such as "The user wants to relax." Specifically, the device uses a camera and microphone to read the user's emotions in real time and obtains the analysis results as text data.

[0610] Step 3:

[0611] The device collects the text data entered by the user and the emotion engine's output (emotion data) and creates an API request to send it to the server. Specifically, the device generates a JSON-formatted request containing the conditions "autumn foliage scenery" and "relaxation." Once the API request is complete, the device sends the data to the server. This sent data is passed on to the next processing step.

[0612] Step 4:

[0613] The server receives the API request sent from the device and first analyzes the data. Analysis is the process of converting the user's desired conditions and the output of the emotion engine into a format that the generative AI model can understand. Specifically, the server tokenizes the sent JSON data, converts text data into character vectors, and quantifies emotion data. This generates a data format suitable for input to the generative AI model.

[0614] Step 5:

[0615] The server inputs the analyzed data into a generative AI model and begins the original image generation process. The generative AI model uses specific algorithms to generate the optimal image based on the user's desired conditions and emotional state. This process creates an image with appropriate visual features, color tone, and style. Specifically, the server inputs tokenized text data and quantified emotional data for "Autumn foliage scenery with a relaxing effect" into the generative AI model and waits for the image to be generated. The generated image data is then passed to the next processing step.

[0616] Step 6:

[0617] The generated image data is returned from the generative AI model to the server, which temporarily stores the generated image data. The server then prepares to send the generated image data to the device as a response. Specifically, the server stores the generated image in temporary storage and encodes it as part of the JSON response.

[0618] Step 7:

[0619] The device receives the generated image data sent from the server. The received image data is displayed on the user interface, allowing the user to confirm the generated image. Specifically, the device extracts the image data from the received JSON response and displays it on the interface. At this point, the user can view the generated image and, if necessary, press the download button to save the image file to their device.

[0620] (Application example 2)

[0621] 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."

[0622] Conventional advertising image generation systems have difficulty generating images that take the user's emotional state into account, making it impossible to generate images that best fit the target or theme of the advertisement. Even when users input desired conditions, the system does not generate images that reflect the emotional elements based on those conditions, limiting their effectiveness as advertising. Furthermore, images generated solely based on user-input conditions and reference images often lack originality and creativity.

[0623] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0624] In this invention, the server includes: a means for a user to input desired image conditions and descriptions; a means for transmitting the input conditions and the user's emotional state to the server; a means for analyzing the input conditions and emotional state and converting them into a format suitable for a generative model; a means for inputting the converted format into the generative model to generate an original image based on the user's emotional state; and a means for transmitting the generated image to a terminal and displaying it. This enables advertising creators and marketers to easily generate high-quality original images that reflect emotional elements and are best suited to their target or theme.

[0625] The "means for the user to input the conditions and description of the image desired" is a function for the user to input the specific conditions and description of the image he or she wants to generate using an input interface such as a text box.

[0626] The "means for transmitting the input conditions and the user's emotional state to the server" is a function for transmitting the conditions and explanations input by the user and data on the user's emotional state detected by the emotion engine to the server via a network.

[0627] "Means for analyzing the input conditions and emotional state and converting them into a format suitable for the generative model" refers to a function that executes a process of analyzing the user's conditions, explanations, and emotional state data and converting them into a format that can be understood by the generative AI model.

[0628] "Means for inputting the converted format into a generative model and generating an original image based on the user's emotional state" refers to a function for inputting the converted data into a generative AI model and generating an original image that reflects emotional elements based on this.

[0629] The "means for transmitting the generated image to the terminal and displaying it" is a function for transmitting the generated original image data from the server to the user's terminal and displaying it on the user interface.

[0630] The "means for uploading reference images" is a function for uploading to the system an image file that serves as a reference for the image that the user wants to generate.

[0631] A "generative model" is an artificial intelligence model that automatically generates original images based on the user's input conditions and emotional state.

[0632] The system embodying the present invention functions as a tool that enables advertising creators and marketers to easily generate high-quality images that reflect emotional elements. Specific embodiments of the system are described below.

[0633] User operations

[0634] Using a smartphone app, users enter the theme and conditions of their ad into a text box. They can also upload a reference image. The system then uses a camera and microphone to recognize facial expressions and voice to detect emotions in real time. For example, a user who enters "Christmas sale ad image" and has a smiling expression may use the app.

[0635] Terminal handling

[0636] It collects the conditions and emotional state entered by the user and generates an API request to send this to the server. For example, it collects data such as "Christmas sale advertising image" and "user is smiling" and sends it to the server.

[0637] Server Processing

[0638] The server analyzes the received API request and converts it into a format that the generative AI model can understand. This conversion process includes tokenizing the text data and tagging the emotional state. The analyzed data is input into the generative AI model, which generates an original image that takes into account the user's emotional state. Examples of generative AI models used are OpenAI's DALL·E and Google AI's Imagen. The generated image will look something like a "Smiling Happy Christmas Sale Advertising Image."

[0639] Finalizing the device

[0640] The generated image data sent from the server is received and displayed on the user interface. The user can check the generated image and, if necessary, press the download button to save the image file to their device.

[0641] The hardware used is a smartphone, and real-time emotion recognition is performed using a camera and microphone. The software used is an emotion engine (e.g., Amazon Rekognition or Microsoft Azure Face API), and the generative AI model is OpenAI's DALL·E or Google AI's Imagen.

[0642] Examples and prompts

[0643] Specifically, if the user desires a "happy Christmas sale advertising image" and has a smiling expression, the system generates a "smiling happy Christmas sale advertising image."

[0644] Example prompt sentence:

[0645] "Christmas sale advertising image"

[0646] "We detected that the user is smiling"

[0647] "Generate smiling, happy Christmas sale advertising images"

[0648] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0649] Step 1:

[0650] The user launches the smartphone app and enters the advertising theme and conditions into a text box. For example, they might enter "Christmas sale advertising image." They can also upload a reference image. Furthermore, facial expressions and voice are recognized using a camera and microphone. The inputs for this step are the text conditions entered by the user, the uploaded reference image, and the emotional state obtained in real time. The output is a combination of the input data and emotional data.

[0651] Step 2:

[0652] The device organizes the collected data and generates an API request to send to the server. Specifically, it combines the text condition entered by the user, "Christmas sale advertising image," and the emotion data, "the user is smiling," into an API request. The input is the text information, reference image, and emotion data provided by the user. The output is a formatted API request to send to the server.

[0653] Step 3:

[0654] The server receives the API request and first tokenizes the text data and tags it with emotion data. This analysis process converts the data into a format that the generative AI model can understand. For example, the terms "Christmas sale advertising image" and "smile" are tokenized. The input is the data received from the API request, and the output is the analyzed data to be input into the generative AI model.

[0655] Step 4:

[0656] The server inputs the analyzed data into a generative AI model to generate an original image. The generative AI model generates an image based on the user's conditions and emotional state. In this case, the model generates a "smiling, happy Christmas sale advertising image." The input is the analyzed data, and the output is the generated image data.

[0657] Step 5:

[0658] The server temporarily stores the generated image data and prepares it for transmission to the terminal. The input is the generated image data, and the output is the prepared data to be transmitted to the terminal.

[0659] Step 6:

[0660] The terminal receives the image data sent from the server and displays it on the user interface. The user can check the generated image and, if necessary, press the download button to save the image file to their terminal. The input is the image data received from the server, and the output is the generated image displayed to the user.

[0661] keyword

[0662] Generative AI model, prompt sentence

[0663] 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.

[0664] 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.

[0665] 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.

[0666] [Third embodiment]

[0667] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0668] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0669] 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).

[0670] 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.

[0671] 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.

[0672] 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).

[0673] 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.

[0674] 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.

[0675] 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.

[0676] 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.

[0677] 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.

[0678] 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."

[0679] This invention is a system in which a user inputs desired conditions and images, and a generative AI model analyzes them to generate original images that can be used for document creation. The invention consists of three main elements: a server, a terminal, and a user.

[0680] User operations

[0681] First, the user accesses the interface of the self-made image generation tool and enters the desired image conditions and image in the text boxes. At this time, it is also possible to upload a reference image file. Once the input is complete, the user presses the "Start Generation" button, and the input data is sent to the device.

[0682] For example, if a user desires "autumn foliage scenery" for creating a document, the user enters "autumn foliage scenery" in the text box and uploads a reference image if necessary.

[0683] Terminal handling

[0684] The device collects the data entered by the user and creates an API request to send it to the server. Once the API request is complete, it sends it to the server and waits for a response.

[0685] Specifically, this is a process of sending the user's input data, text information "autumn foliage scenery," to the server.

[0686] Server Processing

[0687] The server receives the API request sent from the device and first performs data analysis. Analysis is the process of converting the input conditions into a format that the generative AI model can understand. Once this conversion is complete, the data is input into the generative AI model, and the original image generation process begins.

[0688] The generative AI model uses the analyzed data to generate unique images that match the user's desired criteria. In doing so, the model creates images with visual characteristics that match the criteria, resulting in original content created from scratch.

[0689] When image generation is complete, the server receives the generated image data and prepares to send it to the terminal as a response.

[0690] Finalizing the device

[0691] The device receives the generated image data sent from the server and displays it on the user interface. The user can check the generated image and, if necessary, press the download button to save the image file to their device.

[0692] For example, if a user requests an image of "autumn foliage scenery," the generated image is displayed and the user can download it and use it in their materials.

[0693] Specific examples

[0694] For example, if a user needs an image of a "future city" to create educational materials, they enter "future city" into the interface and press the "Start Generation" button. The device sends the data to the server, which analyzes it and passes it to the generative AI model. The generative AI model generates an image of the "future city" and returns it to the server. The device then displays the image to the user, who then downloads it.

[0695] In this way, users can quickly obtain ideal, copyright-free images with minimal effort and use them in creating materials. The system of the present invention is extremely useful in that it provides high-quality images through a complex process, simply by the user inputting their requirements.

