system

Abstract

Provide a system. [Solution] means for uploading photo or video data from a user terminal to a server; A means for analyzing the photo or video data stored in the server using a generative AI model to check for copyright and portrait rights issues; A means for automatically correcting photo or video data based on the analysis results of the generative AI model; A means to perform a final check of the corrected data using a regenerative AI model, and means for notifying a user of the final confirmation result; A means for sharing the final confirmed data with other systems through API linkage; A system including:

Description

[Technical Field]

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

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

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

[0004] The widespread use of digital equipment has led to an increase in the number of new video creators, regardless of age or gender. At the same time, however, the importance of copyright and portrait rights management is growing. Currently, this verification process is performed manually by agencies and communication is conducted using legacy methods, leaving freelancers and small- to medium-sized agencies seeking greater efficiency. However, these agencies face a shortage of IT personnel, making efficient management difficult. Therefore, this invention aims to use generative AI to automate the verification process of copyright and portrait rights, realizing an efficient and appropriate approval process and resolving the challenges faced by freelancers and small- to medium-sized agencies. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems with a system that includes a means for uploading photo or video data from a user device to a server, a means for analyzing the photo or video data stored on the server using a generative AI model to check for copyright and portrait rights issues, a means for automatically correcting the photo or video data based on the analysis results of the generative AI model, a means for performing a final check on the corrected data using the generative AI model again, a means for notifying the user of the final check results, and a means for sharing the final checked data with other systems via API integration.Furthermore, by including a means for notifying the user of the specific reason if the corrected data is not acceptable and requesting additional information or corrections, and a means for using the checked data on other platforms in real time via API integration, more flexible and efficient management is possible.

[0006] "User" means an individual or entity that uses the System to upload photo or video data and undergoes a review and approval process.

[0007] "Terminal" refers to an electronic device used by a user, such as a computer, smartphone, or tablet, and is a device used to access the system and upload data.

[0008] "Server" refers to the central processing unit that manages and operates the entire system, and is the infrastructure for processing data storage, analysis, notifications, and API integration.

[0009] A "generative AI model" refers to an artificial intelligence algorithm that performs image recognition, facial recognition, text recognition, etc., and is a program for analyzing and automatically correcting photo or video data.

[0010] "Photo or video data" refers to images, videos, and other multimedia files taken and uploaded by users.

[0011] An "analysis queue" is a list or array for ordering and waiting data in order to efficiently perform data analysis on the server.

[0012] "Copyright" refers to the legal right to protect creative works and the right to prevent their unauthorized use or distribution.

[0013] "Portrait rights" refers to the legal right to protect an individual's face or figure when it is photographed, used, or made public, and is a right to prevent unauthorized commercial use.

[0014] "Notification system" refers to the communication means used by the server to communicate analysis results and approval results to the user, including email, push notifications, dashboard alerts, etc.

[0015] "API integration" refers to an application programming interface that allows data to be automatically shared and linked between different systems, and is a technology that enables consistent data usage.

[0016] "Approval Process" refers to the series of procedures and decisions for final approval of data analyzed and corrected by a generative AI model. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] This invention relates to a system for efficiently and accurately analyzing and verifying photo or video data. Users upload their photographic data, and a server analyzes the data using a generative AI model, automatically verifying and correcting copyright and portrait rights. Furthermore, the system notifies users of the analysis results and uses an API to link verified data with other systems, enabling immediate use of the data.

[0039] Specifically, the system is constructed as follows:

[0040] Uploading photos and video data

[0041] Users can upload photos or video data taken from their own devices (e.g., PCs or smartphones) by accessing the system's upload page. All they need to do is select the desired file and click the upload button.

[0042] Data storage and analysis

[0043] The server receives the uploaded data and stores it in secure storage, while simultaneously adding it to the analysis queue and preparing it for analysis by the generative AI model.

[0044] Data analysis with generative AI models

[0045] The server passes the stored data to the generative AI model to begin analysis. The generative AI model first performs facial recognition to identify areas where portrait rights are involved. It also checks for copyright infringement through text recognition within the image. Based on the results of this analysis, it issues instructions for any necessary corrections (e.g., blurring faces, trimming unnecessary parts, etc.).

[0046] Auto-correct and double-check

[0047] The server automatically modifies the photo or video data based on instructions from the generative AI model. The modified data is then passed back to the generative AI model for final confirmation. This confirmation determines whether the data is acceptable.

[0048] Notification of approval results

[0049] The server notifies the user of the final analysis and verification results, such as "All data uploaded by the user has been approved" or "The following data needs to be edited due to portrait rights issues."

[0050] Utilizing data through API integration

[0051] The server then connects the approved data to other systems via APIs, allowing it to be instantly used on platforms such as e-commerce sites and social media. For example, verified photos can be displayed on product description pages for online shopping sites or used in advertising campaigns.

[0052] Specific examples

[0053] For example, a freelance photographer user uploads photos taken at an event to the system. The server analyzes the uploaded photos using a generative AI model to check for portrait rights and copyright issues. The generative AI model performs facial recognition to determine whether a specific person is in the photo and blurs the face if necessary. Once this process is complete, the server notifies the user of the results. The notification includes information on whether the photo is approved or requires correction. Finally, the approved data is linked to the e-commerce site via an API and used on the product page.

[0054] In this manner, the present invention provides a user with an efficient and hassle-free process for reviewing and approving photo and video data.

[0055] The processing flow will be explained below.

[0056] Step 1:

[0057] Users access the system's upload page using a device (e.g., PC or smartphone) to select the files they have taken, and then click the upload button to send the data to the system.

[0058] Step 2:

[0059] The terminal generates a request to upload the selected photo or video data to the platform server and transmits the data.

[0060] Step 3:

[0061] The server receives the uploaded data and stores it in secure storage, while simultaneously adding it to an analysis queue, ready for analysis by the generative AI model.

[0062] Step 4:

[0063] The server then passes the data added to the analysis queue to the generative AI model, which performs facial and text recognition on the photos and video data to identify issues with portrait rights and copyright.

[0064] Step 5:

[0065] Based on the analysis results, the generative AI model generates instructions for automatically correcting photos and video data, including blurring faces and cropping unwanted areas.

[0066] Step 6:

[0067] The server receives instructions from the generative AI model and automatically corrects the photo and video data using its internal image processing tools. The corrected data is then passed back to the generative AI model for final confirmation.

[0068] Step 7:

[0069] The generative AI model then re-analyzes the corrected data and performs a final check, which determines whether the data is acceptable.

[0070] Step 8:

[0071] The server collects the final confirmation results and notifies the user. Approved data is marked as "approved" and linked to the marketplace and content delivery system. Data that cannot be approved is fed back to the user with specific reasons.

[0072] Step 9:

[0073] The notification system will inform the user of the results of the check in natural language, for example, sending messages such as "All data uploaded by the customer has been approved" or "The following data needs to be edited due to portrait rights issues."

[0074] Step 10:

[0075] The server then uses an API to connect the verified data to other systems, allowing it to be immediately used on platforms such as e-commerce sites and social media.

[0076] This allows users to have an efficient and accurate photo and video data review and approval process.

[0077] Example 1

[0078] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0079] Efficiently and accurately analyzing photo and video data online and automatically checking and correcting copyright and portrait rights issues is time-consuming and labor-intensive if done manually. Therefore, there is a need to provide a system that automates this work and facilitates the immediate use of data.

[0080] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0081] In this invention, the server includes a means for uploading photo or video data from a user terminal to the server, a means for analyzing the photo or video data stored on the server using a generative AI model to check for copyright and portrait rights issues, a means for automatically correcting the photo or video data based on the analysis results of the generative AI model, a means for performing a final check on the corrected data using the generative AI model again, a means for notifying the user of the final check results, a means for sharing the final checked data with other systems via API integration, a means for storing the data in secure storage, a means for using an analysis queue in the generative AI model, and a means for performing facial recognition and text recognition. This enables accurate and efficient analysis, automatic correction, and checking of photo and video data. It also promotes the immediate use of checked data.

[0082] "Photographic or video data" refers to visual information recorded in the form of an image file or video file.

[0083] "User terminal" refers to an electronic device, such as a computer, smartphone, or tablet, that a user uses to perform operations.

[0084] A "server" refers to a computer system that provides services and data over a network.

[0085] A "generative AI model" refers to an algorithm that has been trained to perform a specific task (e.g., image recognition or text analysis) using machine learning or artificial intelligence techniques.

[0086] "Upload means" refers to the function or process for sending data from a user terminal to a server.

[0087] "Storage" refers to a mechanism or system (e.g., cloud storage) for secure and persistent retention of data.

[0088] An "analysis queue" refers to a data structure or system that queues data to be analyzed in order and waits for processing.

[0089] "Facial recognition" refers to the technology and processes used to detect and identify human faces within image data.

[0090] "Text recognition" refers to the technology and process of extracting character information from image data and processing it as digital text.

[0091] "Automatic correction measures" refers to functions and processes for automatically modifying or improving data based on the analysis results of a generative AI model.

[0092] "Reconfirmation means" refers to the functions and processes for reconfirming the corrected data and giving final approval.

[0093] "Notification means" refers to the functions and processes for transmitting information such as analysis results and the need for corrections from the system to the user.

[0094] "API integration means" refers to the interfaces and processes for exchanging data and functions between different software systems.

[0095] MODE FOR CARRYING OUT THE INVENTION

[0096] This invention relates to a system that efficiently and accurately analyzes photo or video data and automatically checks and corrects copyrights and portrait rights. In this system, users upload their photographic data, and a server analyzes the data using a generative AI model. The system then corrects the data based on the analysis results, and notifies the user after final confirmation. Approved data can also be linked to other systems via an API.

[0097] Data upload and storage

[0098] Users access the system's upload page to upload photos or video data taken from their own devices (PCs, smartphones, etc.), select the files, and click the upload button. The uploaded data is received by the server and stored in secure storage. This storage process uses cloud storage such as Amazon S3.

[0099] Data analysis with generative AI models

[0100] The server adds the stored data to an analysis queue and prepares it for analysis using a generative AI model. Using the analysis queue, the data is analyzed sequentially. The generative AI model performs facial and text recognition and checks for portrait and copyright issues. This analysis uses, for example, Google® Cloud Vision API and Tesseract OCR.

[0101] Auto-correct and re-check

[0102] Based on the analysis results, the server issues instructions for automatic correction of the photo or video data. For example, OpenCV is used to blur faces or trim unwanted parts. The corrected data is then checked again by the generative AI model. This recheck determines whether the corrections were made appropriately.

[0103] User notification and API integration

[0104] The final review result is sent to the user via email or in-app notification. The notification will include information on whether the photo was approved or if any corrections are required. The approved data is shared with other systems via API integration via the server. This integration allows the photo to be used immediately, for example, in product description pages on e-commerce sites or in advertising campaigns.

[0105] Specific examples

[0106] For example, consider the case where a freelance photographer uploads photos taken at an event to the system. The server analyzes the uploaded photos using a generative AI model to check for issues with portrait rights and copyright. The generative AI model performs facial recognition to confirm whether specific people are in the photos and blurs their faces if necessary. After corrections are made, the server notifies the user of the results of the check. The notification may include a message such as "Photo A has been approved" or "Photo B has a portrait rights issue and needs to be corrected." Finally, the approved data is linked to the e-commerce site via an API and used on the product page.

[0107] Prompt Sentence Examples

[0108] Examples of prompts for generative AI models include:

[0109] "Detect all faces in this photo and apply blur according to specific criteria."

[0110] In this way, a system can be provided that enables accurate and efficient analysis, automatic correction, and confirmation of photographic and video data, as well as immediate utilization of the data.

[0111] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0112] Step 1: Upload step

[0113] Users access the system's upload page to upload photos or video data taken from their own device (PC, smartphone, etc.) and click the upload button.

[0114] Input: Photo or video data from the user's device

[0115] Output: Upload data to the server

[0116] Specific operation: The user accesses the upload page of the system using a browser, selects the data file, and clicks the upload button. The system receives the selected file and displays a message on the screen indicating that the upload was successful.

[0117] Step 2: Data storage step

[0118] The server receives the uploaded data and stores it in secure storage (e.g., Amazon S3).

[0119] Input: Photo or video data uploaded by the user

[0120] Output: Data file in cloud storage

[0121] Specific operation: The server converts the received data into a certain format and transfers it to the cloud storage using a communication protocol (e.g., HTTPS). Once the data has been saved in the storage, it returns a status indicating that the data has been saved to the system.

[0122] Step 3: Analysis preparation steps

[0123] The server adds the stored data to an analysis queue and prepares it for analysis by the generative AI model.

[0124] Input: Data files stored in cloud storage

[0125] Output: File paths and metadata in the analysis queue

[0126] Specific operation: The server adds the file path in the storage and related metadata to the analysis queue. The data in the analysis queue is processed sequentially according to priority.

[0127] Step 4: Data analysis step

[0128] The server then passes the stored data to a generative AI model for analysis, which performs facial and text recognition and detects portrait and copyright issues.

[0129] Input: Data files from the analysis queue

[0130] Output: Face recognition results, text recognition results, correction instructions

[0131] How it works: The server takes data from the analysis queue and passes it to a generative AI model (e.g., Google Cloud Vision API or Tesseract OCR). The generative AI model detects faces and text and returns correction instructions based on those to the server.

[0132] Step 5: Auto-correction step

[0133] The server automatically modifies the photo or video data based on the analysis results, for example by blurring faces or trimming unwanted parts.

[0134] Input: Correction instructions returned by the generative AI model

[0135] Output: Modified data file

[0136] Specific operation: Based on the correction instructions, the server uses an image processing library such as OpenCV to perform specific corrections. For example, it performs processing such as blurring or cropping the face. The corrected data is temporarily saved.

[0137] Step 6: Reconfirmation step

[0138] The server then passes the corrected data back to the generative AI model for a final check, determining whether the corrections were made appropriately.

[0139] Input: Modified data file

[0140] Output: Final confirmation result (approval or rejection)

[0141] Specific operation: The server sends the corrected data to the generative AI model again for reanalysis. The generative AI model performs a final check to see if the corrections were made properly and returns the results to the server.

[0142] Step 7: User Notification Step

[0143] The server will send the final confirmation result to the user via email or in-app notification.

[0144] Input: Final confirmation result

[0145] Output: User notification (email or in-app message)

[0146] Specific behavior: The server receives the final review result and notifies the user, including whether the photo is approved or needs corrections.

[0147] Step 8: API integration step

[0148] The approved data is then shared with other systems via API by the server.

[0149] Input: Final confirmed data

[0150] Output: Use of data in other systems (e.g., product page on an e-commerce site)

[0151] What it does: The server sends the approved data to other systems via a RESTful API. This integration allows, for example, the approved photos to be used on product pages on online shopping sites.

[0152] In this way, specific processing is performed at each step, enabling efficient and accurate analysis and confirmation of photo and video data across the entire system.

[0153] (Application example 1)

[0154] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0155] For modern advertising creators and marketers, the process of checking and correcting portrait rights and copyrights in photographs and video data is extremely tedious and time-consuming. Performing this process manually increases the risk of human error and reduces efficiency. Furthermore, it is difficult to utilize corrected data in advertising campaigns in real time, hindering the rapid implementation of marketing strategies. The present invention aims to solve these problems by providing a system that efficiently and accurately analyzes and corrects data and utilizes it in advertising campaigns in real time.

[0156] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0157] In this invention, the server includes means for uploading photo or video data from a user terminal to the server, means for analyzing the photo or video data stored on the server using a generative AI model and checking for copyright and portrait rights issues, means for automatically correcting the photo or video data based on the analysis results of the generative AI model, means for performing a final check of the corrected data using the generative AI model again, means for notifying the user of the final check results, means for sharing the final checked data with other systems via API integration, means for analyzing data taken by advertising creators and marketers and checking and correcting portrait rights and copyrights, and means for utilizing the corrected data in advertising campaigns in real time. This enables efficient and rapid analysis and correction of data, allowing it to be immediately utilized in advertising campaigns.

