system

Abstract

Provide a system. 【Solution means】means for the user to input authentication information, means for the terminal to send the authentication information to the server and confirm the user's access right, means for the user to input requirements for image generation, means for the terminal to send the input requirements to the server, means for the server to send a request to the image generation AI, means for the image generation AI to generate an image based on the requirements, means for the server to temporarily store the generated image and send a link to the user, means for the user to view the generated image and send feedback, A system including means for finally saving the image based on the feedback and generating a link.

Description

Technical Field

[0004] ,

[0006] , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to the 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, in presentation materials and reports used within a company, there has been a demand for quickly and efficiently generating high-quality illustrations and photos. However, when using external image materials, it is a problem within the company that it requires a great deal of effort and time to confirm copyright, and it also incurs costs. In addition, since each department uses images in different styles, there is also a problem that the overall image of the company is not unified. Furthermore, due to a shortage of personnel with image generation skills, it is difficult to quickly generate images within the company.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides the following means.

[0006] Access rights are managed for each user by providing a means for the user to enter authentication information and a means for the terminal to send authentication information to the server to verify the user's access rights. In addition, a means for the user to enter image generation requirements and a means for the terminal to send the entered requirements to the server are provided, making it easy for users to give specific image generation instructions.

[0007] Furthermore, the system provides a means for the server to send requests to the image generation AI, which then generates images based on the requirements, enabling the rapid generation of high-quality images. It also provides a means for temporarily storing the generated images and sending a link to the user, as well as a means for the user to review the generated images and send feedback, allowing the user to review the images and provide instructions for corrections as needed.

[0008] Furthermore, it provides a means to finalize the image based on feedback and generate a link. By providing a means for the server to save the generated image to internal storage, generate a sharing link for the saved image, and notify relevant projects and departments, it enables each department within the company to use images with a unified look.

[0009] Finally, the device displays a list of images saved to the user and provides a means for the user to download images or share them with other departments, thereby enabling the user to effectively utilize the generated images and facilitate smooth communication both inside and outside the company.

[0010] "User authentication information" refers to information such as the ID and password that a user enters to access the system.

[0011] A "terminal" refers to a device such as a computer or smartphone that is operated by a user.

[0012] A "server" is a computer system that communicates with terminals via a network and processes and stores data.

[0013] "Access right" refers to the right of a user to access specific functions and data within a system.

[0014] "Image generation requirements" refer to the specified content such as the theme, style, and specific elements of the image that the user wants to generate.

[0015] "Image generation AI" refers to artificial intelligence that automatically generates images based on the specified requirements.

[0016] "Feedback" refers to the confirmation and correction requests made by the user for the generated image.

[0017] "Internal storage" refers to a digital storage device for storing data used within an enterprise.

[0018] "Shared link" refers to reference information such as a URL that provides a means of accessing the stored data.

[0019] "Projects and departments" refer to specific business units or organizational units within an enterprise.

[0020] "Image list" refers to the thumbnail or list display of images generated and saved in the past.

[0021] "Download" refers to the operation of importing data stored on a terminal.

[0022] "Modification instruction" refers to the request for changes or specific improvement requirements for the generated image.

Brief Description of Drawings

[0023] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0024] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0025] First, the language used in the following description will be explained.

[0026] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0027] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0028] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0029] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0030] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0031] [First Embodiment]

[0032] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0033] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0034] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0035] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0036] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0037] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0038] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0039] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0041] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0042] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0043] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0044] This invention is a system for quickly and efficiently generating high-quality illustrations and photographs for presentation materials and reports used within a company. The system operates through the following steps: user authentication, image generation request, image generation, confirmation and storage, and sharing and utilization.

[0045] User Authentication

[0046] Example of program processing

[0047] The device displays a login screen to the user.

[0048] The user enters their authentication information (ID and password).

[0049] The device sends authentication information to the server.

[0050] The server compares the received authentication information with the database to verify the user's access rights.

[0051] Specific example

[0052] Mr. Tanaka from the public relations department logs into the system, and the server verifies his authentication information. Since Mr. Tanaka has the necessary permissions for the public relations department, he is able to access the public relations project.

[0053] Image generation request

[0054] Example of program processing

[0055] The device displays an input form to the user for image generation.

[0056] The user enters the requirements for the image they want to generate (theme, style, specific elements).

[0057] The terminal sends the entered requirements to the server.

[0058] Specific example

[0059] Mr. Tanaka types "a simple and modern style illustration of the new smartphone" for the new product announcement. The device sends this request to the server.

[0060] Image generation

[0061] Example of program processing

[0062] The server sends a request to the image generation AI.

[0063] The image generation AI generates images based on the specified requirements.

[0064] The server temporarily stores the generated image and sends a link to the user.

[0065] Specific example

[0066] The server receives the request and sends it to the image generation AI. The image generation AI generates an illustration based on the requirements, and the server temporarily stores the illustration. A confirmation link is sent to Mr. Tanaka.

[0067] View and save the image.

[0068] Example of program processing

[0069] The user clicks the link and checks the generated image.

[0070] The device sends user feedback (approval or correction request) to the server.

[0071] Based on the feedback, the server will finally save the image and generate a link.

[0072] Specific example

[0073] Ms. Tanaka clicks the link to review the generated illustration. If revisions are needed, she submits feedback. If she is finally satisfied, she presses the "Approve" button and submits it to the server. The server saves the illustration to the company's internal storage and generates a sharing link.

[0074] Image sharing and use

[0075] Example of program processing

[0076] The device displays a list of images saved to the user.

[0077] Users select images, download them, and share them with other departments.

[0078] Specific example

[0079] Ms. Tanaka will download the generated illustrations and insert them into the PowerPoint presentation for the product announcement. She will also share them with the marketing team for use in social media posts.

[0080] This system significantly reduces the effort required for copyright verification and enables the creation and sharing of consistent images across the entire company. Furthermore, by providing users with an easy way to generate and share high-quality images, it improves operational efficiency. This invention is effectively operated by reskilled employees.

[0081] The following describes the processing flow.

[0082] User Authentication

[0083] Step 1:

[0084] The device displays a login screen to the user.

[0085] Step 2:

[0086] The user enters their ID and password.

[0087] Step 3:

[0088] The terminal sends the entered authentication information to the server.

[0089] Step 4:

[0090] The server compares the received authentication information with the database to verify the user's access rights.

[0091] Step 5:

[0092] If the server successfully authenticates the user, it will set the user's access rights and display the dashboard.

[0093] Image generation request

[0094] Step 1:

[0095] The device displays an input form to the user for image generation.

[0096] Step 2:

[0097] The user enters the requirements for the image they want to generate (theme, style, specific elements).

[0098] Step 3:

[0099] The terminal converts the entered requirements into JSON format and sends it to the server.

[0100] Image generation

[0101] Step 1:

[0102] The server sends the received request to the image generation AI.

[0103] Step 2:

[0104] The image generation AI generates images based on the requirements.

[0105] Step 3:

[0106] The server temporarily stores the generated image and sends a link to the user.

[0107] View and save the image.

[0108] Step 1:

[0109] The user clicks the provided link and checks the generated image.

[0110] Step 2:

[0111] The device sends user feedback (approval or correction request) to the server.

[0112] Step 3:

[0113] If the server requests a correction, it sends another request to the image generation AI, which then makes corrections based on the feedback.

[0114] Step 4:

[0115] Once the user approves the image, the server saves the image and generates a link.

[0116] Image sharing and use

[0117] Step 1:

[0118] The device displays a list of images saved to the user.

[0119] Step 2:

[0120] The user selects the image they need and gives instructions for downloading or sharing it.

[0121] Step 3:

[0122] The device downloads the image and saves it to the selected folder.

[0123] Step 4:

[0124] The server generates a shared link and notifies the relevant parties.

[0125] This allows users to quickly generate high-quality images, review and revise them as needed, and ultimately share them in a format suitable for use both internally and externally.

[0126] (Example 1)

[0127] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0128] Businesses need to quickly generate high-quality illustrations and photographs and efficiently share and utilize those images. Current systems require significant time and effort for image generation, and sharing the generated images is cumbersome, leading to decreased work efficiency and considerable effort in copyright verification. In particular, generating consistent designs and styles is difficult, making it challenging to maintain a consistent brand image within the company.

[0129] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0130] In this invention, the server includes means for sending requests to an image generation AI model, means for temporarily storing the generated image and sending a link to the user, and means for finally saving the image and generating a link based on feedback. This enables users to easily and quickly generate high-quality images and efficiently share and utilize the generated images while maintaining a unified design and style.

[0131] A "user" is a person or group that accesses and uses a system.

[0132] "Authentication information" refers to identification information used to verify a user's identity, and generally includes an ID and password.

[0133] A "terminal" is a device used by a user to access a system, and includes personal computers, smartphones, tablets, and other similar devices.

[0134] A "server" is a central computing system that processes user requests and manages data.

[0135] An "image generation AI model" is a program or software that generates images using artificial intelligence technology, and includes models such as Stable Diffusion and DALL-E.

[0136] A "request" refers to the specific operations or processes that a user asks the system to perform.

[0137] A "link" is a URL or path used to access generated images or other resources.

[0138] "Feedback" refers to opinions, evaluations, observations, and requests for corrections that users provide to a system.

[0139] "Cloud storage" refers to a storage service for saving and managing data via the internet.

[0140] A "session" is a state that uniquely identifies a series of communications and operations performed while a user is logged into a system.

[0141] An "access token" is a digital key that temporarily holds a user's authentication information and grants them access to the system.

[0142] Modes for carrying out the invention

[0143] This invention is a system for quickly and efficiently generating high-quality illustrations and photographs for presentation materials and reports used within a company. The system operates through the following steps: user authentication, image generation request, image generation, confirmation and storage, and sharing and utilization. Details are described below.

[0144] User Authentication

[0145] The terminal displays a login screen to the user, who enters their authentication information (ID and password). The terminal sends this authentication information to the server, which verifies the received authentication information against a database (such as MySQL® or PostgreSQL). If authentication is successful, the server creates a session and sends an access token back to the terminal. This allows the user to access the system.

[0146] Image generation request

[0147] The device displays an input form for image generation to the user, where the user enters the requirements for the image they want to generate (theme, style, specific elements). The device validates the entered requirements in real time, and if there are no problems, it sends them to the server. An AI image generation model (such as Stable Diffusion or DALL-E) generates the image based on these requirements.

[0148] As a concrete example, a user inputs the requirement, "An illustration of a new smartphone in a simple and modern style." The device sends this requirement to the server, which then sends a request to the image generation AI model.

[0149] Image generation

[0150] The server sends a request to an image generation AI model, which generates an image based on the specified requirements. The generated image is temporarily stored by the server in cloud storage or local storage, and a confirmation link is sent to the user.

[0151] View and save the image.

[0152] The user clicks a verification link to review the generated image. The device sends user feedback (approval or correction request) to the server. The server finalizes the image based on the feedback and generates a sharing link. Finally approved images are stored in internal storage.

[0153] Image sharing and use

[0154] The device displays a list of images saved by the user, allowing the user to select and download the desired images or share them with other departments. The device generates download and sharing links for the selected images, which the user can then use to share the images with other departments.

[0155] A specific example of a prompt message is: "Generate four high-quality slides suitable for a sample A4 portrait-size document for public relations. Product group: Smartphones, Theme: New product announcement, Style: Simple and modern."

[0156] This enables the creation and sharing of a unified image across the entire company, while significantly reducing the effort required for copyright verification. Furthermore, by providing users with an easy way to generate and share high-quality images, it improves operational efficiency. This invention is expected to be effectively implemented by reskilled employees.

[0157] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0158] Step 1: User Authentication

[0159] The device displays a login screen to the user. The user enters their ID and password, and the device sends this authentication information to the server. The server compares the received authentication information with the database to verify the user's access rights. MySQL or PostgreSQL are used as the database. If authentication is successful, the server creates a session and sends an access token back to the device.

[0160] Input: ID, Password

[0161] Data processing: Database matching

[0162] Output: Access token

[0163] Specific operation: Mr. Tanaka enters his ID and password on the login screen, and the device sends this information. The server verifies this information, generates an access token, and sends it back.

[0164] Step 2: Enter the image generation request

[0165] The device displays an input form for image generation to the user. The user enters the requirements for image generation (theme, style, specific elements) into the form. The device validates the entered requirements in real time and sends them to the server if there are no problems.

[0166] Input: Generation requirements (theme, style, specific elements)

[0167] Data na: Validation

[0168] Output: Sending requirements data to the server

[0169] Specific action: Mr. Tanaka enters the following: "An illustration of the new smartphone product in a simple and modern style." The terminal validates the requirement and sends it to the server.

[0170] Step 3: Sending the image generation request to the server

[0171] Based on the requirements received by the server, a request is sent to the image generation AI model. Stable Diffusion and DALL-E can be used as the image generation AI model.

[0172] Input: Requirements data

[0173] Data processing: Sending a request to an AI model for image generation.

[0174] Output: Generation Request

[0175] Specific operation: The server sends the requirement "an illustration of a new smartphone in a simple and modern style" to the image generation AI.

[0176] Step 4: Image generation

[0177] The image generation AI model generates images based on the specified requirements. The generated images are then sent back to the server.

[0178] Input: Generation Request

[0179] Data processing: Image generation

[0180] Output: Image data

[0181] Specific operation: The image generation AI model receives a request and generates an image based on the specified requirements. The generated image is then sent back to the server.

[0182] Step 5: Temporarily save the image and generate the link.

[0183] The server temporarily saves the generated image to cloud storage or local storage, generates a verification link, and sends it back to the device.

[0184] Input: Image data

[0185] Data processing: Temporary storage, link generation

[0186] Output: Verification link

[0187] Specific operation: The server saves the generated image, creates a verification link, and sends it to the terminal.

[0188] Step 6: Verify the generated image.

[0189] The user clicks the link received from their device and checks the generated image.

[0190] Input: Confirmation link

[0191] Output: None

[0192] Specific action: Ms. Tanaka clicks the link and checks the generated illustration.

[0193] Step 7: Submitting Feedback

[0194] The device sends user feedback (approval or correction request) to the server.

[0195] Input: Feedback (approval or correction request)

[0196] Output: Feedback data

[0197] Specific operation: Mr. Tanaka enters the confirmation result (approval or correction request), and the terminal sends it to the server.

[0198] Step 8: Final save and link generation

[0199] The server will finalize the image based on the feedback and, if approved, generate a sharing link. The finalized image will be stored in the company's internal storage.

[0200] Input: Feedback data

[0201] Data processing: Final saving, link generation.

[0202] Output: Shared link

[0203] Specific actions: The server performs the final save, generates a shared link, and provides it to Mr. Tanaka.

[0204] Step 9: Sharing and using images

[0205] The device displays a list of images saved to the user, and the user selects and downloads the desired images or shares them with other departments.

[0206] Input: Request to display image list

[0207] Output: Image data, sharing link

[0208] Specific actions: Ms. Tanaka selects and downloads the saved illustration. She also shares it with the marketing team using a shared link.

[0209] (Application Example 1)

[0210] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0211] While systems for rapidly generating high-quality images already exist, there is a lack of systems that provide metadata for those images and further analyze viewing trends and predict social media reactions. In particular, advertising campaigns require predicting how generated images will be received by viewers and providing feedback on subsequent reactions. Effective means to achieve this are needed.

[0212] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0213] In this invention, the server includes means for providing metadata of the generated image, means for performing viewing trend analysis and predicting reactions on social media based on the metadata, and means for collecting and feeding back reactions to the shared image from social media. This makes it possible to predict in advance how the generated image will affect the advertising campaign and to effectively collect and feed back actual reactions.

[0214] User authentication is the process by which a user proves their identity in order to access a system.

[0215] A "device" refers to a device operated by a user, and includes computers, smartphones, tablets, and other similar devices.

[0216] A "server" is a computer system used for managing and processing data.

[0217] "Authentication information" refers to information such as the ID and password that a user uses to log in to a system.

[0218] "Image generation AI" is a system that uses artificial intelligence technology to generate images based on user requirements.

[0219] "Metadata" refers to additional information related to the generated image, including viewing trends and predictions of reactions on social media.

[0220] "Feedback" refers to the user's action of reviewing generated images and sending suggestions for corrections or approvals to the server.

[0221] "Viewing trend analysis" is the process of analyzing how images generated based on metadata are received by viewers.

[0222] "SNS reaction prediction" is the process of predicting what kind of reactions a generated image will evoke on social networking services.

[0223] "Internal storage" refers to a data storage area used for saving and managing data within a company.

[0224] Modes for carrying out the invention

[0225] This invention provides a system for rapidly generating high-quality images for advertising campaigns, providing metadata for those images, analyzing viewing trends, predicting social media reactions, and collecting feedback. The following hardware and software are used to implement the system.

[0226] Hardware and software requirements

[0227] Smartphones (running iOS or Android®)

[0228] Communication modules (Wi-Fi, LTE, etc.)

[0229] Servers (cloud services, such as AWS® or Google® Cloud)

[0230] Image generation AI (e.g., OpenAI's DALL-E, Google's DeepDream, etc.)

[0231] Databases (e.g., MySQL, PostgreSQL)

[0232] System Overview

[0233] 1. User Authentication:

[0234] The smartphone displays a login screen to the user. The user enters their ID and password. The authentication information is sent to the server, and the user's access rights are verified by comparing the authentication information with the database.

[0235] 2. Image generation request:

[0236] The smartphone displays an input form to the user for generating an advertising image. The user enters the requirements for the image they want to generate (theme, style, specific elements). These entered requirements are sent to the server.

[0237] 3. Image generation:

[0238] The server sends a request to the image generation AI. The image generation AI generates an image based on the specified requirements, and the generated image is returned to the server and temporarily stored. The server sends a confirmation link to the user.

[0239] 4. Check and save the image:

[0240] The user clicks the link and reviews the generated image. The user's feedback (approval or revision request) is sent to the server. Based on the feedback, the image is finalized and a sharing link is generated.

[0241] 5. Image sharing and use:

[0242] The server saves the generated images to the company's internal storage. The server generates a sharing link for the saved images and notifies the relevant projects and departments. The terminal displays a list of saved images to the user, allowing the user to download the images or share them with other departments.

[0243] 6. Providing and analyzing metadata:

[0244] The server provides metadata for the generated images. Based on the metadata, it performs viewing trend analysis and predicts reactions on social media, and collects and provides feedback on reactions to shared images from social media.

[0245] Specific example

[0246] This scenario involves an advertising professional using the "AdImageCreator" application to generate new promotional images for smartphones.

[0247] 1. Login: The advertising representative opens the app on their smartphone, enters their ID and password, and logs in.

[0248] 2. Generation Request: Enter "Simple and modern style illustration of a new smartphone for advertising" and press the submit button.

[0249] 3. Image Generation: The server sends a request to the image generation AI, the AI ​​generates an image, and the link is sent to the advertiser.

[0250] 4. Review and Save: The advertiser clicks the link to review the generated image. They then submit feedback for revisions or approvals.

[0251] 5. Sharing and Use: Advertisers download the generated images and share them with marketing teams using internal chat. The images are used in advertising campaigns and also posted on social media.

[0252] 6. Metadata Provision and Analysis: The server provides image metadata to analyze viewing trends and predict social media reactions. Furthermore, it collects social media reactions and provides feedback to advertisers.

[0253] Example of a prompt

[0254] 1. "Illustrations of the new smartphone in a simple and modern style."

[0255] 2. "An image of a family enjoying themselves on the beach for a summer campaign."

[0256] 3. "Photos of Christmas trees and presents for Christmas sales"

[0257] This invention provides a system that can effectively generate and utilize high-quality advertising images, thereby further strengthening a company's marketing activities.

[0258] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0259] Step 1:

[0260] User Authentication

[0261] Input: The user enters their ID and password on the login screen of their smartphone.

[0262] Specific actions:

[0263] The terminal sends the entered authentication information to the server. The server checks against the database to verify the user's access rights.

[0264] Output: If authentication is successful, the user can proceed to the next step. If it fails, a message will be displayed prompting re-entry.

[0265] Step 2:

[0266] Image generation request

[0267] Input: The user enters the requirements for the image they want to generate (theme, style, specific elements).

[0268] Specific actions:

[0269] The terminal sends the entered requirements to the server.

[0270] Output: The server receives the requirements and prepares to send a request to the image generation AI.

[0271] Step 3:

[0272] Image generation

[0273] Input: The server sends the requirements as a request to the image generation AI.

[0274] Specific actions:

[0275] The server sends the requirements to the image generation AI, and the AI ​​generates an image based on the specified requirements.

[0276] Output: The image generation AI returns the generated image to the server, where it is temporarily stored. The server then sends a confirmation link to the user.

[0277] Step 4:

[0278] View and save the image.

[0279] Input: The user checks the image generated by clicking the link received from the server.

[0280] Specific operations:

[0281] The user checks the generated image and enters feedback (approval or request for modification). The terminal sends the feedback to the server.

[0282] Output: Based on the feedback, the server finally saves the image and generates a link for access.

[0283] Step 5:

[0284] Sharing and Saving of Images

[0285] Input: The server notifies the related project or department of the finally saved image and the sharing link.

[0286] Specific operations:

[0287] The server saves the generated image in the company storage and creates a list of the saved images. The terminal displays the list of the saved images to the user.

[0288] Output: The user can download the image or share it with other departments. The sharing link is notified to the related project or department.

[0289] Step 6:

[0290] Provision and Analysis of Metadata

[0291] Input: The server collects metadata for the generated image.

[0292] Specific operations:

[0293] The server uses metadata to analyze viewing trends and predict social media reactions. Furthermore, it collects actual social media reactions to generate feedback.

[0294] Output: Analysis results and response data are provided to the user, and data is obtained to measure the effectiveness of image-based advertising campaigns.

[0295] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0296] This invention provides a system for quickly and efficiently generating high-quality illustrations and photographs for presentation materials and reports used within a company. Furthermore, it combines this with an emotion engine that recognizes user emotions, resulting in a more user-friendly operation. The system includes steps such as user authentication, image generation requests, image generation, confirmation and saving, sharing and utilization, as well as emotion recognition and optimization based on that recognition.

