System

Abstract

A system is provided.SOLUTION: A system, comprising: means for receiving a request from a user; means for searching an in-house database and a copyright free image database based on the request; means for selecting relevant images from the search results; means for generating a custom image using the selected images; means for optimizing the generated custom image; and means for sending the optimized custom image to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] When creating materials, finding the right image to visually express a specific image or concept is a difficult problem. Selecting the right image not only takes a huge amount of time, but also requires careful consideration of copyright issues. This significantly reduces the efficiency of document creation. Furthermore, users without specialized image creation expertise find it even more difficult to create attractive, easy-to-understand custom images. Therefore, there is a need for a method to efficiently and easily generate and provide appropriate custom images. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including: a means for receiving a request from a user; a means for searching an in-house database and a copyright-free image database based on the request; a means for selecting relevant images from the search results; a means for generating a custom image using the selected image; a means for optimizing the generated custom image; and a means for transmitting the optimized custom image to the user. This system allows users to efficiently use high-quality custom images, dramatically improving the efficiency of document creation. Furthermore, by further including an analysis means for analyzing keywords based on the request and passing them to the search means, and a quality check means for checking the quality of the generated custom image and reprocessing it as necessary, it is possible to provide custom images of a quality that satisfies the user.

[0006] "User" means any person or organization that uses the System to create images or materials.

[0007] A "request" refers to a request sent by a user to the system for images containing a particular image or concept.

[0008] The term "receiving means" refers to a component having a function of receiving and analyzing a request sent from a user.

[0009] An "internal database" refers to an electronic storage device that stores documents and image data managed within a company or organization.

[0010] A "copyright-free image database" refers to a database that stores images that are free from copyright infringement issues and can be used freely.

[0011] The term "searching means" refers to a component that has the function of executing a query based on the request content to the database and extracting related images.

[0012] "Selection method" refers to the algorithm or process used to select from the search results the image that best matches the request.

[0013] "Generative means" refers to the AI ​​or algorithms used to create new custom images based on selected images.

[0014] "Optimizing" refers to processes such as compression, resizing, color adjustment, etc. that are performed to improve the quality of the generated custom image.

[0015] "Means for transmitting" refers to a component capable of sending data over a network to provide an optimized custom image to a user.

[0016] The term "analysis means" refers to a component that has the function of analyzing keywords in a user request and generating a search query based on the keywords.

[0017] "Quality check means" refers to a component that has the function of evaluating the quality of the generated custom image and reprocessing it if necessary. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The present invention relates to a system for efficiently generating custom images for document creation and providing them to users. An embodiment of the present invention will be described below.

[0040] The user uses the device to input the image theme or element required for creating the document (e.g., "environmental protection," "nature," "recycle," etc.) into the text box. After inputting, the user clicks the request send button, and the device creates the input information as request data in JSON format and sends it to the server.

[0041] The server receives the HTTP request and analyzes the request data. The analyzed keywords are stored in variables to form a search query. Based on this search query, the server performs a search against the company's internal database and a copyright-free image database. The server extracts and filters relevant images from the search results.

[0042] After filtering, the server selects the most suitable image for the request and passes it to the image fusion AI to generate a custom image. The server checks the quality of the generated custom image and reprocesses it if necessary. The server also performs an optimization process to compress and resize the image, converting it into a user-friendly format.

[0043] The optimized custom image is prepared as the final output data, and the server sends it to the terminal as a JSON-formatted response data. The terminal receives the HTTP response, extracts the image from the JSON data, and displays it to the user. The user can then check the displayed custom image and use it to create documents.

[0044] Specific examples

[0045] Scenario: A user wants to create a presentation on the theme of "environmental protection."

[0046] The user uses the device to input keywords such as "environmental protection," "nature," and "recycle" into a text box and send a request. The device then sends the request to the server, which then analyzes the received request and extracts the necessary keywords.

[0047] The server searches the company's internal database and a copyright-free image database to extract relevant images. From the search results, the server selects images related to "environmental protection," "nature," and "recycling," and passes them to the image fusion AI to generate a custom image. The generated custom image is then quality checked and optimized, and the final custom image is output.

[0048] The server sends the custom image to the user's device, which then displays it. The user can then view the custom image and add it to their presentation. This process allows users to efficiently use high-quality custom images, dramatically improving presentation creation.

[0049] This completes one embodiment of the present invention, which automates and streamlines the process of generating custom images required for creating materials.

[0050] The processing flow will be explained below.

[0051] Step 1:

[0052] The user uses the device to input the theme or element of the image they want to create (e.g., "environmental protection," "nature," "recycling," etc.) into the text box, and then clicks the submit request button.

[0053] Step 2:

[0054] The terminal creates the input information as request data in JSON format and sends it to the server using the HTTP POST method.

[0055] Step 3:

[0056] The server receives the HTTP request and parses the request data. The parsed keywords are stored in variables.

[0057] Step 4:

[0058] The server constructs the search query and prepares the search request against the in-house database and the copyright-free image database.

[0059] Step 5:

[0060] The server runs a search query against the in-house database to retrieve relevant images, as well as against a royalty-free image database to retrieve relevant images.

[0061] Step 6:

[0062] The server generates a list of retrieved images and filters them based on the request.

[0063] Step 7:

[0064] The server selects the image that best suits the request from the filtered image list.

[0065] Step 8:

[0066] The server passes the selected images to an image fusion AI, which generates a custom image based on the user's theme and elements.

[0067] Step 9:

[0068] The server checks the quality of the generated custom images and requests the AI ​​to reprocess them if necessary.

[0069] Step 10:

[0070] The server puts the custom images through an optimization process, compressing and resizing them to convert them into a user-friendly format.

[0071] Step 11:

[0072] The server creates an optimized custom image as response data in JSON format and sends it to the terminal as an HTTP response.

[0073] Step 12:

[0074] The device receives the HTTP response and extracts the image from the JSON data.

[0075] Step 13:

[0076] The terminal updates the interface to display the extracted custom image to the user.

[0077] Step 14:

[0078] The user can check the displayed custom image and use it to create documents.

[0079] Example 1

[0080] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0081] In conventional document creation, the process of users searching for images appropriate to a theme and customizing them is time-consuming and labor-intensive. Furthermore, there is a lack of automated means for efficiently generating high-quality, appropriate custom images. This has led to a lack of efficiency in document creation and a lack of user satisfaction. In particular, there is a need for a comprehensive system that can search multiple image databases, appropriately filter the images, and further optimize custom images.

[0082] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0083] In this invention, the server includes a means for receiving a request from a user, a means for searching a database based on the request, and a means for extracting related images from the search results, allowing users to easily obtain high-quality custom images and streamlining document creation.

[0084] "User" refers to the entity that uses the system to create materials and generate images.

[0085] A "request" is information that a user requests and sends to the system, and includes specific keywords and themes.

[0086] "Database" refers to the company's in-house database and copyright-free image database, a system for managing a large number of images and related information.

[0087] "Search" refers to the process of finding relevant images within a database based on specific keywords or conditions.

[0088] "Extraction" refers to the act of extracting the image that best suits your purpose from the search results.

[0089] A "generative AI model" refers to an artificial intelligence model that synthesizes multiple images to generate new custom images.

[0090] "Optimization" refers to the process of adjusting the file size and improving the image quality of the generated custom image.

[0091] "Quality check" refers to the process of inspecting the quality of the generated custom images to ensure they meet standards.

[0092] The system of the present invention efficiently generates high-quality custom images required for document creation and provides them to users. In this system, the user uses a terminal to input the image theme and elements and submit a request. This request is sent to a server as JSON-formatted data. The server receives the request and searches an in-house database and a copyright-free image database based on analyzed keywords. Relevant images are selected from the search results and a generative AI model is used to generate custom images. The generated custom images undergo a quality check and optimization process before being provided to the user.

[0093] Specifically, the user enters keywords such as "environmental protection," "nature," or "recycle" into the text box on the device and clicks the send request button. The device converts the entered information into JSON-formatted request data and sends it to the server using the HTTP POST method.

[0094] The server analyzes the received request and performs a search against the database using the extracted keywords as a search query. Specifically, it searches for images using SQL queries or API requests. The server then filters relevant images from the search results and passes them to the image fusion AI to generate a custom image. Generative AI models used include "DALL-E 2." In this process, the AI ​​model is given a prompt sentence such as:

[0095] Generate high-quality custom images with environmental, nature, and recycling themes.

[0096] The server checks the quality of the generated custom image and reprocesses it if necessary. If the quality meets the standards, the server runs an optimization process to compress and resize the image. The final optimized custom image is sent to the device as JSON-formatted response data. The device displays the image to the user based on the received data. The user can use this image to create documents.

[0097] This system significantly streamlines the process of generating custom images required for document creation, allowing users to easily obtain high-quality images.

[0098] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0099] Step 1:

[0100] The user uses the device to enter the theme or element of the image in the text box and clicks the send request button. The entered theme or element (e.g., "environmental protection," "nature," "recycle") is passed to the device as input data. In this case, the input data is text-format keywords. The device converts the entered information into request data in JSON format.

[0101] Input: Keywords entered by the user ("environmental protection," "nature," "recycle")

[0102] Output: Request data in JSON format

[0103] Step 2:

[0104] The terminal sends the generated JSON-formatted request data to the server using the HTTP POST method. This transmission process transmits the user's request to the server.

[0105] Input: Request data in JSON format

[0106] Output: HTTP request (JSON data)

[0107] Step 3:

[0108] The server receives the HTTP request. It parses the received JSON data and extracts keywords. The parsing process stores the keywords in variables.

[0109] Input: HTTP request (JSON data)

[0110] Output: Extracted keywords (variables)

[0111] Step 4:

[0112] The server generates a search query based on the extracted keywords and runs it against the company's internal database and a database of royalty-free images, using SQL queries and API requests.

[0113] Input: Extracted keywords

[0114] Output: Search query

[0115] Step 5:

[0116] The server searches the database using the generated search query to extract relevant images. As a result of the search process, multiple images are obtained.

[0117] Input: Search query

[0118] Output: Search results (list of images)

[0119] Step 6:

[0120] The server filters relevant images from the search results, taking into account criteria such as resolution, file format, and usage conditions, and temporarily stores the filtered images.

[0121] Input: Search results (list of images)

[0122] Output: filtered image

[0123] Step 7:

[0124] The server then passes the filtered image to an image fusion AI to generate a custom image. The generative AI model used includes "DALL-E 2." During this process, the following prompt is input to the AI ​​model:

[0125] Generate high-quality custom images with environmental, nature, and recycling themes.

[0126] Input: filtered image, prompt text

[0127] Output: The generated custom image

[0128] Step 8:

[0129] The server checks the quality of the generated custom images, including checking the resolution, color consistency, and whether there is any unwanted noise. If the quality does not meet the standards, the images are reprocessed.

[0130] Input: Generated custom image

[0131] Output: Quality check result (pass / fail)

[0132] Step 9:

[0133] The server then optimizes the custom images that meet the quality criteria. The optimization process involves compressing and resizing the image, specifically using the JPEG compression algorithm to reduce file size while preserving image quality.

[0134] Input: Custom images that pass quality checks

[0135] Output: Optimized custom images

[0136] Step 10:

[0137] The server sends the optimized custom image to the terminal as a JSON response data. The specific JSON data example is as follows:

[0138] json

[0139] {

[0140] "custom_image_url": "https: / / example.com / generated_image.jpg"

[0141] }

[0142] Input: Optimized custom image

[0143] Output: Response data in JSON format

[0144] Step 11:

[0145] The device receives the HTTP response and extracts the image URL from the JSON data. The device uses this image URL to display a custom image to the user. The user can then view the displayed image and use it to create documents.

[0146] Input: Response data in JSON format

[0147] Output: Custom image displayed to the user

[0148] (Application example 1)

[0149] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0150] Creating advertising images and promotional materials in modern brick-and-mortar stores is often done manually, requiring a great deal of time and effort. In particular, brick-and-mortar stores often need to change their advertising content frequently depending on the season or campaign, and new images must be prepared quickly and appropriately each time. Furthermore, generating high-quality custom images requires specialized knowledge and skills, which can lead to inefficient work. Therefore, there is a need for a system that streamlines this work and allows anyone to easily generate and use high-quality custom images.

[0151] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0152] In this invention, the server includes means for receiving a request from a user, means for searching an in-house database and a copyright-free image database based on the request, means for selecting relevant images from the search results, means for using a generative model to generate a custom image using the selected images, means for optimizing the generated custom image, means for transmitting the optimized custom image to the user, and means for displaying the generated custom image to the user, thereby enabling efficient creation and rapid deployment of advertising images and promotional materials in physical stores.

[0153] The "means for receiving a request from a user" is a function that provides an interface for acquiring information input by a user through a terminal and sending it to a server.

[0154] "A means of searching internal databases and copyright-free image databases based on requests" refers to a search function that finds appropriate images from internal databases and external copyright-free image databases based on keywords or themes requested by users.

[0155] A "means for selecting relevant images" is an algorithm or method for selecting from the search results the images that best match the user's requirements.

[0156] "Means for generating custom images using generative models" refers to a function that uses an advanced generative model (e.g., image fusion AI) to create a unique custom image based on a selected image.

[0157] "Means for optimizing generated custom images" refers to a process for compressing or resizing generated images as necessary while preserving their quality.

[0158] The "means for sending an optimized custom image to a user" is a function for sending an optimized image to a user's terminal in the form of an HTTP response or the like.

[0159] The "display means" is an interface on the user terminal for visually displaying the custom image received from the server.

[0160] The "analysis means" is a function for extracting keywords contained in a request sent by a user and generating a search query based on that information.

[0161] "Quality check means" refers to a process or function that checks the quality of the custom images generated and reprocesses them if necessary.

[0162] The present invention relates to a system that improves the efficiency of creating advertising images and sales promotion materials in brick-and-mortar stores and enables the rapid generation and use of high-quality custom images. Hereinafter, an embodiment of the present invention will be described.

[0163] First, the user uses a smartphone or tablet device to input a specific theme or keyword (e.g., "fresh vegetables," "healthy," "organic," etc.). After inputting, the device generates the input information as request data in JSON format and sends it to the server.

[0164] The server receives the request data from the user and extracts keywords using an analysis tool. The extracted keywords are used to generate a search query, which is then used to search the company's internal database and a copyright-free image database. The server then selects relevant images from the search results and proceeds to the next step.

[0165] The selected images are then passed to a process that uses a generative model to generate a custom image. This process uses image fusion AI to synthesize and process the images into a high-quality custom image. The resulting custom image is then verified by a quality checker and reprocessed if necessary. After quality is assured, an optimization process compresses and resizes the image to convert it into a user-friendly format.

[0166] The optimized custom image is finally sent to the user's device, where the received image can be visually displayed and checked using a display device. This allows physical stores to quickly generate and deploy new advertising images and promotional materials.

[0167] The main hardware and software used to implement this system include: On the server side, Python and Flask are used, and a database search module and an image fusion AI module are installed. On the client side, JavaScript, HTML, and CSS are used to build the user interface.

[0168] As a concrete example, consider a case where a user wants to generate a custom image for a display ad on the theme of "fresh vegetables." In this case, the user enters "fresh vegetables," "healthy," and "organic" into the text box on their device and submits a request. Based on this request, the server searches for relevant images and generates a custom image using image fusion AI. As part of this process, the following prompt sentence is used:

[0169] "Fresh vegetables, healthy, organic"

[0170] The server generates a custom image based on the prompt and delivers it to the user's device. The user can then view the image and use it in display ads.

[0171] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0172] Step 1: User Input

[0173] Users use their smartphones or tablet devices to input the desired advertising theme or elements (e.g., "fresh vegetables," "healthy," "organic") into a request form. This input information constitutes the request data.

[0174] Input: Theme or keyword

[0175] Output: Request data (JSON format)

[0176] Step 2: Submitting the request

[0177] The device sends the request data entered by the user to the server as an HTTP POST request, which includes the user's input information in JSON format.

[0178] Input: Request data (JSON format)

[0179] Output: HTTP POST request

[0180] Step 3: Receiving and Parsing Request Data

[0181] The server receives the HTTP POST request and analyzes the request data. The analysis means extracts keywords from the request data and stores them in variables.

[0182] Input: HTTP POST request (JSON format)

[0183] Output: Extracted keywords (variables)

[0184] Step 4: Find an image

[0185] The server searches the in-house database and copyright-free image database based on the extracted keywords, generates a search query, and executes the image search.

[0186] Input: Extracted keywords

[0187] Output: Search results (image list)

[0188] Step 5: Select related images

[0189] The server selects the most relevant images from the search results using algorithms and filtering methods.

[0190] Input: Search results (image list)

[0191] Output: Selected related images (image list)

[0192] Step 6: Generate custom images

[0193] The server passes the selected images to the image fusion AI to generate a custom image. A generative model is used to create a unique custom image.

[0194] Input: Selected related images (image list)

[0195] Output: The generated custom image

[0196] Step 7: Image optimization

[0197] The server checks the quality of the generated custom images, reprocesses them if necessary, and also performs optimizations such as compressing and resizing the images.

[0198] Input: Generated custom image

[0199] Output: Optimized custom images

[0200] Step 8: Submit a custom image

[0201] The server composes the optimized custom image as response data in JSON format and sends it to the user's device via an HTTP response.

[0202] Input: Optimized custom image

[0203] Output: HTTP response (JSON format)

[0204] Step 9: Displaying Custom Images

[0205] The user's terminal receives the HTTP response, extracts the custom image from the response data, and visually displays the custom image to the user using the terminal's display means.

[0206] Input: HTTP response (JSON format)

[0207] Output: Custom image displayed

[0208] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0209] The present invention relates to a system for generating custom images that recognize and reflect the emotional state of a user, thereby efficiently supporting the creation of materials. An embodiment of the present invention will now be described.

[0210] The user uses the device to input the image theme or element required for creating the document (e.g., "environmental protection," "nature," "recycling," etc.) into a text box. The device then captures the user's facial expressions and voice data through an emotion engine to recognize their emotional state. The recognized emotion data is added to the request data.