[0158] "Photo or video data" refers to digital information in the form of still images and videos taken or generated by a user.

[0159] "User terminal" refers to an electronic device such as a computer, smartphone, or tablet that is used to upload photo or video data to a server.

[0160] A "server" is a central control device that receives data from user terminals and processes it, such as storing, analyzing, and modifying.

[0161] A "generative AI model" is a system that uses machine learning algorithms to analyze images and videos and automatically check and correct copyright and portrait rights.

[0162] "Means for uploading" refers to the functions and processes that allow a user to send photo or video data from their own device to the server.

[0163] "Means of analysis" refers to the process of processing data using a generative AI model to perform copyright and image rights checks.

[0164] "Means for automatic correction" refers to a function that automatically applies necessary corrections to data based on the analysis results.

[0165] "Final verification means" refers to the process of re-analyzing the automatically corrected data using a regenerative AI model to finally verify the accuracy and legality of the data.

[0166] "Means for notifying" refers to a communication function for notifying the user of the final confirmation result, and may include notification via an interface.

[0167] "API integration and sharing" refers to standardized protocols and interfaces for connecting and exchanging data with other computer systems and platforms.

[0168] "Advertising creator" refers to an individual or organization that produces photographs and videos for advertising and carries out marketing activities.

[0169] "Marketer" refers to an individual or organization whose role is to plan, execute, and manage marketing activities.

[0170] "Means that can be used in advertising campaigns in real time" refers to the ability to immediately incorporate data that has been analyzed and corrected into advertising strategies.

[0171] This system enables the uploading of photo and video data, analysis, automatic correction, final confirmation, notification, and API integration. This system is designed specifically to enable advertising creators and marketers to effectively and quickly manage data and utilize it in real-time advertising campaigns.

[0172] System Overview

[0173] The system consists of the following main components:

[0174] 1. User device (PC, smartphone, tablet, etc.)

[0175] 2. Server

[0176] 3. Generative AI Models

[0177] 4. Storage services (e.g., Firebase, Amazon S3)

[0178] 5. Message queue systems (e.g. RabbitMQ)

[0179] 6. Image processing libraries (e.g. OpenCV, dlib, Pillow)

[0180] 7. Notification systems (e.g., Twilio API, Firebase Cloud Messaging)

[0181] 8. API integration platform (e.g. RESTful API)

[0182] Natural language explanation of program processing

[0183] 1. Data upload and storage:

[0184] Photo or video data is uploaded from the user's device to the server, which receives it and stores it securely in a storage service such as Firebase or Amazon S3.

[0185] 2. Analysis preparation and queue management:

[0186] The server adds the uploaded data to a message queue system such as RabbitMQ and prepares it for analysis by the generative AI model.

[0187] 3. Analysis by generative AI model:

[0188] The server uses the generative AI model to perform the following analysis:

[0189] Facial Recognition: Use OpenCV and dlib to recognize faces in photos and videos and verify portrait rights.

[0190] Text Recognition: Use Tesseract OCR to recognize text in images and perform copyright verification.

[0191] 4. Autocorrect:

[0192] Based on the analysis results of the generative AI model, the server uses an image processing library such as Pillow to automatically make any necessary corrections (e.g., blurring faces, trimming unnecessary parts, etc.).

[0193] 5. Double check:

[0194] The corrected data is analyzed again by the generated AI model for final confirmation.

[0195] 6. Result notification:

[0196] The server uses the Twilio API or Firebase Cloud Messaging to notify the user of the final validation result, including whether the data was approved or if further corrections are required.

[0197] 7. API Integration:

[0198] Approved data is shared in real time with advertising platforms (e.g., Facebook Ads, Google Ads) via RESTful APIs.

[0199] Examples of concrete examples and prompts

[0200] For example, let's consider the case where an advertising creator shoots a promotional video for a product and uploads it to the system. The server receives the uploaded data and analyzes portrait rights and copyright using a generative AI model. Based on the analysis results, necessary corrections are automatically made, and after final confirmation, the results are notified to the user. This data is connected to the API in real time and immediately used in advertising campaigns.

[0201] Prompt Sentence Examples

[0202] User: Upload a product photo.

[0203] System: Initiate facial recognition and portrait rights checks.

[0204] System: Begins recognizing text in images.

[0205] System: Blurring face. Cropping image.

[0206] System: Final checks underway.

[0207] System: Analysis and corrections completed. Data is ready for advertising campaigns.

[0208] In this way, the present invention provides advertising creators and marketers with efficient and accurate data analysis and real-time utilization.

[0209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0210] Step 1:

[0211] Uploading data

[0212] Users upload photos or video data from their devices. They select the desired files from their devices and send them via the system's upload page. The server receives this data and stores it in secure storage such as Firebase or Amazon S3.

[0213] Input: Photo or video data selected by the user from their device

[0214] Output: Uploaded data stored on the server

[0215] Step 2:

[0216] Analysis preparation and queue management

[0217] The server adds the uploaded data to a message queue system such as RabbitMQ, which prepares the data for analysis tasks and sends it to a generative AI model for analysis.

[0218] Input: Upload data stored on the server

[0219] Output: Data added to the analysis queue

[0220] Step 3:

[0221] Analysis using generative AI models

[0222] The server analyzes the uploaded data using a generative AI model. First, it uses OpenCV and dlib for facial recognition to identify areas where image rights are involved. Second, it uses Tesseract OCR to recognize text within the image and verify copyright.

[0223] Input: Data added to the analysis queue

[0224] Output: Face and text recognition analysis results

[0225] Step 4:

[0226] autocorrect

[0227] Based on the results of the generative AI model's analysis, the server uses image processing libraries such as Pillow to make any necessary corrections, such as adding blur to recognized faces or cropping out unwanted parts.

[0228] Input: Face and text recognition analysis results

[0229] Output: Modified photo or video data

[0230] Step 5:

[0231] Reconfirmation

[0232] The server then analyzes the corrected data again using the generated AI model and performs a final check to ensure the corrections were made correctly and that there are no omissions.

[0233] Input: Modified photo or video data

[0234] Output: Final verified data

[0235] Step 6:

[0236] Result notification

[0237] The server notifies the user of the final validation result using the Twilio API or Firebase Cloud Messaging, including whether the data is approved or requires further correction.

[0238] Input: Final verified data

[0239] Output: A message to inform the user

[0240] Step 7:

[0241] API Integration

[0242] Approved data is shared in real time with advertising platforms (e.g., Facebook Ads, Google Ads) using a RESTful API, allowing advertisers and marketers to instantly use the approved data in their campaigns.

[0243] Input: Approved photo or video data

[0244] Output: Data linked to advertising platform

[0245] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0246] This invention relates to a system for efficiently verifying and correcting copyrights and portrait rights in photo or video data. This system analyzes data uploaded by users using a generative AI model and automatically performs necessary corrections. In addition, by combining it with an emotion engine that recognizes the user's emotions, the system appropriately adjusts notifications and feedback of the analysis results, improving the user experience.

[0247] Uploading photos and video data

[0248] Users access the system's upload page from their own devices (e.g., PCs or smartphones), select the photos or video data they have taken, and upload them. After selecting the files, users send the data by clicking the upload button.

[0249] Data storage and analysis

[0250] The server receives the uploaded data, stores it in storage, and adds it to the analysis queue to prepare it for analysis by the generative AI model.

[0251] Data analysis with generative AI models

[0252] The server passes the data from the analysis queue to the generative AI model, which then begins the analysis process. The generative AI model performs facial and text recognition on the photos and video data to identify issues with portrait rights and copyright. This allows it to extract problematic parts related to copyright and portrait rights.

[0253] Auto-correct and double-check

[0254] The server automatically corrects the photo and video data based on instructions from the generative AI model. Specifically, this includes blurring faces and trimming unnecessary parts. The corrected data is then passed back to the generative AI model for final confirmation.

[0255] Emotional Engine Adjustment

[0256] The emotion engine recognizes the user's emotions in real time and adjusts the analysis results and notifications and feedback appropriately. For example, if the user is feeling stressed, the server will send notifications in a gentler tone. If the user gives positive feedback to the system, the emotion engine uses it to improve the content of future notifications and the accuracy of analysis.

[0257] Notification of approval results

[0258] The server aggregates the final analysis and verification results and notifies the user through the emotion engine. For example, when notifying the user that "all of your uploaded data has been approved," the emotion engine adjusts the wording as necessary.

[0259] Utilizing data through API integration

[0260] The approved data is then linked to other systems via API by the server. This allows the verified data to be immediately used on platforms such as e-commerce sites and social media. For example, verified photos can be displayed on product description pages in online shops or used as material for advertising campaigns.

[0261] Specific examples

[0262] For example, a freelance photographer user can upload photos taken at an event to the system. The server then analyzes the uploaded photos using a generative AI model to check for portrait rights and copyright issues. Based on the analysis results, any necessary face blurring or cropping is automatically performed. The emotion engine then analyzes the user's reaction and provides optimal feedback. Finally, the verified data is linked to the e-commerce site and used as material for product pages.

[0263] In this manner, the present invention provides users with an efficient and flexible photo and video data review, correction and approval process.

[0264] The processing flow will be explained below.

[0265] Step 1:

[0266] Users access the system's upload page from their own device (e.g., PC, smartphone), select the photos or video data they have taken, and then click the upload button to send the data to the system.

[0267] Step 2:

[0268] The terminal generates a request to upload the selected data to the server and transmits the data to the server.

[0269] Step 3:

[0270] The server receives the uploaded photo or video data and stores it in secure storage, checking for duplicate or corrupted data during the process.

[0271] Step 4:

[0272] The server adds the saved data to the analysis queue, preparing it for analysis by the generative AI model, and logs that the data has been successfully added to the analysis queue.

[0273] Step 5:

[0274] The server passes the data from the analysis queue to the generative AI model, which performs facial and text recognition on the photos and video data to identify any issues with portrait rights or copyright.

[0275] Step 6:

[0276] The generative AI model automatically corrects photos and video data based on the analysis results. This correction includes blurring faces and trimming unwanted parts. The correction process is performed by an image processing tool on the server side.

[0277] Step 7:

[0278] The server passes the corrected data back to the generative AI model for final validation, which then re-analyzes the corrected data to ensure all issues have been resolved.

[0279] Step 8:

[0280] The emotion engine analyzes the user's emotional state and adjusts the content and method of notification of the analysis results. The emotion engine analyzes the user's real-time emotional data (e.g., facial expressions, tone of voice).

[0281] Step 9:

[0282] The server aggregates the final confirmation results and notifies the user through the emotion engine. For example, the message "All your uploaded data has been approved" can be softened depending on the emotion, improving the user experience.

[0283] Step 10:

[0284] The server then connects the final verified data to other systems via API. This connection makes the verified data instantly available on platforms such as e-commerce sites and social media. For example, photos can be automatically uploaded to product description pages on e-commerce sites.

[0285] Step 11:

[0286] The notification system sends notifications to users after API integration to let them know how their data is being used. For example, it notifies users by sending a notification that "your customer photo has been uploaded to the product description page."

[0287] Through this process flow, the present invention provides users with an efficient and flexible process for reviewing, correcting, and approving photo and video data.

[0288] Example 2

[0289] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0290] Conventional image data confirmation and correction systems have the problem of requiring a lot of manual work, which is time-consuming and labor-intensive. Furthermore, the accuracy of confirmation and correction tends to vary, making it difficult to improve the user experience. Furthermore, since the final confirmed data is not linked to other systems in real time, it is difficult to immediately utilize the data. There is a demand for a system that solves these issues.

[0291] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for uploading image data from a user terminal to the server; means for analyzing the image data stored in the server using a generative AI model and checking for issues with intellectual property rights and personal rights; means for automatically correcting the image data based on the analysis results of the generative AI model; means for finally checking the corrected data again using the generative AI model; means for notifying the user of the final check results; means for sharing the finally checked data with other information processing systems through API collaboration; and means including an emotion engine that recognizes user emotions in real time and adjusts notification of the analysis results. This enables the efficiency and accuracy of checking and correcting image data to be improved, and further enables an improved user experience and rapid use of the finally checked data.

[0292] "User terminal" refers to a computing device used by an individual, including a PC or smartphone.

[0293] A "server" is a computing system that provides services over a network and stores and processes data.

[0294] "Image data" is digital data of photographs or videos, and is composed of a series of pixel information.

[0295] A "generative AI model" is a data analysis model that uses artificial intelligence technology and analyzes image data using machine learning algorithms.

[0296] "Analysis" refers to the process of examining the contents of data in detail to find specific information or problems.

[0297] "Intellectual property rights" means ownership rights in creative ideas and information that are protected by law, including copyrights and patents.

[0298] "Personal rights" refer to rights that belong to individuals, such as privacy and portrait rights.

[0299] "Automatic correction" means that the system autonomously corrects errors or problems in the data, including blurring or cropping images.

[0300] "Final confirmation" is the process of finally confirming that the analyzed and corrected data is accurate and has no problems.

[0301] "Notification" refers to the act of transmitting information from the system to the user, and is done in the form of email, pop-up, etc.

[0302] "API integration" refers to integration using an application programming interface to share data and functions between different information processing systems.

[0303] The "emotion engine" is a component that analyzes the user's emotional state in real time and adjusts the system's response.

[0304] "Real-time" refers to data processing and communication occurring instantly, with almost no delay.

[0305] The present invention relates to a system for efficiently verifying and correcting intellectual property and personal rights in image data. The system allows users to upload image data from their devices to a server, which then uses a generative AI model to analyze, correct, and finally verify the image data.

[0306] Specifically, the system uses the following hardware and software:

[0307] 1. User terminal: A device such as a desktop computer, laptop, or smartphone that uploads image data.

[0308] 2. Server: A high-performance computing system that stores, analyzes, modifies, and checks data. The server can be a cloud service such as Amazon Web Services (AWS®).

[0309] 3. Generative AI Models: Machine learning models for analyzing image data, built using frameworks such as TENSORFLOW®, that perform facial and text recognition and identify intellectual property and personal rights issues.

[0310] 4. Image processing software: Use libraries such as OpenCV to automatically correct problem areas.

[0311] 5. Emotion Engine: Recognizes the user's emotions in real time and adjusts the notification content based on the analysis results. Specifically, this is achieved by using facial expression recognition APIs and voice analysis software.

[0312] As a concrete example of how the system works, consider a scenario in which a freelance photographer uploads photos taken at an event to the system. The photos uploaded from the user's device are received by the server, stored in storage, and added to the analysis queue. The server then analyzes the photos using a generative AI model, performing facial and text recognition and checking for intellectual property and personal rights issues. Based on the analysis results, the server automatically blurs the face and crops out unnecessary parts. The corrected data is then passed back to the generative AI model for final review.

[0313] The emotion engine recognizes the user's real-time emotions, and if the user is feeling stressed, for example, the server adjusts the tone of the notification to be gentler. The final confirmation result is notified to the user via the emotion engine, and the approved data is shared in real time by the server with other information processing systems via API.

[0314] An example of a prompt might be, "Please explain the process by which a freelance photographer takes photos at an event, uploads them to a system, and automatically checks and corrects copyright and portrait rights."

[0315] In this manner, the present invention provides users with an efficient and flexible image data review, correction and approval process.

[0316] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0317] Step 1:

[0318] The user accesses the upload page of the system using their own terminal, selects image data, and uploads it. When the user selects a file and clicks the "Upload" button, the terminal sends the image data to the server via an HTTP request.