[0297] User Authentication

[0298] Example of program processing

[0299] The device displays a login screen to the user.

[0300] The user enters their authentication information (ID and password).

[0301] The device sends authentication information to the server.

[0302] The server compares the received authentication information with the database to verify the user's access rights.

[0303] Image generation request

[0304] Example of program processing

[0305] The terminal displays an input form for the user to generate an image.

[0306] The user inputs the requirements (theme, style, specific elements) of the image to be generated.

[0307] The terminal converts the input requirements into JSON format and sends them to the server.

[0308] Emotion recognition by the emotion engine

[0309] Example of program processing

[0310] Using the terminal's camera and microphone, capture the user's expression and voice tone.

[0311] The emotion engine analyzes the captured data and recognizes the user's emotion in real time.

[0312] The server receives the user's emotion data and reflects it in the requirements for image generation.

[0313] Specific example

[0314] When Mr. Tanaka inputs "Illustrations of the new product smartphone in a simple and modern style" for the new product launch, the emotion engine senses nervousness from his expression and voice tone. Based on this information, the system proposes adding relaxing design elements.

[0315] Image generation

[0316] Example of program processing

[0317] The server sends a request including emotion data to the image generation AI.

[0318] The image generation AI generates an image based on the requirements.

[0319] The server temporarily saves the generated image and sends a link to the user.

[0320] View and save the image.

[0321] Example of program processing

[0322] The user clicks the link and checks the generated image.

[0323] The device sends user feedback (approval or correction request) to the server.

[0324] If the server requests a correction, it sends a re-request to the image generation AI, which then makes corrections based on the feedback.

[0325] Once the user approves the image, the server saves the image and generates a link.

[0326] Emotional feedback and model optimization

[0327] Example of program processing

[0328] The emotion engine collects emotional feedback on user-generated images.

[0329] The server sends emotional feedback to the image generation AI, which is then used to optimize the model.

[0330] Specific example

[0331] If Ms. Tanaka is satisfied with the generated illustration, the emotion engine recognizes this positive emotion, and the server sends this feedback to the image generation AI to be used for future generation.

[0332] Image sharing and use

[0333] Example of program processing

[0334] The device displays a list of images saved to the user.

[0335] The user selects the image they need and gives instructions for downloading or sharing it.

[0336] The device downloads the image and saves it to the selected folder.

[0337] The server generates a shared link and notifies the relevant parties.

[0338] Specific example

[0339] Ms. Tanaka will download the generated illustrations and insert them into the PowerPoint presentation for the product announcement. She will also share them with the marketing team for use in social media posts.

[0340] This system allows users to quickly generate high-quality images, review and revise them as needed, and ultimately share them in a format suitable for internal and external use. Furthermore, by utilizing an emotion engine, it enables the generation of optimal images that respond to the user's emotions, thereby improving the user experience.

[0341] The following describes the processing flow.

[0342] User Authentication

[0343] Step 1:

[0344] The device displays a login screen to the user.

[0345] Step 2:

[0346] The user enters their ID and password.

[0347] Step 3:

[0348] The terminal sends the entered authentication information to the server.

[0349] Step 4:

[0350] The server compares the received authentication information with the database to verify the user's access rights.

[0351] Step 5:

[0352] If the server successfully authenticates the user, it will set the user's access rights and display the dashboard.

[0353] Image generation request

[0354] Step 1:

[0355] The device displays an input form to the user for image generation.

[0356] Step 2:

[0357] The user enters the requirements for the image they want to generate (theme, style, specific elements).

[0358] Step 3:

[0359] The device activates its camera and microphone to capture the user's facial expressions and voice tone, collecting data.

[0360] Step 4:

[0361] The terminal converts the entered requirements into JSON format and sends them to the server along with sentiment data.

[0362] Emotion recognition by an emotion engine

[0363] Step 1:

[0364] The server receives user sentiment data and sends it to the sentiment engine.

[0365] Step 2:

[0366] The emotion engine analyzes the captured data and recognizes the user's emotions in real time.

[0367] Step 3:

[0368] The server integrates emotional data obtained from the emotion engine into the request and reflects it in the image generation requirements.

[0369] Image generation

[0370] Step 1:

[0371] The server sends a request containing emotional data to the image generation AI.

[0372] Step 2:

[0373] The image generation AI generates images based on the requirements.

[0374] Step 3:

[0375] The server temporarily stores the generated image and sends a link to the user.

[0376] View and save the image.

[0377] Step 1:

[0378] The user clicks the link and checks the generated image.

[0379] Step 2:

[0380] The device sends user feedback (approval or correction request) to the server.

[0381] Step 3:

[0382] If the server requests a correction, it sends a re-request to the image generation AI, which then makes corrections based on the feedback.

[0383] Step 4:

[0384] Once the user approves the image, the server saves the image and generates a link.

[0385] Emotional feedback and model optimization

[0386] Step 1:

[0387] The emotion engine collects emotional feedback on user-generated images.

[0388] Step 2:

[0389] The server sends emotional feedback to the image generation AI, which is then used to optimize the model.

[0390] Image sharing and use

[0391] Step 1:

[0392] The device displays a list of images saved to the user.

[0393] Step 2:

[0394] The user selects the image they need and gives instructions for downloading or sharing it.

[0395] Step 3:

[0396] The device downloads the image and saves it to the selected folder.

[0397] Step 4:

[0398] The server generates a shared link and notifies the relevant parties.

[0399] (Example 2)

[0400] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0401] The problem that this invention aims to solve is to provide a system that can quickly and efficiently generate high-quality illustrations and photographs for presentation materials and reports used within companies, as well as a system that recognizes user emotions and improves usability. Conventional systems have the problem that they cannot take user emotions into consideration when generating images, and therefore the user experience is not improved. In addition, there were insufficient means to effectively save and share the generated images.

[0402] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0403] In this invention, the server includes means for the user to input authentication information, means for the terminal to send authentication information to the server to verify the user's access rights, means for the user to input image generation requirements, means for the terminal to send the input requirements to the server, means for the server to send a request to an image generation AI, means for the image generation AI to generate an image based on the requirements, means for the server to temporarily store the generated image and send a link to the user, means for the user to review the generated image and send feedback, means for the image to be finally saved and a link generated based on the feedback, means for the terminal to capture the user's facial expressions and voice tone using a camera and microphone, means for an emotion engine to analyze the captured data and recognize the user's emotions, and means for the server to receive the user's emotion data and reflect it in the image generation requirements. This enables optimal image generation according to the user's emotions, improves the user experience, and allows for the effective storage and sharing of generated images.

[0404] "User authentication" is the process by which a user enters their authentication information in order to access a system, and the server verifies their access rights.

[0405] An "image generation request" is the process in which a user inputs the requirements for the image they want to generate, and those requirements are sent to the server.

[0406] An "emotion engine" is a combination of software or hardware that analyzes a user's facial expressions and tone of voice to recognize their emotions.

[0407] "Image generation AI" is an artificial intelligence model that generates images based on input requirements.

[0408] A "server" is a computer system that manages the entire system and provides functions such as user authentication, processing image generation requests, and communication with image generation AI.

[0409] A "terminal" is a device that provides an interface for a user to access a system, and includes common computers such as personal computers, smartphones, and tablets.

[0410] "User feedback" is the process by which users send opinions, such as evaluations and requests for modifications, to the system regarding the images they generate.

[0411] A "link" is data that indicates a URL or other reference for a user to access.

[0412] "Storage" refers to physical or cloud-based storage devices used to store generated images and other data.

[0413] "Sharing methods" refer to methods or protocols for sharing generated images or their links with other users or departments.

[0414] This invention provides a system for quickly and efficiently generating high-quality illustrations and photographs for presentation materials and reports used within a company. Furthermore, by incorporating an emotion engine that recognizes user emotions, it achieves user-friendly operation. The system includes the following steps: user authentication, image generation request, emotion recognition by the emotion engine, image generation, confirmation and saving, and sharing and use.

[0415] The main components of the system are the server, terminal, user, image generation AI, and emotion engine. The specific roles and processing flow of each are described below.

[0416] User Authentication

[0417] The device displays a login screen to the user.

[0418] The user enters their authentication information (user ID and password).

[0419] The terminal sends the entered authentication information to the server.

[0420] The server compares the received authentication information with the database to verify the user's access rights.

[0421] Image generation request

[0422] The device displays an input form to the user for image generation.

[0423] The user enters the requirements for the image they want to generate (theme, style, specific elements).

[0424] The terminal converts the entered requirements into JSON format and sends it to the server.

[0425] Emotion recognition by an emotion engine

[0426] The device's camera and microphone are used to capture the user's facial expressions and voice tone.

[0427] The emotion engine analyzes the captured data and recognizes the user's emotions in real time.

[0428] The server receives user emotion data and incorporates it into the image generation requirements.

[0429] Image generation

[0430] The server sends a request containing emotional data to the image generation AI.

[0431] The image generation AI generates images based on the requirements.

[0432] The server temporarily stores the generated image and sends a link to the user.

[0433] View and save the image.

[0434] The user clicks the link and checks the generated image.

[0435] The device sends user feedback (approval or correction request) to the server.

[0436] If the server requests a correction, it sends a re-request to the image generation AI, which then makes corrections based on the feedback.

[0437] Once the user approves the image, the server saves the image and generates a link.

[0438] Emotional feedback and model optimization

[0439] The emotion engine collects emotional feedback on user-generated images.

[0440] The server sends emotional feedback to the image generation AI, which is then used to optimize the model.

[0441] Image sharing and use

[0442] The device displays a list of images saved to the user.

[0443] The user selects the image they need and gives instructions for downloading or sharing it.

[0444] The device downloads the image and saves it to the selected folder.

[0445] The server generates a shared link and notifies the relevant parties.

[0446] Examples of specific cases and prompt statements

[0447] For example, when a user inputs "An illustration of the new smartphone in a simple and modern style" for a new product announcement, the emotion engine senses tension from their facial expression and tone of voice. Based on this information, the system suggests adding relaxed design elements. An example of a prompt from the generative AI model is, "Please draw the new smartphone in a simple and modern style."

[0448] As described above, the system of the present invention allows users to quickly generate high-quality images, review and correct them as needed, and ultimately share them in a format suitable for use both inside and outside the company. Furthermore, by using an emotion engine, it enables the generation of optimal images that respond to the user's emotions, thereby improving the user experience.

[0449] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0450] Step 1: User Authentication

[0451] The device displays a login screen to the user.

[0452] Specific actions: The terminal screen will display input fields for user ID and password, and a "Login" button will be placed there.

[0453] Input: User ID, Password.

[0454] Output: Authentication information entered by the user.

[0455] The user enters their authentication information (user ID and password).

[0456] Specific steps: Enter your ID and password using the keyboard. Then click the login button.

[0457] Input: Keyboard input.

[0458] Output: The entered authentication information is sent to the login field.

[0459] The terminal sends the entered authentication information to the server.

[0460] Specific operation: Serializes the input data into JSON format and sends it to the server as an HTTP POST request.

[0461] Input: Authentication information (User ID, Password).

[0462] Output: Authentication information is sent to the server.

[0463] The server compares the received authentication information with the database to verify the user's access rights.

[0464] Specific operation: The server executes an SQL query against the database to verify user information. If successful, it generates an authentication token and returns it to the terminal.

[0465] Input: Authentication information (User ID, Password).

[0466] Output: Authentication token or error message.

[0467] Step 2: Image generation request

[0468] The device displays an input form to the user for image generation.

[0469] Specific operation: Display fields on the web screen for entering the image theme, style, and specific elements.

[0470] Input: None (initial display).

[0471] Output: A form for entering image generation requests.

[0472] The user enters the requirements for the image they want to generate.

[0473] Specific actions: For example, enter requirements such as, "An illustration of a new smartphone product in a simple and modern style."

[0474] Input: Image theme, style, and specific elements.

[0475] Output: Requirements for generating the input image.

[0476] The terminal converts the entered requirements into JSON format and sends it to the server.

[0477] Specific operation: Format the contents of the input field into JSON format and send it to the server as an HTTP POST request.

[0478] Input: Requirements for image generation.

[0479] Output: Image generation request sent to the server.

[0480] Step 3: Emotion recognition by the emotion engine

[0481] The device's camera and microphone are used to capture the user's facial expressions and voice tone.

[0482] Specific operation: Activates the device's built-in camera and microphone to collect data in real time.

[0483] Input: User's facial expression data, voice tone.

[0484] Output: Captured audio and video data.

[0485] The emotion engine analyzes the captured data and recognizes the user's emotions in real time.

[0486] Specific operation: The captured data is input into a deep learning model, and emotion labels are output.

[0487] Input: Captured audio data, video data.

[0488] Output: Analyzed emotion data (e.g., tension, relaxation).

[0489] The server receives user emotion data and incorporates it into the image generation requirements.

[0490] Specific operation: The server receives emotion data and updates the image generation request based on that data.

[0491] Input: Sentiment data.

[0492] Output: Updated image generation request.

[0493] Step 4: Image Generation

[0494] The server sends a request containing emotional data to the image generation AI.

[0495] Specific operation: Convert the updated image generation request to the appropriate format and send it to the image generation AI.

[0496] Input: Updated image generation request.

[0497] Output: Request sent to the image generation AI.

[0498] The image generation AI generates images based on the requirements.

[0499] Specific operation: The image generation AI model analyzes the prompt text and generates an image based on the specified style and elements.

[0500] Input: Prompt text, image generation requirements.

[0501] Output: The generated image.

[0502] The server temporarily stores the generated image and sends a link to the user.

[0503] Specific operation: The generated image is saved to temporary storage, and a notification containing the image's URL is sent to the user.

[0504] Input: The generated image.

[0505] Output: The link sent to the user.

[0506] Step 5: Check and save the image.

[0507] The user clicks the link and checks the generated image.

[0508] Specific action: Click on an email or in-app notification to be redirected to an image display page.

[0509] Input: Link.

[0510] Output: Display of the generated image.

[0511] The device sends user feedback (approval or correction request) to the server.

[0512] Specific action: Enter approval or correction requests into the feedback input form and send the data to the server.

[0513] Input: Feedback.

[0514] Output: Feedback sent to the server.

[0515] If the server requests a correction, it sends a re-request to the image generation AI, which then makes corrections based on the feedback.

[0516] Specific action: A regeneration request reflecting the changes is sent to the image generation AI.

[0517] Input: Correction request.

[0518] Output: Regenerated image.

[0519] Once the user approves the image, the server saves the image and generates a link.

[0520] Specific operation: Save the image to persistent storage and generate a download link.

[0521] Input: Approved image.

[0522] Output: Final saved image, download link.

[0523] Step 6: Emotional Feedback and Model Optimization

[0524] The emotion engine collects emotional feedback on user-generated images.

[0525] Specific operation: Capture the user's facial expressions and voice again while they are viewing the image to obtain emotion data.

[0526] Input: User's facial expression data, voice tone.

[0527] Output: Collected emotional feedback.

[0528] The server sends emotional feedback to the image generation AI, which is then used to optimize the model.

[0529] Specific operation: The collected emotional feedback is fed back to the image generation AI and used as training data for the model.

[0530] Input: Emotional feedback.

[0531] Output: Optimized image generation AI model.

[0532] Step 7: Sharing and using images

[0533] The device displays a list of images saved to the user.

[0534] Specific action: Display a list of thumbnails of all images generated by the user on the user's dashboard page.

[0535] Input: None (initial display).

[0536] Output: A list of image thumbnails.

[0537] The user selects the image they need and gives instructions for downloading or sharing it.

[0538] Specific actions: Select an image and click the "Download" or "Share" button.

[0539] Input: Selected image.

[0540] Output: Download or share options.

[0541] The device downloads the image and saves it to the selected folder.

[0542] Specific action: Use the browser's download function to save the image to the specified folder.

[0543] Input: Selected download folder.

[0544] Output: Saved image.

[0545] The server generates a shared link and notifies the relevant parties.

[0546] Specific actions: Generate a sharing link and send notifications to designated stakeholders via email or messaging apps.

[0547] Input: The image you want to share.

[0548] Output: Shared link and notification.

[0549] (Application Example 2)

[0550] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0551] Conventional image generation systems were unable to automatically reflect the optimal design elements based on user emotions, resulting in limited improvements to the user experience. Furthermore, it was difficult to immediately reflect the generated images in virtual stores, lacking convenience in situations requiring rapid operation. Additionally, there was no easy way for virtual store operators to use the image generation interface. This led to problems where generated images could not be immediately corrected or optimized if they did not meet user expectations.

[0552] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to input authentication information, means for the terminal to send authentication information to the server to confirm the user's access rights, means for the user to input the requirements for image generation, means for the terminal to send the input requirements to the server, means for the server to send a request to the image generation AI, means for the image generation AI to generate an image based on the requirements, means for the server to temporarily store the generated image and send a link to the user, means for the user to check the generated image and send feedback, means for the image to be finally saved and a link generated based on the feedback, means for the terminal to capture the user's facial expression using a camera and for the emotion engine to recognize the user's emotions, and means for the emotion engine to reflect the recognized emotion data in the image generation request. As a result, the optimal design elements based on the user's emotions are automatically reflected, enabling high-quality image generation quickly and efficiently, and allowing for rapid reflection in product descriptions in virtual stores.

[0553] A "user" is a person or entity that uses this system to input image generation requirements, and to review and provide feedback on the generated images.

[0554] "Authentication information" refers to information necessary for a user to access the system, such as a user ID and password.

[0555] A "terminal" is a device used by a user to access the system, enter authentication information, input requirements for image generation, and verify the generated image.

[0556] A "server" is a central processing system that performs tasks such as verifying authentication information, sending and managing image generation requests, and saving generated images and creating links.

[0557] "Image generation AI" is an artificial intelligence algorithm that generates high-quality images based on requirements entered by the user.

[0558] The "emotion engine" is a system that uses the device's camera to capture the user's facial expressions and recognizes the user's emotions in real time.

[0559] "Requirements" are conditions that include the theme, style, and specific elements of the image the user wants to generate.

[0560] "Feedback" refers to a user's response to an image they have created, seeking approval or modification.

[0561] A "link" is a URL or hyperlink that allows a server to temporarily store an image it has generated and make it accessible to users.

[0562] "Facial expressions" refer to the movements and visual changes of the user's face, and are the input data that the emotion engine uses to recognize emotions.

[0563] "Emotional data" refers to information about emotions that the emotion engine analyzes and recognizes from the user's facial expressions.

[0564] The present invention's system takes the user's input requirements for the image they wish to generate and uses image generation AI to produce high-quality images. In this process, it recognizes the user's emotions in real time and incorporates optimal design elements based on those emotions. This system enables users to quickly and efficiently generate high-quality images, review and modify them as needed, and ultimately share them in a format suitable for use in virtual stores.

[0565] First, the user enters their authentication information (user ID and password) using a terminal. The terminal sends this authentication information to the server, which then compares the received information with a database to verify the user's access rights. This prevents unauthorized access to the system.

[0566] Next, the user enters specific requirements for image generation (theme, style, specific elements) on their device. This input is converted to JSON format and sent to the server.

[0567] Subsequently, the user's facial expressions are captured using the device's camera. The emotion engine analyzes the captured facial data and recognizes the user's emotions in real time. This recognized emotion data is then incorporated into the image generation request.

[0568] The server sends a request containing emotional data to the image generation AI, which generates an image based on the requirements. The generated image is temporarily stored on the server, and an access link is sent to the user.

[0569] The user clicks the received link to view the generated image and sends feedback (approval or correction request) to the server via their device. The server receives the feedback and, if necessary, sends a request back to the image generation AI to make corrections based on the feedback.

[0570] Once the user approves an image, the server saves it and generates a link. The generated image is stored in the company's internal storage, and the sharing link is sent to the relevant projects and departments as needed. The device also displays a list of images saved by the user, allowing them to download images or share them with other departments.

[0571] For example, when a virtual store operator generates custom images based on the theme of "relaxing interior," this system allows the operator to easily input requirements via smartphone, and quickly review, modify, and share the generated images.

[0572] The main hardware and software used are as follows:

[0573] Camera: Captures the user's facial expressions (e.g., smartphone camera)

[0574] EmotionEngine: A software module that recognizes emotions.

[0575] ImageGenerator: AI Algorithm for Image Generation

[0576] API Server: A backend system that manages image generation requests and authentication information.

[0577] Examples of prompt statements include the following:

[0578] Image generation theme: Relaxing interior

[0579] Image style: Simple and modern

[0580] Specific elements: sofa, houseplants, calming color scheme

[0581] This system allows users to generate high-quality images that incorporate design elements tailored to their emotions, enabling efficient product descriptions in virtual stores.

[0582] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0583] Step 1:

[0584] The user enters authentication information on the device. Specifically, they enter their user ID and password into the input form and click the submit button. Input: User ID, password. Output: Authentication information is entered on the device.

[0585] Step 2:

[0586] The terminal sends authentication information to the server, and the server verifies the user's access rights by comparing the received authentication information with the database. Input: Authentication information. Output: Access rights verification result. Specifically, the server queries the database and verifies access rights based on the results.

[0587] Step 3:

[0588] The user enters the image generation requirements (theme, style, specific elements) into the device, which then converts this into JSON format and sends it to the server. Input: Image generation requirements. Output: Request data in JSON format. Specifically, the user fills in the theme, style, and specific elements in the input form and clicks the submit button.

[0589] Step 4:

[0590] The device uses its camera to capture the user's facial expressions, and an emotion engine analyzes this data to recognize the user's emotions in real time. Input: Captured image data. Output: User emotion data. Specifically, the camera captures the facial expression, and the emotion engine analyzes the emotion from the expression.

[0591] Step 5:

[0592] The server adds sentiment data to a JSON-formatted request and sends the request to the image generation AI. Input: Request data, sentiment data. Output: Image generation request. Specifically, the sentiment data is incorporated into the JSON and sent to the image generation AI via the API.