[0696] The processing flow will be explained below.

[0697] Program processing steps

[0698] Step 1:

[0699] The user accesses the interface of the self-made image generation tool and inputs the desired image conditions and image into the text box. For example, they can input "autumn foliage scenery." If they have a reference image, they can upload that image file. Once they have completed the input, they press the "Start Generation" button.

[0700] Step 2:

[0701] The device collects the data entered by the user. The collected data (text information and uploaded images) is organized as an API request. This API request is then prepared for sending to the server.

[0702] Step 3:

[0703] The device sends the organized API request to the server, and then waits for a response from the server.

[0704] Step 4:

[0705] The server receives API requests sent from the device, analyzes the received data, and converts it into a format that the generative AI model can understand. This conversion includes tokenizing text data and preprocessing image data.

[0706] Step 5:

[0707] The server inputs the analyzed data into a generative AI model, which then starts the image generation process based on the input conditions and images.

[0708] Step 6:

[0709] Generative AI models generate original images that fit the criteria, a process that involves running algorithms to create images from scratch with the right visual characteristics.

[0710] Step 7:

[0711] The generated image data is returned from the generative AI model to the server, which temporarily stores the image data and prepares to send it to the device as a response.

[0712] Step 8:

[0713] The server sends the generated image data to the device as a response, which includes metadata and a download link for the generated image.

[0714] Step 9:

[0715] The terminal receives the generated image data sent from the server, displays the received image data on the user interface, and allows the user to confirm it.

[0716] Step 10:

[0717] Users can check the generated image and press the download button to save the image file to their own device, allowing them to freely use the generated image in creating documents.

[0718] Through this series of processes, users can quickly generate original images that meet their desired conditions and use them without copyright issues.

[0719] Example 1

[0720] 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."

[0721] Conventional image generation systems have had difficulty in quickly and easily generating original images that meet the user's desired conditions. They also required complex procedures and settings, which was time-consuming for the user. Furthermore, the generated images were often of low quality, or did not fully meet the user's expectations.

[0722] 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.

[0723] In this invention, the server includes means for a user to input desired image conditions and descriptions, means for transmitting the input conditions to a terminal, means for the terminal to collect the user's input data and transmit it as an API request to the server, means for the server to analyze the received API request and convert it into a format understandable by the generative AI model, means for inputting the converted data into the generative AI model to generate an original image, means for the server to transmit the generated image to the terminal, and means for the terminal to display the image received to the user. This allows a user to quickly generate high-quality original images and easily use them simply by inputting their desired conditions.

[0724] A "user" is a person who uses the system to make a request for image generation.

[0725] The "conditions and description of the desired image" is information indicating the specific characteristics and content of the image that the user wants to generate.

[0726] A "terminal" is a computer device that allows a user to input conditions and explanations and transmit them to a server.

[0727] An "API request" refers to data sent from a device to a server to request specific data or functions.

[0728] A "server" is a computer system that receives and analyzes data sent from a terminal and generates images using a generative AI model.

[0729] A "generative AI model" is an algorithm or program that generates original images from input data based on machine learning and artificial intelligence techniques.

[0730] "Analysis" is the process of converting the conditions and explanations entered by the user into a format that the generative AI model can understand.

[0731] An "original image" is a new, copyright-free image generated from scratch by a generative AI model.

[0732] "Upload" refers to a user sending a reference image or file from their own device to the server.

[0733] "Download" refers to a user saving an image generated from a server or device to their own device.

[0734] A "user interface" is a part of software that provides the screens and operating methods for users to operate a system.

[0735] This invention is a system in which, when a user inputs desired conditions and descriptions, a generative AI model analyzes them and generates original images that can be used for document creation, etc. The invention consists of three main elements: a server, a terminal, and a user.

[0736] User operations

[0737] First, the user accesses the interface of the self-made image generation tool and enters the desired image conditions and description in the text box. At this time, it is also possible to upload a reference image file. Once the input is complete, the user presses the "Start Generation" button, and the input data is sent to the device.

[0738] For example, if a user wants an image of a "future city" for creating a document, they can enter "future city" in the text box and upload a reference image if necessary.

[0739] Terminal handling

[0740] The device collects the data entered by the user and creates an API request to send it to the server. The device converts the text data entered by the user into JSON format and saves the uploaded image in a temporary folder. Once this API request is complete, the device sends the data to the server and waits for a response.

[0741] Server Processing

[0742] The server receives the API request sent from the device and first performs data analysis. Analysis is the process of converting the input conditions into a format that the generative AI model can understand. The server then analyzes the received JSON data and converts it into a format that the generative AI model can understand (for example, tokenized text). Once this conversion is complete, the server inputs the analyzed data into the generative AI model and begins the original image generation process.

[0743] Image generation using generative AI models

[0744] The generative AI model generates unique images that match the user's desired criteria based on the analysis data provided by the server. For example, based on the image of a "future city," an image featuring skyscrapers and futuristic infrastructure is generated. In this generation process, the generative AI model uses past data and learning to create original images that meet the user's requirements.

[0745] Finalizing the device

[0746] The generated image is received by the server and sent to the terminal as a response. The terminal receives the generated image data sent from the server and displays it on the user interface. The user can check the generated image and, if necessary, press the download button to save the image file to their terminal.

[0747] As a specific example, if a user requests an image of a "future city," a generated image is displayed and the user can download it and use it in their materials.

[0748] Using this system, users can quickly obtain ideal, copyright-free images with little effort and use them in document creation. Examples of prompts include: "Generate an image of a city of the future" or "Generate an image of autumn foliage."

[0749] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0750] Step 1: User Input

[0751] Users access the interface of the self-made image generation tool and enter the desired image conditions and description in the text box. They can also upload reference image files. For example, they can enter "future city" in the text box and upload the reference image by dragging and dropping. This input data includes the conditions in text format and the image file.

[0752] input:

[0753] Text image condition "City of the Future"

[0754] Uploaded reference images (if needed)

[0755] output:

[0756] Send user input data to the terminal

[0757] Specific behavior:

[0758] When the user clicks the "Start Generation" button, the text conditions and reference images are sent to the terminal.

[0759] Step 2: Collecting device data and making API requests

[0760] The device collects the data entered by the user. The collected data is the text information "Future City" and (optionally) the uploaded reference image file. Based on this data, the device creates an API request to send to the server. The device converts the input data to JSON format and saves the uploaded image in a temporary folder.

[0761] input:

[0762] User-submitted text information and reference images

[0763] output:

[0764] JSON formatted API request

[0765] Specific behavior:

[0766] The device encodes the text information into JSON format, saves the image to a temporary folder on the local file system, and combines this data into a single API request.

[0767] Step 3: Receiving and analyzing data on the server

[0768] The server receives the API request sent from the device. The received data includes the text information "future city" in JSON format and the path to the reference image. The server then analyzes this data and converts it into a format that the generative AI model can understand (for example, tokenized text). This allows the generative AI model to accurately understand the user's request.

[0769] input:

[0770] API request from the device (JSON format text information and image path)

[0771] output:

[0772] Data after conversion into a format that can be understood by the generative AI model

[0773] Specific behavior:

[0774] The server parses the JSON data, tokenizes the text information, and, if reference images are available, preprocesses the images to convert them into a format suitable for the generative AI model.

[0775] Step 4: Image generation using a generative AI model

[0776] The generative AI model generates unique images that match the user's desired conditions based on the analysis data provided by the server. For example, based on the image of a "future city," it generates an image featuring skyscrapers and futuristic infrastructure. This process is carried out by the generative AI model based on past data and learnings, in accordance with the user's wishes.

[0777] input:

[0778] Converted data from the server

[0779] output:

[0780] Generated original image

[0781] Specific behavior:

[0782] The generative AI model runs its internal algorithms based on the provided text data and reference images to generate images that meet the user's desired criteria.

[0783] Step 5: Server receives image and prepares response

[0784] The generated image is received by the server, which encodes it into an appropriate format and creates a response to send to the device.

[0785] input:

[0786] Generated images from generative AI models

[0787] output:

[0788] A response containing the encoded image data.

[0789] Specific behavior:

[0790] The server encodes the generated image in PNG format and adds it to the body of the HTTP response.

[0791] Step 6: Receive and display images on your device

[0792] The device decodes the image data received from the server and displays it on the user interface. The user can check the displayed image and, if satisfied, press the download button to save the image.

[0793] input:

[0794] Encoded image data from the server

[0795] output:

[0796] The decoded image displayed in the user interface

[0797] Specific behavior:

[0798] The device extracts the image data from the HTTP response and displays it in the user interface using an image viewer.

[0799] (Application example 1)

[0800] 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."

[0801] In the past, when a user requested image generation, they only had to input the desired image conditions and a description, and subsequent usage was limited. In particular, there was a lack of means for users to visually check items without actually trying them on in virtual stores, so there is a need to improve the user experience.

[0802] 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.

[0803] In this invention, the server includes means for a user to input desired image conditions and descriptions, means for transmitting the input conditions to the server, means for analyzing the input conditions and converting them into a format suitable for a generative model, means for inputting the converted format into a generative model to generate an original image, means for transmitting the generated image to a terminal, and means for a user to try on an avatar using the generated image in a virtual store, thereby enabling a user to visually check items without actually picking them up.

[0804] The "means for the user to input the conditions and description of the desired image" is an interface that allows the user to input the content of the desired image by text or other means.

[0805] The "means for transmitting the input conditions to the server" refers to a communication means for transmitting the conditions input by the user to the server via the Internet or the like.

[0806] The "means for analyzing the input conditions and converting them into a format suitable for the generative model" refers to a data analysis and conversion means for receiving the user's transmitted conditions and converting them into a data format that is easy for the generative AI model to understand.

[0807] "Means for inputting the converted format into a generative model and generating an original image" refers to means for inputting the converted data into a generative AI model, which then creates a new original image.

[0808] The "means for transmitting the generated image to the terminal" refers to a communication means for sending the generated original image to the user's terminal.

