System

Abstract

To provide a system.SOLUTION: A system includes: means for acquiring a specific image searched on a network by a search engine crawler; means for determining whether the acquired specific image is an image generated by AI; means for generating summary information containing a summary of an information source where the specific image determined to have been generated by AI is posted; and means for listing the generated summary information and the specific image.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] Certain logo images are increasingly being generated by AI and used fraudulently, but it is difficult to detect and take action against such misuse. [Means for solving the problem]

[0005] This invention uses a search engine crawler to search the Internet for specific logo images and determines whether the found logo images were generated by AI. Furthermore, it generates summaries of websites that contain logo images determined to have been generated by AI, and lists the generated summaries and logo images. This makes it easier to detect and take measures against unauthorized use. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [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. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] 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.

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

[0009] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] 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.

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

[0012] In the following embodiments, a communication I / F (Interface) with a 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.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. 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."

[0014] [First embodiment]

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

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

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

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

[0019] The reception device 38 includes a touch panel 38A, 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.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (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.

[0021] 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.

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

[0023] 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.

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

[0025] In the smart device 14, the processor 46 performs the reception output 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.

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

[0027] "Example 1"

[0028] One embodiment of the present invention is a search engine crawler for searching for specific logo images. This crawler periodically patrols the Internet and searches for specific logo images. For example, it can crawl every night to search for specific logo images.

[0029] "Example 2"

[0030] There are also methods to determine whether a searched logo image was generated by AI. This uses a machine learning model that has learned the characteristics of AI-generated images to determine whether the searched logo image was generated by AI. For example, it is possible to use a model that has learned the characteristics of images generated by a GAN (Generative Adversarial Network).

[0031] "Example 3"

[0032] Furthermore, there are methods for generating summaries of websites that contain logo images determined to have been generated by AI. These methods extract text information from the website and generate summaries. For example, natural language processing techniques such as BERT (Bidirectional Encoder Representations from Transformers) can be used to extract important information from the text information on the website and generate summaries.

[0033] "Example 4"

[0034] Finally, there is a means for listing the generated summary information and logo images. This allows the summary information and logo images to be displayed as a list, making it easier for humans to check the contents. For example, it is possible to display a list of the summary information and logo images through a web interface so that humans can check them.

[0035] The processing flow of each embodiment will be described below.

[0036] "Example 1"

[0037] Step 1: A search engine crawler crawls the Internet to search for specific logo images. This crawler crawls every night, for example, to search for specific logo images.

[0038] Step 2: The crawler stores the logo images it finds in a temporary database, which provides input data for the AI ​​to use in the next step.

[0039] "Example 2"

[0040] Step 1: A means for determining whether the searched logo image was generated by AI is activated. This uses a machine learning model that has learned the characteristics of AI-generated images to determine whether the searched logo image was generated by AI.

[0041] Step 2: Based on the results, we identify logo images that are determined to be AI-generated and store that information in a temporary database, which will provide input data for generating a website summary in the next step.

[0042] "Example 3"

[0043] Step 1: A means for generating a summary of a website containing a logo image determined to have been generated by AI is activated. This extracts text information from the website and generates a summary.

[0044] Step 2: The generated summary information is saved in a temporary database, which will serve as input data for the next step of creating a list of summary information and logo images.

[0045] "Example 4"

[0046] Step 1: A means for listing the generated summary information and logo images is activated. This displays the summary information and logo images as a list, making it easier for humans to check the contents.

[0047] Step 2: Display the listed summary information and logo image in a web interface so that it can be reviewed by humans.

[0048] Example 1

[0049] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0050] Conventional search engine crawlers have difficulty efficiently searching for specific images, and managing and notifying search results is time-consuming. Furthermore, there is a lack of means to identify images generated by generative AI models and organize the information. This makes it difficult for users to quickly and accurately obtain the information they need.

[0051] The identification process by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. In this invention, the server includes means for searching for specific images on the network using a search engine crawler, means for determining whether the searched images were generated by a generative AI model, means for generating summaries of information resources containing images determined to have been generated by a generative AI model, means for listing the generated summary information and images, means for periodically starting the crawler, means for saving the collected images in a database, and means for notifying the search results. This makes it possible to efficiently search for specific images, organize the determination results by the generative AI model, and notify the user quickly and accurately.

[0052] A "search engine crawler" is a program that automatically crawls information resources on a network and collects specific data.

[0053] A "specific image" is an image that has a specific feature designated by the user as a search target.

[0054] "Network" refers to an information and communication network such as the Internet or an intranet.

[0055] A "generative AI model" is an algorithm or system that generates data using artificial intelligence technology.

[0056] A "means for determining" is a device or program that has the function of evaluating the characteristics of data based on specific conditions or criteria and outputting the results.

[0057] An "information resource" is a collection of information such as websites and databases that exist on a network.

[0058] A "summary generation means" is a device or program that has the function of extracting important information from information resources and summarizing it concisely.

[0059] A "listing means" is a device or program that has the function of organizing collected data and displaying it in a list format.

[0060] "Means for periodically launching a crawler" refers to a device or program that has the function of automatically running a crawler at regular intervals.

[0061] A "database" is a system for efficiently storing, searching, and managing data.

[0062] A "notification means" is a device or program that has the function of notifying a user of a specific event or result.

[0063] This invention is a system that efficiently searches for specific images, organizes the judgment results by a generative AI model, and notifies the user quickly and accurately. A specific embodiment of this system is described below.

[0064] The server automatically crawls information resources on the network to collect specific images using a search engine crawler, which is written in a programming language such as Python or JavaScript (registered trademark) and uses libraries such as BeautifulSoup and Puppeteer to analyze the HTML of web pages.

[0065] The collected images are analyzed using a generative AI model, which uses artificial intelligence techniques to determine whether an image is generated, using image recognition libraries such as OpenCV.

[0066] The results of the assessment are generated as a summary of the information resource. Natural language processing technology is used to generate the summary, and important information is extracted. The generated summary information and images are compiled into a list and stored in a database. The database may use MySQL (registered trademark) or MongoDB.

[0067] The server sets up a cron job to periodically launch the crawler. This automatically runs the crawler every night at midnight. The collected images and summary information are stored in a database and notified to the user. An email sending function is used for notification.

[0068] As a concrete example, consider the case where a user searches for logo images of a specific company. For example, when searching for logo images of a "specific company," a crawler traverses web pages on the network and collects logo images of the specific company. The server runs the crawler every night and stores the collected logo images in a database. The server then notifies the user of the search results by email.

[0069] An example of a prompt is as follows:

[0070] "Please set up a crawler to search for logo images of specific companies. The crawler will run every night, crawl web pages on the network, collect specific logo images, and store them in a database. For example, if you search for logo images of "specific companies," the crawler will collect logo images of specific companies."

[0071] In this way, users can build a system that allows them to efficiently search for specific images, organize the judgment results of generative AI models, and quickly and accurately notify the results.

[0072] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0073] Step 1: Start the crawler

[0074] The server starts the crawler every night at midnight. Specifically, a cron job is set up to run the crawler at 2:00 AM every day. The input is a cron job configuration file, and the output is that the crawler will start running. Specifically, the server sets up the cron job as follows:

[0075] 0 2 / usr / bin / python3 / path / to / crawler.py

[0076] Step 2: Collect web pages

[0077] The crawler collects web pages from the network. Specifically, it uses BeautifulSoup (a Python library) to analyze the HTML of the web pages. The input is the URL of the web page, and the output is the analyzed HTML data. Specifically, the crawler performs the following processes:

[0078] Access the URL, get the HTML data, and parse it with BeautifulSoup.

[0079] Step 3: Find your logo image

[0080] The crawler searches for specific logo images from collected web pages. It uses OpenCV (a Python library) for image recognition. The input requires the parsed HTML data and a logo image template, and the output is the URL of the matching logo image. Specifically, the crawler performs the following process:

[0081] Image tags are extracted from the HTML data, and each image is analyzed using OpenCV to determine whether it matches the logo image.

[0082] Step 4: Save your data

[0083] The server saves the search results in a database. It uses MySQL to save the search results. The input requires the URL of the matching logo image, and the output is the search results saved in the database. Specifically, the server performs the following process:

[0084] Insert the matching logo image URL into the database.

[0085] Step 5: Notification of results

[0086] The server notifies the user of the search results. For example, it can notify them by email. The input requires the search results stored in the database, and the output is the notification sent to the user. Specifically, the server performs the following process:

[0087] The search results are inserted into the email body and sent to the user.

[0088] In this way, a system can be constructed in which the server can efficiently search for specific logo images, organize the judgment results of the generative AI model, and notify the user quickly and accurately.

[0089] (Application example 1)

[0090] Next, a description will be given of Application Example 1 of Embodiment 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."

[0091] While conventional search engine crawlers can search the Internet for specific logo images, they lack the functionality to monitor and notify users in real time how those logo images are being used. Furthermore, they lack the means to determine whether searched logo images are generated by AI and provide a list of that information. This makes it difficult for brand managers and marketers to efficiently monitor the usage of their company's logos.

[0092] 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.

[0093] In this invention, the server includes means for searching the Internet for specific logo images using a search engine crawler, means for determining whether the searched logo images were generated by AI, means for generating summaries of information sources containing logo images determined to be generated by AI, means for listing the generated summaries and logo images, means for providing an interface for a human to review the listed summaries and logo images, and means for monitoring how specific logo images are used on the Internet and sending notifications in real time, thereby enabling brand managers and marketers to efficiently monitor the usage of their company's logos and respond in real time.

[0094] A "search engine crawler" is a program that automatically crawls web pages on the Internet and collects specific information.

[0095] A "logo image" is an image that includes a shape or design that symbolizes a company or brand.

[0096] "AI-generated" means created automatically using artificial intelligence technology.

[0097] "Source" refers to a website or online platform where particular data or information is posted.

[0098] A "summary" is a concise summary of the main points of a particular piece of information.

[0099] "Listing" means organizing multiple items into a list format.

[0100] "Interface" refers to the means or screen through which a user interacts with a system.

[0101] "Real-time notifications" means that users are notified immediately when a specific event or piece of information occurs.

[0102] "Monitoring" means continuously observing a specific object and recording its condition and changes.

[0103] The system for implementing this invention includes a search engine crawler, an AI judgment module, a summary generation module, a listing module, an interface module, and a real-time notification module. These modules run on a server, monitor how a specific logo image is used on the Internet, and notify users in real time.

[0104] Program processing explanation

[0105] The server periodically crawls web pages on the Internet using a search engine crawler implemented using Python to search for specific logo images. The crawler retrieves web pages using the requests library and analyzes the HTML using the BeautifulSoup library. If a specific logo image is found, the information is sent to the AI ​​judgment module.

[0106] The AI ​​determination module uses a generative AI model to determine whether the searched logo image is AI-generated. This model uses a pre-trained deep learning model. The determination result is sent to the summary generation module.

[0107] The summary generation module generates a summary of the information source containing the logo image determined to be generated by AI. This summary is automatically generated using natural language processing technology. The generated summary information and logo image are sent to the listing module.

[0108] The list module organizes the generated summary information and logo images into a list format and stores it in a database, which is then sent to the interface module for user confirmation.

[0109] The interface module provides an interface for users to check the listed summary information and logo images, and the interface can be accessed through a web browser or a smartphone application.

[0110] The real-time notification module notifies users in real time when a specific logo image is newly used on the Internet, in the form of push notifications, email notifications, etc.

[0111] Specific examples

[0112] For example, if a brand manager wants to monitor how their company's logo image (URL: https: / / example.com / logo.png) is being used online, they can use this system to receive real-time information about new web pages where the logo image is being used, enabling them to quickly detect and respond to any unauthorized or misused brand use.

[0113] Prompt Sentence Examples

[0114] Implement a crawler to monitor how your brand's logo image (URL: https: / / example.com / logo.png) is used on the Internet. This crawler will traverse the Internet every night, searching for the specific logo image. If it finds one, it will add the URL to a list and notify the user.

[0115] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0116] Step 1:

[0117] The server launches a search engine crawler to periodically crawl web pages on the Internet. As input, it receives the URL of a specific logo image and a list of web pages to crawl. The crawler uses the requests library to retrieve the web pages and parses the HTML using the BeautifulSoup library. As output, it generates the URLs of web pages that contain the specific logo image.

[0118] Step 2:

[0119] The server sends the URL of the webpage obtained in step 1 to the AI ​​judgment module. As input, it receives the URL of the webpage and the logo image data. The AI ​​judgment module uses the generative AI model to determine whether the logo image was generated by AI. As output, it generates a judgment result.

[0120] Step 3:

[0121] The server sends the judgment result obtained in step 2 to the summary generation module. It receives the judgment result and the URL of the web page as input. The summary generation module uses natural language processing technology to generate a summary of the information source where the logo image is posted. It generates summary information as output.

[0122] Step 4:

[0123] The server sends the summary information and logo images generated in step 3 to the listing module. The listing module receives the summary information and logo image data as input. The listing module organizes the generated summary information and logo images into a list format and stores it in a database. The listing module generates the list data as output.

[0124] Step 5:

[0125] The server sends the listed data to an interface module, which receives the listed data as input, provides an interface for a user to view the listed summary information and logo image, and generates an interface accessible to a user as output.

[0126] Step 6:

[0127] The server starts a real-time notification module when a specific logo image is newly used on the Internet. As input, it receives information about the newly used logo image. The real-time notification module notifies the user in the form of a push notification, email notification, or the like. As output, it generates a notification message.

[0128] Example 2

[0129] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0130] In recent years, advances in artificial intelligence technology have led to an increase in the number of generated images. However, there is a lack of means to determine whether these images were generated by artificial intelligence, making it difficult for users and businesses to obtain reliable information. There is also a need for a system that can efficiently determine whether images searched on the network were generated by artificial intelligence and provide the results to users.

[0131] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0132] In this invention, the server includes means for searching a network for specific images using a search engine crawler, means for determining whether the searched image was generated by artificial intelligence, means for generating summaries of information sources containing images determined to have been generated by artificial intelligence, means for listing the generated summary information and images, means for a user to upload images and determine whether the uploaded images were generated by artificial intelligence, means for inputting preprocessed images into a machine learning model and obtaining the determination result, and means for notifying the user of the determination result, thereby enabling a user to easily determine whether an image searched for on a network or an image uploaded by the user was generated by artificial intelligence and obtain the result in a reliable manner.

[0133] A "search engine crawler" is a program that automatically collects specific data from sources on a network.

[0134] "Image" is digital data containing visual information, including logos and photographs.

[0135] A "network" is a system in which multiple computers and devices are interconnected and exchange data.

[0136] "Artificial intelligence" is the technology that enables computer systems to mimic human intelligence and learn, reason, and self-correct.

[0137] A "means for determining" is a process or device that evaluates data based on specific conditions or criteria and derives a result.

[0138] A "source" is a place or system where data or information is generated, stored, or provided.

[0139] A "summary generator" is a process or device that condenses long information into short chunks and extracts the main points.

[0140] A "listing means" is a process or device that organizes multiple pieces of data or information and displays them in a list format.

[0141] "User" means an individual or organization that uses the system or service.

[0142] "Uploading" is the act of sending data from a local device to a server on a network.

[0143] "Preprocessing" refers to the initial stage of processing to convert data into a format suitable for analysis and processing.

[0144] A "machine learning model" is an algorithm or system that learns from data and makes predictions and classifications.

[0145] A "judgment result" is an evaluation or conclusion drawn based on specific conditions or criteria.

[0146] A "means of notification" is a process or device that conveys specific information to a user.

[0147] MODE FOR CARRYING OUT THE INVENTION

[0148] This invention relates to a system for determining whether an image searched on a network or uploaded by a user has been generated by artificial intelligence. A specific embodiment of this system will be described below.

[0149] System configuration

[0150] This system consists of three main elements: a server, a terminal, and a user. The server uses a machine equipped with a high-performance GPU (e.g., NVIDIA Tesla V100) and uses Tensorflow (registered trademark) or PyTorch as software. The terminal is the device through which the user accesses the system, and includes devices such as PCs and smartphones.

[0151] Program processing

[0152] The server performs the following steps:

[0153] 1. Image search using search engine crawlers

[0154] The server searches the network for specific images using a search engine crawler, which periodically traverses the network and collects new images.

[0155] 2. Image Judgment

[0156] The server uses a machine learning model to determine whether the retrieved image was generated by artificial intelligence. The model learns the characteristics of images generated by a Generative Adversarial Network (GAN).

[0157] 3. Summary information generation

[0158] The server generates a summary of the source containing the image determined to be generated by artificial intelligence, which is a concise summary of the main points.

[0159] 4. Listing

[0160] The server lists the generated summary information and images in an organized manner that allows the user to easily review the list.

[0161] 5. User image upload and detection

[0162] Users use their terminals to access the system's web interface and upload images. The server receives the uploaded images and performs pre-processing, which includes resizing and normalizing the images.

[0163] 6. Input to the machine learning model and obtain the judgment result

[0164] The server inputs the preprocessed image into the machine learning model and obtains a judgment result, which is a flag indicating whether the image was generated by artificial intelligence.

[0165] 7. Notification of the decision

[0166] The server notifies the user of the result of the determination, either by a message displayed on the web interface or, if necessary, by email.

[0167] Specific examples

[0168] For example, if a user wants to determine whether a searched logo image was generated by AI, they can input the following prompt to the generative AI model:

[0169] Example prompt sentence:

[0170] "Please determine whether this logo image was generated by AI."

[0171] The server receives this prompt and inputs the retrieved logo image into a machine learning model. The model analyzes the image's features and determines whether it was AI-generated. The result is then notified to the user.

[0172] In this way, users can easily verify whether a logo image is generated by AI.

[0173] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0174] Program processing flow

[0175] Step 1: Image search using a search engine crawler

[0176] The server searches the network for specific images using a search engine crawler, which periodically traverses the network and collects new images.

[0177] Input: Search query

[0178] Output: A list of URLs of the images found

[0179] How it works: The server passes the search query to the crawler, which then visits sources on the network to collect image URLs, which are then returned to the server.

[0180] Step 2: Download and preprocess images

[0181] The server downloads images from the collected URL list and pre-processes them, which includes resizing and normalizing the images.

[0182] Input: List of image URLs

[0183] Output: Preprocessed image data

[0184] What happens: The server downloads the image from each URL, resizes it using Python's Pillow library, and normalizes the pixel values ​​using NumPy (e.g., resizes the image to 256x256 pixels and scales the pixel values ​​to the range 0 to 1).

[0185] Step 3: Image Judgment

[0186] The server inputs the pre-processed image data into a machine learning model to determine whether the image was generated by artificial intelligence.

[0187] Input: Preprocessed image data

[0188] Output: Judgment result (probability of AI generation)

[0189] How it works: The server inputs preprocessed image data into a TensorFlow or PyTorch model. The model analyzes the image features and evaluates the likelihood of AI generation. For example, a probability value such as "AI generation: 90%" is output.

[0190] Step 4: Generate summary information

[0191] The server generates summaries of the sources containing images determined to be generated by artificial intelligence.

[0192] Input: Judgment result, image URL

[0193] Output: Summary information

[0194] Specific operation: If the judgment result exceeds a certain threshold, the server extracts text information from the source where the image is posted and generates a summary using natural language processing technology.

[0195] Step 5: List

[0196] The server lists the generated summary information and images.

[0197] Input: Summary information, image data

[0198] Output: Listed information

[0199] Specific operation: The server compiles the summary information and image data into a single list and organizes them so that the user can easily check them.

[0200] Step 6: Upload user images

[0201] Users use a terminal to access the system's web interface and upload images.

[0202] Input: User uploaded image

[0203] Output: Image data sent to the server

[0204] Specific operation: A user opens a browser and accesses the system's web page. The user clicks the "Select Image" button to select a logo image file from the local disk. Then, the user clicks the "Upload" button.

[0205] Step 7: Preprocessing the uploaded images

[0206] The server receives the uploaded images and performs pre-processing.

[0207] Input: Uploaded image data

[0208] Output: Preprocessed image data

[0209] Specific operation: The server resizes the received image using Python's Pillow library and normalizes the pixel values ​​using NumPy.

[0210] Step 8: Input to the machine learning model and obtain the judgment result

[0211] The server inputs the preprocessed image into the machine learning model and obtains the judgment result.

[0212] Input: Preprocessed image data

[0213] Output: Judgment result (probability of AI generation)

[0214] How it works: The server inputs preprocessed image data into a TensorFlow or PyTorch model, which analyzes the image's features and evaluates the potential for AI generation.

[0215] Step 9: Notification of decision

[0216] The server notifies the user of the result of the determination.

[0217] Input: Judgment result

[0218] Output: Message to be sent to the user

[0219] Specific operation: The server generates the judgment result in HTML format and displays it in the user's browser. For example, it may say, "This logo image is likely to have been generated by AI (90%)."

[0220] In this way, users can easily verify whether a logo image is generated by AI.

[0221] (Application example 2)

[0222] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0223] In recent years, advances in AI technology have led to an increase in the number of AI-generated logo images on the Internet. This has made it difficult to verify the authenticity of logo images used by companies and individuals. In particular, there is a growing risk that AI-generated logo images will be misused, raising concerns about a decline in credibility and damage to brand value. In this situation, there is a need for a method to quickly and accurately determine whether a logo image has been generated by AI.

[0224] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for searching the Internet for specific logo images using a search engine crawler, means for determining whether the searched logo images were generated by AI, means for generating summaries of websites containing logo images determined to have been generated by AI, means for listing the generated summaries and logo images, means for providing an interface for uploading logo images, means for determining whether the uploaded logo images were generated by AI, and means for notifying the user of the determination result. This makes it possible to quickly and accurately determine the authenticity of logo images used by companies and individuals.

[0225] A "search engine crawler" is a program that automatically searches for and collects specific logo images on the Internet.

[0226] A "logo image" is a visual symbol used by businesses and individuals to identify their brand or service.

[0227] "AI-generated" means that it has been automatically created using artificial intelligence technology.

[0228] "Means for determining" refers to a device or program that has the function of determining whether a specific logo image was generated by AI.

[0229] A "means for generating a website summary" is a device or program that has the function of creating information that succinctly summarizes the contents of a particular website.

[0230] The "listing means" is a device or program that has the function of organizing the generated summary information and logo images in a list format.

[0231] The "means for providing an interface" refers to a device or program that provides an operation screen and input means for users to upload logo images.

[0232] "Means for notifying the user of the judgment result" refers to a device or program that has the function of informing the user of the judgment result as to whether the logo image was generated by AI.

[0233] The system for implementing the present invention is configured as follows: The server includes a search engine crawler, an AI judgment module, a summary generation module, a listing module, an interface providing module, and a notification module.