[0593] Step 6:

[0594] The image generation AI generates an image based on the request and sends it back to the server. Input: Image generation request. Output: Generated image data. Specifically, the image generation algorithm generates an image based on the requirements and emotions.

[0595] Step 7:

[0596] The server temporarily stores the generated image and sends an access link to the user. Input: Generated image data. Output: Access link. Specifically, the server saves the image to temporary storage, generates a link, and notifies the user.

[0597] Step 8:

[0598] The user clicks a received link, reviews the generated image, and sends feedback (approval or correction request) to the server via their device. Input: User feedback. Output: Feedback data. Specifically, the user clicks the link to view the image and then clicks the confirmation button.

[0599] Step 9:

[0600] The server receives feedback and, if necessary, sends a re-request to the image generation AI to make corrections based on the feedback. Input: Feedback data. Output: Corrected image data (if necessary). Specifically, the server analyzes the feedback content and sends a re-request if corrections are needed.

[0601] Step 10:

[0602] Once the user approves the image, the server saves the image and generates a link. Input: Final image data, user approval. Output: Final saved image data, sharing link. Specifically, the final image data is saved to permanent storage.

[0603] Step 11:

[0604] The server saves the last saved image to internal storage and notifies relevant projects and departments of the sharing link. Input: Last saved image data. Output: Sharing link, notification data. Specifically, it saves the image to internal storage and sends the link to relevant departments via email or the notification system.

[0605] Step 12:

[0606] The device displays a list of images saved by the user, allowing the user to download or share images with other departments. Input: Saved image data. Output: Download link, Share link. Specifically, it displays a list view and allows the user to select either a download or share link.

[0607] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0608] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0609] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0610] [Second Embodiment]

[0611] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0612] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0613] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0614] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0615] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0616] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0617] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0618] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0619] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0620] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0621] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0622] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0623] This invention is a system for quickly and efficiently generating high-quality illustrations and photographs for presentation materials and reports used within a company. The system operates through the following steps: user authentication, image generation request, image generation, confirmation and storage, and sharing and utilization.

[0624] User Authentication

[0625] Example of program processing

[0626] The device displays a login screen to the user.

[0627] The user enters their authentication information (ID and password).

[0628] The device sends authentication information to the server.

[0629] The server compares the received authentication information with the database to verify the user's access rights.

[0630] Specific example

[0631] Mr. Tanaka from the public relations department logs into the system, and the server verifies his authentication information. Since Mr. Tanaka has the necessary permissions for the public relations department, he is able to access the public relations project.

[0632] Image generation request

[0633] Example of program processing

[0634] The device displays an input form to the user for image generation.

[0635] The user enters the requirements for the image they want to generate (theme, style, specific elements).

[0636] The terminal sends the entered requirements to the server.

[0637] Specific example

[0638] Mr. Tanaka types "a simple and modern style illustration of the new smartphone" for the new product announcement. The device sends this request to the server.

[0639] Image generation

[0640] Example of program processing

[0641] The server sends a request to the image generation AI.

[0642] The image generation AI generates images based on the specified requirements.

[0643] The server temporarily stores the generated image and sends a link to the user.

[0644] Specific example

[0645] The server receives the request and sends it to the image generation AI. The image generation AI generates an illustration based on the requirements, and the server temporarily stores the illustration. A confirmation link is sent to Mr. Tanaka.

[0646] View and save the image.

[0647] Example of program processing

[0648] The user clicks the link and checks the generated image.

[0649] The device sends user feedback (approval or correction request) to the server.

[0650] Based on the feedback, the server will finally save the image and generate a link.

[0651] Specific example

[0652] Ms. Tanaka clicks the link to review the generated illustration. If revisions are needed, she submits feedback. If she is finally satisfied, she presses the "Approve" button and submits it to the server. The server saves the illustration to the company's internal storage and generates a sharing link.

[0653] Image sharing and use

[0654] Example of program processing

[0655] The device displays a list of images saved to the user.

[0656] Users select images, download them, and share them with other departments.

[0657] Specific example

[0658] Ms. Tanaka will download the generated illustrations and insert them into the PowerPoint presentation for the product announcement. She will also share them with the marketing team for use in social media posts.

[0659] This system significantly reduces the effort required for copyright verification and enables the creation and sharing of consistent images across the entire company. Furthermore, by providing users with an easy way to generate and share high-quality images, it improves operational efficiency. This invention is effectively operated by reskilled employees.

[0660] The following describes the processing flow.

[0661] User Authentication

[0662] Step 1:

[0663] The device displays a login screen to the user.

[0664] Step 2:

[0665] The user enters their ID and password.

[0666] Step 3:

[0667] The terminal sends the entered authentication information to the server.

[0668] Step 4:

[0669] The server compares the received authentication information with the database to verify the user's access rights.

[0670] Step 5:

[0671] If the server successfully authenticates the user, it will set the user's access rights and display the dashboard.

[0672] Image generation request

[0673] Step 1:

[0674] The device displays an input form to the user for image generation.

[0675] Step 2:

[0676] The user enters the requirements for the image they want to generate (theme, style, specific elements).

[0677] Step 3:

[0678] The terminal converts the entered requirements into JSON format and sends it to the server.

[0679] Image generation

[0680] Step 1:

[0681] The server sends the received request to the image generation AI.

[0682] Step 2:

[0683] The image generation AI generates images based on the requirements.

[0684] Step 3:

[0685] The server temporarily stores the generated image and sends a link to the user.

[0686] View and save the image.

[0687] Step 1:

[0688] The user clicks the provided link and checks the generated image.

[0689] Step 2:

[0690] The device sends user feedback (approval or correction request) to the server.

[0691] Step 3:

[0692] If the server requests a correction, it sends another request to the image generation AI, which then makes corrections based on the feedback.

[0693] Step 4:

[0694] Once the user approves the image, the server saves the image and generates a link.

[0695] Image sharing and use

[0696] Step 1:

[0697] The device displays a list of images saved to the user.

[0698] Step 2:

[0699] The user selects the image they need and gives instructions for downloading or sharing it.

[0700] Step 3:

[0701] The device downloads the image and saves it to the selected folder.

[0702] Step 4:

[0703] The server generates a shared link and notifies the relevant parties.

[0704] This allows users to quickly generate high-quality images, review and revise them as needed, and ultimately share them in a format suitable for use both internally and externally.

[0705] (Example 1)

[0706] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0707] Businesses need to quickly generate high-quality illustrations and photographs and efficiently share and utilize those images. Current systems require significant time and effort for image generation, and sharing the generated images is cumbersome, leading to decreased work efficiency and considerable effort in copyright verification. In particular, generating consistent designs and styles is difficult, making it challenging to maintain a consistent brand image within the company.

[0708] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0709] In this invention, the server includes means for sending requests to an image generation AI model, means for temporarily storing the generated image and sending a link to the user, and means for finally saving the image and generating a link based on feedback. This enables users to easily and quickly generate high-quality images and efficiently share and utilize the generated images while maintaining a unified design and style.

[0710] A "user" is a person or group that accesses and uses a system.

[0711] "Authentication information" refers to identification information used to verify a user's identity, and generally includes an ID and password.

[0712] A "terminal" is a device used by a user to access a system, and includes personal computers, smartphones, tablets, and other similar devices.

[0713] A "server" is a central computing system that processes user requests and manages data.

[0714] An "image generation AI model" is a program or software that generates images using artificial intelligence technology, and includes models such as Stable Diffusion and DALL-E.

[0715] A "request" refers to the specific operations or processes that a user asks the system to perform.

[0716] A "link" is a URL or path used to access generated images or other resources.

[0717] "Feedback" refers to opinions, evaluations, observations, and requests for corrections that users provide to a system.

[0718] "Cloud storage" refers to a storage service for saving and managing data via the internet.

[0719] A "session" is a state that uniquely identifies a series of communications and operations performed while a user is logged into a system.

[0720] An "access token" is a digital key that temporarily holds a user's authentication information and grants them access to the system.

[0721] Modes for carrying out the invention

[0722] This invention is a system for quickly and efficiently generating high-quality illustrations and photographs for presentation materials and reports used within a company. The system operates through the following steps: user authentication, image generation request, image generation, confirmation and storage, and sharing and utilization. Details are described below.

[0723] User Authentication

[0724] The terminal displays a login screen to the user, who enters their authentication information (ID and password). The terminal sends this authentication information to the server, which verifies the received information against a database (such as MySQL or PostgreSQL). If authentication is successful, the server creates a session and sends an access token back to the terminal. This allows the user to access the system.

[0725] Image generation request

[0726] The device displays an input form for image generation to the user, where the user enters the requirements for the image they want to generate (theme, style, specific elements). The device validates the entered requirements in real time, and if there are no problems, it sends them to the server. An AI image generation model (such as Stable Diffusion or DALL-E) generates the image based on these requirements.

[0727] As a concrete example, a user inputs the requirement, "An illustration of a new smartphone in a simple and modern style." The device sends this requirement to the server, which then sends a request to the image generation AI model.

[0728] Image generation

[0729] The server sends a request to an image generation AI model, which generates an image based on the specified requirements. The generated image is temporarily stored by the server in cloud storage or local storage, and a confirmation link is sent to the user.

[0730] View and save the image.

[0731] The user clicks a verification link to review the generated image. The device sends user feedback (approval or correction request) to the server. The server finalizes the image based on the feedback and generates a sharing link. Finally approved images are stored in internal storage.

[0732] Image sharing and use

[0733] The device displays a list of images saved by the user, allowing the user to select and download the desired images or share them with other departments. The device generates download and sharing links for the selected images, which the user can then use to share the images with other departments.

[0734] A specific example of a prompt message is: "Generate four high-quality slides suitable for a sample A4 portrait-size document for public relations. Product group: Smartphones, Theme: New product announcement, Style: Simple and modern."

[0735] This enables the creation and sharing of a unified image across the entire company, while significantly reducing the effort required for copyright verification. Furthermore, by providing users with an easy way to generate and share high-quality images, it improves operational efficiency. This invention is expected to be effectively implemented by reskilled employees.

[0736] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0737] Step 1: User Authentication

[0738] The device displays a login screen to the user. The user enters their ID and password, and the device sends this authentication information to the server. The server compares the received authentication information with the database to verify the user's access rights. MySQL or PostgreSQL are used as the database. If authentication is successful, the server creates a session and sends an access token back to the device.

[0739] Input: ID, Password

[0740] Data processing: Database matching

[0741] Output: Access token

[0742] Specific operation: Mr. Tanaka enters his ID and password on the login screen, and the device sends this information. The server verifies this information, generates an access token, and sends it back.

[0743] Step 2: Enter the image generation request

[0744] The device displays an input form for image generation to the user. The user enters the requirements for image generation (theme, style, specific elements) into the form. The device validates the entered requirements in real time and sends them to the server if there are no problems.

[0745] Input: Generation requirements (theme, style, specific elements)

[0746] Data na: Validation

[0747] Output: Sending requirements data to the server

[0748] Specific action: Mr. Tanaka enters the following: "An illustration of the new smartphone product in a simple and modern style." The terminal validates the requirement and sends it to the server.

[0749] Step 3: Sending the image generation request to the server

[0750] Based on the requirements received by the server, a request is sent to the image generation AI model. Stable Diffusion and DALL-E can be used as the image generation AI model.

[0751] Input: Requirements data

[0752] Data processing: Sending a request to an AI model for image generation.

[0753] Output: Generation Request

[0754] Specific operation: The server sends the requirement "an illustration of a new smartphone in a simple and modern style" to the image generation AI.

[0755] Step 4: Image generation

[0756] The image generation AI model generates images based on the specified requirements. The generated images are then sent back to the server.

[0757] Input: Generation Request

[0758] Data processing: Image generation

[0759] Output: Image data

[0760] Specific operation: The image generation AI model receives a request and generates an image based on the specified requirements. The generated image is then sent back to the server.

[0761] Step 5: Temporarily save the image and generate the link.

[0762] The server temporarily saves the generated image to cloud storage or local storage, generates a verification link, and sends it back to the device.

[0763] Input: Image data

[0764] Data processing: Temporary storage, link generation

[0765] Output: Verification link

[0766] Specific operation: The server saves the generated image, creates a verification link, and sends it to the terminal.

[0767] Step 6: Verify the generated image.

[0768] The user clicks the link received from their device and checks the generated image.

[0769] Input: Confirmation link

[0770] Output: None

[0771] Specific action: Ms. Tanaka clicks the link and checks the generated illustration.

[0772] Step 7: Submitting Feedback

[0773] The device sends user feedback (approval or correction request) to the server.

[0774] Input: Feedback (approval or correction request)

[0775] Output: Feedback data

[0776] Specific operation: Mr. Tanaka enters the confirmation result (approval or correction request), and the terminal sends it to the server.

[0777] Step 8: Final save and link generation

[0778] The server will finalize the image based on the feedback and, if approved, generate a sharing link. The finalized image will be stored in the company's internal storage.

[0779] Input: Feedback data

[0780] Data processing: Final saving, link generation.

[0781] Output: Shared link

[0782] Specific actions: The server performs the final save, generates a shared link, and provides it to Mr. Tanaka.

[0783] Step 9: Sharing and using images

[0784] The device displays a list of images saved to the user, and the user selects and downloads the desired images or shares them with other departments.

[0785] Input: Request to display image list

[0786] Output: Image data, sharing link

[0787] Specific actions: Ms. Tanaka selects and downloads the saved illustration. She also shares it with the marketing team using a shared link.

[0788] (Application Example 1)

[0789] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0790] While systems for rapidly generating high-quality images already exist, there is a lack of systems that provide metadata for those images and further analyze viewing trends and predict social media reactions. In particular, advertising campaigns require predicting how generated images will be received by viewers and providing feedback on subsequent reactions. Effective means to achieve this are needed.

[0791] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0792] In this invention, the server includes means for providing metadata of the generated image, means for performing viewing trend analysis and predicting reactions on social media based on the metadata, and means for collecting and feeding back reactions to the shared image from social media. This makes it possible to predict in advance how the generated image will affect the advertising campaign and to effectively collect and feed back actual reactions.

[0793] User authentication is the process by which a user proves their identity in order to access a system.

[0794] A "device" refers to a device operated by a user, and includes computers, smartphones, tablets, and other similar devices.

[0795] A "server" is a computer system used for managing and processing data.

[0796] "Authentication information" refers to information such as the ID and password that a user uses to log in to a system.

[0797] "Image generation AI" is a system that uses artificial intelligence technology to generate images based on user requirements.

[0798] "Metadata" refers to additional information related to the generated image, including viewing trends and predictions of reactions on social media.

[0799] "Feedback" refers to the user's action of reviewing generated images and sending suggestions for corrections or approvals to the server.

[0800] "Viewing trend analysis" is the process of analyzing how images generated based on metadata are received by viewers.

[0801] "SNS reaction prediction" is the process of predicting what kind of reactions a generated image will evoke on social networking services.

[0802] "Internal storage" refers to a data storage area used for saving and managing data within a company.

[0803] Modes for carrying out the invention

[0804] This invention provides a system for rapidly generating high-quality images for advertising campaigns, providing metadata for those images, analyzing viewing trends, predicting social media reactions, and collecting feedback. The following hardware and software are used to implement the system.

[0805] Hardware and software requirements

[0806] Smartphone (iOS or Android)

[0807] Communication modules (Wi-Fi, LTE, etc.)

[0808] Servers (cloud services, such as AWS or Google Cloud)

[0809] Image generation AI (e.g., OpenAI's DALL-E, Google's DeepDream, etc.)

[0810] Databases (e.g., MySQL, PostgreSQL)

[0811] System Overview

[0812] 1. User Authentication:

[0813] The smartphone displays a login screen to the user. The user enters their ID and password. The authentication information is sent to the server, and the user's access rights are verified by comparing the authentication information with the database.

[0814] 2. Image generation request:

[0815] The smartphone displays an input form to the user for generating an advertising image. The user enters the requirements for the image they want to generate (theme, style, specific elements). These entered requirements are sent to the server.

[0816] 3. Image generation:

[0817] The server sends a request to the image generation AI. The image generation AI generates an image based on the specified requirements, and the generated image is returned to the server and temporarily stored. The server sends a confirmation link to the user.

[0818] 4. Check and save the image:

[0819] The user clicks the link and reviews the generated image. The user's feedback (approval or revision request) is sent to the server. Based on the feedback, the image is finalized and a sharing link is generated.

[0820] 5. Image sharing and use:

[0821] The server saves the generated images to the company's internal storage. The server generates a sharing link for the saved images and notifies the relevant projects and departments. The terminal displays a list of saved images to the user, allowing the user to download the images or share them with other departments.

[0822] 6. Providing and analyzing metadata:

[0823] The server provides metadata for the generated images. Based on the metadata, it performs viewing trend analysis and predicts reactions on social media, and collects and provides feedback on reactions to shared images from social media.

[0824] Specific example

[0825] This scenario involves an advertising professional using the "AdImageCreator" application to generate new promotional images for smartphones.

[0826] 1. Login: The advertising representative opens the app on their smartphone, enters their ID and password, and logs in.

[0827] 2. Generation Request: Enter "Simple and modern style illustration of a new smartphone for advertising" and press the submit button.

[0828] 3. Image Generation: The server sends a request to the image generation AI, the AI ​​generates an image, and the link is sent to the advertiser.

[0829] 4. Review and Save: The advertiser clicks the link to review the generated image. They then submit feedback for revisions or approvals.

[0830] 5. Sharing and Use: Advertisers download the generated images and share them with marketing teams using internal chat. The images are used in advertising campaigns and also posted on social media.

[0831] 6. Metadata Provision and Analysis: The server provides image metadata to analyze viewing trends and predict social media reactions. Furthermore, it collects social media reactions and provides feedback to advertisers.

[0832] Example of a prompt

[0833] 1. "Illustrations of the new smartphone in a simple and modern style."

[0834] 2. "An image of a family enjoying themselves on the beach for a summer campaign."

[0835] 3. "Photos of Christmas trees and presents for Christmas sales"

[0836] This invention provides a system that can effectively generate and utilize high-quality advertising images, thereby further strengthening a company's marketing activities.

[0837] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0838] Step 1:

[0839] User Authentication

[0840] Input: The user enters their ID and password on the login screen of their smartphone.

[0841] Specific actions:

[0842] The terminal sends the entered authentication information to the server. The server checks against the database to verify the user's access rights.

[0843] Output: If authentication is successful, the user can proceed to the next step. If it fails, a message will be displayed prompting re-entry.

[0844] Step 2:

[0845] Image generation request

[0846] Input: The user enters the requirements for the image they want to generate (theme, style, specific elements).

[0847] Specific actions:

[0848] The terminal sends the entered requirements to the server.

[0849] Output: The server receives the requirements and prepares to send a request to the image generation AI.

[0850] Step 3:

[0851] Image generation

[0852] Input: The server sends the requirements as a request to the image generation AI.

[0853] Specific actions:

[0854] The server sends the requirements to the image generation AI, and the AI ​​generates an image based on the specified requirements.

[0855] Output: The image generation AI returns the generated image to the server, where it is temporarily stored. The server then sends a confirmation link to the user.

[0856] Step 4:

[0857] View and save the image.

[0858] Input: The user clicks a link received from the server to view the generated image.

[0859] Specific actions:

[0860] The user reviews the generated image and enters feedback (approval or correction request). The device then sends the feedback to the server.

[0861] Output: Based on the feedback, the server finalizes the image and generates a link for access.

[0862] Step 5:

[0863] Sharing and saving images

[0864] Input: The server will notify relevant projects and departments of the saved images and sharing links.

[0865] Specific actions:

[0866] The server saves the generated images to the company's internal storage and creates a list of the saved images. The terminal displays the list of saved images to the user.

[0867] Output: Users can download images and share them with other departments. A sharing link will be sent to the relevant project or department.

[0868] Step 6:

[0869] Metadata provision and analysis

[0870] Input: The server collects metadata for the generated image.

[0871] Specific actions:

[0872] The server uses metadata to analyze viewing trends and predict social media reactions. Furthermore, it collects actual social media reactions to generate feedback.

[0873] Output: Analysis results and response data are provided to the user, and data is obtained to measure the effectiveness of image-based advertising campaigns.

[0874] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0875] This invention provides a system for quickly and efficiently generating high-quality illustrations and photographs for presentation materials and reports used within a company. Furthermore, it combines this with an emotion engine that recognizes user emotions, resulting in a more user-friendly operation. The system includes steps such as user authentication, image generation requests, image generation, confirmation and saving, sharing and utilization, as well as emotion recognition and optimization based on that recognition.

[0876] User Authentication

[0877] Example of program processing

[0878] The device displays a login screen to the user.

[0879] The user enters their authentication information (ID and password).

[0880] The device sends authentication information to the server.

[0881] The server compares the received authentication information with the database to verify the user's access rights.

[0882] Image generation request

[0883] Example of program processing

[0884] The device displays an input form to the user for image generation.

[0885] The user enters the requirements for the image they want to generate (theme, style, specific elements).

[0886] The terminal converts the entered requirements into JSON format and sends it to the server.

[0887] Emotion recognition by an emotion engine

[0888] Example of program processing

[0889] The device's camera and microphone are used to capture the user's facial expressions and voice tone.

[0890] The emotion engine analyzes the captured data and recognizes the user's emotions in real time.

[0891] The server receives user emotion data and incorporates it into the image generation requirements.

[0892] Specific example

[0893] When Mr. Tanaka types "a simple and modern style illustration for the new smartphone" for the new product announcement, the emotion engine senses tension from his facial expression and tone of voice. Based on this information, the system suggests adding relaxed design elements.

[0894] Image generation

[0895] Example of program processing

[0896] The server sends a request containing emotional data to the image generation AI.

[0897] The image generation AI generates images based on the requirements.

[0898] The server temporarily stores the generated image and sends a link to the user.

[0899] View and save the image.

[0900] Example of program processing

[0901] The user clicks the link and checks the generated image.

[0902] The device sends user feedback (approval or correction request) to the server.

[0903] If the server requests a correction, it sends a re-request to the image generation AI, which then makes corrections based on the feedback.