[0809] The "means for allowing a user to try on a generated image on an avatar" refers to a means for applying a generated image to a user's avatar and displaying the avatar as if it were trying on the image.

[0810] This invention relates to a system that generates original images using a generative AI model based on user input of desired image conditions and descriptions. In particular, it provides a system that allows users to try on generated images on an avatar in a virtual store.

[0811] Hardware and Software Configuration

[0812] To implement the system, the following hardware and software are required:

[0813] Hardware:

[0814] Smartphone (iOS / Android)

[0815] Server (cloud-based)

[0816] software:

[0817] Mobile app development framework (React Native)

[0818] API management server (Express.js)

[0819] Generative AI model (Stable Diffusion, DALL-E 2)

[0820] Explanation of program processing

[0821] 1. User Action:

[0822] Users access the smartphone app interface and enter the desired image criteria and description into a text box, for example, "business casual style attire." They can also upload reference images if needed.

[0823] 2. Terminal processing:

[0824] It collects the data entered by the user and creates an API request to send it to the server. Specifically, it sends the input text "Business casual style attire" to the server and waits for a response from the server.

[0825] 3. Server processing:

[0826] The server receives the API request, analyzes the input data, and converts it into a format suitable for the generative AI model. This converted data is passed to the generative AI model (e.g., Stable Diffusion) to generate the original image. The server receives the generated image data and prepares it to be sent to the device as a response.

[0827] 4. Finalize your device:

[0828] The terminal receives the generated image data sent from the server and displays it on the user interface. The user can check the generated image and try it on their avatar if necessary.

[0829] Specific examples

[0830] For example, consider the case where a user inputs "I want to try on casual summer clothes." The user inputs "casual summer clothes" into the interface of a smartphone app and presses the start generation button. The device collects the input data and sends it to the server. The server analyzes the received data and passes it to the generative AI model. The generative AI model generates an image of "casual summer clothes" and returns it to the server. The device receives the image and displays it on the user interface. The user can review the image and try it on their avatar. The user can also download the image.

[0831] Prompt Sentence Examples

[0832] "Generate business casual style outfits and have your avatar try them on."

[0833] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0834] Step 1:

[0835] The user accesses the smartphone app interface and enters the desired image criteria and description in the text boxes. The user can also upload reference images if necessary. The input data is "business casual style clothing," and the user presses the "Start Generation" button. The output is the criteria data entered by the user.

[0836] Step 2:

[0837] The device collects the user's input data and generates an API request to send it to the server. Specifically, it converts the input text data into an API request format (such as JSON) and sends it to the server address. The input is the user's condition data, and the output is the data sent to the server as an API request.

[0838] Step 3:

[0839] The server receives the API request and analyzes the input data (user condition data). This analysis is the process of converting the conditions entered by the user into a data format that the generative AI model can understand. Specifically, it converts text data into a model input format (e.g., tokenized text). The input is the API request data, and the output is the data in the converted format.

[0840] Step 4:

[0841] The server inputs the converted data into a generative AI model (e.g., Stable Diffusion) to generate the original image. The model generates an image based on the input data and returns the image data to the server. The input is the converted data, and the output is the generated image data.

[0842] Step 5:

[0843] The server receives the generated image data and prepares to send it to the device. It generates an API response for sending the image data to the device and sends it to the device. The input is the generated image data, and the output is the data sent to the device as an API response.

[0844] Step 6:

[0845] The terminal receives the generated image data sent from the server and displays it on the user interface. The user checks the generated image and selects an option to try it on the avatar. The input is the received generated image data, and the output is the user's visual confirmation and the result of trying it on the avatar.

[0846] 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.

[0847] This invention is a system in which a generative AI model uses an emotion engine to generate original images based on the user's desired conditions and images. This system consists of three main elements: a server, a terminal, and a user, and is combined with an emotion engine that senses the user's emotions and reflects them in the image generation.

[0848] User operations

[0849] The user accesses the interface of the self-made image generation tool and enters the desired image conditions and image into the text box. For example, they can enter "autumn foliage scenery." They can also upload a reference image file. Once the input is complete, the user presses the "Start Generation" button.

[0850] Furthermore, the system also recognizes the user's emotional state. The emotion engine detects emotions from the user's facial expressions, voice, or text, and adds them as complementary conditions for image generation.

[0851] For example, if the user has a tired expression, the emotion engine will modify the conditions to something like "a relaxing autumn scene with autumn leaves."

[0852] Terminal handling

[0853] The device collects the data entered by the user and the output of the emotion engine, creates an API request to send it to the server, and then waits for a response.

[0854] For example, data such as "autumn foliage scenery" and "user needs relaxation" are collected and sent to the server.

[0855] Server Processing

[0856] The server receives the API request sent from the device and first analyzes the data. Analysis is the process of converting the user's desired conditions and the output of the emotion engine into a format that the generative AI model can understand. This conversion includes tokenizing text data and preprocessing image data.

[0857] The analyzed data is then fed into a generative AI model, which then begins the process of generating an original image. The generative AI model then generates the optimal image based on the user's desired conditions and emotional state. This process creates an image with the appropriate visual characteristics, color tone, and style.

[0858] For example, the generative AI model generates "relaxing autumn foliage scenes."

[0859] The generated image data is returned from the generation AI model to the server, which temporarily stores the image data and prepares to send it to the device as a response.

[0860] Finalizing the device

[0861] The device receives the generated image data sent from the server and displays it on the user interface. The user can check the generated image and, if necessary, press the download button to save the image file to their device.

[0862] For example, a user may check out "Relaxing Autumn Foliage Scenery" and download it to use in their materials.

[0863] Specific examples

[0864] For example, if an image of a "future city" is needed to create educational materials, the user enters "future city" into the interface and presses the generation start button. If the emotion engine detects the user's elevated emotions, it will supplement the criteria with an "energetic future city." The device sends the data to the server, which analyzes and converts the data and passes it to the generative AI model. The generative AI model generates an image of the "energetic future city" and returns it to the server. The device then displays the image to the user, who then downloads it.

[0865] In this way, users can quickly generate original images that meet the conditions that take into account their emotional state, and use them without copyright issues. The system of the present invention is extremely useful in that it not only allows users to input conditions, but also reflects their emotional state, thereby providing more suitable, high-quality images.

[0866] The processing flow will be explained below.

[0867] Program processing steps

[0868] Step 1:

[0869] The user accesses the interface of the self-made image generation tool and inputs the desired image conditions and image into the text box. For example, they can input "autumn foliage scenery." They can also upload reference image files. Once the input is complete, the user presses the "Start Generation" button.

[0870] Step 2:

[0871] The device collects data entered by the user, including text information and uploaded image files, and then makes an API request to send the collected data to the server.

[0872] Step 3:

[0873] The device sends the created API request to the server and then waits for a response from the server.

[0874] Step 4:

[0875] The server receives API requests sent from the device, then parses the received data and converts it into a format that the generative AI model can understand. Parsing includes tokenizing text data and preprocessing uploaded images.

[0876] Step 5:

[0877] The server inputs the analyzed data into the generative AI model, which then initiates the process of generating an original image based on the user's input conditions.

[0878] Step 6:

[0879] At the same time, the emotion engine recognizes the user's emotions. The emotion engine detects emotions from the user's facial expressions, voice, and text input. This emotion data is provided as supplementary data to the generative AI model.

[0880] Step 7:

[0881] The generative AI model generates the optimal original image based on the input conditions and emotional data from the emotion engine. For example, if the emotion engine detects "relaxation," the generated "autumn foliage scene" will be adjusted to have a more calming atmosphere.

[0882] Step 8:

[0883] The generated image data is returned from the generative AI model to the server, which temporarily stores the image data and prepares to send it to the device as a response.

[0884] Step 9:

[0885] The server sends the generated image data to the device, and the response includes metadata and a download link for the generated image.

[0886] Step 10:

[0887] The terminal receives the generated image data sent from the server, displays the received image data on the user interface, and allows the user to confirm it.

[0888] Step 11:

[0889] Users can check the generated images and, if necessary, press the download button to save the image files to their own devices, allowing them to freely use the generated images in creating materials.

[0890] As a concrete example, consider a case where a user needs an image of a "future city" for educational materials. The user enters "future city" in the text box and presses the start generation button. If the emotion engine detects the user's excited emotion, the generative AI model will generate an image of an "energetic future city." Through this image generation process, the user can quickly obtain a high-quality original image to use in creating materials.

[0891] Example 2

[0892] 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."

[0893] While modern image generation technology can generate original images based on specific user requirements and descriptions, technology for generating more personalized images by reflecting the user's emotional state is still immature. This is because it is difficult for users to clearly communicate their emotions through text, facial expressions, or voice, and as a result, generated images do not necessarily match the user's emotions or requirements. Furthermore, if the generated image does not meet the user's expectations, it is time-consuming to reset the conditions, making efficient image generation difficult.

[0894] 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.

[0895] In this invention, the server includes a means for a user to input desired image conditions and descriptions, a means for transmitting the input conditions to the server, a means for analyzing the input conditions and converting them into a format suitable for a generative AI model, a means for inputting the converted format into the generative AI model to generate an original image, a means for transmitting the generated image to a terminal, a means including an emotion engine that detects the user's emotions and adds them as complementary conditions for image generation, and a means for integrating the output of the emotion engine into analysis data. This makes it possible to generate images that reflect the specific conditions and emotional state desired by the user.

[0896] A "user" is an individual or entity that utilizes the system to make an image generation request.

[0897] "Conditions" are information for specifying the characteristics and content of the image desired by the user.

[0898] "Description" is text information that allows a user to specify the detailed content and context of a desired image.

[0899] "Means" are hardware and software components used within a system to achieve a particular purpose.

[0900] A "server" is a computing device on a network that receives data sent by a user, analyzes and processes it, and sends the final results to a terminal.