[0234] First, a search engine crawler periodically searches for and collects specific logo images from the Internet. The collected logo images are sent to an AI judgment module. The AI ​​judgment module uses a pre-trained generative AI model (e.g., GAN) to determine whether the logo image was generated by AI. The result of this judgment is sent to a summary generation module, which generates a summary of the website containing the logo image determined to have been generated by AI.

[0235] Next, the generated summary information and logo images are organized in a list format by the listing module. This list is displayed through the interface providing module so that the user can check it. The user can also upload a logo image through the interface. The uploaded logo image is sent again to the AI ​​judgment module, and the judgment result is notified to the user through the notification module.

[0236] The following hardware and software are used to implement this system. Hardware requires a server and user terminal. Software includes TensorFlow, Keras, and PIL (Python Imaging Library). This software is used to process and calculate data. Specifically, it performs processes such as image resizing, normalization, tensor transformation, and judgment using AI models.

[0237] For example, when a company is designing a new logo, they can use the system to check whether the logo was generated by AI. Users upload a logo image through the interface and enter the following prompt:

[0238] "Please determine whether this logo image was generated by AI."

[0239] Based on this prompt, the system will determine whether the logo image is authentic and notify the user, thereby ensuring the reliability of logo images used by companies and individuals.

[0240] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0241] Step 1:

[0242] The server periodically searches for and collects specific logo images from the Internet using a search engine crawler. The input is a search query or search conditions, and the output is the collected logo image data. Specifically, the crawler traverses web pages, detects logo images, and downloads them.

[0243] Step 2:

[0244] The server sends the collected logo images to the AI ​​judgment module. The input is the collected logo image data, and the output is the transfer of image data to the AI ​​judgment module. Specifically, the image data is passed to the AI ​​judgment module.

[0245] Step 3:

[0246] The server uses an AI judgment module to determine whether a logo image was generated by AI. The input is the collected logo image data, and the output is a judgment result. Specifically, the image data is input into a generative AI model (e.g., GAN), and the model analyzes the image features and outputs a judgment result.

[0247] Step 4:

[0248] The server generates a summary of the website that contains the logo image determined to have been generated by AI. The input is the result of the determination and the URL of the corresponding website, and the output is summary information. Specifically, it analyzes the content of the website, extracts important information, and creates a summary.

[0249] Step 5:

[0250] The server creates a list of the generated summary information and logo images. The input is the summary information and logo image data, and the output is a list of the information. Specifically, the server organizes the summary information and logo images in a list format and saves them in a database.

[0251] Step 6:

[0252] The user uploads a logo image through the interface. The input is the logo image data uploaded by the user, and the output is the transfer of the image data to the server. Specifically, the user operates the interface to select an image file and presses the upload button.

[0253] Step 7:

[0254] The server then sends the uploaded logo image back to the AI ​​judgment module for judgment. The input is the logo image data uploaded by the user, and the output is the judgment result. Specifically, the image data is passed to the AI ​​judgment module, and the model analyzes the image features and outputs the judgment result.

[0255] Step 8:

[0256] The server notifies the user of the judgment result through a notification module. The input is the judgment result, and the output is a notification message for the user. Specifically, the judgment result is generated as a notification message and sent to the user's terminal.

[0257] Example 3

[0258] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0259] Conventional technologies have had difficulty determining whether a specific image on the Internet was generated by artificial intelligence and generating a summary of the web page on which the image was posted. Furthermore, there was a lack of a way to provide the generated summary information and image in a format that was easy to visually check. This made it difficult for users to quickly and efficiently obtain the information they needed.

[0260] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0261] In this invention, the server includes means for searching a network for specific images using a search engine crawler, means for determining whether the searched image has been generated by artificial intelligence, means for extracting text information from web pages containing images determined to have been generated by artificial intelligence, means for extracting important information from the extracted text information and generating summaries, and means for listing the generated summaries and images, thereby enabling users to efficiently obtain summaries of web pages containing images generated by artificial intelligence and view them in a format that is easy to visually confirm.

[0262] A "search engine crawler" is a program that automatically crawls web pages on a network and collects specific information.

[0263] An "image" is a visually perceptible still image stored in digital form.

[0264] A "network" is a system in which multiple computers and devices are connected through a communication infrastructure such as the Internet.

[0265] "Artificial intelligence" is the technology that enables computer systems to mimic human intelligence and perform functions such as learning, reasoning, and recognition.

[0266] A "web page" is a page containing documents and media content that is published on the Internet and is written in a markup language such as HTML.

[0267] "Text information" is information expressed as a string of characters, and exists in the form of sentences, words, etc.

[0268] A "summary" is information that extracts important parts from original text information and summarizes them concisely.

[0269] "Listing" refers to organizing multiple pieces of information and displaying them in a list format.

[0270] An "interface" is the means by which a user interacts with a system, including visual and operational elements.

[0271] This invention relates to a system that determines whether a specific image has been generated by artificial intelligence, generates a summary of the web page on which the image is posted, and displays the generated summary information and the image in a list.

[0272] The server implements this system using the following hardware and software:

[0273] Hardware and software used

[0274] Hardware: High-performance server computer, network interface card

[0275] software:

[0276] Programming language: Python

[0277] Libraries: requests, BeautifulSoup, transformers, Jinja2

[0278] Artificial intelligence model: BERT (Bidirectional Encoder Representations from Transformers)

[0279] Details of data processing and calculation

[0280] 1. Search Engine Crawlers:

[0281] The server uses Python's requests library to send an HTTP request to the URL specified by the user to retrieve the HTML content of the web page.

[0282] 2. Parsing HTML content:

[0283] The server uses the BeautifulSoup library to parse the retrieved HTML content and extract text information, specifically by stripping out HTML tags and extracting pure text data.

[0284] 3. Extracting important information:

[0285] The server then inputs the extracted text information into the BERT model to extract important information, which then understands the context of the text and identifies important sentences and phrases.

[0286] 4. Generate a summary:

[0287] The server generates summaries based on key information obtained from the BERT model, using the pipeline from the transformers library.

[0288] 5. Summary and image listing:

[0289] The server embeds the generated summary information and images into an HTML template and displays it in a list format. Specifically, it uses the Jinja2 template engine to create a visually easy-to-read list.

[0290] 6. Providing an interface:

[0291] The server sends the generated HTML to the user's browser, and the user checks the summary information and the list of images through the browser.

[0292] Specific examples

[0293] When a user specifies a specific website (e.g., https: / / example.com), the server retrieves the HTML content of that website and generates a summary using the BERT model. The generated summary and images are displayed as a table through a web interface.

[0294] Example prompt sentence:

[0295] "Extract text information from https: / / example.com and generate a summary using the BERT model. Display the generated summary and images in a table."

[0296] In this way, the server can extract text information from the specified website, summarize important information, and display it in a visually easy-to-read format. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0297] Step 1:

[0298] The user enters a website URL.

[0299] The user accesses the system's web interface, enters "https: / / example.com" in the text box, and clicks the "Submit" button.

[0300] Enter: Website URL

[0301] Output: The URL entered

[0302] Step 2:

[0303] The server retrieves the HTML content of the specified website.

[0304] The server uses the requests library to send an HTTP request to the URL entered by the user to retrieve the HTML content of the website.

[0305] Input: Entered URL

[0306] Output: Retrieved HTML content

[0307] Step 3:

[0308] The server extracts text information from the HTML content.

[0309] The server uses the BeautifulSoup library to parse the retrieved HTML content and extract text information, specifically by stripping out HTML tags and extracting pure text data.

[0310] Input: Retrieved HTML content

[0311] Output: Extracted text information

[0312] Step 4:

[0313] The server uses the BERT model to extract important information.

[0314] The server then inputs the extracted text information into the BERT model to extract important information, which then understands the context of the text and identifies important sentences and phrases.

[0315] Input: Extracted text information

[0316] Output: Extracted important information

[0317] Step 5:

[0318] The server generates a summary based on the extracted information.

[0319] The server generates summaries based on key information obtained from the BERT model, using the pipeline from the transformers library.

[0320] Input: Extracted sensitive information

[0321] Output: The generated summary

[0322] Step 6:

[0323] The server lists the summary information and images.

[0324] The server embeds the generated summary information and images into an HTML template and displays it in a list format. Specifically, it uses the Jinja2 template engine to create a visually easy-to-read list.

[0325] Input: Generated summary,image

[0326] Output: List of summary information and images

[0327] Step 7:

[0328] The server displays a list of information in a web interface.

[0329] The server sends the generated HTML to the user's browser, and the user checks the summary information and the list of images through the browser.

[0330] Input: List of summary information and images

[0331] Output: A list of images and summary information displayed in the web interface.

[0332] (Application example 3)

[0333] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0334] Conventional advertising systems make it difficult for users to efficiently find information of interest from the vast number of advertisements available on the Internet. Furthermore, there was a lack of a way to automatically generate and provide users with a summary of websites that contained AI-generated logo images. This made it difficult for users to quickly understand the content of advertisements and make appropriate decisions.

[0335] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0336] In this invention, the server includes means for searching the Internet for specific logo images using a search engine crawler, means for determining whether the retrieved logo images were generated by artificial intelligence, means for generating summaries of websites containing logo images determined to have been generated by artificial intelligence, means for listing the generated summaries and logo images, means for providing an interface for users to view the listed summaries and logo images, means for functioning as an application to be installed on a smartphone, means for extracting text information from websites and generating summaries using natural language processing technology, and means for displaying a list of the generated summaries and logo images, thereby enabling users to efficiently view advertisement summaries and logo images and quickly find advertisements of interest.

[0337] A "search engine crawler" is a program that automatically crawls websites on the Internet and collects specific information.

[0338] "Artificial intelligence" refers to computer systems that mimic human intelligence and can learn and reason.

[0339] A "website" is a collection of information published on the Internet and written in a format such as HTML.

[0340] A "summary" is a short summary of a long piece of text or information, extracting the important points and expressing them concisely.

[0341] "Listing" means organizing and displaying multiple items in a list format.

[0342] An "interface" is the means or method by which a user and a system interact with each other.

[0343] A "smartphone" is a type of mobile phone, a multi-function device that can connect to the Internet and run applications.

[0344] "Natural language processing technology" is a technology that allows computers to understand and process human language.

[0345] "List display" refers to displaying multiple pieces of information together on one screen or page.

[0346] The system for implementing this invention includes means for a search engine crawler, artificial intelligence, a website, summarization, listing, an interface, a smartphone, natural language processing technology, and list display. Specific embodiments of each means will be described below.

[0347] System configuration

[0348] The server uses a search engine crawler to search for specific logo images on the Internet, uses image recognition technology to determine whether the found logo image was generated by artificial intelligence, extracts text information from the website where the determined logo image is posted, and generates a summary using natural language processing technology.

[0349] The generated summary information and logo images are listed and provided to the user through an interface. This interface functions as an application installed on a smartphone. The user can view the listed summary information and logo images in a list view using their smartphone.

[0350] Hardware and software used

[0351] Hardware: Smartphones, servers

[0352] Software: Python, BeautifulSoup, Transformers library (Hugging Face)

[0353] Data processing and calculation

[0354] The server extracts text information from websites using a search engine crawler. It uses BeautifulSoup to extract text from HTML and filters out only visible text. The extracted text information is summarized using natural language processing technology (BERT model). The generated summary information and logo images are compiled into a list and provided to users via a smartphone application.

[0355] Specific examples

[0356] For example, the server extracts text information from the URL "https: / / example.com / ad1" and generates a summary using the BERT model. The generated summary information and a logo image "logo1.png" are displayed to the user via a smartphone application.

[0357] Prompt Sentence Examples

[0358] Extract the text information from the URL: "https: / / example.com / ad1" and generate a summary using the BERT model. Display the generated summary and the logo image "logo1.png" as a list.

[0359] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0360] Step 1:

[0361] The server launches a search engine crawler to search the Internet for specific logo images. It receives the characteristics and keywords of specific logo images as input and generates a list of URLs for corresponding logo images as output. Specifically, the crawler visits websites and extracts image files from the HTML source code.

[0362] Step 2:

[0363] The server determines whether the searched logo images were generated by artificial intelligence. As input, it receives the list of logo image URLs obtained in step 1, and as output, it generates a list of logo images that have been determined to have been generated by artificial intelligence. Specifically, it uses image recognition technology to analyze the features of the logo images and makes a determination using a generative AI model.

[0364] Step 3:

[0365] The server extracts text information from websites that contain logo images determined to have been generated by artificial intelligence. As input, it receives the list of logo image URLs obtained in step 2, and as output, it generates text information for each website. Specifically, it uses BeautifulSoup to extract text from HTML source code and filters out only visible text.

[0366] Step 4:

[0367] The server summarizes the extracted text information using natural language processing technology. It receives the text information obtained in step 3 as input and generates summary information as output. Specifically, it uses the BERT model to tokenize the text, extract important information, and generate a summary.

[0368] Step 5:

[0369] The server lists the generated summary information and logo images. As input, it receives the list of logo images obtained in step 2 and the summary information obtained in step 4, and generates a list of summary information and logo images as output. Specifically, it associates the summary information with the logo images and organizes them in a list format.

[0370] Step 6:

[0371] The server provides an interface for users to check the list of summary information and logo images. It receives the list of summary information and logo images obtained in step 5 as input and generates a user interface as output. Specifically, it displays a list of summary information and logo images through an application installed on a smartphone.

[0372] Step 7:

[0373] The user uses the smartphone application to check the listed summary information and logo images. As input, the user receives the user interface provided in step 6, and as output, selects the advertisement of interest. Specifically, the user taps the summary information and logo image on the application screen to display detailed information.

[0374] 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.

[0375] "Example 1"

[0376] The invention incorporating an emotion engine recognizes a user's emotion and adjusts the display of the summary information list and logo images based on that emotion. Specifically, the emotion engine recognizes the emotion from the user's facial expression, tone of voice, text input, etc. For example, if the user expresses anger, the emotion engine conveys that information to the system, and the system adjusts the display of the summary information list and logo images. Specifically, for users who express anger, the system takes action such as displaying problematic logo images more prominently.

[0377] "Example 2"

[0378] The emotion engine also recognizes the user's emotions through the user interface. Specifically, the user interface includes input devices such as a camera, microphone, and keyboard. The camera captures the user's facial expressions, the microphone records the user's tone of voice, and the keyboard accepts the user's text input. The information obtained from these input devices is sent to the emotion engine, which then recognizes the user's emotions from this information.

[0379] "Example 3"

[0380] Furthermore, the emotion engine adjusts the display method of the listed summary information and logo image based on the recognized emotion. Specifically, the emotion engine communicates the recognized emotion to the system, and the system adjusts the display method of the summary information and logo image based on the emotion. For example, if the user expresses joy, the system selects a display method that matches the user's emotion, such as displaying the summary information and logo image in bright colors.

[0381] The processing flow of each embodiment will be described below.

[0382] "Example 1"

[0383] Step 1: The emotion engine recognizes emotions from the user's facial expressions, tone of voice, text input, etc.

[0384] Step 2: The emotion engine communicates the recognized emotion information to the system.

[0385] Step 3: The system adjusts the display of the summary information and logo images based on the received emotion information. For example, if a user expresses anger, the system may display the problematic logo image more prominently.

[0386] "Example 2"

[0387] Step 1: The user interface conveys the user's emotions to the emotion engine through input devices such as a camera, microphone, or keyboard.

[0388] Step 2: The emotion engine recognizes the user's emotions based on the information obtained from these input devices.

[0389] Step 3: The emotion engine communicates the recognized emotion information to the system, which then adjusts the display method of the summary information and logo image based on that information.

[0390] "Example 3"

[0391] Step 1: The emotion engine recognizes the user's emotion.

[0392] Step 2: The emotion engine communicates the recognized emotion to the system.

[0393] Step 3: The system adjusts the display method of the summary information and logo image based on the received emotion information. For example, if the user expresses joy, the system will select a display method that matches the user's emotion, such as displaying the summary information and logo image in bright colors.

[0394] Example 1

[0395] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0396] Conventional search engine crawlers can search the Internet for specific logo images, but they lack the ability to adjust how information is displayed based on the user's emotions. This makes it difficult to provide personalized information that reflects the user's emotions, and there is a need to improve the user experience. Furthermore, the identification of logo images generated by artificial intelligence and the generation and listing of summary information are not automated, making efficient information management difficult.

[0397] 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.

[0398] In this invention, the server includes means for searching the Internet for specific logo images using a search engine crawler, means for determining whether the retrieved logo image was generated by artificial intelligence, means for generating summaries of websites containing logo images determined to have been generated by artificial intelligence, means for listing the generated summaries and logo images, means for recognizing a user's emotion, and means for adjusting the display method of the listed summaries and logo images based on the recognized user emotion. This enables the provision of personalized information according to the user's emotion, resulting in efficient information management and an improved user experience.

[0399] A "search engine crawler" is a program that automatically crawls websites on the Internet and collects specific information.

[0400] A "logo image" is an image of a shape or design used to identify a company or brand.

[0401] "Artificial intelligence" refers to computer systems that mimic human intelligence and can learn and reason.

[0402] "Summary information" is information that briefly summarizes the contents of a website.

[0403] "Listing" refers to organizing multiple pieces of information into a list format.

[0404] "User emotion" refers to the psychological state or feelings exhibited by the user.

[0405] "Adjusting the display method" refers to changing the way information is presented based on the user's emotions.

[0406] The present invention is a system for searching for a specific logo image and adjusting the way information is displayed based on the user's emotions. A specific embodiment of this system will be described below.

[0407] The server runs a search engine crawler. This crawler is implemented using Python and analyzes web pages using libraries such as BeautifulSoup and Scrapy. The crawler traverses the Internet every night and collects specific logo images. The collected logo images are stored in a database such as MySQL or PostgreSQL.

[0408] The device runs an emotion engine to recognize the user's emotions. The emotion engine captures the user's facial expressions with a camera, receives voice input with a microphone, and analyzes text input. This data is processed using libraries such as TensorFlow and OpenCV. The emotion engine classifies the user's emotions into categories such as "anger," "joy," and "sadness," and sends the information to a server.

[0409] A user accesses the system through a terminal and browses search results for logo images. If the user expresses anger, the emotion engine communicates this information to the server. The server then adjusts the display method of the listed summary information and logo images based on the user's emotion. For example, if the user expresses anger, problematic logo images are displayed more prominently.

[0410] For example, the server runs a crawler every night to collect specific logo images and store them in a database. The device captures the user's facial expressions and voice and analyzes them using an emotion engine. The emotion engine sends the analysis results to the server, which adjusts the display method based on that information. The user then views the adjusted information in a web browser.

[0411] Examples of prompts to input to a generative AI model include:

[0412] "Please explain how to make the offending logo image more prominent when users express anger."

[0413] The system can dynamically adjust how information is displayed based on the user's emotions, providing a more personalized user experience.

[0414] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0415] Step 1:

[0416] The server runs the crawler.

[0417] The server runs the crawler as a scheduled task every night at midnight. The crawler is implemented using Python and analyzes web pages using libraries such as BeautifulSoup and Scrapy. As input, it receives a list of URLs for the websites that the crawler will visit. As output, it extracts URLs for specific logo images.

[0418] Step 2:

[0419] The crawler collects logo images.

[0420] The crawler visits a specified website and extracts logo image URLs using a specific CSS selector (e.g., img.logo). The input is the HTML data of the web page. The output is a list of logo image URLs. Specifically, it analyzes the HTML of the web page and extracts the logo image URLs.

[0421] Step 3:

[0422] The server stores the collected logo images in a database.

[0423] The server stores the URLs of logo images collected by the crawler in a database. As input, it receives a list of logo image URLs. As output, it generates records of logo images stored in the database. Specifically, it inserts records into the database using an ORM library such as SQLAlchemy.

[0424] Step 4:

[0425] The device recognizes the user's emotions.

[0426] The device uses a camera and microphone to capture the user's facial expressions and voice, and sends them to the emotion engine. Images of the user's facial expressions and voice data are provided as input. The output generates the user's emotion category (e.g., "anger," "joy," or "sadness"). Specifically, the system uses OpenCV to detect faces and TensorFlow to predict emotions.

[0427] Step 5:

[0428] The emotion engine sends the user's emotion to the server.

[0429] The emotion engine sends the recognized emotion data to the server. The user's emotion category is given as input. The emotion data sent to the server is generated as output. Specifically, the emotion data is sent using an HTTP POST request.

[0430] Step 6:

[0431] The server adjusts the display method based on the user's emotions.

[0432] The server adjusts the display method of logo images based on the received emotion data. As input, it receives the user's emotion data and a list of logo images. As output, it generates adjusted HTML and CSS. Specifically, if the user expresses anger, it generates HTML and CSS to display the problematic logo images more prominently.

[0433] Step 7:

[0434] The user views the tailored information.

[0435] The user views the tailored information through the terminal. The tailored HTML page is provided as input. The web page viewed by the user is displayed as output. Specifically, the web browser is used to display the HTML page provided by the server.

[0436] (Application example 1)

[0437] Next, a description will be given of Application Example 1 of Embodiment 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."

[0438] Conventional search engine crawlers can search the internet for specific logo images and identify logo images generated by artificial intelligence, but they cannot adjust the way information is displayed based on the user's emotions. As a result, they are unable to provide appropriate information in response to the user's emotions, which results in a lack of an improved user experience.

[0439] 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.

[0440] In this invention, the server includes means for searching the Internet for specific logo images using a search engine crawler, means for determining whether the retrieved logo image was generated by artificial intelligence, means for generating summaries of websites containing logo images determined to have been generated by artificial intelligence, means for listing the generated summaries and logo images, means for recognizing a user's emotion, and means for adjusting the display method of the listed summaries and logo images based on the recognized emotion, thereby enabling the provision of appropriate information according to the user's emotion.

[0441] A "search engine crawler" is a program that crawls the Internet and automatically collects specific information.

[0442] A "logo image" is a visual symbol that identifies a company or brand.

[0443] "Artificial intelligence" refers to computer systems that mimic human intelligence and are used for data analysis and pattern recognition.

[0444] A "website" is a collection of information publicly available on the Internet and is a page accessible at a specific URL.

[0445] "Summary information" is a concise summary of the original information.

[0446] "Listing" means organizing multiple items into a list format.

[0447] "Emotion recognition" is a technology that determines a user's emotions from facial expressions, tone of voice, text input, etc.

[0448] "Adjusting the display method" means changing the way information is displayed depending on the user's emotions.

[0449] The system for implementing this invention combines a search engine crawler, artificial intelligence, emotion recognition technology, and display adjustment functions. Specific embodiments are described below.

[0450] System configuration

[0451] The server uses the following hardware and software:

[0452] Hardware: Servers with powerful processors, memory, storage, and network interfaces.

[0453] Software: Use libraries such as OpenCV, Keras, and PIL.

[0454] Program processing

[0455] The server processes and calculates data in the following procedure.

[0456] 1. Search engine crawler: The server periodically crawls the Internet and searches for specific logo images, allowing it to collect the latest logo images.