[0904] Once the user approves the image, the server saves the image and generates a link.

[0905] Emotional feedback and model optimization

[0906] Example of program processing

[0907] The emotion engine collects emotional feedback on user-generated images.

[0908] The server sends emotional feedback to the image generation AI, which is then used to optimize the model.

[0909] Specific example

[0910] If Ms. Tanaka is satisfied with the generated illustration, the emotion engine recognizes this positive emotion, and the server sends this feedback to the image generation AI to be used for future generation.

[0911] Image sharing and use

[0912] Example of program processing

[0913] The device displays a list of images saved to the user.

[0914] The user selects the image they need and gives instructions for downloading or sharing it.

[0915] The device downloads the image and saves it to the selected folder.

[0916] The server generates a shared link and notifies the relevant parties.

[0917] Specific example

[0918] Ms. Tanaka will download the generated illustrations and insert them into the PowerPoint presentation for the product announcement. She will also share them with the marketing team for use in social media posts.

[0919] This system allows users to quickly generate high-quality images, review and revise them as needed, and ultimately share them in a format suitable for internal and external use. Furthermore, by utilizing an emotion engine, it enables the generation of optimal images that respond to the user's emotions, thereby improving the user experience.

[0920] The following describes the processing flow.

[0921] User Authentication

[0922] Step 1:

[0923] The device displays a login screen to the user.

[0924] Step 2:

[0925] The user enters their ID and password.

[0926] Step 3:

[0927] The terminal sends the entered authentication information to the server.

[0928] Step 4:

[0929] The server compares the received authentication information with the database to verify the user's access rights.

[0930] Step 5:

[0931] If the server successfully authenticates the user, it will set the user's access rights and display the dashboard.

[0932] Image generation request

[0933] Step 1:

[0934] The device displays an input form to the user for image generation.

[0935] Step 2:

[0936] The user enters the requirements for the image they want to generate (theme, style, specific elements).

[0937] Step 3:

[0938] The device activates its camera and microphone to capture the user's facial expressions and voice tone, collecting data.

[0939] Step 4:

[0940] The terminal converts the entered requirements into JSON format and sends them to the server along with sentiment data.

[0941] Emotion recognition by an emotion engine

[0942] Step 1:

[0943] The server receives user sentiment data and sends it to the sentiment engine.

[0944] Step 2:

[0945] The emotion engine analyzes the captured data and recognizes the user's emotions in real time.

[0946] Step 3:

[0947] The server integrates emotional data obtained from the emotion engine into the request and reflects it in the image generation requirements.

[0948] Image generation

[0949] Step 1:

[0950] The server sends a request containing emotional data to the image generation AI.

[0951] Step 2:

[0952] The image generation AI generates images based on the requirements.

[0953] Step 3:

[0954] The server temporarily stores the generated image and sends a link to the user.

[0955] View and save the image.

[0956] Step 1:

[0957] The user clicks the link and checks the generated image.

[0958] Step 2:

[0959] The device sends user feedback (approval or correction request) to the server.

[0960] Step 3:

[0961] If the server requests a correction, it sends a re-request to the image generation AI, which then makes corrections based on the feedback.

[0962] Step 4:

[0963] Once the user approves the image, the server saves the image and generates a link.

[0964] Emotional feedback and model optimization

[0965] Step 1:

[0966] The emotion engine collects emotional feedback on user-generated images.

[0967] Step 2:

[0968] The server sends emotional feedback to the image generation AI, which is then used to optimize the model.

[0969] Image sharing and use

[0970] Step 1:

[0971] The device displays a list of images saved to the user.

[0972] Step 2:

[0973] The user selects the image they need and gives instructions for downloading or sharing it.

[0974] Step 3:

[0975] The device downloads the image and saves it to the selected folder.

[0976] Step 4:

[0977] The server generates a shared link and notifies the relevant parties.

[0978] (Example 2)

[0979] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0980] The problem that this invention aims to solve is to provide a system that can quickly and efficiently generate high-quality illustrations and photographs for presentation materials and reports used within companies, as well as a system that recognizes user emotions and improves usability. Conventional systems have the problem that they cannot take user emotions into consideration when generating images, and therefore the user experience is not improved. In addition, there were insufficient means to effectively save and share the generated images.

[0981] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0982] In this invention, the server includes means for the user to input authentication information, means for the terminal to send authentication information to the server to verify the user's access rights, means for the user to input image generation requirements, means for the terminal to send the input requirements to the server, means for the server to send a request to an image generation AI, means for the image generation AI to generate an image based on the requirements, means for the server to temporarily store the generated image and send a link to the user, means for the user to review the generated image and send feedback, means for the image to be finally saved and a link generated based on the feedback, means for the terminal to capture the user's facial expressions and voice tone using a camera and microphone, means for an emotion engine to analyze the captured data and recognize the user's emotions, and means for the server to receive the user's emotion data and reflect it in the image generation requirements. This enables optimal image generation according to the user's emotions, improves the user experience, and allows for the effective storage and sharing of generated images.

[0983] "User authentication" is the process by which a user enters their authentication information in order to access a system, and the server verifies their access rights.

[0984] An "image generation request" is the process in which a user inputs the requirements for the image they want to generate, and those requirements are sent to the server.

[0985] An "emotion engine" is a combination of software or hardware that analyzes a user's facial expressions and tone of voice to recognize their emotions.

[0986] "Image generation AI" is an artificial intelligence model that generates images based on input requirements.

[0987] A "server" is a computer system that manages the entire system and provides functions such as user authentication, processing image generation requests, and communication with image generation AI.

[0988] A "terminal" is a device that provides an interface for a user to access a system, and includes common computers such as personal computers, smartphones, and tablets.

[0989] "User feedback" is the process by which users send opinions, such as evaluations and requests for modifications, to the system regarding the images they generate.

[0990] A "link" is data that indicates a URL or other reference for a user to access.

[0991] "Storage" refers to physical or cloud-based storage devices used to store generated images and other data.

[0992] "Sharing methods" refer to methods or protocols for sharing generated images or their links with other users or departments.

[0993] This invention provides a system for quickly and efficiently generating high-quality illustrations and photographs for presentation materials and reports used within a company. Furthermore, by incorporating an emotion engine that recognizes user emotions, it achieves user-friendly operation. The system includes the following steps: user authentication, image generation request, emotion recognition by the emotion engine, image generation, confirmation and saving, and sharing and use.

[0994] The main components of the system are the server, terminal, user, image generation AI, and emotion engine. The specific roles and processing flow of each are described below.

[0995] User Authentication

[0996] The device displays a login screen to the user.

[0997] The user enters their authentication information (user ID and password).

[0998] The terminal sends the entered authentication information to the server.

[0999] The server compares the received authentication information with the database to verify the user's access rights.

[1000] Image generation request

[1001] The device displays an input form to the user for image generation.

[1002] The user enters the requirements for the image they want to generate (theme, style, specific elements).

[1003] The terminal converts the entered requirements into JSON format and sends it to the server.

[1004] Emotion recognition by an emotion engine

[1005] The device's camera and microphone are used to capture the user's facial expressions and voice tone.

[1006] The emotion engine analyzes the captured data and recognizes the user's emotions in real time.

[1007] The server receives user emotion data and incorporates it into the image generation requirements.

[1008] Image generation

[1009] The server sends a request containing emotional data to the image generation AI.

[1010] The image generation AI generates images based on the requirements.

[1011] The server temporarily stores the generated image and sends a link to the user.

[1012] View and save the image.

[1013] The user clicks the link and checks the generated image.

[1014] The device sends user feedback (approval or correction request) to the server.

[1015] If the server requests a correction, it sends a re-request to the image generation AI, which then makes corrections based on the feedback.

[1016] Once the user approves the image, the server saves the image and generates a link.

[1017] Emotional feedback and model optimization

[1018] The emotion engine collects emotional feedback on user-generated images.

[1019] The server sends emotional feedback to the image generation AI, which is then used to optimize the model.

[1020] Image sharing and use

[1021] The device displays a list of images saved to the user.

[1022] The user selects the image they need and gives instructions for downloading or sharing it.

[1023] The device downloads the image and saves it to the selected folder.

[1024] The server generates a shared link and notifies the relevant parties.

[1025] Examples of specific cases and prompt statements

[1026] For example, when a user inputs "An illustration of the new smartphone in a simple and modern style" for a new product announcement, the emotion engine senses tension from their facial expression and tone of voice. Based on this information, the system suggests adding relaxed design elements. An example of a prompt from the generative AI model is, "Please draw the new smartphone in a simple and modern style."

[1027] As described above, the system of the present invention allows users to quickly generate high-quality images, review and correct them as needed, and ultimately share them in a format suitable for use both inside and outside the company. Furthermore, by using an emotion engine, it enables the generation of optimal images that respond to the user's emotions, thereby improving the user experience.

[1028] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1029] Step 1: User Authentication

[1030] The device displays a login screen to the user.

[1031] Specific actions: The terminal screen will display input fields for user ID and password, and a "Login" button will be placed there.

[1032] Input: User ID, Password.

[1033] Output: Authentication information entered by the user.

[1034] The user enters their authentication information (user ID and password).

[1035] Specific steps: Enter your ID and password using the keyboard. Then click the login button.

[1036] Input: Keyboard input.

[1037] Output: The entered authentication information is sent to the login field.

[1038] The terminal sends the entered authentication information to the server.

[1039] Specific operation: Serializes the input data into JSON format and sends it to the server as an HTTP POST request.

[1040] Input: Authentication information (User ID, Password).

[1041] Output: Authentication information is sent to the server.

[1042] The server compares the received authentication information with the database to verify the user's access rights.

[1043] Specific operation: The server executes an SQL query against the database to verify user information. If successful, it generates an authentication token and returns it to the terminal.

[1044] Input: Authentication information (User ID, Password).

[1045] Output: Authentication token or error message.

[1046] Step 2: Image generation request

[1047] The device displays an input form to the user for image generation.

[1048] Specific operation: Display fields on the web screen for entering the image theme, style, and specific elements.

[1049] Input: None (initial display).

[1050] Output: A form for entering image generation requests.

[1051] The user enters the requirements for the image they want to generate.

[1052] Specific actions: For example, enter requirements such as, "An illustration of a new smartphone product in a simple and modern style."

[1053] Input: Image theme, style, and specific elements.

[1054] Output: Requirements for generating the input image.

[1055] The terminal converts the entered requirements into JSON format and sends it to the server.

[1056] Specific operation: Format the contents of the input field into JSON format and send it to the server as an HTTP POST request.

[1057] Input: Requirements for image generation.

[1058] Output: Image generation request sent to the server.

[1059] Step 3: Emotion recognition by the emotion engine

[1060] The device's camera and microphone are used to capture the user's facial expressions and voice tone.

[1061] Specific operation: Activates the device's built-in camera and microphone to collect data in real time.

[1062] Input: User's facial expression data, voice tone.

[1063] Output: Captured audio and video data.

[1064] The emotion engine analyzes the captured data and recognizes the user's emotions in real time.

[1065] Specific operation: The captured data is input into a deep learning model, and emotion labels are output.

[1066] Input: Captured audio data, video data.

[1067] Output: Analyzed emotion data (e.g., tension, relaxation).

[1068] The server receives user emotion data and incorporates it into the image generation requirements.

[1069] Specific operation: The server receives emotion data and updates the image generation request based on that data.

[1070] Input: Sentiment data.

[1071] Output: Updated image generation request.

[1072] Step 4: Image Generation

[1073] The server sends a request containing emotional data to the image generation AI.

[1074] Specific operation: Convert the updated image generation request to the appropriate format and send it to the image generation AI.

[1075] Input: Updated image generation request.

[1076] Output: Request sent to the image generation AI.

[1077] The image generation AI generates images based on the requirements.

[1078] Specific operation: The image generation AI model analyzes the prompt text and generates an image based on the specified style and elements.

[1079] Input: Prompt text, image generation requirements.

[1080] Output: The generated image.

[1081] The server temporarily stores the generated image and sends a link to the user.

[1082] Specific operation: The generated image is saved to temporary storage, and a notification containing the image's URL is sent to the user.

[1083] Input: The generated image.

[1084] Output: The link sent to the user.

[1085] Step 5: Check and save the image.

[1086] The user clicks the link and checks the generated image.

[1087] Specific action: Click on an email or in-app notification to be redirected to an image display page.

[1088] Input: Link.

[1089] Output: Display of the generated image.

[1090] The device sends user feedback (approval or correction request) to the server.

[1091] Specific action: Enter approval or correction requests into the feedback input form and send the data to the server.

[1092] Input: Feedback.

[1093] Output: Feedback sent to the server.

[1094] If the server requests a correction, it sends a re-request to the image generation AI, which then makes corrections based on the feedback.

[1095] Specific action: A regeneration request reflecting the changes is sent to the image generation AI.

[1096] Input: Correction request.

[1097] Output: Regenerated image.

[1098] Once the user approves the image, the server saves the image and generates a link.

[1099] Specific operation: Save the image to persistent storage and generate a download link.

[1100] Input: Approved image.

[1101] Output: Final saved image, download link.

[1102] Step 6: Emotional Feedback and Model Optimization

[1103] The emotion engine collects emotional feedback on user-generated images.

[1104] Specific operation: Capture the user's facial expressions and voice again while they are viewing the image to obtain emotion data.

[1105] Input: User's facial expression data, voice tone.

[1106] Output: Collected emotional feedback.

[1107] The server sends emotional feedback to the image generation AI, which is then used to optimize the model.

[1108] Specific operation: The collected emotional feedback is fed back to the image generation AI and used as training data for the model.

[1109] Input: Emotional feedback.

[1110] Output: Optimized image generation AI model.

[1111] Step 7: Sharing and using images

[1112] The device displays a list of images saved to the user.

[1113] Specific action: Display a list of thumbnails of all images generated by the user on the user's dashboard page.

[1114] Input: None (initial display).

[1115] Output: A list of image thumbnails.

[1116] The user selects the image they need and gives instructions for downloading or sharing it.

[1117] Specific actions: Select an image and click the "Download" or "Share" button.

[1118] Input: Selected image.

[1119] Output: Download or share options.

[1120] The device downloads the image and saves it to the selected folder.

[1121] Specific action: Use the browser's download function to save the image to the specified folder.

[1122] Input: Selected download folder.

[1123] Output: Saved image.

[1124] The server generates a shared link and notifies the relevant parties.

[1125] Specific actions: Generate a sharing link and send notifications to designated stakeholders via email or messaging apps.

[1126] Input: The image you want to share.

[1127] Output: Shared link and notification.

[1128] (Application Example 2)

[1129] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[1130] Conventional image generation systems were unable to automatically reflect the optimal design elements based on user emotions, resulting in limited improvements to the user experience. Furthermore, it was difficult to immediately reflect the generated images in virtual stores, lacking convenience in situations requiring rapid operation. Additionally, there was no easy way for virtual store operators to use the image generation interface. This led to problems where generated images could not be immediately corrected or optimized if they did not meet user expectations.

[1131] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to input authentication information, means for the terminal to send authentication information to the server to confirm the user's access rights, means for the user to input the requirements for image generation, means for the terminal to send the input requirements to the server, means for the server to send a request to the image generation AI, means for the image generation AI to generate an image based on the requirements, means for the server to temporarily store the generated image and send a link to the user, means for the user to check the generated image and send feedback, means for the image to be finally saved and a link generated based on the feedback, means for the terminal to capture the user's facial expression using a camera and for the emotion engine to recognize the user's emotions, and means for the emotion engine to reflect the recognized emotion data in the image generation request. As a result, the optimal design elements based on the user's emotions are automatically reflected, enabling high-quality image generation quickly and efficiently, and allowing for rapid reflection in product descriptions in virtual stores.

[1132] A "user" is a person or entity that uses this system to input image generation requirements, and to review and provide feedback on the generated images.

[1133] "Authentication information" refers to information necessary for a user to access the system, such as a user ID and password.

[1134] A "terminal" is a device used by a user to access the system, enter authentication information, input requirements for image generation, and verify the generated image.

[1135] A "server" is a central processing system that performs tasks such as verifying authentication information, sending and managing image generation requests, and saving generated images and creating links.

[1136] "Image generation AI" is an artificial intelligence algorithm that generates high-quality images based on requirements entered by the user.

[1137] The "emotion engine" is a system that uses the device's camera to capture the user's facial expressions and recognizes the user's emotions in real time.

[1138] "Requirements" are conditions that include the theme, style, and specific elements of the image the user wants to generate.

[1139] "Feedback" refers to a user's response to an image they have created, seeking approval or modification.

[1140] A "link" is a URL or hyperlink that allows a server to temporarily store an image it has generated and make it accessible to users.

[1141] "Facial expressions" refer to the movements and visual changes of the user's face, and are the input data that the emotion engine uses to recognize emotions.

[1142] "Emotional data" refers to information about emotions that the emotion engine analyzes and recognizes from the user's facial expressions.

[1143] The present invention's system takes the user's input requirements for the image they wish to generate and uses image generation AI to produce high-quality images. In this process, it recognizes the user's emotions in real time and incorporates optimal design elements based on those emotions. This system enables users to quickly and efficiently generate high-quality images, review and modify them as needed, and ultimately share them in a format suitable for use in virtual stores.

[1144] First, the user enters their authentication information (user ID and password) using a terminal. The terminal sends this authentication information to the server, which then compares the received information with a database to verify the user's access rights. This prevents unauthorized access to the system.

[1145] Next, the user enters specific requirements for image generation (theme, style, specific elements) on their device. This input is converted to JSON format and sent to the server.

[1146] Subsequently, the user's facial expressions are captured using the device's camera. The emotion engine analyzes the captured facial data and recognizes the user's emotions in real time. This recognized emotion data is then incorporated into the image generation request.

[1147] The server sends a request containing emotional data to the image generation AI, which generates an image based on the requirements. The generated image is temporarily stored on the server, and an access link is sent to the user.

[1148] The user clicks the received link to view the generated image and sends feedback (approval or correction request) to the server via their device. The server receives the feedback and, if necessary, sends a request back to the image generation AI to make corrections based on the feedback.

[1149] Once the user approves an image, the server saves it and generates a link. The generated image is stored in the company's internal storage, and the sharing link is sent to the relevant projects and departments as needed. The device also displays a list of images saved by the user, allowing them to download images or share them with other departments.

[1150] For example, when a virtual store operator generates custom images based on the theme of "relaxing interior," this system allows the operator to easily input requirements via smartphone, and quickly review, modify, and share the generated images.

[1151] The main hardware and software used are as follows:

[1152] Camera: Captures the user's facial expressions (e.g., smartphone camera)

[1153] EmotionEngine: A software module that recognizes emotions.

[1154] ImageGenerator: AI Algorithm for Image Generation

[1155] API Server: A backend system that manages image generation requests and authentication information.

[1156] Examples of prompt statements include the following:

[1157] Image generation theme: Relaxing interior

[1158] Image style: Simple and modern

[1159] Specific elements: sofa, houseplants, calming color scheme

[1160] This system allows users to generate high-quality images that incorporate design elements tailored to their emotions, enabling efficient product descriptions in virtual stores.

[1161] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1162] Step 1:

[1163] The user enters authentication information on the device. Specifically, they enter their user ID and password into the input form and click the submit button. Input: User ID, password. Output: Authentication information is entered on the device.

[1164] Step 2:

[1165] The terminal sends authentication information to the server, and the server verifies the user's access rights by comparing the received authentication information with the database. Input: Authentication information. Output: Access rights verification result. Specifically, the server queries the database and verifies access rights based on the results.

[1166] Step 3:

[1167] The user enters the image generation requirements (theme, style, specific elements) into the device, which then converts this into JSON format and sends it to the server. Input: Image generation requirements. Output: Request data in JSON format. Specifically, the user fills in the theme, style, and specific elements in the input form and clicks the submit button.

[1168] Step 4:

[1169] The device uses its camera to capture the user's facial expressions, and an emotion engine analyzes this data to recognize the user's emotions in real time. Input: Captured image data. Output: User emotion data. Specifically, the camera captures the facial expression, and the emotion engine analyzes the emotion from the expression.

[1170] Step 5:

[1171] The server adds sentiment data to a JSON-formatted request and sends the request to the image generation AI. Input: Request data, sentiment data. Output: Image generation request. Specifically, the sentiment data is incorporated into the JSON and sent to the image generation AI via the API.

[1172] Step 6:

[1173] The image generation AI generates an image based on the request and sends it back to the server. Input: Image generation request. Output: Generated image data. Specifically, the image generation algorithm generates an image based on the requirements and emotions.

[1174] Step 7:

[1175] The server temporarily stores the generated image and sends an access link to the user. Input: Generated image data. Output: Access link. Specifically, the server saves the image to temporary storage, generates a link, and notifies the user.

[1176] Step 8:

[1177] The user clicks a received link, reviews the generated image, and sends feedback (approval or correction request) to the server via their device. Input: User feedback. Output: Feedback data. Specifically, the user clicks the link to view the image and then clicks the confirmation button.

[1178] Step 9:

[1179] The server receives feedback and, if necessary, sends a re-request to the image generation AI to make corrections based on the feedback. Input: Feedback data. Output: Corrected image data (if necessary). Specifically, the server analyzes the feedback content and sends a re-request if corrections are needed.

[1180] Step 10:

[1181] Once the user approves the image, the server saves the image and generates a link. Input: Final image data, user approval. Output: Final saved image data, sharing link. Specifically, the final image data is saved to permanent storage.

[1182] Step 11:

[1183] The server saves the last saved image to internal storage and notifies relevant projects and departments of the sharing link. Input: Last saved image data. Output: Sharing link, notification data. Specifically, it saves the image to internal storage and sends the link to relevant departments via email or the notification system.

[1184] Step 12:

[1185] The device displays a list of images saved by the user, allowing the user to download or share images with other departments. Input: Saved image data. Output: Download link, Share link. Specifically, it displays a list view and allows the user to select either a download or share link.