[0319] Input: Image data file path

[0320] Output: HTTP request to the server

[0321] Step 2:

[0322] The server receives the HTTP request and saves the uploaded image data to storage. At the same time, it adds the data to the analysis queue and prepares it for analysis. The server extracts the image data from the request and saves it in the specified directory.

[0323] Input: Image data via HTTP request

[0324] Output: Updates the path of the image data stored in storage and the analysis queue.

[0325] Step 3:

[0326] The server retrieves the next image data from the analysis queue and passes it to the generative AI model, which performs facial and text recognition and identifies intellectual property and personal rights issues.

[0327] Input: Path to the image data saved in storage

[0328] Output: Analysis results by the generative AI model (location and content of the problem area)

[0329] Step 4:

[0330] Based on the analysis results of the generative AI model, the server uses image processing software to automatically correct the image, specifically by blurring problem areas and trimming unnecessary parts.

[0331] Input: Analysis results of the generative AI model

[0332] Output: Corrected image data

[0333] Step 5:

[0334] The server then passes the corrected image data back to the generative AI model for final confirmation, which then rechecks the corrections and confirms that the problem has been resolved.

[0335] Input: Corrected image data

[0336] Output: Final confirmation result

[0337] Step 6:

[0338] The emotion engine recognizes the user's emotions in real time and adjusts the content of notifications sent from the server. For example, if the user is feeling stressed, the notification will be softened.

[0339] Input: Real-time emotional state of the user (facial expression data, voice data)

[0340] Output: Adjusted notification text

[0341] Step 7:

[0342] The server aggregates the final confirmation results and notifies the user through the emotion engine, for example, by sending a notification such as "All of your uploaded data has been approved."

[0343] Input: Final confirmation results and emotion engine input

[0344] Output: User notification

[0345] Step 8:

[0346] The server shares the final confirmed data with other information processing systems via API integration, allowing the confirmed data to be used in real time on e-commerce sites and social media.

[0347] Input: Final confirmed data

[0348] Output: Data transferred to other systems via API

[0349] In this way, the system efficiently and accurately reviews, corrects, and approves image data.

[0350] (Application example 2)

[0351] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0352] The advertising industry requires the ability to quickly and efficiently check and correct copyright and portrait rights issues in photo and video data. However, doing this manually is time-consuming, labor-intensive, and prone to errors. Furthermore, providing feedback without considering user emotions can lead to a poor user experience and lower satisfaction with the service. To solve these issues, a system is needed that combines automatic data analysis and correction with feedback based on user emotions.

[0353] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0354] In this invention, the server includes: means for uploading photo or video data from a user terminal to the server; means for analyzing the photo or video data stored on the server using a generative AI model to check for copyright and portrait rights issues; means for automatically correcting the photo or video data based on the analysis results of the generative AI model; means for performing a final check of the corrected data using the generative AI model again; means including an emotion engine for recognizing user emotions in real time and appropriately adjusting notifications and feedback of the analysis results; means for notifying the user of the results of the final check; and means for sharing the final checked data with other systems via API integration. This makes it possible to quickly and accurately resolve copyright and portrait rights issues in advertising materials and improve the user experience.

[0355] "Photo or video data" refers to digital files in the form of still images or video images taken by a user using a terminal.

[0356] A "user terminal" is an electronic device that a user can operate by connecting to the Internet, such as a personal computer, smartphone, or tablet.

[0357] A "server" is a computer system connected to a network that stores, processes, and distributes data.

[0358] A "generative AI model" is an algorithm or system that uses artificial intelligence techniques to analyze and generate data.

[0359] "Copyright" is the right of the creator or copyright holder of a work to exclusively exploit that work.

[0360] "Portrait rights" are an individual's right to prevent images of their face or figure from being published or used without their permission.

[0361] An "emotion engine" is a system that recognizes and analyzes a user's emotional state in real time and adjusts responses accordingly.

[0362] "API integration" is a method of using an application programming interface (API) to exchange data between different software and systems and integrate functions.

[0363] "Feedback" refers to response information such as analysis results, notifications, and evaluations that the system provides to the user.

[0364] "Analysis" is the process of analyzing data and clarifying its content and characteristics.

[0365] The system of this invention is composed of a user terminal, a server, a generative AI model, an emotion engine, and API integration.

[0366] User terminal

[0367] The user terminal is an electronic device such as a personal computer, smartphone, or tablet, which the user can use to take photos or video data and upload them to the server. The user selects the photos or video data they have taken and accesses a dedicated upload page to send the data.

[0368] server

[0369] The server is connected to the network and provides many functions such as storing, analyzing, modifying, notifying, and API integration of photo or video data. The server uses the following specific hardware and software:

[0370] Server storage: Amazon S3

[0371] Data analysis: OpenCV, TensorFlow

[0372] Sentiment analysis: Microsoft® Azure® Emotion API

[0373] The data received by the server is stored in storage and added to the analysis queue.

[0374] Generative AI Models

[0375] The generative AI model analyzes photo and video data stored on a server, performs facial and text recognition within the data, and identifies copyright and portrait rights issues. Based on the analysis results, automatic corrections are performed, such as blurring faces and cropping unwanted parts.

[0376] Emotion Engine

[0377] The emotion engine recognizes the user's emotional state in real time and adjusts the analysis results and notifications and feedback. This improves the user experience by sending notifications in a gentler tone if the user is feeling stressed. In addition, if the user gives positive feedback, the emotion engine uses it to improve the content of future notifications and the accuracy of analysis.

[0378] API Integration

[0379] Approved data can be linked to other systems via APIs, allowing it to be immediately used on e-commerce sites and social media platforms. For example, verified photos can be used on product pages in online shops or as material for advertising campaigns.

[0380] Specific examples

[0381] As a concrete example, consider the case where an advertising creator uploads a photo taken at a local tourist spot to this system. The server analyzes the uploaded photo using a generative AI model to check for portrait rights and copyright issues. Based on the analysis results, faces are automatically blurred or cropped. The emotion engine then analyzes the user's reaction and provides appropriate feedback. For example, a prompt such as "Please recognize all faces in this image, check for portrait rights issues, and blur them if necessary. Also, check for objects that may be copyrighted and crop them if necessary" could be input into the generative AI model.

[0382] In this way, the present invention can quickly and accurately resolve copyright and portrait rights issues of advertising materials and improve user experience.

[0383] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0384] Step 1:

[0385] The user takes a photo or video data and uploads it to the server from the user's device. The user selects the file specified and sends it to the server via the upload page. The photo or video data file is required as input, and the data is saved on the server as output. The specific operation is when the user clicks the upload button.

[0386] Step 2:

[0387] The server saves the received data in storage and adds it to the analysis queue at the same time. It takes the received photo or video data as input, stores it in the destination storage as output, and adds a task to the analysis queue. Specifically, it saves the data in Amazon S3 and adds an analysis task to the queue.

[0388] Step 3:

[0389] The server retrieves data from the analysis queue and passes it to the generative AI model to begin analysis. The input is the photo or video data retrieved from the analysis queue, and the output is the analysis results. Specific operations include facial recognition and text recognition using OpenCV and TensorFlow.

[0390] Step 4:

[0391] The server automatically corrects the photo or video data based on the analysis results of the generative AI model. The analysis results are taken as input, and corrected data is generated as output. Specific operations include blurring the face area and trimming unnecessary parts.

[0392] Step 5:

[0393] The server then passes the corrected data back to the generative AI model for final confirmation. The corrected photo or video data is the input, and the final confirmation result is generated as the output. Specifically, final face and text recognition is performed again.

[0394] Step 6:

[0395] The server uses an emotion engine to recognize the user's emotional state in real time and adjusts the analysis results and feedback accordingly. The input is the user's emotional data and the final confirmation result, and the output is an adjusted feedback message. Specifically, emotion analysis is performed using the Microsoft Azure Emotion API.

[0396] Step 7:

[0397] The server notifies the user of the final confirmation result. The input is the adjusted feedback message, and the output is a notification sent to the user. The specific operation is to send the notification message to the user's device via push notification or email.

[0398] Step 8:

[0399] The server shares the final confirmed data with other systems via API. The confirmed data is input, and the output is immediately available on platforms such as e-commerce sites and social media. Specifically, the confirmed data is sent to other systems via API.

[0400] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0401] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search<url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0402] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0403] [Second embodiment]

[0404] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0405] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0406] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0407] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0408] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0409] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0410] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0411] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0412] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0413] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0414] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0415] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0416] This invention relates to a system for efficiently and accurately analyzing and verifying photo or video data. Users upload their photographic data, and a server analyzes the data using a generative AI model, automatically verifying and correcting copyright and portrait rights. Furthermore, the system notifies users of the analysis results and uses an API to link verified data with other systems, enabling immediate use of the data.

[0417] Specifically, the system is constructed as follows:

[0418] Uploading photos and video data

[0419] Users can upload photos or video data taken from their own devices (e.g., PCs or smartphones) by accessing the system's upload page. All they need to do is select the desired file and click the upload button.

[0420] Data storage and analysis

[0421] The server receives the uploaded data and stores it in secure storage, while simultaneously adding it to the analysis queue and preparing it for analysis by the generative AI model.

[0422] Data analysis with generative AI models

[0423] The server passes the stored data to the generative AI model to begin analysis. The generative AI model first performs facial recognition to identify areas where portrait rights are involved. It also checks for copyright infringement through text recognition within the image. Based on the results of this analysis, it issues instructions for any necessary corrections (e.g., blurring faces, trimming unnecessary parts, etc.).

[0424] Auto-correct and double-check

[0425] The server automatically modifies the photo or video data based on instructions from the generative AI model. The modified data is then passed back to the generative AI model for final confirmation. This confirmation determines whether the data is acceptable.

[0426] Notification of approval results

[0427] The server notifies the user of the final analysis and verification results, such as "All data uploaded by the user has been approved" or "The following data needs to be edited due to portrait rights issues."

[0428] Utilizing data through API integration

[0429] The server then connects the approved data to other systems via APIs, allowing it to be instantly used on platforms such as e-commerce sites and social media. For example, verified photos can be displayed on product description pages for online shopping sites or used in advertising campaigns.

[0430] Specific examples

[0431] For example, a freelance photographer user uploads photos taken at an event to the system. The server analyzes the uploaded photos using a generative AI model to check for portrait rights and copyright issues. The generative AI model performs facial recognition to determine whether a specific person is in the photo and blurs the face if necessary. Once this process is complete, the server notifies the user of the results. The notification includes information on whether the photo is approved or requires correction. Finally, the approved data is linked to the e-commerce site via an API and used on the product page.

[0432] In this manner, the present invention provides a user with an efficient and hassle-free process for reviewing and approving photo and video data.

[0433] The processing flow will be explained below.

[0434] Step 1:

[0435] Users access the system's upload page using a device (e.g., PC or smartphone) to select the files they have taken, and then click the upload button to send the data to the system.

[0436] Step 2:

[0437] The terminal generates a request to upload the selected photo or video data to the platform server and transmits the data.

[0438] Step 3:

[0439] The server receives the uploaded data and stores it in secure storage, while simultaneously adding it to an analysis queue, ready for analysis by the generative AI model.

[0440] Step 4:

[0441] The server then passes the data added to the analysis queue to the generative AI model, which performs facial and text recognition on the photos and video data to identify issues with portrait rights and copyright.

[0442] Step 5:

[0443] Based on the analysis results, the generative AI model generates instructions for automatically correcting photos and video data, including blurring faces and cropping unwanted areas.

[0444] Step 6:

[0445] The server receives instructions from the generative AI model and automatically corrects the photo and video data using its internal image processing tools. The corrected data is then passed back to the generative AI model for final confirmation.

[0446] Step 7:

[0447] The generative AI model then re-analyzes the corrected data and performs a final check, which determines whether the data is acceptable.

[0448] Step 8:

[0449] The server collects the final confirmation results and notifies the user. Approved data is marked as "approved" and linked to the marketplace and content delivery system. Data that cannot be approved is fed back to the user with specific reasons.

[0450] Step 9:

[0451] The notification system will inform the user of the results of the check in natural language, for example, sending messages such as "All data uploaded by the customer has been approved" or "The following data needs to be edited due to portrait rights issues."

[0452] Step 10:

[0453] The server then uses an API to connect the verified data to other systems, allowing it to be immediately used on platforms such as e-commerce sites and social media.

[0454] This allows users to have an efficient and accurate photo and video data review and approval process.

[0455] Example 1

[0456] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0457] Efficiently and accurately analyzing photo and video data online and automatically checking and correcting copyright and portrait rights issues is time-consuming and labor-intensive if done manually. Therefore, there is a need to provide a system that automates this work and facilitates the immediate use of data.

[0458] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0459] In this invention, the server includes a means for uploading photo or video data from a user terminal to the server, a means for analyzing the photo or video data stored on the server using a generative AI model to check for copyright and portrait rights issues, a means for automatically correcting the photo or video data based on the analysis results of the generative AI model, a means for performing a final check on the corrected data using the generative AI model again, a means for notifying the user of the final check results, a means for sharing the final checked data with other systems via API integration, a means for storing the data in secure storage, a means for using an analysis queue in the generative AI model, and a means for performing facial recognition and text recognition. This enables accurate and efficient analysis, automatic correction, and checking of photo and video data. It also promotes the immediate use of checked data.

[0460] "Photographic or video data" refers to visual information recorded in the form of an image file or video file.

[0461] "User terminal" refers to an electronic device, such as a computer, smartphone, or tablet, that a user uses to perform operations.

[0462] A "server" refers to a computer system that provides services and data over a network.

[0463] A "generative AI model" refers to an algorithm that has been trained to perform a specific task (e.g., image recognition or text analysis) using machine learning or artificial intelligence techniques.

[0464] "Upload means" refers to the function or process for sending data from a user terminal to a server.

[0465] "Storage" refers to a mechanism or system (e.g., cloud storage) for secure and persistent retention of data.

[0466] An "analysis queue" refers to a data structure or system that queues data to be analyzed in order and waits for processing.

[0467] "Facial recognition" refers to the technology and processes used to detect and identify human faces within image data.

[0468] "Text recognition" refers to the technology and process of extracting character information from image data and processing it as digital text.

[0469] "Automatic correction measures" refers to functions and processes for automatically modifying or improving data based on the analysis results of a generative AI model.

[0470] "Reconfirmation means" refers to the functions and processes for reconfirming the corrected data and giving final approval.

[0471] "Notification means" refers to the functions and processes for transmitting information such as analysis results and the need for corrections from the system to the user.

[0472] "API integration means" refers to the interfaces and processes for exchanging data and functions between different software systems.

[0473] MODE FOR CARRYING OUT THE INVENTION

[0474] This invention relates to a system that efficiently and accurately analyzes photo or video data and automatically checks and corrects copyrights and portrait rights. In this system, users upload their photographic data, and a server analyzes the data using a generative AI model. The system then corrects the data based on the analysis results, and notifies the user after final confirmation. Approved data can also be linked to other systems via an API.

[0475] Data upload and storage

[0476] Users access the system's upload page to upload photos or video data taken from their own devices (PCs, smartphones, etc.), select the files, and click the upload button. The uploaded data is received by the server and stored in secure storage. This storage process uses cloud storage such as Amazon S3.

[0477] Data analysis with generative AI models

[0478] The server adds the stored data to an analysis queue and prepares it for analysis using a generative AI model. Using the analysis queue, the data is analyzed sequentially. The generative AI model performs facial and text recognition and checks for portrait and copyright issues. This analysis is performed using, for example, Google Cloud Vision API and Tesseract OCR.

[0479] Auto-correct and re-check

[0480] Based on the analysis results, the server issues instructions for automatic correction of the photo or video data. For example, OpenCV is used to blur faces or trim unwanted parts. The corrected data is then checked again by the generative AI model. This recheck determines whether the corrections were made appropriately.