[0457] 2. AI detection: Determine whether the searched logo image is generated by AI using a pre-trained generative AI model.

[0458] 3. Summary information generation: A summary information is generated for websites that contain logo images determined to have been generated by AI, allowing users to efficiently understand the information.

[0459] 4. Listing: The generated summary information and logo image are listed, which organizes the information and makes it easy for users to check.

[0460] 5. Emotion Recognition: The server acquires video from the smartphone camera and analyzes the user's facial expressions and tone of voice to recognize the user's emotions. This is done using OpenCV and Keras.

[0461] 6. Display Adjustment: Adjust the display of the summary information and logo images based on the recognized emotion. For example, if the user expresses the emotion "happy," the related logo image will be displayed larger and more detailed information will be provided.

[0462] Specific examples

[0463] When a user is viewing an advertisement on their smartphone, if the emotion recognition model detects "happiness" from the user's facial expression, a related logo image will be displayed in a larger size and detailed information will be provided, making it possible to provide appropriate information according to the user's emotions.

[0464] Prompt Sentence Examples

[0465] An example of a prompt sentence to input to the generative AI model is as follows:

[0466] When a user is viewing an ad, if the emotion recognition model detects "happiness," how can I display a larger image of the relevant logo and provide more information?

[0467] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0468] Step 1:

[0469] The server uses a search engine crawler to crawl the Internet and search for specific logo images. The input is the search query and the website to be crawled, and the output is a list of the searched logo images. Specifically, the crawler accesses the specified URLs sequentially, detecting and collecting logo images.

[0470] Step 2:

[0471] The server determines whether the searched logo images were generated by artificial intelligence. The input is the list of logo images collected in step 1, and the output is a list of logo images determined to have been generated by artificial intelligence. Specifically, the server uses the generative AI model to analyze each logo image and determine whether it was generated.

[0472] Step 3:

[0473] The server generates summary information for websites that contain logo images determined to have been generated by artificial intelligence. The input is a list of logo images determined in step 2 and the URLs of the websites that contain them, and the output is a list of summary information. Specifically, the server analyzes the content of the website, extracts important information, and creates a summary.

[0474] Step 4:

[0475] The server lists the generated summary information and logo images. The input is the list of summary information and logo images generated in step 3, and the output is the list of summary information and logo images. Specifically, the server associates each summary information with a logo image and organizes them in a list format.

[0476] Step 5:

[0477] To recognize the user's emotions, the device acquires video from the smartphone camera and analyzes the user's facial expressions and tone of voice. The input is the smartphone camera video, and the output is the recognized emotion. Specifically, the device uses OpenCV and Keras to analyze the video in real time and determine the emotion.

[0478] Step 6:

[0479] The server adjusts the display method of the listed summary information and logo image based on the recognized emotion. The input is the summary information and logo image listed in step 4 and the emotion recognized in step 5, and the output is the adjusted display method. Specifically, the server operates in accordance with the user's emotion; for example, if the emotion is "happy," it displays a related logo image in a larger size and provides detailed information.

[0480] Example 2

[0481] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0482] While conventional search engines can search for specific images on the Internet, they lack the means to determine whether the image was generated by artificial intelligence. They also lack the means to recognize users' emotions in real time and provide an interactive experience. This makes it difficult for users to obtain reliable information, and the user experience is limited.

[0483] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0484] In this invention, the server includes means for searching a network for specific images using a search engine crawler, means for determining whether the searched image has been generated by artificial intelligence, means for generating summaries of web pages containing images determined to have been generated by artificial intelligence, means for listing the generated summaries and images, means for recognizing a user's emotions through a user interface, and means for analyzing information obtained from input devices such as a camera, microphone, and keyboard to recognize the user's emotions. This allows the user to obtain highly reliable information, and recognizing emotions in real time enables a more interactive and richer user experience.

[0485] A "search engine crawler" is a program that automatically collects specific information from a network.

[0486] A "specific image" is a specific visual data that a user intends to find through a search engine.

[0487] "Network" refers to an information and communication network such as the Internet.

[0488] "Artificial intelligence" is a system that mimics human intelligence using technologies such as machine learning and deep learning.

[0489] A "means for determining" is a method or device for determining an outcome based on specific conditions or criteria.

[0490] A "web page" is a page that contains content such as documents, images, and videos that are publicly available on the Internet.

[0491] A "summary generator" is a method or device for summarizing long documents or information into shorter versions.

[0492] A "listing means" is a method or device for organizing multiple items into a list.

[0493] A "user interface" is an interface such as a screen or input device that allows a user to interact with a system.

[0494] "Means for recognizing emotions" refers to a method or device for analyzing and recognizing emotions from a user's facial expressions, tone of voice, text input, etc.

[0495] A "camera" is a device for taking pictures and videos.

[0496] A "microphone" is a device for recording sound.

[0497] A "keyboard" is a device for inputting characters and symbols.

[0498] An "input device" is a device that allows a user to input information into a system.

[0499] "Analyzing" means examining data in detail to understand its meaning and structure.

[0500] MODE FOR CARRYING OUT THE INVENTION

[0501] The present invention relates to a system for determining whether a specific image has been generated by artificial intelligence and for recognizing a user's emotions in real time. Specific embodiments of this system will be described below.

[0502] Hardware and software used

[0503] Hardware:

[0504] Camera (e.g. generic webcam)

[0505] Microphone (e.g., a general-purpose microphone)

[0506] Keyboard (e.g., a generic keyboard)

[0507] software:

[0508] Machine learning models (e.g., Generative Adversarial Network (GAN) models)

[0509] Emotion engine (e.g., general-purpose emotion recognition API)

[0510] System program processing

[0511] The server uses a search engine crawler to search for specific images on the network. The images are then sent to the server and input into a machine learning model. This model is used to determine whether the image was generated by artificial intelligence. For example, a GAN model is used to analyze the image's features and obtain a determination result.

[0512] Examples:

[0513] When a user searches for "new brand logo" using a web browser, the server receives the logo image and inputs it into a machine learning model. The model analyzes the image's features and determines whether it was generated by artificial intelligence. The result is returned to the user.

[0514] Example prompt sentence:

[0515] "Please determine whether this logo image was generated by artificial intelligence."

[0516] The device collects data to recognize the user's emotions through a user interface, specifically using input devices such as a camera, microphone, and keyboard. The camera captures the user's facial expressions, the microphone records the user's tone of voice, and the keyboard accepts the user's text input.

[0517] The server transmits the information obtained from these input devices to the emotion engine, which analyzes the information and recognizes the user's emotion.

[0518] Examples:

[0519] When a user is smiling and talking in front of the device, the camera captures the smile and the microphone records the tone of the voice. These data are sent to the server, and the emotion engine recognizes that the user is "happy."

[0520] Example prompt sentence:

[0521] "Recognize emotions from users' facial expressions and tone of voice."

[0522] The system recognizes user emotions in real time and judges artificial intelligence-generated images to provide a more interactive and reliable user experience.

[0523] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0524] Program processing flow

[0525] Logo image judgment

[0526] Step 1:

[0527] A user searches for a logo image.

[0528] Input: User types "new brand logo" into the search bar and clicks the search button.

[0529] Output: A search query is generated.

[0530] Specific actions: A user opens a web browser, enters keywords in the search bar, and presses the search button.

[0531] Step 2:

[0532] The terminal sends the logo image to the server.

[0533] Input: A logo image retrieved based on the search query.

[0534] Output: The logo image data is sent to the server.

[0535] Specific operation: The terminal uses an HTTP request to send the acquired logo image data to the server.

[0536] Step 3:

[0537] The server inputs the logo image into the machine learning model.

[0538] Input: Logo image data sent to the server.

[0539] Output: The result of the machine learning model.

[0540] Specific operation: The server inputs the received logo image into a machine learning model (e.g., GAN model), analyzes the image features, and obtains a judgment result.

[0541] Step 4:

[0542] The server returns the result of the determination to the user.

[0543] Input: The outcome from the machine learning model.

[0544] Output: The result of the judgment is displayed to the user.

[0545] Specific operation: The server sends the judgment result to the user's device as an HTTP response and displays the result in the browser.

[0546] User Emotion Recognition

[0547] Step 1:

[0548] The user sits in front of the terminal.

[0549] Input: User presence.

[0550] Output: The camera, microphone, and keyboard are ready to collect user data.

[0551] Specific actions: The user sits in front of the device and prepares to use input devices such as the camera, microphone, and keyboard.

[0552] Step 2:

[0553] The device collects data from the camera, microphone, and keyboard.

[0554] Input: User facial expressions, tone of voice, and text input.

[0555] Output: Collected data.

[0556] Specific operations: The camera captures the user's facial expressions, the microphone records the user's tone of voice, and the keyboard accepts the user's text input.

[0557] Step 3:

[0558] The terminal transmits the collected data to the server.

[0559] Input: Collected facial expression data, voice data, and text data.

[0560] Output: Data is sent to the server.

[0561] Specific operation: The device uses an HTTP request to send the collected data to the server.

[0562] Step 4:

[0563] The server analyzes the data using an emotion engine.

[0564] Input: Facial expression data, voice data, and text data sent to the server.

[0565] Output: Analysis results from the emotion engine.

[0566] Specific operation: The server inputs the received data into the emotion engine and analyzes the user's emotions.

[0567] Step 5:

[0568] The server returns the analysis results to the user.

[0569] Input: Analysis results from the emotion engine.

[0570] Output: The analysis results are displayed to the user.

[0571] Specific operation: The server sends the analysis results to the user's device as an HTTP response and displays the results in the browser.

[0572] (Application example 2)

[0573] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0574] Conventional advertising systems lacked a means to determine whether an advertising image was generated by AI, and were unable to evaluate in real time the emotions users felt when viewing the ad. This made it difficult to accurately evaluate the reliability and effectiveness of the ad. Furthermore, there was no system that combined the judgment of AI-generated images with the recognition of user emotions, making it impossible to obtain feedback to maximize the effectiveness of the ad.

[0575] 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.

[0576] In this invention, the server includes means for searching the Internet for specific logo images using a search engine crawler, means for determining whether the searched logo image was generated by AI, means for generating a summary of the website on which the logo image determined to be generated by AI is posted, means for listing the generated summary information and logo image, means including an emotion engine for recognizing user emotions, and means for evaluating user emotions in real time, thereby making it possible to determine whether an advertising image is AI-generated and evaluate user emotions in real time.

[0577] A "search engine crawler" is a program that automatically collects specific information from the Internet.

[0578] A "logo image" is a combination of shapes and letters that symbolize a company or brand.

[0579] "AI-generated" means that it has been automatically created using artificial intelligence technology.

[0580] A "means for determining" is a method or device for determining an outcome based on specific conditions or criteria.

[0581] A "means for generating a summary" is a method or device for concisely summarizing long texts or information.

[0582] A "listing means" is a method or device for organizing multiple pieces of information into a list.

[0583] "Emotion engine" is a general term for software and hardware that recognizes and analyzes user emotions.

[0584] A "real-time assessment tool" is a method or device for instantly assessing ongoing events or conditions.

[0585] A system for implementing the present invention is configured as follows: A server includes a search engine crawler, an AI-generated image judgment model, an emotion engine, and a user interface.

[0586] Program Generation

[0587] The server periodically searches the Internet for specific logo images using a search engine crawler. The searched logo images are then judged to be AI-generated using an AI-generated image judgment model. Based on the judgment results, a summary of the website on which the logo image judged to have been AI-generated is posted is generated. The generated summary information and logo image are listed and provided to the user via a user interface.

[0588] Furthermore, the emotion engine recognizes user emotions through input devices such as cameras, microphones, and keyboards. It analyzes facial expressions, tone of voice, and text input when a user views an ad, and evaluates emotions in real time. This allows for immediate feedback on the effectiveness of the ad.

[0589] Hardware and software used

[0590] Hardware:

[0591] Camera: Used to capture the user's facial expressions.

[0592] Microphone: Used to record the tone of the user's voice.

[0593] Keyboard: Used to accept text input from the user.

[0594] software:

[0595] OpenCV: Used to capture and process camera footage.

[0596] Keras: Used to load and predict AI generative image judgment models and emotion recognition models.

[0597] SpeechRecognition: Used to convert the user's voice into text.

[0598] TextBlob: Used to analyze sentiment from text.

[0599] Specific examples

[0600] For example, when a user uploads an advertisement image, the system determines whether the image was generated by AI. Furthermore, it can evaluate the user's emotions when viewing the advertisement in real time, providing instant feedback on the effectiveness of the advertisement.

[0601] Example prompt sentence:

[0602] Develop an application that, when a user uploads an ad image, determines whether the image is generated by AI and evaluates the user's emotions when viewing the ad in real time. Use a camera and microphone to capture the user's facial expressions and tone of voice, and use an emotion engine to recognize emotions.

[0603] In this way, a system combining AI-generated image judgment and emotion recognition in the advertising industry can be realized.

[0604] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0605] Step 1:

[0606] The server periodically searches the Internet for specific logo images using a search engine crawler. The input is a search query and a web page on the Internet, and the output is a list of the searched logo images. The crawler crawls web pages based on the specified keywords and collects logo images.

[0607] Step 2:

[0608] The server determines whether the searched logo images are generated by AI. The input is the logo images collected in step 1, and the output is the result of determining whether each logo image is AI-generated. Using an AI-generated image judgment model, each logo image is analyzed to determine whether it has the characteristics of AI generation.

[0609] Step 3:

[0610] The server generates a summary of the website that contains the logo image determined to be generated by AI. The input is the logo image determined to be generated by AI and the URL of the website where it is located, and the output is a summary of the website. The summary generation algorithm is used to concisely summarize the content of the web page.

[0611] Step 4:

[0612] The server lists the generated summary information and logo images. The input is the summary information and logo images, and the output is a list of the summary information and logo images. This organizes the information in a format that is easy for users to check.

[0613] Step 5:

[0614] The device runs an emotion engine to recognize the user's emotions. The input is data from the camera, microphone, and keyboard, and the output is the user's emotional state. The camera captures the user's facial expressions, the microphone records the user's tone of voice, and the keyboard accepts the user's text input. These data are sent to the emotion engine and analyzed.

[0615] Step 6:

[0616] The server evaluates the user's emotions in real time. The input is the user's emotion data obtained in step 5, and the output is the real-time emotion evaluation result. The emotion engine performs facial expression analysis, voice analysis, and text analysis to evaluate the user's emotions.

[0617] Step 7:

[0618] The terminal provides the user with the summarized information and logo image in a list form through a user interface. The input is the summarized information and logo image in a list form, and the output is an interface that the user can check. The user can evaluate the effectiveness of the advertisement based on the provided information.

[0619] Example 3

[0620] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0621] With conventional technology, it was difficult to determine whether an image on a network was generated by artificial intelligence and generate a summary of that information source. It was also not possible to list the generated summary information and images and adjust the display method based on the user's emotions. This resulted in the problem that users could not efficiently check information and obtain a display that suited their emotions.

[0622] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0623] In this invention, the server includes means for searching a network for specific images using a search engine crawler, means for determining whether the retrieved images have been generated by artificial intelligence, means for generating summaries of information sources containing images determined to have been generated by artificial intelligence, means for listing the generated summaries and images, and means for adjusting the display method of the listed summaries and images based on emotions recognized by the emotion engine, thereby enabling users to efficiently check information and obtain a display that corresponds to an appropriate emotion.

[0624] A "search engine crawler" is a program that automatically collects specific data from sources on a network.

[0625] "Artificial intelligence" is a technology that allows machines to mimic human intelligence to learn, reason, and recognize.

[0626] "Source" refers to digital content such as websites and databases that exist on a network.

[0627] A "summary" is information that extracts important parts from original information and summarizes them concisely.

[0628] "Listing" refers to organizing multiple pieces of information into a list format.

[0629] An "emotion engine" is a technology that recognizes the user's emotions and adjusts the system's behavior based on that information.

[0630] "Presentation" refers to the visual format or style in which information is presented to the user.

[0631] MODE FOR CARRYING OUT THE INVENTION

[0632] This invention is a system that determines whether images present on a network have been generated by artificial intelligence, generates a summary of the information source, lists the generated summary information and images, and adjusts the display method based on the user's emotions.

[0633] System program generation

[0634] The server generates the system's program using the following means:

[0635] 1. A method for searching for specific images on the network using a search engine crawler

[0636] 2. A method to determine whether a searched image was generated by artificial intelligence

[0637] 3. A means for generating summaries of sources containing images determined to have been generated by artificial intelligence.

[0638] 4. A means of listing the generated summary information and images

[0639] 5. A means to adjust the display of the summary information and images listed based on the emotions recognized by the emotion engine.

[0640] Program processing

[0641] The server first accesses the specified URL and retrieves the HTML code. Next, it parses the HTML code and extracts text information using a library such as BeautifulSoup. The extracted text information is input into a BERT (Bidirectional Encoder Representations from Transformers) model to generate a summary. The BERT model is loaded using the transformers library.

[0642] The generated summary information and images are compiled into a list and displayed through a web interface using web frameworks such as Flask or Django. When a user interacts with the system through the web interface, the emotion engine recognizes emotions from the user's facial expressions and input text. For example, it uses libraries such as OpenCV and DeepFace for facial expression recognition. The server adjusts the display method using CSS and JavaScript based on the emotion information received from the emotion engine.

[0643] Specific examples

[0644] For example, the following prompts can be fed into a generative AI model:

[0645] Example prompt sentence:

[0646] "Extract text information from the website https: / / example.com and generate a summary using BERT. Then, list the generated summary and logo image, and adjust the display based on the user's sentiment."

[0647] Using this prompt, the system can extract text information from the specified website, generate a summary, and then adjust the display method based on the user's sentiment. The flow of the identification process in Example 3 will be described with reference to Figure 21.

[0648] Step 1:

[0649] The server accesses the specified URL and obtains the HTML code.

[0650] Input: The specified URL

[0651] Output: Retrieved HTML code

[0652] Specific operation: The server sends an HTTP request and receives the HTML code returned as a response. For example, it uses the requests library to access the URL and obtain the HTML code.

[0653] Step 2:

[0654] The server parses the HTML code and extracts the text information.

[0655] Input: The HTML code you obtained

[0656] Output: Extracted text information

[0657] Specific operation: The server uses a library such as BeautifulSoup to parse the HTML code and extract text information. Specifically, it parses HTML tags and extracts the text.

[0658] Step 3:

[0659] The server inputs the extracted text information into the BERT model to generate a summary.

[0660] Input: Extracted text information

[0661] Output: Generated summary

[0662] How it works: The server loads the BERT model using the transformers library and provides text information as input. The BERT model extracts important information and generates a summary.

[0663] Step 4:

[0664] The server lists the generated summary information and images.

[0665] Input: Generated summary information, image

[0666] Output: List of summary information and images

[0667] How it works: The server compiles the summary information and images into a data structure (e.g., a list or dictionary), and then displays the list through a web interface using a web framework such as Flask or Django.

[0668] Step 5:

[0669] The emotion engine recognizes the user's emotions, and the server adjusts the display method based on that information.

[0670] Input: User's emotional information

[0671] Output: Adjusted display method

[0672] Specific operation: When a user interacts with the system through a web interface, the emotion engine recognizes emotions from the user's facial expressions and input text. For example, it uses libraries such as OpenCV and DeepFace to recognize facial expressions. The server adjusts the display method using CSS and JavaScript based on the emotion information received from the emotion engine. For example, if the user expresses joy, it changes the background color to a brighter color.

[0673] (Application example 3)

[0674] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0675] Previous systems lacked the means to determine whether logo images on the Internet were generated by artificial intelligence and provide that information to users. They also lacked the ability to adjust the way information is displayed based on the user's emotions, limiting the user experience. This made it difficult for users to quickly and effectively obtain the information they needed.

[0676] The identification process performed by the identification processing unit 290 of the data processing device 12 in Application Example 3 is implemented by the following means. In this invention, the server includes: means for searching the Internet for specific logo images using a search engine crawler; means for determining whether the searched logo image was generated by artificial intelligence; means for generating a summary of the website containing the logo image determined to have been generated by artificial intelligence; means for listing the generated summary information and logo image; and means for adjusting the display method of the listed summary information and logo image based on the emotion recognized by the emotion engine. This allows users to determine whether a logo image on the Internet was generated by artificial intelligence and to quickly and effectively obtain that information. Furthermore, adjusting the display method of information based on the user's emotion can provide a more personalized user experience.

[0677] A "search engine crawler" is a program that automatically crawls web pages on the Internet and collects specific information.

[0678] A "logo image" is an image designed to represent a company or brand.

[0679] "Artificial intelligence" refers to computer systems that mimic human intelligence and can learn and reason.

[0680] A "website" is a collection of information made publicly available on the Internet, a collection of pages with a specific URL.

[0681] A "summary" is a short summary of a long piece of text or information, extracting the important points.

[0682] "Listing" means organizing and displaying multiple items in a list format.

[0683] An "emotion engine" is a technology that recognizes a user's emotions and adjusts the system's behavior based on those emotions.

[0684] An "interface" is the means or method by which a user and a system interact with each other.

[0685] To implement the present invention, the following system configuration is required.

[0686] First, the server uses a search engine crawler to search for specific logo images on the Internet. To determine whether the searched logo image was generated by AI, the generative AI model is used. This model analyzes the characteristics of the logo image and determines whether it was generated by AI.

[0687] Next, the system extracts text information from websites that contain logo images determined to have been generated by AI, and generates summaries of the images using natural language processing techniques such as BERT (Bidirectional Encoder Representations from Transformers). This produces a concise summary of the website's key information.

[0688] The generated summary information and logo image are listed and displayed through an interface so that the user can check them. This interface is designed so that the user can easily view the listed information.

[0689] Additionally, the system uses an emotion engine to recognize the user's emotions and adjust the display of the summary information and logo images in the list based on that emotion. For example, if the user expresses joy, the system will display the summary information and logo images in brighter tones. This emotion recognition uses the BERT model to classify emotions from the user's input text.

[0690] For example, the following prompts can be used:

[0691] URL: "https: / / example.com / product-page"

[0692] Logo URL: "https: / / example.com / logo.png"

[0693] Extracted text: "This product is of very high quality and is loved by many users."

[0694] Generated summary: "This is the summary."

[0695] Perceived Emotion: Positive

[0696] In this way, users can view summary information and logo images on product pages of online shopping sites in a personalized way based on their emotions through a smartphone app. This system allows users to obtain the information they need quickly and effectively, enabling them to enjoy a better shopping experience.

[0697] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0698] Step 1:

[0699] The server uses a search engine crawler to search for specific logo images on the Internet. The crawler traverses a specified URL list and collects web pages that contain logo images. The input is a list of URLs, and the output is data on web pages that contain logo images.