[0211] The device creates the input information and emotion data as request data in JSON format and sends it to the server using the HTTP POST method. The server receives the HTTP request and analyzes the request data. The analyzed keywords and emotion data are stored in variables to form a search query. Based on this search query, the server performs a search against its in-house database and a copyright-free image database. The server extracts and filters related images from the search results.

[0212] After filtering, the server selects the image that best fits the request and emotional data, and passes the selected image to the image fusion AI to generate a custom image. For example, if a user requests an image on the theme of "environmental protection," and the emotion engine recognizes the user's positive emotions such as "joy" or "satisfaction," a bright, positive image may be selected. Conversely, if the user expresses emotions such as "tension" or "anxiety," a calm design to ease anxiety may be selected.

[0213] The server checks the quality of the generated custom image and reprocesses it if necessary. It also performs an optimization process to compress and resize the image and convert it into a format that is easy for users to use. The optimized custom image is prepared as the final output data and sent to the terminal as a JSON-formatted response data.

[0214] The device receives the HTTP response and extracts the image from the JSON data. The extracted custom image is displayed to the user. The user can check the displayed custom image and use it to create documents.

[0215] Specific examples

[0216] Scenario: A user wants to create a presentation on the theme of "environmental protection."

[0217] The user uses the device to input keywords such as "environmental protection," "nature," and "recycle" into a text box, and the emotion engine analyzes the user's facial expressions and voice to recognize "joy." The request and emotion data are then sent to the server, which analyzes the received request and extracts the necessary keywords and emotion data.

[0218] The server searches the company's internal database and a copyright-free image database to extract relevant images. From the search results, the server selects images related to "environmental protection" and "joy" and passes them to the image fusion AI to generate a custom image. The generated custom image is then quality checked and optimized, and the final custom image is output.

[0219] The server sends the custom image to the user's device, which then displays it to the user. The user can then view the custom image and add it to their presentation materials. This process allows users to efficiently use high-quality custom images that suit their emotions, dramatically improving presentation creation.

[0220] The foregoing describes one embodiment of the present invention, which enables and streamlines the process of generating custom images that respond to the user's emotional state.

[0221] The processing flow will be explained below.

[0222] Step 1:

[0223] The user uses the device to input the image theme or element required for creating the document (e.g., "environmental protection," "nature," "recycling," etc.) into a text box. In addition, the user's facial expressions and voice data are captured through the emotion engine.

[0224] Step 2:

[0225] The device's emotion engine analyzes the user's emotional state and adds recognized emotion data (e.g., "joy," "satisfaction," etc.) to the request data.

[0226] Step 3:

[0227] The device creates the input information and emotion data as request data in JSON format and sends it to the server using the HTTP POST method.

[0228] Step 4:

[0229] The server receives the HTTP request and analyzes the request data. The analyzed keywords and emotion data are stored in variables.

[0230] Step 5:

[0231] The server constructs the search query and prepares the search request against the in-house database and the copyright-free image database.

[0232] Step 6:

[0233] The server runs a search query against the in-house database to retrieve relevant images, as well as against a royalty-free image database to retrieve relevant images.

[0234] Step 7:

[0235] The server generates a list of retrieved images and filters them based on the request, taking into account the user's emotional data.

[0236] Step 8:

[0237] The server selects the image that best suits the request and emotional data: if the emotional state is "joy," an image with a positive impression is selected.

[0238] Step 9:

[0239] The server passes the selected images to an image fusion AI, which generates a custom image based on the user's theme and emotion.

[0240] Step 10:

[0241] The server checks the quality of the generated custom images and requests the AI ​​to reprocess them if necessary.

[0242] Step 11:

[0243] The server puts the custom images through an optimization process, compressing and resizing them to convert them into a user-friendly format.

[0244] Step 12:

[0245] The server creates an optimized custom image as response data in JSON format and sends it to the terminal as an HTTP response.

[0246] Step 13:

[0247] The device receives the HTTP response and extracts the image from the JSON data.

[0248] Step 14:

[0249] The terminal updates the interface to display the extracted custom image to the user.

[0250] Step 15:

[0251] The user can check the displayed custom image and use it to create documents.

[0252] Example 2

[0253] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0254] Conventional document creation systems have difficulty generating custom images that reflect the user's emotional state, which has resulted in inefficient document creation. Furthermore, image selection and image optimization according to emotions are insufficient, making it impossible to efficiently provide high-quality custom images that reflect the user's intentions.

[0255] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a request from a user, means for searching an internal database and a copyright-free database, means for capturing the user's facial expression and voice data using an emotion engine and recognizing the user's emotional state, means for adding emotion data to request data, creating JSON-formatted request data, and transmitting the request data to the server, means for selecting related images from the search results and generating a custom image using the selected image, means for optimizing the generated custom image, and means for transmitting the optimized custom image to the user. This makes it possible to efficiently generate and provide high-quality custom images suited to the user's emotional state.

[0256] "User" refers to a person who uses the system to create materials.

[0257] A "request" refers to information requested by a user to input a theme or element and send it to the system.

[0258] "Terminal" refers to a device (such as a PC, smartphone, or tablet) that a user uses to access the system.

[0259] "Server" refers to a computer system that performs the main processing of the system and stores data.

[0260] An "emotion engine" refers to an algorithm or software that analyzes a user's facial expressions and voice data to recognize their emotional state.

[0261] "JSON format" stands for JavaScript Object Notation and refers to a lightweight data exchange format.

[0262] "Internal database" refers to a database that stores various data (such as image data) stored within the system.

[0263] A "copyright-free database" refers to a database that provides data (mainly image data) that can be used without copyright restrictions.

[0264] A "search query" refers to a series of keywords or expressions constructed to conduct a database search.

[0265] "Image selection" refers to the process of selecting appropriate images from search results.

[0266] "Custom Image" refers to a personalized image generated based on a user's request and emotional state.

[0267] "Optimization" refers to the process of compressing, resizing, or otherwise improving an image into a more usable format.

[0268] "Quality Check" refers to the process of checking the quality of the custom images generated and reprocessing them if necessary.

[0269] "HTTP protocol" is an abbreviation for Hypertext Transfer Protocol, and refers to a communication protocol for sending and receiving data over the Internet.

[0270] This invention relates to a system that efficiently supports document creation by recognizing the user's emotional state and generating custom images that reflect that state. This system operates mainly through three main components: the user, the terminal, and the server, and each processing step is clearly separated.

[0271] User input of themes and elements

[0272] The user uses the terminal to input the themes and elements required for creating materials into a text box, for example, keywords such as "environmental protection," "nature," and "recycling."

[0273] Capturing Emotional Data

[0274] The device uses a camera and microphone to capture the user's facial expressions and voice data. Specifically, it uses an emotion engine (e.g., a general face recognition API or voice recognition API) to recognize the user's emotional state. Examples of emotion engines that can be used include Microsoft Azure Face API and Google Cloud's Speech-to-Text.

[0275] Creating and sending request data

[0276] Based on the recognized emotion data and the themes and elements entered by the user, the device creates request data in JSON format, which is then sent to the server using the HTTP POST method.

[0277] Receiving and parsing request data

[0278] When the server receives an HTTP request, it analyzes the request data. This analysis involves extracting keywords and sentiment data from the request data and storing them in variables. A search query is then constructed based on these variables.

[0279] Image search

[0280] Based on the search query, the server performs a search in its internal database and royalty-free databases (e.g., Pexels, Pixabay), extracts relevant images from the search results, and filters them.

[0281] Custom Image Generation

[0282] The server passes the selected images after filtering to an image fusion AI (e.g., a general image generation AI) to generate the most suitable custom image. Examples of image fusion AI that can be used include OpenAI's DALL-E and Google's Imagen. For example, if the user's emotion of "joy" is recognized in relation to the theme of "environmental protection," a bright and positive image will be generated.

[0283] Image quality check and optimization

[0284] The server checks the quality of the custom images generated, reprocesses them if necessary, and puts them through an optimization process to compress, resize, and convert them into a user-friendly format.

[0285] Sending optimized images

[0286] The optimized custom image is prepared as the final output data and sent to the device as an HTTP response.

[0287] Receive and display custom images

[0288] The device receives the HTTP response and extracts the custom image from the JSON data, which is then displayed to the user.

[0289] User Use

[0290] The user can check the displayed custom image and use it in presentations and document creation, thereby enabling the user to efficiently obtain high-quality custom images and improve the quality and efficiency of document creation.

[0291] Specific examples

[0292] For example, if a user is creating a presentation on the topic of "environmental protection," they might use the following prompt:

[0293] "Generate custom images that reflect the sentiments of environmentalism and joy."

[0294] This system can streamline and improve the quality of document creation by generating custom images that reflect the user's emotional state.

[0295] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0296] Step 1:

[0297] The user inputs the image theme or element (e.g., "environmental protection," "nature," "recycle," etc.) required for creating the document into a text box on the terminal. This becomes the input data. The terminal prepares to send this theme or element to the server as character string data.

[0298] Step 2:

[0299] The device receives user input and simultaneously captures the user's facial expressions and voice using a camera and microphone. Specifically, it uses an emotion engine (e.g., Microsoft Azure Face API or Google Cloud's Speech-to-Text) to recognize the user's emotional state and output it as emotion data. This emotion data must be included in the request data.

[0300] Step 3:

[0301] The device combines the input themes and elements (string data) with the recognized emotion data to create request data in JSON format, which is then sent to the server using the HTTP POST method.

[0302] Step 4:

[0303] The server receives HTTP requests and analyzes the received request data. Specifically, it extracts keywords (themes and elements) and sentiment data from the request data, analyzes them, and constructs a search query. The analyzed data becomes input for searching the internal database and copyright-free databases.

[0304] Step 5:

[0305] The server performs a search on its internal database and royalty-free databases (e.g., Pexels, Pixabay) based on the constructed search query. As a result, relevant images are extracted. These extracted images become the input data for the next step.

[0306] Step 6:

[0307] The server selects from the extracted images the one that is most relevant to the request and emotional data, based on the image's metadata and text information. The selected image serves as input data for generating a custom image in the next step.

[0308] Step 7:

[0309] The server then passes the selected images to an image fusion AI (e.g., OpenAI's DALL-E or Google's Imagen) to generate a custom image. This process uses image processing algorithms to output an image that reflects the user's emotional state.

[0310] Step 8:

[0311] The server checks the quality of the generated custom image and reprocesses it if necessary. Specifically, it checks the image resolution and color, and if there are any problems, it passes it back to the image fusion AI for correction. At this stage, the quality of the final custom image is guaranteed.

[0312] Step 9:

[0313] The server optimizes images that pass the quality check by compressing and resizing them and converting them into a format that is easier for users to use, such as JPEG or PNG.

[0314] Step 10:

[0315] The server sends the optimized custom image to the device as response data in JSON format, generates an HTTP response as the response data, and returns it to the device.

[0316] Step 11:

[0317] The device receives the HTTP response, extracts the custom image from the JSON data, and displays the extracted custom image to the user.

[0318] Step 12:

[0319] The user can check the custom image displayed on the terminal and use it in creating materials. The user can efficiently obtain the generated custom image, thereby improving the quality and efficiency of presentations and material creation.

[0320] Above are the detailed processing steps of the system, through which users can efficiently obtain high-quality custom images that reflect their emotional state.

[0321] (Application example 2)

[0322] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0323] Conventional document creation support systems were unable to recognize the user's emotional state and generate custom images that matched those emotions, resulting in the provision of images that did not match the user's intentions. Furthermore, content recommendations did not take the user's emotions into consideration, making it difficult to provide optimal content. This resulted in users wasting time creating documents and selecting content, resulting in reduced efficiency.

[0324] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0325] In this invention, the server includes means for receiving requests from users, means for searching an in-house database and a copyright-free image database, means for selecting relevant images, means for recognizing the user's emotional state and customizing content based on that emotion, means for optimizing the generated custom images, and means for transmitting the optimized custom images to the user. This enables the recommendation of high-quality custom images and content according to the user's emotional state, thereby realizing more efficient document creation and content selection.

[0326] "Means for receiving requests from a user" refers to a device or software that has the function of receiving input or requests from a user and transmitting them to the system.

[0327] "Means for searching a database" refers to devices or software that have the function of searching and extracting relevant information from in-house databases and copyright-free image databases, etc., based on specified keywords or conditions.

[0328] The "means for selecting relevant images" refers to a device or software that has the function of selecting from the search results the image that best suits the user's request or emotional state.

[0329] The "means for generating a custom image" refers to a device or software that has the function of creating a customized image based on a selected image that matches the user's needs and emotions.

[0330] The "means for recognizing the emotional state of the user" refers to a device or software that has the function of analyzing the user's facial expressions and voice data and determining the user's emotional state.

[0331] A "means for customizing content" is a device or software that has the function of adjusting content according to the recognized emotional state of the user and changing it into an appropriate format or content.

[0332] A "means for optimizing a custom image" is any device or software capable of compressing or resizing a generated custom image to improve its quality.

[0333] The "means for transmitting a custom image to a user" refers to a device or software that has the function of transferring an optimized custom image to a user's terminal and displaying it.

[0334] "Emotion recognition is performed based on facial expression and voice data" refers to the process of determining the emotional state of a user based on their facial expression and voice.

[0335] The system of the present invention has the function of generating custom images that recognize and reflect the emotional state of a user, and efficiently supports document creation and content recommendation. Specific embodiments for carrying out the present invention will be described below.

[0336] 1. System Configuration

[0337] The system is mainly composed of a server and a user terminal, and operates in the following steps.

[0338] 1. A means of receiving requests from users

[0339] The user inputs a keyword through an interface installed on the user terminal.

[0340] For example, by using an application on a smartphone or tablet.

[0341] 2. A means of recording the user's emotional state

[0342] The camera and microphone on the user's device are used to capture facial expressions and voice.

[0343] The software used is the open source OpenCV library and Microsoft's Azure Face API as a facial recognition tool.

[0344] 3. Data submission and analysis

[0345] The device sends the user's input keywords and the recognized emotional state to the server as request data in JSON format.

[0346] The server uses the Python requests library to receive and analyze data via the HTTP POST method.

[0347] 4. Image Search and Selection

[0348] The server searches its internal database and its copyright-free image database to extract relevant images.

[0349] The system selects the most suitable image from the search results based on the user's request and emotional state.

[0350] 5. Creating Custom Images

[0351] Based on the selected image, a custom image is generated according to the user's emotion, using image fusion AI.

[0352] For example, if the user expresses the emotion "joy," an image with a bright and positive design is generated.

[0353] 6. Image Optimization

[0354] Quality check the generated custom images and reprocess them if necessary.

[0355] Compress and resize images to convert them into a user-friendly format.

[0356] 7. Sending images

[0357] The optimized custom image is sent to the user's device and displayed to the user, who can use it to create documents and recommend content.

[0358] 2. Data processing and calculation

[0359] Emotion recognition:

[0360] Hardware used: Webcam and microphone (a common brand is the Logitech Brio camera)

[0361] Software used: OpenCV library, Azure Face API

[0362] Processing: Video and audio data is captured, and the emotion recognition engine analyzes facial expressions and voice to generate emotion data.

[0363] Data transmission and analysis:

[0364] Software used: requests library

[0365] Processing: User input and emotion data are packaged in JSON format and sent to the server via HTTP POST. The server then parses the received data using a Python script.

[0366] Image selection and generation:

[0367] Software used: In-house search engine, image fusion AI

[0368] Processing: Search the image database based on the search query to select suitable images. Based on the selected images, the image fusion AI generates a custom image.

[0369] 3. Examples and prompts

[0370] Scenario: A user is looking for content on the theme "environmental documentaries."

[0371] The user opens the app and enters the keyword "environmental documentary." The app uses the camera and microphone to recognize the user's emotion of "excitement." This data is then sent to the server, which then recommends related documentaries. For example, documentaries such as "Our Planet" and "Blue Planet" are recommended.

[0372] Prompt Sentence Examples

[0373] "Given the given keyword 'environment,' generate server request data to recommend content that corresponds to the user's emotions."

[0374] This configuration provides appropriate images and content that take into consideration the user's emotions, improving the efficiency of creating materials and viewing content.

[0375] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0376] Step 1:

[0377] A user launches the smartphone app and enters a keyword into the text box. This keyword indicates the theme for which they wish to create materials or have content recommended. The user enters a keyword such as "environmental documentary." The keyword is obtained as input data.

[0378] Step 2:

[0379] The device uses a camera and microphone to capture the user's facial expressions and voice, and the emotion recognition engine analyzes this data. Specifically, it uses the OpenCV library and Azure Face API to identify emotions from facial expression and voice data. Facial expression images and voice data are obtained as input data, and emotion data is generated based on these.

[0380] Step 3:

[0381] The device packages the user's input keywords and recognized emotional data in JSON format, which includes information on the keyword and emotional state. This package is sent to the server using the HTTP POST method. Keywords and emotional data are obtained as input data, and JSON-formatted request data is generated as output data.

[0382] Step 4:

[0383] The server parses the received JSON request data and extracts keywords and sentiment data. The data is received using the Python requests library, and the parsing engine breaks down the data to extract keywords and sentiment. The JSON request data is obtained as input data, and keywords and sentiment data are provided as output data.

[0384] Step 5:

[0385] The server searches the company's in-house database and copyright-free image database based on the extracted keywords and emotion data, thereby extracting related images. Keywords and emotion data are obtained as input data, and a list of related images is generated as output data.

[0386] Step 6:

[0387] The server selects from the search results the image that best suits the user's request and emotional state. The search engine evaluates and ranks the image list obtained based on emotional data. The list of related images and emotional data are obtained as input data, and the optimal image is selected as output data.

[0388] Step 7:

[0389] The server uses image fusion AI to generate a custom image using the optimal image. For example, if the user expresses the emotion of "joy," it generates an image with a bright, positive design. The selected image and emotion data are obtained as input data, and a custom image is generated as output data.

[0390] Step 8:

[0391] The server checks the quality of the custom image and reprocesses it if necessary. Specifically, it checks the clarity of the image and the appropriateness of the layout, and regenerates it if the quality is low. The custom image is taken as input data, and a quality-checked custom image is generated as output data.