[1186] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1187] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1188] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[1189] [Third Embodiment]

[1190] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1191] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1192] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1193] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[1194] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1195] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1196] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1197] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1198] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1199] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1200] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1201] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[1202] This invention is a system for quickly and efficiently generating high-quality illustrations and photographs for presentation materials and reports used within a company. The system operates through the following steps: user authentication, image generation request, image generation, confirmation and storage, and sharing and utilization.

[1203] User Authentication

[1204] Example of program processing

[1205] The device displays a login screen to the user.

[1206] The user enters their authentication information (ID and password).

[1207] The device sends authentication information to the server.

[1208] The server compares the received authentication information with the database to verify the user's access rights.

[1209] Specific example

[1210] Mr. Tanaka from the public relations department logs into the system, and the server verifies his authentication information. Since Mr. Tanaka has the necessary permissions for the public relations department, he is able to access the public relations project.

[1211] Image generation request

[1212] Example of program processing

[1213] The device displays an input form to the user for image generation.

[1214] The user enters the requirements for the image they want to generate (theme, style, specific elements).

[1215] The terminal sends the entered requirements to the server.

[1216] Specific example

[1217] Mr. Tanaka types "a simple and modern style illustration of the new smartphone" for the new product announcement. The device sends this request to the server.

[1218] Image generation

[1219] Example of program processing

[1220] The server sends a request to the image generation AI.

[1221] The image generation AI generates images based on the specified requirements.

[1222] The server temporarily stores the generated image and sends a link to the user.

[1223] Specific example

[1224] The server receives the request and sends it to the image generation AI. The image generation AI generates an illustration based on the requirements, and the server temporarily stores the illustration. A confirmation link is sent to Mr. Tanaka.

[1225] View and save the image.

[1226] Example of program processing

[1227] The user clicks the link and checks the generated image.

[1228] The device sends user feedback (approval or correction request) to the server.

[1229] Based on the feedback, the server will finally save the image and generate a link.

[1230] Specific example

[1231] Ms. Tanaka clicks the link to review the generated illustration. If revisions are needed, she submits feedback. If she is finally satisfied, she presses the "Approve" button and submits it to the server. The server saves the illustration to the company's internal storage and generates a sharing link.

[1232] Image sharing and use

[1233] Example of program processing

[1234] The device displays a list of images saved to the user.

[1235] Users select images, download them, and share them with other departments.

[1236] Specific example

[1237] Ms. Tanaka will download the generated illustrations and insert them into the PowerPoint presentation for the product announcement. She will also share them with the marketing team for use in social media posts.

[1238] This system significantly reduces the effort required for copyright verification and enables the creation and sharing of consistent images across the entire company. Furthermore, by providing users with an easy way to generate and share high-quality images, it improves operational efficiency. This invention is effectively operated by reskilled employees.

[1239] The following describes the processing flow.

[1240] User Authentication

[1241] Step 1:

[1242] The device displays a login screen to the user.

[1243] Step 2:

[1244] The user enters their ID and password.

[1245] Step 3:

[1246] The terminal sends the entered authentication information to the server.

[1247] Step 4:

[1248] The server compares the received authentication information with the database to verify the user's access rights.

[1249] Step 5:

[1250] If the server successfully authenticates the user, it will set the user's access rights and display the dashboard.

[1251] Image generation request

[1252] Step 1:

[1253] The device displays an input form to the user for image generation.

[1254] Step 2:

[1255] The user enters the requirements for the image they want to generate (theme, style, specific elements).

[1256] Step 3:

[1257] The terminal converts the entered requirements into JSON format and sends it to the server.

[1258] Image generation

[1259] Step 1:

[1260] The server sends the received request to the image generation AI.

[1261] Step 2:

[1262] The image generation AI generates images based on the requirements.

[1263] Step 3:

[1264] The server temporarily stores the generated image and sends a link to the user.

[1265] View and save the image.

[1266] Step 1:

[1267] The user clicks the provided link and checks the generated image.

[1268] Step 2:

[1269] The device sends user feedback (approval or correction request) to the server.

[1270] Step 3:

[1271] If the server requests a correction, it sends another request to the image generation AI, which then makes corrections based on the feedback.

[1272] Step 4:

[1273] Once the user approves the image, the server saves the image and generates a link.

[1274] Image sharing and use

[1275] Step 1:

[1276] The device displays a list of images saved to the user.

[1277] Step 2:

[1278] The user selects the image they need and gives instructions for downloading or sharing it.

[1279] Step 3:

[1280] The device downloads the image and saves it to the selected folder.

[1281] Step 4:

[1282] The server generates a shared link and notifies the relevant parties.

[1283] This allows users to quickly generate high-quality images, review and revise them as needed, and ultimately share them in a format suitable for use both internally and externally.

[1284] (Example 1)

[1285] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1286] Businesses need to quickly generate high-quality illustrations and photographs and efficiently share and utilize those images. Current systems require significant time and effort for image generation, and sharing the generated images is cumbersome, leading to decreased work efficiency and considerable effort in copyright verification. In particular, generating consistent designs and styles is difficult, making it challenging to maintain a consistent brand image within the company.

[1287] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1288] In this invention, the server includes means for sending requests to an image generation AI model, means for temporarily storing the generated image and sending a link to the user, and means for finally saving the image and generating a link based on feedback. This enables users to easily and quickly generate high-quality images and efficiently share and utilize the generated images while maintaining a unified design and style.

[1289] A "user" is a person or group that accesses and uses a system.

[1290] "Authentication information" refers to identification information used to verify a user's identity, and generally includes an ID and password.

[1291] A "terminal" is a device used by a user to access a system, and includes personal computers, smartphones, tablets, and other similar devices.

[1292] A "server" is a central computing system that processes user requests and manages data.

[1293] An "image generation AI model" is a program or software that generates images using artificial intelligence technology, and includes models such as Stable Diffusion and DALL-E.

[1294] A "request" refers to the specific operations or processes that a user asks the system to perform.

[1295] A "link" is a URL or path used to access generated images or other resources.

[1296] "Feedback" refers to opinions, evaluations, observations, and requests for corrections that users provide to a system.

[1297] "Cloud storage" refers to a storage service for saving and managing data via the internet.

[1298] A "session" is a state that uniquely identifies a series of communications and operations performed while a user is logged into a system.

[1299] An "access token" is a digital key that temporarily holds a user's authentication information and grants them access to the system.

[1300] Modes for carrying out the invention

[1301] This invention is a system for quickly and efficiently generating high-quality illustrations and photographs for presentation materials and reports used within a company. The system operates through the following steps: user authentication, image generation request, image generation, confirmation and storage, and sharing and utilization. Details are described below.

[1302] User Authentication

[1303] The terminal displays a login screen to the user, who enters their authentication information (ID and password). The terminal sends this authentication information to the server, which verifies the received information against a database (such as MySQL or PostgreSQL). If authentication is successful, the server creates a session and sends an access token back to the terminal. This allows the user to access the system.

[1304] Image generation request

[1305] The device displays an input form for image generation to the user, where the user enters the requirements for the image they want to generate (theme, style, specific elements). The device validates the entered requirements in real time, and if there are no problems, it sends them to the server. An AI image generation model (such as Stable Diffusion or DALL-E) generates the image based on these requirements.

[1306] As a concrete example, a user inputs the requirement, "An illustration of a new smartphone in a simple and modern style." The device sends this requirement to the server, which then sends a request to the image generation AI model.

[1307] Image generation

[1308] The server sends a request to an image generation AI model, which generates an image based on the specified requirements. The generated image is temporarily stored by the server in cloud storage or local storage, and a confirmation link is sent to the user.

[1309] View and save the image.

[1310] The user clicks a verification link to review the generated image. The device sends user feedback (approval or correction request) to the server. The server finalizes the image based on the feedback and generates a sharing link. Finally approved images are stored in internal storage.

[1311] Image sharing and use

[1312] The device displays a list of images saved by the user, allowing the user to select and download the desired images or share them with other departments. The device generates download and sharing links for the selected images, which the user can then use to share the images with other departments.

[1313] A specific example of a prompt message is: "Generate four high-quality slides suitable for a sample A4 portrait-size document for public relations. Product group: Smartphones, Theme: New product announcement, Style: Simple and modern."

[1314] This enables the creation and sharing of a unified image across the entire company, while significantly reducing the effort required for copyright verification. Furthermore, by providing users with an easy way to generate and share high-quality images, it improves operational efficiency. This invention is expected to be effectively implemented by reskilled employees.

[1315] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1316] Step 1: User Authentication

[1317] The device displays a login screen to the user. The user enters their ID and password, and the device sends this authentication information to the server. The server compares the received authentication information with the database to verify the user's access rights. MySQL or PostgreSQL are used as the database. If authentication is successful, the server creates a session and sends an access token back to the device.

[1318] Input: ID, Password

[1319] Data processing: Database matching

[1320] Output: Access token

[1321] Specific operation: Mr. Tanaka enters his ID and password on the login screen, and the device sends this information. The server verifies this information, generates an access token, and sends it back.

[1322] Step 2: Enter the image generation request

[1323] The device displays an input form for image generation to the user. The user enters the requirements for image generation (theme, style, specific elements) into the form. The device validates the entered requirements in real time and sends them to the server if there are no problems.

[1324] Input: Generation requirements (theme, style, specific elements)

[1325] Data na: Validation

[1326] Output: Sending requirements data to the server

[1327] Specific action: Mr. Tanaka enters the following: "An illustration of the new smartphone product in a simple and modern style." The terminal validates the requirement and sends it to the server.

[1328] Step 3: Sending the image generation request to the server

[1329] Based on the requirements received by the server, a request is sent to the image generation AI model. Stable Diffusion and DALL-E can be used as the image generation AI model.

[1330] Input: Requirements data

[1331] Data processing: Sending a request to an AI model for image generation.

[1332] Output: Generation Request

[1333] Specific operation: The server sends the requirement "an illustration of a new smartphone in a simple and modern style" to the image generation AI.

[1334] Step 4: Image generation

[1335] The image generation AI model generates images based on the specified requirements. The generated images are then sent back to the server.

[1336] Input: Generation Request

[1337] Data processing: Image generation

[1338] Output: Image data

[1339] Specific operation: The image generation AI model receives a request and generates an image based on the specified requirements. The generated image is then sent back to the server.

[1340] Step 5: Temporarily save the image and generate the link.

[1341] The server temporarily saves the generated image to cloud storage or local storage, generates a verification link, and sends it back to the device.

[1342] Input: Image data

[1343] Data processing: Temporary storage, link generation

[1344] Output: Verification link

[1345] Specific operation: The server saves the generated image, creates a verification link, and sends it to the terminal.

[1346] Step 6: Verify the generated image.

[1347] The user clicks the link received from their device and checks the generated image.

[1348] Input: Confirmation link

[1349] Output: None

[1350] Specific action: Ms. Tanaka clicks the link and checks the generated illustration.

[1351] Step 7: Submitting Feedback

[1352] The device sends user feedback (approval or correction request) to the server.

[1353] Input: Feedback (approval or correction request)

[1354] Output: Feedback data

[1355] Specific operation: Mr. Tanaka enters the confirmation result (approval or correction request), and the terminal sends it to the server.

[1356] Step 8: Final save and link generation

[1357] The server will finalize the image based on the feedback and, if approved, generate a sharing link. The finalized image will be stored in the company's internal storage.

[1358] Input: Feedback data

[1359] Data processing: Final saving, link generation.

[1360] Output: Shared link

[1361] Specific actions: The server performs the final save, generates a shared link, and provides it to Mr. Tanaka.

[1362] Step 9: Sharing and using images

[1363] The device displays a list of images saved to the user, and the user selects and downloads the desired images or shares them with other departments.

[1364] Input: Request to display image list

[1365] Output: Image data, sharing link

[1366] Specific actions: Ms. Tanaka selects and downloads the saved illustration. She also shares it with the marketing team using a shared link.

[1367] (Application Example 1)

[1368] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1369] While systems for rapidly generating high-quality images already exist, there is a lack of systems that provide metadata for those images and further analyze viewing trends and predict social media reactions. In particular, advertising campaigns require predicting how generated images will be received by viewers and providing feedback on subsequent reactions. Effective means to achieve this are needed.

[1370] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1371] In this invention, the server includes means for providing metadata of the generated image, means for performing viewing trend analysis and predicting reactions on social media based on the metadata, and means for collecting and feeding back reactions to the shared image from social media. This makes it possible to predict in advance how the generated image will affect the advertising campaign and to effectively collect and feed back actual reactions.

[1372] User authentication is the process by which a user proves their identity in order to access a system.

[1373] A "device" refers to a device operated by a user, and includes computers, smartphones, tablets, and other similar devices.

[1374] A "server" is a computer system used for managing and processing data.

[1375] "Authentication information" refers to information such as the ID and password that a user uses to log in to a system.

[1376] "Image generation AI" is a system that uses artificial intelligence technology to generate images based on user requirements.

[1377] "Metadata" refers to additional information related to the generated image, including viewing trends and predictions of reactions on social media.

[1378] "Feedback" refers to the user's action of reviewing generated images and sending suggestions for corrections or approvals to the server.

[1379] "Viewing trend analysis" is the process of analyzing how images generated based on metadata are received by viewers.

[1380] "SNS reaction prediction" is the process of predicting what kind of reactions a generated image will evoke on social networking services.

[1381] "Internal storage" refers to a data storage area used for saving and managing data within a company.

[1382] Modes for carrying out the invention

[1383] This invention provides a system for rapidly generating high-quality images for advertising campaigns, providing metadata for those images, analyzing viewing trends, predicting social media reactions, and collecting feedback. The following hardware and software are used to implement the system.

[1384] Hardware and software requirements

[1385] Smartphone (iOS or Android)

[1386] Communication modules (Wi-Fi, LTE, etc.)

[1387] Servers (cloud services, such as AWS or Google Cloud)

[1388] Image generation AI (e.g., OpenAI's DALL-E, Google's DeepDream, etc.)

[1389] Databases (e.g., MySQL, PostgreSQL)

[1390] System Overview

[1391] 1. User Authentication:

[1392] The smartphone displays a login screen to the user. The user enters their ID and password. The authentication information is sent to the server, and the user's access rights are verified by comparing the authentication information with the database.

[1393] 2. Image generation request:

[1394] The smartphone displays an input form to the user for generating an advertising image. The user enters the requirements for the image they want to generate (theme, style, specific elements). These entered requirements are sent to the server.

[1395] 3. Image generation:

[1396] The server sends a request to the image generation AI. The image generation AI generates an image based on the specified requirements, and the generated image is returned to the server and temporarily stored. The server sends a confirmation link to the user.

[1397] 4. Check and save the image:

[1398] The user clicks the link and reviews the generated image. The user's feedback (approval or revision request) is sent to the server. Based on the feedback, the image is finalized and a sharing link is generated.

[1399] 5. Image sharing and use:

[1400] The server saves the generated images to the company's internal storage. The server generates a sharing link for the saved images and notifies the relevant projects and departments. The terminal displays a list of saved images to the user, allowing the user to download the images or share them with other departments.

[1401] 6. Providing and analyzing metadata:

[1402] The server provides metadata for the generated images. Based on the metadata, it performs viewing trend analysis and predicts reactions on social media, and collects and provides feedback on reactions to shared images from social media.

[1403] Specific example

[1404] This scenario involves an advertising professional using the "AdImageCreator" application to generate new promotional images for smartphones.

[1405] 1. Login: The advertising representative opens the app on their smartphone, enters their ID and password, and logs in.

[1406] 2. Generation Request: Enter "Simple and modern style illustration of a new smartphone for advertising" and press the submit button.

[1407] 3. Image Generation: The server sends a request to the image generation AI, the AI ​​generates an image, and the link is sent to the advertiser.

[1408] 4. Review and Save: The advertiser clicks the link to review the generated image. They then submit feedback for revisions or approvals.

[1409] 5. Sharing and Use: Advertisers download the generated images and share them with marketing teams using internal chat. The images are used in advertising campaigns and also posted on social media.

[1410] 6. Metadata Provision and Analysis: The server provides image metadata to analyze viewing trends and predict social media reactions. Furthermore, it collects social media reactions and provides feedback to advertisers.

[1411] Example of a prompt

[1412] 1. "Illustrations of the new smartphone in a simple and modern style."

[1413] 2. "An image of a family enjoying themselves on the beach for a summer campaign."

[1414] 3. "Photos of Christmas trees and presents for Christmas sales"

[1415] This invention provides a system that can effectively generate and utilize high-quality advertising images, thereby further strengthening a company's marketing activities.

[1416] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1417] Step 1:

[1418] User Authentication

[1419] Input: The user enters their ID and password on the login screen of their smartphone.

[1420] Specific actions:

[1421] The terminal sends the entered authentication information to the server. The server checks against the database to verify the user's access rights.

[1422] Output: If authentication is successful, the user can proceed to the next step. If it fails, a message will be displayed prompting re-entry.

[1423] Step 2:

[1424] Image generation request

[1425] Input: The user enters the requirements for the image they want to generate (theme, style, specific elements).

[1426] Specific actions:

[1427] The terminal sends the entered requirements to the server.

[1428] Output: The server receives the requirements and prepares to send a request to the image generation AI.

[1429] Step 3:

[1430] Image generation

[1431] Input: The server sends the requirements as a request to the image generation AI.

[1432] Specific actions:

[1433] The server sends the requirements to the image generation AI, and the AI ​​generates an image based on the specified requirements.

[1434] Output: The image generation AI returns the generated image to the server, where it is temporarily stored. The server then sends a confirmation link to the user.

[1435] Step 4:

[1436] View and save the image.

[1437] Input: The user clicks a link received from the server to view the generated image.

[1438] Specific actions:

[1439] The user reviews the generated image and enters feedback (approval or correction request). The device then sends the feedback to the server.

[1440] Output: Based on the feedback, the server finalizes the image and generates a link for access.

[1441] Step 5:

[1442] Sharing and saving images

[1443] Input: The server will notify relevant projects and departments of the saved images and sharing links.

[1444] Specific actions:

[1445] The server saves the generated images to the company's internal storage and creates a list of the saved images. The terminal displays the list of saved images to the user.

[1446] Output: Users can download images and share them with other departments. A sharing link will be sent to the relevant project or department.

[1447] Step 6:

[1448] Metadata provision and analysis

[1449] Input: The server collects metadata for the generated image.

[1450] Specific actions:

[1451] The server uses metadata to analyze viewing trends and predict social media reactions. Furthermore, it collects actual social media reactions to generate feedback.

[1452] Output: Analysis results and response data are provided to the user, and data is obtained to measure the effectiveness of image-based advertising campaigns.

[1453] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1454] This invention provides a system for quickly and efficiently generating high-quality illustrations and photographs for presentation materials and reports used within a company. Furthermore, it combines this with an emotion engine that recognizes user emotions, resulting in a more user-friendly operation. The system includes steps such as user authentication, image generation requests, image generation, confirmation and saving, sharing and utilization, as well as emotion recognition and optimization based on that recognition.

[1455] User Authentication

[1456] Example of program processing

[1457] The device displays a login screen to the user.

[1458] The user enters their authentication information (ID and password).

[1459] The device sends authentication information to the server.

[1460] The server compares the received authentication information with the database to verify the user's access rights.

[1461] Image generation request

[1462] Example of program processing

[1463] The device displays an input form to the user for image generation.

[1464] The user enters the requirements for the image they want to generate (theme, style, specific elements).

[1465] The terminal converts the entered requirements into JSON format and sends it to the server.

[1466] Emotion recognition by an emotion engine

[1467] Example of program processing

[1468] The device's camera and microphone are used to capture the user's facial expressions and voice tone.

[1469] The emotion engine analyzes the captured data and recognizes the user's emotions in real time.

[1470] The server receives user emotion data and incorporates it into the image generation requirements.

[1471] Specific example

[1472] When Mr. Tanaka types "a simple and modern style illustration for the new smartphone" for the new product announcement, the emotion engine senses tension from his facial expression and tone of voice. Based on this information, the system suggests adding relaxed design elements.

[1473] Image generation

[1474] Example of program processing

[1475] The server sends a request containing emotional data to the image generation AI.

[1476] The image generation AI generates images based on the requirements.

[1477] The server temporarily stores the generated image and sends a link to the user.

[1478] View and save the image.

[1479] Example of program processing

[1480] The user clicks the link and checks the generated image.

[1481] The device sends user feedback (approval or correction request) to the server.

[1482] If the server requests a correction, it sends a re-request to the image generation AI, which then makes corrections based on the feedback.

[1483] Once the user approves the image, the server saves the image and generates a link.

[1484] Emotional feedback and model optimization

[1485] Example of program processing

[1486] The emotion engine collects emotional feedback on user-generated images.

[1487] The server sends emotional feedback to the image generation AI, which is then used to optimize the model.

[1488] Specific example

[1489] If Ms. Tanaka is satisfied with the generated illustration, the emotion engine recognizes this positive emotion, and the server sends this feedback to the image generation AI to be used for future generation.

[1490] Image sharing and use

[1491] Example of program processing

[1492] The device displays a list of images saved to the user.

[1493] The user selects the image they need and gives instructions for downloading or sharing it.

[1494] The device downloads the image and saves it to the selected folder.

[1495] The server generates a shared link and notifies the relevant parties.

[1496] Specific example

[1497] Ms. Tanaka will download the generated illustrations and insert them into the PowerPoint presentation for the product announcement. She will also share them with the marketing team for use in social media posts.

[1498] This system allows users to quickly generate high-quality images, review and revise them as needed, and ultimately share them in a format suitable for internal and external use. Furthermore, by utilizing an emotion engine, it enables the generation of optimal images that respond to the user's emotions, thereby improving the user experience.

[1499] The following describes the processing flow.

[1500] User Authentication

[1501] Step 1:

[1502] The device displays a login screen to the user.

[1503] Step 2:

[1504] The user enters their ID and password.

[1505] Step 3:

[1506] The terminal sends the entered authentication information to the server.

[1507] Step 4:

[1508] The server compares the received authentication information with the database to verify the user's access rights.

[1509] Step 5:

[1510] If the server successfully authenticates the user, it will set the user's access rights and display the dashboard.