[0901] "Terminal" refers to the device through which a user accesses the system, inputs conditions, and checks and downloads generated images.

[0902] "Input conditions" refers to the characteristics and descriptions specified by the user for image generation.

[0903] "Means for sending" refers to the process or function for sending the conditions entered by the user to the server.

[0904] "Means of analysis" refers to the process or function for converting the input conditions into a format that the generative AI model can understand.

[0905] A "generative AI model" is an algorithm or program that uses artificial intelligence to generate original images based on user input.

[0906] "Means for converting" refers to the process or function for analyzing the input conditions and converting them into a format suitable for the generative AI model.

[0907] "Means of generation" refers to the process or function for inputting converted data into a generative AI model and creating an original image.

[0908] "Means for generating an image" refers to the process or function for inputting the converted format into a generative AI model to generate the original image.

[0909] "Transmitting means" refers to the process or function for transmitting the generated image to the terminal.

[0910] An "emotion engine" is software or hardware that has the function of detecting emotions from a user's facial expressions, voice, text, etc., and adding them as complementary conditions for image generation.

[0911] "Means of integration" refers to the processes and functions for integrating the output of the emotion engine into the analytical data.

[0912] This invention is a system that generates original images based on the user's desired conditions and emotions. The system consists of three main elements: the user, the terminal, and the server. It also includes an emotion engine that detects the user's emotions and reflects them in the image generation.

[0913] User operations

[0914] Users access the interface of the self-made image generation tool and enter the conditions and description of the desired image in the text box. Specifically, they can enter a prompt such as "Autumn foliage scenery." They can also upload a reference image. Once the input is complete, the user presses the "Start Generation" button.

[0915] Emotion detection

[0916] The system is equipped with an emotion engine that recognizes the user's emotional state in real time. The emotion engine detects emotions from the user's facial expressions, voice, or input text and adds them as complementary conditions for image generation. For example, if the user has a tired expression, the emotion engine will modify the conditions to something like "relaxing autumn foliage scenery."

[0917] Device Role

[0918] The device collects the data entered by the user and the output of the emotion engine, and creates an API request to send it to the server. It generates a JSON-formatted request containing the user-entered emotion data "Autumn foliage scenery" and "Relaxed," and sends it to the server. The device waits for a response from the server and displays the generated image on the user interface.

[0919] Server Processing

[0920] The server receives the API request sent from the device and first analyzes the data. This analysis is the process of converting the user's desired conditions and the output of the emotion engine into a format that the generative AI model can understand. The analysis includes tokenizing the text data and converting the emotion data into numbers. The analyzed data is input into the generative AI model, which starts the original image generation process. For example, an image of a "relaxing autumn foliage scene" is generated. The generated image data is then sent from the server to the device.

[0921] Specific examples

[0922] For example, if an image of a "future city" is needed to create educational materials, the user enters "future city" into the interface and presses the generation start button. If the emotion engine detects the user's elevated emotions, it will supplement the criteria with an "energetic future city." The device sends the data to the server, which analyzes and converts the data and passes it to the generative AI model. The generative AI model generates an image of the "energetic future city" and returns it to the server. The device then displays the image to the user, who then downloads it.

[0923] Prompt Sentence Examples

[0924] "Generate an image of autumn foliage. The user wants a relaxing image."

[0925] In this way, users can quickly generate original images that match their emotional state and use them without copyright issues. This system is extremely useful in that it not only allows users to input conditions, but also reflects their emotional state, providing more suitable, high-quality images.

[0926] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0927] Step 1:

[0928] The user accesses the interface of the self-made image generation tool and enters the conditions and description of the desired image in the text box. For example, they can enter a prompt such as "Autumn foliage scenery." Once they have completed the input, they press the "Start generation" button. The input data is the text data "Autumn foliage scenery," and this is passed to the next processing step.

[0929] Step 2:

[0930] The device receives the text data entered by the user and activates the emotion engine. The emotion engine detects emotions from the user's facial expressions, voice, and entered text. This analysis generates emotion data such as "The user wants to relax." Specifically, the device uses a camera and microphone to read the user's emotions in real time and obtains the analysis results as text data.

[0931] Step 3:

[0932] The device collects the text data entered by the user and the emotion engine's output (emotion data) and creates an API request to send it to the server. Specifically, the device generates a JSON-formatted request containing the conditions "autumn foliage scenery" and "relaxation." Once the API request is complete, the device sends the data to the server. This sent data is passed on to the next processing step.

[0933] Step 4:

[0934] The server receives the API request sent from the device and first analyzes the data. Analysis is the process of converting the user's desired conditions and the output of the emotion engine into a format that the generative AI model can understand. Specifically, the server tokenizes the sent JSON data, converts text data into character vectors, and quantifies emotion data. This generates a data format suitable for input to the generative AI model.

[0935] Step 5:

[0936] The server inputs the analyzed data into a generative AI model and begins the original image generation process. The generative AI model uses specific algorithms to generate the optimal image based on the user's desired conditions and emotional state. This process creates an image with appropriate visual features, color tone, and style. Specifically, the server inputs tokenized text data and quantified emotional data for "Autumn foliage scenery with a relaxing effect" into the generative AI model and waits for the image to be generated. The generated image data is then passed to the next processing step.

[0937] Step 6:

[0938] The generated image data is returned from the generative AI model to the server, which temporarily stores the generated image data. The server then prepares to send the generated image data to the device as a response. Specifically, the server stores the generated image in temporary storage and encodes it as part of the JSON response.

[0939] Step 7:

[0940] The device receives the generated image data sent from the server. The received image data is displayed on the user interface, allowing the user to confirm the generated image. Specifically, the device extracts the image data from the received JSON response and displays it on the interface. At this point, the user can view the generated image and, if necessary, press the download button to save the image file to their device.

[0941] (Application example 2)

[0942] 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."

[0943] Conventional advertising image generation systems have difficulty generating images that take the user's emotional state into account, making it impossible to generate images that best fit the target or theme of the advertisement. Even when users input desired conditions, the system does not generate images that reflect the emotional elements based on those conditions, limiting their effectiveness as advertising. Furthermore, images generated solely based on user-input conditions and reference images often lack originality and creativity.

[0944] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0945] In this invention, the server includes: a means for a user to input desired image conditions and descriptions; a means for transmitting the input conditions and the user's emotional state to the server; a means for analyzing the input conditions and emotional state and converting them into a format suitable for a generative model; a means for inputting the converted format into the generative model to generate an original image based on the user's emotional state; and a means for transmitting the generated image to a terminal and displaying it. This enables advertising creators and marketers to easily generate high-quality original images that reflect emotional elements and are best suited to their target or theme.

[0946] The "means for the user to input the conditions and description of the image desired" is a function for the user to input the specific conditions and description of the image he or she wants to generate using an input interface such as a text box.

[0947] The "means for transmitting the input conditions and the user's emotional state to the server" is a function for transmitting the conditions and explanations input by the user and data on the user's emotional state detected by the emotion engine to the server via a network.

[0948] "Means for analyzing the input conditions and emotional state and converting them into a format suitable for the generative model" refers to a function that executes a process of analyzing the user's conditions, explanations, and emotional state data and converting them into a format that can be understood by the generative AI model.

[0949] "Means for inputting the converted format into a generative model and generating an original image based on the user's emotional state" refers to a function for inputting the converted data into a generative AI model and generating an original image that reflects emotional elements based on this.

[0950] The "means for transmitting the generated image to the terminal and displaying it" is a function for transmitting the generated original image data from the server to the user's terminal and displaying it on the user interface.

[0951] The "means for uploading reference images" is a function for uploading to the system an image file that serves as a reference for the image that the user wants to generate.

[0952] A "generative model" is an artificial intelligence model that automatically generates original images based on the user's input conditions and emotional state.

[0953] The system embodying the present invention functions as a tool that enables advertising creators and marketers to easily generate high-quality images that reflect emotional elements. Specific embodiments of the system are described below.

[0954] User operations

[0955] Using a smartphone app, users enter the theme and conditions of their ad into a text box. They can also upload a reference image. The system then uses a camera and microphone to recognize facial expressions and voice to detect emotions in real time. For example, a user who enters "Christmas sale ad image" and has a smiling expression may use the app.

[0956] Terminal handling

[0957] It collects the conditions and emotional state entered by the user and generates an API request to send this to the server. For example, it collects data such as "Christmas sale advertising image" and "user is smiling" and sends it to the server.

[0958] Server Processing

[0959] The server analyzes the received API request and converts it into a format that the generative AI model can understand. This conversion process includes tokenizing the text data and tagging the emotional state. The analyzed data is input into the generative AI model, which generates an original image that takes into account the user's emotional state. Examples of generative AI models used are OpenAI's DALL·E and Google AI's Imagen. The generated image will look something like a "Smiling Happy Christmas Sale Advertising Image."

[0960] Finalizing the device

[0961] The generated image data sent from the server is received and displayed on the user interface. The user can check the generated image and, if necessary, press the download button to save the image file to their device.

[0962] The hardware used is a smartphone, and real-time emotion recognition is performed using a camera and microphone. The software used is an emotion engine (e.g., Amazon Rekognition or Microsoft Azure Face API), and the generative AI model is OpenAI's DALL·E or Google AI's Imagen.

[0963] Examples and prompts

[0964] Specifically, if the user desires a "happy Christmas sale advertising image" and has a smiling expression, the system generates a "smiling happy Christmas sale advertising image."

[0965] Example prompt sentence:

[0966] "Christmas sale advertising image"

[0967] "We detected that the user is smiling"

[0968] "Generate smiling, happy Christmas sale advertising images"

[0969] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0970] Step 1:

[0971] The user launches the smartphone app and enters the advertising theme and conditions into a text box. For example, they might enter "Christmas sale advertising image." They can also upload a reference image. Furthermore, facial expressions and voice are recognized using a camera and microphone. The inputs for this step are the text conditions entered by the user, the uploaded reference image, and the emotional state obtained in real time. The output is a combination of the input data and emotional data.