[0481] User notification and API integration

[0482] The final review result is sent to the user via email or in-app notification. The notification will include information on whether the photo was approved or if any corrections are required. The approved data is shared with other systems via API integration via the server. This integration allows the photo to be used immediately, for example, in product description pages on e-commerce sites or in advertising campaigns.

[0483] Specific examples

[0484] For example, consider the case where a freelance photographer uploads photos taken at an event to the system. The server analyzes the uploaded photos using a generative AI model to check for issues with portrait rights and copyright. The generative AI model performs facial recognition to confirm whether specific people are in the photos and blurs their faces if necessary. After corrections are made, the server notifies the user of the results of the check. The notification may include a message such as "Photo A has been approved" or "Photo B has a portrait rights issue and needs to be corrected." Finally, the approved data is linked to the e-commerce site via an API and used on the product page.

[0485] Prompt Sentence Examples

[0486] Examples of prompts for generative AI models include:

[0487] "Detect all faces in this photo and apply blur according to specific criteria."

[0488] In this way, a system can be provided that enables accurate and efficient analysis, automatic correction, and confirmation of photographic and video data, as well as immediate utilization of the data.

[0489] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0490] Step 1: Upload step

[0491] Users access the system's upload page to upload photos or video data taken from their own device (PC, smartphone, etc.) and click the upload button.

[0492] Input: Photo or video data from the user's device

[0493] Output: Upload data to the server

[0494] Specific operation: The user accesses the upload page of the system using a browser, selects the data file, and clicks the upload button. The system receives the selected file and displays a message on the screen indicating that the upload was successful.

[0495] Step 2: Data storage step

[0496] The server receives the uploaded data and stores it in secure storage (e.g., Amazon S3).

[0497] Input: Photo or video data uploaded by the user

[0498] Output: Data file in cloud storage

[0499] Specific operation: The server converts the received data into a certain format and transfers it to the cloud storage using a communication protocol (e.g., HTTPS). Once the data has been saved in the storage, it returns a status indicating that the data has been saved to the system.

[0500] Step 3: Analysis preparation steps

[0501] The server adds the stored data to an analysis queue and prepares it for analysis by the generative AI model.

[0502] Input: Data files stored in cloud storage

[0503] Output: File paths and metadata in the analysis queue

[0504] Specific operation: The server adds the file path in the storage and related metadata to the analysis queue. The data in the analysis queue is processed sequentially according to priority.

[0505] Step 4: Data analysis step

[0506] The server then passes the stored data to a generative AI model for analysis, which performs facial and text recognition and detects portrait and copyright issues.

[0507] Input: Data files from the analysis queue

[0508] Output: Face recognition results, text recognition results, correction instructions

[0509] How it works: The server takes data from the analysis queue and passes it to a generative AI model (e.g., Google Cloud Vision API or Tesseract OCR). The generative AI model detects faces and text and returns correction instructions based on those to the server.

[0510] Step 5: Auto-correction step

[0511] The server automatically modifies the photo or video data based on the analysis results, for example by blurring faces or trimming unwanted parts.

[0512] Input: Correction instructions returned by the generative AI model

[0513] Output: Modified data file

[0514] Specific operation: Based on the correction instructions, the server uses an image processing library such as OpenCV to perform specific corrections. For example, it performs processing such as blurring or cropping the face. The corrected data is temporarily saved.

[0515] Step 6: Reconfirmation step

[0516] The server then passes the corrected data back to the generative AI model for a final check, determining whether the corrections were made appropriately.

[0517] Input: Modified data file

[0518] Output: Final confirmation result (approval or rejection)

[0519] Specific operation: The server sends the corrected data to the generative AI model again for reanalysis. The generative AI model performs a final check to see if the corrections were made properly and returns the results to the server.

[0520] Step 7: User Notification Step

[0521] The server will send the final confirmation result to the user via email or in-app notification.

[0522] Input: Final confirmation result

[0523] Output: User notification (email or in-app message)

[0524] Specific behavior: The server receives the final review result and notifies the user, including whether the photo is approved or needs corrections.

[0525] Step 8: API integration step

[0526] The approved data is then shared with other systems via API by the server.

[0527] Input: Final confirmed data

[0528] Output: Use of data in other systems (e.g., product page on an e-commerce site)

[0529] What it does: The server sends the approved data to other systems via a RESTful API. This integration allows, for example, the approved photos to be used on product pages on online shopping sites.

[0530] In this way, specific processing is performed at each step, enabling efficient and accurate analysis and confirmation of photo and video data across the entire system.

[0531] (Application example 1)

[0532] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0533] For modern advertising creators and marketers, the process of checking and correcting portrait rights and copyrights in photographs and video data is extremely tedious and time-consuming. Performing this process manually increases the risk of human error and reduces efficiency. Furthermore, it is difficult to utilize corrected data in advertising campaigns in real time, hindering the rapid implementation of marketing strategies. The present invention aims to solve these problems by providing a system that efficiently and accurately analyzes and corrects data and utilizes it in advertising campaigns in real time.

[0534] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0535] In this invention, the server includes means for uploading photo or video data from a user terminal to the server, means for analyzing the photo or video data stored on the server using a generative AI model and checking for copyright and portrait rights issues, means for automatically correcting the photo or video data based on the analysis results of the generative AI model, means for performing a final check of the corrected data using the generative AI model again, means for notifying the user of the final check results, means for sharing the final checked data with other systems via API integration, means for analyzing data taken by advertising creators and marketers and checking and correcting portrait rights and copyrights, and means for utilizing the corrected data in advertising campaigns in real time. This enables efficient and rapid analysis and correction of data, allowing it to be immediately utilized in advertising campaigns.

[0536] "Photo or video data" refers to digital information in the form of still images and videos taken or generated by a user.

[0537] "User terminal" refers to an electronic device such as a computer, smartphone, or tablet that is used to upload photo or video data to a server.

[0538] A "server" is a central control device that receives data from user terminals and processes it, such as storing, analyzing, and modifying.

[0539] A "generative AI model" is a system that uses machine learning algorithms to analyze images and videos and automatically check and correct copyright and portrait rights.

[0540] "Means for uploading" refers to the functions and processes that allow a user to send photo or video data from their own device to the server.

[0541] "Means of analysis" refers to the process of processing data using a generative AI model to perform copyright and image rights checks.

[0542] "Means for automatic correction" refers to a function that automatically applies necessary corrections to data based on the analysis results.

[0543] "Final verification means" refers to the process of re-analyzing the automatically corrected data using a regenerative AI model to finally verify the accuracy and legality of the data.

[0544] "Means for notifying" refers to a communication function for notifying the user of the final confirmation result, and may include notification via an interface.

[0545] "API integration and sharing" refers to standardized protocols and interfaces for connecting and exchanging data with other computer systems and platforms.

[0546] "Advertising creator" refers to an individual or organization that produces photographs and videos for advertising and carries out marketing activities.

[0547] "Marketer" refers to an individual or organization whose role is to plan, execute, and manage marketing activities.

[0548] "Means that can be used in advertising campaigns in real time" refers to the ability to immediately incorporate data that has been analyzed and corrected into advertising strategies.

[0549] This system enables the uploading of photo and video data, analysis, automatic correction, final confirmation, notification, and API integration. This system is designed specifically to enable advertising creators and marketers to effectively and quickly manage data and utilize it in real-time advertising campaigns.

[0550] System Overview

[0551] The system consists of the following main components:

[0552] 1. User device (PC, smartphone, tablet, etc.)

[0553] 2. Server

[0554] 3. Generative AI Models

[0555] 4. Storage services (e.g., Firebase, Amazon S3)

[0556] 5. Message queue systems (e.g. RabbitMQ)

[0557] 6. Image processing libraries (e.g. OpenCV, dlib, Pillow)

[0558] 7. Notification systems (e.g., Twilio API, Firebase Cloud Messaging)

[0559] 8. API integration platform (e.g. RESTful API)

[0560] Natural language explanation of program processing

[0561] 1. Data upload and storage:

[0562] Photo or video data is uploaded from the user's device to the server, which receives it and stores it securely in a storage service such as Firebase or Amazon S3.

[0563] 2. Analysis preparation and queue management:

[0564] The server adds the uploaded data to a message queue system such as RabbitMQ and prepares it for analysis by the generative AI model.

[0565] 3. Analysis by generative AI model:

[0566] The server uses the generative AI model to perform the following analysis:

[0567] Facial Recognition: Use OpenCV and dlib to recognize faces in photos and videos and verify portrait rights.

[0568] Text Recognition: Use Tesseract OCR to recognize text in images and perform copyright verification.

[0569] 4. Autocorrect:

[0570] Based on the analysis results of the generative AI model, the server uses an image processing library such as Pillow to automatically make any necessary corrections (e.g., blurring faces, trimming unnecessary parts, etc.).

[0571] 5. Double check:

[0572] The corrected data is analyzed again by the generated AI model for final confirmation.

[0573] 6. Result notification:

[0574] The server uses the Twilio API or Firebase Cloud Messaging to notify the user of the final validation result, including whether the data was approved or if further corrections are required.

[0575] 7. API Integration:

[0576] Approved data is shared in real time with advertising platforms (e.g., Facebook Ads, Google Ads) via RESTful APIs.

[0577] Examples of concrete examples and prompts

[0578] For example, let's consider the case where an advertising creator shoots a promotional video for a product and uploads it to the system. The server receives the uploaded data and analyzes portrait rights and copyright using a generative AI model. Based on the analysis results, necessary corrections are automatically made, and after final confirmation, the results are notified to the user. This data is connected to the API in real time and immediately used in advertising campaigns.

[0579] Prompt Sentence Examples

[0580] User: Upload a product photo.

[0581] System: Initiate facial recognition and portrait rights checks.

[0582] System: Begins recognizing text in images.

[0583] System: Blurring face. Cropping image.

[0584] System: Final checks underway.

[0585] System: Analysis and corrections completed. Data is ready for advertising campaigns.

[0586] In this way, the present invention provides advertising creators and marketers with efficient and accurate data analysis and real-time utilization.

[0587] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0588] Step 1:

[0589] Uploading data

[0590] Users upload photos or video data from their devices. They select the desired files from their devices and send them via the system's upload page. The server receives this data and stores it in secure storage such as Firebase or Amazon S3.

[0591] Input: Photo or video data selected by the user from their device

[0592] Output: Uploaded data stored on the server

[0593] Step 2:

[0594] Analysis preparation and queue management

[0595] The server adds the uploaded data to a message queue system such as RabbitMQ, which prepares the data for analysis tasks and sends it to a generative AI model for analysis.

[0596] Input: Upload data stored on the server

[0597] Output: Data added to the analysis queue

[0598] Step 3:

[0599] Analysis using generative AI models

[0600] The server analyzes the uploaded data using a generative AI model. First, it uses OpenCV and dlib for facial recognition to identify areas where image rights are involved. Second, it uses Tesseract OCR to recognize text within the image and verify copyright.

[0601] Input: Data added to the analysis queue

[0602] Output: Face and text recognition analysis results

[0603] Step 4:

[0604] autocorrect

[0605] Based on the results of the generative AI model's analysis, the server uses image processing libraries such as Pillow to make any necessary corrections, such as adding blur to recognized faces or cropping out unwanted parts.

[0606] Input: Face and text recognition analysis results

[0607] Output: Modified photo or video data

[0608] Step 5:

[0609] Reconfirmation

[0610] The server then analyzes the corrected data again using the generated AI model and performs a final check to ensure the corrections were made correctly and that there are no omissions.

[0611] Input: Modified photo or video data

[0612] Output: Final verified data

[0613] Step 6:

[0614] Result notification

[0615] The server notifies the user of the final validation result using the Twilio API or Firebase Cloud Messaging, including whether the data is approved or requires further correction.

[0616] Input: Final verified data

[0617] Output: A message to inform the user

[0618] Step 7:

[0619] API Integration

[0620] Approved data is shared in real time with advertising platforms (e.g., Facebook Ads, Google Ads) using a RESTful API, allowing advertisers and marketers to instantly use the approved data in their campaigns.

[0621] Input: Approved photo or video data

[0622] Output: Data linked to advertising platform

[0623] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0624] This invention relates to a system for efficiently verifying and correcting copyrights and portrait rights in photo or video data. This system analyzes data uploaded by users using a generative AI model and automatically performs necessary corrections. In addition, by combining it with an emotion engine that recognizes the user's emotions, the system appropriately adjusts notifications and feedback of the analysis results, improving the user experience.

[0625] Uploading photos and video data

[0626] Users access the system's upload page from their own devices (e.g., PCs or smartphones), select the photos or video data they have taken, and upload them. After selecting the files, users send the data by clicking the upload button.

[0627] Data storage and analysis

[0628] The server receives the uploaded data, stores it in storage, and adds it to the analysis queue to prepare it for analysis by the generative AI model.

[0629] Data analysis with generative AI models

[0630] The server passes the data from the analysis queue to the generative AI model, which then begins the analysis process. The generative AI model performs facial and text recognition on the photos and video data to identify issues with portrait rights and copyright. This allows it to extract problematic parts related to copyright and portrait rights.

[0631] Auto-correct and double-check

[0632] The server automatically corrects the photo and video data based on instructions from the generative AI model. Specifically, this includes blurring faces and trimming unnecessary parts. The corrected data is then passed back to the generative AI model for final confirmation.

[0633] Emotional Engine Adjustment

[0634] The emotion engine recognizes the user's emotions in real time and adjusts the analysis results and notifications and feedback appropriately. For example, if the user is feeling stressed, the server will send notifications in a gentler tone. If the user gives positive feedback to the system, the emotion engine uses it to improve the content of future notifications and the accuracy of analysis.

[0635] Notification of approval results

[0636] The server aggregates the final analysis and verification results and notifies the user through the emotion engine. For example, when notifying the user that "all of your uploaded data has been approved," the emotion engine adjusts the wording as necessary.

[0637] Utilizing data through API integration

[0638] The approved data is then linked to other systems via API by the server. This allows the verified data to be immediately used on platforms such as e-commerce sites and social media. For example, verified photos can be displayed on product description pages in online shops or used as material for advertising campaigns.

[0639] Specific examples

[0640] For example, a freelance photographer user can upload photos taken at an event to the system. The server then analyzes the uploaded photos using a generative AI model to check for portrait rights and copyright issues. Based on the analysis results, any necessary face blurring or cropping is automatically performed. The emotion engine then analyzes the user's reaction and provides optimal feedback. Finally, the verified data is linked to the e-commerce site and used as material for product pages.

[0641] In this manner, the present invention provides users with an efficient and flexible photo and video data review, correction and approval process.

[0642] The processing flow will be explained below.

[0643] Step 1:

[0644] Users access the system's upload page from their own device (e.g., PC, smartphone), select the photos or video data they have taken, and then click the upload button to send the data to the system.

[0645] Step 2:

[0646] The terminal generates a request to upload the selected data to the server and transmits the data to the server.

[0647] Step 3:

[0648] The server receives the uploaded photo or video data and stores it in secure storage, checking for duplicate or corrupted data during the process.

[0649] Step 4:

[0650] The server adds the saved data to the analysis queue, preparing it for analysis by the generative AI model, and logs that the data has been successfully added to the analysis queue.

[0651] Step 5:

[0652] The server passes the data from the analysis queue to the generative AI model, which performs facial and text recognition on the photos and video data to identify any issues with portrait rights or copyright.

[0653] Step 6:

[0654] The generative AI model automatically corrects photos and video data based on the analysis results. This correction includes blurring faces and trimming unwanted parts. The correction process is performed by an image processing tool on the server side.

[0655] Step 7:

[0656] The server passes the corrected data back to the generative AI model for final validation, which then re-analyzes the corrected data to ensure all issues have been resolved.

[0657] Step 8:

[0658] The emotion engine analyzes the user's emotional state and adjusts the content and method of notification of the analysis results. The emotion engine analyzes the user's real-time emotional data (e.g., facial expressions, tone of voice).

