system

Abstract

We provide the system. [Solution] A means for receiving generated visual or textual data, A means for analyzing the received data and detecting unnatural or unrealistic elements, Based on the analysis results, means for identifying matches or similarities by comparing them with existing digital data libraries or commercial platforms, A system that includes means of notifying the user and instructing them to correct or change the relevant data when a match or similarity is identified.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In recent years, with the development of generative AI technology, users can easily generate visual data and text data. However, the generated data may often be similar to the works of others, which may cause copyright infringement problems. In addition, the generated products may contain unnatural or unrealistic elements, and there is also a risk of damaging reliability if users use them as they are. Due to such problems, it is difficult to use generative AI with confidence. 【Means for Solving the Problems】 【0005】 This invention provides a system that receives generated visual or text data, analyzes the received data, and detects unnatural or unrealistic elements. Furthermore, based on the analysis results, it identifies matches or similarities by comparing them with existing digital data libraries or commercial platforms, notifies the user if any are found, and prompts them to correct or change them. This system makes it easier for users to verify the legality and reliability of the generated data, enabling them to confidently utilize generation AI technology. 【0006】 "Generated visual data" refers to images or videos created by a computer program using AI technology. 【0007】 "Generated text data" refers to text content that is automatically generated using AI technology. 【0008】 "Means of receiving data" refers to a function or process that receives data from a user and converts it into a format that can be processed within the system. 【0009】 "Means for analyzing and detecting unnatural or unrealistic elements" refers to algorithms or programs that analyze data content to identify unusual patterns or unnatural descriptions. 【0010】 "Existing digital data libraries or commercial platforms" refers to databases containing publicly available content or commercially offered digital content services. 【0011】 "Means of identifying matches or similarities" refers to a program or function for comparing the data in question and finding matches or similarities with existing data. 【0012】 "Means of notifying users and instructing them to correct or change" refers to a process for informing users about detected limitations or problems and presenting them with steps or options for taking action. [Brief explanation of the drawing] 【0013】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, when an emotion engine is combined. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention] 【0014】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0015】 First, the terms used in the following description will be explained. 【0016】 In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like. 【0017】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0018】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc. 【0019】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0020】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0021】 [First Embodiment] 【0022】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0023】 As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. 【0024】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0025】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0026】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0027】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0028】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0029】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0030】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0031】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0032】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0033】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0034】 This invention relates to an embodiment of a system for automatically checking the legality and reliability of generated visual and textual data. This system is constructed as follows: 【0035】 The user first sends visual or text data created using AI generation from their device to the server via an online platform. When uploading, the user must adhere to the specified file format. The device provides a user interface via the internet, enabling file selection and uploading. 【0036】 The transmitted data is received by the server and analyzed by a dedicated content analysis module. The server uses this analysis module to evaluate whether there are any unnatural or unrealistic elements. Specifically, it applies an AI-based pattern recognition algorithm to check the color and shape of visual data, and the grammatical consistency and contextual relevance of text data. 【0037】 Next, the server uses a data comparison module to cross-reference the analyzed data with existing digital data libraries and existing content on commercial platforms. Text matching and image recognition technologies are used here, and if a certain level of similarity is detected, the data is deemed potentially infringing on copyright. 【0038】 Based on the results obtained through this process, the server notifies the user of the results through the user interface. The result notification includes the detection of unnatural elements and details of suspected copyright infringement. The user can view this information and modify or regenerate the generated data as needed. 【0039】 As a concrete example, suppose a user uploads a new image they have created. If this image is very similar to a specific image that exists on a commercial image platform, the server will detect this and notify the user of the similarity. Based on the notification, the user is given the option to adjust part of the image or regenerate different visual data. 【0040】 This system allows users to reduce the risk of copyright infringement and more easily guarantee the quality of the generated content during the content creation process using generative AI. 【0041】 The following describes the processing flow. 【0042】 Step 1: 【0043】 Users use their devices to select visual and text data generated by the AI ​​and access an upload interface. This interface, available via a browser or dedicated application, sends data to the server through an upload form that includes checks for file format specification and size limits. 【0044】 Step 2: 【0045】 The server first verifies the data format before analyzing the data received from the user. Here, it checks if the file format is correct and if the content is corrupted. If the format or content is inappropriate, it returns an error message to the user and prompts them to try again. 【0046】 Step 3: 【0047】 The server uses an analysis module to examine the data content in detail. At this stage, AI-based image and text analysis techniques are used to detect unnatural elements and unrealistic features in the data. For example, in image data, it evaluates color inconsistencies and unnatural shadows, and in text data, it identifies grammatical errors and semantic inconsistencies. 【0048】 Step 4: 【0049】 The server uses the evaluation results to match the data against existing digital data libraries and commercial content platforms. Here, image recognition algorithms and text matching techniques are used to search for similarities. This process determines whether there is a potential for copyright infringement. 【0050】 Step 5: 【0051】 The server aggregates all analysis results and prepares to notify the user. If any unnatural elements are found or if a risk of copyright infringement is identified, a detailed report is generated. Through this report, the user can see which parts of the generated data are problematic. 【0052】 Step 6: 【0053】 The user reviews the report provided by the server and corrects or regenerates the data as needed. If the user makes corrections and re-uploads the data, the process restarts from step 1. 【0054】 (Example 1) 【0055】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0056】 In recent years, advancements in generative AI models have enabled users to generate diverse data. However, this has also led to increased concerns about the integrity of the generated data, the presence of unnatural elements, and copyright infringement. This invention aims to solve the problem of automating the verification of the legality and reliability of such generated data, thereby providing an environment in which users can engage in creative activities with peace of mind. 【0057】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0058】 In this invention, the server includes means for analyzing the structure of received data and verifying the integrity of the information, means for analyzing the characteristics of the content, and means for matching it with an existing information base and identifying matches or similarities. This allows users to check for any unnatural elements in the generated data, improve the quality of the data while mitigating copyright risks. 【0059】 "Generated data" refers to visual or textual data created by a generative AI model. 【0060】 A "terminal" refers to a computer system used by a user to generate data and send it to a server. 【0061】 A "server" refers to a central computing system that analyzes received data, detects unnatural elements, and compares them with existing information. 【0062】 "Means of analyzing structure and verifying information integrity" refers to a function that evaluates whether the data file format and data content are appropriate. 【0063】 "Means for analyzing content characteristics" refers to a function that uses pattern recognition technology to verify whether there are any unnatural elements in the data. 【0064】 "Means of matching with existing information bases to identify matches or similarities" refers to a function that compares incoming data with information from existing data libraries or commercial platforms to confirm similarity. 【0065】 "Unnatural elements" refer to unusual patterns or content features that are not normally expected and are recognized within the data. 【0066】 "Copyright" refers to the right that provides legal protection for creative works. 【0067】 This invention describes a specific embodiment of a system for automatically verifying the legality and reliability of data created using a generative AI model. 【0068】 The user first uses a terminal to select visual and text data obtained using a generative AI model. The terminal provides a graphical user interface, allowing the user to easily select files and send them to the server. The data sent should preferably be in common digital file formats (e.g., JPEG, PNG, TXT, DOCX, etc.). 【0069】 When the server receives data sent from a terminal, it utilizes a high-performance content analysis module to analyze the data. In particular, pattern recognition algorithms are applied to visual data to analyze characteristics such as color and shape. For text data, grammar checks and contextual analysis are performed to detect inconsistencies. Machine learning techniques are used for analysis, specifically by applying existing pattern recognition libraries and grammar analysis engines. 【0070】 The analysis results are cross-referenced with existing digital data libraries and data on public platforms by a comparison module on the server. This verifies whether the newly generated data is similar to existing data. For example, visual similarity is detected using image recognition technology, or text similarity is determined using text matching technology. 【0071】 Based on the analysis and comparison results, the server notifies the user via the user interface. The notification includes details about detected unnatural elements and suspected copyright infringements. The user can then use this information to modify or regenerate the generated content. 【0072】 As a concrete example, consider a scenario where a user uploads an image generated in response to the prompt, "Generate a new character illustration with a beautiful sunset over the sea as the background." If this image is similar to an existing commercial image, the server detects this and reports it to the user. The user can then proceed to modify the similar parts or generate a new illustration based on other ideas. 【0073】 This system allows users to improve the quality of their generated content while avoiding legal risks. 【0074】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0075】 Step 1: 【0076】 The user selects visual or text data generated using a generative AI model on their device and sends it to the server. The selected data is the input, and the data sent to the server is the output. The device displays a user interface, providing the user with an easy way to select and upload files. 【0077】 Step 2: 【0078】 The server receives data sent from the terminal. The input is the received data, and the output is the data stored within the server. The server first verifies the file format of the received data to ensure it is in the correct format. 【0079】 Step 3: 【0080】 The server activates a content analysis module to analyze the received data. The input is the stored data, and the output is the analysis result. To detect unnatural elements, a pattern recognition algorithm is applied to the visual data, and grammatical and contextual analysis is performed on the text data. 【0081】 Step 4: 【0082】 The server compares the analyzed data with data from existing digital data libraries and commercial platforms. The input is the analysis results, and the output is the similarity identification results. Image recognition and text matching technologies are used to detect similarity with existing data. 【0083】 Step 5: 【0084】 The server notifies the user based on the comparison results. The input is the similarity identification result, and the output is a detailed notification to the user. By displaying the results, including alerts and recommended actions, to the user through the user interface, the user can choose their next course of action. 【0085】 (Application Example 1) 【0086】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0087】 If the generated content has unnatural characteristics or carries the risk of copyright infringement, users may unintentionally distribute inaccurate or illegal content. It is essential to prevent such situations and provide an environment where users can create and distribute content with peace of mind. 【0088】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0089】 In this invention, the server includes means for receiving generated content, means for analyzing the received content and detecting unnatural or unrealistic characteristics, means for identifying correspondences or similarities by comparing it with existing information databases or commercial platforms based on the analysis results, and means for providing immediate feedback on the legality of the content before the user uploads it and giving instructions for correction. This enables users to prevent the generation and distribution of inaccurate or illegal content and to create content with peace of mind. 【0090】 "Generated content" refers to visual or textual data created using AI or other digital technologies. 【0091】 "Means of receiving data" refers to a system for transferring user-created content to a server and receiving that data. 【0092】 "Means of analysis" refers to methods and techniques for analyzing received content and detecting unnatural or unrealistic characteristics. 【0093】 "Unnatural characteristics" refer to elements that deviate from their natural state or the properties of data that lack consistency with the real world. 【0094】 An "unrealistic characteristic" refers to a feature or state that lacks physical or contextual reality. 【0095】 An "existing information database" is a collection of digital data accumulated in the past, used for comparing and referencing content. 【0096】 A "commercial platform" refers to an online space or system used for commercial activities. 【0097】 "Means for identifying correspondence or similarity" refers to methods for comparing analyzed data with existing data to find similarities or matches. 【0098】 "Means of providing feedback" refers to a system that informs users of analysis results and comments regarding content. 【0099】 "Means of providing instructions for correction" refers to methods for presenting users with specific improvement plans or action guidelines regarding identified problems. 【0100】 The system that implements this application primarily functions based on the interaction between a server, a terminal, and a user. The user uploads content generated via the terminal to the server. The server analyzes the visual data using image processing libraries such as PIL (Python Imaging Library) and OpenCV. During the analysis, machine learning models are utilized to detect unnatural or unrealistic characteristics, thereby evaluating whether the user-generated content is realistic. 