[0700] Step 2:

[0701] The server determines whether the collected logo images were generated by artificial intelligence. A generative AI model is used to make this determination. A logo image is given as input, and the output is a determination result as to whether the logo image was generated by artificial intelligence. Specifically, the features of the logo image are analyzed, and a determination is made using the generative AI model.

[0702] Step 3:

[0703] The server extracts text information from websites that contain logo images determined to have been generated by artificial intelligence. The input is web page data, and the output is text information. Specifically, it uses libraries such as BeautifulSoup to extract text from HTML.

[0704] Step 4:

[0705] The server then summarizes the extracted text information. This summary generation uses natural language processing techniques such as BERT. The input is text information, and the output is a summarized text. Specifically, the BERT model is used to extract important information and generate a summary.

[0706] Step 5:

[0707] The server lists the generated summary information and logo images. The summary information and logo images are given as input, and the list data is obtained as output. Specifically, the summary information and logo images are organized in a list format and stored in a data structure for display.

[0708] Step 6:

[0709] The server recognizes the user's emotions using an emotion engine. The input is the user's input text, and the output is the recognized emotion. Specifically, it uses the BERT model to classify the emotion from the user's input text.

[0710] Step 7:

[0711] The server adjusts the display method of the listed summary information and logo image based on the recognized emotion. The recognized emotion and the listed data are given as input, and the adjusted display data is obtained as output. Specifically, the color tone and layout are changed according to the emotion, and the information is displayed in the most optimal form for the user.

[0712] Step 8:

[0713] The device provides the adjusted display data to the user. The adjusted display data is provided as input, and an interface that can be viewed by the user is provided as output. Specifically, the information is displayed to the user through a smartphone app or a web browser.

[0714] 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.

[0715] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) 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.

[0716] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[0717] 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.

[0718] [Second embodiment]

[0719] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0720] 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.

[0721] 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).

[0722] 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.

[0723] 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.

[0724] 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).

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] 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.

[0730] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0731] "Example 1"

[0732] One embodiment of the present invention is a search engine crawler for searching for specific logo images. This crawler periodically patrols the Internet and searches for specific logo images. For example, it can crawl every night to search for specific logo images.

[0733] "Example 2"

[0734] There are also methods to determine whether a searched logo image was generated by AI. This uses a machine learning model that has learned the characteristics of AI-generated images to determine whether the searched logo image was generated by AI. For example, it is possible to use a model that has learned the characteristics of images generated by a GAN (Generative Adversarial Network).

[0735] "Example 3"

[0736] Furthermore, there are methods for generating summaries of websites that contain logo images determined to have been generated by AI. These methods extract text information from the website and generate summaries. For example, natural language processing techniques such as BERT (Bidirectional Encoder Representations from Transformers) can be used to extract important information from the text information on the website and generate summaries.

[0737] "Example 4"

[0738] Finally, there is a means for listing the generated summary information and logo images. This allows the summary information and logo images to be displayed as a list, making it easier for humans to check the contents. For example, it is possible to display a list of the summary information and logo images through a web interface so that humans can check them.

[0739] The processing flow of each embodiment will be described below.

[0740] "Example 1"

[0741] Step 1: A search engine crawler crawls the Internet to search for specific logo images. This crawler crawls every night, for example, to search for specific logo images.

[0742] Step 2: The crawler stores the logo images it finds in a temporary database, which provides input data for the AI ​​to use in the next step.

[0743] "Example 2"

[0744] Step 1: A means for determining whether the searched logo image was generated by AI is activated. This uses a machine learning model that has learned the characteristics of AI-generated images to determine whether the searched logo image was generated by AI.

[0745] Step 2: Based on the results, we identify logo images that are determined to be AI-generated and store that information in a temporary database, which will provide input data for generating a website summary in the next step.

[0746] "Example 3"

[0747] Step 1: A means for generating a summary of a website containing a logo image determined to have been generated by AI is activated. This extracts text information from the website and generates a summary.

[0748] Step 2: The generated summary information is saved in a temporary database, which will serve as input data for the next step of creating a list of summary information and logo images.

[0749] "Example 4"

[0750] Step 1: A means for listing the generated summary information and logo images is activated. This displays the summary information and logo images as a list, making it easier for humans to check the contents.

[0751] Step 2: Display the listed summary information and logo image in a web interface so that it can be reviewed by humans.

[0752] Example 1

[0753] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0754] Conventional search engine crawlers have difficulty efficiently searching for specific images, and managing and notifying search results is time-consuming. Furthermore, there is a lack of means to identify images generated by generative AI models and organize the information. This makes it difficult for users to quickly and accurately obtain the information they need.

[0755] The identification process by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. In this invention, the server includes means for searching for specific images on the network using a search engine crawler, means for determining whether the searched images were generated by a generative AI model, means for generating summaries of information resources containing images determined to have been generated by a generative AI model, means for listing the generated summary information and images, means for periodically starting the crawler, means for saving the collected images in a database, and means for notifying the search results. This makes it possible to efficiently search for specific images, organize the determination results by the generative AI model, and notify the user quickly and accurately.

[0756] A "search engine crawler" is a program that automatically crawls information resources on a network and collects specific data.

[0757] A "specific image" is an image that has a specific feature designated by the user as a search target.

[0758] "Network" refers to an information and communication network such as the Internet or an intranet.

[0759] A "generative AI model" is an algorithm or system that generates data using artificial intelligence technology.

[0760] A "means for determining" is a device or program that has the function of evaluating the characteristics of data based on specific conditions or criteria and outputting the results.

[0761] An "information resource" is a collection of information such as websites and databases that exist on a network.

[0762] A "summary generation means" is a device or program that has the function of extracting important information from information resources and summarizing it concisely.

[0763] A "listing means" is a device or program that has the function of organizing collected data and displaying it in a list format.

[0764] "Means for periodically launching a crawler" refers to a device or program that has the function of automatically running a crawler at regular intervals.

[0765] A "database" is a system for efficiently storing, searching, and managing data.

[0766] A "notification means" is a device or program that has the function of notifying a user of a specific event or result.

[0767] This invention is a system that efficiently searches for specific images, organizes the judgment results by a generative AI model, and notifies the user quickly and accurately. A specific embodiment of this system is described below.

[0768] The server automatically crawls network resources to collect specific images using a search engine crawler, written in a programming language such as Python or JavaScript, that uses libraries such as BeautifulSoup or Puppeteer to parse the HTML of web pages.

[0769] The collected images are analyzed using a generative AI model, which uses artificial intelligence techniques to determine whether an image is generated, using image recognition libraries such as OpenCV.

[0770] The results of the assessment are generated as a summary of the information resource. Natural language processing technology is used to generate the summary, and important information is extracted. The generated summary information and images are compiled into a list and stored in a database. Databases such as MySQL and MongoDB are used.

[0771] The server sets up a cron job to periodically launch the crawler. This automatically runs the crawler every night at midnight. The collected images and summary information are stored in a database and notified to the user. An email sending function is used for notification.

[0772] As a concrete example, consider the case where a user searches for logo images of a specific company. For example, when searching for logo images of a "specific company," a crawler traverses web pages on the network and collects logo images of the specific company. The server runs the crawler every night and stores the collected logo images in a database. The server then notifies the user of the search results by email.

[0773] An example of a prompt is as follows:

[0774] "Please set up a crawler to search for logo images of specific companies. The crawler will run every night, crawl web pages on the network, collect specific logo images, and store them in a database. For example, if you search for logo images of "specific companies," the crawler will collect logo images of specific companies."

[0775] In this way, users can build a system that allows them to efficiently search for specific images, organize the judgment results of generative AI models, and quickly and accurately notify the results.

[0776] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0777] Step 1: Start the crawler

[0778] The server starts the crawler every night at midnight. Specifically, a cron job is set up to run the crawler at 2:00 AM every day. The input is a cron job configuration file, and the output is that the crawler will start running. Specifically, the server sets up the cron job as follows:

[0779] 0 2 / usr / bin / python3 / path / to / crawler.py

[0780] Step 2: Collect web pages

[0781] The crawler collects web pages from the network. Specifically, it uses BeautifulSoup (a Python library) to analyze the HTML of the web pages. The input is the URL of the web page, and the output is the analyzed HTML data. Specifically, the crawler performs the following processes:

[0782] Access the URL, get the HTML data, and parse it with BeautifulSoup.

[0783] Step 3: Find your logo image

[0784] The crawler searches for specific logo images from collected web pages. It uses OpenCV (a Python library) for image recognition. The input requires the parsed HTML data and a logo image template, and the output is the URL of the matching logo image. Specifically, the crawler performs the following process:

[0785] Image tags are extracted from the HTML data, and each image is analyzed using OpenCV to determine whether it matches the logo image.

[0786] Step 4: Save your data

[0787] The server saves the search results in a database. It uses MySQL to save the search results. The input requires the URL of the matching logo image, and the output is the search results saved in the database. Specifically, the server performs the following process:

[0788] Insert the matching logo image URL into the database.

[0789] Step 5: Notification of results

[0790] The server notifies the user of the search results. For example, it can notify them by email. The input requires the search results stored in the database, and the output is the notification sent to the user. Specifically, the server performs the following process:

[0791] The search results are inserted into the email body and sent to the user.

[0792] In this way, a system can be constructed in which the server can efficiently search for specific logo images, organize the judgment results of the generative AI model, and notify the user quickly and accurately.

[0793] (Application example 1)

[0794] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0795] While conventional search engine crawlers can search the Internet for specific logo images, they lack the functionality to monitor and notify users in real time how those logo images are being used. Furthermore, they lack the means to determine whether searched logo images are generated by AI and provide a list of that information. This makes it difficult for brand managers and marketers to efficiently monitor the usage of their company's logos.

[0796] 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.

[0797] In this invention, the server includes means for searching the Internet for specific logo images using a search engine crawler, means for determining whether the searched logo images were generated by AI, means for generating summaries of information sources containing logo images determined to be generated by AI, means for listing the generated summaries and logo images, means for providing an interface for a human to review the listed summaries and logo images, and means for monitoring how specific logo images are used on the Internet and sending notifications in real time, thereby enabling brand managers and marketers to efficiently monitor the usage of their company's logos and respond in real time.

[0798] A "search engine crawler" is a program that automatically crawls web pages on the Internet and collects specific information.

[0799] A "logo image" is an image that includes a shape or design that symbolizes a company or brand.

[0800] "AI-generated" means created automatically using artificial intelligence technology.

[0801] "Source" refers to a website or online platform where particular data or information is posted.

[0802] A "summary" is a concise summary of the main points of a particular piece of information.

[0803] "Listing" means organizing multiple items into a list format.

[0804] "Interface" refers to the means or screen through which a user interacts with a system.

[0805] "Real-time notifications" means that users are notified immediately when a specific event or piece of information occurs.

[0806] "Monitoring" means continuously observing a specific object and recording its condition and changes.

[0807] The system for implementing this invention includes a search engine crawler, an AI judgment module, a summary generation module, a listing module, an interface module, and a real-time notification module. These modules run on a server, monitor how a specific logo image is used on the Internet, and notify users in real time.

[0808] Program processing explanation

[0809] The server periodically crawls web pages on the Internet using a search engine crawler implemented using Python to search for specific logo images. The crawler retrieves web pages using the requests library and analyzes the HTML using the BeautifulSoup library. If a specific logo image is found, the information is sent to the AI ​​judgment module.

[0810] The AI ​​determination module uses a generative AI model to determine whether the searched logo image is AI-generated. This model uses a pre-trained deep learning model. The determination result is sent to the summary generation module.

[0811] The summary generation module generates a summary of the information source containing the logo image determined to be generated by AI. This summary is automatically generated using natural language processing technology. The generated summary information and logo image are sent to the listing module.

[0812] The list module organizes the generated summary information and logo images into a list format and stores it in a database, which is then sent to the interface module for user confirmation.

[0813] The interface module provides an interface for users to check the listed summary information and logo images, and the interface can be accessed through a web browser or a smartphone application.

[0814] The real-time notification module notifies users in real time when a specific logo image is newly used on the Internet, in the form of push notifications, email notifications, etc.

[0815] Specific examples

[0816] For example, if a brand manager wants to monitor how their company's logo image (URL: https: / / example.com / logo.png) is being used online, they can use this system to receive real-time information about new web pages where the logo image is being used, enabling them to quickly detect and respond to any unauthorized or misused brand use.

[0817] Prompt Sentence Examples

[0818] Implement a crawler to monitor how your brand's logo image (URL: https: / / example.com / logo.png) is used on the Internet. This crawler will traverse the Internet every night, searching for the specific logo image. If it finds one, it will add the URL to a list and notify the user.

[0819] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0820] Step 1:

[0821] The server launches a search engine crawler to periodically crawl web pages on the Internet. As input, it receives the URL of a specific logo image and a list of web pages to crawl. The crawler uses the requests library to retrieve the web pages and parses the HTML using the BeautifulSoup library. As output, it generates the URLs of web pages that contain the specific logo image.

[0822] Step 2:

[0823] The server sends the URL of the webpage obtained in step 1 to the AI ​​judgment module. As input, it receives the URL of the webpage and the logo image data. The AI ​​judgment module uses the generative AI model to determine whether the logo image was generated by AI. As output, it generates a judgment result.

[0824] Step 3:

[0825] The server sends the judgment result obtained in step 2 to the summary generation module. It receives the judgment result and the URL of the web page as input. The summary generation module uses natural language processing technology to generate a summary of the information source where the logo image is posted. It generates summary information as output.

[0826] Step 4:

[0827] The server sends the summary information and logo images generated in step 3 to the listing module. The listing module receives the summary information and logo image data as input. The listing module organizes the generated summary information and logo images into a list format and stores it in a database. The listing module generates the list data as output.

[0828] Step 5:

[0829] The server sends the listed data to an interface module, which receives the listed data as input, provides an interface for a user to view the listed summary information and logo image, and generates an interface accessible to a user as output.

[0830] Step 6:

[0831] The server starts a real-time notification module when a specific logo image is newly used on the Internet. As input, it receives information about the newly used logo image. The real-time notification module notifies the user in the form of a push notification, email notification, or the like. As output, it generates a notification message.

[0832] Example 2

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

[0834] In recent years, advances in artificial intelligence technology have led to an increase in the number of generated images. However, there is a lack of means to determine whether these images were generated by artificial intelligence, making it difficult for users and businesses to obtain reliable information. There is also a need for a system that can efficiently determine whether images searched on the network were generated by artificial intelligence and provide the results to users.

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

[0836] In this invention, the server includes means for searching a network for specific images using a search engine crawler, means for determining whether the searched image was generated by artificial intelligence, means for generating summaries of information sources containing images determined to have been generated by artificial intelligence, means for listing the generated summary information and images, means for a user to upload images and determine whether the uploaded images were generated by artificial intelligence, means for inputting preprocessed images into a machine learning model and obtaining the determination result, and means for notifying the user of the determination result, thereby enabling a user to easily determine whether an image searched for on a network or an image uploaded by the user was generated by artificial intelligence and obtain the result in a reliable manner.

[0837] A "search engine crawler" is a program that automatically collects specific data from sources on a network.

[0838] "Image" is digital data containing visual information, including logos and photographs.

[0839] A "network" is a system in which multiple computers and devices are interconnected and exchange data.

[0840] "Artificial intelligence" is the technology that enables computer systems to mimic human intelligence and learn, reason, and self-correct.

[0841] A "means for determining" is a process or device that evaluates data based on specific conditions or criteria and derives a result.

[0842] A "source" is a place or system where data or information is generated, stored, or provided.

[0843] A "summary generator" is a process or device that condenses long information into short chunks and extracts the main points.

[0844] A "listing means" is a process or device that organizes multiple pieces of data or information and displays them in a list format.

[0845] "User" means an individual or organization that uses the system or service.

[0846] "Uploading" is the act of sending data from a local device to a server on a network.

[0847] "Preprocessing" refers to the initial stage of processing to convert data into a format suitable for analysis and processing.

[0848] A "machine learning model" is an algorithm or system that learns from data and makes predictions and classifications.

[0849] A "judgment result" is an evaluation or conclusion drawn based on specific conditions or criteria.

[0850] A "means of notification" is a process or device that conveys specific information to a user.

[0851] MODE FOR CARRYING OUT THE INVENTION

[0852] This invention relates to a system for determining whether an image searched on a network or uploaded by a user has been generated by artificial intelligence. A specific embodiment of this system will be described below.

[0853] System configuration

[0854] This system consists of three main elements: a server, a terminal, and a user. The server uses a machine equipped with a high-performance GPU (e.g., NVIDIA Tesla V100) and uses TensorFlow or PyTorch as software. The terminal is the device through which the user accesses the system, and includes devices such as PCs and smartphones.

[0855] Program processing

[0856] The server performs the following steps:

[0857] 1. Image search using search engine crawlers

[0858] The server searches the network for specific images using a search engine crawler, which periodically traverses the network and collects new images.

[0859] 2. Image Judgment

[0860] The server uses a machine learning model to determine whether the retrieved image was generated by artificial intelligence. The model learns the characteristics of images generated by a Generative Adversarial Network (GAN).

[0861] 3. Summary information generation

[0862] The server generates a summary of the source containing the image determined to be generated by artificial intelligence, which is a concise summary of the main points.

[0863] 4. Listing

[0864] The server lists the generated summary information and images in an organized manner that allows the user to easily review the list.

[0865] 5. User image upload and detection

[0866] Users use their terminals to access the system's web interface and upload images. The server receives the uploaded images and performs pre-processing, which includes resizing and normalizing the images.

[0867] 6. Input to the machine learning model and obtain the judgment result

[0868] The server inputs the preprocessed image into the machine learning model and obtains a judgment result, which is a flag indicating whether the image was generated by artificial intelligence.

[0869] 7. Notification of the decision

[0870] The server notifies the user of the result of the determination, either by a message displayed on the web interface or, if necessary, by email.

[0871] Specific examples

[0872] For example, if a user wants to determine whether a searched logo image was generated by AI, they can input the following prompt to the generative AI model:

[0873] Example prompt sentence:

[0874] "Please determine whether this logo image was generated by AI."

[0875] The server receives this prompt and inputs the retrieved logo image into a machine learning model. The model analyzes the image's features and determines whether it was AI-generated. The result is then notified to the user.

[0876] In this way, users can easily verify whether a logo image is generated by AI.

[0877] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0878] Program processing flow

[0879] Step 1: Image search using a search engine crawler

[0880] The server searches the network for specific images using a search engine crawler, which periodically traverses the network and collects new images.

[0881] Input: Search query

[0882] Output: A list of URLs of the images found

[0883] How it works: The server passes the search query to the crawler, which then visits sources on the network to collect image URLs, which are then returned to the server.

[0884] Step 2: Download and preprocess images

[0885] The server downloads images from the collected URL list and pre-processes them, which includes resizing and normalizing the images.

[0886] Input: List of image URLs

[0887] Output: Preprocessed image data

[0888] What happens: The server downloads the image from each URL, resizes it using Python's Pillow library, and normalizes the pixel values ​​using NumPy (e.g., resizes the image to 256x256 pixels and scales the pixel values ​​to the range 0 to 1).

[0889] Step 3: Image Judgment

[0890] The server inputs the pre-processed image data into a machine learning model to determine whether the image was generated by artificial intelligence.

[0891] Input: Preprocessed image data

[0892] Output: Judgment result (probability of AI generation)

[0893] How it works: The server inputs preprocessed image data into a TensorFlow or PyTorch model. The model analyzes the image features and evaluates the likelihood of AI generation. For example, a probability value such as "AI generation: 90%" is output.

[0894] Step 4: Generate summary information

[0895] The server generates summaries of the sources containing images determined to be generated by artificial intelligence.

[0896] Input: Judgment result, image URL

[0897] Output: Summary information

[0898] Specific operation: If the judgment result exceeds a certain threshold, the server extracts text information from the source where the image is posted and generates a summary using natural language processing technology.

[0899] Step 5: List

[0900] The server lists the generated summary information and images.

[0901] Input: Summary information, image data

[0902] Output: Listed information

[0903] Specific operation: The server compiles the summary information and image data into a single list and organizes them so that the user can easily check them.

[0904] Step 6: Upload user images

[0905] Users use a terminal to access the system's web interface and upload images.

[0906] Input: User uploaded image

[0907] Output: Image data sent to the server

[0908] Specific operation: A user opens a browser and accesses the system's web page. The user clicks the "Select Image" button to select a logo image file from the local disk. Then, the user clicks the "Upload" button.

[0909] Step 7: Preprocessing the uploaded images

[0910] The server receives the uploaded images and performs pre-processing.

[0911] Input: Uploaded image data

[0912] Output: Preprocessed image data

[0913] Specific operation: The server resizes the received image using Python's Pillow library and normalizes the pixel values ​​using NumPy.

[0914] Step 8: Input to the machine learning model and obtain the judgment result

[0915] The server inputs the preprocessed image into the machine learning model and obtains the judgment result.

[0916] Input: Preprocessed image data

[0917] Output: Judgment result (probability of AI generation)

[0918] How it works: The server inputs preprocessed image data into a TensorFlow or PyTorch model, which analyzes the image's features and evaluates the potential for AI generation.

[0919] Step 9: Notification of decision

[0920] The server notifies the user of the result of the determination.

[0921] Input: Judgment result

[0922] Output: Message to be sent to the user

[0923] Specific operation: The server generates the judgment result in HTML format and displays it in the user's browser. For example, it may say, "This logo image is likely to have been generated by AI (90%)."

[0924] In this way, users can easily verify whether a logo image is generated by AI.

[0925] (Application example 2)

[0926] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0927] In recent years, advances in AI technology have led to an increase in the number of AI-generated logo images on the Internet. This has made it difficult to verify the authenticity of logo images used by companies and individuals. In particular, there is a growing risk that AI-generated logo images will be misused, raising concerns about a decline in credibility and damage to brand value. In this situation, there is a need for a method to quickly and accurately determine whether a logo image has been generated by AI.

[0928] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for searching the Internet for specific logo images using a search engine crawler, means for determining whether the searched logo images were generated by AI, means for generating summaries of websites containing logo images determined to have been generated by AI, means for listing the generated summaries and logo images, means for providing an interface for uploading logo images, means for determining whether the uploaded logo images were generated by AI, and means for notifying the user of the determination result. This makes it possible to quickly and accurately determine the authenticity of logo images used by companies and individuals.

[0929] A "search engine crawler" is a program that automatically searches for and collects specific logo images on the Internet.

[0930] A "logo image" is a visual symbol used by businesses and individuals to identify their brand or service.

[0931] "AI-generated" means that it has been automatically created using artificial intelligence technology.

[0932] "Means for determining" refers to a device or program that has the function of determining whether a specific logo image was generated by AI.

[0933] A "means for generating a website summary" is a device or program that has the function of creating information that succinctly summarizes the contents of a particular website.

[0934] The "listing means" is a device or program that has the function of organizing the generated summary information and logo images in a list format.