[0392] Step 9:

[0393] The server compresses and resizes your custom images to produce optimized images in a format that is easily consumed by users. The input is a quality-checked custom image, and the output is an optimized custom image.

[0394] Step 10:

[0395] The server packages the optimized custom image in JSON format and sends it to the device. The device displays the received custom image, and the user uses the image for document creation and content recommendation. The optimized custom image is obtained as input data, and the custom image displayed on the user's device is generated as output data.

[0396] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0397] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0398] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0399] [Second embodiment]

[0400] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0401] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0402] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0403] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0404] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0405] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0406] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0407] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0408] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0409] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0410] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0411] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0412] The present invention relates to a system for efficiently generating custom images for document creation and providing them to users. An embodiment of the present invention will be described below.

[0413] The user uses the device to input the image theme or element required for creating the document (e.g., "environmental protection," "nature," "recycle," etc.) into the text box. After inputting, the user clicks the request send button, and the device creates the input information as request data in JSON format and sends it to the server.

[0414] The server receives the HTTP request and analyzes the request data. The analyzed keywords are stored in variables to form a search query. Based on this search query, the server performs a search against the company's internal database and a copyright-free image database. The server extracts and filters relevant images from the search results.

[0415] After filtering, the server selects the most suitable image for the request and passes it to the image fusion AI to generate a custom image. The server checks the quality of the generated custom image and reprocesses it if necessary. The server also performs an optimization process to compress and resize the image, converting it into a user-friendly format.

[0416] The optimized custom image is prepared as the final output data, and the server sends it to the terminal as a JSON-formatted response data. The terminal receives the HTTP response, extracts the image from the JSON data, and displays it to the user. The user can then check the displayed custom image and use it to create documents.

[0417] Specific examples

[0418] Scenario: A user wants to create a presentation on the theme of "environmental protection."

[0419] The user uses the device to input keywords such as "environmental protection," "nature," and "recycle" into a text box and send a request. The device then sends the request to the server, which then analyzes the received request and extracts the necessary keywords.

[0420] The server searches the company's internal database and a copyright-free image database to extract relevant images. From the search results, the server selects images related to "environmental protection," "nature," and "recycling," and passes them to the image fusion AI to generate a custom image. The generated custom image is then quality checked and optimized, and the final custom image is output.

[0421] The server sends the custom image to the user's device, which then displays it. The user can then view the custom image and add it to their presentation. This process allows users to efficiently use high-quality custom images, dramatically improving presentation creation.

[0422] This completes one embodiment of the present invention, which automates and streamlines the process of generating custom images required for creating materials.

[0423] The processing flow will be explained below.

[0424] Step 1:

[0425] The user uses the device to input the theme or element of the image they want to create (e.g., "environmental protection," "nature," "recycling," etc.) into the text box, and then clicks the submit request button.

[0426] Step 2:

[0427] The terminal creates the input information as request data in JSON format and sends it to the server using the HTTP POST method.

[0428] Step 3:

[0429] The server receives the HTTP request and parses the request data. The parsed keywords are stored in variables.

[0430] Step 4:

[0431] The server constructs the search query and prepares the search request against the in-house database and the copyright-free image database.

[0432] Step 5:

[0433] The server runs a search query against the in-house database to retrieve relevant images, as well as against a royalty-free image database to retrieve relevant images.

[0434] Step 6:

[0435] The server generates a list of retrieved images and filters them based on the request.

[0436] Step 7:

[0437] The server selects the image that best suits the request from the filtered image list.

[0438] Step 8:

[0439] The server passes the selected images to an image fusion AI, which generates a custom image based on the user's theme and elements.

[0440] Step 9:

[0441] The server checks the quality of the generated custom images and requests the AI ​​to reprocess them if necessary.

[0442] Step 10:

[0443] The server puts the custom images through an optimization process, compressing and resizing them to convert them into a user-friendly format.

[0444] Step 11:

[0445] The server creates an optimized custom image as response data in JSON format and sends it to the terminal as an HTTP response.

[0446] Step 12:

[0447] The device receives the HTTP response and extracts the image from the JSON data.

[0448] Step 13:

[0449] The terminal updates the interface to display the extracted custom image to the user.

[0450] Step 14:

[0451] The user can check the displayed custom image and use it to create documents.

[0452] Example 1

[0453] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0454] In conventional document creation, the process of users searching for images appropriate to a theme and customizing them is time-consuming and labor-intensive. Furthermore, there is a lack of automated means for efficiently generating high-quality, appropriate custom images. This has led to a lack of efficiency in document creation and a lack of user satisfaction. In particular, there is a need for a comprehensive system that can search multiple image databases, appropriately filter the images, and further optimize custom images.

[0455] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0456] In this invention, the server includes a means for receiving a request from a user, a means for searching a database based on the request, and a means for extracting related images from the search results, allowing users to easily obtain high-quality custom images and streamlining document creation.

[0457] "User" refers to the entity that uses the system to create materials and generate images.

[0458] A "request" is information that a user requests and sends to the system, and includes specific keywords and themes.

[0459] "Database" refers to the company's in-house database and copyright-free image database, a system for managing a large number of images and related information.

[0460] "Search" refers to the process of finding relevant images within a database based on specific keywords or conditions.

[0461] "Extraction" refers to the act of extracting the image that best suits your purpose from the search results.

[0462] A "generative AI model" refers to an artificial intelligence model that synthesizes multiple images to generate new custom images.

[0463] "Optimization" refers to the process of adjusting the file size and improving the image quality of the generated custom image.

[0464] "Quality check" refers to the process of inspecting the quality of the generated custom images to ensure they meet standards.

[0465] The system of the present invention efficiently generates high-quality custom images required for document creation and provides them to users. In this system, the user uses a terminal to input the image theme and elements and submit a request. This request is sent to a server as JSON-formatted data. The server receives the request and searches an in-house database and a copyright-free image database based on analyzed keywords. Relevant images are selected from the search results and a generative AI model is used to generate custom images. The generated custom images undergo a quality check and optimization process before being provided to the user.

[0466] Specifically, the user enters keywords such as "environmental protection," "nature," or "recycle" into the text box on the device and clicks the send request button. The device converts the entered information into JSON-formatted request data and sends it to the server using the HTTP POST method.

[0467] The server analyzes the received request and performs a search against the database using the extracted keywords as a search query. Specifically, it searches for images using SQL queries or API requests. The server then filters relevant images from the search results and passes them to the image fusion AI to generate a custom image. Generative AI models used include "DALL-E 2." In this process, the AI ​​model is given a prompt sentence such as:

[0468] Generate high-quality custom images with environmental, nature, and recycling themes.

[0469] The server checks the quality of the generated custom image and reprocesses it if necessary. If the quality meets the standards, the server runs an optimization process to compress and resize the image. The final optimized custom image is sent to the device as JSON-formatted response data. The device displays the image to the user based on the received data. The user can use this image to create documents.

[0470] This system significantly streamlines the process of generating custom images required for document creation, allowing users to easily obtain high-quality images.

[0471] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0472] Step 1:

[0473] The user uses the device to enter the theme or element of the image in the text box and clicks the send request button. The entered theme or element (e.g., "environmental protection," "nature," "recycle") is passed to the device as input data. In this case, the input data is text-format keywords. The device converts the entered information into request data in JSON format.

[0474] Input: Keywords entered by the user ("environmental protection," "nature," "recycle")

[0475] Output: Request data in JSON format

[0476] Step 2:

[0477] The terminal sends the generated JSON-formatted request data to the server using the HTTP POST method. This transmission process transmits the user's request to the server.

[0478] Input: Request data in JSON format

[0479] Output: HTTP request (JSON data)

[0480] Step 3:

[0481] The server receives the HTTP request. It parses the received JSON data and extracts keywords. The parsing process stores the keywords in variables.

[0482] Input: HTTP request (JSON data)

[0483] Output: Extracted keywords (variables)

[0484] Step 4:

[0485] The server generates a search query based on the extracted keywords and runs it against the company's internal database and a database of royalty-free images, using SQL queries and API requests.

[0486] Input: Extracted keywords

[0487] Output: Search query

[0488] Step 5:

[0489] The server searches the database using the generated search query to extract relevant images. As a result of the search process, multiple images are obtained.

[0490] Input: Search query

[0491] Output: Search results (list of images)

[0492] Step 6:

[0493] The server filters relevant images from the search results, taking into account criteria such as resolution, file format, and usage conditions, and temporarily stores the filtered images.

[0494] Input: Search results (list of images)

[0495] Output: filtered image

[0496] Step 7:

[0497] The server then passes the filtered image to an image fusion AI to generate a custom image. The generative AI model used includes "DALL-E 2." During this process, the following prompt is input to the AI ​​model:

[0498] Generate high-quality custom images with environmental, nature, and recycling themes.

[0499] Input: filtered image, prompt text

[0500] Output: The generated custom image

[0501] Step 8:

[0502] The server checks the quality of the generated custom images, including checking the resolution, color consistency, and whether there is any unwanted noise. If the quality does not meet the standards, the images are reprocessed.

[0503] Input: Generated custom image

[0504] Output: Quality check result (pass / fail)

[0505] Step 9:

[0506] The server then optimizes the custom images that meet the quality criteria. The optimization process involves compressing and resizing the image, specifically using the JPEG compression algorithm to reduce file size while preserving image quality.

[0507] Input: Custom images that pass quality checks

[0508] Output: Optimized custom images

[0509] Step 10:

[0510] The server sends the optimized custom image to the terminal as a JSON response data. The specific JSON data example is as follows:

[0511] json

[0512] {

[0513] "custom_image_url": "https: / / example.com / generated_image.jpg"

[0514] }

[0515] Input: Optimized custom image

[0516] Output: Response data in JSON format

[0517] Step 11:

[0518] The device receives the HTTP response and extracts the image URL from the JSON data. The device uses this image URL to display a custom image to the user. The user can then view the displayed image and use it to create documents.

[0519] Input: Response data in JSON format

[0520] Output: Custom image displayed to the user

[0521] (Application example 1)

[0522] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0523] Creating advertising images and promotional materials in modern brick-and-mortar stores is often done manually, requiring a great deal of time and effort. In particular, brick-and-mortar stores often need to change their advertising content frequently depending on the season or campaign, and new images must be prepared quickly and appropriately each time. Furthermore, generating high-quality custom images requires specialized knowledge and skills, which can lead to inefficient work. Therefore, there is a need for a system that streamlines this work and allows anyone to easily generate and use high-quality custom images.

[0524] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0525] In this invention, the server includes means for receiving a request from a user, means for searching an in-house database and a copyright-free image database based on the request, means for selecting relevant images from the search results, means for using a generative model to generate a custom image using the selected images, means for optimizing the generated custom image, means for transmitting the optimized custom image to the user, and means for displaying the generated custom image to the user, thereby enabling efficient creation and rapid deployment of advertising images and promotional materials in physical stores.

[0526] The "means for receiving a request from a user" is a function that provides an interface for acquiring information input by a user through a terminal and sending it to a server.

[0527] "A means of searching internal databases and copyright-free image databases based on requests" refers to a search function that finds appropriate images from internal databases and external copyright-free image databases based on keywords or themes requested by users.

[0528] A "means for selecting relevant images" is an algorithm or method for selecting from the search results the images that best match the user's requirements.

[0529] "Means for generating custom images using generative models" refers to a function that uses an advanced generative model (e.g., image fusion AI) to create a unique custom image based on a selected image.

[0530] "Means for optimizing generated custom images" refers to a process for compressing or resizing generated images as necessary while preserving their quality.

[0531] The "means for sending an optimized custom image to a user" is a function for sending an optimized image to a user's terminal in the form of an HTTP response or the like.

[0532] The "display means" is an interface on the user terminal for visually displaying the custom image received from the server.

[0533] The "analysis means" is a function for extracting keywords contained in a request sent by a user and generating a search query based on that information.

[0534] "Quality check means" refers to a process or function that checks the quality of the custom images generated and reprocesses them if necessary.

[0535] The present invention relates to a system that improves the efficiency of creating advertising images and sales promotion materials in brick-and-mortar stores and enables the rapid generation and use of high-quality custom images. Hereinafter, an embodiment of the present invention will be described.

[0536] First, the user uses a smartphone or tablet device to input a specific theme or keyword (e.g., "fresh vegetables," "healthy," "organic," etc.). After inputting, the device generates the input information as request data in JSON format and sends it to the server.

[0537] The server receives the request data from the user and extracts keywords using an analysis tool. The extracted keywords are used to generate a search query, which is then used to search the company's internal database and a copyright-free image database. The server then selects relevant images from the search results and proceeds to the next step.

[0538] The selected images are then passed to a process that uses a generative model to generate a custom image. This process uses image fusion AI to synthesize and process the images into a high-quality custom image. The resulting custom image is then verified by a quality checker and reprocessed if necessary. After quality is assured, an optimization process compresses and resizes the image to convert it into a user-friendly format.

[0539] The optimized custom image is finally sent to the user's device, where the received image can be visually displayed and checked using a display device. This allows physical stores to quickly generate and deploy new advertising images and promotional materials.

[0540] The main hardware and software used to implement this system include: On the server side, Python and Flask are used, and a database search module and an image fusion AI module are installed. On the client side, JavaScript, HTML, and CSS are used to build the user interface.

[0541] As a concrete example, consider a case where a user wants to generate a custom image for a display ad on the theme of "fresh vegetables." In this case, the user enters "fresh vegetables," "healthy," and "organic" into the text box on their device and submits a request. Based on this request, the server searches for relevant images and generates a custom image using image fusion AI. As part of this process, the following prompt sentence is used:

[0542] "Fresh vegetables, healthy, organic"

[0543] The server generates a custom image based on the prompt and delivers it to the user's device. The user can then view the image and use it in display ads.

[0544] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0545] Step 1: User Input

[0546] Users use their smartphones or tablet devices to input the desired advertising theme or elements (e.g., "fresh vegetables," "healthy," "organic") into a request form. This input information constitutes the request data.

[0547] Input: Theme or keyword

[0548] Output: Request data (JSON format)

[0549] Step 2: Submitting the request

[0550] The device sends the request data entered by the user to the server as an HTTP POST request, which includes the user's input information in JSON format.

[0551] Input: Request data (JSON format)

[0552] Output: HTTP POST request

[0553] Step 3: Receiving and Parsing Request Data

[0554] The server receives the HTTP POST request and analyzes the request data. The analysis means extracts keywords from the request data and stores them in variables.

[0555] Input: HTTP POST request (JSON format)

[0556] Output: Extracted keywords (variables)

[0557] Step 4: Find an image

[0558] The server searches the in-house database and copyright-free image database based on the extracted keywords, generates a search query, and executes the image search.

[0559] Input: Extracted keywords

[0560] Output: Search results (image list)

[0561] Step 5: Select related images

[0562] The server selects the most relevant images from the search results using algorithms and filtering methods.

[0563] Input: Search results (image list)

[0564] Output: Selected related images (image list)

[0565] Step 6: Generate custom images

[0566] The server passes the selected images to the image fusion AI to generate a custom image. A generative model is used to create a unique custom image.

[0567] Input: Selected related images (image list)

[0568] Output: The generated custom image

[0569] Step 7: Image optimization

[0570] The server checks the quality of the generated custom images, reprocesses them if necessary, and also performs optimizations such as compressing and resizing the images.

[0571] Input: Generated custom image

[0572] Output: Optimized custom images

[0573] Step 8: Submit a custom image

[0574] The server composes the optimized custom image as response data in JSON format and sends it to the user's device via an HTTP response.

[0575] Input: Optimized custom image

[0576] Output: HTTP response (JSON format)

[0577] Step 9: Displaying Custom Images

[0578] The user's terminal receives the HTTP response, extracts the custom image from the response data, and visually displays the custom image to the user using the terminal's display means.

[0579] Input: HTTP response (JSON format)

[0580] Output: Custom image displayed

[0581] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0582] The present invention relates to a system for generating custom images that recognize and reflect the emotional state of a user, thereby efficiently supporting the creation of materials. An embodiment of the present invention will now be described.

[0583] The user uses the device to input the image theme or element required for creating the document (e.g., "environmental protection," "nature," "recycling," etc.) into a text box. The device then captures the user's facial expressions and voice data through an emotion engine to recognize their emotional state. The recognized emotion data is added to the request data.

[0584] The device creates the input information and emotion data as request data in JSON format and sends it to the server using the HTTP POST method. The server receives the HTTP request and analyzes the request data. The analyzed keywords and emotion data are stored in variables to form a search query. Based on this search query, the server performs a search against its in-house database and a copyright-free image database. The server extracts and filters related images from the search results.

[0585] After filtering, the server selects the image that best fits the request and emotional data, and passes the selected image to the image fusion AI to generate a custom image. For example, if a user requests an image on the theme of "environmental protection," and the emotion engine recognizes the user's positive emotions such as "joy" or "satisfaction," a bright, positive image may be selected. Conversely, if the user expresses emotions such as "tension" or "anxiety," a calm design to ease anxiety may be selected.

[0586] The server checks the quality of the generated custom image and reprocesses it if necessary. It also performs an optimization process to compress and resize the image and convert it into a format that is easy for users to use. The optimized custom image is prepared as the final output data and sent to the terminal as a JSON-formatted response data.

[0587] The device receives the HTTP response and extracts the image from the JSON data. The extracted custom image is displayed to the user. The user can check the displayed custom image and use it to create documents.

[0588] Specific examples

[0589] Scenario: A user wants to create a presentation on the theme of "environmental protection."

[0590] The user uses the device to input keywords such as "environmental protection," "nature," and "recycle" into a text box, and the emotion engine analyzes the user's facial expressions and voice to recognize "joy." The request and emotion data are then sent to the server, which analyzes the received request and extracts the necessary keywords and emotion data.

[0591] The server searches the company's internal database and a copyright-free image database to extract relevant images. From the search results, the server selects images related to "environmental protection" and "joy" and passes them to the image fusion AI to generate a custom image. The generated custom image is then quality checked and optimized, and the final custom image is output.