【0101】 Based on the analysis results, the server performs comparison calculations to identify similarities and matches by comparing them with data contained in existing information databases and commercial platforms. If the similarity is high, the server provides feedback and notifies the user of the potential copyright infringement. Specifically, it presents prompt messages such as, "Does this content potentially infringe on existing copyrights? If so, which parts should be modified?" 【0102】 Users who receive this notification can receive specific correction instructions through their device interface, enabling them to make appropriate revisions and adjustments to their content. This process reduces the risk of creating and distributing inaccurate or illegal content, allowing users to proceed with content creation with peace of mind. 【0103】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0104】 Step 1: 【0105】 The user uploads the generated content from their device to the server. The input consists of images and text data selected by the user, and the output is the transfer of this data to the server. This process involves data transfer via an internet connection. 【0106】 Step 2: 【0107】 The server receives uploaded content and analyzes the data using image processing libraries (e.g., PIL or OpenCV). The input is data sent by the user, and the output is information on the detection of unnatural characteristics as a result of the analysis. Specifically, it extracts color and shape information from the image and applies an AI algorithm to identify unnatural patterns. 【0108】 Step 3: 【0109】 The server compares the analysis results with data from existing information databases and commercial platforms. The input is the analyzed content information, and the output is the result of identifying similarities or matches. Here, text matching and image recognition technologies are used to evaluate data similarity. 【0110】 Step 4: 【0111】 If the similarity is high, the server sends a notification to the user as feedback. The input is the result of the similarity identification, and the output is the detailed notification content for the user. Specifically, it generates content that informs the user about what the similar content is and whether corrections are needed. 【0112】 Step 5: 【0113】 Users receive feedback from the server via their devices and consider revising their content. The input is notification information from the server, and the output is the user's decision on whether or not to make revisions. This allows users to optimize their content and complete revisions as needed. 【0114】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0115】 This invention relates to a system that combines generated visual or textual data with an emotion engine to provide appropriate feedback to the user. This system uses emotion recognition technology to analyze the user's emotional state and can effectively customize feedback based on the synthesized data. 【0116】 The user uploads data created through the generative AI using their device to the system. The uploaded data is received by the server in the first stage of processing, and analysis begins. The server first checks the data format, and if there are no problems, it then analyzes the content of the data. Here, pattern recognition using machine learning models is performed to check for unnatural or unrealistic elements. 【0117】 Based on the analysis results, the server activates a newly integrated emotion engine to identify the user's emotions. This emotion engine determines the emotional state by capturing facial expression data and text data when the user provides input to the system. For example, it analyzes the user's intent and tone from the text data, and if negative emotions are detected, it prepares special feedback. 【0118】 As a concrete example, consider a scenario where a user uploads visual data, but the server notifies them of a potential copyright infringement. The emotion engine analyzes the user's emotional response to this notification through text input and facial recognition during video chats. If the server determines that the user is feeling confused or stressed, it provides supportive messages, including encouragement and detailed instructions, to help alleviate the user's anxiety. 【0119】 This system, which incorporates an emotion engine, allows users to improve the safety and quality of generated data, as well as how they receive feedback. This, in turn, can reduce stress and improve learning efficiency. 【0120】 The following describes the processing flow. 【0121】 Step 1: 【0122】 The user uses a device to select visual or text data generated by the AI ​​and uploads it to the system. The data is then sent to a server in the cloud. The device displays the upload status to the user through its interface and guides them through the next steps. 【0123】 Step 2: 【0124】 The server receives the uploaded data and first checks its format. Here, it verifies that the data is in a supported file format (e.g., JPEG, PNG, or text file). After this verification is complete, the data is securely stored and prepared for analysis. 【0125】 Step 3: 【0126】 The server sends the received data to an analysis module, which uses AI to detect unnatural or unrealistic elements. It applies algorithms to identify unnatural colors or shapes, grammatical errors, or contextual inconsistencies. 【0127】 Step 4: 【0128】 The server compares the analyzed data with existing digital data libraries and commercial platforms to check for similar data. Here, image recognition technology and text similarity algorithms are used to search for matches that may infringe copyright. 【0129】 Step 5: 【0130】 The server passes the analysis results to the emotion engine, which makes a decision to determine the user's emotional state. In this process, the emotion engine analyzes how the user expresses themselves while operating the system (such as input text and changes in facial expressions) and infers whether the emotion is positive or negative. 【0131】 Step 6: 【0132】 Based on the sentiment data and analysis results obtained by the server, the content of notifications sent to the user is adjusted. If a negative reaction is detected, the tone and content of the message are modified to enhance user support, such as encouragement or suggestions for problem solving. 【0133】 Step 7: 【0134】 Users review feedback sent from the server via their device and decide whether to correct or re-upload data, or take further action as needed. The device organizes the feedback in an easy-to-understand format and provides users with smooth instructions for the next steps. 【0135】 (Example 2) 【0136】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0137】 In modern society, vast amounts of digital data are generated daily, potentially containing unnatural elements or copyright issues. However, it is difficult for users to manually analyze this data appropriately and receive necessary corrections and feedback. Furthermore, providing appropriate feedback that takes into account the impact digital data has on users' emotions is also a challenging task. 【0138】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0139】 In this invention, the server includes means for receiving generated digital data, means for performing analysis to detect unnatural elements and similarities, and means for analyzing emotions and generating appropriate feedback. This enhances the safety and quality of data generated by users and enables them to receive emotionally sensitive feedback. 【0140】 "Generated digital data" refers to information in visual or text format newly created by a user or electronic device. 【0141】 "Means of receiving" refers to methods or devices that have the function of taking digital data into a server or system and preparing it for processing. 【0142】 An "unnatural element" refers to an abnormal feature in digital data that deviates from normal patterns or expected content. 【0143】 "Unrealistic elements" refer to information within digital data that appears to contradict real-world situations or physical laws. 【0144】 "Means based on analysis results" refers to methods or devices that utilize the results of digital data analysis to proceed to the next processing step. 【0145】 "Existing information collections" refer to databases and digital libraries that have been accumulated over time. 【0146】 "Commercial infrastructure" refers to online platforms and marketplaces used for commercial activities. 【0147】 "Means of identifying matches or similarities" refers to methods or devices for determining how similar analyzed digital data is to existing data. 【0148】 "Means for analyzing emotions and generating appropriate feedback" refers to a method or device that understands the user's emotional state and provides accurate responses or suggestions based on the results. 【0149】 This invention relates to a system that analyzes generated digital data and provides appropriate feedback to the user. The system consists of a terminal, a server, and an emotion analysis engine. 【0150】 The terminal is used by users to generate and upload digital data. Users generate visual and text data on the terminal and send it to the system. The terminal has an interface for sending data from the input screen to the server and is equipped with a display device to notify the user of the data transmission status. 【0151】 The server is responsible for analyzing the received digital data. First, the server checks the data format and evaluates whether it is in an appropriate format. After verification, it analyzes the data using machine learning models and detects unnatural or unrealistic elements using pattern recognition technology. At this time, the server compares the data's similarity to existing databases and generates a message containing necessary correction instructions if it is necessary to notify the user. 【0152】 Furthermore, the server incorporates an emotion analysis engine that analyzes the user's emotional state based on facial expression data and text content. Based on this analysis, it constructs personalized feedback for the user. For example, if negative emotions are detected, the server can offer encouraging messages or solutions. 【0153】 As a concrete example, when a user uploads visual data of a natural landscape to the server, the server checks whether the data is similar to existing works. If similarities are found, the emotion engine analyzes the user's emotions upon receiving the notification. If the user is confused, the server provides feedback such as, "Please confirm that this image is original, and we will support you if further copyright investigation is necessary." 【0154】 An example of a prompt message is as follows: 【0155】 "Based on the following text, please generate a reassuring support message for the user: 'The image you uploaded has been determined to be a copyright infringement.'" 【0156】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0157】 Step 1: 【0158】 Users generate visual or text data and upload it to the system using a terminal. The input is the digital data generated by the user, and the output is request data sent to the server. During this process, the terminal performs an initial check of the data format and transfers the data to the server in the appropriate format. A progress bar is displayed on the terminal to visualize the progress of the data transfer. 【0159】 Step 2: 【0160】 The server receives digital data sent from the user. The input is digital data from the user, and the output is data in a format that can be analyzed. The server checks whether the format of the received data is JPEG, PNG, or UTF-8 if it is text. If there are no problems, it temporarily stores the data as format-checked data and proceeds to the next analysis step. 【0161】 Step 3: 【0162】 The server performs analysis on stored data using machine learning models. The input is format-verified data, and the output is the analysis results. Pattern recognition techniques are used to detect unnatural or unrealistic elements within the data. During this process, the data is compared with existing information sets to check for matches and similarities. This data processing generates metadata as an analysis result. 【0163】 Step 4: 【0164】 The server activates the emotion analysis engine based on the analysis results. The inputs used are the user's facial expression data, text data, and the analysis results, and the output identifies the user's emotional state. The server utilizes natural language processing techniques to analyze emotional tone from the user's text. Simultaneously, it uses facial recognition technology to identify emotional characteristics from videos and images. 【0165】 Step 5: 【0166】 The server uses a generative AI model to create appropriate feedback based on the results of sentiment analysis. The input is the emotional state and analysis metadata, and the output is a supportive message for the user. For example, if the user is confused, the server generates a message containing encouragement and suggestions for improving the situation. This provides real-time support to the user. 【0167】 (Application Example 2) 【0168】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0169】 Traditional data analysis systems focused on issuing warnings about the appropriateness of generated information and whether copyright infringement occurred, but they did not consider providing feedback based on users' emotions. As a result, responses that ignored users' emotional reactions were provided, hindering the user's information exchange experience. Furthermore, there is a need for a system that can enhance users' sense of security and trust through flexible feedback that responds to their emotional state. 【0170】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0171】 In this invention, the server includes means for receiving generated informational data or linguistic data, means for analyzing the received data and detecting unnatural or unrealistic elements, and means for activating an emotion engine, identifying the user's emotions, and adapting the dialogue based on the identified emotions. This makes it possible to provide appropriate feedback that takes into account the user's emotional state. 【0172】 "Generated information data" refers to digital information in visual or text format created by the user through the generation AI. 【0173】 "Linguistic data" refers to text information based on natural language, used to express the user's intentions and emotions. 【0174】 An "emotion engine" is a device or program that identifies an emotional state based on user input and generates feedback accordingly. 【0175】 "Emotional state" refers to the user's psychological feelings and mood at a particular moment. 【0176】 "Adapting the dialogue" means changing the feedback and support messages provided according to the user's emotional state. 【0177】 "Information resources" refer to existing records and data stored in various digital formats that are used for comparison and analysis. 【0178】 A "commercial infrastructure" refers to an electronic platform or system used for trading goods or providing services. 【0179】 The system for realizing this invention operates through cooperation between a server and a user's terminal. The server receives information data or language data generated from the user's terminal and analyzes its content. The analysis includes software that uses a learning model to detect unnatural or unrealistic elements. Based on the analysis results, the server activates an emotion engine. The emotion engine identifies the user's emotional state, generates feedback according to the identified emotion, and sends it to the user. 【0180】 Software such as Amazon Web Services (AWS®) sentiment analysis services and Google® Cloud Natural Language API can be used to analyze emotional states. The feedback users receive is tailored to their emotional state; for example, a user feeling anxious will be provided with a reassuring message. 【0181】 For example, if a user enters "I'm worried about this purchase," the server analyzes this text and, based on the identified emotion of anxiety, returns feedback such as "Rest assured, this product comes with a money-back guarantee." Examples of prompts include "Please provide information to alleviate your concerns about this product." In this way, the system optimizes the user's information exchange experience and enables flexible responses tailored to their emotions. 【0182】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0183】 Step 1: 【0184】 The user uploads generated informational or linguistic data to the server using a device. The input is the visual or text data created by the user using a generated AI model, and the output is its transfer to the server. 【0185】 Step 2: 【0186】 The server analyzes the received data to verify its format and structure. This step determines whether the data is in the correct digital format and prepares it for appropriate data processing. The input is the received raw data, and the output is the well-formed data after format verification. The server then processes the data into a format suitable for analysis. 【0187】 Step 3: 【0188】 The server uses a machine learning model to detect unnatural or unrealistic elements in the data. The input is well-formed data, and the output is the analysis results regarding the unnatural elements. Specifically, it uses AWS and Google Cloud services to scrutinize the data. 【0189】 Step 4: 【0190】 The server activates the emotion engine based on the analysis results to identify the user's emotional state. The input is the analysis results, and the output is the identified emotional state. At this stage, the emotion analysis API is used to determine the emotions contained in the user's input. 