[0935] The "means for providing an interface" refers to a device or program that provides an operation screen and input means for users to upload logo images.

[0936] "Means for notifying the user of the judgment result" refers to a device or program that has the function of informing the user of the judgment result as to whether the logo image was generated by AI.

[0937] The system for implementing the present invention is configured as follows: The server includes a search engine crawler, an AI judgment module, a summary generation module, a listing module, an interface providing module, and a notification module.

[0938] First, a search engine crawler periodically searches for and collects specific logo images from the Internet. The collected logo images are sent to an AI judgment module. The AI ​​judgment module uses a pre-trained generative AI model (e.g., GAN) to determine whether the logo image was generated by AI. The result of this judgment is sent to a summary generation module, which generates a summary of the website containing the logo image determined to have been generated by AI.

[0939] Next, the generated summary information and logo images are organized in a list format by the listing module. This list is displayed through the interface providing module so that the user can check it. The user can also upload a logo image through the interface. The uploaded logo image is sent again to the AI ​​judgment module, and the judgment result is notified to the user through the notification module.

[0940] The following hardware and software are used to implement this system. Hardware requires a server and user terminal. Software includes TensorFlow, Keras, and PIL (Python Imaging Library). This software is used to process and calculate data. Specifically, it performs processes such as image resizing, normalization, tensor transformation, and judgment using AI models.

[0941] For example, when a company is designing a new logo, they can use the system to check whether the logo was generated by AI. Users upload a logo image through the interface and enter the following prompt:

[0942] "Please determine whether this logo image was generated by AI."

[0943] Based on this prompt, the system will determine whether the logo image is authentic and notify the user, thereby ensuring the reliability of logo images used by companies and individuals.

[0944] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0945] Step 1:

[0946] The server periodically searches for and collects specific logo images from the Internet using a search engine crawler. The input is a search query or search conditions, and the output is the collected logo image data. Specifically, the crawler traverses web pages, detects logo images, and downloads them.

[0947] Step 2:

[0948] The server sends the collected logo images to the AI ​​judgment module. The input is the collected logo image data, and the output is the transfer of image data to the AI ​​judgment module. Specifically, the image data is passed to the AI ​​judgment module.

[0949] Step 3:

[0950] The server uses an AI judgment module to determine whether a logo image was generated by AI. The input is the collected logo image data, and the output is a judgment result. Specifically, the image data is input into a generative AI model (e.g., GAN), and the model analyzes the image features and outputs a judgment result.

[0951] Step 4:

[0952] The server generates a summary of the website that contains the logo image determined to have been generated by AI. The input is the result of the determination and the URL of the corresponding website, and the output is summary information. Specifically, it analyzes the content of the website, extracts important information, and creates a summary.

[0953] Step 5:

[0954] The server creates a list of the generated summary information and logo images. The input is the summary information and logo image data, and the output is a list of the information. Specifically, the server organizes the summary information and logo images in a list format and saves them in a database.

[0955] Step 6:

[0956] The user uploads a logo image through the interface. The input is the logo image data uploaded by the user, and the output is the transfer of the image data to the server. Specifically, the user operates the interface to select an image file and presses the upload button.

[0957] Step 7:

[0958] The server then sends the uploaded logo image back to the AI ​​judgment module for judgment. The input is the logo image data uploaded by the user, and the output is the judgment result. Specifically, the image data is passed to the AI ​​judgment module, and the model analyzes the image features and outputs the judgment result.

[0959] Step 8:

[0960] The server notifies the user of the judgment result through a notification module. The input is the judgment result, and the output is a notification message for the user. Specifically, the judgment result is generated as a notification message and sent to the user's terminal.

[0961] Example 3

[0962] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0963] Conventional technologies have had difficulty determining whether a specific image on the Internet was generated by artificial intelligence and generating a summary of the web page on which the image was posted. Furthermore, there was a lack of a way to provide the generated summary information and image in a format that was easy to visually check. This made it difficult for users to quickly and efficiently obtain the information they needed.

[0964] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0965] In this invention, the server includes means for searching a network for specific images using a search engine crawler, means for determining whether the searched image has been generated by artificial intelligence, means for extracting text information from web pages containing images determined to have been generated by artificial intelligence, means for extracting important information from the extracted text information and generating summaries, and means for listing the generated summaries and images, thereby enabling users to efficiently obtain summaries of web pages containing images generated by artificial intelligence and view them in a format that is easy to visually confirm.

[0966] A "search engine crawler" is a program that automatically crawls web pages on a network and collects specific information.

[0967] An "image" is a visually perceptible still image stored in digital form.

[0968] A "network" is a system in which multiple computers and devices are connected through a communication infrastructure such as the Internet.

[0969] "Artificial intelligence" is the technology that enables computer systems to mimic human intelligence and perform functions such as learning, reasoning, and recognition.

[0970] A "web page" is a page containing documents and media content that is published on the Internet and is written in a markup language such as HTML.

[0971] "Text information" is information expressed as a string of characters, and exists in the form of sentences, words, etc.

[0972] A "summary" is information that extracts important parts from original text information and summarizes them concisely.

[0973] "Listing" refers to organizing multiple pieces of information and displaying them in a list format.

[0974] An "interface" is the means by which a user interacts with a system, including visual and operational elements.

[0975] This invention relates to a system that determines whether a specific image has been generated by artificial intelligence, generates a summary of the web page on which the image is posted, and displays the generated summary information and the image in a list.

[0976] The server implements this system using the following hardware and software:

[0977] Hardware and software used

[0978] Hardware: High-performance server computer, network interface card

[0979] software:

[0980] Programming language: Python

[0981] Libraries: requests, BeautifulSoup, transformers, Jinja2

[0982] Artificial intelligence model: BERT (Bidirectional Encoder Representations from Transformers)

[0983] Details of data processing and calculation

[0984] 1. Search Engine Crawlers:

[0985] The server uses Python's requests library to send an HTTP request to the URL specified by the user to retrieve the HTML content of the web page.

[0986] 2. Parsing HTML content:

[0987] The server uses the BeautifulSoup library to parse the retrieved HTML content and extract text information, specifically by stripping out HTML tags and extracting pure text data.

[0988] 3. Extracting important information:

[0989] The server then inputs the extracted text information into the BERT model to extract important information, which then understands the context of the text and identifies important sentences and phrases.

[0990] 4. Generate a summary:

[0991] The server generates summaries based on key information obtained from the BERT model, using the pipeline from the transformers library.

[0992] 5. Summary and image listing:

[0993] The server embeds the generated summary information and images into an HTML template and displays it in a list format. Specifically, it uses the Jinja2 template engine to create a visually easy-to-read list.

[0994] 6. Providing an interface:

[0995] The server sends the generated HTML to the user's browser, and the user checks the summary information and the list of images through the browser.

[0996] Specific examples

[0997] When a user specifies a specific website (e.g., https: / / example.com), the server retrieves the HTML content of that website and generates a summary using the BERT model. The generated summary and images are displayed as a table through a web interface.

[0998] Example prompt sentence:

[0999] "Extract text information from https: / / example.com and generate a summary using the BERT model. Display the generated summary and images in a table."

[1000] In this way, the server can extract text information from the specified website, summarize important information, and display it in a visually easy-to-read format. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1001] Step 1:

[1002] The user enters a website URL.

[1003] The user accesses the system's web interface, enters "https: / / example.com" in the text box, and clicks the "Submit" button.

[1004] Enter: Website URL

[1005] Output: The URL entered

[1006] Step 2:

[1007] The server retrieves the HTML content of the specified website.

[1008] The server uses the requests library to send an HTTP request to the URL entered by the user to retrieve the HTML content of the website.

[1009] Input: Entered URL

[1010] Output: Retrieved HTML content

[1011] Step 3:

[1012] The server extracts text information from the HTML content.

[1013] The server uses the BeautifulSoup library to parse the retrieved HTML content and extract text information, specifically by stripping out HTML tags and extracting pure text data.

[1014] Input: Retrieved HTML content

[1015] Output: Extracted text information

[1016] Step 4:

[1017] The server uses the BERT model to extract important information.

[1018] The server then inputs the extracted text information into the BERT model to extract important information, which then understands the context of the text and identifies important sentences and phrases.

[1019] Input: Extracted text information

[1020] Output: Extracted important information

[1021] Step 5:

[1022] The server generates a summary based on the extracted information.

[1023] The server generates summaries based on key information obtained from the BERT model, using the pipeline from the transformers library.

[1024] Input: Extracted sensitive information

[1025] Output: The generated summary

[1026] Step 6:

[1027] The server lists the summary information and images.

[1028] The server embeds the generated summary information and images into an HTML template and displays it in a list format. Specifically, it uses the Jinja2 template engine to create a visually easy-to-read list.

[1029] Input: Generated summary,image

[1030] Output: List of summary information and images

[1031] Step 7:

[1032] The server displays a list of information in a web interface.

[1033] The server sends the generated HTML to the user's browser, and the user checks the summary information and the list of images through the browser.

[1034] Input: List of summary information and images

[1035] Output: A list of images and summary information displayed in the web interface.

[1036] (Application example 3)

[1037] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1038] Conventional advertising systems make it difficult for users to efficiently find information of interest from the vast number of advertisements available on the Internet. Furthermore, there was a lack of a way to automatically generate and provide users with a summary of websites that contained AI-generated logo images. This made it difficult for users to quickly understand the content of advertisements and make appropriate decisions.

[1039] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1040] In this invention, the server includes means for searching the Internet for specific logo images using a search engine crawler, means for determining whether the retrieved logo images were generated by artificial intelligence, means for generating summaries of websites containing logo images determined to have been generated by artificial intelligence, means for listing the generated summaries and logo images, means for providing an interface for users to view the listed summaries and logo images, means for functioning as an application to be installed on a smartphone, means for extracting text information from websites and generating summaries using natural language processing technology, and means for displaying a list of the generated summaries and logo images, thereby enabling users to efficiently view advertisement summaries and logo images and quickly find advertisements of interest.

[1041] A "search engine crawler" is a program that automatically crawls websites on the Internet and collects specific information.

[1042] "Artificial intelligence" refers to computer systems that mimic human intelligence and can learn and reason.

[1043] A "website" is a collection of information published on the Internet and written in a format such as HTML.

[1044] A "summary" is a short summary of a long piece of text or information, extracting the important points and expressing them concisely.

[1045] "Listing" means organizing and displaying multiple items in a list format.

[1046] An "interface" is the means or method by which a user and a system interact with each other.

[1047] A "smartphone" is a type of mobile phone, a multi-function device that can connect to the Internet and run applications.

[1048] "Natural language processing technology" is a technology that allows computers to understand and process human language.

[1049] "List display" refers to displaying multiple pieces of information together on one screen or page.

[1050] The system for implementing this invention includes means for a search engine crawler, artificial intelligence, a website, summarization, listing, an interface, a smartphone, natural language processing technology, and list display. Specific embodiments of each means will be described below.

[1051] System configuration

[1052] The server uses a search engine crawler to search for specific logo images on the Internet, uses image recognition technology to determine whether the found logo image was generated by artificial intelligence, extracts text information from the website where the determined logo image is posted, and generates a summary using natural language processing technology.

[1053] The generated summary information and logo images are listed and provided to the user through an interface. This interface functions as an application installed on a smartphone. The user can view the listed summary information and logo images in a list view using their smartphone.

[1054] Hardware and software used

[1055] Hardware: Smartphones, servers

[1056] Software: Python, BeautifulSoup, Transformers library (Hugging Face)

[1057] Data processing and calculation

[1058] The server extracts text information from websites using a search engine crawler. It uses BeautifulSoup to extract text from HTML and filters out only visible text. The extracted text information is summarized using natural language processing technology (BERT model). The generated summary information and logo images are compiled into a list and provided to users via a smartphone application.

[1059] Specific examples

[1060] For example, the server extracts text information from the URL "https: / / example.com / ad1" and generates a summary using the BERT model. The generated summary information and a logo image "logo1.png" are displayed to the user via a smartphone application.

[1061] Prompt Sentence Examples

[1062] Extract the text information from the URL: "https: / / example.com / ad1" and generate a summary using the BERT model. Display the generated summary and the logo image "logo1.png" as a list.

[1063] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1064] Step 1:

[1065] The server launches a search engine crawler to search the Internet for specific logo images. It receives the characteristics and keywords of specific logo images as input and generates a list of URLs for corresponding logo images as output. Specifically, the crawler visits websites and extracts image files from the HTML source code.

[1066] Step 2:

[1067] The server determines whether the searched logo images were generated by artificial intelligence. As input, it receives the list of logo image URLs obtained in step 1, and as output, it generates a list of logo images that have been determined to have been generated by artificial intelligence. Specifically, it uses image recognition technology to analyze the features of the logo images and makes a determination using a generative AI model.

[1068] Step 3:

[1069] The server extracts text information from websites that contain logo images determined to have been generated by artificial intelligence. As input, it receives the list of logo image URLs obtained in step 2, and as output, it generates text information for each website. Specifically, it uses BeautifulSoup to extract text from HTML source code and filters out only visible text.

[1070] Step 4:

[1071] The server summarizes the extracted text information using natural language processing technology. It receives the text information obtained in step 3 as input and generates summary information as output. Specifically, it uses the BERT model to tokenize the text, extract important information, and generate a summary.

[1072] Step 5:

[1073] The server lists the generated summary information and logo images. As input, it receives the list of logo images obtained in step 2 and the summary information obtained in step 4, and generates a list of summary information and logo images as output. Specifically, it associates the summary information with the logo images and organizes them in a list format.

[1074] Step 6:

[1075] The server provides an interface for users to check the list of summary information and logo images. It receives the list of summary information and logo images obtained in step 5 as input and generates a user interface as output. Specifically, it displays a list of summary information and logo images through an application installed on a smartphone.

[1076] Step 7:

[1077] The user uses the smartphone application to check the listed summary information and logo images. As input, the user receives the user interface provided in step 6, and as output, selects the advertisement of interest. Specifically, the user taps the summary information and logo image on the application screen to display detailed information.

[1078] 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.

[1079] "Example 1"

[1080] The invention incorporating an emotion engine recognizes a user's emotion and adjusts the display of the summary information list and logo images based on that emotion. Specifically, the emotion engine recognizes the emotion from the user's facial expression, tone of voice, text input, etc. For example, if the user expresses anger, the emotion engine conveys that information to the system, and the system adjusts the display of the summary information list and logo images. Specifically, for users who express anger, the system takes action such as displaying problematic logo images more prominently.

[1081] "Example 2"

[1082] The emotion engine also recognizes the user's emotions through the user interface. Specifically, the user interface includes input devices such as a camera, microphone, and keyboard. The camera captures the user's facial expressions, the microphone records the user's tone of voice, and the keyboard accepts the user's text input. The information obtained from these input devices is sent to the emotion engine, which then recognizes the user's emotions from this information.

[1083] "Example 3"

[1084] Furthermore, the emotion engine adjusts the display method of the listed summary information and logo image based on the recognized emotion. Specifically, the emotion engine communicates the recognized emotion to the system, and the system adjusts the display method of the summary information and logo image based on the emotion. For example, if the user expresses joy, the system selects a display method that matches the user's emotion, such as displaying the summary information and logo image in bright colors.

[1085] The processing flow of each embodiment will be described below.

[1086] "Example 1"

[1087] Step 1: The emotion engine recognizes emotions from the user's facial expressions, tone of voice, text input, etc.

[1088] Step 2: The emotion engine communicates the recognized emotion information to the system.

[1089] Step 3: The system adjusts the display of the summary information and logo images based on the received emotion information. For example, if a user expresses anger, the system may display the problematic logo image more prominently.

[1090] "Example 2"

[1091] Step 1: The user interface conveys the user's emotions to the emotion engine through input devices such as a camera, microphone, or keyboard.

[1092] Step 2: The emotion engine recognizes the user's emotions based on the information obtained from these input devices.

[1093] Step 3: The emotion engine communicates the recognized emotion information to the system, which then adjusts the display method of the summary information and logo image based on that information.

[1094] "Example 3"

[1095] Step 1: The emotion engine recognizes the user's emotion.

[1096] Step 2: The emotion engine communicates the recognized emotion to the system.

[1097] Step 3: The system adjusts the display method of the summary information and logo image based on the received emotion information. For example, if the user expresses joy, the system will select a display method that matches the user's emotion, such as displaying the summary information and logo image in bright colors.

[1098] Example 1

[1099] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1100] Conventional search engine crawlers can search the Internet for specific logo images, but they lack the ability to adjust how information is displayed based on the user's emotions. This makes it difficult to provide personalized information that reflects the user's emotions, and there is a need to improve the user experience. Furthermore, the identification of logo images generated by artificial intelligence and the generation and listing of summary information are not automated, making efficient information management difficult.

[1101] 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.

[1102] In this invention, the server includes means for searching the Internet for specific logo images using a search engine crawler, means for determining whether the retrieved logo image was generated by artificial intelligence, means for generating summaries of websites containing logo images determined to have been generated by artificial intelligence, means for listing the generated summaries and logo images, means for recognizing a user's emotion, and means for adjusting the display method of the listed summaries and logo images based on the recognized user emotion. This enables the provision of personalized information according to the user's emotion, resulting in efficient information management and an improved user experience.

[1103] A "search engine crawler" is a program that automatically crawls websites on the Internet and collects specific information.

[1104] A "logo image" is an image of a shape or design used to identify a company or brand.

[1105] "Artificial intelligence" refers to computer systems that mimic human intelligence and can learn and reason.

[1106] "Summary information" is information that briefly summarizes the contents of a website.

[1107] "Listing" refers to organizing multiple pieces of information into a list format.

[1108] "User emotion" refers to the psychological state or feelings exhibited by the user.

[1109] "Adjusting the display method" refers to changing the way information is presented based on the user's emotions.

[1110] The present invention is a system for searching for a specific logo image and adjusting the way information is displayed based on the user's emotions. A specific embodiment of this system will be described below.

[1111] The server runs a search engine crawler. This crawler is implemented using Python and analyzes web pages using libraries such as BeautifulSoup and Scrapy. The crawler traverses the Internet every night and collects specific logo images. The collected logo images are stored in a database such as MySQL or PostgreSQL.

[1112] The device runs an emotion engine to recognize the user's emotions. The emotion engine captures the user's facial expressions with a camera, receives voice input with a microphone, and analyzes text input. This data is processed using libraries such as TensorFlow and OpenCV. The emotion engine classifies the user's emotions into categories such as "anger," "joy," and "sadness," and sends the information to a server.

[1113] A user accesses the system through a terminal and browses search results for logo images. If the user expresses anger, the emotion engine communicates this information to the server. The server then adjusts the display method of the listed summary information and logo images based on the user's emotion. For example, if the user expresses anger, problematic logo images are displayed more prominently.

[1114] For example, the server runs a crawler every night to collect specific logo images and store them in a database. The device captures the user's facial expressions and voice and analyzes them using an emotion engine. The emotion engine sends the analysis results to the server, which adjusts the display method based on that information. The user then views the adjusted information in a web browser.

[1115] Examples of prompts to input to a generative AI model include:

[1116] "Please explain how to make the offending logo image more prominent when users express anger."

[1117] The system can dynamically adjust how information is displayed based on the user's emotions, providing a more personalized user experience.

[1118] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1119] Step 1:

[1120] The server runs the crawler.

[1121] The server runs the crawler as a scheduled task every night at midnight. The crawler is implemented using Python and analyzes web pages using libraries such as BeautifulSoup and Scrapy. As input, it receives a list of URLs for the websites that the crawler will visit. As output, it extracts URLs for specific logo images.

[1122] Step 2:

[1123] The crawler collects logo images.

[1124] The crawler visits a specified website and extracts logo image URLs using a specific CSS selector (e.g., img.logo). The input is the HTML data of the web page. The output is a list of logo image URLs. Specifically, it analyzes the HTML of the web page and extracts the logo image URLs.

[1125] Step 3:

[1126] The server stores the collected logo images in a database.

[1127] The server stores the URLs of logo images collected by the crawler in a database. As input, it receives a list of logo image URLs. As output, it generates records of logo images stored in the database. Specifically, it inserts records into the database using an ORM library such as SQLAlchemy.

[1128] Step 4:

[1129] The device recognizes the user's emotions.

[1130] The device uses a camera and microphone to capture the user's facial expressions and voice, and sends them to the emotion engine. Images of the user's facial expressions and voice data are provided as input. The output generates the user's emotion category (e.g., "anger," "joy," or "sadness"). Specifically, the system uses OpenCV to detect faces and TensorFlow to predict emotions.

[1131] Step 5:

[1132] The emotion engine sends the user's emotion to the server.

[1133] The emotion engine sends the recognized emotion data to the server. The user's emotion category is given as input. The emotion data sent to the server is generated as output. Specifically, the emotion data is sent using an HTTP POST request.

[1134] Step 6:

[1135] The server adjusts the display method based on the user's emotions.

[1136] The server adjusts the display method of logo images based on the received emotion data. As input, it receives the user's emotion data and a list of logo images. As output, it generates adjusted HTML and CSS. Specifically, if the user expresses anger, it generates HTML and CSS to display the problematic logo images more prominently.

[1137] Step 7:

[1138] The user views the tailored information.

[1139] The user views the tailored information through the terminal. The tailored HTML page is provided as input. The web page viewed by the user is displayed as output. Specifically, the web browser is used to display the HTML page provided by the server.

[1140] (Application example 1)

[1141] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1142] Conventional search engine crawlers can search the internet for specific logo images and identify logo images generated by artificial intelligence, but they cannot adjust the way information is displayed based on the user's emotions. As a result, they are unable to provide appropriate information in response to the user's emotions, which results in a lack of an improved user experience.

[1143] 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.

[1144] In this invention, the server includes means for searching the Internet for specific logo images using a search engine crawler, means for determining whether the retrieved logo image was generated by artificial intelligence, means for generating summaries of websites containing logo images determined to have been generated by artificial intelligence, means for listing the generated summaries and logo images, means for recognizing a user's emotion, and means for adjusting the display method of the listed summaries and logo images based on the recognized emotion, thereby enabling the provision of appropriate information according to the user's emotion.

[1145] A "search engine crawler" is a program that crawls the Internet and automatically collects specific information.

[1146] A "logo image" is a visual symbol that identifies a company or brand.

[1147] "Artificial intelligence" refers to computer systems that mimic human intelligence and are used for data analysis and pattern recognition.

[1148] A "website" is a collection of information publicly available on the Internet and is a page accessible at a specific URL.

[1149] "Summary information" is a concise summary of the original information.

[1150] "Listing" means organizing multiple items into a list format.

[1151] "Emotion recognition" is a technology that determines a user's emotions from facial expressions, tone of voice, text input, etc.

[1152] "Adjusting the display method" means changing the way information is displayed depending on the user's emotions.