[0659] Step 9:

[0660] The server aggregates the final confirmation results and notifies the user through the emotion engine. For example, the message "All your uploaded data has been approved" can be softened depending on the emotion, improving the user experience.

[0661] Step 10:

[0662] The server then connects the final verified data to other systems via API. This connection makes the verified data instantly available on platforms such as e-commerce sites and social media. For example, photos can be automatically uploaded to product description pages on e-commerce sites.

[0663] Step 11:

[0664] The notification system sends notifications to users after API integration to let them know how their data is being used. For example, it notifies users by sending a notification that "your customer photo has been uploaded to the product description page."

[0665] Through this process flow, the present invention provides users with an efficient and flexible process for reviewing, correcting, and approving photo and video data.

[0666] Example 2

[0667] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0668] Conventional image data confirmation and correction systems have the problem of requiring a lot of manual work, which is time-consuming and labor-intensive. Furthermore, the accuracy of confirmation and correction tends to vary, making it difficult to improve the user experience. Furthermore, since the final confirmed data is not linked to other systems in real time, it is difficult to immediately utilize the data. There is a demand for a system that solves these issues.

[0669] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for uploading image data from a user terminal to the server; means for analyzing the image data stored in the server using a generative AI model and checking for issues with intellectual property rights and personal rights; means for automatically correcting the image data based on the analysis results of the generative AI model; means for finally checking the corrected data again using the generative AI model; means for notifying the user of the final check results; means for sharing the finally checked data with other information processing systems through API collaboration; and means including an emotion engine that recognizes user emotions in real time and adjusts notification of the analysis results. This enables the efficiency and accuracy of checking and correcting image data to be improved, and further enables an improved user experience and rapid use of the finally checked data.

[0670] "User terminal" refers to a computing device used by an individual, including a PC or smartphone.

[0671] A "server" is a computing system that provides services over a network and stores and processes data.

[0672] "Image data" is digital data of photographs or videos, and is composed of a series of pixel information.

[0673] A "generative AI model" is a data analysis model that uses artificial intelligence technology and analyzes image data using machine learning algorithms.

[0674] "Analysis" refers to the process of examining the contents of data in detail to find specific information or problems.

[0675] "Intellectual property rights" means ownership rights in creative ideas and information that are protected by law, including copyrights and patents.

[0676] "Personal rights" refer to rights that belong to individuals, such as privacy and portrait rights.

[0677] "Automatic correction" means that the system autonomously corrects errors or problems in the data, including blurring or cropping images.

[0678] "Final confirmation" is the process of finally confirming that the analyzed and corrected data is accurate and has no problems.

[0679] "Notification" refers to the act of transmitting information from the system to the user, and is done in the form of email, pop-up, etc.

[0680] "API integration" refers to integration using an application programming interface to share data and functions between different information processing systems.

[0681] The "emotion engine" is a component that analyzes the user's emotional state in real time and adjusts the system's response.

[0682] "Real-time" refers to data processing and communication occurring instantly, with almost no delay.

[0683] The present invention relates to a system for efficiently verifying and correcting intellectual property and personal rights in image data. The system allows users to upload image data from their devices to a server, which then uses a generative AI model to analyze, correct, and finally verify the image data.

[0684] Specifically, the system uses the following hardware and software:

[0685] 1. User terminal: A device such as a desktop computer, laptop, or smartphone that uploads image data.

[0686] 2. Server: A high-performance computing system that stores, analyzes, modifies, and validates data. The server can be a cloud service such as Amazon Web Services (AWS).

[0687] 3. Generative AI models: These are machine learning models built using frameworks such as TensorFlow to analyze image data, perform facial recognition, text recognition, and identify intellectual property and personal rights issues.

[0688] 4. Image processing software: Use libraries such as OpenCV to automatically correct problem areas.

[0689] 5. Emotion Engine: Recognizes the user's emotions in real time and adjusts the notification content based on the analysis results. Specifically, this is achieved by using facial expression recognition APIs and voice analysis software.

[0690] As a concrete example of how the system works, consider a scenario in which a freelance photographer uploads photos taken at an event to the system. The photos uploaded from the user's device are received by the server, stored in storage, and added to the analysis queue. The server then analyzes the photos using a generative AI model, performing facial and text recognition and checking for intellectual property and personal rights issues. Based on the analysis results, the server automatically blurs the face and crops out unnecessary parts. The corrected data is then passed back to the generative AI model for final review.

[0691] The emotion engine recognizes the user's real-time emotions, and if the user is feeling stressed, for example, the server adjusts the tone of the notification to be gentler. The final confirmation result is notified to the user via the emotion engine, and the approved data is shared in real time by the server with other information processing systems via API.

[0692] An example of a prompt might be, "Please explain the process by which a freelance photographer takes photos at an event, uploads them to a system, and automatically checks and corrects copyright and portrait rights."

[0693] In this manner, the present invention provides users with an efficient and flexible image data review, correction and approval process.

[0694] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0695] Step 1:

[0696] The user accesses the upload page of the system using their own terminal, selects image data, and uploads it. When the user selects a file and clicks the "Upload" button, the terminal sends the image data to the server via an HTTP request.

[0697] Input: Image data file path

[0698] Output: HTTP request to the server

[0699] Step 2:

[0700] The server receives the HTTP request and saves the uploaded image data to storage. At the same time, it adds the data to the analysis queue and prepares it for analysis. The server extracts the image data from the request and saves it in the specified directory.

[0701] Input: Image data via HTTP request

[0702] Output: Updates the path of the image data stored in storage and the analysis queue.

[0703] Step 3:

[0704] The server retrieves the next image data from the analysis queue and passes it to the generative AI model, which performs facial and text recognition and identifies intellectual property and personal rights issues.

[0705] Input: Path to the image data saved in storage

[0706] Output: Analysis results by the generative AI model (location and content of the problem area)

[0707] Step 4:

[0708] Based on the analysis results of the generative AI model, the server uses image processing software to automatically correct the image, specifically by blurring problem areas and trimming unnecessary parts.

[0709] Input: Analysis results of the generative AI model

[0710] Output: Corrected image data

[0711] Step 5:

[0712] The server then passes the corrected image data back to the generative AI model for final confirmation, which then rechecks the corrections and confirms that the problem has been resolved.

[0713] Input: Corrected image data

[0714] Output: Final confirmation result

[0715] Step 6:

[0716] The emotion engine recognizes the user's emotions in real time and adjusts the content of notifications sent from the server. For example, if the user is feeling stressed, the notification will be softened.

[0717] Input: Real-time emotional state of the user (facial expression data, voice data)

[0718] Output: Adjusted notification text

[0719] Step 7:

[0720] The server aggregates the final confirmation results and notifies the user through the emotion engine, for example, by sending a notification such as "All of your uploaded data has been approved."

[0721] Input: Final confirmation results and emotion engine input

[0722] Output: User notification

[0723] Step 8:

[0724] The server shares the final confirmed data with other information processing systems via API integration, allowing the confirmed data to be used in real time on e-commerce sites and social media.

[0725] Input: Final confirmed data

[0726] Output: Data transferred to other systems via API

[0727] In this way, the system efficiently and accurately reviews, corrects, and approves image data.

[0728] (Application example 2)

[0729] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0730] The advertising industry requires the ability to quickly and efficiently check and correct copyright and portrait rights issues in photo and video data. However, doing this manually is time-consuming, labor-intensive, and prone to errors. Furthermore, providing feedback without considering user emotions can lead to a poor user experience and lower satisfaction with the service. To solve these issues, a system is needed that combines automatic data analysis and correction with feedback based on user emotions.

[0731] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0732] In this invention, the server includes: means for uploading photo or video data from a user terminal to the server; means for analyzing the photo or video data stored on the server using a generative AI model to check for copyright and portrait rights issues; means for automatically correcting the photo or video data based on the analysis results of the generative AI model; means for performing a final check of the corrected data using the generative AI model again; means including an emotion engine for recognizing user emotions in real time and appropriately adjusting notifications and feedback of the analysis results; means for notifying the user of the results of the final check; and means for sharing the final checked data with other systems via API integration. This makes it possible to quickly and accurately resolve copyright and portrait rights issues in advertising materials and improve the user experience.

[0733] "Photo or video data" refers to digital files in the form of still images or video images taken by a user using a terminal.

[0734] A "user terminal" is an electronic device that a user can operate by connecting to the Internet, such as a personal computer, smartphone, or tablet.

[0735] A "server" is a computer system connected to a network that stores, processes, and distributes data.

[0736] A "generative AI model" is an algorithm or system that uses artificial intelligence techniques to analyze and generate data.

[0737] "Copyright" is the right of the creator or copyright holder of a work to exclusively exploit that work.

[0738] "Portrait rights" are an individual's right to prevent images of their face or figure from being published or used without their permission.

[0739] An "emotion engine" is a system that recognizes and analyzes a user's emotional state in real time and adjusts responses accordingly.

[0740] "API integration" is a method of using an application programming interface (API) to exchange data between different software and systems and integrate functions.

[0741] "Feedback" refers to response information such as analysis results, notifications, and evaluations that the system provides to the user.

[0742] "Analysis" is the process of analyzing data and clarifying its content and characteristics.

[0743] The system of this invention is composed of a user terminal, a server, a generative AI model, an emotion engine, and API integration.

[0744] User terminal

[0745] The user terminal is an electronic device such as a personal computer, smartphone, or tablet, which the user can use to take photos or video data and upload them to the server. The user selects the photos or video data they have taken and accesses a dedicated upload page to send the data.

[0746] server

[0747] The server is connected to the network and provides many functions such as storing, analyzing, modifying, notifying, and API integration of photo or video data. The server uses the following specific hardware and software:

[0748] Server storage: Amazon S3

[0749] Data analysis: OpenCV, TensorFlow

[0750] Emotion analysis: Microsoft Azure Emotion API

[0751] The data received by the server is stored in storage and added to the analysis queue.

[0752] Generative AI Models

[0753] The generative AI model analyzes photo and video data stored on a server, performs facial and text recognition within the data, and identifies copyright and portrait rights issues. Based on the analysis results, automatic corrections are performed, such as blurring faces and cropping unwanted parts.

[0754] Emotion Engine

[0755] The emotion engine recognizes the user's emotional state in real time and adjusts the analysis results and notifications and feedback. This improves the user experience by sending notifications in a gentler tone if the user is feeling stressed. In addition, if the user gives positive feedback, the emotion engine uses it to improve the content of future notifications and the accuracy of analysis.

[0756] API Integration

[0757] Approved data can be linked to other systems via APIs, allowing it to be immediately used on e-commerce sites and social media platforms. For example, verified photos can be used on product pages in online shops or as material for advertising campaigns.

[0758] Specific examples

[0759] As a concrete example, consider the case where an advertising creator uploads a photo taken at a local tourist spot to this system. The server analyzes the uploaded photo using a generative AI model to check for portrait rights and copyright issues. Based on the analysis results, faces are automatically blurred or cropped. The emotion engine then analyzes the user's reaction and provides appropriate feedback. For example, a prompt such as "Please recognize all faces in this image, check for portrait rights issues, and blur them if necessary. Also, check for objects that may be copyrighted and crop them if necessary" could be input into the generative AI model.

[0760] In this way, the present invention can quickly and accurately resolve copyright and portrait rights issues of advertising materials and improve user experience.

[0761] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0762] Step 1:

[0763] The user takes a photo or video data and uploads it to the server from the user's device. The user selects the file specified and sends it to the server via the upload page. The photo or video data file is required as input, and the data is saved on the server as output. The specific operation is when the user clicks the upload button.

[0764] Step 2:

[0765] The server saves the received data in storage and adds it to the analysis queue at the same time. It takes the received photo or video data as input, stores it in the destination storage as output, and adds a task to the analysis queue. Specifically, it saves the data in Amazon S3 and adds an analysis task to the queue.

[0766] Step 3:

[0767] The server retrieves data from the analysis queue and passes it to the generative AI model to begin analysis. The input is the photo or video data retrieved from the analysis queue, and the output is the analysis results. Specific operations include facial recognition and text recognition using OpenCV and TensorFlow.

[0768] Step 4:

[0769] The server automatically corrects the photo or video data based on the analysis results of the generative AI model. The analysis results are taken as input, and corrected data is generated as output. Specific operations include blurring the face area and trimming unnecessary parts.

[0770] Step 5:

[0771] The server then passes the corrected data back to the generative AI model for final confirmation. The corrected photo or video data is the input, and the final confirmation result is generated as the output. Specifically, final face and text recognition is performed again.

[0772] Step 6:

[0773] The server uses an emotion engine to recognize the user's emotional state in real time and adjusts the analysis results and feedback accordingly. The input is the user's emotional data and the final confirmation result, and the output is an adjusted feedback message. Specifically, emotion analysis is performed using the Microsoft Azure Emotion API.

[0774] Step 7:

[0775] The server notifies the user of the final confirmation result. The input is the adjusted feedback message, and the output is a notification sent to the user. The specific operation is to send the notification message to the user's device via push notification or email.

[0776] Step 8:

[0777] The server shares the final confirmed data with other systems via API. The confirmed data is input, and the output is immediately available on platforms such as e-commerce sites and social media. Specifically, the confirmed data is sent to other systems via API.

[0778] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0779] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0780] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0781] [Third embodiment]

[0782] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0783] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0784] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0785] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0786] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0787] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0788] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0789] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0790] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0791] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0792] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0793] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0794] This invention relates to a system for efficiently and accurately analyzing and verifying photo or video data. Users upload their photographic data, and a server analyzes the data using a generative AI model, automatically verifying and correcting copyright and portrait rights. Furthermore, the system notifies users of the analysis results and uses an API to link verified data with other systems, enabling immediate use of the data.

[0795] Specifically, the system is constructed as follows:

[0796] Uploading photos and video data

[0797] Users can upload photos or video data taken from their own devices (e.g., PCs or smartphones) by accessing the system's upload page. All they need to do is select the desired file and click the upload button.

[0798] Data storage and analysis

[0799] The server receives the uploaded data and stores it in secure storage, while simultaneously adding it to the analysis queue and preparing it for analysis by the generative AI model.

[0800] Data analysis with generative AI models

[0801] The server passes the stored data to the generative AI model to begin analysis. The generative AI model first performs facial recognition to identify areas where portrait rights are involved. It also checks for copyright infringement through text recognition within the image. Based on the results of this analysis, it issues instructions for any necessary corrections (e.g., blurring faces, trimming unnecessary parts, etc.).

[0802] Auto-correct and double-check

[0803] The server automatically modifies the photo or video data based on instructions from the generative AI model. The modified data is then passed back to the generative AI model for final confirmation. This confirmation determines whether the data is acceptable.

[0804] Notification of approval results

[0805] The server notifies the user of the final analysis and verification results, such as "All data uploaded by the user has been approved" or "The following data needs to be edited due to portrait rights issues."

[0806] Utilizing data through API integration

[0807] The server then connects the approved data to other systems via APIs, allowing it to be instantly used on platforms such as e-commerce sites and social media. For example, verified photos can be displayed on product description pages for online shopping sites or used in advertising campaigns.

[0808] Specific examples

[0809] For example, a freelance photographer user uploads photos taken at an event to the system. The server analyzes the uploaded photos using a generative AI model to check for portrait rights and copyright issues. The generative AI model performs facial recognition to determine whether a specific person is in the photo and blurs the face if necessary. Once this process is complete, the server notifies the user of the results. The notification includes information on whether the photo is approved or requires correction. Finally, the approved data is linked to the e-commerce site via an API and used on the product page.

[0810] In this manner, the present invention provides a user with an efficient and hassle-free process for reviewing and approving photo and video data.

[0811] The processing flow will be explained below.