【0191】 Step 5: 【0192】 Based on the emotional state identified by the emotion engine, the server generates appropriate feedback and sends it to the user. The input is the identified emotional state, and the output is a feedback message directed to the user. Specifically, it prepares a customized response according to the emotion and forwards the message to the user's terminal. 【0193】 The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data. 【0194】 Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0195】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0196】 [Second Embodiment] 【0197】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0198】 As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server. 【0199】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0200】 The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52. 【0201】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0202】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0203】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0204】 Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0205】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0206】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0207】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0208】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0209】 This invention relates to an embodiment of a system for automatically checking the legality and reliability of generated visual and textual data. This system is constructed as follows: 【0210】 The user first sends visual or text data created using AI generation from their device to the server via an online platform. When uploading, the user must adhere to the specified file format. The device provides a user interface via the internet, enabling file selection and uploading. 【0211】 The transmitted data is received by the server and analyzed by a dedicated content analysis module. The server uses this analysis module to evaluate whether there are any unnatural or unrealistic elements. Specifically, it applies an AI-based pattern recognition algorithm to check the color and shape of visual data, and the grammatical consistency and contextual relevance of text data. 【0212】 Next, the server uses a data comparison module to cross-reference the analyzed data with existing digital data libraries and existing content on commercial platforms. Text matching and image recognition technologies are used here, and if a certain level of similarity is detected, the data is deemed potentially infringing on copyright. 【0213】 Based on the results obtained through this process, the server notifies the user of the results through the user interface. The result notification includes the detection of unnatural elements and details of suspected copyright infringement. The user can view this information and modify or regenerate the generated data as needed. 【0214】 As a concrete example, suppose a user uploads a new image they have created. If this image is very similar to a specific image that exists on a commercial image platform, the server will detect this and notify the user of the similarity. Based on the notification, the user is given the option to adjust part of the image or regenerate different visual data. 【0215】 This system allows users to reduce the risk of copyright infringement and more easily guarantee the quality of the generated content during the content creation process using generative AI. 【0216】 The following describes the processing flow. 【0217】 Step 1: 【0218】 Users use their devices to select visual and text data generated by the AI ​​and access an upload interface. This interface, available via a browser or dedicated application, sends data to the server through an upload form that includes checks for file format specification and size limits. 【0219】 Step 2: 【0220】 The server first verifies the data format before analyzing the data received from the user. Here, it checks if the file format is correct and if the content is corrupted. If the format or content is inappropriate, it returns an error message to the user and prompts them to try again. 【0221】 Step 3: 【0222】 The server uses an analysis module to examine the data content in detail. At this stage, AI-based image and text analysis techniques are used to detect unnatural elements and unrealistic features in the data. For example, in image data, it evaluates color inconsistencies and unnatural shadows, and in text data, it identifies grammatical errors and semantic inconsistencies. 【0223】 Step 4: 【0224】 The server uses the evaluation results to match the data against existing digital data libraries and commercial content platforms. Here, image recognition algorithms and text matching techniques are used to search for similarities. This process determines whether there is a potential for copyright infringement. 【0225】 Step 5: 【0226】 The server aggregates all analysis results and prepares to notify the user. If any unnatural elements are found or if a risk of copyright infringement is identified, a detailed report is generated. Through this report, the user can see which parts of the generated data are problematic. 【0227】 Step 6: 【0228】 The user reviews the report provided by the server and corrects or regenerates the data as needed. If the user makes corrections and re-uploads the data, the process restarts from step 1. 【0229】 (Example 1) 【0230】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0231】 In recent years, advancements in generative AI models have enabled users to generate diverse data. However, this has also led to increased concerns about the integrity of the generated data, the presence of unnatural elements, and copyright infringement. This invention aims to solve the problem of automating the verification of the legality and reliability of such generated data, thereby providing an environment in which users can engage in creative activities with peace of mind. 【0232】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0233】 In this invention, the server includes means for analyzing the structure of received data and verifying the integrity of the information, means for analyzing the characteristics of the content, and means for matching it with an existing information base and identifying matches or similarities. This allows users to check for any unnatural elements in the generated data, improve the quality of the data while mitigating copyright risks. 【0234】 "Generated data" refers to visual or textual data created by a generative AI model. 【0235】 A "terminal" refers to a computer system used by a user to generate data and send it to a server. 【0236】 A "server" refers to a central computing system that analyzes received data, detects unnatural elements, and compares them with existing information. 【0237】 "Means of analyzing structure and verifying information integrity" refers to a function that evaluates whether the data file format and data content are appropriate. 【0238】 "Means for analyzing content characteristics" refers to a function that uses pattern recognition technology to verify whether there are any unnatural elements in the data. 【0239】 "Means of matching with existing information bases to identify matches or similarities" refers to a function that compares incoming data with information from existing data libraries or commercial platforms to confirm similarity. 【0240】 "Unnatural elements" refer to unusual patterns or content features that are not normally expected and are recognized within the data. 【0241】 "Copyright" refers to the right that provides legal protection for creative works. 【0242】 This invention describes a specific embodiment of a system for automatically verifying the legality and reliability of data created using a generative AI model. 【0243】 The user first uses a terminal to select visual and text data obtained using a generative AI model. The terminal provides a graphical user interface, allowing the user to easily select files and send them to the server. The data sent should preferably be in common digital file formats (e.g., JPEG, PNG, TXT, DOCX, etc.). 【0244】 When the server receives data sent from a terminal, it utilizes a high-performance content analysis module to analyze the data. In particular, pattern recognition algorithms are applied to visual data to analyze characteristics such as color and shape. For text data, grammar checks and contextual analysis are performed to detect inconsistencies. Machine learning techniques are used for analysis, specifically by applying existing pattern recognition libraries and grammar analysis engines. 【0245】 The analysis results are cross-referenced with existing digital data libraries and data on public platforms by a comparison module on the server. This verifies whether the newly generated data is similar to existing data. For example, visual similarity is detected using image recognition technology, or text similarity is determined using text matching technology. 【0246】 Based on the analysis and comparison results, the server notifies the user via the user interface. The notification includes details about detected unnatural elements and suspected copyright infringements. The user can then use this information to modify or regenerate the generated content. 【0247】 As a concrete example, consider a scenario where a user uploads an image generated in response to the prompt, "Generate a new character illustration with a beautiful sunset over the sea as the background." If this image is similar to an existing commercial image, the server detects this and reports it to the user. The user can then proceed to modify the similar parts or generate a new illustration based on other ideas. 【0248】 This system allows users to improve the quality of their generated content while avoiding legal risks. 【0249】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0250】 Step 1: 【0251】 The user selects visual or text data generated using a generative AI model on their device and sends it to the server. The selected data is the input, and the data sent to the server is the output. The device displays a user interface, providing the user with an easy way to select and upload files. 【0252】 Step 2: 【0253】 The server receives data sent from the terminal. The input is the received data, and the output is the data stored within the server. The server first verifies the file format of the received data to ensure it is in the correct format. 【0254】 Step 3: 【0255】 The server activates a content analysis module to analyze the received data. The input is the stored data, and the output is the analysis result. To detect unnatural elements, a pattern recognition algorithm is applied to the visual data, and grammatical and contextual analysis is performed on the text data. 【0256】 Step 4: 【0257】 The server compares the analyzed data with data from existing digital data libraries and commercial platforms. The input is the analysis results, and the output is the similarity identification results. Image recognition and text matching technologies are used to detect similarity with existing data. 【0258】 Step 5: 【0259】 The server notifies the user based on the comparison results. The input is the similarity identification result, and the output is a detailed notification to the user. By displaying the results, including alerts and recommended actions, to the user through the user interface, the user can choose their next course of action. 【0260】 (Application Example 1) 【0261】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0262】 If the generated content has unnatural characteristics or carries the risk of copyright infringement, users may unintentionally distribute inaccurate or illegal content. It is essential to prevent such situations and provide an environment where users can create and distribute content with peace of mind. 【0263】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0264】 In this invention, the server includes means for receiving generated content, means for analyzing the received content and detecting unnatural or unrealistic characteristics, means for identifying correspondences or similarities by comparing it with existing information databases or commercial platforms based on the analysis results, and means for providing immediate feedback on the legality of the content before the user uploads it and giving instructions for correction. This enables users to prevent the generation and distribution of inaccurate or illegal content and to create content with peace of mind. 【0265】 "Generated content" refers to visual or textual data created using AI or other digital technologies. 【0266】 "Means of receiving data" refers to a system for transferring user-created content to a server and receiving that data. 【0267】 "Means of analysis" refers to methods and techniques for analyzing received content and detecting unnatural or unrealistic characteristics. 【0268】 "Unnatural characteristics" refer to elements that deviate from their natural state or the properties of data that lack consistency with the real world. 【0269】 An "unrealistic characteristic" refers to a feature or state that lacks physical or contextual reality. 【0270】 An "existing information database" is a collection of digital data accumulated in the past, used for comparing and referencing content. 【0271】 A "commercial platform" refers to an online space or system used for commercial activities. 【0272】 "Means for identifying correspondence or similarity" refers to methods for comparing analyzed data with existing data to find similarities or matches. 【0273】 "Means of providing feedback" refers to a system that informs users of analysis results and comments regarding content. 【0274】 "Means of providing instructions for correction" refers to methods for presenting users with specific improvement plans or action guidelines regarding identified problems. 【0275】 The system that implements this application primarily functions based on the interaction between a server, a terminal, and a user. The user uploads content generated via the terminal to the server. The server analyzes the visual data using image processing libraries such as PIL (Python Imaging Library) and OpenCV. During the analysis, machine learning models are utilized to detect unnatural or unrealistic characteristics, thereby evaluating whether the user-generated content is realistic. 【0276】 Based on the analysis results, the server performs comparison calculations to identify similarities and matches by comparing them with data contained in existing information databases and commercial platforms. If the similarity is high, the server provides feedback and notifies the user of the potential copyright infringement. Specifically, it presents prompt messages such as, "Does this content potentially infringe on existing copyrights? If so, which parts should be modified?" 【0277】 Users who receive this notification can receive specific correction instructions through their device interface, enabling them to make appropriate revisions and adjustments to their content. This process reduces the risk of creating and distributing inaccurate or illegal content, allowing users to proceed with content creation with peace of mind. 【0278】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0279】 Step 1: 【0280】 The user uploads the generated content from their device to the server. The input consists of images and text data selected by the user, and the output is the transfer of this data to the server. This process involves data transfer via an internet connection. 【0281】 Step 2: 【0282】 The server receives the uploaded content and analyzes the data using an image processing library (e.g., PIL or OpenCV). The input is the data sent from the user, and the output is the detection information of unnatural characteristics as the analysis result. As specific operations, it extracts the color information and shape information of the image, and applies an AI algorithm to identify unnatural patterns. 【0283】 Step 3: 【0284】 Based on the analysis result, the server compares it with the data in the existing information database or commercial platform. The input is the analyzed content information, and the output is the identification result of similarity or coincidence. Here, text matching and image recognition technologies are utilized to evaluate the similarity of the data. 【0285】 Step 4: 【0286】 If the similarity is high, the server sends a notification to the user as feedback. The input is the result of similarity identification, and the output is the detailed notification content to the user. As specific operations, it generates content to inform the user about what the similar content is and whether correction is needed. 【0287】 Step 5: 【0288】 The user receives the feedback provided by the server through the terminal and considers modifying the content. The input is the notification information from the server, and the output is the decision on whether the user will perform the modification operation. Thereby, the user can optimize the content and complete the correction if necessary. 【0289】 Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion. 