[1511] Image generation request

[1512] Step 1:

[1513] The device displays an input form to the user for image generation.

[1514] Step 2:

[1515] The user enters the requirements for the image they want to generate (theme, style, specific elements).

[1516] Step 3:

[1517] The device activates its camera and microphone to capture the user's facial expressions and voice tone, collecting data.

[1518] Step 4:

[1519] The terminal converts the entered requirements into JSON format and sends them to the server along with sentiment data.

[1520] Emotion recognition by an emotion engine

[1521] Step 1:

[1522] The server receives user sentiment data and sends it to the sentiment engine.

[1523] Step 2:

[1524] The emotion engine analyzes the captured data and recognizes the user's emotions in real time.

[1525] Step 3:

[1526] The server integrates emotional data obtained from the emotion engine into the request and reflects it in the image generation requirements.

[1527] Image generation

[1528] Step 1:

[1529] The server sends a request containing emotional data to the image generation AI.

[1530] Step 2:

[1531] The image generation AI generates images based on the requirements.

[1532] Step 3:

[1533] The server temporarily stores the generated image and sends a link to the user.

[1534] View and save the image.

[1535] Step 1:

[1536] The user clicks the link and checks the generated image.

[1537] Step 2:

[1538] The device sends user feedback (approval or correction request) to the server.

[1539] Step 3:

[1540] If the server requests a correction, it sends a re-request to the image generation AI, which then makes corrections based on the feedback.

[1541] Step 4:

[1542] Once the user approves the image, the server saves the image and generates a link.

[1543] Emotional feedback and model optimization

[1544] Step 1:

[1545] The emotion engine collects emotional feedback on user-generated images.

[1546] Step 2:

[1547] The server sends emotional feedback to the image generation AI, which is then used to optimize the model.

[1548] Image sharing and use

[1549] Step 1:

[1550] The device displays a list of images saved to the user.

[1551] Step 2:

[1552] The user selects the image they need and gives instructions for downloading or sharing it.

[1553] Step 3:

[1554] The device downloads the image and saves it to the selected folder.

[1555] Step 4:

[1556] The server generates a shared link and notifies the relevant parties.

[1557] (Example 2)

[1558] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1559] The problem that this invention aims to solve is to provide a system that can quickly and efficiently generate high-quality illustrations and photographs for presentation materials and reports used within companies, as well as a system that recognizes user emotions and improves usability. Conventional systems have the problem that they cannot take user emotions into consideration when generating images, and therefore the user experience is not improved. In addition, there were insufficient means to effectively save and share the generated images.

[1560] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1561] In this invention, the server includes means for the user to input authentication information, means for the terminal to send authentication information to the server to verify the user's access rights, means for the user to input image generation requirements, means for the terminal to send the input requirements to the server, means for the server to send a request to an image generation AI, means for the image generation AI to generate an image based on the requirements, means for the server to temporarily store the generated image and send a link to the user, means for the user to review the generated image and send feedback, means for the image to be finally saved and a link generated based on the feedback, means for the terminal to capture the user's facial expressions and voice tone using a camera and microphone, means for an emotion engine to analyze the captured data and recognize the user's emotions, and means for the server to receive the user's emotion data and reflect it in the image generation requirements. This enables optimal image generation according to the user's emotions, improves the user experience, and allows for the effective storage and sharing of generated images.

[1562] "User authentication" is the process by which a user enters their authentication information in order to access a system, and the server verifies their access rights.

[1563] An "image generation request" is the process in which a user inputs the requirements for the image they want to generate, and those requirements are sent to the server.

[1564] An "emotion engine" is a combination of software or hardware that analyzes a user's facial expressions and tone of voice to recognize their emotions.

[1565] "Image generation AI" is an artificial intelligence model that generates images based on input requirements.

[1566] A "server" is a computer system that manages the entire system and provides functions such as user authentication, processing image generation requests, and communication with image generation AI.

[1567] A "terminal" is a device that provides an interface for a user to access a system, and includes common computers such as personal computers, smartphones, and tablets.

[1568] "User feedback" is the process by which users send opinions, such as evaluations and requests for modifications, to the system regarding the images they generate.

[1569] A "link" is data that indicates a URL or other reference for a user to access.

[1570] "Storage" refers to physical or cloud-based storage devices used to store generated images and other data.

[1571] "Sharing methods" refer to methods or protocols for sharing generated images or their links with other users or departments.

[1572] This invention provides a system for quickly and efficiently generating high-quality illustrations and photographs for presentation materials and reports used within a company. Furthermore, by incorporating an emotion engine that recognizes user emotions, it achieves user-friendly operation. The system includes the following steps: user authentication, image generation request, emotion recognition by the emotion engine, image generation, confirmation and saving, and sharing and use.

[1573] The main components of the system are the server, terminal, user, image generation AI, and emotion engine. The specific roles and processing flow of each are described below.

[1574] User Authentication

[1575] The device displays a login screen to the user.

[1576] The user enters their authentication information (user ID and password).

[1577] The terminal sends the entered authentication information to the server.

[1578] The server compares the received authentication information with the database to verify the user's access rights.

[1579] Image generation request

[1580] The device displays an input form to the user for image generation.

[1581] The user enters the requirements for the image they want to generate (theme, style, specific elements).

[1582] The terminal converts the entered requirements into JSON format and sends it to the server.

[1583] Emotion recognition by an emotion engine

[1584] The device's camera and microphone are used to capture the user's facial expressions and voice tone.

[1585] The emotion engine analyzes the captured data and recognizes the user's emotions in real time.

[1586] The server receives user emotion data and incorporates it into the image generation requirements.

[1587] Image generation

[1588] The server sends a request containing emotional data to the image generation AI.

[1589] The image generation AI generates images based on the requirements.

[1590] The server temporarily stores the generated image and sends a link to the user.

[1591] View and save the image.

[1592] The user clicks the link and checks the generated image.

[1593] The device sends user feedback (approval or correction request) to the server.

[1594] If the server requests a correction, it sends a re-request to the image generation AI, which then makes corrections based on the feedback.

[1595] Once the user approves the image, the server saves the image and generates a link.

[1596] Emotional feedback and model optimization

[1597] The emotion engine collects emotional feedback on user-generated images.

[1598] The server sends emotional feedback to the image generation AI, which is then used to optimize the model.

[1599] Image sharing and use

[1600] The device displays a list of images saved to the user.

[1601] The user selects the image they need and gives instructions for downloading or sharing it.

[1602] The device downloads the image and saves it to the selected folder.

[1603] The server generates a shared link and notifies the relevant parties.

[1604] Examples of specific cases and prompt statements

[1605] For example, when a user inputs "An illustration of the new smartphone in a simple and modern style" for a new product announcement, the emotion engine senses tension from their facial expression and tone of voice. Based on this information, the system suggests adding relaxed design elements. An example of a prompt from the generative AI model is, "Please draw the new smartphone in a simple and modern style."

[1606] As described above, the system of the present invention allows users to quickly generate high-quality images, review and correct them as needed, and ultimately share them in a format suitable for use both inside and outside the company. Furthermore, by using an emotion engine, it enables the generation of optimal images that respond to the user's emotions, thereby improving the user experience.

[1607] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1608] Step 1: User Authentication

[1609] The device displays a login screen to the user.

[1610] Specific actions: The terminal screen will display input fields for user ID and password, and a "Login" button will be placed there.

[1611] Input: User ID, Password.

[1612] Output: Authentication information entered by the user.

[1613] The user enters their authentication information (user ID and password).

[1614] Specific steps: Enter your ID and password using the keyboard. Then click the login button.

[1615] Input: Keyboard input.

[1616] Output: The entered authentication information is sent to the login field.

[1617] The terminal sends the entered authentication information to the server.

[1618] Specific operation: Serializes the input data into JSON format and sends it to the server as an HTTP POST request.

[1619] Input: Authentication information (User ID, Password).

[1620] Output: Authentication information is sent to the server.

[1621] The server compares the received authentication information with the database to verify the user's access rights.

[1622] Specific operation: The server executes an SQL query against the database to verify user information. If successful, it generates an authentication token and returns it to the terminal.

[1623] Input: Authentication information (User ID, Password).

[1624] Output: Authentication token or error message.

[1625] Step 2: Image generation request

[1626] The device displays an input form to the user for image generation.

[1627] Specific operation: Display fields on the web screen for entering the image theme, style, and specific elements.

[1628] Input: None (initial display).

[1629] Output: A form for entering image generation requests.

[1630] The user enters the requirements for the image they want to generate.

[1631] Specific actions: For example, enter requirements such as, "An illustration of a new smartphone product in a simple and modern style."

[1632] Input: Image theme, style, and specific elements.

[1633] Output: Requirements for generating the input image.

[1634] The terminal converts the entered requirements into JSON format and sends it to the server.

[1635] Specific operation: Format the contents of the input field into JSON format and send it to the server as an HTTP POST request.

[1636] Input: Requirements for image generation.

[1637] Output: Image generation request sent to the server.

[1638] Step 3: Emotion recognition by the emotion engine

[1639] The device's camera and microphone are used to capture the user's facial expressions and voice tone.

[1640] Specific operation: Activates the device's built-in camera and microphone to collect data in real time.

[1641] Input: User's facial expression data, voice tone.

[1642] Output: Captured audio and video data.

[1643] The emotion engine analyzes the captured data and recognizes the user's emotions in real time.

[1644] Specific operation: The captured data is input into a deep learning model, and emotion labels are output.

[1645] Input: Captured audio data, video data.

[1646] Output: Analyzed emotion data (e.g., tension, relaxation).

[1647] The server receives user emotion data and incorporates it into the image generation requirements.

[1648] Specific operation: The server receives emotion data and updates the image generation request based on that data.

[1649] Input: Sentiment data.

[1650] Output: Updated image generation request.

[1651] Step 4: Image Generation

[1652] The server sends a request containing emotional data to the image generation AI.

[1653] Specific operation: Convert the updated image generation request to the appropriate format and send it to the image generation AI.

[1654] Input: Updated image generation request.

[1655] Output: Request sent to the image generation AI.

[1656] The image generation AI generates images based on the requirements.

[1657] Specific operation: The image generation AI model analyzes the prompt text and generates an image based on the specified style and elements.

[1658] Input: Prompt text, image generation requirements.

[1659] Output: The generated image.

[1660] The server temporarily stores the generated image and sends a link to the user.

[1661] Specific operation: The generated image is saved to temporary storage, and a notification containing the image's URL is sent to the user.

[1662] Input: The generated image.

[1663] Output: The link sent to the user.

[1664] Step 5: Check and save the image.

[1665] The user clicks the link and checks the generated image.

[1666] Specific action: Click on an email or in-app notification to be redirected to an image display page.

[1667] Input: Link.

[1668] Output: Display of the generated image.

[1669] The device sends user feedback (approval or correction request) to the server.

[1670] Specific action: Enter approval or correction requests into the feedback input form and send the data to the server.

[1671] Input: Feedback.

[1672] Output: Feedback sent to the server.

[1673] If the server requests a correction, it sends a re-request to the image generation AI, which then makes corrections based on the feedback.

[1674] Specific action: A regeneration request reflecting the changes is sent to the image generation AI.

[1675] Input: Correction request.

[1676] Output: Regenerated image.

[1677] Once the user approves the image, the server saves the image and generates a link.

[1678] Specific operation: Save the image to persistent storage and generate a download link.

[1679] Input: Approved image.

[1680] Output: Final saved image, download link.

[1681] Step 6: Emotional Feedback and Model Optimization

[1682] The emotion engine collects emotional feedback on user-generated images.

[1683] Specific operation: Capture the user's facial expressions and voice again while they are viewing the image to obtain emotion data.

[1684] Input: User's facial expression data, voice tone.

[1685] Output: Collected emotional feedback.

[1686] The server sends emotional feedback to the image generation AI, which is then used to optimize the model.

[1687] Specific operation: The collected emotional feedback is fed back to the image generation AI and used as training data for the model.

[1688] Input: Emotional feedback.

[1689] Output: Optimized image generation AI model.

[1690] Step 7: Sharing and using images

[1691] The device displays a list of images saved to the user.

[1692] Specific action: Display a list of thumbnails of all images generated by the user on the user's dashboard page.

[1693] Input: None (initial display).

[1694] Output: A list of image thumbnails.

[1695] The user selects the image they need and gives instructions for downloading or sharing it.

[1696] Specific actions: Select an image and click the "Download" or "Share" button.

[1697] Input: Selected image.

[1698] Output: Download or share options.

[1699] The device downloads the image and saves it to the selected folder.

[1700] Specific action: Use the browser's download function to save the image to the specified folder.

[1701] Input: Selected download folder.

[1702] Output: Saved image.

[1703] The server generates a shared link and notifies the relevant parties.

[1704] Specific actions: Generate a sharing link and send notifications to designated stakeholders via email or messaging apps.

[1705] Input: The image you want to share.

[1706] Output: Shared link and notification.

[1707] (Application Example 2)

[1708] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1709] Conventional image generation systems were unable to automatically reflect the optimal design elements based on user emotions, resulting in limited improvements to the user experience. Furthermore, it was difficult to immediately reflect the generated images in virtual stores, lacking convenience in situations requiring rapid operation. Additionally, there was no easy way for virtual store operators to use the image generation interface. This led to problems where generated images could not be immediately corrected or optimized if they did not meet user expectations.

[1710] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to input authentication information, means for the terminal to send authentication information to the server to confirm the user's access rights, means for the user to input the requirements for image generation, means for the terminal to send the input requirements to the server, means for the server to send a request to the image generation AI, means for the image generation AI to generate an image based on the requirements, means for the server to temporarily store the generated image and send a link to the user, means for the user to check the generated image and send feedback, means for the image to be finally saved and a link generated based on the feedback, means for the terminal to capture the user's facial expression using a camera and for the emotion engine to recognize the user's emotions, and means for the emotion engine to reflect the recognized emotion data in the image generation request. As a result, the optimal design elements based on the user's emotions are automatically reflected, enabling high-quality image generation quickly and efficiently, and allowing for rapid reflection in product descriptions in virtual stores.

[1711] A "user" is a person or entity that uses this system to input image generation requirements, and to review and provide feedback on the generated images.

[1712] "Authentication information" refers to information necessary for a user to access the system, such as a user ID and password.

[1713] A "terminal" is a device used by a user to access the system, enter authentication information, input requirements for image generation, and verify the generated image.

[1714] A "server" is a central processing system that performs tasks such as verifying authentication information, sending and managing image generation requests, and saving generated images and creating links.

[1715] "Image generation AI" is an artificial intelligence algorithm that generates high-quality images based on requirements entered by the user.

[1716] The "emotion engine" is a system that uses the device's camera to capture the user's facial expressions and recognizes the user's emotions in real time.

[1717] "Requirements" are conditions that include the theme, style, and specific elements of the image the user wants to generate.

[1718] "Feedback" refers to a user's response to an image they have created, seeking approval or modification.

[1719] A "link" is a URL or hyperlink that allows a server to temporarily store an image it has generated and make it accessible to users.

[1720] "Facial expressions" refer to the movements and visual changes of the user's face, and are the input data that the emotion engine uses to recognize emotions.

[1721] "Emotional data" refers to information about emotions that the emotion engine analyzes and recognizes from the user's facial expressions.

[1722] The present invention's system takes the user's input requirements for the image they wish to generate and uses image generation AI to produce high-quality images. In this process, it recognizes the user's emotions in real time and incorporates optimal design elements based on those emotions. This system enables users to quickly and efficiently generate high-quality images, review and modify them as needed, and ultimately share them in a format suitable for use in virtual stores.

[1723] First, the user enters their authentication information (user ID and password) using a terminal. The terminal sends this authentication information to the server, which then compares the received information with a database to verify the user's access rights. This prevents unauthorized access to the system.

[1724] Next, the user enters specific requirements for image generation (theme, style, specific elements) on their device. This input is converted to JSON format and sent to the server.

[1725] Subsequently, the user's facial expressions are captured using the device's camera. The emotion engine analyzes the captured facial data and recognizes the user's emotions in real time. This recognized emotion data is then incorporated into the image generation request.

[1726] The server sends a request containing emotional data to the image generation AI, which generates an image based on the requirements. The generated image is temporarily stored on the server, and an access link is sent to the user.

[1727] The user clicks the received link to view the generated image and sends feedback (approval or correction request) to the server via their device. The server receives the feedback and, if necessary, sends a request back to the image generation AI to make corrections based on the feedback.

[1728] Once the user approves an image, the server saves it and generates a link. The generated image is stored in the company's internal storage, and the sharing link is sent to the relevant projects and departments as needed. The device also displays a list of images saved by the user, allowing them to download images or share them with other departments.

[1729] For example, when a virtual store operator generates custom images based on the theme of "relaxing interior," this system allows the operator to easily input requirements via smartphone, and quickly review, modify, and share the generated images.

[1730] The main hardware and software used are as follows:

[1731] Camera: Captures the user's facial expressions (e.g., smartphone camera)

[1732] EmotionEngine: A software module that recognizes emotions.

[1733] ImageGenerator: AI Algorithm for Image Generation

[1734] API Server: A backend system that manages image generation requests and authentication information.

[1735] Examples of prompt statements include the following:

[1736] Image generation theme: Relaxing interior

[1737] Image style: Simple and modern

[1738] Specific elements: sofa, houseplants, calming color scheme

[1739] This system allows users to generate high-quality images that incorporate design elements tailored to their emotions, enabling efficient product descriptions in virtual stores.

[1740] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1741] Step 1:

[1742] The user enters authentication information on the device. Specifically, they enter their user ID and password into the input form and click the submit button. Input: User ID, password. Output: Authentication information is entered on the device.

[1743] Step 2:

[1744] The terminal sends authentication information to the server, and the server verifies the user's access rights by comparing the received authentication information with the database. Input: Authentication information. Output: Access rights verification result. Specifically, the server queries the database and verifies access rights based on the results.

[1745] Step 3:

[1746] The user enters the image generation requirements (theme, style, specific elements) into the device, which then converts this into JSON format and sends it to the server. Input: Image generation requirements. Output: Request data in JSON format. Specifically, the user fills in the theme, style, and specific elements in the input form and clicks the submit button.

[1747] Step 4:

[1748] The device uses its camera to capture the user's facial expressions, and an emotion engine analyzes this data to recognize the user's emotions in real time. Input: Captured image data. Output: User emotion data. Specifically, the camera captures the facial expression, and the emotion engine analyzes the emotion from the expression.

[1749] Step 5:

[1750] The server adds sentiment data to a JSON-formatted request and sends the request to the image generation AI. Input: Request data, sentiment data. Output: Image generation request. Specifically, the sentiment data is incorporated into the JSON and sent to the image generation AI via the API.

[1751] Step 6:

[1752] The image generation AI generates an image based on the request and sends it back to the server. Input: Image generation request. Output: Generated image data. Specifically, the image generation algorithm generates an image based on the requirements and emotions.

[1753] Step 7:

[1754] The server temporarily stores the generated image and sends an access link to the user. Input: Generated image data. Output: Access link. Specifically, the server saves the image to temporary storage, generates a link, and notifies the user.

[1755] Step 8:

[1756] The user clicks a received link, reviews the generated image, and sends feedback (approval or correction request) to the server via their device. Input: User feedback. Output: Feedback data. Specifically, the user clicks the link to view the image and then clicks the confirmation button.

[1757] Step 9:

[1758] The server receives feedback and, if necessary, sends a re-request to the image generation AI to make corrections based on the feedback. Input: Feedback data. Output: Corrected image data (if necessary). Specifically, the server analyzes the feedback content and sends a re-request if corrections are needed.

[1759] Step 10:

[1760] Once the user approves the image, the server saves the image and generates a link. Input: Final image data, user approval. Output: Final saved image data, sharing link. Specifically, the final image data is saved to permanent storage.

[1761] Step 11:

[1762] The server saves the last saved image to internal storage and notifies relevant projects and departments of the sharing link. Input: Last saved image data. Output: Sharing link, notification data. Specifically, it saves the image to internal storage and sends the link to relevant departments via email or the notification system.

[1763] Step 12:

[1764] The device displays a list of images saved by the user, allowing the user to download or share images with other departments. Input: Saved image data. Output: Download link, Share link. Specifically, it displays a list view and allows the user to select either a download or share link.

[1765] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1766] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1767] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1768] [Fourth Embodiment]

[1769] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1770] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1771] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1772] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1773] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1774] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1775] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1776] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1777] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1778] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1779] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1780] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1781] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1782] This invention is a system for quickly and efficiently generating high-quality illustrations and photographs for presentation materials and reports used within a company. The system operates through the following steps: user authentication, image generation request, image generation, confirmation and storage, and sharing and utilization.

[1783] User Authentication

[1784] Example of program processing

[1785] The device displays a login screen to the user.

[1786] The user enters their authentication information (ID and password).

[1787] The device sends authentication information to the server.

[1788] The server compares the received authentication information with the database to verify the user's access rights.

[1789] Specific example

[1790] Mr. Tanaka from the public relations department logs into the system, and the server verifies his authentication information. Since Mr. Tanaka has the necessary permissions for the public relations department, he is able to access the public relations project.

[1791] Image generation request

[1792] Example of program processing

[1793] The device displays an input form to the user for image generation.

[1794] The user enters the requirements for the image they want to generate (theme, style, specific elements).

[1795] The terminal sends the entered requirements to the server.

[1796] Specific example

[1797] Mr. Tanaka types "a simple and modern style illustration of the new smartphone" for the new product announcement. The device sends this request to the server.

[1798] Image generation

[1799] Example of program processing

[1800] The server sends a request to the image generation AI.

[1801] The image generation AI generates images based on the specified requirements.

[1802] The server temporarily stores the generated image and sends a link to the user.

[1803] Specific example

[1804] The server receives the request and sends it to the image generation AI. The image generation AI generates an illustration based on the requirements, and the server temporarily stores the illustration. A confirmation link is sent to Mr. Tanaka.

[1805] View and save the image.