[0592] The server sends the custom image to the user's device, which then displays it to the user. The user can then view the custom image and add it to their presentation materials. This process allows users to efficiently use high-quality custom images that suit their emotions, dramatically improving presentation creation.

[0593] The foregoing describes one embodiment of the present invention, which enables and streamlines the process of generating custom images that respond to the user's emotional state.

[0594] The processing flow will be explained below.

[0595] Step 1:

[0596] The user uses the device to input the image theme or element required for creating the document (e.g., "environmental protection," "nature," "recycling," etc.) into a text box. In addition, the user's facial expressions and voice data are captured through the emotion engine.

[0597] Step 2:

[0598] The device's emotion engine analyzes the user's emotional state and adds recognized emotion data (e.g., "joy," "satisfaction," etc.) to the request data.

[0599] Step 3:

[0600] The device creates the input information and emotion data as request data in JSON format and sends it to the server using the HTTP POST method.

[0601] Step 4:

[0602] The server receives the HTTP request and analyzes the request data. The analyzed keywords and emotion data are stored in variables.

[0603] Step 5:

[0604] The server constructs the search query and prepares the search request against the in-house database and the copyright-free image database.

[0605] Step 6:

[0606] The server runs a search query against the in-house database to retrieve relevant images, as well as against a royalty-free image database to retrieve relevant images.

[0607] Step 7:

[0608] The server generates a list of retrieved images and filters them based on the request, taking into account the user's emotional data.

[0609] Step 8:

[0610] The server selects the image that best suits the request and emotional data: if the emotional state is "joy," an image with a positive impression is selected.

[0611] Step 9:

[0612] The server passes the selected images to an image fusion AI, which generates a custom image based on the user's theme and emotion.

[0613] Step 10:

[0614] The server checks the quality of the generated custom images and requests the AI ​​to reprocess them if necessary.

[0615] Step 11:

[0616] The server puts the custom images through an optimization process, compressing and resizing them to convert them into a user-friendly format.

[0617] Step 12:

[0618] The server creates an optimized custom image as response data in JSON format and sends it to the terminal as an HTTP response.

[0619] Step 13:

[0620] The device receives the HTTP response and extracts the image from the JSON data.

[0621] Step 14:

[0622] The terminal updates the interface to display the extracted custom image to the user.

[0623] Step 15:

[0624] The user can check the displayed custom image and use it to create documents.

[0625] Example 2

[0626] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0627] Conventional document creation systems have difficulty generating custom images that reflect the user's emotional state, which has resulted in inefficient document creation. Furthermore, image selection and image optimization according to emotions are insufficient, making it impossible to efficiently provide high-quality custom images that reflect the user's intentions.

[0628] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a request from a user, means for searching an internal database and a copyright-free database, means for capturing the user's facial expression and voice data using an emotion engine and recognizing the user's emotional state, means for adding emotion data to request data, creating JSON-formatted request data, and transmitting the request data to the server, means for selecting related images from the search results and generating a custom image using the selected image, means for optimizing the generated custom image, and means for transmitting the optimized custom image to the user. This makes it possible to efficiently generate and provide high-quality custom images suited to the user's emotional state.

[0629] "User" refers to a person who uses the system to create materials.

[0630] A "request" refers to information requested by a user to input a theme or element and send it to the system.

[0631] "Terminal" refers to a device (such as a PC, smartphone, or tablet) that a user uses to access the system.

[0632] "Server" refers to a computer system that performs the main processing of the system and stores data.

[0633] An "emotion engine" refers to an algorithm or software that analyzes a user's facial expressions and voice data to recognize their emotional state.

[0634] "JSON format" stands for JavaScript Object Notation and refers to a lightweight data exchange format.

[0635] "Internal database" refers to a database that stores various data (such as image data) stored within the system.

[0636] A "copyright-free database" refers to a database that provides data (mainly image data) that can be used without copyright restrictions.

[0637] A "search query" refers to a series of keywords or expressions constructed to conduct a database search.

[0638] "Image selection" refers to the process of selecting appropriate images from search results.

[0639] "Custom Image" refers to a personalized image generated based on a user's request and emotional state.

[0640] "Optimization" refers to the process of compressing, resizing, or otherwise improving an image into a more usable format.

[0641] "Quality Check" refers to the process of checking the quality of the custom images generated and reprocessing them if necessary.

[0642] "HTTP protocol" is an abbreviation for Hypertext Transfer Protocol, and refers to a communication protocol for sending and receiving data over the Internet.

[0643] This invention relates to a system that efficiently supports document creation by recognizing the user's emotional state and generating custom images that reflect that state. This system operates mainly through three main components: the user, the terminal, and the server, and each processing step is clearly separated.

[0644] User input of themes and elements

[0645] The user uses the terminal to input the themes and elements required for creating materials into a text box, for example, keywords such as "environmental protection," "nature," and "recycling."

[0646] Capturing Emotional Data

[0647] The device uses a camera and microphone to capture the user's facial expressions and voice data. Specifically, it uses an emotion engine (e.g., a general face recognition API or voice recognition API) to recognize the user's emotional state. Examples of emotion engines that can be used include Microsoft Azure Face API and Google Cloud's Speech-to-Text.

[0648] Creating and sending request data

[0649] Based on the recognized emotion data and the themes and elements entered by the user, the device creates request data in JSON format, which is then sent to the server using the HTTP POST method.

[0650] Receiving and parsing request data

[0651] When the server receives an HTTP request, it analyzes the request data. This analysis involves extracting keywords and sentiment data from the request data and storing them in variables. A search query is then constructed based on these variables.

[0652] Image search

[0653] Based on the search query, the server performs a search in its internal database and royalty-free databases (e.g., Pexels, Pixabay), extracts relevant images from the search results, and filters them.

[0654] Custom Image Generation

[0655] The server passes the selected images after filtering to an image fusion AI (e.g., a general image generation AI) to generate the most suitable custom image. Examples of image fusion AI that can be used include OpenAI's DALL-E and Google's Imagen. For example, if the user's emotion of "joy" is recognized in relation to the theme of "environmental protection," a bright and positive image will be generated.

[0656] Image quality check and optimization

[0657] The server checks the quality of the custom images generated, reprocesses them if necessary, and puts them through an optimization process to compress, resize, and convert them into a user-friendly format.

[0658] Sending optimized images

[0659] The optimized custom image is prepared as the final output data and sent to the device as an HTTP response.

[0660] Receive and display custom images

[0661] The device receives the HTTP response and extracts the custom image from the JSON data, which is then displayed to the user.

[0662] User Use

[0663] The user can check the displayed custom image and use it in presentations and document creation, thereby enabling the user to efficiently obtain high-quality custom images and improve the quality and efficiency of document creation.

[0664] Specific examples

[0665] For example, if a user is creating a presentation on the topic of "environmental protection," they might use the following prompt:

[0666] "Generate custom images that reflect the sentiments of environmentalism and joy."

[0667] This system can streamline and improve the quality of document creation by generating custom images that reflect the user's emotional state.

[0668] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0669] Step 1:

[0670] The user inputs the image theme or element (e.g., "environmental protection," "nature," "recycle," etc.) required for creating the document into a text box on the terminal. This becomes the input data. The terminal prepares to send this theme or element to the server as character string data.

[0671] Step 2:

[0672] The device receives user input and simultaneously captures the user's facial expressions and voice using a camera and microphone. Specifically, it uses an emotion engine (e.g., Microsoft Azure Face API or Google Cloud's Speech-to-Text) to recognize the user's emotional state and output it as emotion data. This emotion data must be included in the request data.

[0673] Step 3:

[0674] The device combines the input themes and elements (string data) with the recognized emotion data to create request data in JSON format, which is then sent to the server using the HTTP POST method.

[0675] Step 4:

[0676] The server receives HTTP requests and analyzes the received request data. Specifically, it extracts keywords (themes and elements) and sentiment data from the request data, analyzes them, and constructs a search query. The analyzed data becomes input for searching the internal database and copyright-free databases.

[0677] Step 5:

[0678] The server performs a search on its internal database and royalty-free databases (e.g., Pexels, Pixabay) based on the constructed search query. As a result, relevant images are extracted. These extracted images become the input data for the next step.

[0679] Step 6:

[0680] The server selects from the extracted images the one that is most relevant to the request and emotional data, based on the image's metadata and text information. The selected image serves as input data for generating a custom image in the next step.

[0681] Step 7:

[0682] The server then passes the selected images to an image fusion AI (e.g., OpenAI's DALL-E or Google's Imagen) to generate a custom image. This process uses image processing algorithms to output an image that reflects the user's emotional state.

[0683] Step 8:

[0684] The server checks the quality of the generated custom image and reprocesses it if necessary. Specifically, it checks the image resolution and color, and if there are any problems, it passes it back to the image fusion AI for correction. At this stage, the quality of the final custom image is guaranteed.

[0685] Step 9:

[0686] The server optimizes images that pass the quality check by compressing and resizing them and converting them into a format that is easier for users to use, such as JPEG or PNG.

[0687] Step 10:

[0688] The server sends the optimized custom image to the device as response data in JSON format, generates an HTTP response as the response data, and returns it to the device.

[0689] Step 11:

[0690] The device receives the HTTP response, extracts the custom image from the JSON data, and displays the extracted custom image to the user.

[0691] Step 12:

[0692] The user can check the custom image displayed on the terminal and use it in creating materials. The user can efficiently obtain the generated custom image, thereby improving the quality and efficiency of presentations and material creation.

[0693] Above are the detailed processing steps of the system, through which users can efficiently obtain high-quality custom images that reflect their emotional state.

[0694] (Application example 2)

[0695] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0696] Conventional document creation support systems were unable to recognize the user's emotional state and generate custom images that matched those emotions, resulting in the provision of images that did not match the user's intentions. Furthermore, content recommendations did not take the user's emotions into consideration, making it difficult to provide optimal content. This resulted in users wasting time creating documents and selecting content, resulting in reduced efficiency.

[0697] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0698] In this invention, the server includes means for receiving requests from users, means for searching an in-house database and a copyright-free image database, means for selecting relevant images, means for recognizing the user's emotional state and customizing content based on that emotion, means for optimizing the generated custom images, and means for transmitting the optimized custom images to the user. This enables the recommendation of high-quality custom images and content according to the user's emotional state, thereby realizing more efficient document creation and content selection.

[0699] "Means for receiving requests from a user" refers to a device or software that has the function of receiving input or requests from a user and transmitting them to the system.

[0700] "Means for searching a database" refers to devices or software that have the function of searching and extracting relevant information from in-house databases and copyright-free image databases, etc., based on specified keywords or conditions.

[0701] The "means for selecting relevant images" refers to a device or software that has the function of selecting from the search results the image that best suits the user's request or emotional state.

[0702] The "means for generating a custom image" refers to a device or software that has the function of creating a customized image based on a selected image that matches the user's needs and emotions.

[0703] The "means for recognizing the emotional state of the user" refers to a device or software that has the function of analyzing the user's facial expressions and voice data and determining the user's emotional state.

[0704] A "means for customizing content" is a device or software that has the function of adjusting content according to the recognized emotional state of the user and changing it into an appropriate format or content.

[0705] A "means for optimizing a custom image" is any device or software capable of compressing or resizing a generated custom image to improve its quality.

[0706] The "means for transmitting a custom image to a user" refers to a device or software that has the function of transferring an optimized custom image to a user's terminal and displaying it.

[0707] "Emotion recognition is performed based on facial expression and voice data" refers to the process of determining the emotional state of a user based on their facial expression and voice.

[0708] The system of the present invention has the function of generating custom images that recognize and reflect the emotional state of a user, and efficiently supports document creation and content recommendation. Specific embodiments for carrying out the present invention will be described below.

[0709] 1. System Configuration

[0710] The system is mainly composed of a server and a user terminal, and operates in the following steps.

[0711] 1. A means of receiving requests from users

[0712] The user inputs a keyword through an interface installed on the user terminal.

[0713] For example, by using an application on a smartphone or tablet.

[0714] 2. A means of recording the user's emotional state

[0715] The camera and microphone on the user's device are used to capture facial expressions and voice.

[0716] The software used is the open source OpenCV library and Microsoft's Azure Face API as a facial recognition tool.

[0717] 3. Data submission and analysis

[0718] The device sends the user's input keywords and the recognized emotional state to the server as request data in JSON format.

[0719] The server uses the Python requests library to receive and analyze data via the HTTP POST method.

[0720] 4. Image Search and Selection

[0721] The server searches its internal database and its copyright-free image database to extract relevant images.

[0722] The system selects the most suitable image from the search results based on the user's request and emotional state.

[0723] 5. Creating Custom Images

[0724] Based on the selected image, a custom image is generated according to the user's emotion, using image fusion AI.

[0725] For example, if the user expresses the emotion "joy," an image with a bright and positive design is generated.

[0726] 6. Image Optimization

[0727] Quality check the generated custom images and reprocess them if necessary.

[0728] Compress and resize images to convert them into a user-friendly format.

[0729] 7. Sending images

[0730] The optimized custom image is sent to the user's device and displayed to the user, who can use it to create documents and recommend content.

[0731] 2. Data processing and calculation

[0732] Emotion recognition:

[0733] Hardware used: Webcam and microphone (a common brand is the Logitech Brio camera)

[0734] Software used: OpenCV library, Azure Face API

[0735] Processing: Video and audio data is captured, and the emotion recognition engine analyzes facial expressions and voice to generate emotion data.

[0736] Data transmission and analysis:

[0737] Software used: requests library

[0738] Processing: User input and emotion data are packaged in JSON format and sent to the server via HTTP POST. The server then parses the received data using a Python script.

[0739] Image selection and generation:

[0740] Software used: In-house search engine, image fusion AI

[0741] Processing: Search the image database based on the search query to select suitable images. Based on the selected images, the image fusion AI generates a custom image.

[0742] 3. Examples and prompts

[0743] Scenario: A user is looking for content on the theme "environmental documentaries."

[0744] The user opens the app and enters the keyword "environmental documentary." The app uses the camera and microphone to recognize the user's emotion of "excitement." This data is then sent to the server, which then recommends related documentaries. For example, documentaries such as "Our Planet" and "Blue Planet" are recommended.

[0745] Prompt Sentence Examples

[0746] "Given the given keyword 'environment,' generate server request data to recommend content that corresponds to the user's emotions."

[0747] This configuration provides appropriate images and content that take into consideration the user's emotions, improving the efficiency of creating materials and viewing content.

[0748] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0749] Step 1:

[0750] A user launches the smartphone app and enters a keyword into the text box. This keyword indicates the theme for which they wish to create materials or have content recommended. The user enters a keyword such as "environmental documentary." The keyword is obtained as input data.

[0751] Step 2:

[0752] The device uses a camera and microphone to capture the user's facial expressions and voice, and the emotion recognition engine analyzes this data. Specifically, it uses the OpenCV library and Azure Face API to identify emotions from facial expression and voice data. Facial expression images and voice data are obtained as input data, and emotion data is generated based on these.

[0753] Step 3:

[0754] The device packages the user's input keywords and recognized emotional data in JSON format, which includes information on the keyword and emotional state. This package is sent to the server using the HTTP POST method. Keywords and emotional data are obtained as input data, and JSON-formatted request data is generated as output data.

[0755] Step 4:

[0756] The server parses the received JSON request data and extracts keywords and sentiment data. The data is received using the Python requests library, and the parsing engine breaks down the data to extract keywords and sentiment. The JSON request data is obtained as input data, and keywords and sentiment data are provided as output data.

[0757] Step 5:

[0758] The server searches the company's in-house database and copyright-free image database based on the extracted keywords and emotion data, thereby extracting related images. Keywords and emotion data are obtained as input data, and a list of related images is generated as output data.

[0759] Step 6:

[0760] The server selects from the search results the image that best suits the user's request and emotional state. The search engine evaluates and ranks the image list obtained based on emotional data. The list of related images and emotional data are obtained as input data, and the optimal image is selected as output data.

[0761] Step 7:

[0762] The server uses image fusion AI to generate a custom image using the optimal image. For example, if the user expresses the emotion of "joy," it generates an image with a bright, positive design. The selected image and emotion data are obtained as input data, and a custom image is generated as output data.

[0763] Step 8:

[0764] The server checks the quality of the custom image and reprocesses it if necessary. Specifically, it checks the clarity of the image and the appropriateness of the layout, and regenerates it if the quality is low. The custom image is taken as input data, and a quality-checked custom image is generated as output data.

[0765] Step 9:

[0766] The server compresses and resizes your custom images to produce optimized images in a format that is easily consumed by users. The input is a quality-checked custom image, and the output is an optimized custom image.

[0767] Step 10:

[0768] The server packages the optimized custom image in JSON format and sends it to the device. The device displays the received custom image, and the user uses the image for document creation and content recommendation. The optimized custom image is obtained as input data, and the custom image displayed on the user's device is generated as output data.

[0769] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0770] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0771] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0772] [Third embodiment]

[0773] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0774] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0775] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0776] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0777] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0778] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0779] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0780] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0781] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0782] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0783] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0784] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0785] The present invention relates to a system for efficiently generating custom images for document creation and providing them to users. An embodiment of the present invention will be described below.

[0786] The user uses the device to input the image theme or element required for creating the document (e.g., "environmental protection," "nature," "recycle," etc.) into the text box. After inputting, the user clicks the request send button, and the device creates the input information as request data in JSON format and sends it to the server.

[0787] The server receives the HTTP request and analyzes the request data. The analyzed keywords are stored in variables to form a search query. Based on this search query, the server performs a search against the company's internal database and a copyright-free image database. The server extracts and filters relevant images from the search results.

[0788] After filtering, the server selects the most suitable image for the request and passes it to the image fusion AI to generate a custom image. The server checks the quality of the generated custom image and reprocesses it if necessary. The server also performs an optimization process to compress and resize the image, converting it into a user-friendly format.

[0789] The optimized custom image is prepared as the final output data, and the server sends it to the terminal as a JSON-formatted response data. The terminal receives the HTTP response, extracts the image from the JSON data, and displays it to the user. The user can then check the displayed custom image and use it to create documents.

[0790] Specific examples

[0791] Scenario: A user wants to create a presentation on the theme of "environmental protection."