【0290】 This invention relates to a system that combines generated visual or textual data with an emotion engine to provide appropriate feedback to the user. This system uses emotion recognition technology to analyze the user's emotional state and can effectively customize feedback based on the synthesized data. 【0291】 The user uploads data created through the generative AI using their device to the system. The uploaded data is received by the server in the first stage of processing, and analysis begins. The server first checks the data format, and if there are no problems, it then analyzes the content of the data. Here, pattern recognition using machine learning models is performed to check for unnatural or unrealistic elements. 【0292】 Based on the analysis results, the server activates a newly integrated emotion engine to identify the user's emotions. This emotion engine determines the emotional state by capturing facial expression data and text data when the user provides input to the system. For example, it analyzes the user's intent and tone from the text data, and if negative emotions are detected, it prepares special feedback. 【0293】 As a concrete example, consider a scenario where a user uploads visual data, but the server notifies them of a potential copyright infringement. The emotion engine analyzes the user's emotional response to this notification through text input and facial recognition during video chats. If the server determines that the user is feeling confused or stressed, it provides supportive messages, including encouragement and detailed instructions, to help alleviate the user's anxiety. 【0294】 This system, which incorporates an emotion engine, allows users to improve the safety and quality of generated data, as well as how they receive feedback. This, in turn, can reduce stress and improve learning efficiency. 【0295】 The following describes the processing flow. 【0296】 Step 1: 【0297】 The user uses a device to select visual or text data generated by the AI ​​and uploads it to the system. The data is then sent to a server in the cloud. The device displays the upload status to the user through its interface and guides them through the next steps. 【0298】 Step 2: 【0299】 The server receives the uploaded data and first checks its format. Here, it verifies that the data is in a supported file format (e.g., JPEG, PNG, or text file). After this verification is complete, the data is securely stored and prepared for analysis. 【0300】 Step 3: 【0301】 The server sends the received data to an analysis module, which uses AI to detect unnatural or unrealistic elements. It applies algorithms to identify unnatural colors or shapes, grammatical errors, or contextual inconsistencies. 【0302】 Step 4: 【0303】 The server compares the analyzed data with existing digital data libraries and commercial platforms to check for similar data. Here, image recognition technology and text similarity algorithms are used to search for matches that may infringe copyright. 【0304】 Step 5: 【0305】 The server passes the analysis result to the emotion engine to make a judgment for identifying the user's emotional state. At this time, the emotion engine analyzes the expression methods (such as input text and changes in expressions) when the user is operating the system, and infers positive or negative emotions. 【0306】 Step 6: 【0307】 Based on the emotion data and analysis result obtained by the server, adjust the content to be notified to the user. If a negative reaction is detected, devise the tone and content of the message, and incorporate content that strengthens user support, such as encouragement and suggestions for problem-solving. 【0308】 Step 7: 【0309】 The user checks the feedback sent from the server through the terminal and decides whether to modify or re-upload the data or take further actions as needed. The terminal arranges the feedback in an easy-to-view manner and provides the user with smooth instructions for the next step. 【0310】 (Example 2) 【0311】 Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal". 【0312】 In modern society, a large amount of digital data is generated every day, and there may be potential unnatural elements and copyright-related problems in it. However, it is difficult for users to manually analyze these data appropriately and receive necessary corrections and feedback. Also, it is a difficult task to provide appropriate feedback considering the impact of digital data on users' emotions. 【0313】 The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0314】 In this invention, the server includes means for receiving generated digital data, means for performing analysis to detect unnatural elements and similarities, and means for analyzing emotions and generating appropriate feedback. This enhances the safety and quality of data generated by users and enables them to receive emotionally sensitive feedback. 【0315】 "Generated digital data" refers to information in visual or text format newly created by a user or electronic device. 【0316】 "Means of receiving" refers to methods or devices that have the function of taking digital data into a server or system and preparing it for processing. 【0317】 An "unnatural element" refers to an abnormal feature in digital data that deviates from normal patterns or expected content. 【0318】 "Unrealistic elements" refer to information within digital data that appears to contradict real-world situations or physical laws. 【0319】 "Means based on analysis results" refers to methods or devices that utilize the results of digital data analysis to proceed to the next processing step. 【0320】 "Existing information collections" refer to databases and digital libraries that have been accumulated over time. 【0321】 "Commercial infrastructure" refers to online platforms and marketplaces used for commercial activities. 【0322】 "Means of identifying matches or similarities" refers to methods or devices for determining how similar analyzed digital data is to existing data. 【0323】 "Means for analyzing emotions and generating appropriate feedback" refers to a method or device that understands the user's emotional state and provides accurate responses or suggestions based on the results. 【0324】 This invention relates to a system that analyzes generated digital data and provides appropriate feedback to the user. The system consists of a terminal, a server, and an emotion analysis engine. 【0325】 The terminal is used by users to generate and upload digital data. Users generate visual and text data on the terminal and send it to the system. The terminal has an interface for sending data from the input screen to the server and is equipped with a display device to notify the user of the data transmission status. 【0326】 The server is responsible for analyzing the received digital data. First, the server checks the data format and evaluates whether it is in an appropriate format. After verification, it analyzes the data using machine learning models and detects unnatural or unrealistic elements using pattern recognition technology. At this time, the server compares the data's similarity to existing databases and generates a message containing necessary correction instructions if it is necessary to notify the user. 【0327】 Furthermore, the server incorporates an emotion analysis engine that analyzes the user's emotional state based on facial expression data and text content. Based on this analysis, it constructs personalized feedback for the user. For example, if negative emotions are detected, the server can offer encouraging messages or solutions. 【0328】 As a concrete example, when a user uploads visual data of a natural landscape to the server, the server checks whether the data is similar to existing works. If similarities are found, the emotion engine analyzes the user's emotions upon receiving the notification. If the user is confused, the server provides feedback such as, "Please confirm that this image is original, and we will support you if further copyright investigation is necessary." 【0329】 An example of a prompt message is as follows: 【0330】 "Based on the following text, please generate a reassuring support message for the user: 'The image you uploaded has been determined to be a copyright infringement.'" 【0331】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0332】 Step 1: 【0333】 Users generate visual or text data and upload it to the system using a terminal. The input is the digital data generated by the user, and the output is request data sent to the server. During this process, the terminal performs an initial check of the data format and transfers the data to the server in the appropriate format. A progress bar is displayed on the terminal to visualize the progress of the data transfer. 【0334】 Step 2: 【0335】 The server receives digital data sent from the user. The input is digital data from the user, and the output is data in a format that can be analyzed. The server checks whether the format of the received data is JPEG, PNG, or UTF-8 if it is text. If there are no problems, it temporarily stores the data as format-checked data and proceeds to the next analysis step. 【0336】 Step 3: 【0337】 The server performs analysis on stored data using machine learning models. The input is format-verified data, and the output is the analysis results. Pattern recognition techniques are used to detect unnatural or unrealistic elements within the data. During this process, the data is compared with existing information sets to check for matches and similarities. This data processing generates metadata as an analysis result. 【0338】 Step 4: 【0339】 The server activates the emotion analysis engine based on the analysis results. The inputs used are the user's facial expression data, text data, and the analysis results, and the output identifies the user's emotional state. The server utilizes natural language processing techniques to analyze emotional tone from the user's text. Simultaneously, it uses facial recognition technology to identify emotional characteristics from videos and images. 【0340】 Step 5: 【0341】 The server uses a generative AI model to create appropriate feedback based on the results of sentiment analysis. The input is the emotional state and analysis metadata, and the output is a supportive message for the user. For example, if the user is confused, the server generates a message containing encouragement and suggestions for improving the situation. This provides real-time support to the user. 【0342】 (Application Example 2) 【0343】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0344】 Traditional data analysis systems focused on issuing warnings about the appropriateness of generated information and whether copyright infringement occurred, but they did not consider providing feedback based on users' emotions. As a result, responses that ignored users' emotional reactions were provided, hindering the user's information exchange experience. Furthermore, there is a need for a system that can enhance users' sense of security and trust through flexible feedback that responds to their emotional state. 【0345】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0346】 In this invention, the server includes means for receiving generated informational data or linguistic data, means for analyzing the received data and detecting unnatural or unrealistic elements, and means for activating an emotion engine, identifying the user's emotions, and adapting the dialogue based on the identified emotions. This makes it possible to provide appropriate feedback that takes into account the user's emotional state. 【0347】 "Generated information data" refers to digital information in visual or text format created by the user through the generation AI. 【0348】 "Linguistic data" refers to text information based on natural language, used to express the user's intentions and emotions. 【0349】 An "emotion engine" is a device or program that identifies an emotional state based on user input and generates feedback accordingly. 【0350】 "Emotional state" refers to the user's psychological feelings and mood at a particular moment. 【0351】 "Adapting the dialogue" means changing the feedback and support messages provided according to the user's emotional state. 【0352】 "Information resources" refer to existing records and data stored in various digital formats that are used for comparison and analysis. 【0353】 A "commercial infrastructure" refers to an electronic platform or system used for trading goods or providing services. 【0354】 The system for realizing this invention operates through cooperation between a server and a user's terminal. The server receives information data or language data generated from the user's terminal and analyzes its content. The analysis includes software that uses a learning model to detect unnatural or unrealistic elements. Based on the analysis results, the server activates an emotion engine. The emotion engine identifies the user's emotional state, generates feedback according to the identified emotion, and sends it to the user. 【0355】 Software such as Amazon Web Services (AWS) sentiment analysis services and Google Cloud Natural Language API can be used to analyze emotional states. The feedback users receive is tailored to their emotional state; for example, a user feeling anxious will be provided with a reassuring message. 【0356】 For example, if a user enters "I'm worried about this purchase," the server analyzes this text and, based on the identified emotion of anxiety, returns feedback such as "Rest assured, this product comes with a money-back guarantee." Examples of prompts include "Please provide information to alleviate your concerns about this product." In this way, the system optimizes the user's information exchange experience and enables flexible responses tailored to their emotions. 【0357】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0358】 Step 1: 【0359】 The user uploads generated informational or linguistic data to the server using a device. The input is the visual or text data created by the user using a generated AI model, and the output is its transfer to the server. 【0360】 Step 2: 【0361】 The server analyzes the received data to verify its format and structure. This step determines whether the data is in the correct digital format and prepares it for appropriate data processing. The input is the received raw data, and the output is the well-formed data after format verification. The server then processes the data into a format suitable for analysis. 【0362】 Step 3: 【0363】 The server uses a machine learning model to detect unnatural or unrealistic elements in the data. The input is well-formed data, and the output is the analysis results regarding the unnatural elements. Specifically, it uses AWS and Google Cloud services to scrutinize the data. 【0364】 Step 4: 【0365】 The server activates the emotion engine based on the analysis results to identify the user's emotional state. The input is the analysis results, and the output is the identified emotional state. At this stage, the emotion analysis API is used to determine the emotions contained in the user's input. 【0366】 Step 5: 【0367】 Based on the emotional state identified by the emotion engine, the server generates appropriate feedback and sends it to the user. The input is the identified emotional state, and the output is a feedback message directed to the user. Specifically, it prepares a customized response according to the emotion and forwards the message to the user's terminal. 【0368】 The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0369】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0370】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214. 【0371】 [Third Embodiment] 【0372】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0373】 As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server. 【0374】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0375】 The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52. 【0376】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0377】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0378】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0379】 Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0380】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0381】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0382】 In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0383】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal". 【0384】 This invention relates to an embodiment of a system for automatically checking the legality and reliability of generated visual and textual data. This system is constructed as follows: 【0385】 The user first sends visual or text data created using AI generation from their device to the server via an online platform. When uploading, the user must adhere to the specified file format. The device provides a user interface via the internet, enabling file selection and uploading. 【0386】 The transmitted data is received by the server and analyzed by a dedicated content analysis module. The server uses this analysis module to evaluate whether there are any unnatural or unrealistic elements. Specifically, it applies an AI-based pattern recognition algorithm to check the color and shape of visual data, and the grammatical consistency and contextual relevance of text data. 【0387】 Next, the server uses a data comparison module to cross-reference the analyzed data with existing digital data libraries and existing content on commercial platforms. Text matching and image recognition technologies are used here, and if a certain level of similarity is detected, the data is deemed potentially infringing on copyright. 