[1806] Example of program processing

[1807] The user clicks the link and checks the generated image.

[1808] The device sends user feedback (approval or correction request) to the server.

[1809] Based on the feedback, the server will finally save the image and generate a link.

[1810] Specific example

[1811] Ms. Tanaka clicks the link to review the generated illustration. If revisions are needed, she submits feedback. If she is finally satisfied, she presses the "Approve" button and submits it to the server. The server saves the illustration to the company's internal storage and generates a sharing link.

[1812] Image sharing and use

[1813] Example of program processing

[1814] The device displays a list of images saved to the user.

[1815] Users select images, download them, and share them with other departments.

[1816] Specific example

[1817] Ms. Tanaka will download the generated illustrations and insert them into the PowerPoint presentation for the product announcement. She will also share them with the marketing team for use in social media posts.

[1818] This system significantly reduces the effort required for copyright verification and enables the creation and sharing of consistent images across the entire company. Furthermore, by providing users with an easy way to generate and share high-quality images, it improves operational efficiency. This invention is effectively operated by reskilled employees.

[1819] The following describes the processing flow.

[1820] User Authentication

[1821] Step 1:

[1822] The device displays a login screen to the user.

[1823] Step 2:

[1824] The user enters their ID and password.

[1825] Step 3:

[1826] The terminal sends the entered authentication information to the server.

[1827] Step 4:

[1828] The server compares the received authentication information with the database to verify the user's access rights.

[1829] Step 5:

[1830] If the server successfully authenticates the user, it will set the user's access rights and display the dashboard.

[1831] Image generation request

[1832] Step 1:

[1833] The device displays an input form to the user for image generation.

[1834] Step 2:

[1835] The user enters the requirements for the image they want to generate (theme, style, specific elements).

[1836] Step 3:

[1837] The terminal converts the entered requirements into JSON format and sends it to the server.

[1838] Image generation

[1839] Step 1:

[1840] The server sends the received request to the image generation AI.

[1841] Step 2:

[1842] The image generation AI generates images based on the requirements.

[1843] Step 3:

[1844] The server temporarily stores the generated image and sends a link to the user.

[1845] View and save the image.

[1846] Step 1:

[1847] The user clicks the provided link and checks the generated image.

[1848] Step 2:

[1849] The device sends user feedback (approval or correction request) to the server.

[1850] Step 3:

[1851] If the server requests a correction, it sends another request to the image generation AI, which then makes corrections based on the feedback.

[1852] Step 4:

[1853] Once the user approves the image, the server saves the image and generates a link.

[1854] Image sharing and use

[1855] Step 1:

[1856] The device displays a list of images saved to the user.

[1857] Step 2:

[1858] The user selects the image they need and gives instructions for downloading or sharing it.

[1859] Step 3:

[1860] The device downloads the image and saves it to the selected folder.

[1861] Step 4:

[1862] The server generates a shared link and notifies the relevant parties.

[1863] This allows users to quickly generate high-quality images, review and revise them as needed, and ultimately share them in a format suitable for use both internally and externally.

[1864] (Example 1)

[1865] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1866] Businesses need to quickly generate high-quality illustrations and photographs and efficiently share and utilize those images. Current systems require significant time and effort for image generation, and sharing the generated images is cumbersome, leading to decreased work efficiency and considerable effort in copyright verification. In particular, generating consistent designs and styles is difficult, making it challenging to maintain a consistent brand image within the company.

[1867] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1868] In this invention, the server includes means for sending requests to an image generation AI model, means for temporarily storing the generated image and sending a link to the user, and means for finally saving the image and generating a link based on feedback. This enables users to easily and quickly generate high-quality images and efficiently share and utilize the generated images while maintaining a unified design and style.

[1869] A "user" is a person or group that accesses and uses a system.

[1870] "Authentication information" refers to identification information used to verify a user's identity, and generally includes an ID and password.

[1871] A "terminal" is a device used by a user to access a system, and includes personal computers, smartphones, tablets, and other similar devices.

[1872] A "server" is a central computing system that processes user requests and manages data.

[1873] An "image generation AI model" is a program or software that generates images using artificial intelligence technology, and includes models such as Stable Diffusion and DALL-E.

[1874] A "request" refers to the specific operations or processes that a user asks the system to perform.

[1875] A "link" is a URL or path used to access generated images or other resources.

[1876] "Feedback" refers to opinions, evaluations, observations, and requests for corrections that users provide to a system.

[1877] "Cloud storage" refers to a storage service for saving and managing data via the internet.

[1878] A "session" is a state that uniquely identifies a series of communications and operations performed while a user is logged into a system.

[1879] An "access token" is a digital key that temporarily holds a user's authentication information and grants them access to the system.

[1880] Modes for carrying out the invention

[1881] This invention is a system for quickly and efficiently generating high-quality illustrations and photographs for presentation materials and reports used within a company. The system operates through the following steps: user authentication, image generation request, image generation, confirmation and storage, and sharing and utilization. Details are described below.

[1882] User Authentication

[1883] The terminal displays a login screen to the user, who enters their authentication information (ID and password). The terminal sends this authentication information to the server, which verifies the received information against a database (such as MySQL or PostgreSQL). If authentication is successful, the server creates a session and sends an access token back to the terminal. This allows the user to access the system.

[1884] Image generation request

[1885] The device displays an input form for image generation to the user, where the user enters the requirements for the image they want to generate (theme, style, specific elements). The device validates the entered requirements in real time, and if there are no problems, it sends them to the server. An AI image generation model (such as Stable Diffusion or DALL-E) generates the image based on these requirements.

[1886] As a concrete example, a user inputs the requirement, "An illustration of a new smartphone in a simple and modern style." The device sends this requirement to the server, which then sends a request to the image generation AI model.

[1887] Image generation

[1888] The server sends a request to an image generation AI model, which generates an image based on the specified requirements. The generated image is temporarily stored by the server in cloud storage or local storage, and a confirmation link is sent to the user.

[1889] View and save the image.

[1890] The user clicks a verification link to review the generated image. The device sends user feedback (approval or correction request) to the server. The server finalizes the image based on the feedback and generates a sharing link. Finally approved images are stored in internal storage.

[1891] Image sharing and use

[1892] The device displays a list of images saved by the user, allowing the user to select and download the desired images or share them with other departments. The device generates download and sharing links for the selected images, which the user can then use to share the images with other departments.

[1893] A specific example of a prompt message is: "Generate four high-quality slides suitable for a sample A4 portrait-size document for public relations. Product group: Smartphones, Theme: New product announcement, Style: Simple and modern."

[1894] This enables the creation and sharing of a unified image across the entire company, while significantly reducing the effort required for copyright verification. Furthermore, by providing users with an easy way to generate and share high-quality images, it improves operational efficiency. This invention is expected to be effectively implemented by reskilled employees.

[1895] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1896] Step 1: User Authentication

[1897] The device displays a login screen to the user. The user enters their ID and password, and the device sends this authentication information to the server. The server compares the received authentication information with the database to verify the user's access rights. MySQL or PostgreSQL are used as the database. If authentication is successful, the server creates a session and sends an access token back to the device.

[1898] Input: ID, Password

[1899] Data processing: Database matching

[1900] Output: Access token

[1901] Specific operation: Mr. Tanaka enters his ID and password on the login screen, and the device sends this information. The server verifies this information, generates an access token, and sends it back.

[1902] Step 2: Enter the image generation request

[1903] The device displays an input form for image generation to the user. The user enters the requirements for image generation (theme, style, specific elements) into the form. The device validates the entered requirements in real time and sends them to the server if there are no problems.

[1904] Input: Generation requirements (theme, style, specific elements)

[1905] Data na: Validation

[1906] Output: Sending requirements data to the server

[1907] Specific action: Mr. Tanaka enters the following: "An illustration of the new smartphone product in a simple and modern style." The terminal validates the requirement and sends it to the server.

[1908] Step 3: Sending the image generation request to the server

[1909] Based on the requirements received by the server, a request is sent to the image generation AI model. Stable Diffusion and DALL-E can be used as the image generation AI model.

[1910] Input: Requirements data

[1911] Data processing: Sending a request to an AI model for image generation.

[1912] Output: Generation Request

[1913] Specific operation: The server sends the requirement "an illustration of a new smartphone in a simple and modern style" to the image generation AI.

[1914] Step 4: Image generation

[1915] The image generation AI model generates images based on the specified requirements. The generated images are then sent back to the server.

[1916] Input: Generation Request

[1917] Data processing: Image generation

[1918] Output: Image data

[1919] Specific operation: The image generation AI model receives a request and generates an image based on the specified requirements. The generated image is then sent back to the server.

[1920] Step 5: Temporarily save the image and generate the link.

[1921] The server temporarily saves the generated image to cloud storage or local storage, generates a verification link, and sends it back to the device.

[1922] Input: Image data

[1923] Data processing: Temporary storage, link generation

[1924] Output: Verification link

[1925] Specific operation: The server saves the generated image, creates a verification link, and sends it to the terminal.

[1926] Step 6: Verify the generated image.

[1927] The user clicks the link received from their device and checks the generated image.

[1928] Input: Confirmation link

[1929] Output: None

[1930] Specific action: Ms. Tanaka clicks the link and checks the generated illustration.

[1931] Step 7: Submitting Feedback

[1932] The device sends user feedback (approval or correction request) to the server.

[1933] Input: Feedback (approval or correction request)

[1934] Output: Feedback data

[1935] Specific operation: Mr. Tanaka enters the confirmation result (approval or correction request), and the terminal sends it to the server.

[1936] Step 8: Final save and link generation

[1937] The server will finalize the image based on the feedback and, if approved, generate a sharing link. The finalized image will be stored in the company's internal storage.

[1938] Input: Feedback data

[1939] Data processing: Final saving, link generation.

[1940] Output: Shared link

[1941] Specific actions: The server performs the final save, generates a shared link, and provides it to Mr. Tanaka.

[1942] Step 9: Sharing and using images

[1943] The device displays a list of images saved to the user, and the user selects and downloads the desired images or shares them with other departments.

[1944] Input: Request to display image list

[1945] Output: Image data, sharing link

[1946] Specific actions: Ms. Tanaka selects and downloads the saved illustration. She also shares it with the marketing team using a shared link.

[1947] (Application Example 1)

[1948] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1949] While systems for rapidly generating high-quality images already exist, there is a lack of systems that provide metadata for those images and further analyze viewing trends and predict social media reactions. In particular, advertising campaigns require predicting how generated images will be received by viewers and providing feedback on subsequent reactions. Effective means to achieve this are needed.

[1950] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1951] In this invention, the server includes means for providing metadata of the generated image, means for performing viewing trend analysis and predicting reactions on social media based on the metadata, and means for collecting and feeding back reactions to the shared image from social media. This makes it possible to predict in advance how the generated image will affect the advertising campaign and to effectively collect and feed back actual reactions.

[1952] User authentication is the process by which a user proves their identity in order to access a system.

[1953] A "device" refers to a device operated by a user, and includes computers, smartphones, tablets, and other similar devices.

[1954] A "server" is a computer system used for managing and processing data.

[1955] "Authentication information" refers to information such as the ID and password that a user uses to log in to a system.

[1956] "Image generation AI" is a system that uses artificial intelligence technology to generate images based on user requirements.

[1957] "Metadata" refers to additional information related to the generated image, including viewing trends and predictions of reactions on social media.

[1958] "Feedback" refers to the user's action of reviewing generated images and sending suggestions for corrections or approvals to the server.

[1959] "Viewing trend analysis" is the process of analyzing how images generated based on metadata are received by viewers.

[1960] "SNS reaction prediction" is the process of predicting what kind of reactions a generated image will evoke on social networking services.

[1961] "Internal storage" refers to a data storage area used for saving and managing data within a company.

[1962] Modes for carrying out the invention

[1963] This invention provides a system for rapidly generating high-quality images for advertising campaigns, providing metadata for those images, analyzing viewing trends, predicting social media reactions, and collecting feedback. The following hardware and software are used to implement the system.

[1964] Hardware and software requirements

[1965] Smartphone (iOS or Android)

[1966] Communication modules (Wi-Fi, LTE, etc.)

[1967] Servers (cloud services, such as AWS or Google Cloud)

[1968] Image generation AI (e.g., OpenAI's DALL-E, Google's DeepDream, etc.)

[1969] Databases (e.g., MySQL, PostgreSQL)

[1970] System Overview

[1971] 1. User Authentication:

[1972] The smartphone displays a login screen to the user. The user enters their ID and password. The authentication information is sent to the server, and the user's access rights are verified by comparing the authentication information with the database.

[1973] 2. Image generation request:

[1974] The smartphone displays an input form to the user for generating an advertising image. The user enters the requirements for the image they want to generate (theme, style, specific elements). These entered requirements are sent to the server.

[1975] 3. Image generation:

[1976] The server sends a request to the image generation AI. The image generation AI generates an image based on the specified requirements, and the generated image is returned to the server and temporarily stored. The server sends a confirmation link to the user.

[1977] 4. Check and save the image:

[1978] The user clicks the link and reviews the generated image. The user's feedback (approval or revision request) is sent to the server. Based on the feedback, the image is finalized and a sharing link is generated.

[1979] 5. Image sharing and use:

[1980] The server saves the generated images to the company's internal storage. The server generates a sharing link for the saved images and notifies the relevant projects and departments. The terminal displays a list of saved images to the user, allowing the user to download the images or share them with other departments.

[1981] 6. Providing and analyzing metadata:

[1982] The server provides metadata for the generated images. Based on the metadata, it performs viewing trend analysis and predicts reactions on social media, and collects and provides feedback on reactions to shared images from social media.

[1983] Specific example

[1984] This scenario involves an advertising professional using the "AdImageCreator" application to generate new promotional images for smartphones.

[1985] 1. Login: The advertising representative opens the app on their smartphone, enters their ID and password, and logs in.

[1986] 2. Generation Request: Enter "Simple and modern style illustration of a new smartphone for advertising" and press the submit button.

[1987] 3. Image Generation: The server sends a request to the image generation AI, the AI ​​generates an image, and the link is sent to the advertiser.

[1988] 4. Review and Save: The advertiser clicks the link to review the generated image. They then submit feedback for revisions or approvals.

[1989] 5. Sharing and Use: Advertisers download the generated images and share them with marketing teams using internal chat. The images are used in advertising campaigns and also posted on social media.

[1990] 6. Metadata Provision and Analysis: The server provides image metadata to analyze viewing trends and predict social media reactions. Furthermore, it collects social media reactions and provides feedback to advertisers.

[1991] Example of a prompt

[1992] 1. "Illustrations of the new smartphone in a simple and modern style."

[1993] 2. "An image of a family enjoying themselves on the beach for a summer campaign."

[1994] 3. "Photos of Christmas trees and presents for Christmas sales"

[1995] This invention provides a system that can effectively generate and utilize high-quality advertising images, thereby further strengthening a company's marketing activities.

[1996] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1997] Step 1:

[1998] User Authentication

[1999] Input: The user enters their ID and password on the login screen of their smartphone.

[2000] Specific actions:

[2001] The terminal sends the entered authentication information to the server. The server checks against the database to verify the user's access rights.

[2002] Output: If authentication is successful, the user can proceed to the next step. If it fails, a message will be displayed prompting re-entry.

[2003] Step 2:

[2004] Image generation request

[2005] Input: The user enters the requirements for the image they want to generate (theme, style, specific elements).

[2006] Specific actions:

[2007] The terminal sends the entered requirements to the server.

[2008] Output: The server receives the requirements and prepares to send a request to the image generation AI.

[2009] Step 3:

[2010] Image generation

[2011] Input: The server sends the requirements as a request to the image generation AI.

[2012] Specific actions:

[2013] The server sends the requirements to the image generation AI, and the AI ​​generates an image based on the specified requirements.

[2014] Output: The image generation AI returns the generated image to the server, where it is temporarily stored. The server then sends a confirmation link to the user.

[2015] Step 4:

[2016] View and save the image.

[2017] Input: The user clicks a link received from the server to view the generated image.

[2018] Specific actions:

[2019] The user reviews the generated image and enters feedback (approval or correction request). The device then sends the feedback to the server.

[2020] Output: Based on the feedback, the server finalizes the image and generates a link for access.

[2021] Step 5:

[2022] Sharing and saving images

[2023] Input: The server will notify relevant projects and departments of the saved images and sharing links.

[2024] Specific actions:

[2025] The server saves the generated images to the company's internal storage and creates a list of the saved images. The terminal displays the list of saved images to the user.

[2026] Output: Users can download images and share them with other departments. A sharing link will be sent to the relevant project or department.

[2027] Step 6:

[2028] Metadata provision and analysis

[2029] Input: The server collects metadata for the generated image.

[2030] Specific actions:

[2031] The server uses metadata to analyze viewing trends and predict social media reactions. Furthermore, it collects actual social media reactions to generate feedback.

[2032] Output: Analysis results and response data are provided to the user, and data is obtained to measure the effectiveness of image-based advertising campaigns.

[2033] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[2034] This invention provides a system for quickly and efficiently generating high-quality illustrations and photographs for presentation materials and reports used within a company. Furthermore, it combines this with an emotion engine that recognizes user emotions, resulting in a more user-friendly operation. The system includes steps such as user authentication, image generation requests, image generation, confirmation and saving, sharing and utilization, as well as emotion recognition and optimization based on that recognition.

[2035] User Authentication

[2036] Example of program processing

[2037] The device displays a login screen to the user.

[2038] The user enters their authentication information (ID and password).

[2039] The device sends authentication information to the server.

[2040] The server compares the received authentication information with the database to verify the user's access rights.

[2041] Image generation request

[2042] Example of program processing

[2043] The device displays an input form to the user for image generation.

[2044] The user enters the requirements for the image they want to generate (theme, style, specific elements).

[2045] The terminal converts the entered requirements into JSON format and sends it to the server.

[2046] Emotion recognition by an emotion engine

[2047] Example of program processing

[2048] The device's camera and microphone are used to capture the user's facial expressions and voice tone.

[2049] The emotion engine analyzes the captured data and recognizes the user's emotions in real time.

[2050] The server receives user emotion data and incorporates it into the image generation requirements.

[2051] Specific example

[2052] When Mr. Tanaka types "a simple and modern style illustration for the new smartphone" for the new product announcement, the emotion engine senses tension from his facial expression and tone of voice. Based on this information, the system suggests adding relaxed design elements.

[2053] Image generation

[2054] Example of program processing

[2055] The server sends a request containing emotional data to the image generation AI.

[2056] The image generation AI generates images based on the requirements.

[2057] The server temporarily stores the generated image and sends a link to the user.

[2058] View and save the image.

[2059] Example of program processing

[2060] The user clicks the link and checks the generated image.

[2061] The device sends user feedback (approval or correction request) to the server.

[2062] If the server requests a correction, it sends a re-request to the image generation AI, which then makes corrections based on the feedback.

[2063] Once the user approves the image, the server saves the image and generates a link.

[2064] Emotional feedback and model optimization

[2065] Example of program processing

[2066] The emotion engine collects emotional feedback on user-generated images.

[2067] The server sends emotional feedback to the image generation AI, which is then used to optimize the model.

[2068] Specific example

[2069] If Ms. Tanaka is satisfied with the generated illustration, the emotion engine recognizes this positive emotion, and the server sends this feedback to the image generation AI to be used for future generation.

[2070] Image sharing and use

[2071] Example of program processing

[2072] The device displays a list of images saved to the user.

[2073] The user selects the image they need and gives instructions for downloading or sharing it.

[2074] The device downloads the image and saves it to the selected folder.

[2075] The server generates a shared link and notifies the relevant parties.

[2076] Specific example

[2077] Ms. Tanaka will download the generated illustrations and insert them into the PowerPoint presentation for the product announcement. She will also share them with the marketing team for use in social media posts.

[2078] This system allows users to quickly generate high-quality images, review and revise them as needed, and ultimately share them in a format suitable for internal and external use. Furthermore, by utilizing an emotion engine, it enables the generation of optimal images that respond to the user's emotions, thereby improving the user experience.

[2079] The following describes the processing flow.

[2080] User Authentication

[2081] Step 1:

[2082] The device displays a login screen to the user.

[2083] Step 2:

[2084] The user enters their ID and password.

[2085] Step 3:

[2086] The terminal sends the entered authentication information to the server.

[2087] Step 4:

[2088] The server compares the received authentication information with the database to verify the user's access rights.

[2089] Step 5:

[2090] If the server successfully authenticates the user, it will set the user's access rights and display the dashboard.

[2091] Image generation request

[2092] Step 1:

[2093] The device displays an input form to the user for image generation.

[2094] Step 2:

[2095] The user enters the requirements for the image they want to generate (theme, style, specific elements).

[2096] Step 3:

[2097] The device activates its camera and microphone to capture the user's facial expressions and voice tone, collecting data.

[2098] Step 4:

[2099] The terminal converts the entered requirements into JSON format and sends them to the server along with sentiment data.

[2100] Emotion recognition by an emotion engine

[2101] Step 1:

[2102] The server receives user sentiment data and sends it to the sentiment engine.

[2103] Step 2:

[2104] The emotion engine analyzes the captured data and recognizes the user's emotions in real time.

[2105] Step 3:

[2106] The server integrates emotional data obtained from the emotion engine into the request and reflects it in the image generation requirements.

[2107] Image generation

[2108] Step 1:

[2109] The server sends a request containing emotional data to the image generation AI.

[2110] Step 2:

[2111] The image generation AI generates images based on the requirements.

[2112] Step 3:

[2113] The server temporarily stores the generated image and sends a link to the user.

[2114] View and save the image.

[2115] Step 1:

[2116] The user clicks the link and checks the generated image.

[2117] Step 2:

[2118] The device sends user feedback (approval or correction request) to the server.

[2119] Step 3:

[2120] If the server requests a correction, it sends a re-request to the image generation AI, which then makes corrections based on the feedback.

[2121] Step 4:

[2122] Once the user approves the image, the server saves the image and generates a link.

[2123] Emotional feedback and model optimization

[2124] Step 1:

[2125] The emotion engine collects emotional feedback on user-generated images.