[0972] Step 2:

[0973] The device organizes the collected data and generates an API request to send to the server. Specifically, it combines the text condition entered by the user, "Christmas sale advertising image," and the emotion data, "the user is smiling," into an API request. The input is the text information, reference image, and emotion data provided by the user. The output is a formatted API request to send to the server.

[0974] Step 3:

[0975] The server receives the API request and first tokenizes the text data and tags it with emotion data. This analysis process converts the data into a format that the generative AI model can understand. For example, the terms "Christmas sale advertising image" and "smile" are tokenized. The input is the data received from the API request, and the output is the analyzed data to be input into the generative AI model.

[0976] Step 4:

[0977] The server inputs the analyzed data into a generative AI model to generate an original image. The generative AI model generates an image based on the user's conditions and emotional state. In this case, the model generates a "smiling, happy Christmas sale advertising image." The input is the analyzed data, and the output is the generated image data.

[0978] Step 5:

[0979] The server temporarily stores the generated image data and prepares it for transmission to the terminal. The input is the generated image data, and the output is the prepared data to be transmitted to the terminal.

[0980] Step 6:

[0981] The terminal receives the image data sent from the server and displays it on the user interface. The user can check the generated image and, if necessary, press the download button to save the image file to their terminal. The input is the image data received from the server, and the output is the generated image displayed to the user.

[0982] keyword

[0983] Generative AI model, prompt sentence

[0984] 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.

[0985] 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.

[0986] 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.

[0987] [Fourth embodiment]

[0988] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0989] 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.

[0990] 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).

[0991] 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.

[0992] 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.

[0993] 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).

[0994] 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.

[0995] 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.

[0996] 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.

[0997] 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.

[0998] 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.

[0999] 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.

[1000] 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."

[1001] This invention is a system in which a user inputs desired conditions and images, and a generative AI model analyzes them to generate original images that can be used for document creation. The invention consists of three main elements: a server, a terminal, and a user.

[1002] User operations

[1003] First, the user accesses the interface of the self-made image generation tool and enters the desired image conditions and image in the text boxes. At this time, it is also possible to upload a reference image file. Once the input is complete, the user presses the "Start Generation" button, and the input data is sent to the device.

[1004] For example, if a user desires "autumn foliage scenery" for creating a document, the user enters "autumn foliage scenery" in the text box and uploads a reference image if necessary.

[1005] Terminal handling

[1006] The device collects the data entered by the user and creates an API request to send it to the server. Once the API request is complete, it sends it to the server and waits for a response.

[1007] Specifically, this is a process of sending the user's input data, text information "autumn foliage scenery," to the server.

[1008] Server Processing

[1009] The server receives the API request sent from the device and first performs data analysis. Analysis is the process of converting the input conditions into a format that the generative AI model can understand. Once this conversion is complete, the data is input into the generative AI model, and the original image generation process begins.

[1010] The generative AI model uses the analyzed data to generate unique images that match the user's desired criteria. In doing so, the model creates images with visual characteristics that match the criteria, resulting in original content created from scratch.

[1011] When image generation is complete, the server receives the generated image data and prepares to send it to the terminal as a response.

[1012] Finalizing the device

[1013] The device receives the generated image data sent from the server and displays it on the user interface. The user can check the generated image and, if necessary, press the download button to save the image file to their device.

[1014] For example, if a user requests an image of "autumn foliage scenery," the generated image is displayed and the user can download it and use it in their materials.

[1015] Specific examples

[1016] For example, if a user needs an image of a "future city" to create educational materials, they enter "future city" into the interface and press the "Start Generation" button. The device sends the data to the server, which analyzes it and passes it to the generative AI model. The generative AI model generates an image of the "future city" and returns it to the server. The device then displays the image to the user, who then downloads it.

[1017] In this way, users can quickly obtain ideal, copyright-free images with minimal effort and use them in creating materials. The system of the present invention is extremely useful in that it provides high-quality images through a complex process, simply by the user inputting their requirements.

[1018] The processing flow will be explained below.

[1019] Program processing steps

[1020] Step 1:

[1021] The user accesses the interface of the self-made image generation tool and inputs the desired image conditions and image into the text box. For example, they can input "autumn foliage scenery." If they have a reference image, they can upload that image file. Once they have completed the input, they press the "Start Generation" button.

[1022] Step 2:

[1023] The device collects the data entered by the user. The collected data (text information and uploaded images) is organized as an API request. This API request is then prepared for sending to the server.

[1024] Step 3:

[1025] The device sends the organized API request to the server, and then waits for a response from the server.

[1026] Step 4:

[1027] The server receives API requests sent from the device, analyzes the received data, and converts it into a format that the generative AI model can understand. This conversion includes tokenizing text data and preprocessing image data.

[1028] Step 5:

[1029] The server inputs the analyzed data into a generative AI model, which then starts the image generation process based on the input conditions and images.

[1030] Step 6:

[1031] Generative AI models generate original images that fit the criteria, a process that involves running algorithms to create images from scratch with the right visual characteristics.

[1032] Step 7:

[1033] The generated image data is returned from the generative AI model to the server, which temporarily stores the image data and prepares to send it to the device as a response.

[1034] Step 8:

[1035] The server sends the generated image data to the device as a response, which includes metadata and a download link for the generated image.

[1036] Step 9:

[1037] The terminal receives the generated image data sent from the server, displays the received image data on the user interface, and allows the user to confirm it.

[1038] Step 10:

[1039] Users can check the generated image and press the download button to save the image file to their own device, allowing them to freely use the generated image in creating documents.

[1040] Through this series of processes, users can quickly generate original images that meet their desired conditions and use them without copyright issues.

[1041] Example 1

[1042] 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."

[1043] Conventional image generation systems have had difficulty in quickly and easily generating original images that meet the user's desired conditions. They also required complex procedures and settings, which was time-consuming for the user. Furthermore, the generated images were often of low quality, or did not fully meet the user's expectations.

[1044] 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.

[1045] In this invention, the server includes means for a user to input desired image conditions and descriptions, means for transmitting the input conditions to a terminal, means for the terminal to collect the user's input data and transmit it as an API request to the server, means for the server to analyze the received API request and convert it into a format understandable by the generative AI model, means for inputting the converted data into the generative AI model to generate an original image, means for the server to transmit the generated image to the terminal, and means for the terminal to display the image received to the user. This allows a user to quickly generate high-quality original images and easily use them simply by inputting their desired conditions.

[1046] A "user" is a person who uses the system to make a request for image generation.

[1047] The "conditions and description of the desired image" is information indicating the specific characteristics and content of the image that the user wants to generate.

[1048] A "terminal" is a computer device that allows a user to input conditions and explanations and transmit them to a server.

[1049] An "API request" refers to data sent from a device to a server to request specific data or functions.

[1050] A "server" is a computer system that receives and analyzes data sent from a terminal and generates images using a generative AI model.

[1051] A "generative AI model" is an algorithm or program that generates original images from input data based on machine learning and artificial intelligence techniques.

[1052] "Analysis" is the process of converting the conditions and explanations entered by the user into a format that the generative AI model can understand.

[1053] An "original image" is a new, copyright-free image generated from scratch by a generative AI model.

[1054] "Upload" refers to a user sending a reference image or file from their own device to the server.

[1055] "Download" refers to a user saving an image generated from a server or device to their own device.

[1056] A "user interface" is a part of software that provides the screens and operating methods for users to operate a system.

[1057] This invention is a system in which, when a user inputs desired conditions and descriptions, a generative AI model analyzes them and generates original images that can be used for document creation, etc. The invention consists of three main elements: a server, a terminal, and a user.

[1058] User operations

[1059] First, the user accesses the interface of the self-made image generation tool and enters the desired image conditions and description in the text box. At this time, it is also possible to upload a reference image file. Once the input is complete, the user presses the "Start Generation" button, and the input data is sent to the device.

[1060] For example, if a user wants an image of a "future city" for creating a document, they can enter "future city" in the text box and upload a reference image if necessary.

[1061] Terminal handling

[1062] The device collects the data entered by the user and creates an API request to send it to the server. The device converts the text data entered by the user into JSON format and saves the uploaded image in a temporary folder. Once this API request is complete, the device sends the data to the server and waits for a response.

[1063] Server Processing

[1064] The server receives the API request sent from the device and first performs data analysis. Analysis is the process of converting the input conditions into a format that the generative AI model can understand. The server then analyzes the received JSON data and converts it into a format that the generative AI model can understand (for example, tokenized text). Once this conversion is complete, the server inputs the analyzed data into the generative AI model and begins the original image generation process.

[1065] Image generation using generative AI models

[1066] The generative AI model generates unique images that match the user's desired criteria based on the analysis data provided by the server. For example, based on the image of a "future city," an image featuring skyscrapers and futuristic infrastructure is generated. In this generation process, the generative AI model uses past data and learning to create original images that meet the user's requirements.

[1067] Finalizing the device

[1068] The generated image is received by the server and sent to the terminal as a response. The terminal receives the generated image data sent from the server and displays it on the user interface. The user can check the generated image and, if necessary, press the download button to save the image file to their terminal.

[1069] As a specific example, if a user requests an image of a "future city," a generated image is displayed and the user can download it and use it in their materials.

[1070] Using this system, users can quickly obtain ideal, copyright-free images with little effort and use them in document creation. Examples of prompts include: "Generate an image of a city of the future" or "Generate an image of autumn foliage."

[1071] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1072] Step 1: User Input

[1073] Users access the interface of the self-made image generation tool and enter the desired image conditions and description in the text box. They can also upload reference image files. For example, they can enter "future city" in the text box and upload the reference image by dragging and dropping. This input data includes the conditions in text format and the image file.

[1074] input:

[1075] Text image condition "City of the Future"

[1076] Uploaded reference images (if needed)

[1077] output:

[1078] Send user input data to the terminal

[1079] Specific behavior:

[1080] When the user clicks the "Start Generation" button, the text conditions and reference images are sent to the terminal.