【0388】 Based on the results obtained through this process, the server notifies the user of the results through the user interface. The result notification includes the detection of unnatural elements and details of suspected copyright infringement. The user can view this information and modify or regenerate the generated data as needed. 【0389】 As a concrete example, suppose a user uploads a new image they have created. If this image is very similar to a specific image that exists on a commercial image platform, the server will detect this and notify the user of the similarity. Based on the notification, the user is given the option to adjust part of the image or regenerate different visual data. 【0390】 This system allows users to reduce the risk of copyright infringement and more easily guarantee the quality of the generated content during the content creation process using generative AI. 【0391】 The following describes the processing flow. 【0392】 Step 1: 【0393】 Users use their devices to select visual and text data generated by the AI ​​and access an upload interface. This interface, available via a browser or dedicated application, sends data to the server through an upload form that includes checks for file format specification and size limits. 【0394】 Step 2: 【0395】 The server first verifies the data format before analyzing the data received from the user. Here, it checks if the file format is correct and if the content is corrupted. If the format or content is inappropriate, it returns an error message to the user and prompts them to try again. 【0396】 Step 3: 【0397】 The server uses an analysis module to examine the data content in detail. At this stage, AI-based image and text analysis techniques are used to detect unnatural elements and unrealistic features in the data. For example, in image data, it evaluates color inconsistencies and unnatural shadows, and in text data, it identifies grammatical errors and semantic inconsistencies. 【0398】 Step 4: 【0399】 The server uses the evaluation results to match the data against existing digital data libraries and commercial content platforms. Here, image recognition algorithms and text matching techniques are used to search for similarities. This process determines whether there is a potential for copyright infringement. 【0400】 Step 5: 【0401】 The server aggregates all analysis results and prepares to notify the user. If any unnatural elements are found or if a risk of copyright infringement is identified, a detailed report is generated. Through this report, the user can see which parts of the generated data are problematic. 【0402】 Step 6: 【0403】 The user reviews the report provided by the server and corrects or regenerates the data as needed. If the user makes corrections and re-uploads the data, the process restarts from step 1. 【0404】 (Example 1) 【0405】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0406】 In recent years, advancements in generative AI models have enabled users to generate diverse data. However, this has also led to increased concerns about the integrity of the generated data, the presence of unnatural elements, and copyright infringement. This invention aims to solve the problem of automating the verification of the legality and reliability of such generated data, thereby providing an environment in which users can engage in creative activities with peace of mind. 【0407】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0408】 In this invention, the server includes means for analyzing the structure of received data and verifying the integrity of the information, means for analyzing the characteristics of the content, and means for matching it with an existing information base and identifying matches or similarities. This allows users to check for any unnatural elements in the generated data, improve the quality of the data while mitigating copyright risks. 【0409】 "Generated data" refers to visual or textual data created by a generative AI model. 【0410】 A "terminal" refers to a computer system used by a user to generate data and send it to a server. 【0411】 A "server" refers to a central computing system that analyzes received data, detects unnatural elements, and compares them with existing information. 【0412】 "Means of analyzing structure and verifying information integrity" refers to a function that evaluates whether the data file format and data content are appropriate. 【0413】 "Means for analyzing content characteristics" refers to a function that uses pattern recognition technology to verify whether there are any unnatural elements in the data. 【0414】 "Means of matching with existing information bases to identify matches or similarities" refers to a function that compares incoming data with information from existing data libraries or commercial platforms to confirm similarity. 【0415】 "Unnatural elements" refer to unusual patterns or content features that are not normally expected and are recognized within the data. 【0416】 "Copyright" refers to the right that provides legal protection for creative works. 【0417】 This invention describes a specific embodiment of a system for automatically verifying the legality and reliability of data created using a generative AI model. 【0418】 The user first uses a terminal to select visual and text data obtained using a generative AI model. The terminal provides a graphical user interface, allowing the user to easily select files and send them to the server. The data sent should preferably be in common digital file formats (e.g., JPEG, PNG, TXT, DOCX, etc.). 【0419】 When the server receives data sent from a terminal, it utilizes a high-performance content analysis module to analyze the data. In particular, pattern recognition algorithms are applied to visual data to analyze characteristics such as color and shape. For text data, grammar checks and contextual analysis are performed to detect inconsistencies. Machine learning techniques are used for analysis, specifically by applying existing pattern recognition libraries and grammar analysis engines. 【0420】 The analysis results are cross-referenced with existing digital data libraries and data on public platforms by a comparison module on the server. This verifies whether the newly generated data is similar to existing data. For example, visual similarity is detected using image recognition technology, or text similarity is determined using text matching technology. 【0421】 Based on the analysis and comparison results, the server notifies the user via the user interface. The notification includes details about detected unnatural elements and suspected copyright infringements. The user can then use this information to modify or regenerate the generated content. 【0422】 As a concrete example, consider a scenario where a user uploads an image generated in response to the prompt, "Generate a new character illustration with a beautiful sunset over the sea as the background." If this image is similar to an existing commercial image, the server detects this and reports it to the user. The user can then proceed to modify the similar parts or generate a new illustration based on other ideas. 【0423】 This system allows users to improve the quality of their generated content while avoiding legal risks. 【0424】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0425】 Step 1: 【0426】 The user selects visual or text data generated using a generative AI model on their device and sends it to the server. The selected data is the input, and the data sent to the server is the output. The device displays a user interface, providing the user with an easy way to select and upload files. 【0427】 Step 2: 【0428】 The server receives data sent from the terminal. The input is the received data, and the output is the data stored within the server. The server first verifies the file format of the received data to ensure it is in the correct format. 【0429】 Step 3: 【0430】 The server activates a content analysis module to analyze the received data. The input is the stored data, and the output is the analysis result. To detect unnatural elements, a pattern recognition algorithm is applied to the visual data, and grammatical and contextual analysis is performed on the text data. 【0431】 Step 4: 【0432】 The server compares the analyzed data with data from existing digital data libraries and commercial platforms. The input is the analysis results, and the output is the similarity identification results. Image recognition and text matching technologies are used to detect similarity with existing data. 【0433】 Step 5: 【0434】 The server notifies the user based on the comparison results. The input is the similarity identification result, and the output is a detailed notification to the user. By displaying the results, including alerts and recommended actions, to the user through the user interface, the user can choose their next course of action. 【0435】 (Application Example 1) 【0436】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0437】 If the generated content has unnatural characteristics or carries the risk of copyright infringement, users may unintentionally distribute inaccurate or illegal content. It is essential to prevent such situations and provide an environment where users can create and distribute content with peace of mind. 【0438】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0439】 In this invention, the server includes means for receiving generated content, means for analyzing the received content and detecting unnatural or unrealistic characteristics, means for identifying correspondences or similarities by comparing it with existing information databases or commercial platforms based on the analysis results, and means for providing immediate feedback on the legality of the content before the user uploads it and giving instructions for correction. This enables users to prevent the generation and distribution of inaccurate or illegal content and to create content with peace of mind. 【0440】 "Generated content" refers to visual or textual data created using AI or other digital technologies. 【0441】 "Means of receiving data" refers to a system for transferring user-created content to a server and receiving that data. 【0442】 "Means of analysis" refers to methods and techniques for analyzing received content and detecting unnatural or unrealistic characteristics. 【0443】 "Unnatural characteristics" refer to elements that deviate from their natural state or the properties of data that lack consistency with the real world. 【0444】 An "unrealistic characteristic" refers to a feature or state that lacks physical or contextual reality. 【0445】 An "existing information database" is a collection of digital data accumulated in the past, used for comparing and referencing content. 【0446】 A "commercial platform" refers to an online space or system used for commercial activities. 【0447】 "Means for identifying correspondence or similarity" refers to methods for comparing analyzed data with existing data to find similarities or matches. 【0448】 "Means of providing feedback" refers to a system that informs users of analysis results and comments regarding content. 【0449】 "Means of providing instructions for correction" refers to methods for presenting users with specific improvement plans or action guidelines regarding identified problems. 【0450】 The system that implements this application primarily functions based on the interaction between a server, a terminal, and a user. The user uploads content generated via the terminal to the server. The server analyzes the visual data using image processing libraries such as PIL (Python Imaging Library) and OpenCV. During the analysis, machine learning models are utilized to detect unnatural or unrealistic characteristics, thereby evaluating whether the user-generated content is realistic. 【0451】 Based on the analysis results, the server performs comparison calculations to identify similarities and matches by comparing them with data contained in existing information databases and commercial platforms. If the similarity is high, the server provides feedback and notifies the user of the potential copyright infringement. Specifically, it presents prompt messages such as, "Does this content potentially infringe on existing copyrights? If so, which parts should be modified?" 【0452】 Users who receive this notification can receive specific correction instructions through their device interface, enabling them to make appropriate revisions and adjustments to their content. This process reduces the risk of creating and distributing inaccurate or illegal content, allowing users to proceed with content creation with peace of mind. 【0453】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0454】 Step 1: 【0455】 The user uploads the generated content from their device to the server. The input consists of images and text data selected by the user, and the output is the transfer of this data to the server. This process involves data transfer via an internet connection. 【0456】 Step 2: 【0457】 The server receives uploaded content and analyzes the data using image processing libraries (e.g., PIL or OpenCV). The input is data sent by the user, and the output is information on the detection of unnatural characteristics as a result of the analysis. Specifically, it extracts color and shape information from the image and applies an AI algorithm to identify unnatural patterns. 【0458】 Step 3: 【0459】 The server compares the analysis results with data from existing information databases and commercial platforms. The input is the analyzed content information, and the output is the result of identifying similarities or matches. Here, text matching and image recognition technologies are used to evaluate data similarity. 【0460】 Step 4: 【0461】 If the similarity is high, the server sends a notification to the user as feedback. The input is the result of the similarity identification, and the output is the detailed notification content for the user. Specifically, it generates content that informs the user about what the similar content is and whether corrections are needed. 【0462】 Step 5: 【0463】 Users receive feedback from the server via their devices and consider revising their content. The input is notification information from the server, and the output is the user's decision on whether or not to make revisions. This allows users to optimize their content and complete revisions as needed. 【0464】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0465】 This invention relates to a system that combines generated visual or textual data with an emotion engine to provide appropriate feedback to the user. This system uses emotion recognition technology to analyze the user's emotional state and can effectively customize feedback based on the synthesized data. 【0466】 The user uploads data created through the generative AI using their device to the system. The uploaded data is received by the server in the first stage of processing, and analysis begins. The server first checks the data format, and if there are no problems, it then analyzes the content of the data. Here, pattern recognition using machine learning models is performed to check for unnatural or unrealistic elements. 【0467】 Based on the analysis results, the server activates a newly integrated emotion engine to identify the user's emotions. This emotion engine determines the emotional state by capturing facial expression data and text data when the user provides input to the system. For example, it analyzes the user's intent and tone from the text data, and if negative emotions are detected, it prepares special feedback. 【0468】 As a concrete example, consider a scenario where a user uploads visual data, but the server notifies them of a potential copyright infringement. The emotion engine analyzes the user's emotional response to this notification through text input and facial recognition during video chats. If the server determines that the user is feeling confused or stressed, it provides supportive messages, including encouragement and detailed instructions, to help alleviate the user's anxiety. 【0469】 This system, which incorporates an emotion engine, allows users to improve the safety and quality of generated data, as well as how they receive feedback. This, in turn, can reduce stress and improve learning efficiency. 【0470】 The following describes the processing flow. 【0471】 Step 1: 【0472】 The user uses a device to select visual or text data generated by the AI ​​and uploads it to the system. The data is then sent to a server in the cloud. The device displays the upload status to the user through its interface and guides them through the next steps. 【0473】 Step 2: 【0474】 The server receives the uploaded data and first checks its format. Here, it verifies that the data is in a supported file format (e.g., JPEG, PNG, or text file). After this verification is complete, the data is securely stored and prepared for analysis. 【0475】 Step 3: 【0476】 The server sends the received data to an analysis module, which uses AI to detect unnatural or unrealistic elements. It applies algorithms to identify unnatural colors or shapes, grammatical errors, or contextual inconsistencies. 