[0792] The user uses the device to input keywords such as "environmental protection," "nature," and "recycle" into a text box and send a request. The device then sends the request to the server, which then analyzes the received request and extracts the necessary keywords.

[0793] The server searches the company's internal database and a copyright-free image database to extract relevant images. From the search results, the server selects images related to "environmental protection," "nature," and "recycling," and passes them to the image fusion AI to generate a custom image. The generated custom image is then quality checked and optimized, and the final custom image is output.

[0794] The server sends the custom image to the user's device, which then displays it. The user can then view the custom image and add it to their presentation. This process allows users to efficiently use high-quality custom images, dramatically improving presentation creation.

[0795] This completes one embodiment of the present invention, which automates and streamlines the process of generating custom images required for creating materials.

[0796] The processing flow will be explained below.

[0797] Step 1:

[0798] The user uses the device to input the theme or element of the image they want to create (e.g., "environmental protection," "nature," "recycling," etc.) into the text box, and then clicks the submit request button.

[0799] Step 2:

[0800] The terminal creates the input information as request data in JSON format and sends it to the server using the HTTP POST method.

[0801] Step 3:

[0802] The server receives the HTTP request and parses the request data. The parsed keywords are stored in variables.

[0803] Step 4:

[0804] The server constructs the search query and prepares the search request against the in-house database and the copyright-free image database.

[0805] Step 5:

[0806] The server runs a search query against the in-house database to retrieve relevant images, as well as against a royalty-free image database to retrieve relevant images.

[0807] Step 6:

[0808] The server generates a list of retrieved images and filters them based on the request.

[0809] Step 7:

[0810] The server selects the image that best suits the request from the filtered image list.

[0811] Step 8:

[0812] The server passes the selected images to an image fusion AI, which generates a custom image based on the user's theme and elements.

[0813] Step 9:

[0814] The server checks the quality of the generated custom images and requests the AI ​​to reprocess them if necessary.

[0815] Step 10:

[0816] The server puts the custom images through an optimization process, compressing and resizing them to convert them into a user-friendly format.

[0817] Step 11:

[0818] The server creates an optimized custom image as response data in JSON format and sends it to the terminal as an HTTP response.

[0819] Step 12:

[0820] The device receives the HTTP response and extracts the image from the JSON data.

[0821] Step 13:

[0822] The terminal updates the interface to display the extracted custom image to the user.

[0823] Step 14:

[0824] The user can check the displayed custom image and use it to create documents.

[0825] Example 1

[0826] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0827] In conventional document creation, the process of users searching for images appropriate to a theme and customizing them is time-consuming and labor-intensive. Furthermore, there is a lack of automated means for efficiently generating high-quality, appropriate custom images. This has led to a lack of efficiency in document creation and a lack of user satisfaction. In particular, there is a need for a comprehensive system that can search multiple image databases, appropriately filter the images, and further optimize custom images.

[0828] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0829] In this invention, the server includes a means for receiving a request from a user, a means for searching a database based on the request, and a means for extracting related images from the search results, allowing users to easily obtain high-quality custom images and streamlining document creation.

[0830] "User" refers to the entity that uses the system to create materials and generate images.

[0831] A "request" is information that a user requests and sends to the system, and includes specific keywords and themes.

[0832] "Database" refers to the company's in-house database and copyright-free image database, a system for managing a large number of images and related information.

[0833] "Search" refers to the process of finding relevant images within a database based on specific keywords or conditions.

[0834] "Extraction" refers to the act of extracting the image that best suits your purpose from the search results.

[0835] A "generative AI model" refers to an artificial intelligence model that synthesizes multiple images to generate new custom images.

[0836] "Optimization" refers to the process of adjusting the file size and improving the image quality of the generated custom image.

[0837] "Quality check" refers to the process of inspecting the quality of the generated custom images to ensure they meet standards.

[0838] The system of the present invention efficiently generates high-quality custom images required for document creation and provides them to users. In this system, the user uses a terminal to input the image theme and elements and submit a request. This request is sent to a server as JSON-formatted data. The server receives the request and searches an in-house database and a copyright-free image database based on analyzed keywords. Relevant images are selected from the search results and a generative AI model is used to generate custom images. The generated custom images undergo a quality check and optimization process before being provided to the user.

[0839] Specifically, the user enters keywords such as "environmental protection," "nature," or "recycle" into the text box on the device and clicks the send request button. The device converts the entered information into JSON-formatted request data and sends it to the server using the HTTP POST method.

[0840] The server analyzes the received request and performs a search against the database using the extracted keywords as a search query. Specifically, it searches for images using SQL queries or API requests. The server then filters relevant images from the search results and passes them to the image fusion AI to generate a custom image. Generative AI models used include "DALL-E 2." In this process, the AI ​​model is given a prompt sentence such as:

[0841] Generate high-quality custom images with environmental, nature, and recycling themes.

[0842] The server checks the quality of the generated custom image and reprocesses it if necessary. If the quality meets the standards, the server runs an optimization process to compress and resize the image. The final optimized custom image is sent to the device as JSON-formatted response data. The device displays the image to the user based on the received data. The user can use this image to create documents.

[0843] This system significantly streamlines the process of generating custom images required for document creation, allowing users to easily obtain high-quality images.

[0844] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0845] Step 1:

[0846] The user uses the device to enter the theme or element of the image in the text box and clicks the send request button. The entered theme or element (e.g., "environmental protection," "nature," "recycle") is passed to the device as input data. In this case, the input data is text-format keywords. The device converts the entered information into request data in JSON format.

[0847] Input: Keywords entered by the user ("environmental protection," "nature," "recycle")

[0848] Output: Request data in JSON format

[0849] Step 2:

[0850] The terminal sends the generated JSON-formatted request data to the server using the HTTP POST method. This transmission process transmits the user's request to the server.

[0851] Input: Request data in JSON format

[0852] Output: HTTP request (JSON data)

[0853] Step 3:

[0854] The server receives the HTTP request. It parses the received JSON data and extracts keywords. The parsing process stores the keywords in variables.

[0855] Input: HTTP request (JSON data)

[0856] Output: Extracted keywords (variables)

[0857] Step 4:

[0858] The server generates a search query based on the extracted keywords and runs it against the company's internal database and a database of royalty-free images, using SQL queries and API requests.

[0859] Input: Extracted keywords

[0860] Output: Search query

[0861] Step 5:

[0862] The server searches the database using the generated search query to extract relevant images. As a result of the search process, multiple images are obtained.

[0863] Input: Search query

[0864] Output: Search results (list of images)

[0865] Step 6:

[0866] The server filters relevant images from the search results, taking into account criteria such as resolution, file format, and usage conditions, and temporarily stores the filtered images.

[0867] Input: Search results (list of images)

[0868] Output: filtered image

[0869] Step 7:

[0870] The server then passes the filtered image to an image fusion AI to generate a custom image. The generative AI model used includes "DALL-E 2." During this process, the following prompt is input to the AI ​​model:

[0871] Generate high-quality custom images with environmental, nature, and recycling themes.

[0872] Input: filtered image, prompt text

[0873] Output: The generated custom image

[0874] Step 8:

[0875] The server checks the quality of the generated custom images, including checking the resolution, color consistency, and whether there is any unwanted noise. If the quality does not meet the standards, the images are reprocessed.

[0876] Input: Generated custom image

[0877] Output: Quality check result (pass / fail)

[0878] Step 9:

[0879] The server then optimizes the custom images that meet the quality criteria. The optimization process involves compressing and resizing the image, specifically using the JPEG compression algorithm to reduce file size while preserving image quality.

[0880] Input: Custom images that pass quality checks

[0881] Output: Optimized custom images

[0882] Step 10:

[0883] The server sends the optimized custom image to the terminal as a JSON response data. The specific JSON data example is as follows:

[0884] json

[0885] {

[0886] "custom_image_url": "https: / / example.com / generated_image.jpg"

[0887] }

[0888] Input: Optimized custom image

[0889] Output: Response data in JSON format

[0890] Step 11:

[0891] The device receives the HTTP response and extracts the image URL from the JSON data. The device uses this image URL to display a custom image to the user. The user can then view the displayed image and use it to create documents.

[0892] Input: Response data in JSON format

[0893] Output: Custom image displayed to the user

[0894] (Application example 1)

[0895] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0896] Creating advertising images and promotional materials in modern brick-and-mortar stores is often done manually, requiring a great deal of time and effort. In particular, brick-and-mortar stores often need to change their advertising content frequently depending on the season or campaign, and new images must be prepared quickly and appropriately each time. Furthermore, generating high-quality custom images requires specialized knowledge and skills, which can lead to inefficient work. Therefore, there is a need for a system that streamlines this work and allows anyone to easily generate and use high-quality custom images.

[0897] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0898] In this invention, the server includes means for receiving a request from a user, means for searching an in-house database and a copyright-free image database based on the request, means for selecting relevant images from the search results, means for using a generative model to generate a custom image using the selected images, means for optimizing the generated custom image, means for transmitting the optimized custom image to the user, and means for displaying the generated custom image to the user, thereby enabling efficient creation and rapid deployment of advertising images and promotional materials in physical stores.

[0899] The "means for receiving a request from a user" is a function that provides an interface for acquiring information input by a user through a terminal and sending it to a server.

[0900] "A means of searching internal databases and copyright-free image databases based on requests" refers to a search function that finds appropriate images from internal databases and external copyright-free image databases based on keywords or themes requested by users.

[0901] A "means for selecting relevant images" is an algorithm or method for selecting from the search results the images that best match the user's requirements.

[0902] "Means for generating custom images using generative models" refers to a function that uses an advanced generative model (e.g., image fusion AI) to create a unique custom image based on a selected image.

[0903] "Means for optimizing generated custom images" refers to a process for compressing or resizing generated images as necessary while preserving their quality.

[0904] The "means for sending an optimized custom image to a user" is a function for sending an optimized image to a user's terminal in the form of an HTTP response or the like.

[0905] The "display means" is an interface on the user terminal for visually displaying the custom image received from the server.

[0906] The "analysis means" is a function for extracting keywords contained in a request sent by a user and generating a search query based on that information.

[0907] "Quality check means" refers to a process or function that checks the quality of the custom images generated and reprocesses them if necessary.

[0908] The present invention relates to a system that improves the efficiency of creating advertising images and sales promotion materials in brick-and-mortar stores and enables the rapid generation and use of high-quality custom images. Hereinafter, an embodiment of the present invention will be described.

[0909] First, the user uses a smartphone or tablet device to input a specific theme or keyword (e.g., "fresh vegetables," "healthy," "organic," etc.). After inputting, the device generates the input information as request data in JSON format and sends it to the server.

[0910] The server receives the request data from the user and extracts keywords using an analysis tool. The extracted keywords are used to generate a search query, which is then used to search the company's internal database and a copyright-free image database. The server then selects relevant images from the search results and proceeds to the next step.

[0911] The selected images are then passed to a process that uses a generative model to generate a custom image. This process uses image fusion AI to synthesize and process the images into a high-quality custom image. The resulting custom image is then verified by a quality checker and reprocessed if necessary. After quality is assured, an optimization process compresses and resizes the image to convert it into a user-friendly format.

[0912] The optimized custom image is finally sent to the user's device, where the received image can be visually displayed and checked using a display device. This allows physical stores to quickly generate and deploy new advertising images and promotional materials.

[0913] The main hardware and software used to implement this system include: On the server side, Python and Flask are used, and a database search module and an image fusion AI module are installed. On the client side, JavaScript, HTML, and CSS are used to build the user interface.

[0914] As a concrete example, consider a case where a user wants to generate a custom image for a display ad on the theme of "fresh vegetables." In this case, the user enters "fresh vegetables," "healthy," and "organic" into the text box on their device and submits a request. Based on this request, the server searches for relevant images and generates a custom image using image fusion AI. As part of this process, the following prompt sentence is used:

[0915] "Fresh vegetables, healthy, organic"

[0916] The server generates a custom image based on the prompt and delivers it to the user's device. The user can then view the image and use it in display ads.

[0917] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0918] Step 1: User Input

[0919] Users use their smartphones or tablet devices to input the desired advertising theme or elements (e.g., "fresh vegetables," "healthy," "organic") into a request form. This input information constitutes the request data.

[0920] Input: Theme or keyword

[0921] Output: Request data (JSON format)

[0922] Step 2: Submitting the request

[0923] The device sends the request data entered by the user to the server as an HTTP POST request, which includes the user's input information in JSON format.

[0924] Input: Request data (JSON format)

[0925] Output: HTTP POST request

[0926] Step 3: Receiving and Parsing Request Data

[0927] The server receives the HTTP POST request and analyzes the request data. The analysis means extracts keywords from the request data and stores them in variables.

[0928] Input: HTTP POST request (JSON format)

[0929] Output: Extracted keywords (variables)

[0930] Step 4: Find an image

[0931] The server searches the in-house database and copyright-free image database based on the extracted keywords, generates a search query, and executes the image search.

[0932] Input: Extracted keywords

[0933] Output: Search results (image list)

[0934] Step 5: Select related images

[0935] The server selects the most relevant images from the search results using algorithms and filtering methods.

[0936] Input: Search results (image list)

[0937] Output: Selected related images (image list)

[0938] Step 6: Generate custom images

[0939] The server passes the selected images to the image fusion AI to generate a custom image. A generative model is used to create a unique custom image.

[0940] Input: Selected related images (image list)

[0941] Output: The generated custom image

[0942] Step 7: Image optimization

[0943] The server checks the quality of the generated custom images, reprocesses them if necessary, and also performs optimizations such as compressing and resizing the images.

[0944] Input: Generated custom image

[0945] Output: Optimized custom images

[0946] Step 8: Submit a custom image

[0947] The server composes the optimized custom image as response data in JSON format and sends it to the user's device via an HTTP response.

[0948] Input: Optimized custom image

[0949] Output: HTTP response (JSON format)

[0950] Step 9: Displaying Custom Images

[0951] The user's terminal receives the HTTP response, extracts the custom image from the response data, and visually displays the custom image to the user using the terminal's display means.

[0952] Input: HTTP response (JSON format)

[0953] Output: Custom image displayed

[0954] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0955] The present invention relates to a system for generating custom images that recognize and reflect the emotional state of a user, thereby efficiently supporting the creation of materials. An embodiment of the present invention will now be described.

[0956] The user uses the device to input the image theme or element required for creating the document (e.g., "environmental protection," "nature," "recycling," etc.) into a text box. The device then captures the user's facial expressions and voice data through an emotion engine to recognize their emotional state. The recognized emotion data is added to the request data.

[0957] The device creates the input information and emotion data as request data in JSON format and sends it to the server using the HTTP POST method. The server receives the HTTP request and analyzes the request data. The analyzed keywords and emotion data are stored in variables to form a search query. Based on this search query, the server performs a search against its in-house database and a copyright-free image database. The server extracts and filters related images from the search results.

[0958] After filtering, the server selects the image that best fits the request and emotional data, and passes the selected image to the image fusion AI to generate a custom image. For example, if a user requests an image on the theme of "environmental protection," and the emotion engine recognizes the user's positive emotions such as "joy" or "satisfaction," a bright, positive image may be selected. Conversely, if the user expresses emotions such as "tension" or "anxiety," a calm design to ease anxiety may be selected.

[0959] The server checks the quality of the generated custom image and reprocesses it if necessary. It also performs an optimization process to compress and resize the image and convert it into a format that is easy for users to use. The optimized custom image is prepared as the final output data and sent to the terminal as a JSON-formatted response data.

[0960] The device receives the HTTP response and extracts the image from the JSON data. The extracted custom image is displayed to the user. The user can check the displayed custom image and use it to create documents.

[0961] Specific examples

[0962] Scenario: A user wants to create a presentation on the theme of "environmental protection."

[0963] The user uses the device to input keywords such as "environmental protection," "nature," and "recycle" into a text box, and the emotion engine analyzes the user's facial expressions and voice to recognize "joy." The request and emotion data are then sent to the server, which analyzes the received request and extracts the necessary keywords and emotion data.

[0964] The server searches the company's internal database and a copyright-free image database to extract relevant images. From the search results, the server selects images related to "environmental protection" and "joy" and passes them to the image fusion AI to generate a custom image. The generated custom image is then quality checked and optimized, and the final custom image is output.

[0965] The server sends the custom image to the user's device, which then displays it to the user. The user can then view the custom image and add it to their presentation materials. This process allows users to efficiently use high-quality custom images that suit their emotions, dramatically improving presentation creation.

[0966] The foregoing describes one embodiment of the present invention, which enables and streamlines the process of generating custom images that respond to the user's emotional state.

[0967] The processing flow will be explained below.

[0968] Step 1:

[0969] The user uses the device to input the image theme or element required for creating the document (e.g., "environmental protection," "nature," "recycling," etc.) into a text box. In addition, the user's facial expressions and voice data are captured through the emotion engine.

[0970] Step 2:

[0971] The device's emotion engine analyzes the user's emotional state and adds recognized emotion data (e.g., "joy," "satisfaction," etc.) to the request data.

[0972] Step 3:

[0973] The device creates the input information and emotion data as request data in JSON format and sends it to the server using the HTTP POST method.

[0974] Step 4:

[0975] The server receives the HTTP request and analyzes the request data. The analyzed keywords and emotion data are stored in variables.

[0976] Step 5:

[0977] The server constructs the search query and prepares the search request against the in-house database and the copyright-free image database.

[0978] Step 6:

[0979] The server runs a search query against the in-house database to retrieve relevant images, as well as against a royalty-free image database to retrieve relevant images.

[0980] Step 7:

[0981] The server generates a list of retrieved images and filters them based on the request, taking into account the user's emotional data.

[0982] Step 8:

[0983] The server selects the image that best suits the request and emotional data: if the emotional state is "joy," an image with a positive impression is selected.

[0984] Step 9:

[0985] The server passes the selected images to an image fusion AI, which generates a custom image based on the user's theme and emotion.

[0986] Step 10:

[0987] The server checks the quality of the generated custom images and requests the AI ​​to reprocess them if necessary.

[0988] Step 11:

[0989] The server puts the custom images through an optimization process, compressing and resizing them to convert them into a user-friendly format.