[0812] Step 1:

[0813] Users access the system's upload page using a device (e.g., PC or smartphone) to select the files they have taken, and then click the upload button to send the data to the system.

[0814] Step 2:

[0815] The terminal generates a request to upload the selected photo or video data to the platform server and transmits the data.

[0816] Step 3:

[0817] The server receives the uploaded data and stores it in secure storage, while simultaneously adding it to an analysis queue, ready for analysis by the generative AI model.

[0818] Step 4:

[0819] The server then passes the data added to the analysis queue to the generative AI model, which performs facial and text recognition on the photos and video data to identify issues with portrait rights and copyright.

[0820] Step 5:

[0821] Based on the analysis results, the generative AI model generates instructions for automatically correcting photos and video data, including blurring faces and cropping unwanted areas.

[0822] Step 6:

[0823] The server receives instructions from the generative AI model and automatically corrects the photo and video data using its internal image processing tools. The corrected data is then passed back to the generative AI model for final confirmation.

[0824] Step 7:

[0825] The generative AI model then re-analyzes the corrected data and performs a final check, which determines whether the data is acceptable.

[0826] Step 8:

[0827] The server collects the final confirmation results and notifies the user. Approved data is marked as "approved" and linked to the marketplace and content delivery system. Data that cannot be approved is fed back to the user with specific reasons.

[0828] Step 9:

[0829] The notification system will inform the user of the results of the check in natural language, for example, sending messages such as "All data uploaded by the customer has been approved" or "The following data needs to be edited due to portrait rights issues."

[0830] Step 10:

[0831] The server then uses an API to connect the verified data to other systems, allowing it to be immediately used on platforms such as e-commerce sites and social media.

[0832] This allows users to have an efficient and accurate photo and video data review and approval process.

[0833] Example 1

[0834] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0835] Efficiently and accurately analyzing photo and video data online and automatically checking and correcting copyright and portrait rights issues is time-consuming and labor-intensive if done manually. Therefore, there is a need to provide a system that automates this work and facilitates the immediate use of data.

[0836] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0837] In this invention, the server includes a means for uploading photo or video data from a user terminal to the server, a means for analyzing the photo or video data stored on the server using a generative AI model to check for copyright and portrait rights issues, a means for automatically correcting the photo or video data based on the analysis results of the generative AI model, a means for performing a final check on the corrected data using the generative AI model again, a means for notifying the user of the final check results, a means for sharing the final checked data with other systems via API integration, a means for storing the data in secure storage, a means for using an analysis queue in the generative AI model, and a means for performing facial recognition and text recognition. This enables accurate and efficient analysis, automatic correction, and checking of photo and video data. It also promotes the immediate use of checked data.

[0838] "Photographic or video data" refers to visual information recorded in the form of an image file or video file.

[0839] "User terminal" refers to an electronic device, such as a computer, smartphone, or tablet, that a user uses to perform operations.

[0840] A "server" refers to a computer system that provides services and data over a network.

[0841] A "generative AI model" refers to an algorithm that has been trained to perform a specific task (e.g., image recognition or text analysis) using machine learning or artificial intelligence techniques.

[0842] "Upload means" refers to the function or process for sending data from a user terminal to a server.

[0843] "Storage" refers to a mechanism or system (e.g., cloud storage) for secure and persistent retention of data.

[0844] An "analysis queue" refers to a data structure or system that queues data to be analyzed in order and waits for processing.

[0845] "Facial recognition" refers to the technology and processes used to detect and identify human faces within image data.

[0846] "Text recognition" refers to the technology and process of extracting character information from image data and processing it as digital text.

[0847] "Automatic correction measures" refers to functions and processes for automatically modifying or improving data based on the analysis results of a generative AI model.

[0848] "Reconfirmation means" refers to the functions and processes for reconfirming the corrected data and giving final approval.

[0849] "Notification means" refers to the functions and processes for transmitting information such as analysis results and the need for corrections from the system to the user.

[0850] "API integration means" refers to the interfaces and processes for exchanging data and functions between different software systems.

[0851] MODE FOR CARRYING OUT THE INVENTION

[0852] This invention relates to a system that efficiently and accurately analyzes photo or video data and automatically checks and corrects copyrights and portrait rights. In this system, users upload their photographic data, and a server analyzes the data using a generative AI model. The system then corrects the data based on the analysis results, and notifies the user after final confirmation. Approved data can also be linked to other systems via an API.

[0853] Data upload and storage

[0854] Users access the system's upload page to upload photos or video data taken from their own devices (PCs, smartphones, etc.), select the files, and click the upload button. The uploaded data is received by the server and stored in secure storage. This storage process uses cloud storage such as Amazon S3.

[0855] Data analysis with generative AI models

[0856] The server adds the stored data to an analysis queue and prepares it for analysis using a generative AI model. Using the analysis queue, the data is analyzed sequentially. The generative AI model performs facial and text recognition and checks for portrait and copyright issues. This analysis is performed using, for example, Google Cloud Vision API and Tesseract OCR.

[0857] Auto-correct and re-check

[0858] Based on the analysis results, the server issues instructions for automatic correction of the photo or video data. For example, OpenCV is used to blur faces or trim unwanted parts. The corrected data is then checked again by the generative AI model. This recheck determines whether the corrections were made appropriately.

[0859] User notification and API integration

[0860] The final review result is sent to the user via email or in-app notification. The notification will include information on whether the photo was approved or if any corrections are required. The approved data is shared with other systems via API integration via the server. This integration allows the photo to be used immediately, for example, in product description pages on e-commerce sites or in advertising campaigns.

[0861] Specific examples

[0862] For example, consider the case where a freelance photographer uploads photos taken at an event to the system. The server analyzes the uploaded photos using a generative AI model to check for issues with portrait rights and copyright. The generative AI model performs facial recognition to confirm whether specific people are in the photos and blurs their faces if necessary. After corrections are made, the server notifies the user of the results of the check. The notification may include a message such as "Photo A has been approved" or "Photo B has a portrait rights issue and needs to be corrected." Finally, the approved data is linked to the e-commerce site via an API and used on the product page.

[0863] Prompt Sentence Examples

[0864] Examples of prompts for generative AI models include:

[0865] "Detect all faces in this photo and apply blur according to specific criteria."

[0866] In this way, a system can be provided that enables accurate and efficient analysis, automatic correction, and confirmation of photographic and video data, as well as immediate utilization of the data.

[0867] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0868] Step 1: Upload step

[0869] Users access the system's upload page to upload photos or video data taken from their own device (PC, smartphone, etc.) and click the upload button.

[0870] Input: Photo or video data from the user's device

[0871] Output: Upload data to the server

[0872] Specific operation: The user accesses the upload page of the system using a browser, selects the data file, and clicks the upload button. The system receives the selected file and displays a message on the screen indicating that the upload was successful.

[0873] Step 2: Data storage step

[0874] The server receives the uploaded data and stores it in secure storage (e.g., Amazon S3).

[0875] Input: Photo or video data uploaded by the user

[0876] Output: Data file in cloud storage

[0877] Specific operation: The server converts the received data into a certain format and transfers it to the cloud storage using a communication protocol (e.g., HTTPS). Once the data has been saved in the storage, it returns a status indicating that the data has been saved to the system.

[0878] Step 3: Analysis preparation steps

[0879] The server adds the stored data to an analysis queue and prepares it for analysis by the generative AI model.

[0880] Input: Data files stored in cloud storage

[0881] Output: File paths and metadata in the analysis queue

[0882] Specific operation: The server adds the file path in the storage and related metadata to the analysis queue. The data in the analysis queue is processed sequentially according to priority.

[0883] Step 4: Data analysis step

[0884] The server then passes the stored data to a generative AI model for analysis, which performs facial and text recognition and detects portrait and copyright issues.

[0885] Input: Data files from the analysis queue

[0886] Output: Face recognition results, text recognition results, correction instructions

[0887] How it works: The server takes data from the analysis queue and passes it to a generative AI model (e.g., Google Cloud Vision API or Tesseract OCR). The generative AI model detects faces and text and returns correction instructions based on those to the server.

[0888] Step 5: Auto-correction step

[0889] The server automatically modifies the photo or video data based on the analysis results, for example by blurring faces or trimming unwanted parts.

[0890] Input: Correction instructions returned by the generative AI model

[0891] Output: Modified data file

[0892] Specific operation: Based on the correction instructions, the server uses an image processing library such as OpenCV to perform specific corrections. For example, it performs processing such as blurring or cropping the face. The corrected data is temporarily saved.

[0893] Step 6: Reconfirmation step

[0894] The server then passes the corrected data back to the generative AI model for a final check, determining whether the corrections were made appropriately.

[0895] Input: Modified data file

[0896] Output: Final confirmation result (approval or rejection)

[0897] Specific operation: The server sends the corrected data to the generative AI model again for reanalysis. The generative AI model performs a final check to see if the corrections were made properly and returns the results to the server.

[0898] Step 7: User Notification Step

[0899] The server will send the final confirmation result to the user via email or in-app notification.

[0900] Input: Final confirmation result

[0901] Output: User notification (email or in-app message)

[0902] Specific behavior: The server receives the final review result and notifies the user, including whether the photo is approved or needs corrections.

[0903] Step 8: API integration step

[0904] The approved data is then shared with other systems via API by the server.

[0905] Input: Final confirmed data

[0906] Output: Use of data in other systems (e.g., product page on an e-commerce site)

[0907] What it does: The server sends the approved data to other systems via a RESTful API. This integration allows, for example, the approved photos to be used on product pages on online shopping sites.

[0908] In this way, specific processing is performed at each step, enabling efficient and accurate analysis and confirmation of photo and video data across the entire system.

[0909] (Application example 1)

[0910] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0911] For modern advertising creators and marketers, the process of checking and correcting portrait rights and copyrights in photographs and video data is extremely tedious and time-consuming. Performing this process manually increases the risk of human error and reduces efficiency. Furthermore, it is difficult to utilize corrected data in advertising campaigns in real time, hindering the rapid implementation of marketing strategies. The present invention aims to solve these problems by providing a system that efficiently and accurately analyzes and corrects data and utilizes it in advertising campaigns in real time.

[0912] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0913] In this invention, the server includes means for uploading photo or video data from a user terminal to the server, means for analyzing the photo or video data stored on the server using a generative AI model and checking for copyright and portrait rights issues, means for automatically correcting the photo or video data based on the analysis results of the generative AI model, means for performing a final check of the corrected data using the generative AI model again, means for notifying the user of the final check results, means for sharing the final checked data with other systems via API integration, means for analyzing data taken by advertising creators and marketers and checking and correcting portrait rights and copyrights, and means for utilizing the corrected data in advertising campaigns in real time. This enables efficient and rapid analysis and correction of data, allowing it to be immediately utilized in advertising campaigns.

[0914] "Photo or video data" refers to digital information in the form of still images and videos taken or generated by a user.

[0915] "User terminal" refers to an electronic device such as a computer, smartphone, or tablet that is used to upload photo or video data to a server.

[0916] A "server" is a central control device that receives data from user terminals and processes it, such as storing, analyzing, and modifying.

[0917] A "generative AI model" is a system that uses machine learning algorithms to analyze images and videos and automatically check and correct copyright and portrait rights.

[0918] "Means for uploading" refers to the functions and processes that allow a user to send photo or video data from their own device to the server.

[0919] "Means of analysis" refers to the process of processing data using a generative AI model to perform copyright and image rights checks.

[0920] "Means for automatic correction" refers to a function that automatically applies necessary corrections to data based on the analysis results.

[0921] "Final verification means" refers to the process of re-analyzing the automatically corrected data using a regenerative AI model to finally verify the accuracy and legality of the data.

[0922] "Means for notifying" refers to a communication function for notifying the user of the final confirmation result, and may include notification via an interface.

[0923] "API integration and sharing" refers to standardized protocols and interfaces for connecting and exchanging data with other computer systems and platforms.

[0924] "Advertising creator" refers to an individual or organization that produces photographs and videos for advertising and carries out marketing activities.

[0925] "Marketer" refers to an individual or organization whose role is to plan, execute, and manage marketing activities.

[0926] "Means that can be used in advertising campaigns in real time" refers to the ability to immediately incorporate data that has been analyzed and corrected into advertising strategies.

[0927] This system enables the uploading of photo and video data, analysis, automatic correction, final confirmation, notification, and API integration. This system is designed specifically to enable advertising creators and marketers to effectively and quickly manage data and utilize it in real-time advertising campaigns.

[0928] System Overview

[0929] The system consists of the following main components:

[0930] 1. User device (PC, smartphone, tablet, etc.)

[0931] 2. Server

[0932] 3. Generative AI Models

[0933] 4. Storage services (e.g., Firebase, Amazon S3)

[0934] 5. Message queue systems (e.g. RabbitMQ)

[0935] 6. Image processing libraries (e.g. OpenCV, dlib, Pillow)

[0936] 7. Notification systems (e.g., Twilio API, Firebase Cloud Messaging)

[0937] 8. API integration platform (e.g. RESTful API)

[0938] Natural language explanation of program processing

[0939] 1. Data upload and storage:

[0940] Photo or video data is uploaded from the user's device to the server, which receives it and stores it securely in a storage service such as Firebase or Amazon S3.

[0941] 2. Analysis preparation and queue management:

[0942] The server adds the uploaded data to a message queue system such as RabbitMQ and prepares it for analysis by the generative AI model.

[0943] 3. Analysis by generative AI model:

[0944] The server uses the generative AI model to perform the following analysis:

[0945] Facial Recognition: Use OpenCV and dlib to recognize faces in photos and videos and verify portrait rights.

[0946] Text Recognition: Use Tesseract OCR to recognize text in images and perform copyright verification.

[0947] 4. Autocorrect:

[0948] Based on the analysis results of the generative AI model, the server uses an image processing library such as Pillow to automatically make any necessary corrections (e.g., blurring faces, trimming unnecessary parts, etc.).

[0949] 5. Double check:

[0950] The corrected data is analyzed again by the generated AI model for final confirmation.

[0951] 6. Result notification:

[0952] The server uses the Twilio API or Firebase Cloud Messaging to notify the user of the final validation result, including whether the data was approved or if further corrections are required.

[0953] 7. API Integration:

[0954] Approved data is shared in real time with advertising platforms (e.g., Facebook Ads, Google Ads) via RESTful APIs.

[0955] Examples of concrete examples and prompts

[0956] For example, let's consider the case where an advertising creator shoots a promotional video for a product and uploads it to the system. The server receives the uploaded data and analyzes portrait rights and copyright using a generative AI model. Based on the analysis results, necessary corrections are automatically made, and after final confirmation, the results are notified to the user. This data is connected to the API in real time and immediately used in advertising campaigns.

[0957] Prompt Sentence Examples

[0958] User: Upload a product photo.

[0959] System: Initiate facial recognition and portrait rights checks.

[0960] System: Begins recognizing text in images.

[0961] System: Blurring face. Cropping image.

[0962] System: Final checks underway.

[0963] System: Analysis and corrections completed. Data is ready for advertising campaigns.

[0964] In this way, the present invention provides advertising creators and marketers with efficient and accurate data analysis and real-time utilization.

[0965] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0966] Step 1:

[0967] Uploading data

[0968] Users upload photos or video data from their devices. They select the desired files from their devices and send them via the system's upload page. The server receives this data and stores it in secure storage such as Firebase or Amazon S3.

[0969] Input: Photo or video data selected by the user from their device

[0970] Output: Uploaded data stored on the server

[0971] Step 2:

[0972] Analysis preparation and queue management

[0973] The server adds the uploaded data to a message queue system such as RabbitMQ, which prepares the data for analysis tasks and sends it to a generative AI model for analysis.