[2126] Step 2:

[2127] The server sends emotional feedback to the image generation AI, which is then used to optimize the model.

[2128] Image sharing and use

[2129] Step 1:

[2130] The device displays a list of images saved to the user.

[2131] Step 2:

[2132] The user selects the image they need and gives instructions for downloading or sharing it.

[2133] Step 3:

[2134] The device downloads the image and saves it to the selected folder.

[2135] Step 4:

[2136] The server generates a shared link and notifies the relevant parties.

[2137] (Example 2)

[2138] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2139] The problem that this invention aims to solve is to provide a system that can quickly and efficiently generate high-quality illustrations and photographs for presentation materials and reports used within companies, as well as a system that recognizes user emotions and improves usability. Conventional systems have the problem that they cannot take user emotions into consideration when generating images, and therefore the user experience is not improved. In addition, there were insufficient means to effectively save and share the generated images.

[2140] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[2141] In this invention, the server includes means for the user to input authentication information, means for the terminal to send authentication information to the server to verify the user's access rights, means for the user to input image generation requirements, means for the terminal to send the input requirements to the server, means for the server to send a request to an image generation AI, means for the image generation AI to generate an image based on the requirements, means for the server to temporarily store the generated image and send a link to the user, means for the user to review the generated image and send feedback, means for the image to be finally saved and a link generated based on the feedback, means for the terminal to capture the user's facial expressions and voice tone using a camera and microphone, means for an emotion engine to analyze the captured data and recognize the user's emotions, and means for the server to receive the user's emotion data and reflect it in the image generation requirements. This enables optimal image generation according to the user's emotions, improves the user experience, and allows for the effective storage and sharing of generated images.

[2142] "User authentication" is the process by which a user enters their authentication information in order to access a system, and the server verifies their access rights.

[2143] An "image generation request" is the process in which a user inputs the requirements for the image they want to generate, and those requirements are sent to the server.

[2144] An "emotion engine" is a combination of software or hardware that analyzes a user's facial expressions and tone of voice to recognize their emotions.

[2145] "Image generation AI" is an artificial intelligence model that generates images based on input requirements.

[2146] A "server" is a computer system that manages the entire system and provides functions such as user authentication, processing image generation requests, and communication with image generation AI.

[2147] A "terminal" is a device that provides an interface for a user to access a system, and includes common computers such as personal computers, smartphones, and tablets.

[2148] "User feedback" is the process by which users send opinions, such as evaluations and requests for modifications, to the system regarding the images they generate.

[2149] A "link" is data that indicates a URL or other reference for a user to access.

[2150] "Storage" refers to physical or cloud-based storage devices used to store generated images and other data.

[2151] "Sharing methods" refer to methods or protocols for sharing generated images or their links with other users or departments.

[2152] This invention provides a system for quickly and efficiently generating high-quality illustrations and photographs for presentation materials and reports used within a company. Furthermore, by incorporating an emotion engine that recognizes user emotions, it achieves user-friendly operation. The system includes the following steps: user authentication, image generation request, emotion recognition by the emotion engine, image generation, confirmation and saving, and sharing and use.

[2153] The main components of the system are the server, terminal, user, image generation AI, and emotion engine. The specific roles and processing flow of each are described below.

[2154] User Authentication

[2155] The device displays a login screen to the user.

[2156] The user enters their authentication information (user ID and password).

[2157] The terminal sends the entered authentication information to the server.

[2158] The server compares the received authentication information with the database to verify the user's access rights.

[2159] Image generation request

[2160] The device displays an input form to the user for image generation.

[2161] The user enters the requirements for the image they want to generate (theme, style, specific elements).

[2162] The terminal converts the entered requirements into JSON format and sends it to the server.

[2163] Emotion recognition by an emotion engine

[2164] The device's camera and microphone are used to capture the user's facial expressions and voice tone.

[2165] The emotion engine analyzes the captured data and recognizes the user's emotions in real time.

[2166] The server receives user emotion data and incorporates it into the image generation requirements.

[2167] Image generation

[2168] The server sends a request containing emotional data to the image generation AI.

[2169] The image generation AI generates images based on the requirements.

[2170] The server temporarily stores the generated image and sends a link to the user.

[2171] View and save the image.

[2172] The user clicks the link and checks the generated image.

[2173] The device sends user feedback (approval or correction request) to the server.

[2174] If the server requests a correction, it sends a re-request to the image generation AI, which then makes corrections based on the feedback.

[2175] Once the user approves the image, the server saves the image and generates a link.

[2176] Emotional feedback and model optimization

[2177] The emotion engine collects emotional feedback on user-generated images.

[2178] The server sends emotional feedback to the image generation AI, which is then used to optimize the model.

[2179] Image sharing and use

[2180] The device displays a list of images saved to the user.

[2181] The user selects the image they need and gives instructions for downloading or sharing it.

[2182] The device downloads the image and saves it to the selected folder.

[2183] The server generates a shared link and notifies the relevant parties.

[2184] Examples of specific cases and prompt statements

[2185] For example, when a user inputs "An illustration of the new smartphone in a simple and modern style" for a new product announcement, the emotion engine senses tension from their facial expression and tone of voice. Based on this information, the system suggests adding relaxed design elements. An example of a prompt from the generative AI model is, "Please draw the new smartphone in a simple and modern style."

[2186] As described above, the system of the present invention allows users to quickly generate high-quality images, review and correct them as needed, and ultimately share them in a format suitable for use both inside and outside the company. Furthermore, by using an emotion engine, it enables the generation of optimal images that respond to the user's emotions, thereby improving the user experience.

[2187] The flow of the specific processing in Example 2 will be explained using Figure 13.

[2188] Step 1: User Authentication

[2189] The device displays a login screen to the user.

[2190] Specific actions: The terminal screen will display input fields for user ID and password, and a "Login" button will be placed there.

[2191] Input: User ID, Password.

[2192] Output: Authentication information entered by the user.

[2193] The user enters their authentication information (user ID and password).

[2194] Specific steps: Enter your ID and password using the keyboard. Then click the login button.

[2195] Input: Keyboard input.

[2196] Output: The entered authentication information is sent to the login field.

[2197] The terminal sends the entered authentication information to the server.

[2198] Specific operation: Serializes the input data into JSON format and sends it to the server as an HTTP POST request.

[2199] Input: Authentication information (User ID, Password).

[2200] Output: Authentication information is sent to the server.

[2201] The server compares the received authentication information with the database to verify the user's access rights.

[2202] Specific operation: The server executes an SQL query against the database to verify user information. If successful, it generates an authentication token and returns it to the terminal.

[2203] Input: Authentication information (User ID, Password).

[2204] Output: Authentication token or error message.

[2205] Step 2: Image generation request

[2206] The device displays an input form to the user for image generation.

[2207] Specific operation: Display fields on the web screen for entering the image theme, style, and specific elements.

[2208] Input: None (initial display).

[2209] Output: A form for entering image generation requests.

[2210] The user enters the requirements for the image they want to generate.

[2211] Specific actions: For example, enter requirements such as, "An illustration of a new smartphone product in a simple and modern style."

[2212] Input: Image theme, style, and specific elements.

[2213] Output: Requirements for generating the input image.

[2214] The terminal converts the entered requirements into JSON format and sends it to the server.

[2215] Specific operation: Format the contents of the input field into JSON format and send it to the server as an HTTP POST request.

[2216] Input: Requirements for image generation.

[2217] Output: Image generation request sent to the server.

[2218] Step 3: Emotion recognition by the emotion engine

[2219] The device's camera and microphone are used to capture the user's facial expressions and voice tone.

[2220] Specific operation: Activates the device's built-in camera and microphone to collect data in real time.

[2221] Input: User's facial expression data, voice tone.

[2222] Output: Captured audio and video data.

[2223] The emotion engine analyzes the captured data and recognizes the user's emotions in real time.

[2224] Specific operation: The captured data is input into a deep learning model, and emotion labels are output.

[2225] Input: Captured audio data, video data.

[2226] Output: Analyzed emotion data (e.g., tension, relaxation).

[2227] The server receives user emotion data and incorporates it into the image generation requirements.

[2228] Specific operation: The server receives emotion data and updates the image generation request based on that data.

[2229] Input: Sentiment data.

[2230] Output: Updated image generation request.

[2231] Step 4: Image Generation

[2232] The server sends a request containing emotional data to the image generation AI.

[2233] Specific operation: Convert the updated image generation request to the appropriate format and send it to the image generation AI.

[2234] Input: Updated image generation request.

[2235] Output: Request sent to the image generation AI.

[2236] The image generation AI generates images based on the requirements.

[2237] Specific operation: The image generation AI model analyzes the prompt text and generates an image based on the specified style and elements.

[2238] Input: Prompt text, image generation requirements.

[2239] Output: The generated image.

[2240] The server temporarily stores the generated image and sends a link to the user.

[2241] Specific operation: The generated image is saved to temporary storage, and a notification containing the image's URL is sent to the user.

[2242] Input: The generated image.

[2243] Output: The link sent to the user.

[2244] Step 5: Check and save the image.

[2245] The user clicks the link and checks the generated image.

[2246] Specific action: Click on an email or in-app notification to be redirected to an image display page.

[2247] Input: Link.

[2248] Output: Display of the generated image.

[2249] The device sends user feedback (approval or correction request) to the server.

[2250] Specific action: Enter approval or correction requests into the feedback input form and send the data to the server.

[2251] Input: Feedback.

[2252] Output: Feedback sent to the server.

[2253] If the server requests a correction, it sends a re-request to the image generation AI, which then makes corrections based on the feedback.

[2254] Specific action: A regeneration request reflecting the changes is sent to the image generation AI.

[2255] Input: Correction request.

[2256] Output: Regenerated image.

[2257] Once the user approves the image, the server saves the image and generates a link.

[2258] Specific operation: Save the image to persistent storage and generate a download link.

[2259] Input: Approved image.

[2260] Output: Final saved image, download link.

[2261] Step 6: Emotional Feedback and Model Optimization

[2262] The emotion engine collects emotional feedback on user-generated images.

[2263] Specific operation: Capture the user's facial expressions and voice again while they are viewing the image to obtain emotion data.

[2264] Input: User's facial expression data, voice tone.

[2265] Output: Collected emotional feedback.

[2266] The server sends emotional feedback to the image generation AI, which is then used to optimize the model.

[2267] Specific operation: The collected emotional feedback is fed back to the image generation AI and used as training data for the model.

[2268] Input: Emotional feedback.

[2269] Output: Optimized image generation AI model.

[2270] Step 7: Sharing and using images

[2271] The device displays a list of images saved to the user.

[2272] Specific action: Display a list of thumbnails of all images generated by the user on the user's dashboard page.

[2273] Input: None (initial display).

[2274] Output: A list of image thumbnails.

[2275] The user selects the image they need and gives instructions for downloading or sharing it.

[2276] Specific actions: Select an image and click the "Download" or "Share" button.

[2277] Input: Selected image.

[2278] Output: Download or share options.

[2279] The device downloads the image and saves it to the selected folder.

[2280] Specific action: Use the browser's download function to save the image to the specified folder.

[2281] Input: Selected download folder.

[2282] Output: Saved image.

[2283] The server generates a shared link and notifies the relevant parties.

[2284] Specific actions: Generate a sharing link and send notifications to designated stakeholders via email or messaging apps.

[2285] Input: The image you want to share.

[2286] Output: Shared link and notification.

[2287] (Application Example 2)

[2288] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2289] Conventional image generation systems were unable to automatically reflect the optimal design elements based on user emotions, resulting in limited improvements to the user experience. Furthermore, it was difficult to immediately reflect the generated images in virtual stores, lacking convenience in situations requiring rapid operation. Additionally, there was no easy way for virtual store operators to use the image generation interface. This led to problems where generated images could not be immediately corrected or optimized if they did not meet user expectations.

[2290] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to input authentication information, means for the terminal to send authentication information to the server to confirm the user's access rights, means for the user to input the requirements for image generation, means for the terminal to send the input requirements to the server, means for the server to send a request to the image generation AI, means for the image generation AI to generate an image based on the requirements, means for the server to temporarily store the generated image and send a link to the user, means for the user to check the generated image and send feedback, means for the image to be finally saved and a link generated based on the feedback, means for the terminal to capture the user's facial expression using a camera and for the emotion engine to recognize the user's emotions, and means for the emotion engine to reflect the recognized emotion data in the image generation request. As a result, the optimal design elements based on the user's emotions are automatically reflected, enabling high-quality image generation quickly and efficiently, and allowing for rapid reflection in product descriptions in virtual stores.

[2291] A "user" is a person or entity that uses this system to input image generation requirements, and to review and provide feedback on the generated images.

[2292] "Authentication information" refers to information necessary for a user to access the system, such as a user ID and password.

[2293] A "terminal" is a device used by a user to access the system, enter authentication information, input requirements for image generation, and verify the generated image.

[2294] A "server" is a central processing system that performs tasks such as verifying authentication information, sending and managing image generation requests, and saving generated images and creating links.

[2295] "Image generation AI" is an artificial intelligence algorithm that generates high-quality images based on requirements entered by the user.

[2296] The "emotion engine" is a system that uses the device's camera to capture the user's facial expressions and recognizes the user's emotions in real time.

[2297] "Requirements" are conditions that include the theme, style, and specific elements of the image the user wants to generate.

[2298] "Feedback" refers to a user's response to an image they have created, seeking approval or modification.

[2299] A "link" is a URL or hyperlink that allows a server to temporarily store an image it has generated and make it accessible to users.

[2300] "Facial expressions" refer to the movements and visual changes of the user's face, and are the input data that the emotion engine uses to recognize emotions.

[2301] "Emotional data" refers to information about emotions that the emotion engine analyzes and recognizes from the user's facial expressions.

[2302] The present invention's system takes the user's input requirements for the image they wish to generate and uses image generation AI to produce high-quality images. In this process, it recognizes the user's emotions in real time and incorporates optimal design elements based on those emotions. This system enables users to quickly and efficiently generate high-quality images, review and modify them as needed, and ultimately share them in a format suitable for use in virtual stores.

[2303] First, the user enters their authentication information (user ID and password) using a terminal. The terminal sends this authentication information to the server, which then compares the received information with a database to verify the user's access rights. This prevents unauthorized access to the system.

[2304] Next, the user enters specific requirements for image generation (theme, style, specific elements) on their device. This input is converted to JSON format and sent to the server.

[2305] Subsequently, the user's facial expressions are captured using the device's camera. The emotion engine analyzes the captured facial data and recognizes the user's emotions in real time. This recognized emotion data is then incorporated into the image generation request.

[2306] The server sends a request containing emotional data to the image generation AI, which generates an image based on the requirements. The generated image is temporarily stored on the server, and an access link is sent to the user.

[2307] The user clicks the received link to view the generated image and sends feedback (approval or correction request) to the server via their device. The server receives the feedback and, if necessary, sends a request back to the image generation AI to make corrections based on the feedback.

[2308] Once the user approves an image, the server saves it and generates a link. The generated image is stored in the company's internal storage, and the sharing link is sent to the relevant projects and departments as needed. The device also displays a list of images saved by the user, allowing them to download images or share them with other departments.

[2309] For example, when a virtual store operator generates custom images based on the theme of "relaxing interior," this system allows the operator to easily input requirements via smartphone, and quickly review, modify, and share the generated images.

[2310] The main hardware and software used are as follows:

[2311] Camera: Captures the user's facial expressions (e.g., smartphone camera)

[2312] EmotionEngine: A software module that recognizes emotions.

[2313] ImageGenerator: AI Algorithm for Image Generation

[2314] API Server: A backend system that manages image generation requests and authentication information.

[2315] Examples of prompt statements include the following:

[2316] Image generation theme: Relaxing interior

[2317] Image style: Simple and modern

[2318] Specific elements: sofa, houseplants, calming color scheme

[2319] This system allows users to generate high-quality images that incorporate design elements tailored to their emotions, enabling efficient product descriptions in virtual stores.

[2320] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[2321] Step 1:

[2322] The user enters authentication information on the device. Specifically, they enter their user ID and password into the input form and click the submit button. Input: User ID, password. Output: Authentication information is entered on the device.

[2323] Step 2:

[2324] The terminal sends authentication information to the server, and the server verifies the user's access rights by comparing the received authentication information with the database. Input: Authentication information. Output: Access rights verification result. Specifically, the server queries the database and verifies access rights based on the results.

[2325] Step 3:

[2326] The user enters the image generation requirements (theme, style, specific elements) into the device, which then converts this into JSON format and sends it to the server. Input: Image generation requirements. Output: Request data in JSON format. Specifically, the user fills in the theme, style, and specific elements in the input form and clicks the submit button.

[2327] Step 4:

[2328] The device uses its camera to capture the user's facial expressions, and an emotion engine analyzes this data to recognize the user's emotions in real time. Input: Captured image data. Output: User emotion data. Specifically, the camera captures the facial expression, and the emotion engine analyzes the emotion from the expression.

[2329] Step 5:

[2330] The server adds sentiment data to a JSON-formatted request and sends the request to the image generation AI. Input: Request data, sentiment data. Output: Image generation request. Specifically, the sentiment data is incorporated into the JSON and sent to the image generation AI via the API.

[2331] Step 6:

[2332] The image generation AI generates an image based on the request and sends it back to the server. Input: Image generation request. Output: Generated image data. Specifically, the image generation algorithm generates an image based on the requirements and emotions.

[2333] Step 7:

[2334] The server temporarily stores the generated image and sends an access link to the user. Input: Generated image data. Output: Access link. Specifically, the server saves the image to temporary storage, generates a link, and notifies the user.

[2335] Step 8:

[2336] The user clicks a received link, reviews the generated image, and sends feedback (approval or correction request) to the server via their device. Input: User feedback. Output: Feedback data. Specifically, the user clicks the link to view the image and then clicks the confirmation button.

[2337] Step 9:

[2338] The server receives feedback and, if necessary, sends a re-request to the image generation AI to make corrections based on the feedback. Input: Feedback data. Output: Corrected image data (if necessary). Specifically, the server analyzes the feedback content and sends a re-request if corrections are needed.

[2339] Step 10:

[2340] Once the user approves the image, the server saves the image and generates a link. Input: Final image data, user approval. Output: Final saved image data, sharing link. Specifically, the final image data is saved to permanent storage.

[2341] Step 11:

[2342] The server saves the last saved image to internal storage and notifies relevant projects and departments of the sharing link. Input: Last saved image data. Output: Sharing link, notification data. Specifically, it saves the image to internal storage and sends the link to relevant departments via email or the notification system.

[2343] Step 12:

[2344] The device displays a list of images saved by the user, allowing the user to download or share images with other departments. Input: Saved image data. Output: Download link, Share link. Specifically, it displays a list view and allows the user to select either a download or share link.

[2345] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[2346] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2347] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[2348] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2349] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[2350] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[2351] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[2352] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[2353] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[2354] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[2355] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[2356] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[2357] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[2358] 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.

[2359] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[2360] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[2361] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[2362] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[2363] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[2364] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[2365] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[2366] The following is further disclosed regarding the embodiments described above.

[2367] (Claim 1)

[2368] A means for the user to enter authentication information,

[2369] A means by which the terminal sends authentication information to the server to verify the user's access rights,

[2370] A means for the user to input the requirements for image generation,

[2371] A means for the terminal to send the entered requirements to the server,

[2372] A means by which the server sends a request to the image generation AI,

[2373] A means by which an image generation AI generates images based on requirements,

[2374] A means by which the server temporarily stores the generated image and sends a link to the user,

[2375] A means for users to review generated images and send feedback,

[2376] A system that includes means for finalizing images and generating links based on feedback.

[2377] (Claim 2)

[2378] A means of saving the images generated by the server to the company's internal storage,

[2379] The system according to claim 1, comprising means for the server to generate a sharing link for stored images and notify relevant projects or departments.

[2380] (Claim 3)

[2381] A means by which the device displays a list of images saved to the user,

[2382] The system according to claim 1, which includes means for a user to download an image or share it with another department.

[2383] "Example 1"

[2384] (Claim 1)

[2385] A means for the user to enter authentication information,

[2386] A means by which the terminal sends authentication information to the server to verify the user's access rights,

[2387] A means for the user to input the requirements for image generation,

[2388] A means for the terminal to send the entered requirements to the server,

[2389] A means by which the server sends a request to the image generation AI model,

[2390] A means by which an image generation AI model generates images based on requirements,

[2391] A means by which the server temporarily stores the generated image and sends a link to the user,

[2392] A means for users to review generated images and send feedback,

[2393] A means of saving the image and generating a link based on feedback,

[2394] A means for the device to generate a link for sharing or downloading the generated image,

[2395] A system that includes a means for users to share images with other departments using a shared link.

[2396] (Claim 2)

[2397] A means for the server to save the generated images to internal storage,

[2398] The system according to claim 1, comprising means for the server to generate a sharing link for stored images and notify the relevant projects or departments.

[2399] (Claim 3)

[2400] A means by which the device displays a list of images saved to the user,

[2401] The system according to claim 1, which includes means for a user to download an image or share it with another department.

[2402] "Application Example 1"

[2403] (Claim 1)

[2404] A means for the user to enter authentication information,

[2405] A means by which the terminal sends authentication information to the server to verify the user's access rights,

[2406] A means for the user to input the requirements for image generation, ...

Claims

1. A means for the user to enter authentication information, A means by which the terminal sends authentication information to the server to verify the user's access rights, A means for the user to input the requirements for image generation, A means for the terminal to send the entered requirements to the server, A means by which the server sends a request to the image generation AI, A means by which an image generation AI generates images based on requirements, A means by which the server temporarily stores the generated image and sends a link to the user, A means for users to review generated images and send feedback, A system that includes means for finalizing images and generating links based on feedback.

2. A means of saving the images generated by the server to the company's internal storage, The system according to claim 1, comprising means for the server to generate a sharing link for stored images and notify relevant projects or departments.

3. A means by which the device displays a list of images saved to the user, The system according to claim 1, comprising means for a user to download an image or share it with another department.

Images