[0990] Step 12:

[0991] The server creates an optimized custom image as response data in JSON format and sends it to the terminal as an HTTP response.

[0992] Step 13:

[0993] The device receives the HTTP response and extracts the image from the JSON data.

[0994] Step 14:

[0995] The terminal updates the interface to display the extracted custom image to the user.

[0996] Step 15:

[0997] The user can check the displayed custom image and use it to create documents.

[0998] Example 2

[0999] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1000] Conventional document creation systems have difficulty generating custom images that reflect the user's emotional state, which has resulted in inefficient document creation. Furthermore, image selection and image optimization according to emotions are insufficient, making it impossible to efficiently provide high-quality custom images that reflect the user's intentions.

[1001] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a request from a user, means for searching an internal database and a copyright-free database, means for capturing the user's facial expression and voice data using an emotion engine and recognizing the user's emotional state, means for adding emotion data to request data, creating JSON-formatted request data, and transmitting the request data to the server, means for selecting related images from the search results and generating a custom image using the selected image, means for optimizing the generated custom image, and means for transmitting the optimized custom image to the user. This makes it possible to efficiently generate and provide high-quality custom images suited to the user's emotional state.

[1002] "User" refers to a person who uses the system to create materials.

[1003] A "request" refers to information requested by a user to input a theme or element and send it to the system.

[1004] "Terminal" refers to a device (such as a PC, smartphone, or tablet) that a user uses to access the system.

[1005] "Server" refers to a computer system that performs the main processing of the system and stores data.

[1006] An "emotion engine" refers to an algorithm or software that analyzes a user's facial expressions and voice data to recognize their emotional state.

[1007] "JSON format" stands for JavaScript Object Notation and refers to a lightweight data exchange format.

[1008] "Internal database" refers to a database that stores various data (such as image data) stored within the system.

[1009] A "copyright-free database" refers to a database that provides data (mainly image data) that can be used without copyright restrictions.

[1010] A "search query" refers to a series of keywords or expressions constructed to conduct a database search.

[1011] "Image selection" refers to the process of selecting appropriate images from search results.

[1012] "Custom Image" refers to a personalized image generated based on a user's request and emotional state.

[1013] "Optimization" refers to the process of compressing, resizing, or otherwise improving an image into a more usable format.

[1014] "Quality Check" refers to the process of checking the quality of the custom images generated and reprocessing them if necessary.

[1015] "HTTP protocol" is an abbreviation for Hypertext Transfer Protocol, and refers to a communication protocol for sending and receiving data over the Internet.

[1016] This invention relates to a system that efficiently supports document creation by recognizing the user's emotional state and generating custom images that reflect that state. This system operates mainly through three main components: the user, the terminal, and the server, and each processing step is clearly separated.

[1017] User input of themes and elements

[1018] The user uses the terminal to input the themes and elements required for creating materials into a text box, for example, keywords such as "environmental protection," "nature," and "recycling."

[1019] Capturing Emotional Data

[1020] The device uses a camera and microphone to capture the user's facial expressions and voice data. Specifically, it uses an emotion engine (e.g., a general face recognition API or voice recognition API) to recognize the user's emotional state. Examples of emotion engines that can be used include Microsoft Azure Face API and Google Cloud's Speech-to-Text.

[1021] Creating and sending request data

[1022] Based on the recognized emotion data and the themes and elements entered by the user, the device creates request data in JSON format, which is then sent to the server using the HTTP POST method.

[1023] Receiving and parsing request data

[1024] When the server receives an HTTP request, it analyzes the request data. This analysis involves extracting keywords and sentiment data from the request data and storing them in variables. A search query is then constructed based on these variables.

[1025] Image search

[1026] Based on the search query, the server performs a search in its internal database and royalty-free databases (e.g., Pexels, Pixabay), extracts relevant images from the search results, and filters them.

[1027] Custom Image Generation

[1028] The server passes the selected images after filtering to an image fusion AI (e.g., a general image generation AI) to generate the most suitable custom image. Examples of image fusion AI that can be used include OpenAI's DALL-E and Google's Imagen. For example, if the user's emotion of "joy" is recognized in relation to the theme of "environmental protection," a bright and positive image will be generated.

[1029] Image quality check and optimization

[1030] The server checks the quality of the custom images generated, reprocesses them if necessary, and puts them through an optimization process to compress, resize, and convert them into a user-friendly format.

[1031] Sending optimized images

[1032] The optimized custom image is prepared as the final output data and sent to the device as an HTTP response.

[1033] Receive and display custom images

[1034] The device receives the HTTP response and extracts the custom image from the JSON data, which is then displayed to the user.

[1035] User Use

[1036] The user can check the displayed custom image and use it in presentations and document creation, thereby enabling the user to efficiently obtain high-quality custom images and improve the quality and efficiency of document creation.

[1037] Specific examples

[1038] For example, if a user is creating a presentation on the topic of "environmental protection," they might use the following prompt:

[1039] "Generate custom images that reflect the sentiments of environmentalism and joy."

[1040] This system can streamline and improve the quality of document creation by generating custom images that reflect the user's emotional state.

[1041] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1042] Step 1:

[1043] The user inputs the image theme or element (e.g., "environmental protection," "nature," "recycle," etc.) required for creating the document into a text box on the terminal. This becomes the input data. The terminal prepares to send this theme or element to the server as character string data.

[1044] Step 2:

[1045] The device receives user input and simultaneously captures the user's facial expressions and voice using a camera and microphone. Specifically, it uses an emotion engine (e.g., Microsoft Azure Face API or Google Cloud's Speech-to-Text) to recognize the user's emotional state and output it as emotion data. This emotion data must be included in the request data.

[1046] Step 3:

[1047] The device combines the input themes and elements (string data) with the recognized emotion data to create request data in JSON format, which is then sent to the server using the HTTP POST method.

[1048] Step 4:

[1049] The server receives HTTP requests and analyzes the received request data. Specifically, it extracts keywords (themes and elements) and sentiment data from the request data, analyzes them, and constructs a search query. The analyzed data becomes input for searching the internal database and copyright-free databases.

[1050] Step 5:

[1051] The server performs a search on its internal database and royalty-free databases (e.g., Pexels, Pixabay) based on the constructed search query. As a result, relevant images are extracted. These extracted images become the input data for the next step.

[1052] Step 6:

[1053] The server selects from the extracted images the one that is most relevant to the request and emotional data, based on the image's metadata and text information. The selected image serves as input data for generating a custom image in the next step.

[1054] Step 7:

[1055] The server then passes the selected images to an image fusion AI (e.g., OpenAI's DALL-E or Google's Imagen) to generate a custom image. This process uses image processing algorithms to output an image that reflects the user's emotional state.

[1056] Step 8:

[1057] The server checks the quality of the generated custom image and reprocesses it if necessary. Specifically, it checks the image resolution and color, and if there are any problems, it passes it back to the image fusion AI for correction. At this stage, the quality of the final custom image is guaranteed.

[1058] Step 9:

[1059] The server optimizes images that pass the quality check by compressing and resizing them and converting them into a format that is easier for users to use, such as JPEG or PNG.

[1060] Step 10:

[1061] The server sends the optimized custom image to the device as response data in JSON format, generates an HTTP response as the response data, and returns it to the device.

[1062] Step 11:

[1063] The device receives the HTTP response, extracts the custom image from the JSON data, and displays the extracted custom image to the user.

[1064] Step 12:

[1065] The user can check the custom image displayed on the terminal and use it in creating materials. The user can efficiently obtain the generated custom image, thereby improving the quality and efficiency of presentations and material creation.

[1066] Above are the detailed processing steps of the system, through which users can efficiently obtain high-quality custom images that reflect their emotional state.

[1067] (Application example 2)

[1068] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1069] Conventional document creation support systems were unable to recognize the user's emotional state and generate custom images that matched those emotions, resulting in the provision of images that did not match the user's intentions. Furthermore, content recommendations did not take the user's emotions into consideration, making it difficult to provide optimal content. This resulted in users wasting time creating documents and selecting content, resulting in reduced efficiency.

[1070] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1071] In this invention, the server includes means for receiving requests from users, means for searching an in-house database and a copyright-free image database, means for selecting relevant images, means for recognizing the user's emotional state and customizing content based on that emotion, means for optimizing the generated custom images, and means for transmitting the optimized custom images to the user. This enables the recommendation of high-quality custom images and content according to the user's emotional state, thereby realizing more efficient document creation and content selection.

[1072] "Means for receiving requests from a user" refers to a device or software that has the function of receiving input or requests from a user and transmitting them to the system.

[1073] "Means for searching a database" refers to devices or software that have the function of searching and extracting relevant information from in-house databases and copyright-free image databases, etc., based on specified keywords or conditions.

[1074] The "means for selecting relevant images" refers to a device or software that has the function of selecting from the search results the image that best suits the user's request or emotional state.

[1075] The "means for generating a custom image" refers to a device or software that has the function of creating a customized image based on a selected image that matches the user's needs and emotions.

[1076] The "means for recognizing the emotional state of the user" refers to a device or software that has the function of analyzing the user's facial expressions and voice data and determining the user's emotional state.

[1077] A "means for customizing content" is a device or software that has the function of adjusting content according to the recognized emotional state of the user and changing it into an appropriate format or content.

[1078] A "means for optimizing a custom image" is any device or software capable of compressing or resizing a generated custom image to improve its quality.

[1079] The "means for transmitting a custom image to a user" refers to a device or software that has the function of transferring an optimized custom image to a user's terminal and displaying it.

[1080] "Emotion recognition is performed based on facial expression and voice data" refers to the process of determining the emotional state of a user based on their facial expression and voice.

[1081] The system of the present invention has the function of generating custom images that recognize and reflect the emotional state of a user, and efficiently supports document creation and content recommendation. Specific embodiments for carrying out the present invention will be described below.

[1082] 1. System Configuration

[1083] The system is mainly composed of a server and a user terminal, and operates in the following steps.

[1084] 1. A means of receiving requests from users

[1085] The user inputs a keyword through an interface installed on the user terminal.

[1086] For example, by using an application on a smartphone or tablet.

[1087] 2. A means of recording the user's emotional state

[1088] The camera and microphone on the user's device are used to capture facial expressions and voice.

[1089] The software used is the open source OpenCV library and Microsoft's Azure Face API as a facial recognition tool.

[1090] 3. Data submission and analysis

[1091] The device sends the user's input keywords and the recognized emotional state to the server as request data in JSON format.

[1092] The server uses the Python requests library to receive and analyze data via the HTTP POST method.

[1093] 4. Image Search and Selection

[1094] The server searches its internal database and its copyright-free image database to extract relevant images.

[1095] The system selects the most suitable image from the search results based on the user's request and emotional state.

[1096] 5. Creating Custom Images

[1097] Based on the selected image, a custom image is generated according to the user's emotion, using image fusion AI.

[1098] For example, if the user expresses the emotion "joy," an image with a bright and positive design is generated.

[1099] 6. Image Optimization

[1100] Quality check the generated custom images and reprocess them if necessary.

[1101] Compress and resize images to convert them into a user-friendly format.

[1102] 7. Sending images

[1103] The optimized custom image is sent to the user's device and displayed to the user, who can use it to create documents and recommend content.

[1104] 2. Data processing and calculation

[1105] Emotion recognition:

[1106] Hardware used: Webcam and microphone (a common brand is the Logitech Brio camera)

[1107] Software used: OpenCV library, Azure Face API

[1108] Processing: Video and audio data is captured, and the emotion recognition engine analyzes facial expressions and voice to generate emotion data.

[1109] Data transmission and analysis:

[1110] Software used: requests library

[1111] Processing: User input and emotion data are packaged in JSON format and sent to the server via HTTP POST. The server then parses the received data using a Python script.

[1112] Image selection and generation:

[1113] Software used: In-house search engine, image fusion AI

[1114] Processing: Search the image database based on the search query to select suitable images. Based on the selected images, the image fusion AI generates a custom image.

[1115] 3. Examples and prompts

[1116] Scenario: A user is looking for content on the theme "environmental documentaries."

[1117] The user opens the app and enters the keyword "environmental documentary." The app uses the camera and microphone to recognize the user's emotion of "excitement." This data is then sent to the server, which then recommends related documentaries. For example, documentaries such as "Our Planet" and "Blue Planet" are recommended.

[1118] Prompt Sentence Examples

[1119] "Given the given keyword 'environment,' generate server request data to recommend content that corresponds to the user's emotions."

[1120] This configuration provides appropriate images and content that take into consideration the user's emotions, improving the efficiency of creating materials and viewing content.

[1121] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1122] Step 1:

[1123] A user launches the smartphone app and enters a keyword into the text box. This keyword indicates the theme for which they wish to create materials or have content recommended. The user enters a keyword such as "environmental documentary." The keyword is obtained as input data.

[1124] Step 2:

[1125] The device uses a camera and microphone to capture the user's facial expressions and voice, and the emotion recognition engine analyzes this data. Specifically, it uses the OpenCV library and Azure Face API to identify emotions from facial expression and voice data. Facial expression images and voice data are obtained as input data, and emotion data is generated based on these.

[1126] Step 3:

[1127] The device packages the user's input keywords and recognized emotional data in JSON format, which includes information on the keyword and emotional state. This package is sent to the server using the HTTP POST method. Keywords and emotional data are obtained as input data, and JSON-formatted request data is generated as output data.

[1128] Step 4:

[1129] The server parses the received JSON request data and extracts keywords and sentiment data. The data is received using the Python requests library, and the parsing engine breaks down the data to extract keywords and sentiment. The JSON request data is obtained as input data, and keywords and sentiment data are provided as output data.

[1130] Step 5:

[1131] The server searches the company's in-house database and copyright-free image database based on the extracted keywords and emotion data, thereby extracting related images. Keywords and emotion data are obtained as input data, and a list of related images is generated as output data.

[1132] Step 6:

[1133] The server selects from the search results the image that best suits the user's request and emotional state. The search engine evaluates and ranks the image list obtained based on emotional data. The list of related images and emotional data are obtained as input data, and the optimal image is selected as output data.

[1134] Step 7:

[1135] The server uses image fusion AI to generate a custom image using the optimal image. For example, if the user expresses the emotion of "joy," it generates an image with a bright, positive design. The selected image and emotion data are obtained as input data, and a custom image is generated as output data.

[1136] Step 8:

[1137] The server checks the quality of the custom image and reprocesses it if necessary. Specifically, it checks the clarity of the image and the appropriateness of the layout, and regenerates it if the quality is low. The custom image is taken as input data, and a quality-checked custom image is generated as output data.

[1138] Step 9:

[1139] The server compresses and resizes your custom images to produce optimized images in a format that is easily consumed by users. The input is a quality-checked custom image, and the output is an optimized custom image.

[1140] Step 10:

[1141] The server packages the optimized custom image in JSON format and sends it to the device. The device displays the received custom image, and the user uses the image for document creation and content recommendation. The optimized custom image is obtained as input data, and the custom image displayed on the user's device is generated as output data.

[1142] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1143] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1144] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1145] [Fourth embodiment]

[1146] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1147] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1150] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1153] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1154] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1155] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1157] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1158] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1159] The present invention relates to a system for efficiently generating custom images for document creation and providing them to users. An embodiment of the present invention will be described below.

[1160] The user uses the device to input the image theme or element required for creating the document (e.g., "environmental protection," "nature," "recycle," etc.) into the text box. After inputting, the user clicks the request send button, and the device creates the input information as request data in JSON format and sends it to the server.

[1161] The server receives the HTTP request and analyzes the request data. The analyzed keywords are stored in variables to form a search query. Based on this search query, the server performs a search against the company's internal database and a copyright-free image database. The server extracts and filters relevant images from the search results.

[1162] After filtering, the server selects the most suitable image for the request and passes it to the image fusion AI to generate a custom image. The server checks the quality of the generated custom image and reprocesses it if necessary. The server also performs an optimization process to compress and resize the image, converting it into a user-friendly format.

[1163] The optimized custom image is prepared as the final output data, and the server sends it to the terminal as a JSON-formatted response data. The terminal receives the HTTP response, extracts the image from the JSON data, and displays it to the user. The user can then check the displayed custom image and use it to create documents.

[1164] Specific examples

[1165] Scenario: A user wants to create a presentation on the theme of "environmental protection."

[1166] The user uses the device to input keywords such as "environmental protection," "nature," and "recycle" into a text box and send a request. The device then sends the request to the server, which then analyzes the received request and extracts the necessary keywords.

[1167] The server searches the company's internal database and a copyright-free image database to extract relevant images. From the search results, the server selects images related to "environmental protection," "nature," and "recycling," and passes them to the image fusion AI to generate a custom image. The generated custom image is then quality checked and optimized, and the final custom image is output.

[1168] The server sends the custom image to the user's device, which then displays it. The user can then view the custom image and add it to their presentation. This process allows users to efficiently use high-quality custom images, dramatically improving presentation creation.

[1169] This completes one embodiment of the present invention, which automates and streamlines the process of generating custom images required for creating materials.

[1170] The processing flow will be explained below.

[1171] Step 1:

[1172] The user uses the device to input the theme or element of the image they want to create (e.g., "environmental protection," "nature," "recycling," etc.) into the text box, and then clicks the submit request button.

[1173] Step 2:

[1174] The terminal creates the input information as request data in JSON format and sends it to the server using the HTTP POST method.

[1175] Step 3:

[1176] The server receives the HTTP request and parses the request data. The parsed keywords are stored in variables.

[1177] Step 4:

[1178] The server constructs the search query and prepares the search request against the in-house database and the copyright-free image database.

[1179] Step 5:

[1180] The server runs a search query against the in-house database to retrieve relevant images, as well as against a royalty-free image database to retrieve relevant images.

[1181] Step 6:

[1182] The server generates a list of retrieved images and filters them based on the request.

[1183] Step 7:

[1184] The server selects the image that best suits the request from the filtered image list.

[1185] Step 8:

[1186] The server passes the selected images to an image fusion AI, which generates a custom image based on the user's theme and elements.

[1187] Step 9:

[1188] The server checks the quality of the generated custom images and requests the AI ​​to reprocess them if necessary.