[1153] The system for implementing this invention combines a search engine crawler, artificial intelligence, emotion recognition technology, and display adjustment functions. Specific embodiments are described below.

[1154] System configuration

[1155] The server uses the following hardware and software:

[1156] Hardware: Servers with powerful processors, memory, storage, and network interfaces.

[1157] Software: Use libraries such as OpenCV, Keras, and PIL.

[1158] Program processing

[1159] The server processes and calculates data in the following procedure.

[1160] 1. Search engine crawler: The server periodically crawls the Internet and searches for specific logo images, allowing it to collect the latest logo images.

[1161] 2. AI detection: Determine whether the searched logo image is generated by AI using a pre-trained generative AI model.

[1162] 3. Summary information generation: A summary information is generated for websites that contain logo images determined to have been generated by AI, allowing users to efficiently understand the information.

[1163] 4. Listing: The generated summary information and logo image are listed, which organizes the information and makes it easy for users to check.

[1164] 5. Emotion Recognition: The server acquires video from the smartphone camera and analyzes the user's facial expressions and tone of voice to recognize the user's emotions. This is done using OpenCV and Keras.

[1165] 6. Display Adjustment: Adjust the display of the summary information and logo images based on the recognized emotion. For example, if the user expresses the emotion "happy," the related logo image will be displayed larger and more detailed information will be provided.

[1166] Specific examples

[1167] When a user is viewing an advertisement on their smartphone, if the emotion recognition model detects "happiness" from the user's facial expression, a related logo image will be displayed in a larger size and detailed information will be provided, making it possible to provide appropriate information according to the user's emotions.

[1168] Prompt Sentence Examples

[1169] An example of a prompt sentence to input to the generative AI model is as follows:

[1170] When a user is viewing an ad, if the emotion recognition model detects "happiness," how can I display a larger image of the relevant logo and provide more information?

[1171] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1172] Step 1:

[1173] The server uses a search engine crawler to crawl the Internet and search for specific logo images. The input is the search query and the website to be crawled, and the output is a list of the searched logo images. Specifically, the crawler accesses the specified URLs sequentially, detecting and collecting logo images.

[1174] Step 2:

[1175] The server determines whether the searched logo images were generated by artificial intelligence. The input is the list of logo images collected in step 1, and the output is a list of logo images determined to have been generated by artificial intelligence. Specifically, the server uses the generative AI model to analyze each logo image and determine whether it was generated.

[1176] Step 3:

[1177] The server generates summary information for websites that contain logo images determined to have been generated by artificial intelligence. The input is a list of logo images determined in step 2 and the URLs of the websites that contain them, and the output is a list of summary information. Specifically, the server analyzes the content of the website, extracts important information, and creates a summary.

[1178] Step 4:

[1179] The server lists the generated summary information and logo images. The input is the list of summary information and logo images generated in step 3, and the output is the list of summary information and logo images. Specifically, the server associates each summary information with a logo image and organizes them in a list format.

[1180] Step 5:

[1181] To recognize the user's emotions, the device acquires video from the smartphone camera and analyzes the user's facial expressions and tone of voice. The input is the smartphone camera video, and the output is the recognized emotion. Specifically, the device uses OpenCV and Keras to analyze the video in real time and determine the emotion.

[1182] Step 6:

[1183] The server adjusts the display method of the listed summary information and logo image based on the recognized emotion. The input is the summary information and logo image listed in step 4 and the emotion recognized in step 5, and the output is the adjusted display method. Specifically, the server operates in accordance with the user's emotion; for example, if the emotion is "happy," it displays a related logo image in a larger size and provides detailed information.

[1184] Example 2

[1185] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1186] While conventional search engines can search for specific images on the Internet, they lack the means to determine whether the image was generated by artificial intelligence. They also lack the means to recognize users' emotions in real time and provide an interactive experience. This makes it difficult for users to obtain reliable information, and the user experience is limited.

[1187] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1188] In this invention, the server includes means for searching a network for specific images using a search engine crawler, means for determining whether the searched image has been generated by artificial intelligence, means for generating summaries of web pages containing images determined to have been generated by artificial intelligence, means for listing the generated summaries and images, means for recognizing a user's emotions through a user interface, and means for analyzing information obtained from input devices such as a camera, microphone, and keyboard to recognize the user's emotions. This allows the user to obtain highly reliable information, and recognizing emotions in real time enables a more interactive and richer user experience.

[1189] A "search engine crawler" is a program that automatically collects specific information from a network.

[1190] A "specific image" is a specific visual data that a user intends to find through a search engine.

[1191] "Network" refers to an information and communication network such as the Internet.

[1192] "Artificial intelligence" is a system that mimics human intelligence using technologies such as machine learning and deep learning.

[1193] A "means for determining" is a method or device for determining an outcome based on specific conditions or criteria.

[1194] A "web page" is a page that contains content such as documents, images, and videos that are publicly available on the Internet.

[1195] A "summary generator" is a method or device for summarizing long documents or information into shorter versions.

[1196] A "listing means" is a method or device for organizing multiple items into a list.

[1197] A "user interface" is an interface such as a screen or input device that allows a user to interact with a system.

[1198] "Means for recognizing emotions" refers to a method or device for analyzing and recognizing emotions from a user's facial expressions, tone of voice, text input, etc.

[1199] A "camera" is a device for taking pictures and videos.

[1200] A "microphone" is a device for recording sound.

[1201] A "keyboard" is a device for inputting characters and symbols.

[1202] An "input device" is a device that allows a user to input information into a system.

[1203] "Analyzing" means examining data in detail to understand its meaning and structure.

[1204] MODE FOR CARRYING OUT THE INVENTION

[1205] The present invention relates to a system for determining whether a specific image has been generated by artificial intelligence and for recognizing a user's emotions in real time. Specific embodiments of this system will be described below.

[1206] Hardware and software used

[1207] Hardware:

[1208] Camera (e.g. generic webcam)

[1209] Microphone (e.g., a general-purpose microphone)

[1210] Keyboard (e.g., a generic keyboard)

[1211] software:

[1212] Machine learning models (e.g., Generative Adversarial Network (GAN) models)

[1213] Emotion engine (e.g., general-purpose emotion recognition API)

[1214] System program processing

[1215] The server uses a search engine crawler to search for specific images on the network. The images are then sent to the server and input into a machine learning model. This model is used to determine whether the image was generated by artificial intelligence. For example, a GAN model is used to analyze the image's features and obtain a determination result.

[1216] Examples:

[1217] When a user searches for "new brand logo" using a web browser, the server receives the logo image and inputs it into a machine learning model. The model analyzes the image's features and determines whether it was generated by artificial intelligence. The result is returned to the user.

[1218] Example prompt sentence:

[1219] "Please determine whether this logo image was generated by artificial intelligence."

[1220] The device collects data to recognize the user's emotions through a user interface, specifically using input devices such as a camera, microphone, and keyboard. The camera captures the user's facial expressions, the microphone records the user's tone of voice, and the keyboard accepts the user's text input.

[1221] The server transmits the information obtained from these input devices to the emotion engine, which analyzes the information and recognizes the user's emotion.

[1222] Examples:

[1223] When a user is smiling and talking in front of the device, the camera captures the smile and the microphone records the tone of the voice. These data are sent to the server, and the emotion engine recognizes that the user is "happy."

[1224] Example prompt sentence:

[1225] "Recognize emotions from users' facial expressions and tone of voice."

[1226] The system recognizes user emotions in real time and judges artificial intelligence-generated images to provide a more interactive and reliable user experience.

[1227] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1228] Program processing flow

[1229] Logo image judgment

[1230] Step 1:

[1231] A user searches for a logo image.

[1232] Input: User types "new brand logo" into the search bar and clicks the search button.

[1233] Output: A search query is generated.

[1234] Specific actions: A user opens a web browser, enters keywords in the search bar, and presses the search button.

[1235] Step 2:

[1236] The terminal sends the logo image to the server.

[1237] Input: A logo image retrieved based on the search query.

[1238] Output: The logo image data is sent to the server.

[1239] Specific operation: The terminal uses an HTTP request to send the acquired logo image data to the server.

[1240] Step 3:

[1241] The server inputs the logo image into the machine learning model.

[1242] Input: Logo image data sent to the server.

[1243] Output: The result of the machine learning model.

[1244] Specific operation: The server inputs the received logo image into a machine learning model (e.g., GAN model), analyzes the image features, and obtains a judgment result.

[1245] Step 4:

[1246] The server returns the result of the determination to the user.

[1247] Input: The outcome from the machine learning model.

[1248] Output: The result of the judgment is displayed to the user.

[1249] Specific operation: The server sends the judgment result to the user's device as an HTTP response and displays the result in the browser.

[1250] User Emotion Recognition

[1251] Step 1:

[1252] The user sits in front of the terminal.

[1253] Input: User presence.

[1254] Output: The camera, microphone, and keyboard are ready to collect user data.

[1255] Specific actions: The user sits in front of the device and prepares to use input devices such as the camera, microphone, and keyboard.

[1256] Step 2:

[1257] The device collects data from the camera, microphone, and keyboard.

[1258] Input: User facial expressions, tone of voice, and text input.

[1259] Output: Collected data.

[1260] Specific operations: The camera captures the user's facial expressions, the microphone records the user's tone of voice, and the keyboard accepts the user's text input.

[1261] Step 3:

[1262] The terminal transmits the collected data to the server.

[1263] Input: Collected facial expression data, voice data, and text data.

[1264] Output: Data is sent to the server.

[1265] Specific operation: The device uses an HTTP request to send the collected data to the server.

[1266] Step 4:

[1267] The server analyzes the data using an emotion engine.

[1268] Input: Facial expression data, voice data, and text data sent to the server.

[1269] Output: Analysis results from the emotion engine.

[1270] Specific operation: The server inputs the received data into the emotion engine and analyzes the user's emotions.

[1271] Step 5:

[1272] The server returns the analysis results to the user.

[1273] Input: Analysis results from the emotion engine.

[1274] Output: The analysis results are displayed to the user.

[1275] Specific operation: The server sends the analysis results to the user's device as an HTTP response and displays the results in the browser.

[1276] (Application example 2)

[1277] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1278] Conventional advertising systems lacked a means to determine whether an advertising image was generated by AI, and were unable to evaluate in real time the emotions users felt when viewing the ad. This made it difficult to accurately evaluate the reliability and effectiveness of the ad. Furthermore, there was no system that combined the judgment of AI-generated images with the recognition of user emotions, making it impossible to obtain feedback to maximize the effectiveness of the ad.

[1279] 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.

[1280] In this invention, the server includes means for searching the Internet for specific logo images using a search engine crawler, means for determining whether the searched logo image was generated by AI, means for generating a summary of the website on which the logo image determined to be generated by AI is posted, means for listing the generated summary information and logo image, means including an emotion engine for recognizing user emotions, and means for evaluating user emotions in real time, thereby making it possible to determine whether an advertising image is AI-generated and evaluate user emotions in real time.

[1281] A "search engine crawler" is a program that automatically collects specific information from the Internet.

[1282] A "logo image" is a combination of shapes and letters that symbolize a company or brand.

[1283] "AI-generated" means that it has been automatically created using artificial intelligence technology.

[1284] A "means for determining" is a method or device for determining an outcome based on specific conditions or criteria.

[1285] A "means for generating a summary" is a method or device for concisely summarizing long texts or information.

[1286] A "listing means" is a method or device for organizing multiple pieces of information into a list.

[1287] "Emotion engine" is a general term for software and hardware that recognizes and analyzes user emotions.

[1288] A "real-time assessment tool" is a method or device for instantly assessing ongoing events or conditions.

[1289] A system for implementing the present invention is configured as follows: A server includes a search engine crawler, an AI-generated image judgment model, an emotion engine, and a user interface.

[1290] Program Generation

[1291] The server periodically searches the Internet for specific logo images using a search engine crawler. The searched logo images are then judged to be AI-generated using an AI-generated image judgment model. Based on the judgment results, a summary of the website on which the logo image judged to have been AI-generated is posted is generated. The generated summary information and logo image are listed and provided to the user via a user interface.

[1292] Furthermore, the emotion engine recognizes user emotions through input devices such as cameras, microphones, and keyboards. It analyzes facial expressions, tone of voice, and text input when a user views an ad, and evaluates emotions in real time. This allows for immediate feedback on the effectiveness of the ad.

[1293] Hardware and software used

[1294] Hardware:

[1295] Camera: Used to capture the user's facial expressions.

[1296] Microphone: Used to record the tone of the user's voice.

[1297] Keyboard: Used to accept text input from the user.

[1298] software:

[1299] OpenCV: Used to capture and process camera footage.

[1300] Keras: Used to load and predict AI generative image judgment models and emotion recognition models.

[1301] SpeechRecognition: Used to convert the user's voice into text.

[1302] TextBlob: Used to analyze sentiment from text.

[1303] Specific examples

[1304] For example, when a user uploads an advertisement image, the system determines whether the image was generated by AI. Furthermore, it can evaluate the user's emotions when viewing the advertisement in real time, providing instant feedback on the effectiveness of the advertisement.

[1305] Example prompt sentence:

[1306] Develop an application that, when a user uploads an ad image, determines whether the image is generated by AI and evaluates the user's emotions when viewing the ad in real time. Use a camera and microphone to capture the user's facial expressions and tone of voice, and use an emotion engine to recognize emotions.

[1307] In this way, a system combining AI-generated image judgment and emotion recognition in the advertising industry can be realized.

[1308] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1309] Step 1:

[1310] The server periodically searches the Internet for specific logo images using a search engine crawler. The input is a search query and a web page on the Internet, and the output is a list of the searched logo images. The crawler crawls web pages based on the specified keywords and collects logo images.

[1311] Step 2:

[1312] The server determines whether the searched logo images are generated by AI. The input is the logo images collected in step 1, and the output is the result of determining whether each logo image is AI-generated. Using an AI-generated image judgment model, each logo image is analyzed to determine whether it has the characteristics of AI generation.

[1313] Step 3:

[1314] The server generates a summary of the website that contains the logo image determined to be generated by AI. The input is the logo image determined to be generated by AI and the URL of the website where it is located, and the output is a summary of the website. The summary generation algorithm is used to concisely summarize the content of the web page.

[1315] Step 4:

[1316] The server lists the generated summary information and logo images. The input is the summary information and logo images, and the output is a list of the summary information and logo images. This organizes the information in a format that is easy for users to check.

[1317] Step 5:

[1318] The device runs an emotion engine to recognize the user's emotions. The input is data from the camera, microphone, and keyboard, and the output is the user's emotional state. The camera captures the user's facial expressions, the microphone records the user's tone of voice, and the keyboard accepts the user's text input. These data are sent to the emotion engine and analyzed.

[1319] Step 6:

[1320] The server evaluates the user's emotions in real time. The input is the user's emotion data obtained in step 5, and the output is the real-time emotion evaluation result. The emotion engine performs facial expression analysis, voice analysis, and text analysis to evaluate the user's emotions.

[1321] Step 7:

[1322] The terminal provides the user with the summarized information and logo image in a list form through a user interface. The input is the summarized information and logo image in a list form, and the output is an interface that the user can check. The user can evaluate the effectiveness of the advertisement based on the provided information.

[1323] Example 3

[1324] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1325] With conventional technology, it was difficult to determine whether an image on a network was generated by artificial intelligence and generate a summary of that information source. It was also not possible to list the generated summary information and images and adjust the display method based on the user's emotions. This resulted in the problem that users could not efficiently check information and obtain a display that suited their emotions.

[1326] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1327] In this invention, the server includes means for searching a network for specific images using a search engine crawler, means for determining whether the retrieved images have been generated by artificial intelligence, means for generating summaries of information sources containing images determined to have been generated by artificial intelligence, means for listing the generated summaries and images, and means for adjusting the display method of the listed summaries and images based on emotions recognized by the emotion engine, thereby enabling users to efficiently check information and obtain a display that corresponds to an appropriate emotion.

[1328] A "search engine crawler" is a program that automatically collects specific data from sources on a network.

[1329] "Artificial intelligence" is a technology that allows machines to mimic human intelligence to learn, reason, and recognize.

[1330] "Source" refers to digital content such as websites and databases that exist on a network.

[1331] A "summary" is information that extracts important parts from original information and summarizes them concisely.

[1332] "Listing" refers to organizing multiple pieces of information into a list format.

[1333] An "emotion engine" is a technology that recognizes the user's emotions and adjusts the system's behavior based on that information.

[1334] "Presentation" refers to the visual format or style in which information is presented to the user.

[1335] MODE FOR CARRYING OUT THE INVENTION

[1336] This invention is a system that determines whether images present on a network have been generated by artificial intelligence, generates a summary of the information source, lists the generated summary information and images, and adjusts the display method based on the user's emotions.

[1337] System program generation

[1338] The server generates the system's program using the following means:

[1339] 1. A method for searching for specific images on the network using a search engine crawler

[1340] 2. A method to determine whether a searched image was generated by artificial intelligence

[1341] 3. A means for generating summaries of sources containing images determined to have been generated by artificial intelligence.

[1342] 4. A means of listing the generated summary information and images

[1343] 5. A means to adjust the display of the summary information and images listed based on the emotions recognized by the emotion engine.

[1344] Program processing

[1345] The server first accesses the specified URL and retrieves the HTML code. Next, it parses the HTML code and extracts text information using a library such as BeautifulSoup. The extracted text information is input into a BERT (Bidirectional Encoder Representations from Transformers) model to generate a summary. The BERT model is loaded using the transformers library.

[1346] The generated summary information and images are compiled into a list and displayed through a web interface using web frameworks such as Flask or Django. When a user interacts with the system through the web interface, the emotion engine recognizes emotions from the user's facial expressions and input text. For example, it uses libraries such as OpenCV and DeepFace for facial expression recognition. The server adjusts the display method using CSS and JavaScript based on the emotion information received from the emotion engine.

[1347] Specific examples

[1348] For example, the following prompts can be fed into a generative AI model:

[1349] Example prompt sentence:

[1350] "Extract text information from the website https: / / example.com and generate a summary using BERT. Then, list the generated summary and logo image, and adjust the display based on the user's sentiment."

[1351] Using this prompt, the system can extract text information from the specified website, generate a summary, and then adjust the display method based on the user's sentiment. The flow of the identification process in Example 3 will be described with reference to Figure 21.

[1352] Step 1:

[1353] The server accesses the specified URL and obtains the HTML code.

[1354] Input: The specified URL

[1355] Output: Retrieved HTML code

[1356] Specific operation: The server sends an HTTP request and receives the HTML code returned as a response. For example, it uses the requests library to access the URL and obtain the HTML code.

[1357] Step 2:

[1358] The server parses the HTML code and extracts the text information.

[1359] Input: The HTML code you obtained

[1360] Output: Extracted text information

[1361] Specific operation: The server uses a library such as BeautifulSoup to parse the HTML code and extract text information. Specifically, it parses HTML tags and extracts the text.

[1362] Step 3:

[1363] The server inputs the extracted text information into the BERT model to generate a summary.

[1364] Input: Extracted text information

[1365] Output: Generated summary

[1366] How it works: The server loads the BERT model using the transformers library and provides text information as input. The BERT model extracts important information and generates a summary.

[1367] Step 4:

[1368] The server lists the generated summary information and images.

[1369] Input: Generated summary information, image

[1370] Output: List of summary information and images

[1371] How it works: The server compiles the summary information and images into a data structure (e.g., a list or dictionary), and then displays the list through a web interface using a web framework such as Flask or Django.

[1372] Step 5:

[1373] The emotion engine recognizes the user's emotions, and the server adjusts the display method based on that information.

[1374] Input: User's emotional information

[1375] Output: Adjusted display method

[1376] Specific operation: When a user interacts with the system through a web interface, the emotion engine recognizes emotions from the user's facial expressions and input text. For example, it uses libraries such as OpenCV and DeepFace to recognize facial expressions. The server adjusts the display method using CSS and JavaScript based on the emotion information received from the emotion engine. For example, if the user expresses joy, it changes the background color to a brighter color.

[1377] (Application example 3)

[1378] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1379] Previous systems lacked the means to determine whether logo images on the Internet were generated by artificial intelligence and provide that information to users. They also lacked the ability to adjust the way information is displayed based on the user's emotions, limiting the user experience. This made it difficult for users to quickly and effectively obtain the information they needed.

[1380] The identification process performed by the identification processing unit 290 of the data processing device 12 in Application Example 3 is implemented by the following means. In this invention, the server includes: means for searching the Internet for specific logo images using a search engine crawler; means for determining whether the searched logo image was generated by artificial intelligence; means for generating a summary of the website containing the logo image determined to have been generated by artificial intelligence; means for listing the generated summary information and logo image; and means for adjusting the display method of the listed summary information and logo image based on the emotion recognized by the emotion engine. This allows users to determine whether a logo image on the Internet was generated by artificial intelligence and to quickly and effectively obtain that information. Furthermore, adjusting the display method of information based on the user's emotion can provide a more personalized user experience.

[1381] A "search engine crawler" is a program that automatically crawls web pages on the Internet and collects specific information.

[1382] A "logo image" is an image designed to represent a company or brand.

[1383] "Artificial intelligence" refers to computer systems that mimic human intelligence and can learn and reason.

[1384] A "website" is a collection of information made publicly available on the Internet, a collection of pages with a specific URL.

[1385] A "summary" is a short summary of a long piece of text or information, extracting the important points.

[1386] "Listing" means organizing and displaying multiple items in a list format.

[1387] An "emotion engine" is a technology that recognizes a user's emotions and adjusts the system's behavior based on those emotions.

[1388] An "interface" is the means or method by which a user and a system interact with each other.

[1389] To implement the present invention, the following system configuration is required.

[1390] First, the server uses a search engine crawler to search for specific logo images on the Internet. To determine whether the searched logo image was generated by AI, the generative AI model is used. This model analyzes the characteristics of the logo image and determines whether it was generated by AI.

[1391] Next, the system extracts text information from websites that contain logo images determined to have been generated by AI, and generates summaries of the images using natural language processing techniques such as BERT (Bidirectional Encoder Representations from Transformers). This produces a concise summary of the website's key information.

[1392] The generated summary information and logo image are listed and displayed through an interface so that the user can check them. This interface is designed so that the user can easily view the listed information.

[1393] Additionally, the system uses an emotion engine to recognize the user's emotions and adjust the display of the summary information and logo images in the list based on that emotion. For example, if the user expresses joy, the system will display the summary information and logo images in brighter tones. This emotion recognition uses the BERT model to classify emotions from the user's input text.

[1394] For example, the following prompts can be used:

[1395] URL: "https: / / example.com / product-page"

[1396] Logo URL: "https: / / example.com / logo.png"

[1397] Extracted text: "This product is of very high quality and is loved by many users."

[1398] Generated summary: "This is the summary."