【0477】 Step 4: 【0478】 The server compares the analyzed data with existing digital data libraries and commercial platforms to check for similar data. Here, image recognition technology and text similarity algorithms are used to search for matches that may infringe copyright. 【0479】 Step 5: 【0480】 The server passes the analysis results to the emotion engine, which makes a decision to determine the user's emotional state. In this process, the emotion engine analyzes how the user expresses themselves while operating the system (such as input text and changes in facial expressions) and infers whether the emotion is positive or negative. 【0481】 Step 6: 【0482】 Based on the sentiment data and analysis results obtained by the server, the content of notifications sent to the user is adjusted. If a negative reaction is detected, the tone and content of the message are modified to enhance user support, such as encouragement or suggestions for problem solving. 【0483】 Step 7: 【0484】 Users review feedback sent from the server via their device and decide whether to correct or re-upload data, or take further action as needed. The device organizes the feedback in an easy-to-understand format and provides users with smooth instructions for the next steps. 【0485】 (Example 2) 【0486】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0487】 In modern society, vast amounts of digital data are generated daily, potentially containing unnatural elements or copyright issues. However, it is difficult for users to manually analyze this data appropriately and receive necessary corrections and feedback. Furthermore, providing appropriate feedback that takes into account the impact digital data has on users' emotions is also a challenging task. 【0488】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0489】 In this invention, the server includes means for receiving generated digital data, means for performing analysis to detect unnatural elements and similarities, and means for analyzing emotions and generating appropriate feedback. This enhances the safety and quality of data generated by users and enables them to receive emotionally sensitive feedback. 【0490】 "Generated digital data" refers to information in visual or text format newly created by a user or electronic device. 【0491】 "Means of receiving" refers to methods or devices that have the function of taking digital data into a server or system and preparing it for processing. 【0492】 An "unnatural element" refers to an abnormal feature in digital data that deviates from normal patterns or expected content. 【0493】 "Unrealistic elements" refer to information within digital data that appears to contradict real-world situations or physical laws. 【0494】 "Means based on analysis results" refers to methods or devices that utilize the results of digital data analysis to proceed to the next processing step. 【0495】 "Existing information collections" refer to databases and digital libraries that have been accumulated over time. 【0496】 "Commercial infrastructure" refers to online platforms and marketplaces used for commercial activities. 【0497】 "Means of identifying matches or similarities" refers to methods or devices for determining how similar analyzed digital data is to existing data. 【0498】 "Means for analyzing emotions and generating appropriate feedback" refers to a method or device that understands the user's emotional state and provides accurate responses or suggestions based on the results. 【0499】 This invention relates to a system that analyzes generated digital data and provides appropriate feedback to the user. The system consists of a terminal, a server, and an emotion analysis engine. 【0500】 The terminal is used by users to generate and upload digital data. Users generate visual and text data on the terminal and send it to the system. The terminal has an interface for sending data from the input screen to the server and is equipped with a display device to notify the user of the data transmission status. 【0501】 The server is responsible for analyzing the received digital data. First, the server checks the data format and evaluates whether it is in an appropriate format. After verification, it analyzes the data using machine learning models and detects unnatural or unrealistic elements using pattern recognition technology. At this time, the server compares the data's similarity to existing databases and generates a message containing necessary correction instructions if it is necessary to notify the user. 【0502】 Furthermore, the server incorporates an emotion analysis engine that analyzes the user's emotional state based on facial expression data and text content. Based on this analysis, it constructs personalized feedback for the user. For example, if negative emotions are detected, the server can offer encouraging messages or solutions. 【0503】 As a concrete example, when a user uploads visual data of a natural landscape to the server, the server checks whether the data is similar to existing works. If similarities are found, the emotion engine analyzes the user's emotions upon receiving the notification. If the user is confused, the server provides feedback such as, "Please confirm that this image is original, and we will support you if further copyright investigation is necessary." 【0504】 An example of a prompt message is as follows: 【0505】 "Based on the following text, please generate a reassuring support message for the user: 'The image you uploaded has been determined to be a copyright infringement.'" 【0506】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0507】 Step 1: 【0508】 Users generate visual or text data and upload it to the system using a terminal. The input is the digital data generated by the user, and the output is request data sent to the server. During this process, the terminal performs an initial check of the data format and transfers the data to the server in the appropriate format. A progress bar is displayed on the terminal to visualize the progress of the data transfer. 【0509】 Step 2: 【0510】 The server receives digital data sent from the user. The input is digital data from the user, and the output is data in a format that can be analyzed. The server checks whether the format of the received data is JPEG, PNG, or UTF-8 if it is text. If there are no problems, it temporarily stores the data as format-checked data and proceeds to the next analysis step. 【0511】 Step 3: 【0512】 The server performs analysis on stored data using machine learning models. The input is format-verified data, and the output is the analysis results. Pattern recognition techniques are used to detect unnatural or unrealistic elements within the data. During this process, the data is compared with existing information sets to check for matches and similarities. This data processing generates metadata as an analysis result. 【0513】 Step 4: 【0514】 The server activates the emotion analysis engine based on the analysis results. The inputs used are the user's facial expression data, text data, and the analysis results, and the output identifies the user's emotional state. The server utilizes natural language processing techniques to analyze emotional tone from the user's text. Simultaneously, it uses facial recognition technology to identify emotional characteristics from videos and images. 【0515】 Step 5: 【0516】 The server uses a generative AI model to create appropriate feedback based on the results of sentiment analysis. The input is the emotional state and analysis metadata, and the output is a supportive message for the user. For example, if the user is confused, the server generates a message containing encouragement and suggestions for improving the situation. This provides real-time support to the user. 【0517】 (Application Example 2) 【0518】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0519】 Traditional data analysis systems focused on issuing warnings about the appropriateness of generated information and whether copyright infringement occurred, but they did not consider providing feedback based on users' emotions. As a result, responses that ignored users' emotional reactions were provided, hindering the user's information exchange experience. Furthermore, there is a need for a system that can enhance users' sense of security and trust through flexible feedback that responds to their emotional state. 【0520】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0521】 In this invention, the server includes means for receiving generated informational data or linguistic data, means for analyzing the received data and detecting unnatural or unrealistic elements, and means for activating an emotion engine, identifying the user's emotions, and adapting the dialogue based on the identified emotions. This makes it possible to provide appropriate feedback that takes into account the user's emotional state. 【0522】 "Generated information data" refers to digital information in visual or text format created by the user through the generation AI. 【0523】 "Linguistic data" refers to text information based on natural language, used to express the user's intentions and emotions. 【0524】 An "emotion engine" is a device or program that identifies an emotional state based on user input and generates feedback accordingly. 【0525】 "Emotional state" refers to the user's psychological feelings and mood at a particular moment. 【0526】 "Adapting the dialogue" means changing the feedback and support messages provided according to the user's emotional state. 【0527】 "Information resources" refer to existing records and data stored in various digital formats that are used for comparison and analysis. 【0528】 A "commercial infrastructure" refers to an electronic platform or system used for trading goods or providing services. 【0529】 The system for realizing this invention operates through cooperation between a server and a user's terminal. The server receives information data or language data generated from the user's terminal and analyzes its content. The analysis includes software that uses a learning model to detect unnatural or unrealistic elements. Based on the analysis results, the server activates an emotion engine. The emotion engine identifies the user's emotional state, generates feedback according to the identified emotion, and sends it to the user. 【0530】 Software such as Amazon Web Services (AWS) sentiment analysis services and Google Cloud Natural Language API can be used to analyze emotional states. The feedback users receive is tailored to their emotional state; for example, a user feeling anxious will be provided with a reassuring message. 【0531】 For example, if a user enters "I'm worried about this purchase," the server analyzes this text and, based on the identified emotion of anxiety, returns feedback such as "Rest assured, this product comes with a money-back guarantee." Examples of prompts include "Please provide information to alleviate your concerns about this product." In this way, the system optimizes the user's information exchange experience and enables flexible responses tailored to their emotions. 【0532】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0533】 Step 1: 【0534】 The user uploads generated informational or linguistic data to the server using a device. The input is the visual or text data created by the user using a generated AI model, and the output is its transfer to the server. 【0535】 Step 2: 【0536】 The server analyzes the received data to verify its format and structure. This step determines whether the data is in the correct digital format and prepares it for appropriate data processing. The input is the received raw data, and the output is the well-formed data after format verification. The server then processes the data into a format suitable for analysis. 【0537】 Step 3: 【0538】 The server uses a machine learning model to detect unnatural or unrealistic elements in the data. The input is well-formed data, and the output is the analysis results regarding the unnatural elements. Specifically, it uses AWS and Google Cloud services to scrutinize the data. 【0539】 Step 4: 【0540】 The server activates the emotion engine based on the analysis results to identify the user's emotional state. The input is the analysis results, and the output is the identified emotional state. At this stage, the emotion analysis API is used to determine the emotions contained in the user's input. 【0541】 Step 5: 【0542】 Based on the emotional state identified by the emotion engine, the server generates appropriate feedback and sends it to the user. The input is the identified emotional state, and the output is a feedback message directed to the user. Specifically, it prepares a customized response according to the emotion and forwards the message to the user's terminal. 【0543】 The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0544】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0545】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314. 【0546】 [Fourth Embodiment] 【0547】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0548】 As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server. 【0549】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0550】 The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52. 【0551】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0552】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0553】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0554】 The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes. 【0555】 Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0556】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0557】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0558】 In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0559】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0560】 This invention relates to an embodiment of a system for automatically checking the legality and reliability of generated visual and textual data. This system is constructed as follows: 【0561】 The user first sends visual or text data created using AI generation from their device to the server via an online platform. When uploading, the user must adhere to the specified file format. The device provides a user interface via the internet, enabling file selection and uploading. 【0562】 The transmitted data is received by the server and analyzed by a dedicated content analysis module. The server uses this analysis module to evaluate whether there are any unnatural or unrealistic elements. Specifically, it applies an AI-based pattern recognition algorithm to check the color and shape of visual data, and the grammatical consistency and contextual relevance of text data. 【0563】 Next, the server uses a data comparison module to cross-reference the analyzed data with existing digital data libraries and existing content on commercial platforms. Text matching and image recognition technologies are used here, and if a certain level of similarity is detected, the data is deemed potentially infringing on copyright. 【0564】 Based on the results obtained through this process, the server notifies the user of the results through the user interface. The result notification includes the detection of unnatural elements and details of suspected copyright infringement. The user can view this information and modify or regenerate the generated data as needed. 【0565】 As a concrete example, suppose a user uploads a new image they have created. If this image is very similar to a specific image that exists on a commercial image platform, the server will detect this and notify the user of the similarity. Based on the notification, the user is given the option to adjust part of the image or regenerate different visual data. 【0566】 This system allows users to reduce the risk of copyright infringement and more easily guarantee the quality of the generated content during the content creation process using generative AI. 【0567】 The following describes the processing flow. 【0568】 Step 1: 【0569】 Users use their devices to select visual and text data generated by the AI ​​and access an upload interface. This interface, available via a browser or dedicated application, sends data to the server through an upload form that includes checks for file format specification and size limits. 【0570】 Step 2: 【0571】 The server first verifies the data format before analyzing the data received from the user. Here, it checks if the file format is correct and if the content is corrupted. If the format or content is inappropriate, it returns an error message to the user and prompts them to try again. 【0572】 Step 3: 【0573】 The server uses an analysis module to examine the data content in detail. At this stage, AI-based image and text analysis techniques are used to detect unnatural elements and unrealistic features in the data. For example, in image data, it evaluates color inconsistencies and unnatural shadows, and in text data, it identifies grammatical errors and semantic inconsistencies. 【0574】 Step 4: 【0575】 The server uses the evaluation results to match the data against existing digital data libraries and commercial content platforms. Here, image recognition algorithms and text matching techniques are used to search for similarities. This process determines whether there is a potential for copyright infringement. 【0576】 Step 5: 【0577】 The server aggregates all analysis results and prepares to notify the user. If any unnatural elements are found or if a risk of copyright infringement is identified, a detailed report is generated. Through this report, the user can see which parts of the generated data are problematic. 