[1189] Step 10:

[1190] The server puts the custom images through an optimization process, compressing and resizing them to convert them into a user-friendly format.

[1191] Step 11:

[1192] The server creates an optimized custom image as response data in JSON format and sends it to the terminal as an HTTP response.

[1193] Step 12:

[1194] The device receives the HTTP response and extracts the image from the JSON data.

[1195] Step 13:

[1196] The terminal updates the interface to display the extracted custom image to the user.

[1197] Step 14:

[1198] The user can check the displayed custom image and use it to create documents.

[1199] Example 1

[1200] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1201] In conventional document creation, the process of users searching for images appropriate to a theme and customizing them is time-consuming and labor-intensive. Furthermore, there is a lack of automated means for efficiently generating high-quality, appropriate custom images. This has led to a lack of efficiency in document creation and a lack of user satisfaction. In particular, there is a need for a comprehensive system that can search multiple image databases, appropriately filter the images, and further optimize custom images.

[1202] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1203] In this invention, the server includes a means for receiving a request from a user, a means for searching a database based on the request, and a means for extracting related images from the search results, allowing users to easily obtain high-quality custom images and streamlining document creation.

[1204] "User" refers to the entity that uses the system to create materials and generate images.

[1205] A "request" is information that a user requests and sends to the system, and includes specific keywords and themes.

[1206] "Database" refers to the company's in-house database and copyright-free image database, a system for managing a large number of images and related information.

[1207] "Search" refers to the process of finding relevant images within a database based on specific keywords or conditions.

[1208] "Extraction" refers to the act of extracting the image that best suits your purpose from the search results.

[1209] A "generative AI model" refers to an artificial intelligence model that synthesizes multiple images to generate new custom images.

[1210] "Optimization" refers to the process of adjusting the file size and improving the image quality of the generated custom image.

[1211] "Quality check" refers to the process of inspecting the quality of the generated custom images to ensure they meet standards.

[1212] The system of the present invention efficiently generates high-quality custom images required for document creation and provides them to users. In this system, the user uses a terminal to input the image theme and elements and submit a request. This request is sent to a server as JSON-formatted data. The server receives the request and searches an in-house database and a copyright-free image database based on analyzed keywords. Relevant images are selected from the search results and a generative AI model is used to generate custom images. The generated custom images undergo a quality check and optimization process before being provided to the user.

[1213] Specifically, the user enters keywords such as "environmental protection," "nature," or "recycle" into the text box on the device and clicks the send request button. The device converts the entered information into JSON-formatted request data and sends it to the server using the HTTP POST method.

[1214] The server analyzes the received request and performs a search against the database using the extracted keywords as a search query. Specifically, it searches for images using SQL queries or API requests. The server then filters relevant images from the search results and passes them to the image fusion AI to generate a custom image. Generative AI models used include "DALL-E 2." In this process, the AI ​​model is given a prompt sentence such as:

[1215] Generate high-quality custom images with environmental, nature, and recycling themes.

[1216] The server checks the quality of the generated custom image and reprocesses it if necessary. If the quality meets the standards, the server runs an optimization process to compress and resize the image. The final optimized custom image is sent to the device as JSON-formatted response data. The device displays the image to the user based on the received data. The user can use this image to create documents.

[1217] This system significantly streamlines the process of generating custom images required for document creation, allowing users to easily obtain high-quality images.

[1218] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1219] Step 1:

[1220] The user uses the device to enter the theme or element of the image in the text box and clicks the send request button. The entered theme or element (e.g., "environmental protection," "nature," "recycle") is passed to the device as input data. In this case, the input data is text-format keywords. The device converts the entered information into request data in JSON format.

[1221] Input: Keywords entered by the user ("environmental protection," "nature," "recycle")

[1222] Output: Request data in JSON format

[1223] Step 2:

[1224] The terminal sends the generated JSON-formatted request data to the server using the HTTP POST method. This transmission process transmits the user's request to the server.

[1225] Input: Request data in JSON format

[1226] Output: HTTP request (JSON data)

[1227] Step 3:

[1228] The server receives the HTTP request. It parses the received JSON data and extracts keywords. The parsing process stores the keywords in variables.

[1229] Input: HTTP request (JSON data)

[1230] Output: Extracted keywords (variables)

[1231] Step 4:

[1232] The server generates a search query based on the extracted keywords and runs it against the company's internal database and a database of royalty-free images, using SQL queries and API requests.

[1233] Input: Extracted keywords

[1234] Output: Search query

[1235] Step 5:

[1236] The server searches the database using the generated search query to extract relevant images. As a result of the search process, multiple images are obtained.

[1237] Input: Search query

[1238] Output: Search results (list of images)

[1239] Step 6:

[1240] The server filters relevant images from the search results, taking into account criteria such as resolution, file format, and usage conditions, and temporarily stores the filtered images.

[1241] Input: Search results (list of images)

[1242] Output: filtered image

[1243] Step 7:

[1244] The server then passes the filtered image to an image fusion AI to generate a custom image. The generative AI model used includes "DALL-E 2." During this process, the following prompt is input to the AI ​​model:

[1245] Generate high-quality custom images with environmental, nature, and recycling themes.

[1246] Input: filtered image, prompt text

[1247] Output: The generated custom image

[1248] Step 8:

[1249] The server checks the quality of the generated custom images, including checking the resolution, color consistency, and whether there is any unwanted noise. If the quality does not meet the standards, the images are reprocessed.

[1250] Input: Generated custom image

[1251] Output: Quality check result (pass / fail)

[1252] Step 9:

[1253] The server then optimizes the custom images that meet the quality criteria. The optimization process involves compressing and resizing the image, specifically using the JPEG compression algorithm to reduce file size while preserving image quality.

[1254] Input: Custom images that pass quality checks

[1255] Output: Optimized custom images

[1256] Step 10:

[1257] The server sends the optimized custom image to the terminal as a JSON response data. The specific JSON data example is as follows:

[1258] json

[1259] {

[1260] "custom_image_url": "https: / / example.com / generated_image.jpg"

[1261] }

[1262] Input: Optimized custom image

[1263] Output: Response data in JSON format

[1264] Step 11:

[1265] The device receives the HTTP response and extracts the image URL from the JSON data. The device uses this image URL to display a custom image to the user. The user can then view the displayed image and use it to create documents.

[1266] Input: Response data in JSON format

[1267] Output: Custom image displayed to the user

[1268] (Application example 1)

[1269] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1270] Creating advertising images and promotional materials in modern brick-and-mortar stores is often done manually, requiring a great deal of time and effort. In particular, brick-and-mortar stores often need to change their advertising content frequently depending on the season or campaign, and new images must be prepared quickly and appropriately each time. Furthermore, generating high-quality custom images requires specialized knowledge and skills, which can lead to inefficient work. Therefore, there is a need for a system that streamlines this work and allows anyone to easily generate and use high-quality custom images.

[1271] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1272] In this invention, the server includes means for receiving a request from a user, means for searching an in-house database and a copyright-free image database based on the request, means for selecting relevant images from the search results, means for using a generative model to generate a custom image using the selected images, means for optimizing the generated custom image, means for transmitting the optimized custom image to the user, and means for displaying the generated custom image to the user, thereby enabling efficient creation and rapid deployment of advertising images and promotional materials in physical stores.

[1273] The "means for receiving a request from a user" is a function that provides an interface for acquiring information input by a user through a terminal and sending it to a server.

[1274] "A means of searching internal databases and copyright-free image databases based on requests" refers to a search function that finds appropriate images from internal databases and external copyright-free image databases based on keywords or themes requested by users.

[1275] A "means for selecting relevant images" is an algorithm or method for selecting from the search results the images that best match the user's requirements.

[1276] "Means for generating custom images using generative models" refers to a function that uses an advanced generative model (e.g., image fusion AI) to create a unique custom image based on a selected image.

[1277] "Means for optimizing generated custom images" refers to a process for compressing or resizing generated images as necessary while preserving their quality.

[1278] The "means for sending an optimized custom image to a user" is a function for sending an optimized image to a user's terminal in the form of an HTTP response or the like.

[1279] The "display means" is an interface on the user terminal for visually displaying the custom image received from the server.

[1280] The "analysis means" is a function for extracting keywords contained in a request sent by a user and generating a search query based on that information.

[1281] "Quality check means" refers to a process or function that checks the quality of the custom images generated and reprocesses them if necessary.

[1282] The present invention relates to a system that improves the efficiency of creating advertising images and sales promotion materials in brick-and-mortar stores and enables the rapid generation and use of high-quality custom images. Hereinafter, an embodiment of the present invention will be described.

[1283] First, the user uses a smartphone or tablet device to input a specific theme or keyword (e.g., "fresh vegetables," "healthy," "organic," etc.). After inputting, the device generates the input information as request data in JSON format and sends it to the server.

[1284] The server receives the request data from the user and extracts keywords using an analysis tool. The extracted keywords are used to generate a search query, which is then used to search the company's internal database and a copyright-free image database. The server then selects relevant images from the search results and proceeds to the next step.

[1285] The selected images are then passed to a process that uses a generative model to generate a custom image. This process uses image fusion AI to synthesize and process the images into a high-quality custom image. The resulting custom image is then verified by a quality checker and reprocessed if necessary. After quality is assured, an optimization process compresses and resizes the image to convert it into a user-friendly format.

[1286] The optimized custom image is finally sent to the user's device, where the received image can be visually displayed and checked using a display device. This allows physical stores to quickly generate and deploy new advertising images and promotional materials.

[1287] The main hardware and software used to implement this system include: On the server side, Python and Flask are used, and a database search module and an image fusion AI module are installed. On the client side, JavaScript, HTML, and CSS are used to build the user interface.

[1288] As a concrete example, consider a case where a user wants to generate a custom image for a display ad on the theme of "fresh vegetables." In this case, the user enters "fresh vegetables," "healthy," and "organic" into the text box on their device and submits a request. Based on this request, the server searches for relevant images and generates a custom image using image fusion AI. As part of this process, the following prompt sentence is used:

[1289] "Fresh vegetables, healthy, organic"

[1290] The server generates a custom image based on the prompt and delivers it to the user's device. The user can then view the image and use it in display ads.

[1291] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1292] Step 1: User Input

[1293] Users use their smartphones or tablet devices to input the desired advertising theme or elements (e.g., "fresh vegetables," "healthy," "organic") into a request form. This input information constitutes the request data.

[1294] Input: Theme or keyword

[1295] Output: Request data (JSON format)

[1296] Step 2: Submitting the request

[1297] The device sends the request data entered by the user to the server as an HTTP POST request, which includes the user's input information in JSON format.

[1298] Input: Request data (JSON format)

[1299] Output: HTTP POST request

[1300] Step 3: Receiving and Parsing Request Data

[1301] The server receives the HTTP POST request and analyzes the request data. The analysis means extracts keywords from the request data and stores them in variables.

[1302] Input: HTTP POST request (JSON format)

[1303] Output: Extracted keywords (variables)

[1304] Step 4: Find an image

[1305] The server searches the in-house database and copyright-free image database based on the extracted keywords, generates a search query, and executes the image search.

[1306] Input: Extracted keywords

[1307] Output: Search results (image list)

[1308] Step 5: Select related images

[1309] The server selects the most relevant images from the search results using algorithms and filtering methods.

[1310] Input: Search results (image list)

[1311] Output: Selected related images (image list)

[1312] Step 6: Generate custom images

[1313] The server passes the selected images to the image fusion AI to generate a custom image. A generative model is used to create a unique custom image.

[1314] Input: Selected related images (image list)

[1315] Output: The generated custom image

[1316] Step 7: Image optimization

[1317] The server checks the quality of the generated custom images, reprocesses them if necessary, and also performs optimizations such as compressing and resizing the images.

[1318] Input: Generated custom image

[1319] Output: Optimized custom images

[1320] Step 8: Submit a custom image

[1321] The server composes the optimized custom image as response data in JSON format and sends it to the user's device via an HTTP response.

[1322] Input: Optimized custom image

[1323] Output: HTTP response (JSON format)

[1324] Step 9: Displaying Custom Images

[1325] The user's terminal receives the HTTP response, extracts the custom image from the response data, and visually displays the custom image to the user using the terminal's display means.

[1326] Input: HTTP response (JSON format)

[1327] Output: Custom image displayed

[1328] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1329] The present invention relates to a system for generating custom images that recognize and reflect the emotional state of a user, thereby efficiently supporting the creation of materials. An embodiment of the present invention will now be described.

[1330] The user uses the device to input the image theme or element required for creating the document (e.g., "environmental protection," "nature," "recycling," etc.) into a text box. The device then captures the user's facial expressions and voice data through an emotion engine to recognize their emotional state. The recognized emotion data is added to the request data.

[1331] The device creates the input information and emotion data as request data in JSON format and sends it to the server using the HTTP POST method. The server receives the HTTP request and analyzes the request data. The analyzed keywords and emotion data are stored in variables to form a search query. Based on this search query, the server performs a search against its in-house database and a copyright-free image database. The server extracts and filters related images from the search results.

[1332] After filtering, the server selects the image that best fits the request and emotional data, and passes the selected image to the image fusion AI to generate a custom image. For example, if a user requests an image on the theme of "environmental protection," and the emotion engine recognizes the user's positive emotions such as "joy" or "satisfaction," a bright, positive image may be selected. Conversely, if the user expresses emotions such as "tension" or "anxiety," a calm design to ease anxiety may be selected.

[1333] The server checks the quality of the generated custom image and reprocesses it if necessary. It also performs an optimization process to compress and resize the image and convert it into a format that is easy for users to use. The optimized custom image is prepared as the final output data and sent to the terminal as a JSON-formatted response data.

[1334] The device receives the HTTP response and extracts the image from the JSON data. The extracted custom image is displayed to the user. The user can check the displayed custom image and use it to create documents.

[1335] Specific examples

[1336] Scenario: A user wants to create a presentation on the theme of "environmental protection."

[1337] The user uses the device to input keywords such as "environmental protection," "nature," and "recycle" into a text box, and the emotion engine analyzes the user's facial expressions and voice to recognize "joy." The request and emotion data are then sent to the server, which analyzes the received request and extracts the necessary keywords and emotion data.

[1338] The server searches the company's internal database and a copyright-free image database to extract relevant images. From the search results, the server selects images related to "environmental protection" and "joy" and passes them to the image fusion AI to generate a custom image. The generated custom image is then quality checked and optimized, and the final custom image is output.

[1339] The server sends the custom image to the user's device, which then displays it to the user. The user can then view the custom image and add it to their presentation materials. This process allows users to efficiently use high-quality custom images that suit their emotions, dramatically improving presentation creation.

[1340] The foregoing describes one embodiment of the present invention, which enables and streamlines the process of generating custom images that respond to the user's emotional state.

[1341] The processing flow will be explained below.

[1342] Step 1:

[1343] The user uses the device to input the image theme or element required for creating the document (e.g., "environmental protection," "nature," "recycling," etc.) into a text box. In addition, the user's facial expressions and voice data are captured through the emotion engine.

[1344] Step 2:

[1345] The device's emotion engine analyzes the user's emotional state and adds recognized emotion data (e.g., "joy," "satisfaction," etc.) to the request data.

[1346] Step 3:

[1347] The device creates the input information and emotion data as request data in JSON format and sends it to the server using the HTTP POST method.

[1348] Step 4:

[1349] The server receives the HTTP request and analyzes the request data. The analyzed keywords and emotion data are stored in variables.

[1350] Step 5:

[1351] The server constructs the search query and prepares the search request against the in-house database and the copyright-free image database.

[1352] Step 6:

[1353] The server runs a search query against the in-house database to retrieve relevant images, as well as against a royalty-free image database to retrieve relevant images.

[1354] Step 7:

[1355] The server generates a list of retrieved images and filters them based on the request, taking into account the user's emotional data.

[1356] Step 8:

[1357] The server selects the image that best suits the request and emotional data: if the emotional state is "joy," an image with a positive impression is selected.

[1358] Step 9:

[1359] The server passes the selected images to an image fusion AI, which generates a custom image based on the user's theme and emotion.

[1360] Step 10:

[1361] The server checks the quality of the generated custom images and requests the AI ​​to reprocess them if necessary.

[1362] Step 11:

[1363] The server puts the custom images through an optimization process, compressing and resizing them to convert them into a user-friendly format.

[1364] Step 12:

[1365] The server creates an optimized custom image as response data in JSON format and sends it to the terminal as an HTTP response.

[1366] Step 13:

[1367] The device receives the HTTP response and extracts the image from the JSON data.

[1368] Step 14:

[1369] The terminal updates the interface to display the extracted custom image to the user.

[1370] Step 15:

[1371] The user can check the displayed custom image and use it to create documents.

[1372] Example 2

[1373] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1374] Conventional document creation systems have difficulty generating custom images that reflect the user's emotional state, which has resulted in inefficient document creation. Furthermore, image selection and image optimization according to emotions are insufficient, making it impossible to efficiently provide high-quality custom images that reflect the user's intentions.

[1375] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a request from a user, means for searching an internal database and a copyright-free database, means for capturing the user's facial expression and voice data using an emotion engine and recognizing the user's emotional state, means for adding emotion data to request data, creating JSON-formatted request data, and transmitting the request data to the server, means for selecting related images from the search results and generating a custom image using the selected image, means for optimizing the generated custom image, and means for transmitting the optimized custom image to the user. This makes it possible to efficiently generate and provide high-quality custom images suited to the user's emotional state.

[1376] "User" refers to a person who uses the system to create materials.

[1377] A "request" refers to information requested by a user to input a theme or element and send it to the system.

[1378] "Terminal" refers to a device (such as a PC, smartphone, or tablet) that a user uses to access the system.

[1379] "Server" refers to a computer system that performs the main processing of the system and stores data.

[1380] An "emotion engine" refers to an algorithm or software that analyzes a user's facial expressions and voice data to recognize their emotional state.

[1381] "JSON format" stands for JavaScript Object Notation and refers to a lightweight data exchange format.

[1382] "Internal database" refers to a database that stores various data (such as image data) stored within the system.