[1399] Perceived Emotion: Positive

[1400] In this way, users can view summary information and logo images on product pages of online shopping sites in a personalized way based on their emotions through a smartphone app. This system allows users to obtain the information they need quickly and effectively, enabling them to enjoy a better shopping experience.

[1401] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1402] Step 1:

[1403] The server uses a search engine crawler to search for specific logo images on the Internet. The crawler traverses a specified URL list and collects web pages that contain logo images. The input is a list of URLs, and the output is data on web pages that contain logo images.

[1404] Step 2:

[1405] The server determines whether the collected logo images were generated by artificial intelligence. A generative AI model is used to make this determination. A logo image is given as input, and the output is a determination result as to whether the logo image was generated by artificial intelligence. Specifically, the features of the logo image are analyzed, and a determination is made using the generative AI model.

[1406] Step 3:

[1407] The server extracts text information from websites that contain logo images determined to have been generated by artificial intelligence. The input is web page data, and the output is text information. Specifically, it uses libraries such as BeautifulSoup to extract text from HTML.

[1408] Step 4:

[1409] The server then summarizes the extracted text information. This summary generation uses natural language processing techniques such as BERT. The input is text information, and the output is a summarized text. Specifically, the BERT model is used to extract important information and generate a summary.

[1410] Step 5:

[1411] The server lists the generated summary information and logo images. The summary information and logo images are given as input, and the list data is obtained as output. Specifically, the summary information and logo images are organized in a list format and stored in a data structure for display.

[1412] Step 6:

[1413] The server recognizes the user's emotions using an emotion engine. The input is the user's input text, and the output is the recognized emotion. Specifically, it uses the BERT model to classify the emotion from the user's input text.

[1414] Step 7:

[1415] The server adjusts the display method of the listed summary information and logo image based on the recognized emotion. The recognized emotion and the listed data are given as input, and the adjusted display data is obtained as output. Specifically, the color tone and layout are changed according to the emotion, and the information is displayed in the most optimal form for the user.

[1416] Step 8:

[1417] The device provides the adjusted display data to the user. The adjusted display data is provided as input, and an interface that can be viewed by the user is provided as output. Specifically, the information is displayed to the user through a smartphone app or a web browser.

[1418] 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.

[1419] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by 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.

[1420] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[1421] 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.

[1422] [Third embodiment]

[1423] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1424] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1425] 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).

[1426] 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.

[1427] 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.

[1428] 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).

[1429] 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.

[1430] 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.

[1431] 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.

[1432] 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.

[1433] 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.

[1434] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1435] "Example 1"

[1436] One embodiment of the present invention is a search engine crawler for searching for specific logo images. This crawler periodically patrols the Internet and searches for specific logo images. For example, it can crawl every night to search for specific logo images.

[1437] "Example 2"

[1438] There are also methods to determine whether a searched logo image was generated by AI. This uses a machine learning model that has learned the characteristics of AI-generated images to determine whether the searched logo image was generated by AI. For example, it is possible to use a model that has learned the characteristics of images generated by a GAN (Generative Adversarial Network).

[1439] "Example 3"

[1440] Furthermore, there are methods for generating summaries of websites that contain logo images determined to have been generated by AI. These methods extract text information from the website and generate summaries. For example, natural language processing techniques such as BERT (Bidirectional Encoder Representations from Transformers) can be used to extract important information from the text information on the website and generate summaries.

[1441] "Example 4"

[1442] Finally, there is a means for listing the generated summary information and logo images. This allows the summary information and logo images to be displayed as a list, making it easier for humans to check the contents. For example, it is possible to display a list of the summary information and logo images through a web interface so that humans can check them.

[1443] The processing flow of each embodiment will be described below.

[1444] "Example 1"

[1445] Step 1: A search engine crawler crawls the Internet to search for specific logo images. This crawler crawls every night, for example, to search for specific logo images.

[1446] Step 2: The crawler stores the logo images it finds in a temporary database, which provides input data for the AI ​​to use in the next step.

[1447] "Example 2"

[1448] Step 1: A means for determining whether the searched logo image was generated by AI is activated. This uses a machine learning model that has learned the characteristics of AI-generated images to determine whether the searched logo image was generated by AI.

[1449] Step 2: Based on the results, we identify logo images that are determined to be AI-generated and store that information in a temporary database, which will provide input data for generating a website summary in the next step.

[1450] "Example 3"

[1451] Step 1: A means for generating a summary of a website containing a logo image determined to have been generated by AI is activated. This extracts text information from the website and generates a summary.

[1452] Step 2: The generated summary information is saved in a temporary database, which will serve as input data for the next step of creating a list of summary information and logo images.

[1453] "Example 4"

[1454] Step 1: A means for listing the generated summary information and logo images is activated. This displays the summary information and logo images as a list, making it easier for humans to check the contents.

[1455] Step 2: Display the listed summary information and logo image in a web interface so that it can be reviewed by humans.

[1456] Example 1

[1457] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1458] Conventional search engine crawlers have difficulty efficiently searching for specific images, and managing and notifying search results is time-consuming. Furthermore, there is a lack of means to identify images generated by generative AI models and organize the information. This makes it difficult for users to quickly and accurately obtain the information they need.

[1459] The identification process by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. In this invention, the server includes means for searching for specific images on the network using a search engine crawler, means for determining whether the searched images were generated by a generative AI model, means for generating summaries of information resources containing images determined to have been generated by a generative AI model, means for listing the generated summary information and images, means for periodically starting the crawler, means for saving the collected images in a database, and means for notifying the search results. This makes it possible to efficiently search for specific images, organize the determination results by the generative AI model, and notify the user quickly and accurately.

[1460] A "search engine crawler" is a program that automatically crawls information resources on a network and collects specific data.

[1461] A "specific image" is an image that has a specific feature designated by the user as a search target.

[1462] "Network" refers to an information and communication network such as the Internet or an intranet.

[1463] A "generative AI model" is an algorithm or system that generates data using artificial intelligence technology.

[1464] A "means for determining" is a device or program that has the function of evaluating the characteristics of data based on specific conditions or criteria and outputting the results.

[1465] An "information resource" is a collection of information such as websites and databases that exist on a network.

[1466] A "summary generation means" is a device or program that has the function of extracting important information from information resources and summarizing it concisely.

[1467] A "listing means" is a device or program that has the function of organizing collected data and displaying it in a list format.

[1468] "Means for periodically launching a crawler" refers to a device or program that has the function of automatically running a crawler at regular intervals.

[1469] A "database" is a system for efficiently storing, searching, and managing data.

[1470] A "notification means" is a device or program that has the function of notifying a user of a specific event or result.

[1471] This invention is a system that efficiently searches for specific images, organizes the judgment results by a generative AI model, and notifies the user quickly and accurately. A specific embodiment of this system is described below.

[1472] The server automatically crawls network resources to collect specific images using a search engine crawler, written in a programming language such as Python or JavaScript, that uses libraries such as BeautifulSoup or Puppeteer to parse the HTML of web pages.

[1473] The collected images are analyzed using a generative AI model, which uses artificial intelligence techniques to determine whether an image is generated, using image recognition libraries such as OpenCV.

[1474] The results of the assessment are generated as a summary of the information resource. Natural language processing technology is used to generate the summary, and important information is extracted. The generated summary information and images are compiled into a list and stored in a database. Databases such as MySQL and MongoDB are used.

[1475] The server sets up a cron job to periodically launch the crawler. This automatically runs the crawler every night at midnight. The collected images and summary information are stored in a database and notified to the user. An email sending function is used for notification.

[1476] As a concrete example, consider the case where a user searches for logo images of a specific company. For example, when searching for logo images of a "specific company," a crawler traverses web pages on the network and collects logo images of the specific company. The server runs the crawler every night and stores the collected logo images in a database. The server then notifies the user of the search results by email.

[1477] An example of a prompt is as follows:

[1478] "Please set up a crawler to search for logo images of specific companies. The crawler will run every night, crawl web pages on the network, collect specific logo images, and store them in a database. For example, if you search for logo images of "specific companies," the crawler will collect logo images of specific companies."

[1479] In this way, users can build a system that allows them to efficiently search for specific images, organize the judgment results of generative AI models, and quickly and accurately notify the results.

[1480] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1481] Step 1: Start the crawler

[1482] The server starts the crawler every night at midnight. Specifically, a cron job is set up to run the crawler at 2:00 AM every day. The input is a cron job configuration file, and the output is that the crawler will start running. Specifically, the server sets up the cron job as follows:

[1483] 0 2 / usr / bin / python3 / path / to / crawler.py

[1484] Step 2: Collect web pages

[1485] The crawler collects web pages from the network. Specifically, it uses BeautifulSoup (a Python library) to analyze the HTML of the web pages. The input is the URL of the web page, and the output is the analyzed HTML data. Specifically, the crawler performs the following processes:

[1486] Access the URL, get the HTML data, and parse it with BeautifulSoup.

[1487] Step 3: Find your logo image

[1488] The crawler searches for specific logo images from collected web pages. It uses OpenCV (a Python library) for image recognition. The input requires the parsed HTML data and a logo image template, and the output is the URL of the matching logo image. Specifically, the crawler performs the following process:

[1489] Image tags are extracted from the HTML data, and each image is analyzed using OpenCV to determine whether it matches the logo image.

[1490] Step 4: Save your data

[1491] The server saves the search results in a database. It uses MySQL to save the search results. The input requires the URL of the matching logo image, and the output is the search results saved in the database. Specifically, the server performs the following process:

[1492] Insert the matching logo image URL into the database.

[1493] Step 5: Notification of results

[1494] The server notifies the user of the search results. For example, it can notify them by email. The input requires the search results stored in the database, and the output is the notification sent to the user. Specifically, the server performs the following process:

[1495] The search results are inserted into the email body and sent to the user.

[1496] In this way, a system can be constructed in which the server can efficiently search for specific logo images, organize the judgment results of the generative AI model, and notify the user quickly and accurately.

[1497] (Application example 1)

[1498] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1499] While conventional search engine crawlers can search the Internet for specific logo images, they lack the functionality to monitor and notify users in real time how those logo images are being used. Furthermore, they lack the means to determine whether searched logo images are generated by AI and provide a list of that information. This makes it difficult for brand managers and marketers to efficiently monitor the usage of their company's logos.

[1500] 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.

[1501] In this invention, the server includes means for searching the Internet for specific logo images using a search engine crawler, means for determining whether the searched logo images were generated by AI, means for generating summaries of information sources containing logo images determined to be generated by AI, means for listing the generated summaries and logo images, means for providing an interface for a human to review the listed summaries and logo images, and means for monitoring how specific logo images are used on the Internet and sending notifications in real time, thereby enabling brand managers and marketers to efficiently monitor the usage of their company's logos and respond in real time.

[1502] A "search engine crawler" is a program that automatically crawls web pages on the Internet and collects specific information.

[1503] A "logo image" is an image that includes a shape or design that symbolizes a company or brand.

[1504] "AI-generated" means created automatically using artificial intelligence technology.

[1505] "Source" refers to a website or online platform where particular data or information is posted.

[1506] A "summary" is a concise summary of the main points of a particular piece of information.

[1507] "Listing" means organizing multiple items into a list format.

[1508] "Interface" refers to the means or screen through which a user interacts with a system.

[1509] "Real-time notifications" means that users are notified immediately when a specific event or piece of information occurs.

[1510] "Monitoring" means continuously observing a specific object and recording its condition and changes.

[1511] The system for implementing this invention includes a search engine crawler, an AI judgment module, a summary generation module, a listing module, an interface module, and a real-time notification module. These modules run on a server, monitor how a specific logo image is used on the Internet, and notify users in real time.

[1512] Program processing explanation

[1513] The server periodically crawls web pages on the Internet using a search engine crawler implemented using Python to search for specific logo images. The crawler retrieves web pages using the requests library and analyzes the HTML using the BeautifulSoup library. If a specific logo image is found, the information is sent to the AI ​​judgment module.

[1514] The AI ​​determination module uses a generative AI model to determine whether the searched logo image is AI-generated. This model uses a pre-trained deep learning model. The determination result is sent to the summary generation module.

[1515] The summary generation module generates a summary of the information source containing the logo image determined to be generated by AI. This summary is automatically generated using natural language processing technology. The generated summary information and logo image are sent to the listing module.

[1516] The list module organizes the generated summary information and logo images into a list format and stores it in a database, which is then sent to the interface module for user confirmation.

[1517] The interface module provides an interface for users to check the listed summary information and logo images, and the interface can be accessed through a web browser or a smartphone application.

[1518] The real-time notification module notifies users in real time when a specific logo image is newly used on the Internet, in the form of push notifications, email notifications, etc.

[1519] Specific examples

[1520] For example, if a brand manager wants to monitor how their company's logo image (URL: https: / / example.com / logo.png) is being used online, they can use this system to receive real-time information about new web pages where the logo image is being used, enabling them to quickly detect and respond to any unauthorized or misused brand use.

[1521] Prompt Sentence Examples

[1522] Implement a crawler to monitor how your brand's logo image (URL: https: / / example.com / logo.png) is used on the Internet. This crawler will traverse the Internet every night, searching for the specific logo image. If it finds one, it will add the URL to a list and notify the user.

[1523] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1524] Step 1:

[1525] The server launches a search engine crawler to periodically crawl web pages on the Internet. As input, it receives the URL of a specific logo image and a list of web pages to crawl. The crawler uses the requests library to retrieve the web pages and parses the HTML using the BeautifulSoup library. As output, it generates the URLs of web pages that contain the specific logo image.

[1526] Step 2:

[1527] The server sends the URL of the webpage obtained in step 1 to the AI ​​judgment module. As input, it receives the URL of the webpage and the logo image data. The AI ​​judgment module uses the generative AI model to determine whether the logo image was generated by AI. As output, it generates a judgment result.

[1528] Step 3:

[1529] The server sends the judgment result obtained in step 2 to the summary generation module. It receives the judgment result and the URL of the web page as input. The summary generation module uses natural language processing technology to generate a summary of the information source where the logo image is posted. It generates summary information as output.

[1530] Step 4:

[1531] The server sends the summary information and logo images generated in step 3 to the listing module. The listing module receives the summary information and logo image data as input. The listing module organizes the generated summary information and logo images into a list format and stores it in a database. The listing module generates the list data as output.

[1532] Step 5:

[1533] The server sends the listed data to an interface module, which receives the listed data as input, provides an interface for a user to view the listed summary information and logo image, and generates an interface accessible to a user as output.

[1534] Step 6:

[1535] The server starts a real-time notification module when a specific logo image is newly used on the Internet. As input, it receives information about the newly used logo image. The real-time notification module notifies the user in the form of a push notification, email notification, or the like. As output, it generates a notification message.

[1536] Example 2

[1537] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1538] In recent years, advances in artificial intelligence technology have led to an increase in the number of generated images. However, there is a lack of means to determine whether these images were generated by artificial intelligence, making it difficult for users and businesses to obtain reliable information. There is also a need for a system that can efficiently determine whether images searched on the network were generated by artificial intelligence and provide the results to users.

[1539] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1540] In this invention, the server includes means for searching a network for specific images using a search engine crawler, means for determining whether the searched image was generated by artificial intelligence, means for generating summaries of information sources containing images determined to have been generated by artificial intelligence, means for listing the generated summary information and images, means for a user to upload images and determine whether the uploaded images were generated by artificial intelligence, means for inputting preprocessed images into a machine learning model and obtaining the determination result, and means for notifying the user of the determination result, thereby enabling a user to easily determine whether an image searched for on a network or an image uploaded by the user was generated by artificial intelligence and obtain the result in a reliable manner.

[1541] A "search engine crawler" is a program that automatically collects specific data from sources on a network.

[1542] "Image" is digital data containing visual information, including logos and photographs.

[1543] A "network" is a system in which multiple computers and devices are interconnected and exchange data.

[1544] "Artificial intelligence" is the technology that enables computer systems to mimic human intelligence and learn, reason, and self-correct.

[1545] A "means for determining" is a process or device that evaluates data based on specific conditions or criteria and derives a result.

[1546] A "source" is a place or system where data or information is generated, stored, or provided.

[1547] A "summary generator" is a process or device that condenses long information into short chunks and extracts the main points.

[1548] A "listing means" is a process or device that organizes multiple pieces of data or information and displays them in a list format.

[1549] "User" means an individual or organization that uses the system or service.

[1550] "Uploading" is the act of sending data from a local device to a server on a network.

[1551] "Preprocessing" refers to the initial stage of processing to convert data into a format suitable for analysis and processing.

[1552] A "machine learning model" is an algorithm or system that learns from data and makes predictions and classifications.

[1553] A "judgment result" is an evaluation or conclusion drawn based on specific conditions or criteria.

[1554] A "means of notification" is a process or device that conveys specific information to a user.

[1555] MODE FOR CARRYING OUT THE INVENTION

[1556] This invention relates to a system for determining whether an image searched on a network or uploaded by a user has been generated by artificial intelligence. A specific embodiment of this system will be described below.

[1557] System configuration

[1558] This system consists of three main elements: a server, a terminal, and a user. The server uses a machine equipped with a high-performance GPU (e.g., NVIDIA Tesla V100) and uses TensorFlow or PyTorch as software. The terminal is the device through which the user accesses the system, and includes devices such as PCs and smartphones.

[1559] Program processing

[1560] The server performs the following steps:

[1561] 1. Image search using search engine crawlers

[1562] The server searches the network for specific images using a search engine crawler, which periodically traverses the network and collects new images.

[1563] 2. Image Judgment

[1564] The server uses a machine learning model to determine whether the retrieved image was generated by artificial intelligence. The model learns the characteristics of images generated by a Generative Adversarial Network (GAN).

[1565] 3. Summary information generation

[1566] The server generates a summary of the source containing the image determined to be generated by artificial intelligence, which is a concise summary of the main points.

[1567] 4. Listing

[1568] The server lists the generated summary information and images in an organized manner that allows the user to easily review the list.

[1569] 5. User image upload and detection

[1570] Users use their terminals to access the system's web interface and upload images. The server receives the uploaded images and performs pre-processing, which includes resizing and normalizing the images.

[1571] 6. Input to the machine learning model and obtain the judgment result

[1572] The server inputs the preprocessed image into the machine learning model and obtains a judgment result, which is a flag indicating whether the image was generated by artificial intelligence.

[1573] 7. Notification of the decision

[1574] The server notifies the user of the result of the determination, either by a message displayed on the web interface or, if necessary, by email.

[1575] Specific examples

[1576] For example, if a user wants to determine whether a searched logo image was generated by AI, they can input the following prompt to the generative AI model:

[1577] Example prompt sentence:

[1578] "Please determine whether this logo image was generated by AI."

[1579] The server receives this prompt and inputs the retrieved logo image into a machine learning model. The model analyzes the image's features and determines whether it was AI-generated. The result is then notified to the user.

[1580] In this way, users can easily verify whether a logo image is generated by AI.

[1581] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1582] Program processing flow

[1583] Step 1: Image search using a search engine crawler

[1584] The server searches the network for specific images using a search engine crawler, which periodically traverses the network and collects new images.

[1585] Input: Search query

[1586] Output: A list of URLs of the images found

[1587] How it works: The server passes the search query to the crawler, which then visits sources on the network to collect image URLs, which are then returned to the server.

[1588] Step 2: Download and preprocess images

[1589] The server downloads images from the collected URL list and pre-processes them, which includes resizing and normalizing the images.

[1590] Input: List of image URLs

[1591] Output: Preprocessed image data

[1592] What happens: The server downloads the image from each URL, resizes it using Python's Pillow library, and normalizes the pixel values ​​using NumPy (e.g., resizes the image to 256x256 pixels and scales the pixel values ​​to the range 0 to 1).

[1593] Step 3: Image Judgment

[1594] The server inputs the pre-processed image data into a machine learning model to determine whether the image was generated by artificial intelligence.

[1595] Input: Preprocessed image data

[1596] Output: Judgment result (probability of AI generation)

[1597] How it works: The server inputs preprocessed image data into a TensorFlow or PyTorch model. The model analyzes the image features and evaluates the likelihood of AI generation. For example, a probability value such as "AI generation: 90%" is output.

[1598] Step 4: Generate summary information

[1599] The server generates summaries of the sources containing images determined to be generated by artificial intelligence.

[1600] Input: Judgment result, image URL

[1601] Output: Summary information

[1602] Specific operation: If the judgment result exceeds a certain threshold, the server extracts text information from the source where the image is posted and generates a summary using natural language processing technology.

[1603] Step 5: List

[1604] The server lists the generated summary information and images.

[1605] Input: Summary information, image data

[1606] Output: Listed information

[1607] Specific operation: The server compiles the summary information and image data into a single list and organizes them so that the user can easily check them.

[1608] Step 6: Upload user images

[1609] Users use a terminal to access the system's web interface and upload images.

[1610] Input: User uploaded image

[1611] Output: Image data sent to the server

[1612] Specific operation: A user opens a browser and accesses the system's web page. The user clicks the "Select Image" button to select a logo image file from the local disk. Then, the user clicks the "Upload" button.

[1613] Step 7: Preprocessing the uploaded images

[1614] The server receives the uploaded images and performs pre-processing.

[1615] Input: Uploaded image data

[1616] Output: Preprocessed image data

[1617] Specific operation: The server resizes the received image using Python's Pillow library and normalizes the pixel values ​​using NumPy.

[1618] Step 8: Input to the machine learning model and obtain the judgment result

[1619] The server inputs the preprocessed image into the machine learning model and obtains the judgment result.

[1620] Input: Preprocessed image data

[1621] Output: Judgment result (probability of AI generation)

[1622] How it works: The server inputs preprocessed image data into a TensorFlow or PyTorch model, which analyzes the image's features and evaluates the potential for AI generation.

[1623] Step 9: Notification of decision

[1624] The server notifies the user of the result of the determination.

[1625] Input: Judgment result

[1626] Output: Message to be sent to the user

[1627] Specific operation: The server generates the judgment result in HTML format and displays it in the user's browser. For example, it may say, "This logo image is likely to have been generated by AI (90%)."

[1628] In this way, users can easily verify whether a logo image is generated by AI.

[1629] (Application example 2)

[1630] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1631] In recent years, advances in AI technology have led to an increase in the number of AI-generated logo images on the Internet. This has made it difficult to verify the authenticity of logo images used by companies and individuals. In particular, there is a growing risk that AI-generated logo images will be misused, raising concerns about a decline in credibility and damage to brand value. In this situation, there is a need for a method to quickly and accurately determine whether a logo image has been generated by AI.

[1632] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for searching the Internet for specific logo images using a search engine crawler, means for determining whether the searched logo images were generated by AI, means for generating summaries of websites containing logo images determined to have been generated by AI, means for listing the generated summaries and logo images, means for providing an interface for uploading logo images, means for determining whether the uploaded logo images were generated by AI, and means for notifying the user of the determination result. This makes it possible to quickly and accurately determine the authenticity of logo images used by companies and individuals.