[0974] Input: Upload data stored on the server

[0975] Output: Data added to the analysis queue

[0976] Step 3:

[0977] Analysis using generative AI models

[0978] The server analyzes the uploaded data using a generative AI model. First, it uses OpenCV and dlib for facial recognition to identify areas where image rights are involved. Second, it uses Tesseract OCR to recognize text within the image and verify copyright.

[0979] Input: Data added to the analysis queue

[0980] Output: Face and text recognition analysis results

[0981] Step 4:

[0982] autocorrect

[0983] Based on the results of the generative AI model's analysis, the server uses image processing libraries such as Pillow to make any necessary corrections, such as adding blur to recognized faces or cropping out unwanted parts.

[0984] Input: Face and text recognition analysis results

[0985] Output: Modified photo or video data

[0986] Step 5:

[0987] Reconfirmation

[0988] The server then analyzes the corrected data again using the generated AI model and performs a final check to ensure the corrections were made correctly and that there are no omissions.

[0989] Input: Modified photo or video data

[0990] Output: Final verified data

[0991] Step 6:

[0992] Result notification

[0993] The server notifies the user of the final validation result using the Twilio API or Firebase Cloud Messaging, including whether the data is approved or requires further correction.

[0994] Input: Final verified data

[0995] Output: A message to inform the user

[0996] Step 7:

[0997] API Integration

[0998] Approved data is shared in real time with advertising platforms (e.g., Facebook Ads, Google Ads) using a RESTful API, allowing advertisers and marketers to instantly use the approved data in their campaigns.

[0999] Input: Approved photo or video data

[1000] Output: Data linked to advertising platform

[1001] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1002] This invention relates to a system for efficiently verifying and correcting copyrights and portrait rights in photo or video data. This system analyzes data uploaded by users using a generative AI model and automatically performs necessary corrections. In addition, by combining it with an emotion engine that recognizes the user's emotions, the system appropriately adjusts notifications and feedback of the analysis results, improving the user experience.

[1003] Uploading photos and video data

[1004] Users access the system's upload page from their own devices (e.g., PCs or smartphones), select the photos or video data they have taken, and upload them. After selecting the files, users send the data by clicking the upload button.

[1005] Data storage and analysis

[1006] The server receives the uploaded data, stores it in storage, and adds it to the analysis queue to prepare it for analysis by the generative AI model.

[1007] Data analysis with generative AI models

[1008] The server passes the data from the analysis queue to the generative AI model, which then begins the analysis process. The generative AI model performs facial and text recognition on the photos and video data to identify issues with portrait rights and copyright. This allows it to extract problematic parts related to copyright and portrait rights.

[1009] Auto-correct and double-check

[1010] The server automatically corrects the photo and video data based on instructions from the generative AI model. Specifically, this includes blurring faces and trimming unnecessary parts. The corrected data is then passed back to the generative AI model for final confirmation.

[1011] Emotional Engine Adjustment

[1012] The emotion engine recognizes the user's emotions in real time and adjusts the analysis results and notifications and feedback appropriately. For example, if the user is feeling stressed, the server will send notifications in a gentler tone. If the user gives positive feedback to the system, the emotion engine uses it to improve the content of future notifications and the accuracy of analysis.

[1013] Notification of approval results

[1014] The server aggregates the final analysis and verification results and notifies the user through the emotion engine. For example, when notifying the user that "all of your uploaded data has been approved," the emotion engine adjusts the wording as necessary.

[1015] Utilizing data through API integration

[1016] The approved data is then linked to other systems via API by the server. This allows the verified data to be immediately used on platforms such as e-commerce sites and social media. For example, verified photos can be displayed on product description pages in online shops or used as material for advertising campaigns.

[1017] Specific examples

[1018] For example, a freelance photographer user can upload photos taken at an event to the system. The server then analyzes the uploaded photos using a generative AI model to check for portrait rights and copyright issues. Based on the analysis results, any necessary face blurring or cropping is automatically performed. The emotion engine then analyzes the user's reaction and provides optimal feedback. Finally, the verified data is linked to the e-commerce site and used as material for product pages.

[1019] In this manner, the present invention provides users with an efficient and flexible photo and video data review, correction and approval process.

[1020] The processing flow will be explained below.

[1021] Step 1:

[1022] Users access the system's upload page from their own device (e.g., PC, smartphone), select the photos or video data they have taken, and then click the upload button to send the data to the system.

[1023] Step 2:

[1024] The terminal generates a request to upload the selected data to the server and transmits the data to the server.

[1025] Step 3:

[1026] The server receives the uploaded photo or video data and stores it in secure storage, checking for duplicate or corrupted data during the process.

[1027] Step 4:

[1028] The server adds the saved data to the analysis queue, preparing it for analysis by the generative AI model, and logs that the data has been successfully added to the analysis queue.

[1029] Step 5:

[1030] The server passes the data from the analysis queue to the generative AI model, which performs facial and text recognition on the photos and video data to identify any issues with portrait rights or copyright.

[1031] Step 6:

[1032] The generative AI model automatically corrects photos and video data based on the analysis results. This correction includes blurring faces and trimming unwanted parts. The correction process is performed by an image processing tool on the server side.

[1033] Step 7:

[1034] The server passes the corrected data back to the generative AI model for final validation, which then re-analyzes the corrected data to ensure all issues have been resolved.

[1035] Step 8:

[1036] The emotion engine analyzes the user's emotional state and adjusts the content and method of notification of the analysis results. The emotion engine analyzes the user's real-time emotional data (e.g., facial expressions, tone of voice).

[1037] Step 9:

[1038] The server aggregates the final confirmation results and notifies the user through the emotion engine. For example, the message "All your uploaded data has been approved" can be softened depending on the emotion, improving the user experience.

[1039] Step 10:

[1040] The server then connects the final verified data to other systems via API. This connection makes the verified data instantly available on platforms such as e-commerce sites and social media. For example, photos can be automatically uploaded to product description pages on e-commerce sites.

[1041] Step 11:

[1042] The notification system sends notifications to users after API integration to let them know how their data is being used. For example, it notifies users by sending a notification that "your customer photo has been uploaded to the product description page."

[1043] Through this process flow, the present invention provides users with an efficient and flexible process for reviewing, correcting, and approving photo and video data.

[1044] Example 2

[1045] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1046] Conventional image data confirmation and correction systems have the problem of requiring a lot of manual work, which is time-consuming and labor-intensive. Furthermore, the accuracy of confirmation and correction tends to vary, making it difficult to improve the user experience. Furthermore, since the final confirmed data is not linked to other systems in real time, it is difficult to immediately utilize the data. There is a demand for a system that solves these issues.

[1047] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for uploading image data from a user terminal to the server; means for analyzing the image data stored in the server using a generative AI model and checking for issues with intellectual property rights and personal rights; means for automatically correcting the image data based on the analysis results of the generative AI model; means for finally checking the corrected data again using the generative AI model; means for notifying the user of the final check results; means for sharing the finally checked data with other information processing systems through API collaboration; and means including an emotion engine that recognizes user emotions in real time and adjusts notification of the analysis results. This enables the efficiency and accuracy of checking and correcting image data to be improved, and further enables an improved user experience and rapid use of the finally checked data.

[1048] "User terminal" refers to a computing device used by an individual, including a PC or smartphone.

[1049] A "server" is a computing system that provides services over a network and stores and processes data.

[1050] "Image data" is digital data of photographs or videos, and is composed of a series of pixel information.

[1051] A "generative AI model" is a data analysis model that uses artificial intelligence technology and analyzes image data using machine learning algorithms.

[1052] "Analysis" refers to the process of examining the contents of data in detail to find specific information or problems.

[1053] "Intellectual property rights" means ownership rights in creative ideas and information that are protected by law, including copyrights and patents.

[1054] "Personal rights" refer to rights that belong to individuals, such as privacy and portrait rights.

[1055] "Automatic correction" means that the system autonomously corrects errors or problems in the data, including blurring or cropping images.

[1056] "Final confirmation" is the process of finally confirming that the analyzed and corrected data is accurate and has no problems.

[1057] "Notification" refers to the act of transmitting information from the system to the user, and is done in the form of email, pop-up, etc.

[1058] "API integration" refers to integration using an application programming interface to share data and functions between different information processing systems.

[1059] The "emotion engine" is a component that analyzes the user's emotional state in real time and adjusts the system's response.

[1060] "Real-time" refers to data processing and communication occurring instantly, with almost no delay.

[1061] The present invention relates to a system for efficiently verifying and correcting intellectual property and personal rights in image data. The system allows users to upload image data from their devices to a server, which then uses a generative AI model to analyze, correct, and finally verify the image data.

[1062] Specifically, the system uses the following hardware and software:

[1063] 1. User terminal: A device such as a desktop computer, laptop, or smartphone that uploads image data.

[1064] 2. Server: A high-performance computing system that stores, analyzes, modifies, and validates data. The server can be a cloud service such as Amazon Web Services (AWS).

[1065] 3. Generative AI models: These are machine learning models built using frameworks such as TensorFlow to analyze image data, perform facial recognition, text recognition, and identify intellectual property and personal rights issues.

[1066] 4. Image processing software: Use libraries such as OpenCV to automatically correct problem areas.

[1067] 5. Emotion Engine: Recognizes the user's emotions in real time and adjusts the notification content based on the analysis results. Specifically, this is achieved by using facial expression recognition APIs and voice analysis software.

[1068] As a concrete example of how the system works, consider a scenario in which a freelance photographer uploads photos taken at an event to the system. The photos uploaded from the user's device are received by the server, stored in storage, and added to the analysis queue. The server then analyzes the photos using a generative AI model, performing facial and text recognition and checking for intellectual property and personal rights issues. Based on the analysis results, the server automatically blurs the face and crops out unnecessary parts. The corrected data is then passed back to the generative AI model for final review.

[1069] The emotion engine recognizes the user's real-time emotions, and if the user is feeling stressed, for example, the server adjusts the tone of the notification to be gentler. The final confirmation result is notified to the user via the emotion engine, and the approved data is shared in real time by the server with other information processing systems via API.

[1070] An example of a prompt might be, "Please explain the process by which a freelance photographer takes photos at an event, uploads them to a system, and automatically checks and corrects copyright and portrait rights."

[1071] In this manner, the present invention provides users with an efficient and flexible image data review, correction and approval process.

[1072] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1073] Step 1:

[1074] The user accesses the upload page of the system using their own terminal, selects image data, and uploads it. When the user selects a file and clicks the "Upload" button, the terminal sends the image data to the server via an HTTP request.

[1075] Input: Image data file path

[1076] Output: HTTP request to the server

[1077] Step 2:

[1078] The server receives the HTTP request and saves the uploaded image data to storage. At the same time, it adds the data to the analysis queue and prepares it for analysis. The server extracts the image data from the request and saves it in the specified directory.

[1079] Input: Image data via HTTP request

[1080] Output: Updates the path of the image data stored in storage and the analysis queue.

[1081] Step 3:

[1082] The server retrieves the next image data from the analysis queue and passes it to the generative AI model, which performs facial and text recognition and identifies intellectual property and personal rights issues.

[1083] Input: Path to the image data saved in storage

[1084] Output: Analysis results by the generative AI model (location and content of the problem area)

[1085] Step 4:

[1086] Based on the analysis results of the generative AI model, the server uses image processing software to automatically correct the image, specifically by blurring problem areas and trimming unnecessary parts.

[1087] Input: Analysis results of the generative AI model

[1088] Output: Corrected image data

[1089] Step 5:

[1090] The server then passes the corrected image data back to the generative AI model for final confirmation, which then rechecks the corrections and confirms that the problem has been resolved.

[1091] Input: Corrected image data

[1092] Output: Final confirmation result

[1093] Step 6:

[1094] The emotion engine recognizes the user's emotions in real time and adjusts the content of notifications sent from the server. For example, if the user is feeling stressed, the notification will be softened.

[1095] Input: Real-time emotional state of the user (facial expression data, voice data)

[1096] Output: Adjusted notification text

[1097] Step 7:

[1098] The server aggregates the final confirmation results and notifies the user through the emotion engine, for example, by sending a notification such as "All of your uploaded data has been approved."

[1099] Input: Final confirmation results and emotion engine input

[1100] Output: User notification

[1101] Step 8:

[1102] The server shares the final confirmed data with other information processing systems via API integration, allowing the confirmed data to be used in real time on e-commerce sites and social media.

[1103] Input: Final confirmed data

[1104] Output: Data transferred to other systems via API

[1105] In this way, the system efficiently and accurately reviews, corrects, and approves image data.

[1106] (Application example 2)

[1107] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1108] The advertising industry requires the ability to quickly and efficiently check and correct copyright and portrait rights issues in photo and video data. However, doing this manually is time-consuming, labor-intensive, and prone to errors. Furthermore, providing feedback without considering user emotions can lead to a poor user experience and lower satisfaction with the service. To solve these issues, a system is needed that combines automatic data analysis and correction with feedback based on user emotions.

[1109] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1110] In this invention, the server includes: means for uploading photo or video data from a user terminal to the server; means for analyzing the photo or video data stored on the server using a generative AI model to check for copyright and portrait rights issues; means for automatically correcting the photo or video data based on the analysis results of the generative AI model; means for performing a final check of the corrected data using the generative AI model again; means including an emotion engine for recognizing user emotions in real time and appropriately adjusting notifications and feedback of the analysis results; means for notifying the user of the results of the final check; and means for sharing the final checked data with other systems via API integration. This makes it possible to quickly and accurately resolve copyright and portrait rights issues in advertising materials and improve the user experience.

[1111] "Photo or video data" refers to digital files in the form of still images or video images taken by a user using a terminal.

[1112] A "user terminal" is an electronic device that a user can operate by connecting to the Internet, such as a personal computer, smartphone, or tablet.

[1113] A "server" is a computer system connected to a network that stores, processes, and distributes data.

[1114] A "generative AI model" is an algorithm or system that uses artificial intelligence techniques to analyze and generate data.

[1115] "Copyright" is the right of the creator or copyright holder of a work to exclusively exploit that work.

[1116] "Portrait rights" are an individual's right to prevent images of their face or figure from being published or used without their permission.

[1117] An "emotion engine" is a system that recognizes and analyzes a user's emotional state in real time and adjusts responses accordingly.

[1118] "API integration" is a method of using an application programming interface (API) to exchange data between different software and systems and integrate functions.

[1119] "Feedback" refers to response information such as analysis results, notifications, and evaluations that the system provides to the user.

[1120] "Analysis" is the process of analyzing data and clarifying its content and characteristics.

[1121] The system of this invention is composed of a user terminal, a server, a generative AI model, an emotion engine, and API integration.

[1122] User terminal

[1123] The user terminal is an electronic device such as a personal computer, smartphone, or tablet, which the user can use to take photos or video data and upload them to the server. The user selects the photos or video data they have taken and accesses a dedicated upload page to send the data.

[1124] server

[1125] The server is connected to the network and provides many functions such as storing, analyzing, modifying, notifying, and API integration of photo or video data. The server uses the following specific hardware and software:

[1126] Server storage: Amazon S3

[1127] Data analysis: OpenCV, TensorFlow

[1128] Emotion analysis: Microsoft Azure Emotion API

[1129] The data received by the server is stored in storage and added to the analysis queue.

[1130] Generative AI Models

[1131] The generative AI model analyzes photo and video data stored on a server, performs facial and text recognition within the data, and identifies copyright and portrait rights issues. Based on the analysis results, automatic corrections are performed, such as blurring faces and cropping unwanted parts.

[1132] Emotion Engine

[1133] The emotion engine recognizes the user's emotional state in real time and adjusts the analysis results and notifications and feedback. This improves the user experience by sending notifications in a gentler tone if the user is feeling stressed. In addition, if the user gives positive feedback, the emotion engine uses it to improve the content of future notifications and the accuracy of analysis.

[1134] API Integration

[1135] Approved data can be linked to other systems via APIs, allowing it to be immediately used on e-commerce sites and social media platforms. For example, verified photos can be used on product pages in online shops or as material for advertising campaigns.