[1383] A "copyright-free database" refers to a database that provides data (mainly image data) that can be used without copyright restrictions.

[1384] A "search query" refers to a series of keywords or expressions constructed to conduct a database search.

[1385] "Image selection" refers to the process of selecting appropriate images from search results.

[1386] "Custom Image" refers to a personalized image generated based on a user's request and emotional state.

[1387] "Optimization" refers to the process of compressing, resizing, or otherwise improving an image into a more usable format.

[1388] "Quality Check" refers to the process of checking the quality of the custom images generated and reprocessing them if necessary.

[1389] "HTTP protocol" is an abbreviation for Hypertext Transfer Protocol, and refers to a communication protocol for sending and receiving data over the Internet.

[1390] This invention relates to a system that efficiently supports document creation by recognizing the user's emotional state and generating custom images that reflect that state. This system operates mainly through three main components: the user, the terminal, and the server, and each processing step is clearly separated.

[1391] User input of themes and elements

[1392] The user uses the terminal to input the themes and elements required for creating materials into a text box, for example, keywords such as "environmental protection," "nature," and "recycling."

[1393] Capturing Emotional Data

[1394] The device uses a camera and microphone to capture the user's facial expressions and voice data. Specifically, it uses an emotion engine (e.g., a general face recognition API or voice recognition API) to recognize the user's emotional state. Examples of emotion engines that can be used include Microsoft Azure Face API and Google Cloud's Speech-to-Text.

[1395] Creating and sending request data

[1396] Based on the recognized emotion data and the themes and elements entered by the user, the device creates request data in JSON format, which is then sent to the server using the HTTP POST method.

[1397] Receiving and parsing request data

[1398] When the server receives an HTTP request, it analyzes the request data. This analysis involves extracting keywords and sentiment data from the request data and storing them in variables. A search query is then constructed based on these variables.

[1399] Image search

[1400] Based on the search query, the server performs a search in its internal database and royalty-free databases (e.g., Pexels, Pixabay), extracts relevant images from the search results, and filters them.

[1401] Custom Image Generation

[1402] The server passes the selected images after filtering to an image fusion AI (e.g., a general image generation AI) to generate the most suitable custom image. Examples of image fusion AI that can be used include OpenAI's DALL-E and Google's Imagen. For example, if the user's emotion of "joy" is recognized in relation to the theme of "environmental protection," a bright and positive image will be generated.

[1403] Image quality check and optimization

[1404] The server checks the quality of the custom images generated, reprocesses them if necessary, and puts them through an optimization process to compress, resize, and convert them into a user-friendly format.

[1405] Sending optimized images

[1406] The optimized custom image is prepared as the final output data and sent to the device as an HTTP response.

[1407] Receive and display custom images

[1408] The device receives the HTTP response and extracts the custom image from the JSON data, which is then displayed to the user.

[1409] User Use

[1410] The user can check the displayed custom image and use it in presentations and document creation, thereby enabling the user to efficiently obtain high-quality custom images and improve the quality and efficiency of document creation.

[1411] Specific examples

[1412] For example, if a user is creating a presentation on the topic of "environmental protection," they might use the following prompt:

[1413] "Generate custom images that reflect the sentiments of environmentalism and joy."

[1414] This system can streamline and improve the quality of document creation by generating custom images that reflect the user's emotional state.

[1415] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1416] Step 1:

[1417] The user inputs the image theme or element (e.g., "environmental protection," "nature," "recycle," etc.) required for creating the document into a text box on the terminal. This becomes the input data. The terminal prepares to send this theme or element to the server as character string data.

[1418] Step 2:

[1419] The device receives user input and simultaneously captures the user's facial expressions and voice using a camera and microphone. Specifically, it uses an emotion engine (e.g., Microsoft Azure Face API or Google Cloud's Speech-to-Text) to recognize the user's emotional state and output it as emotion data. This emotion data must be included in the request data.

[1420] Step 3:

[1421] The device combines the input themes and elements (string data) with the recognized emotion data to create request data in JSON format, which is then sent to the server using the HTTP POST method.

[1422] Step 4:

[1423] The server receives HTTP requests and analyzes the received request data. Specifically, it extracts keywords (themes and elements) and sentiment data from the request data, analyzes them, and constructs a search query. The analyzed data becomes input for searching the internal database and copyright-free databases.

[1424] Step 5:

[1425] The server performs a search on its internal database and royalty-free databases (e.g., Pexels, Pixabay) based on the constructed search query. As a result, relevant images are extracted. These extracted images become the input data for the next step.

[1426] Step 6:

[1427] The server selects from the extracted images the one that is most relevant to the request and emotional data, based on the image's metadata and text information. The selected image serves as input data for generating a custom image in the next step.

[1428] Step 7:

[1429] The server then passes the selected images to an image fusion AI (e.g., OpenAI's DALL-E or Google's Imagen) to generate a custom image. This process uses image processing algorithms to output an image that reflects the user's emotional state.

[1430] Step 8:

[1431] The server checks the quality of the generated custom image and reprocesses it if necessary. Specifically, it checks the image resolution and color, and if there are any problems, it passes it back to the image fusion AI for correction. At this stage, the quality of the final custom image is guaranteed.

[1432] Step 9:

[1433] The server optimizes images that pass the quality check by compressing and resizing them and converting them into a format that is easier for users to use, such as JPEG or PNG.

[1434] Step 10:

[1435] The server sends the optimized custom image to the device as response data in JSON format, generates an HTTP response as the response data, and returns it to the device.

[1436] Step 11:

[1437] The device receives the HTTP response, extracts the custom image from the JSON data, and displays the extracted custom image to the user.

[1438] Step 12:

[1439] The user can check the custom image displayed on the terminal and use it in creating materials. The user can efficiently obtain the generated custom image, thereby improving the quality and efficiency of presentations and material creation.

[1440] Above are the detailed processing steps of the system, through which users can efficiently obtain high-quality custom images that reflect their emotional state.

[1441] (Application example 2)

[1442] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1443] Conventional document creation support systems were unable to recognize the user's emotional state and generate custom images that matched those emotions, resulting in the provision of images that did not match the user's intentions. Furthermore, content recommendations did not take the user's emotions into consideration, making it difficult to provide optimal content. This resulted in users wasting time creating documents and selecting content, resulting in reduced efficiency.

[1444] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1445] In this invention, the server includes means for receiving requests from users, means for searching an in-house database and a copyright-free image database, means for selecting relevant images, means for recognizing the user's emotional state and customizing content based on that emotion, means for optimizing the generated custom images, and means for transmitting the optimized custom images to the user. This enables the recommendation of high-quality custom images and content according to the user's emotional state, thereby realizing more efficient document creation and content selection.

[1446] "Means for receiving requests from a user" refers to a device or software that has the function of receiving input or requests from a user and transmitting them to the system.

[1447] "Means for searching a database" refers to devices or software that have the function of searching and extracting relevant information from in-house databases and copyright-free image databases, etc., based on specified keywords or conditions.

[1448] The "means for selecting relevant images" refers to a device or software that has the function of selecting from the search results the image that best suits the user's request or emotional state.

[1449] The "means for generating a custom image" refers to a device or software that has the function of creating a customized image based on a selected image that matches the user's needs and emotions.

[1450] The "means for recognizing the emotional state of the user" refers to a device or software that has the function of analyzing the user's facial expressions and voice data and determining the user's emotional state.

[1451] A "means for customizing content" is a device or software that has the function of adjusting content according to the recognized emotional state of the user and changing it into an appropriate format or content.

[1452] A "means for optimizing a custom image" is any device or software capable of compressing or resizing a generated custom image to improve its quality.

[1453] The "means for transmitting a custom image to a user" refers to a device or software that has the function of transferring an optimized custom image to a user's terminal and displaying it.

[1454] "Emotion recognition is performed based on facial expression and voice data" refers to the process of determining the emotional state of a user based on their facial expression and voice.

[1455] The system of the present invention has the function of generating custom images that recognize and reflect the emotional state of a user, and efficiently supports document creation and content recommendation. Specific embodiments for carrying out the present invention will be described below.

[1456] 1. System Configuration

[1457] The system is mainly composed of a server and a user terminal, and operates in the following steps.

[1458] 1. A means of receiving requests from users

[1459] The user inputs a keyword through an interface installed on the user terminal.

[1460] For example, by using an application on a smartphone or tablet.

[1461] 2. A means of recording the user's emotional state

[1462] The camera and microphone on the user's device are used to capture facial expressions and voice.

[1463] The software used is the open source OpenCV library and Microsoft's Azure Face API as a facial recognition tool.

[1464] 3. Data submission and analysis

[1465] The device sends the user's input keywords and the recognized emotional state to the server as request data in JSON format.

[1466] The server uses the Python requests library to receive and analyze data via the HTTP POST method.

[1467] 4. Image Search and Selection

[1468] The server searches its internal database and its copyright-free image database to extract relevant images.

[1469] The system selects the most suitable image from the search results based on the user's request and emotional state.

[1470] 5. Creating Custom Images

[1471] Based on the selected image, a custom image is generated according to the user's emotion, using image fusion AI.

[1472] For example, if the user expresses the emotion "joy," an image with a bright and positive design is generated.

[1473] 6. Image Optimization

[1474] Quality check the generated custom images and reprocess them if necessary.

[1475] Compress and resize images to convert them into a user-friendly format.

[1476] 7. Sending images

[1477] The optimized custom image is sent to the user's device and displayed to the user, who can use it to create documents and recommend content.

[1478] 2. Data processing and calculation

[1479] Emotion recognition:

[1480] Hardware used: Webcam and microphone (a common brand is the Logitech Brio camera)

[1481] Software used: OpenCV library, Azure Face API

[1482] Processing: Video and audio data is captured, and the emotion recognition engine analyzes facial expressions and voice to generate emotion data.

[1483] Data transmission and analysis:

[1484] Software used: requests library

[1485] Processing: User input and emotion data are packaged in JSON format and sent to the server via HTTP POST. The server then parses the received data using a Python script.

[1486] Image selection and generation:

[1487] Software used: In-house search engine, image fusion AI

[1488] Processing: Search the image database based on the search query to select suitable images. Based on the selected images, the image fusion AI generates a custom image.

[1489] 3. Examples and prompts

[1490] Scenario: A user is looking for content on the theme "environmental documentaries."

[1491] The user opens the app and enters the keyword "environmental documentary." The app uses the camera and microphone to recognize the user's emotion of "excitement." This data is then sent to the server, which then recommends related documentaries. For example, documentaries such as "Our Planet" and "Blue Planet" are recommended.

[1492] Prompt Sentence Examples

[1493] "Given the given keyword 'environment,' generate server request data to recommend content that corresponds to the user's emotions."

[1494] This configuration provides appropriate images and content that take into consideration the user's emotions, improving the efficiency of creating materials and viewing content.

[1495] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1496] Step 1:

[1497] A user launches the smartphone app and enters a keyword into the text box. This keyword indicates the theme for which they wish to create materials or have content recommended. The user enters a keyword such as "environmental documentary." The keyword is obtained as input data.

[1498] Step 2:

[1499] The device uses a camera and microphone to capture the user's facial expressions and voice, and the emotion recognition engine analyzes this data. Specifically, it uses the OpenCV library and Azure Face API to identify emotions from facial expression and voice data. Facial expression images and voice data are obtained as input data, and emotion data is generated based on these.

[1500] Step 3:

[1501] The device packages the user's input keywords and recognized emotional data in JSON format, which includes information on the keyword and emotional state. This package is sent to the server using the HTTP POST method. Keywords and emotional data are obtained as input data, and JSON-formatted request data is generated as output data.

[1502] Step 4:

[1503] The server parses the received JSON request data and extracts keywords and sentiment data. The data is received using the Python requests library, and the parsing engine breaks down the data to extract keywords and sentiment. The JSON request data is obtained as input data, and keywords and sentiment data are provided as output data.

[1504] Step 5:

[1505] The server searches the company's in-house database and copyright-free image database based on the extracted keywords and emotion data, thereby extracting related images. Keywords and emotion data are obtained as input data, and a list of related images is generated as output data.

[1506] Step 6:

[1507] The server selects from the search results the image that best suits the user's request and emotional state. The search engine evaluates and ranks the image list obtained based on emotional data. The list of related images and emotional data are obtained as input data, and the optimal image is selected as output data.

[1508] Step 7:

[1509] The server uses image fusion AI to generate a custom image using the optimal image. For example, if the user expresses the emotion of "joy," it generates an image with a bright, positive design. The selected image and emotion data are obtained as input data, and a custom image is generated as output data.

[1510] Step 8:

[1511] The server checks the quality of the custom image and reprocesses it if necessary. Specifically, it checks the clarity of the image and the appropriateness of the layout, and regenerates it if the quality is low. The custom image is taken as input data, and a quality-checked custom image is generated as output data.

[1512] Step 9:

[1513] The server compresses and resizes your custom images to produce optimized images in a format that is easily consumed by users. The input is a quality-checked custom image, and the output is an optimized custom image.

[1514] Step 10:

[1515] The server packages the optimized custom image in JSON format and sends it to the device. The device displays the received custom image, and the user uses the image for document creation and content recommendation. The optimized custom image is obtained as input data, and the custom image displayed on the user's device is generated as output data.

[1516] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1517] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1518] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1519] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1520] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1521] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1522] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1523] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1524] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1525] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1526] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1527] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1528] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1529] 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.

[1530] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1531] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1532] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1533] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1534] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1535] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1536] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1537] The following is further disclosed regarding the above embodiment.

[1538] (Claim 1)

[1539] means for receiving a request from a user;

[1540] means for searching an in-house database and a copyright-free image database based on said request;

[1541] means for selecting related images from the search results;

[1542] means for generating a custom image using the selected image;

[1543] means for optimizing the generated custom image;

[1544] means for transmitting the optimized custom image to the user;

[1545] A system including:

[1546] (Claim 2)

[1547] an analysis means for analyzing keywords based on the request from the user and passing the keywords to the search means;

[1548] quality checking means for checking the quality of the generated custom image and reprocessing it if necessary;

[1549] The system of claim 1 further comprising:

[1550] (Claim 3)

[1551] 10. The system of claim 1, wherein the optimization means compresses and resizes images.

[1552] "Example 1"

[1553] (Claim 1)

[1554] means for receiving a request from a user;

[1555] means for searching a database based on said request;

[1556] means for extracting related images from the search results;

[1557] a generative AI model means for generating a custom image using the extracted image;

[1558] means for optimizing the generated custom image;

[1559] means for transmitting the optimized custom image to the user;

[1560] A system including:

[1561] (Claim 2)

[1562] an analysis means for analyzing keywords based on the request from the user and passing the keywords to the search means;

[1563] quality checking means for checking the quality of the generated custom image and reprocessing it if necessary;

[1564] The system of claim 1 further comprising:

[1565] (Claim 3)

[1566] 10. The system of claim 1, wherein the optimization means compresses and resizes images.

[1567] "Application Example 1"

[1568] (Claim 1)

[1569] means for receiving a request from a user;

[1570] means for searching an in-house database and a copyright-free image database based on said request;

[1571] means for selecting related images from the search results;

[1572] means for using a generative model to generate a custom image using the selected image;

[1573] means for optimizing the generated custom image;

[1574] means for transmitting the optimized custom image to the user;

[1575] display means for allowing a user to view and use the generated custom image;

[1576] A system including:

[1577] (Claim 2)

[1578] an analysis means for analyzing keywords based on the request from the user and passing the keywords to the search means;

[1579] quality checking means for checking the quality of the generated custom image and reprocessing it if necessary;

[1580] means for displaying the custom image on the display means;

[1581] The system of claim 1 further comprising:

[1582] (Claim 3)

[1583] 10. The system of claim 1, wherein the optimization means compresses and resizes images.

[1584] "Example 2: Combining Emotion Engines"

[1585] (Claim 1)

[1586] means for receiving a request from a user;

[1587] means for searching an internal database and a copyright-free database based on said request;

[1588] a means for capturing facial expressions and voice data of the user using an emotion engine and recognizing the emotional state of the user;

[1589] means for adding the emotion data to the request data, creating JSON format request data, and transmitting the request data to a server;

[1590] means for selecting relevant images from the search results and generating a custom image using the selected images;

[1591] means for optimizing the generated custom image;

[1592] means for transmitting the optimized custom image to the user;

[1593] A system including:

[1594] (Claim 2)

[1595] an analysis means for analyzing keywords based on the request from the user and passing the keywords to the search means;

[1596] means for constructing a search query based on the analyzed keywords and emotion data;

[1597] quality checking means for checking the quality of the generated custom image and reprocessing it if necessary;

[1598] The system of claim 1 further comprising:

[1599] (Claim 3)

[1600] 10. The system of claim 1, wherein the optimization means compresses and resizes images.

[1601] "Application example 2 when combining emotion engines"

[1602] (Claim 1)

[1603] means for receiving a request from a user;

[1604] means for searching an in-house database and a copyright-free image database based on said request;

[1605] means for selecting related images from the search results;

[1606] means for generating a custom image using the selected image;

[1607] means for recognizing a user's emotional state and customizing content based on that emotion;

[1608] means for optimizing the generated custom image;

[1609] means for transmitting the optimized custom image to the user;

[1610] A system including:

[1611] (Claim 2)

[1612] an analysis means for analyzing keywords based on the request from the user and passing the keywords to the search means;

[1613] quality checking means for checking the quality of the generated custom image and reprocessing it if necessary;

[1614] 2. The system of claim 1, wherein emotion recognition is based on facial and voice data.

[1615] (Claim 3)

[1616] 10. The system of claim 1, wherein the optimization means compresses and resizes images. [Explanation of symbols]

[1617] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving a request from a user; means for searching an in-house database and a copyright-free image database based on said request; means for selecting related images from the search results; means for generating a custom image using the selected image; means for optimizing the generated custom image; means for transmitting the optimized custom image to the user; A system including:

2. an analysis means for analyzing keywords based on the request from the user and passing the keywords to the search means; quality checking means for checking the quality of the generated custom image and reprocessing it if necessary; The system of claim 1 further comprising:

3. 2. The system of claim 1, wherein said optimization means compresses and resizes images.

Images