[1633] A "search engine crawler" is a program that automatically searches for and collects specific logo images on the Internet.

[1634] A "logo image" is a visual symbol used by businesses and individuals to identify their brand or service.

[1635] "AI-generated" means that it has been automatically created using artificial intelligence technology.

[1636] "Means for determining" refers to a device or program that has the function of determining whether a specific logo image was generated by AI.

[1637] A "means for generating a website summary" is a device or program that has the function of creating information that succinctly summarizes the contents of a particular website.

[1638] The "listing means" is a device or program that has the function of organizing the generated summary information and logo images in a list format.

[1639] The "means for providing an interface" refers to a device or program that provides an operation screen and input means for users to upload logo images.

[1640] "Means for notifying the user of the judgment result" refers to a device or program that has the function of informing the user of the judgment result as to whether the logo image was generated by AI.

[1641] The system for implementing the present invention is configured as follows: The server includes a search engine crawler, an AI judgment module, a summary generation module, a listing module, an interface providing module, and a notification module.

[1642] First, a search engine crawler periodically searches for and collects specific logo images from the Internet. The collected logo images are sent to an AI judgment module. The AI ​​judgment module uses a pre-trained generative AI model (e.g., GAN) to determine whether the logo image was generated by AI. The result of this judgment is sent to a summary generation module, which generates a summary of the website containing the logo image determined to have been generated by AI.

[1643] Next, the generated summary information and logo images are organized in a list format by the listing module. This list is displayed through the interface providing module so that the user can check it. The user can also upload a logo image through the interface. The uploaded logo image is sent again to the AI ​​judgment module, and the judgment result is notified to the user through the notification module.

[1644] The following hardware and software are used to implement this system. Hardware requires a server and user terminal. Software includes TensorFlow, Keras, and PIL (Python Imaging Library). This software is used to process and calculate data. Specifically, it performs processes such as image resizing, normalization, tensor transformation, and judgment using AI models.

[1645] For example, when a company is designing a new logo, they can use the system to check whether the logo was generated by AI. Users upload a logo image through the interface and enter the following prompt:

[1646] "Please determine whether this logo image was generated by AI."

[1647] Based on this prompt, the system will determine whether the logo image is authentic and notify the user, thereby ensuring the reliability of logo images used by companies and individuals.

[1648] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1649] Step 1:

[1650] The server periodically searches for and collects specific logo images from the Internet using a search engine crawler. The input is a search query or search conditions, and the output is the collected logo image data. Specifically, the crawler traverses web pages, detects logo images, and downloads them.

[1651] Step 2:

[1652] The server sends the collected logo images to the AI ​​judgment module. The input is the collected logo image data, and the output is the transfer of image data to the AI ​​judgment module. Specifically, the image data is passed to the AI ​​judgment module.

[1653] Step 3:

[1654] The server uses an AI judgment module to determine whether a logo image was generated by AI. The input is the collected logo image data, and the output is a judgment result. Specifically, the image data is input into a generative AI model (e.g., GAN), and the model analyzes the image features and outputs a judgment result.

[1655] Step 4:

[1656] The server generates a summary of the website that contains the logo image determined to have been generated by AI. The input is the result of the determination and the URL of the corresponding website, and the output is summary information. Specifically, it analyzes the content of the website, extracts important information, and creates a summary.

[1657] Step 5:

[1658] The server creates a list of the generated summary information and logo images. The input is the summary information and logo image data, and the output is a list of the information. Specifically, the server organizes the summary information and logo images in a list format and saves them in a database.

[1659] Step 6:

[1660] The user uploads a logo image through the interface. The input is the logo image data uploaded by the user, and the output is the transfer of the image data to the server. Specifically, the user operates the interface to select an image file and presses the upload button.

[1661] Step 7:

[1662] The server then sends the uploaded logo image back to the AI ​​judgment module for judgment. The input is the logo image data uploaded by the user, and the output is the judgment result. Specifically, the image data is passed to the AI ​​judgment module, and the model analyzes the image features and outputs the judgment result.

[1663] Step 8:

[1664] The server notifies the user of the judgment result through a notification module. The input is the judgment result, and the output is a notification message for the user. Specifically, the judgment result is generated as a notification message and sent to the user's terminal.

[1665] Example 3

[1666] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1667] Conventional technologies have had difficulty determining whether a specific image on the Internet was generated by artificial intelligence and generating a summary of the web page on which the image was posted. Furthermore, there was a lack of a way to provide the generated summary information and image in a format that was easy to visually check. This made it difficult for users to quickly and efficiently obtain the information they needed.

[1668] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1669] In this invention, the server includes means for searching a network for specific images using a search engine crawler, means for determining whether the searched image has been generated by artificial intelligence, means for extracting text information from web pages containing images determined to have been generated by artificial intelligence, means for extracting important information from the extracted text information and generating summaries, and means for listing the generated summaries and images, thereby enabling users to efficiently obtain summaries of web pages containing images generated by artificial intelligence and view them in a format that is easy to visually confirm.

[1670] A "search engine crawler" is a program that automatically crawls web pages on a network and collects specific information.

[1671] An "image" is a visually perceptible still image stored in digital form.

[1672] A "network" is a system in which multiple computers and devices are connected through a communication infrastructure such as the Internet.

[1673] "Artificial intelligence" is the technology that enables computer systems to mimic human intelligence and perform functions such as learning, reasoning, and recognition.

[1674] A "web page" is a page containing documents and media content that is published on the Internet and is written in a markup language such as HTML.

[1675] "Text information" is information expressed as a string of characters, and exists in the form of sentences, words, etc.

[1676] A "summary" is information that extracts important parts from original text information and summarizes them concisely.

[1677] "Listing" refers to organizing multiple pieces of information and displaying them in a list format.

[1678] An "interface" is the means by which a user interacts with a system, including visual and operational elements.

[1679] This invention relates to a system that determines whether a specific image has been generated by artificial intelligence, generates a summary of the web page on which the image is posted, and displays the generated summary information and the image in a list.

[1680] The server implements this system using the following hardware and software:

[1681] Hardware and software used

[1682] Hardware: High-performance server computer, network interface card

[1683] software:

[1684] Programming language: Python

[1685] Libraries: requests, BeautifulSoup, transformers, Jinja2

[1686] Artificial intelligence model: BERT (Bidirectional Encoder Representations from Transformers)

[1687] Details of data processing and calculation

[1688] 1. Search Engine Crawlers:

[1689] The server uses Python's requests library to send an HTTP request to the URL specified by the user to retrieve the HTML content of the web page.

[1690] 2. Parsing HTML content:

[1691] The server uses the BeautifulSoup library to parse the retrieved HTML content and extract text information, specifically by stripping out HTML tags and extracting pure text data.

[1692] 3. Extracting important information:

[1693] The server then inputs the extracted text information into the BERT model to extract important information, which then understands the context of the text and identifies important sentences and phrases.

[1694] 4. Generate a summary:

[1695] The server generates summaries based on key information obtained from the BERT model, using the pipeline from the transformers library.

[1696] 5. Summary and image listing:

[1697] The server embeds the generated summary information and images into an HTML template and displays it in a list format. Specifically, it uses the Jinja2 template engine to create a visually easy-to-read list.

[1698] 6. Providing an interface:

[1699] The server sends the generated HTML to the user's browser, and the user checks the summary information and the list of images through the browser.

[1700] Specific examples

[1701] When a user specifies a specific website (e.g., https: / / example.com), the server retrieves the HTML content of that website and generates a summary using the BERT model. The generated summary and images are displayed as a table through a web interface.

[1702] Example prompt sentence:

[1703] "Extract text information from https: / / example.com and generate a summary using the BERT model. Display the generated summary and images in a table."

[1704] In this way, the server can extract text information from the specified website, summarize important information, and display it in a visually easy-to-read format. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1705] Step 1:

[1706] The user enters a website URL.

[1707] The user accesses the system's web interface, enters "https: / / example.com" in the text box, and clicks the "Submit" button.

[1708] Enter: Website URL

[1709] Output: The URL entered

[1710] Step 2:

[1711] The server retrieves the HTML content of the specified website.

[1712] The server uses the requests library to send an HTTP request to the URL entered by the user to retrieve the HTML content of the website.

[1713] Input: Entered URL

[1714] Output: Retrieved HTML content

[1715] Step 3:

[1716] The server extracts text information from the HTML content.

[1717] The server uses the BeautifulSoup library to parse the retrieved HTML content and extract text information, specifically by stripping out HTML tags and extracting pure text data.

[1718] Input: Retrieved HTML content

[1719] Output: Extracted text information

[1720] Step 4:

[1721] The server uses the BERT model to extract important information.

[1722] The server then inputs the extracted text information into the BERT model to extract important information, which then understands the context of the text and identifies important sentences and phrases.

[1723] Input: Extracted text information

[1724] Output: Extracted important information

[1725] Step 5:

[1726] The server generates a summary based on the extracted information.

[1727] The server generates summaries based on key information obtained from the BERT model, using the pipeline from the transformers library.

[1728] Input: Extracted sensitive information

[1729] Output: The generated summary

[1730] Step 6:

[1731] The server lists the summary information and images.

[1732] The server embeds the generated summary information and images into an HTML template and displays it in a list format. Specifically, it uses the Jinja2 template engine to create a visually easy-to-read list.

[1733] Input: Generated summary,image

[1734] Output: List of summary information and images

[1735] Step 7:

[1736] The server displays a list of information in a web interface.

[1737] The server sends the generated HTML to the user's browser, and the user checks the summary information and the list of images through the browser.

[1738] Input: List of summary information and images

[1739] Output: A list of images and summary information displayed in the web interface.

[1740] (Application example 3)

[1741] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1742] Conventional advertising systems make it difficult for users to efficiently find information of interest from the vast number of advertisements available on the Internet. Furthermore, there was a lack of a way to automatically generate and provide users with a summary of websites that contained AI-generated logo images. This made it difficult for users to quickly understand the content of advertisements and make appropriate decisions.

[1743] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1744] In this invention, the server includes means for searching the Internet for specific logo images using a search engine crawler, means for determining whether the retrieved logo images were generated by artificial intelligence, means for generating summaries of websites containing logo images determined to have been generated by artificial intelligence, means for listing the generated summaries and logo images, means for providing an interface for users to view the listed summaries and logo images, means for functioning as an application to be installed on a smartphone, means for extracting text information from websites and generating summaries using natural language processing technology, and means for displaying a list of the generated summaries and logo images, thereby enabling users to efficiently view advertisement summaries and logo images and quickly find advertisements of interest.

[1745] A "search engine crawler" is a program that automatically crawls websites on the Internet and collects specific information.

[1746] "Artificial intelligence" refers to computer systems that mimic human intelligence and can learn and reason.

[1747] A "website" is a collection of information published on the Internet and written in a format such as HTML.

[1748] A "summary" is a short summary of a long piece of text or information, extracting the important points and expressing them concisely.

[1749] "Listing" means organizing and displaying multiple items in a list format.

[1750] An "interface" is the means or method by which a user and a system interact with each other.

[1751] A "smartphone" is a type of mobile phone, a multi-function device that can connect to the Internet and run applications.

[1752] "Natural language processing technology" is a technology that allows computers to understand and process human language.

[1753] "List display" refers to displaying multiple pieces of information together on one screen or page.

[1754] The system for implementing this invention includes means for a search engine crawler, artificial intelligence, a website, summarization, listing, an interface, a smartphone, natural language processing technology, and list display. Specific embodiments of each means will be described below.

[1755] System configuration

[1756] The server uses a search engine crawler to search for specific logo images on the Internet, uses image recognition technology to determine whether the found logo image was generated by artificial intelligence, extracts text information from the website where the determined logo image is posted, and generates a summary using natural language processing technology.

[1757] The generated summary information and logo images are listed and provided to the user through an interface. This interface functions as an application installed on a smartphone. The user can view the listed summary information and logo images in a list view using their smartphone.

[1758] Hardware and software used

[1759] Hardware: Smartphones, servers

[1760] Software: Python, BeautifulSoup, Transformers library (Hugging Face)

[1761] Data processing and calculation

[1762] The server extracts text information from websites using a search engine crawler. It uses BeautifulSoup to extract text from HTML and filters out only visible text. The extracted text information is summarized using natural language processing technology (BERT model). The generated summary information and logo images are compiled into a list and provided to users via a smartphone application.

[1763] Specific examples

[1764] For example, the server extracts text information from the URL "https: / / example.com / ad1" and generates a summary using the BERT model. The generated summary information and a logo image "logo1.png" are displayed to the user via a smartphone application.

[1765] Prompt Sentence Examples

[1766] Extract the text information from the URL: "https: / / example.com / ad1" and generate a summary using the BERT model. Display the generated summary and the logo image "logo1.png" as a list.

[1767] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1768] Step 1:

[1769] The server launches a search engine crawler to search the Internet for specific logo images. It receives the characteristics and keywords of specific logo images as input and generates a list of URLs for corresponding logo images as output. Specifically, the crawler visits websites and extracts image files from the HTML source code.

[1770] Step 2:

[1771] The server determines whether the searched logo images were generated by artificial intelligence. As input, it receives the list of logo image URLs obtained in step 1, and as output, it generates a list of logo images that have been determined to have been generated by artificial intelligence. Specifically, it uses image recognition technology to analyze the features of the logo images and makes a determination using a generative AI model.

[1772] Step 3:

[1773] The server extracts text information from websites that contain logo images determined to have been generated by artificial intelligence. As input, it receives the list of logo image URLs obtained in step 2, and as output, it generates text information for each website. Specifically, it uses BeautifulSoup to extract text from HTML source code and filters out only visible text.

[1774] Step 4:

[1775] The server summarizes the extracted text information using natural language processing technology. It receives the text information obtained in step 3 as input and generates summary information as output. Specifically, it uses the BERT model to tokenize the text, extract important information, and generate a summary.

[1776] Step 5:

[1777] The server lists the generated summary information and logo images. As input, it receives the list of logo images obtained in step 2 and the summary information obtained in step 4, and generates a list of summary information and logo images as output. Specifically, it associates the summary information with the logo images and organizes them in a list format.

[1778] Step 6:

[1779] The server provides an interface for users to check the list of summary information and logo images. It receives the list of summary information and logo images obtained in step 5 as input and generates a user interface as output. Specifically, it displays a list of summary information and logo images through an application installed on a smartphone.

[1780] Step 7:

[1781] The user uses the smartphone application to check the listed summary information and logo images. As input, the user receives the user interface provided in step 6, and as output, selects the advertisement of interest. Specifically, the user taps the summary information and logo image on the application screen to display detailed information.

[1782] 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.

[1783] "Example 1"

[1784] The invention incorporating an emotion engine recognizes a user's emotion and adjusts the display of the summary information list and logo images based on that emotion. Specifically, the emotion engine recognizes the emotion from the user's facial expression, tone of voice, text input, etc. For example, if the user expresses anger, the emotion engine conveys that information to the system, and the system adjusts the display of the summary information list and logo images. Specifically, for users who express anger, the system takes action such as displaying problematic logo images more prominently.

[1785] "Example 2"

[1786] The emotion engine also recognizes the user's emotions through the user interface. Specifically, the user interface includes input devices such as a camera, microphone, and keyboard. The camera captures the user's facial expressions, the microphone records the user's tone of voice, and the keyboard accepts the user's text input. The information obtained from these input devices is sent to the emotion engine, which then recognizes the user's emotions from this information.

[1787] "Example 3"

[1788] Furthermore, the emotion engine adjusts the display method of the listed summary information and logo image based on the recognized emotion. Specifically, the emotion engine communicates the recognized emotion to the system, and the system adjusts the display method of the summary information and logo image based on the emotion. For example, if the user expresses joy, the system selects a display method that matches the user's emotion, such as displaying the summary information and logo image in bright colors.

[1789] The processing flow of each embodiment will be described below.

[1790] "Example 1"

[1791] Step 1: The emotion engine recognizes emotions from the user's facial expressions, tone of voice, text input, etc.

[1792] Step 2: The emotion engine communicates the recognized emotion information to the system.

[1793] Step 3: The system adjusts the display of the summary information and logo images based on the received emotion information. For example, if a user expresses anger, the system may display the problematic logo image more prominently.

[1794] "Example 2"

[1795] Step 1: The user interface conveys the user's emotions to the emotion engine through input devices such as a camera, microphone, or keyboard.

[1796] Step 2: The emotion engine recognizes the user's emotions based on the information obtained from these input devices.

[1797] Step 3: The emotion engine communicates the recognized emotion information to the system, which then adjusts the display method of the summary information and logo image based on that information.

[1798] "Example 3"

[1799] Step 1: The emotion engine recognizes the user's emotion.

[1800] Step 2: The emotion engine communicates the recognized emotion to the system.

[1801] Step 3: The system adjusts the display method of the summary information and logo image based on the received emotion information. For example, if the user expresses joy, the system will select a display method that matches the user's emotion, such as displaying the summary information and logo image in bright colors.

[1802] Example 1

[1803] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1804] Conventional search engine crawlers can search the Internet for specific logo images, but they lack the ability to adjust how information is displayed based on the user's emotions. This makes it difficult to provide personalized information that reflects the user's emotions, and there is a need to improve the user experience. Furthermore, the identification of logo images generated by artificial intelligence and the generation and listing of summary information are not automated, making efficient information management difficult.

[1805] 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.

[1806] In this invention, the server includes means for searching the Internet for specific logo images using a search engine crawler, means for determining whether the retrieved logo image was generated by artificial intelligence, means for generating summaries of websites containing logo images determined to have been generated by artificial intelligence, means for listing the generated summaries and logo images, means for recognizing a user's emotion, and means for adjusting the display method of the listed summaries and logo images based on the recognized user emotion. This enables the provision of personalized information according to the user's emotion, resulting in efficient information management and an improved user experience.

[1807] A "search engine crawler" is a program that automatically crawls websites on the Internet and collects specific information.

[1808] A "logo image" is an image of a shape or design used to identify a company or brand.

[1809] "Artificial intelligence" refers to computer systems that mimic human intelligence and can learn and reason.

[1810] "Summary information" is information that briefly summarizes the contents of a website.

[1811] "Listing" refers to organizing multiple pieces of information into a list format.

[1812] "User emotion" refers to the psychological state or feelings exhibited by the user.

[1813] "Adjusting the display method" refers to changing the way information is presented based on the user's emotions.

[1814] The present invention is a system for searching for a specific logo image and adjusting the way information is displayed based on the user's emotions. A specific embodiment of this system will be described below.

[1815] The server runs a search engine crawler. This crawler is implemented using Python and analyzes web pages using libraries such as BeautifulSoup and Scrapy. The crawler traverses the Internet every night and collects specific logo images. The collected logo images are stored in a database such as MySQL or PostgreSQL.

[1816] The device runs an emotion engine to recognize the user's emotions. The emotion engine captures the user's facial expressions with a camera, receives voice input with a microphone, and analyzes text input. This data is processed using libraries such as TensorFlow and OpenCV. The emotion engine classifies the user's emotions into categories such as "anger," "joy," and "sadness," and sends the information to a server.

[1817] A user accesses the system through a terminal and browses search results for logo images. If the user expresses anger, the emotion engine communicates this information to the server. The server then adjusts the display method of the listed summary information and logo images based on the user's emotion. For example, if the user expresses anger, problematic logo images are displayed more prominently.

[1818] For example, the server runs a crawler every night to collect specific logo images and store them in a database. The device captures the user's facial expressions and voice and analyzes them using an emotion engine. The emotion engine sends the analysis results to the server, which adjusts the display method based on that information. The user then views the adjusted information in a web browser.

[1819] Examples of prompts to input to a generative AI model include:

[1820] "Please explain how to make the offending logo image more prominent when users express anger."

[1821] The system can dynamically adjust how information is displayed based on the user's emotions, providing a more personalized user experience.

[1822] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1823] Step 1:

[1824] The server runs the crawler.

[1825] The server runs the crawler as a scheduled task every night at midnight. The crawler is implemented using Python and analyzes web pages using libraries such as BeautifulSoup and Scrapy. As input, it receives a list of URLs for the websites that the crawler will visit. As output, it extracts URLs for specific logo images.

[1826] Step 2:

[1827] The crawler collects logo images.

[1828] The crawler visits a specified website and extracts logo image URLs using a specific CSS selector (e.g., img.logo). The input is the HTML data of the web page. The output is a list of logo image URLs. Specifically, it analyzes the HTML of the web page and extracts the logo image URLs.

[1829] Step 3:

[1830] The server stores the collected logo images in a database.

[1831] The server stores the URLs of logo images collected by the crawler in a database. As input, it receives a list of logo image URLs. As output, it generates records of logo images stored in the database. Specifically, it inserts records into the database using an ORM library such as SQLAlchemy.

[1832] Step 4:

[1833] The device recognizes the user's emotions.

[1834] The device uses a camera and microphone to capture the user's facial expressions and voice, and sends them to the emotion engine. Images of the user's facial expressions and voice data are provided as input. The output generates the user's emotion category (e.g., "anger," "joy," or "sadness"). Specifically, the system uses OpenCV to detect faces and TensorFlow to predict emotions.

[1835] Step 5:

[1836] The emotion engine sends the user's emotion to the server.

[1837] The emotion engine sends the recognized emotion data to the server. The user's emotion category is given as input. The emotion data sent to the server is generated as output. Specifically, the emotion data is sent using an HTTP POST request.

[1838] Step 6:

[1839] The server adjusts the display method based on the user's emotions.

[1840] The server adjusts the display method of logo images based on the received emotion data. As input, it receives the user's emotion data and a list of logo images. As output, it generates adjusted HTML and CSS. Specifically, if the user expresses anger, it generates HTML and CSS to display the problematic logo images more prominently.

[1841] Step 7:

[1842] The user views the tailored information.

[1843] The user views the tailored information through the terminal. The tailored HTML page is provided as input. The web page viewed by the user is displayed as output. Specifically, the web browser is used to display the HTML page provided by the server.

[1844] (Application example 1)

[1845] Next, a description will be given of Application Example 1 of Form Example 1. In the followin...

Claims

1. A means for acquiring specific images searched for on a network by a search engine crawler; A means for determining whether or not a specific image acquired has been generated by AI; A means for generating summary information including a summary of a source in which a particular image determined to have been generated by AI is posted; A means for listing the generated summary information and the particular image; A system including:

2. A means for retrieving a logo image uploaded by a user; A means for determining whether the acquired logo image is generated by AI using an AI determination module; means for notifying the user of the result of the determination; A system including:

3. A means for searching a network for a specific image using a search engine crawler; means for determining whether the retrieved image is generated by artificial intelligence; A means for extracting text information from a web page on which an image determined to have been generated by artificial intelligence is posted; A means for extracting important information from the extracted text information and generating a summary; A means for listing the generated summary information and images; A system including:

Images