【0578】 Step 6: 【0579】 The user reviews the report provided by the server and corrects or regenerates the data as needed. If the user makes corrections and re-uploads the data, the process restarts from step 1. 【0580】 (Example 1) 【0581】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0582】 In recent years, advancements in generative AI models have enabled users to generate diverse data. However, this has also led to increased concerns about the integrity of the generated data, the presence of unnatural elements, and copyright infringement. This invention aims to solve the problem of automating the verification of the legality and reliability of such generated data, thereby providing an environment in which users can engage in creative activities with peace of mind. 【0583】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0584】 In this invention, the server includes means for analyzing the structure of received data and verifying the integrity of the information, means for analyzing the characteristics of the content, and means for matching it with an existing information base and identifying matches or similarities. This allows users to check for any unnatural elements in the generated data, improve the quality of the data while mitigating copyright risks. 【0585】 "Generated data" refers to visual or textual data created by a generative AI model. 【0586】 A "terminal" refers to a computer system used by a user to generate data and send it to a server. 【0587】 A "server" refers to a central computing system that analyzes received data, detects unnatural elements, and compares them with existing information. 【0588】 "Means of analyzing structure and verifying information integrity" refers to a function that evaluates whether the data file format and data content are appropriate. 【0589】 "Means for analyzing content characteristics" refers to a function that uses pattern recognition technology to verify whether there are any unnatural elements in the data. 【0590】 "Means of matching with existing information bases to identify matches or similarities" refers to a function that compares incoming data with information from existing data libraries or commercial platforms to confirm similarity. 【0591】 "Unnatural elements" refer to unusual patterns or content features that are not normally expected and are recognized within the data. 【0592】 "Copyright" refers to the right that provides legal protection for creative works. 【0593】 This invention describes a specific embodiment of a system for automatically verifying the legality and reliability of data created using a generative AI model. 【0594】 The user first uses a terminal to select visual and text data obtained using a generative AI model. The terminal provides a graphical user interface, allowing the user to easily select files and send them to the server. The data sent should preferably be in common digital file formats (e.g., JPEG, PNG, TXT, DOCX, etc.). 【0595】 When the server receives data sent from a terminal, it utilizes a high-performance content analysis module to analyze the data. In particular, pattern recognition algorithms are applied to visual data to analyze characteristics such as color and shape. For text data, grammar checks and contextual analysis are performed to detect inconsistencies. Machine learning techniques are used for analysis, specifically by applying existing pattern recognition libraries and grammar analysis engines. 【0596】 The analysis results are cross-referenced with existing digital data libraries and data on public platforms by a comparison module on the server. This verifies whether the newly generated data is similar to existing data. For example, visual similarity is detected using image recognition technology, or text similarity is determined using text matching technology. 【0597】 Based on the analysis and comparison results, the server notifies the user via the user interface. The notification includes details about detected unnatural elements and suspected copyright infringements. The user can then use this information to modify or regenerate the generated content. 【0598】 As a concrete example, consider a scenario where a user uploads an image generated in response to the prompt, "Generate a new character illustration with a beautiful sunset over the sea as the background." If this image is similar to an existing commercial image, the server detects this and reports it to the user. The user can then proceed to modify the similar parts or generate a new illustration based on other ideas. 【0599】 This system allows users to improve the quality of their generated content while avoiding legal risks. 【0600】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0601】 Step 1: 【0602】 The user selects visual or text data generated using a generative AI model on their device and sends it to the server. The selected data is the input, and the data sent to the server is the output. The device displays a user interface, providing the user with an easy way to select and upload files. 【0603】 Step 2: 【0604】 The server receives data sent from the terminal. The input is the received data, and the output is the data stored within the server. The server first verifies the file format of the received data to ensure it is in the correct format. 【0605】 Step 3: 【0606】 The server activates a content analysis module to analyze the received data. The input is the stored data, and the output is the analysis result. To detect unnatural elements, a pattern recognition algorithm is applied to the visual data, and grammatical and contextual analysis is performed on the text data. 【0607】 Step 4: 【0608】 The server compares the analyzed data with data from existing digital data libraries and commercial platforms. The input is the analysis results, and the output is the similarity identification results. Image recognition and text matching technologies are used to detect similarity with existing data. 【0609】 Step 5: 【0610】 The server notifies the user based on the comparison results. The input is the similarity identification result, and the output is a detailed notification to the user. By displaying the results, including alerts and recommended actions, to the user through the user interface, the user can choose their next course of action. 【0611】 (Application Example 1) 【0612】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0613】 If the generated content has unnatural characteristics or carries the risk of copyright infringement, users may unintentionally distribute inaccurate or illegal content. It is essential to prevent such situations and provide an environment where users can create and distribute content with peace of mind. 【0614】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0615】 In this invention, the server includes means for receiving generated content, means for analyzing the received content and detecting unnatural or unrealistic characteristics, means for identifying correspondences or similarities by comparing it with existing information databases or commercial platforms based on the analysis results, and means for providing immediate feedback on the legality of the content before the user uploads it and giving instructions for correction. This enables users to prevent the generation and distribution of inaccurate or illegal content and to create content with peace of mind. 【0616】 "Generated content" refers to visual or textual data created using AI or other digital technologies. 【0617】 "Means of receiving data" refers to a system for transferring user-created content to a server and receiving that data. 【0618】 "Means of analysis" refers to methods and techniques for analyzing received content and detecting unnatural or unrealistic characteristics. 【0619】 "Unnatural characteristics" refer to elements that deviate from their natural state or the properties of data that lack consistency with the real world. 【0620】 An "unrealistic characteristic" refers to a feature or state that lacks physical or contextual reality. 【0621】 An "existing information database" is a collection of digital data accumulated in the past, used for comparing and referencing content. 【0622】 A "commercial platform" refers to an online space or system used for commercial activities. 【0623】 "Means for identifying correspondence or similarity" refers to methods for comparing analyzed data with existing data to find similarities or matches. 【0624】 "Means of providing feedback" refers to a system that informs users of analysis results and comments regarding content. 【0625】 "Means of providing instructions for correction" refers to methods for presenting users with specific improvement plans or action guidelines regarding identified problems. 【0626】 The system that implements this application primarily functions based on the interaction between a server, a terminal, and a user. The user uploads content generated via the terminal to the server. The server analyzes the visual data using image processing libraries such as PIL (Python Imaging Library) and OpenCV. During the analysis, machine learning models are utilized to detect unnatural or unrealistic characteristics, thereby evaluating whether the user-generated content is realistic. 【0627】 Based on the analysis results, the server performs comparison calculations to identify similarities and matches by comparing them with data contained in existing information databases and commercial platforms. If the similarity is high, the server provides feedback and notifies the user of the potential copyright infringement. Specifically, it presents prompt messages such as, "Does this content potentially infringe on existing copyrights? If so, which parts should be modified?" 【0628】 Users who receive this notification can receive specific correction instructions through their device interface, enabling them to make appropriate revisions and adjustments to their content. This process reduces the risk of creating and distributing inaccurate or illegal content, allowing users to proceed with content creation with peace of mind. 【0629】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0630】 Step 1: 【0631】 The user uploads the generated content from their device to the server. The input consists of images and text data selected by the user, and the output is the transfer of this data to the server. This process involves data transfer via an internet connection. 【0632】 Step 2: 【0633】 The server receives uploaded content and analyzes the data using image processing libraries (e.g., PIL or OpenCV). The input is data sent by the user, and the output is information on the detection of unnatural characteristics as a result of the analysis. Specifically, it extracts color and shape information from the image and applies an AI algorithm to identify unnatural patterns. 【0634】 Step 3: 【0635】 The server compares the analysis results with data from existing information databases and commercial platforms. The input is the analyzed content information, and the output is the result of identifying similarities or matches. Here, text matching and image recognition technologies are used to evaluate data similarity. 【0636】 Step 4: 【0637】 If the similarity is high, the server sends a notification to the user as feedback. The input is the result of the similarity identification, and the output is the detailed notification content for the user. Specifically, it generates content that informs the user about what the similar content is and whether corrections are needed. 【0638】 Step 5: 【0639】 Users receive feedback from the server via their devices and consider revising their content. The input is notification information from the server, and the output is the user's decision on whether or not to make revisions. This allows users to optimize their content and complete revisions as needed. 【0640】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0641】 This invention relates to a system that combines generated visual or textual data with an emotion engine to provide appropriate feedback to the user. This system uses emotion recognition technology to analyze the user's emotional state and can effectively customize feedback based on the synthesized data. 【0642】 The user uploads data created through the generative AI using their device to the system. The uploaded data is received by the server in the first stage of processing, and analysis begins. The server first checks the data format, and if there are no problems, it then analyzes the content of the data. Here, pattern recognition using machine learning models is performed to check for unnatural or unrealistic elements. 【0643】 Based on the analysis results, the server activates a newly integrated emotion engine to identify the user's emotions. This emotion engine determines the emotional state by capturing facial expression data and text data when the user provides input to the system. For example, it analyzes the user's intent and tone from the text data, and if negative emotions are detected, it prepares special feedback. 【0644】 As a concrete example, consider a scenario where a user uploads visual data, but the server notifies them of a potential copyright infringement. The emotion engine analyzes the user's emotional response to this notification through text input and facial recognition during video chats. If the server determines that the user is feeling confused or stressed, it provides supportive messages, including encouragement and detailed instructions, to help alleviate the user's anxiety. 【0645】 This system, which incorporates an emotion engine, allows users to improve the safety and quality of generated data, as well as how they receive feedback. This, in turn, can reduce stress and improve learning efficiency. 【0646】 The following describes the processing flow. 【0647】 Step 1: 【0648】 The user uses a device to select visual or text data generated by the AI ​​and uploads it to the system. The data is then sent to a server in the cloud. The device displays the upload status to the user through its interface and guides them through the next steps. 【0649】 Step 2: 【0650】 The server receives the uploaded data and first checks its format. Here, it verifies that the data is in a supported file format (e.g., JPEG, PNG, or text file). After this verification is complete, the data is securely stored and prepared for analysis. 【0651】 Step 3: 【0652】 The server sends the received data to an analysis module, which uses AI to detect unnatural or unrealistic elements. It applies algorithms to identify unnatural colors or shapes, grammatical errors, or contextual inconsistencies. 【0653】 Step 4: 【0654】 The server compares the analyzed data with existing digital data libraries and commercial platforms to check for similar data. Here, image recognition technology and text similarity algorithms are used to search for matches that may infringe copyright. 【0655】 Step 5: 【0656】 The server passes the analysis results to the emotion engine, which makes a decision to determine the user's emotional state. In this process, the emotion engine analyzes how the user expresses themselves while operating the system (such as input text and changes in facial expressions) and infers whether the emotion is positive or negative. 【0657】 Step 6: 【0658】 Based on the sentiment data and analysis results obtained by the server, the content of notifications sent to the user is adjusted. If a negative reaction is detected, the tone and content of the message are modified to enhance user support, such as encouragement or suggestions for problem solving. 【0659】 Step 7: 【0660】 Users review feedback sent from the server via their device and decide whether to correct or re-upload data, or take further action as needed. The device organizes the feedback in an easy-to-understand format and provides users with smooth instructions for the next steps. 【0661】 (Example 2) 【0662】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0663】 In modern society, vast amounts of digital data are generated daily, potentially containing unnatural elements or copyright issues. However, it is difficult for users to manually analyze this data appropriately and receive necessary corrections and feedback. Furthermore, providing appropriate feedback that takes into account the impact digital data has on users' emotions is also a challenging task. 【0664】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0665】 In this invention, the server includes means for receiving generated digital data, means for performing analysis to detect unnatural elements and similarities, and means for analyzing emotions and generating appropriate feedback. This enhances the safety and quality of data generated by users and enables them to receive emotionally sensitive feedback. 【0666】 "Generated digital data" refers to information in visual or text format newly created by a user or electronic device. 【0667】 "Means of receiving" refers to methods or devices that have the function of taking digital data into a server or system and preparing it for processing. 【0668】 An "unnatural element" refers to an abnormal feature in digital data that deviates from normal patterns or expected content. 【0669】 "Unrealistic elements" refer to information within digital data that appears to contradict real-world situations or physical laws. 