[1081] Step 2: Collecting device data and making API requests

[1082] The device collects the data entered by the user. The collected data is the text information "Future City" and (optionally) the uploaded reference image file. Based on this data, the device creates an API request to send to the server. The device converts the input data to JSON format and saves the uploaded image in a temporary folder.

[1083] input:

[1084] User-submitted text information and reference images

[1085] output:

[1086] JSON formatted API request

[1087] Specific behavior:

[1088] The device encodes the text information into JSON format, saves the image to a temporary folder on the local file system, and combines this data into a single API request.

[1089] Step 3: Receiving and analyzing data on the server

[1090] The server receives the API request sent from the device. The received data includes the text information "future city" in JSON format and the path to the reference image. The server then analyzes this data and converts it into a format that the generative AI model can understand (for example, tokenized text). This allows the generative AI model to accurately understand the user's request.

[1091] input:

[1092] API request from the device (JSON format text information and image path)

[1093] output:

[1094] Data after conversion into a format that can be understood by the generative AI model

[1095] Specific behavior:

[1096] The server parses the JSON data, tokenizes the text information, and, if reference images are available, preprocesses the images to convert them into a format suitable for the generative AI model.

[1097] Step 4: Image generation using a generative AI model

[1098] The generative AI model generates unique images that match the user's desired conditions based on the analysis data provided by the server. For example, based on the image of a "future city," it generates an image featuring skyscrapers and futuristic infrastructure. This process is carried out by the generative AI model based on past data and learnings, in accordance with the user's wishes.

[1099] input:

[1100] Converted data from the server

[1101] output:

[1102] Generated original image

[1103] Specific behavior:

[1104] The generative AI model runs its internal algorithms based on the provided text data and reference images to generate images that meet the user's desired criteria.

[1105] Step 5: Server receives image and prepares response

[1106] The generated image is received by the server, which encodes it into an appropriate format and creates a response to send to the device.

[1107] input:

[1108] Generated images from generative AI models

[1109] output:

[1110] A response containing the encoded image data.

[1111] Specific behavior:

[1112] The server encodes the generated image in PNG format and adds it to the body of the HTTP response.

[1113] Step 6: Receive and display images on your device

[1114] The device decodes the image data received from the server and displays it on the user interface. The user can check the displayed image and, if satisfied, press the download button to save the image.

[1115] input:

[1116] Encoded image data from the server

[1117] output:

[1118] The decoded image displayed in the user interface

[1119] Specific behavior:

[1120] The device extracts the image data from the HTTP response and displays it in the user interface using an image viewer.

[1121] (Application example 1)

[1122] 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."

[1123] In the past, when a user requested image generation, they only had to input the desired image conditions and a description, and subsequent usage was limited. In particular, there was a lack of means for users to visually check items without actually trying them on in virtual stores, so there is a need to improve the user experience.

[1124] 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.

[1125] In this invention, the server includes means for a user to input desired image conditions and descriptions, means for transmitting the input conditions to the server, means for analyzing the input conditions and converting them into a format suitable for a generative model, means for inputting the converted format into a generative model to generate an original image, means for transmitting the generated image to a terminal, and means for a user to try on an avatar using the generated image in a virtual store, thereby enabling a user to visually check items without actually picking them up.

[1126] The "means for the user to input the conditions and description of the desired image" is an interface that allows the user to input the content of the desired image by text or other means.

[1127] The "means for transmitting the input conditions to the server" refers to a communication means for transmitting the conditions input by the user to the server via the Internet or the like.

[1128] The "means for analyzing the input conditions and converting them into a format suitable for the generative model" refers to a data analysis and conversion means for receiving the user's transmitted conditions and converting them into a data format that is easy for the generative AI model to understand.

[1129] "Means for inputting the converted format into a generative model and generating an original image" refers to means for inputting the converted data into a generative AI model, which then creates a new original image.

[1130] The "means for transmitting the generated image to the terminal" refers to a communication means for sending the generated original image to the user's terminal.

[1131] The "means for allowing a user to try on a generated image on an avatar" refers to a means for applying a generated image to a user's avatar and displaying the avatar as if it were trying on the image.

[1132] This invention relates to a system that generates original images using a generative AI model based on user input of desired image conditions and descriptions. In particular, it provides a system that allows users to try on generated images on an avatar in a virtual store.

[1133] Hardware and Software Configuration

[1134] To implement the system, the following hardware and software are required:

[1135] Hardware:

[1136] Smartphone (iOS / Android)

[1137] Server (cloud-based)

[1138] software:

[1139] Mobile app development framework (React Native)

[1140] API management server (Express.js)

[1141] Generative AI model (Stable Diffusion, DALL-E 2)

[1142] Explanation of program processing

[1143] 1. User Action:

[1144] Users access the smartphone app interface and enter the desired image criteria and description into a text box, for example, "business casual style attire." They can also upload reference images if needed.

[1145] 2. Terminal processing:

[1146] It collects the data entered by the user and creates an API request to send it to the server. Specifically, it sends the input text "Business casual style attire" to the server and waits for a response from the server.

[1147] 3. Server processing:

[1148] The server receives the API request, analyzes the input data, and converts it into a format suitable for the generative AI model. This converted data is passed to the generative AI model (e.g., Stable Diffusion) to generate the original image. The server receives the generated image data and prepares it to be sent to the device as a response.

[1149] 4. Finalize your device:

[1150] The terminal receives the generated image data sent from the server and displays it on the user interface. The user can check the generated image and try it on their avatar if necessary.

[1151] Specific examples

[1152] For example, consider the case where a user inputs "I want to try on casual summer clothes." The user inputs "casual summer clothes" into the interface of a smartphone app and presses the start generation button. The device collects the input data and sends it to the server. The server analyzes the received data and passes it to the generative AI model. The generative AI model generates an image of "casual summer clothes" and returns it to the server. The device receives the image and displays it on the user interface. The user can review the image and try it on their avatar. The user can also download the image.

[1153] Prompt Sentence Examples

[1154] "Generate business casual style outfits and have your avatar try them on."

[1155] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1156] Step 1:

[1157] The user accesses the smartphone app interface and enters the desired image criteria and description in the text boxes. The user can also upload reference images if necessary. The input data is "business casual style clothing," and the user presses the "Start Generation" button. The output is the criteria data entered by the user.

[1158] Step 2:

[1159] The device collects the user's input data and generates an API request to send it to the server. Specifically, it converts the input text data into an API request format (such as JSON) and sends it to the server address. The input is the user's condition data, and the output is the data sent to the server as an API request.

[1160] Step 3:

[1161] The server receives the API request and analyzes the input data (user condition data). This analysis is the process of converting the conditions entered by the user into a data format that the generative AI model can understand. Specifically, it converts text data into a model input format (e.g., tokenized text). The input is the API request data, and the output is the data in the converted format.

[1162] Step 4:

[1163] The server inputs the converted data into a generative AI model (e.g., Stable Diffusion) to generate the original image. The model generates an image based on the input data and returns the image data to the server. The input is the converted data, and the output is the generated image data.

[1164] Step 5:

[1165] The server receives the generated image data and prepares to send it to the device. It generates an API response for sending the image data to the device and sends it to the device. The input is the generated image data, and the output is the data sent to the device as an API response.

[1166] Step 6:

[1167] The terminal receives the generated image data sent from the server and displays it on the user interface. The user checks the generated image and selects an option to try it on the avatar. The input is the received generated image data, and the output is the user's visual confirmation and the result of trying it on the avatar.

[1168] 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.

[1169] This invention is a system in which a generative AI model uses an emotion engine to generate original images based on the user's desired conditions and images. This system consists of three main elements: a server, a terminal, and a user, and is combined with an emotion engine that senses the user's emotions and reflects them in the image generation.

[1170] User operations

[1171] The user accesses the interface of the self-made image generation tool and enters the desired image conditions and image into the text box. For example, they can enter "autumn foliage scenery." They can also upload a reference image file. Once the input is complete, the user presses the "Start Generation" button.

[1172] Furthermore, the system also recognizes the user's emotional state. The emotion engine detects emotions from the user's facial expressions, voice, or text, and adds them as complementary conditions for image generation.

[1173] For example, if the user has a tired expression, the emotion engine will modify the conditions to something like "a relaxing autumn scene with autumn leaves."

[1174] Terminal handling

[1175] The device collects the data entered by the user and the output of the emotion engine, creates an API request to send it to the server, and then waits for a response.

[1176] For example, data such as "autumn foliage scenery" and "user needs relaxation" are collected and sent to the server.

[1177] Server Processing

[1178] The server receives the API request sent from the device and first analyzes the data. Analysis is the process of converting the user's desired conditions and the output of the emotion engine into a format that the generative AI model can understand. This conversion includes tokenizing text data and preprocessing image data.

[1179] The analyzed data is then fed into a generative AI model, which then begins the process of generating an original image. The generative AI model then generates the optimal image based on the user's desired conditions and emotional state. This process creates an image with the appropriate visual characteristics, color tone, and style.

[1180] For example, the generative AI model generates "relaxing autumn foliage scenes."

[1181] The generated image data is returned from the generation AI model to the server, which temporarily stores the image data and prepares to send it to the device as a response.

[1182] Finalizing the device

[1183] The device receives the generated image data sent from the server and displays it on the user interface. The user can check the generated image and, if necessary, press the download button to save the image file to their device.

[1184] For example, a user may check out "Relaxing Autumn Foliage Scenery" and download it to use in their materials.

[1185] Specific examples

[1186] For example, if an image of a "future city" is needed to create educational materials, the user enters "future city" into the interface and presses the generation start button. If the emotion engine detects the user's elevated emotions, it will supplement the criteria with an "energetic future city." The device sends the data to the server, which analyzes and converts the data and passes it to the generative AI model. The generative AI model generates an image of the "energetic future city" and returns it to the server. The device then displays the image to the user, who then downloads it.