【0670】 "Means based on analysis results" refers to methods or devices that utilize the results of digital data analysis to proceed to the next processing step. 【0671】 "Existing information collections" refer to databases and digital libraries that have been accumulated over time. 【0672】 "Commercial infrastructure" refers to online platforms and marketplaces used for commercial activities. 【0673】 "Means of identifying matches or similarities" refers to methods or devices for determining how similar analyzed digital data is to existing data. 【0674】 "Means for analyzing emotions and generating appropriate feedback" refers to a method or device that understands the user's emotional state and provides accurate responses or suggestions based on the results. 【0675】 This invention relates to a system that analyzes generated digital data and provides appropriate feedback to the user. The system consists of a terminal, a server, and an emotion analysis engine. 【0676】 The terminal is used by users to generate and upload digital data. Users generate visual and text data on the terminal and send it to the system. The terminal has an interface for sending data from the input screen to the server and is equipped with a display device to notify the user of the data transmission status. 【0677】 The server is responsible for analyzing the received digital data. First, the server checks the data format and evaluates whether it is in an appropriate format. After verification, it analyzes the data using machine learning models and detects unnatural or unrealistic elements using pattern recognition technology. At this time, the server compares the data's similarity to existing databases and generates a message containing necessary correction instructions if it is necessary to notify the user. 【0678】 Furthermore, the server incorporates an emotion analysis engine that analyzes the user's emotional state based on facial expression data and text content. Based on this analysis, it constructs personalized feedback for the user. For example, if negative emotions are detected, the server can offer encouraging messages or solutions. 【0679】 As a concrete example, when a user uploads visual data of a natural landscape to the server, the server checks whether the data is similar to existing works. If similarities are found, the emotion engine analyzes the user's emotions upon receiving the notification. If the user is confused, the server provides feedback such as, "Please confirm that this image is original, and we will support you if further copyright investigation is necessary." 【0680】 An example of a prompt message is as follows: 【0681】 "Based on the following text, please generate a reassuring support message for the user: 'The image you uploaded has been determined to be a copyright infringement.'" 【0682】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0683】 Step 1: 【0684】 Users generate visual or text data and upload it to the system using a terminal. The input is the digital data generated by the user, and the output is request data sent to the server. During this process, the terminal performs an initial check of the data format and transfers the data to the server in the appropriate format. A progress bar is displayed on the terminal to visualize the progress of the data transfer. 【0685】 Step 2: 【0686】 The server receives digital data sent from the user. The input is digital data from the user, and the output is data in a format that can be analyzed. The server checks whether the format of the received data is JPEG, PNG, or UTF-8 if it is text. If there are no problems, it temporarily stores the data as format-checked data and proceeds to the next analysis step. 【0687】 Step 3: 【0688】 The server performs analysis on stored data using machine learning models. The input is format-verified data, and the output is the analysis results. Pattern recognition techniques are used to detect unnatural or unrealistic elements within the data. During this process, the data is compared with existing information sets to check for matches and similarities. This data processing generates metadata as an analysis result. 【0689】 Step 4: 【0690】 The server activates the emotion analysis engine based on the analysis results. The inputs used are the user's facial expression data, text data, and the analysis results, and the output identifies the user's emotional state. The server utilizes natural language processing techniques to analyze emotional tone from the user's text. Simultaneously, it uses facial recognition technology to identify emotional characteristics from videos and images. 【0691】 Step 5: 【0692】 The server uses a generative AI model to create appropriate feedback based on the results of sentiment analysis. The input is the emotional state and analysis metadata, and the output is a supportive message for the user. For example, if the user is confused, the server generates a message containing encouragement and suggestions for improving the situation. This provides real-time support to the user. 【0693】 (Application Example 2) 【0694】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0695】 Traditional data analysis systems focused on issuing warnings about the appropriateness of generated information and whether copyright infringement occurred, but they did not consider providing feedback based on users' emotions. As a result, responses that ignored users' emotional reactions were provided, hindering the user's information exchange experience. Furthermore, there is a need for a system that can enhance users' sense of security and trust through flexible feedback that responds to their emotional state. 【0696】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0697】 In this invention, the server includes means for receiving generated informational data or linguistic data, means for analyzing the received data and detecting unnatural or unrealistic elements, and means for activating an emotion engine, identifying the user's emotions, and adapting the dialogue based on the identified emotions. This makes it possible to provide appropriate feedback that takes into account the user's emotional state. 【0698】 "Generated information data" refers to digital information in visual or text format created by the user through the generation AI. 【0699】 "Linguistic data" refers to text information based on natural language, used to express the user's intentions and emotions. 【0700】 An "emotion engine" is a device or program that identifies an emotional state based on user input and generates feedback accordingly. 【0701】 "Emotional state" refers to the user's psychological feelings and mood at a particular moment. 【0702】 "Adapting the dialogue" means changing the feedback and support messages provided according to the user's emotional state. 【0703】 "Information resources" refer to existing records and data stored in various digital formats that are used for comparison and analysis. 【0704】 A "commercial infrastructure" refers to an electronic platform or system used for trading goods or providing services. 【0705】 The system for realizing this invention operates through cooperation between a server and a user's terminal. The server receives information data or language data generated from the user's terminal and analyzes its content. The analysis includes software that uses a learning model to detect unnatural or unrealistic elements. Based on the analysis results, the server activates an emotion engine. The emotion engine identifies the user's emotional state, generates feedback according to the identified emotion, and sends it to the user. 【0706】 Software such as Amazon Web Services (AWS) sentiment analysis services and Google Cloud Natural Language API can be used to analyze emotional states. The feedback users receive is tailored to their emotional state; for example, a user feeling anxious will be provided with a reassuring message. 【0707】 For example, if a user enters "I'm worried about this purchase," the server analyzes this text and, based on the identified emotion of anxiety, returns feedback such as "Rest assured, this product comes with a money-back guarantee." Examples of prompts include "Please provide information to alleviate your concerns about this product." In this way, the system optimizes the user's information exchange experience and enables flexible responses tailored to their emotions. 【0708】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0709】 Step 1: 【0710】 The user uploads generated informational or linguistic data to the server using a device. The input is the visual or text data created by the user using a generated AI model, and the output is its transfer to the server. 【0711】 Step 2: 【0712】 The server analyzes the received data to verify its format and structure. This step determines whether the data is in the correct digital format and prepares it for appropriate data processing. The input is the received raw data, and the output is the well-formed data after format verification. The server then processes the data into a format suitable for analysis. 【0713】 Step 3: 【0714】 The server uses a machine learning model to detect unnatural or unrealistic elements in the data. The input is well-formed data, and the output is the analysis results regarding the unnatural elements. Specifically, it uses AWS and Google Cloud services to scrutinize the data. 【0715】 Step 4: 【0716】 The server activates the emotion engine based on the analysis results to identify the user's emotional state. The input is the analysis results, and the output is the identified emotional state. At this stage, the emotion analysis API is used to determine the emotions contained in the user's input. 【0717】 Step 5: 【0718】 Based on the emotional state identified by the emotion engine, the server generates appropriate feedback and sends it to the user. The input is the identified emotional state, and the output is a feedback message directed to the user. Specifically, it prepares a customized response according to the emotion and forwards the message to the user's terminal. 【0719】 The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0720】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0721】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0722】 Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion. 【0723】 Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together. 【0724】 These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression. 【0725】 The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become. 【0726】 Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant. 【0727】 The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more." 【0728】 The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values. 【0729】 The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format. 【0730】 In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data. 【0731】 In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56. 【0732】 Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12. 【0733】 Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56. 【0734】 The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory. 【0735】 The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor. 【0736】 Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources. 【0737】 Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose. 【0738】 The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above. 【0739】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference. 【0740】 The following is further disclosed regarding the embodiments described above. 【0741】 (Claim 1) 【0742】 A means for receiving generated visual or textual data, 【0743】 A means for analyzing the received data and detecting unnatural or unrealistic elements, 【0744】 Based on the analysis results, means for identifying matches or similarities by comparing them with existing digital data libraries or commercial platforms, 【0745】 A system that includes means of notifying the user and instructing them to correct or change the relevant data when a match or similarity is identified. 【0746】 (Claim 2) 【0747】 The system according to claim 1, which determines the file format of the received data and applies appropriate data processing. 【0748】 (Claim 3) 【0749】 The system according to claim 1, which uses a machine learning model to detect unnatural elements for analysis and comparison. 【0750】 "Example 1" 【0751】 (Claim 1) 【0752】 A means of sending the generated data from the terminal to the server, 【0753】 A means to analyze the structure of the data received by the server and verify the integrity of the information, 【0754】 A server analyzes the characteristics of the content and uses pattern recognition technology to detect unnatural elements, 【0755】 A means for comparing the analyzed data with existing information bases to identify matches or similarities, 【0756】 A means of notifying the user of the results and prompting them to correct or regenerate the data, 【0757】 A system that includes this. 【0758】 (Claim 2) 【0759】 The system according to claim 1, which determines the format of received data and changes the format as necessary. 【0760】 (Claim 3) 【0761】 The system according to claim 1, which uses machine learning techniques to detect discrepancies or anomalies based on analysis and comparison. 【0762】 "Application Example 1" 【0763】 (Claim 1) 【0764】 A means of receiving the generated content, 【0765】 A means for analyzing the received content and detecting unnatural or unrealistic characteristics, 【0766】 A means of identifying correspondences or similarities by comparing the analysis results with existing information databases or commercial platforms, 【0767】 If a match or similarity is identified, a means of notifying the user and instructing them to correct or change the relevant content, 【0768】 A system that includes means to provide immediate feedback on the legality of content before users upload it, and to give instructions for correction. 【0769】 (Claim 2) 【0770】 The system according to claim 1, which determines the format of the received content and applies appropriate processing. 【0771】 (Claim 3) 【0772】 The system according to claim 1, which uses a machine learning model to detect unnatural characteristics for analysis and comparison, and provides the user with specific correction suggestions. 【0773】 "Example 2 of combining an emotion engine" 【0774】 (Claim 1) 【0775】 A means of receiving the generated digital data, 【0776】 A means for analyzing the received information and detecting unnatural or unrealistic elements, 【0777】 A means of identifying matches or similarities based on the analysis results by comparing them with existing information sets or commercial infrastructure, 【0778】 Means for notifying users and instructing them to correct or change the relevant information when a match or similarity is identified, 【0779】 A means of analyzing user emotions from received information and generating appropriate feedback, 【0780】 A system that includes means for providing generated feedback to users. 【0781】 (Claim 2) 【0782】 The system according to claim 1, which determines the format of the received information and applies appropriate information processing. 【0783】 (Claim 3) 【0784】 The system according to claim 1, which uses a learning model to detect unnatural elements for analysis and comparison. 【0785】 "Application example 2 when combining with an emotional engine" 【0786】 (Claim 1) 【0787】 A means for receiving generated information data or language data, 【0788】 A means for analyzing the received data and detecting unnatural or unrealistic elements, 【0789】 A means of identifying similarities or similarities by comparing them with existing information resources or commercial infrastructure based on the analysis results, 【0790】 A means of notifying the user and instructing them to correct or change the relevant data if a match or similarity is identified, 【0791】 A means to activate an emotion engine, identify the user's emotions, and adapt the dialogue based on the identified emotions, 【0792】 A system that includes means to provide specific responses according to the user's emotional state and optimize the information exchange experience. 【0793】 (Claim 2) 【0794】 The system according to claim 1, which determines the digital format of the received data and applies appropriate information processing. 【0795】 (Claim 3) 【0796】 The system according to claim 1, which uses a learning model to detect unnatural elements for analysis and comparison. [Explanation of symbols] 【0797】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] A means for receiving generated visual or textual data, A means for analyzing the received data and detecting unnatural or unrealistic elements, Based on the analysis results, means for identifying matches or similarities by comparing them with existing digital data libraries or commercial platforms, A system that includes means of notifying the user and instructing them to correct or change the relevant data when a match or similarity is identified. [Claim 2] The system according to claim 1, which determines the file format of the received data and applies appropriate data processing. [Claim 3] The system according to claim 1, which uses a machine learning model to detect unnatural elements for analysis and comparison.

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