[1187] In this way, users can quickly generate original images that meet the conditions that take into account their emotional state, and use them without copyright issues. The system of the present invention is extremely useful in that it not only allows users to input conditions, but also reflects their emotional state, thereby providing more suitable, high-quality images.

[1188] The processing flow will be explained below.

[1189] Program processing steps

[1190] Step 1:

[1191] The user accesses the interface of the self-made image generation tool and inputs the desired image conditions and image into the text box. For example, they can input "autumn foliage scenery." They can also upload reference image files. Once the input is complete, the user presses the "Start Generation" button.

[1192] Step 2:

[1193] The device collects data entered by the user, including text information and uploaded image files, and then makes an API request to send the collected data to the server.

[1194] Step 3:

[1195] The device sends the created API request to the server and then waits for a response from the server.

[1196] Step 4:

[1197] The server receives API requests sent from the device, then parses the received data and converts it into a format that the generative AI model can understand. Parsing includes tokenizing text data and preprocessing uploaded images.

[1198] Step 5:

[1199] The server inputs the analyzed data into the generative AI model, which then initiates the process of generating an original image based on the user's input conditions.

[1200] Step 6:

[1201] At the same time, the emotion engine recognizes the user's emotions. The emotion engine detects emotions from the user's facial expressions, voice, and text input. This emotion data is provided as supplementary data to the generative AI model.

[1202] Step 7:

[1203] The generative AI model generates the optimal original image based on the input conditions and emotional data from the emotion engine. For example, if the emotion engine detects "relaxation," the generated "autumn foliage scene" will be adjusted to have a more calming atmosphere.

[1204] Step 8:

[1205] The generated image data is returned from the generative AI model to the server, which temporarily stores the image data and prepares to send it to the device as a response.

[1206] Step 9:

[1207] The server sends the generated image data to the device, and the response includes metadata and a download link for the generated image.

[1208] Step 10:

[1209] The terminal receives the generated image data sent from the server, displays the received image data on the user interface, and allows the user to confirm it.

[1210] Step 11:

[1211] Users can check the generated images and, if necessary, press the download button to save the image files to their own devices, allowing them to freely use the generated images in creating materials.

[1212] As a concrete example, consider a case where a user needs an image of a "future city" for educational materials. The user enters "future city" in the text box and presses the start generation button. If the emotion engine detects the user's excited emotion, the generative AI model will generate an image of an "energetic future city." Through this image generation process, the user can quickly obtain a high-quality original image to use in creating materials.

[1213] Example 2

[1214] 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."

[1215] While modern image generation technology can generate original images based on specific user requirements and descriptions, technology for generating more personalized images by reflecting the user's emotional state is still immature. This is because it is difficult for users to clearly communicate their emotions through text, facial expressions, or voice, and as a result, generated images do not necessarily match the user's emotions or requirements. Furthermore, if the generated image does not meet the user's expectations, it is time-consuming to reset the conditions, making efficient image generation difficult.

[1216] 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.

[1217] In this invention, the server includes a means for a user to input desired image conditions and descriptions, a means for transmitting the input conditions to the server, a means for analyzing the input conditions and converting them into a format suitable for a generative AI model, a means for inputting the converted format into the generative AI model to generate an original image, a means for transmitting the generated image to a terminal, a means including an emotion engine that detects the user's emotions and adds them as complementary conditions for image generation, and a means for integrating the output of the emotion engine into analysis data. This makes it possible to generate images that reflect the specific conditions and emotional state desired by the user.

[1218] A "user" is an individual or entity that utilizes the system to make an image generation request.

[1219] "Conditions" are information for specifying the characteristics and content of the image desired by the user.

[1220] "Description" is text information that allows a user to specify the detailed content and context of a desired image.

[1221] "Means" are hardware and software components used within a system to achieve a particular purpose.

[1222] A "server" is a computing device on a network that receives data sent by a user, analyzes and processes it, and sends the final results to a terminal.

[1223] "Terminal" refers to the device through which a user accesses the system, inputs conditions, and checks and downloads generated images.

[1224] "Input conditions" refers to the characteristics and descriptions specified by the user for image generation.

[1225] "Means for sending" refers to the process or function for sending the conditions entered by the user to the server.

[1226] "Means of analysis" refers to the process or function for converting the input conditions into a format that the generative AI model can understand.

[1227] A "generative AI model" is an algorithm or program that uses artificial intelligence to generate original images based on user input.

[1228] "Means for converting" refers to the process or function for analyzing the input conditions and converting them into a format suitable for the generative AI model.

[1229] "Means of generation" refers to the process or function for inputting converted data into a generative AI model and creating an original image.

[1230] "Means for generating an image" refers to the process or function for inputting the converted format into a generative AI model to generate the original image.

[1231] "Transmitting means" refers to the process or function for transmitting the generated image to the terminal.

[1232] An "emotion engine" is software or hardware that has the function of detecting emotions from a user's facial expressions, voice, text, etc., and adding them as complementary conditions for image generation.

[1233] "Means of integration" refers to the processes and functions for integrating the output of the emotion engine into the analytical data.

[1234] This invention is a system that generates original images based on the user's desired conditions and emotions. The system consists of three main elements: the user, the terminal, and the server. It also includes an emotion engine that detects the user's emotions and reflects them in the image generation.

[1235] User operations

[1236] Users access the interface of the self-made image generation tool and enter the conditions and description of the desired image in the text box. Specifically, they can enter a prompt such as "Autumn foliage scenery." They can also upload a reference image. Once the input is complete, the user presses the "Start Generation" button.

[1237] Emotion detection

[1238] The system is equipped with an emotion engine that recognizes the user's emotional state in real time. The emotion engine detects emotions from the user's facial expressions, voice, or input text and adds them as complementary conditions for image generation. For example, if the user has a tired expression, the emotion engine will modify the conditions to something like "relaxing autumn foliage scenery."

[1239] Device Role

[1240] The device collects the data entered by the user and the output of the emotion engine, and creates an API request to send it to the server. It generates a JSON-formatted request containing the user-entered emotion data "Autumn foliage scenery" and "Relaxed," and sends it to the server. The device waits for a response from the server and displays the generated image on the user interface.

[1241] Server Processing

[1242] The server receives the API request sent from the device and first analyzes the data. This analysis is the process of converting the user's desired conditions and the output of the emotion engine into a format that the generative AI model can understand. The analysis includes tokenizing the text data and converting the emotion data into numbers. The analyzed data is input into the generative AI model, which starts the original image generation process. For example, an image of a "relaxing autumn foliage scene" is generated. The generated image data is then sent from the server to the device.

[1243] Specific examples

[1244] For example, if an image of a "future city" is needed to create educational materials, the user enters "future city" into the interface and presses the generation start button. If the emotion engine detects the user's elevated emotions, it will supplement the criteria with an "energetic future city." The device sends the data to the server, which analyzes and converts the data and passes it to the generative AI model. The generative AI model generates an image of the "energetic future city" and returns it to the server. The device then displays the image to the user, who then downloads it.

[1245] Prompt Sentence Examples

[1246] "Generate an image of autumn foliage. The user wants a relaxing image."

[1247] In this way, users can quickly generate original images that match their emotional state and use them without copyright issues. This system is extremely useful in that it not only allows users to input conditions, but also reflects their emotional state, providing more suitable, high-quality images.

[1248] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1249] Step 1:

[1250] The user accesses the interface of the self-made image generation tool and enters the conditions and description of the desired image in the text box. For example, they can enter a prompt such as "Autumn foliage scenery." Once they have completed the input, they press the "Start generation" button. The input data is the text data "Autumn foliage scenery," and this is passed to the next processing step.

[1251] Step 2:

[1252] The device receives the text data entered by the user and activates the emotion engine. The emotion engine detects emotions from the user's facial expressions, voice, and entered text. This analysis generates emotion data such as "The user wants to relax." Specifically, the device uses a camera and microphone to read the user's emotions in real time and obtains the analysis results as text data.

[1253] Step 3:

[1254] The device collects the text data entered by the user and the emotion engine's output (emotion data) and creates an API request to send it to the server. Specifically, the device generates a JSON-formatted request containing the conditions "autumn foliage scenery" and "relaxation." Once the API request is complete, the device sends the data to the server. This sent data is passed on to the next processing step.

[1255] Step 4:

[1256] The server receives the API request sent from the device and first analyzes the data. Analysis is the process of converting the user's desired conditions and the output of the emotion engine into a format that the generative AI model can understand. Specifically, the server tokenizes the sent JSON data, converts text data into character vectors, and quantifies emotion data. This generates a data format suitable for input to the generative AI model.

[1257] Step 5:

[1258] The server inputs the analyzed data into a generative AI model and begins the original image generation process. The generative AI model uses specific algorithms to generate the optimal image based on the user's desired conditions and emotional state. This process creates an image with appropriate visual features, color tone, and style. Specifically, the server inputs tokenized text data and quantified emotional data for "Autumn foliage scenery with a relaxing effect" into the generative AI model and waits for the image to be generated. The generated image data is then passed to the next processing step.

[1259] Step 6:

[1260] The generated image data is returned from the generative AI model to the server, which temporarily stores the generated image data. The server then prepares to send the generated image data to the device as a response. Specifically, the server stores the generated image in temporary storage and encodes it as part of the JSON response.

[1261] Step 7:

[1262] The device receives the generated image data sent from the server. The received image data is displayed on the user interface, allowing the user to confirm the generated image. Specifically, the device extracts the image data from the received JSON response and displays it on the interface. At this point, the user can view the generated image and, if necessary, press the download button to save the image file to their device.

[1263] (Application example 2)

[1264] 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."

[1265] Conventional advertising image generation systems have difficulty generating images that take the user's emotional state into account, making it impossible to generate images that best fit the target or theme of the advertisement. Even when users input desired conditions, the system does not generate images that reflect the emotional elements based on those conditions, limiting their effectiveness as advertising. Furthermore, images generated solely based on user-input conditions and reference images often lack originality and creativity.

[1266] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1267] In this invention, the server includes: a means for a user to input desired image conditions and descriptions; a means for transmitting the input conditions and the user's emotional state to the server; a means for analyzing the input conditions and emotional state and converting them into a format suitable for a generative model; a means for inputting the converted format into the generative model to generate an original image based on the user's emotional state; and a means for transmitting the generated image to a terminal and displaying it. This enables advertising creators and marketers to easily generate high-quality original images that reflect emotional elements and are best suited to their target or theme.

[1268] The "means for the user to input the conditions and description of the image desired" is a function for the user to input the specific conditions and description of the image he or she wants to generate using an input interface such as a text box.

[1269] The "means for transmitting the input conditions and the user's emotional state to the server" is a function for transmitting the conditions and explanations input by the user and data on the user's emotional state detected by the emotion engine to the server via a network.

[1270] "Means for analyzing the input conditions and emotional state and converting them into a format suitable for the generative model" refers to a function that executes a process of analyzing the user's conditions, explanations, and emotional state data and converting them into a format that can be understood by the generative AI model.

[1271] "Means for inputting the converted format into a generative model and generating an original image based on the user's emotional state" refers to a function for inputting the converted data into a generative AI model and generating an original image that reflects emotional elements based on this.

[1272] The "means for transmitting the generated image to the terminal and displaying it" is a function for transmitting the generated original image data from the server to the user's terminal and displaying it on the user interface.

[1273] The "means for uploading reference images" is a function for uploading to the system an image file that serves as a reference for the image that the user wants to generate.

[1274] A "generative model" is an artificial intelligence model that automatically generates original images based on the user's input conditions and emotional state.

[1275] The system embodying the present invention functions as a tool that enables advertising creators and marketers to easily generate high-quality images that reflect emotional elements. Specific embodiments of the system are described below.

[1276] User operations

[1277] Using a smartphone app, users enter the theme and conditions of their ad into a text box. They can also upload a reference image. The system then uses a camera and microphone to recognize facial expressions and voice to detect emotions in real time. For example, a user who enters "Christmas sale ad image" and has a smiling expression may use the app.

[1278] Terminal handling

[1279] It collects the conditions and emotional state entered by the user and generates an API request to send this to the server. For example, it collects data such as "Christmas sale advertising image" and "user is smiling" and sends it to the server.

[1280] Server Processing

[1281] The server analyzes the received API request and converts it into a format that the generative AI model can understand. This conversion process includes tokenizing the text data and tagging the emotional state. The analyzed data is input into the generative AI model, which generates an original image that takes into account the user's emotional state. Examples of generative AI models used are OpenAI's DALL·E and Google AI's Imagen. The generated image will look something like a "Smiling Happy Christmas Sale Advertising Image."

[1282] Finalizing the device

[1283] The generated image data sent from the server is received and displayed on the user interface. The user can check the generated image and, if necessary, press the download button to save the image file to their device.

[1284] The hardware used is a smartphone, and real-time emotion recognition is performed using a camera and microphone. The software used is an emotion engine (e.g., Amazon Rekognition or Microsoft Azure Face API), and the generative AI model is OpenAI's DALL·E or Google AI's Imagen.

[1285] Examples and prompts

[1286] Specifically, if the user desires a "happy Christmas sale advertising image" and has a smiling expression, the system generates a "smiling happy Christmas sale advertising image."

[1287] Example prompt sentence:

[1288] "Christmas sale advertising image"

[1289] "We detected that the user is smiling"

[1290] "Generate smiling, happy Christmas sale advertising images"

[1291] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1292] Step 1:

[1293] The user launches the smartphone app and enters the advertising theme and conditions into a text box. For example, they might enter "Christmas sale advertising image." They can also upload a reference image. Furthermore, facial expressions and voice are recognized using a camera and microphone. The inputs for this step are the text conditions entered by the user, the uploaded reference image, and the emotional state obtained in real time. The output is a combination of the input data and emotional data.

[1294] Step 2:

[1295] The device organizes the collected data and generates an API request to send to the server. Specifically, it combines the text condition entered by the user, "Christmas sale advertising image," and the emotion data, "the user is smiling," into an API request. The input is the text information, reference image, and emotion data provided by the user. The output is a formatted API request to send to the server.

[1296] Step 3:

[1297] The server receives the API request and first tokenizes the text data and tags it with emotion data. This analysis process converts the data into a format that the generative AI model can understand. For example, the terms "Christmas sale advertising image" and "smile" are tokenized. The input is the data received from the API request, and the output is the analyzed data to be input into the generative AI model.

[1298] Step 4:

[1299] The server inputs the analyzed data into a generative AI model to generate an original image. The generative AI model generates an image based on the user's conditions and emotional state. In this case, the model generates a "smiling, happy Christmas sale advertising image." The input is the analyzed data, and the output is the generated image data.

[1300] Step 5:

[1301] The server temporarily stores the generated image data and prepares it for transmission to the terminal. The input is the generated image data, and the output is the prepared data to be transmitted to the terminal.

[1302] Step 6:

[1303] The terminal receives the image data sent from the server and displays it on the user interface. The user can check the generated image and, if necessary, press the download button to save the image file to their terminal. The input is the image data received from the server, and the output is the generated image displayed to the user.

[1304] keyword

[1305] Generative AI model, prompt sentence

[1306] 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.

[1307] 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.

[1308] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1309] 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.

[1310] 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.

[1311] 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.

[1312] 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).

[1313] 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.

[1314] 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."

[1315] 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.

[1316] 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).

[1317] 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.

[1318] 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.

[1319] 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.

[1320] 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.

[1321] 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.

[1322] 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.

[1323] 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.

[1324] 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.

[1325] 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.

[1326] 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.

[1327] The following is further disclosed regarding the above embodiment.

[1328] (Claim 1)

[1329] a means for the user to input desired image conditions and descriptions;

[1330] means for transmitting the input conditions to a server;

[1331] means for analyzing the input conditions and converting them into a format suitable for a generative model;

[1332] a means for inputting the converted format into a generative model to generate an original image;

[1333] The system includes means for transmitting the generated image to a terminal.

[1334] (Claim 2)

[1335] 2. The system according to claim 1, further comprising means for uploading a reference image based on the input conditions.

[1336] (Claim 3)

[1337] 10. The system of claim 1, further comprising means for enabling the generated images to be viewed and downloaded by a user.

[1338] "Example 1"

[1339] (Claim 1)

[1340] a means for the user to input desired image conditions and descriptions;

[1341] means for transmitting the input conditions to a terminal;

[1342] A means for the terminal to collect user input data and send it to a server as an API request;

[1343] A means for analyzing the API request received by the server and converting it into a format understandable by the generative AI model;

[1344] A means for inputting the converted data into a generative AI model to generate an original image;

[1345] A means for the server to transmit the generated image to the terminal;

[1346] The system includes means for displaying the images received by the terminal to a user.

[1347] (Claim 2)

[1348] 2. The system according to claim 1, further comprising means for uploading a reference image based on the input conditions.

[1349] (Claim 3)

[1350] 10. The system of claim 1, further comprising means for enabling the generated images to be viewed and downloaded by a user.

[1351] "Application Example 1"

[1352] (Claim 1)

[1353] a means for the user to input desired image conditions and descriptions;

[1354] means for transmitting the input conditions to a server;

[1355] means for analyzing the input conditions and converting them into a format suitable for a generative model;

[1356] a means for inputting the converted format into a generative model to generate an original image;

[1357] means for transmitting the generated image to a terminal;

[1358] The system includes a means for a user to try on an avatar using the generated image.

[1359] (Claim 2)

[1360] 2. The system according to claim 1, further comprising means for uploading a reference image based on the input conditions.

[1361] (Claim 3)

[1362] 10. The system of claim 1, further comprising means for enabling the generated images to be viewed and downloaded by a user.

[1363] "Example 2: Combining Emotion Engines"

[1364] (Claim 1)

[1365] a means for the user to input desired image conditions and descriptions;

[1366] means for transmitting the input conditions to a server;

[1367] A means for analyzing the input conditions and converting them into a format suitable for a generative AI model;

[1368] A means for inputting the converted format into a generative AI model to generate an original image;

[1369] means for transmitting the generated image to a terminal;

[1370] A means including an emotion engine that detects a user's emotion and adds it as a complementary condition for image generation;

[1371] The system includes means for integrating the output of the emotion engine into the analysis data.

[1372] (Claim 2)

[1373] 2. The system according to claim 1, further comprising means for uploading a reference image based on the input conditions.

[1374] (Claim 3)

[1375] 10. The system of claim 1, further comprising means for enabling the generated images to be viewed and downloaded by a user.

[1376] "Application example 2 when combining emotion engines"

[1377] (Claim 1)

[1378] a means for the user to input desired image conditions and descriptions;

[1379] means for transmitting the input conditions and the user's emotional state to a server;

[1380] means for analyzing the input conditions and emotional states and converting them into a format suitable for a generative model;

[1381] means for inputting the converted form into a generative model to generate an original image based on the user's emotional state;

[1382] The system includes means for transmitting the generated image to a terminal and displaying it.

[1383] (Claim 2)

[1384] 10. The system of claim 1, further comprising means for uploading a reference image based on the input condition and emotional state.

[1385] (Claim 3)

[1386] 10. The system of claim 1, further comprising means for enabling the generated images to be viewed and downloaded by a user. [Explanation of symbols]

[1387] 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 the user to input desired image conditions and descriptions; means for transmitting the input conditions to a server; means for analyzing the input conditions and converting them into a format suitable for a generative model; a means for inputting the converted format into a generative model to generate an original image; The system includes means for transmitting the generated image to a terminal.

2. 2. The system according to claim 1, further comprising means for uploading a reference image based on the input conditions.

3. 2. The system of claim 1, further comprising means for enabling the generated images to be viewed and downloaded by a user.

Images