[1136] Specific examples

[1137] As a concrete example, consider the case where an advertising creator uploads a photo taken at a local tourist spot to this system. The server analyzes the uploaded photo using a generative AI model to check for portrait rights and copyright issues. Based on the analysis results, faces are automatically blurred or cropped. The emotion engine then analyzes the user's reaction and provides appropriate feedback. For example, a prompt such as "Please recognize all faces in this image, check for portrait rights issues, and blur them if necessary. Also, check for objects that may be copyrighted and crop them if necessary" could be input into the generative AI model.

[1138] In this way, the present invention can quickly and accurately resolve copyright and portrait rights issues of advertising materials and improve user experience.

[1139] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1140] Step 1:

[1141] The user takes a photo or video data and uploads it to the server from the user's device. The user selects the file specified and sends it to the server via the upload page. The photo or video data file is required as input, and the data is saved on the server as output. The specific operation is when the user clicks the upload button.

[1142] Step 2:

[1143] The server saves the received data in storage and adds it to the analysis queue at the same time. It takes the received photo or video data as input, stores it in the destination storage as output, and adds a task to the analysis queue. Specifically, it saves the data in Amazon S3 and adds an analysis task to the queue.

[1144] Step 3:

[1145] The server retrieves data from the analysis queue and passes it to the generative AI model to begin analysis. The input is the photo or video data retrieved from the analysis queue, and the output is the analysis results. Specific operations include facial recognition and text recognition using OpenCV and TensorFlow.

[1146] Step 4:

[1147] The server automatically corrects the photo or video data based on the analysis results of the generative AI model. The analysis results are taken as input, and corrected data is generated as output. Specific operations include blurring the face area and trimming unnecessary parts.

[1148] Step 5:

[1149] The server then passes the corrected data back to the generative AI model for final confirmation. The corrected photo or video data is the input, and the final confirmation result is generated as the output. Specifically, final face and text recognition is performed again.

[1150] Step 6:

[1151] The server uses an emotion engine to recognize the user's emotional state in real time and adjusts the analysis results and feedback accordingly. The input is the user's emotional data and the final confirmation result, and the output is an adjusted feedback message. Specifically, emotion analysis is performed using the Microsoft Azure Emotion API.

[1152] Step 7:

[1153] The server notifies the user of the final confirmation result. The input is the adjusted feedback message, and the output is a notification sent to the user. The specific operation is to send the notification message to the user's device via push notification or email.

[1154] Step 8:

[1155] The server shares the final confirmed data with other systems via API. The confirmed data is input, and the output is immediately available on platforms such as e-commerce sites and social media. Specifically, the confirmed data is sent to other systems via API.

[1156] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1157] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1158] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1159] [Fourth embodiment]

[1160] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1161] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1162] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1164] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1166] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1167] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1168] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1169] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1170] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1171] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1172] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1173] This invention relates to a system for efficiently and accurately analyzing and verifying photo or video data. Users upload their photographic data, and a server analyzes the data using a generative AI model, automatically verifying and correcting copyright and portrait rights. Furthermore, the system notifies users of the analysis results and uses an API to link verified data with other systems, enabling immediate use of the data.

[1174] Specifically, the system is constructed as follows:

[1175] Uploading photos and video data

[1176] Users can upload photos or video data taken from their own devices (e.g., PCs or smartphones) by accessing the system's upload page. All they need to do is select the desired file and click the upload button.

[1177] Data storage and analysis

[1178] The server receives the uploaded data and stores it in secure storage, while simultaneously adding it to the analysis queue and preparing it for analysis by the generative AI model.

[1179] Data analysis with generative AI models

[1180] The server passes the stored data to the generative AI model to begin analysis. The generative AI model first performs facial recognition to identify areas where portrait rights are involved. It also checks for copyright infringement through text recognition within the image. Based on the results of this analysis, it issues instructions for any necessary corrections (e.g., blurring faces, trimming unnecessary parts, etc.).

[1181] Auto-correct and double-check

[1182] The server automatically modifies the photo or video data based on instructions from the generative AI model. The modified data is then passed back to the generative AI model for final confirmation. This confirmation determines whether the data is acceptable.

[1183] Notification of approval results

[1184] The server notifies the user of the final analysis and verification results, such as "All data uploaded by the user has been approved" or "The following data needs to be edited due to portrait rights issues."

[1185] Utilizing data through API integration

[1186] The server then connects the approved data to other systems via APIs, allowing it to be instantly used on platforms such as e-commerce sites and social media. For example, verified photos can be displayed on product description pages for online shopping sites or used in advertising campaigns.

[1187] Specific examples

[1188] For example, a freelance photographer user uploads photos taken at an event to the system. The server analyzes the uploaded photos using a generative AI model to check for portrait rights and copyright issues. The generative AI model performs facial recognition to determine whether a specific person is in the photo and blurs the face if necessary. Once this process is complete, the server notifies the user of the results. The notification includes information on whether the photo is approved or requires correction. Finally, the approved data is linked to the e-commerce site via an API and used on the product page.

[1189] In this manner, the present invention provides a user with an efficient and hassle-free process for reviewing and approving photo and video data.

[1190] The processing flow will be explained below.

[1191] Step 1:

[1192] Users access the system's upload page using a device (e.g., PC or smartphone) to select the files they have taken, and then click the upload button to send the data to the system.

[1193] Step 2:

[1194] The terminal generates a request to upload the selected photo or video data to the platform server and transmits the data.

[1195] Step 3:

[1196] The server receives the uploaded data and stores it in secure storage, while simultaneously adding it to an analysis queue, ready for analysis by the generative AI model.

[1197] Step 4:

[1198] The server then passes the data added to the analysis queue to the generative AI model, which performs facial and text recognition on the photos and video data to identify issues with portrait rights and copyright.

[1199] Step 5:

[1200] Based on the analysis results, the generative AI model generates instructions for automatically correcting photos and video data, including blurring faces and cropping unwanted areas.

[1201] Step 6:

[1202] The server receives instructions from the generative AI model and automatically corrects the photo and video data using its internal image processing tools. The corrected data is then passed back to the generative AI model for final confirmation.

[1203] Step 7:

[1204] The generative AI model then re-analyzes the corrected data and performs a final check, which determines whether the data is acceptable.

[1205] Step 8:

[1206] The server collects the final confirmation results and notifies the user. Approved data is marked as "approved" and linked to the marketplace and content delivery system. Data that cannot be approved is fed back to the user with specific reasons.

[1207] Step 9:

[1208] The notification system will inform the user of the results of the check in natural language, for example, sending messages such as "All data uploaded by the customer has been approved" or "The following data needs to be edited due to portrait rights issues."

[1209] Step 10:

[1210] The server then uses an API to connect the verified data to other systems, allowing it to be immediately used on platforms such as e-commerce sites and social media.

[1211] This allows users to have an efficient and accurate photo and video data review and approval process.

[1212] Example 1

[1213] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1214] Efficiently and accurately analyzing photo and video data online and automatically checking and correcting copyright and portrait rights issues is time-consuming and labor-intensive if done manually. Therefore, there is a need to provide a system that automates this work and facilitates the immediate use of data.

[1215] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1216] In this invention, the server includes a means for uploading photo or video data from a user terminal to the server, a means for analyzing the photo or video data stored on the server using a generative AI model to check for copyright and portrait rights issues, a means for automatically correcting the photo or video data based on the analysis results of the generative AI model, a means for performing a final check on the corrected data using the generative AI model again, a means for notifying the user of the final check results, a means for sharing the final checked data with other systems via API integration, a means for storing the data in secure storage, a means for using an analysis queue in the generative AI model, and a means for performing facial recognition and text recognition. This enables accurate and efficient analysis, automatic correction, and checking of photo and video data. It also promotes the immediate use of checked data.

[1217] "Photographic or video data" refers to visual information recorded in the form of an image file or video file.

[1218] "User terminal" refers to an electronic device, such as a computer, smartphone, or tablet, that a user uses to perform operations.

[1219] A "server" refers to a computer system that provides services and data over a network.

[1220] A "generative AI model" refers to an algorithm that has been trained to perform a specific task (e.g., image recognition or text analysis) using machine learning or artificial intelligence techniques.

[1221] "Upload means" refers to the function or process for sending data from a user terminal to a server.

[1222] "Storage" refers to a mechanism or system (e.g., cloud storage) for secure and persistent retention of data.

[1223] An "analysis queue" refers to a data structure or system that queues data to be analyzed in order and waits for processing.

[1224] "Facial recognition" refers to the technology and processes used to detect and identify human faces within image data.

[1225] "Text recognition" refers to the technology and process of extracting character information from image data and processing it as digital text.

[1226] "Automatic correction measures" refers to functions and processes for automatically modifying or improving data based on the analysis results of a generative AI model.

[1227] "Reconfirmation means" refers to the functions and processes for reconfirming the corrected data and giving final approval.

[1228] "Notification means" refers to the functions and processes for transmitting information such as analysis results and the need for corrections from the system to the user.

[1229] "API integration means" refers to the interfaces and processes for exchanging data and functions between different software systems.

[1230] MODE FOR CARRYING OUT THE INVENTION

[1231] This invention relates to a system that efficiently and accurately analyzes photo or video data and automatically checks and corrects copyrights and portrait rights. In this system, users upload their photographic data, and a server analyzes the data using a generative AI model. The system then corrects the data based on the analysis results, and notifies the user after final confirmation. Approved data can also be linked to other systems via an API.

[1232] Data upload and storage

[1233] Users access the system's upload page to upload photos or video data taken from their own devices (PCs, smartphones, etc.), select the files, and click the upload button. The uploaded data is received by the server and stored in secure storage. This storage process uses cloud storage such as Amazon S3.

[1234] Data analysis with generative AI models

[1235] The server adds the stored data to an analysis queue and prepares it for analysis using a generative AI model. Using the analysis queue, the data is analyzed sequentially. The generative AI model performs facial and text recognition and checks for portrait and copyright issues. This analysis is performed using, for example, Google Cloud Vision API and Tesseract OCR.

[1236] Auto-correct and re-check

[1237] Based on the analysis results, the server issues instructions for automatic correction of the photo or video data. For example, OpenCV is used to blur faces or trim unwanted parts. The corrected data is then checked again by the generative AI model. This recheck determines whether the corrections were made appropriately.

[1238] User notification and API integration

[1239] The final review result is sent to the user via email or in-app notification. The notification will include information on whether the photo was approved or if any corrections are required. The approved data is shared with other systems via API integration via the server. This integration allows the photo to be used immediately, for example, in product description pages on e-commerce sites or in advertising campaigns.

[1240] Specific examples

[1241] For example, consider the case where a freelance photographer uploads photos taken at an event to the system. The server analyzes the uploaded photos using a generative AI model to check for issues with portrait rights and copyright. The generative AI model performs facial recognition to confirm whether specific people are in the photos and blurs their faces if necessary. After corrections are made, the server notifies the user of the results of the check. The notification may include a message such as "Photo A has been approved" or "Photo B has a portrait rights issue and needs to be corrected." Finally, the approved data is linked to the e-commerce site via an API and used on the product page.

[1242] Prompt Sentence Examples

[1243] Examples of prompts for generative AI models include:

[1244] "Detect all faces in this photo and apply blur according to specific criteria."

[1245] In this way, a system can be provided that enables accurate and efficient analysis, automatic correction, and confirmation of photographic and video data, as well as immediate utilization of the data.

[1246] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1247] Step 1: Upload step

[1248] Users access the system's upload page to upload photos or video data taken from their own device (PC, smartphone, etc.) and click the upload button.

[1249] Input: Photo or video data from the user's device

[1250] Output: Upload data to the server

[1251] Specific operation: The user accesses the upload page of the system using a browser, selects the data file, and clicks the upload button. The system receives the selected file and displays a message on the screen indicating that the upload was successful.

[1252] Step 2: Data storage step

[1253] The server receives the uploaded data and stores it in secure storage (e.g., Amazon S3).

[1254] Input: Photo or video data uploaded by the user

[1255] Output: Data file in cloud storage

[1256] Specific operation: The server converts the received data into a certain format and transfers it to the cloud storage using a communication protocol (e.g., HTTPS). Once the data has been saved in the storage, it returns a status indicating that the data has been saved to the system.

[1257] Step 3: Analysis preparation steps

[1258] The server adds the stored data to an analysis queue and prepares it for analysis by the generative AI model.

[1259] Input: Data files stored in cloud storage

[1260] Output: File paths and metadata in the analysis queue

[1261] Specific operation: The server adds the file path in the storage and related metadata to the analysis queue. The data in the analysis queue is processed sequentially according to priority.

[1262] Step 4: Data analysis step

[1263] The server then passes the stored data to a generative AI model for analysis, which performs facial and text recognition and detects portrait and copyright issues.

[1264] Input: Data files from the analysis queue

[1265] Output: Face recognition results, text recognition results, correction instructions

[1266] How it works: The server takes data from the analysis queue and passes it to a generative AI model (e.g., Google Cloud Vision API or Tesseract OCR). The generative AI model detects faces and text and returns correction instructions based on those to the server.

[1267] Step 5: Auto-correction step

[1268] The server automatically modifies the photo or video data based on the analysis results, for example by blurring faces or trimming unwanted parts.

[1269] Input: Correction instructions returned by the generative AI model

[1270] Output: Modified data file

[1271] Specific operation: Based on the correction instructions, the server uses an image processing library such as OpenCV to perform specific corrections. For example, it performs processing such as blurring or cropping the face. The corrected data is temporarily saved.

[1272] Step 6: Reconfirmation step

[1273] The server then passes the corrected data back to the generative AI model for a final check, determining whether the corrections were made appropriately.

[1274] Input: Modified data file

[1275] Output: Final confirmation result (approval or rejection)

[1276] Specific operation: The server sends the corrected data to the generative AI model again for reanalysis. The generative AI model performs a final check to see if the corrections were made properly and returns the results to the server.

[1277] Step 7: User Notification Step

[1278] The server will send the final confirmation result to the user via email or in-app notification.

[1279] Input: Final confirmation result

[1280] Output: User notification (email or in-app message)

[1281] Specific behavior: The server receives the final review result and notifies the user, including whether the photo is approved or needs corrections.

[1282] Step 8: API integration step

[1283] The approved data is then shared with other systems via API by the server.

[1284] Input: Final confirmed data

[1285] Output: Use of data in other systems (e.g., product page on an e-commerce site)

[1286] What it does: The server sends the approved data to other systems via a RESTful API. This integration allows, for example, the approved photos to be used on product pages on online shopping sites.

[1287] In this way, specific processing is performed at each step, enabling efficient and accurate analysis and confirmation of photo and video data across the entire system.

[1288] (Application example 1)

[1289] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1290] For modern advertising creators and marketers, the process of checking and correcting portrait rights and copyrights in photographs and video data is extremely tedious and time-consuming. Performing this process manually increases the risk of human error and reduces efficiency. Furthermore, it is difficult to utilize corrected data in advertising campaigns in real time, hindering the rapid implementation of marketing strategies. The present invention aims to solve these problems by providing a system that efficiently and accurately analyzes and corrects data and utilizes it in advertising campaigns in real time.

[1291] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1292] In this invention, the server includes means for uploading photo or video data from a user terminal to the server, means for analyzing the photo or video data stored on the server using a generative AI model and checking for copyright and portrait rights issues, means for automatically correcting the photo or video data based on the analysis results of the generative AI model, means for performing a final check of the corrected data using the generative AI model again, means for notifying the user of the final check results, means for sharing the final checked data with other systems via API integration, means for analyzing data taken by advertising creators and marketers and checking and correcting portrait rights and copyrights, and means for utilizing the corrected data in advertising campaigns in real time. This enables efficient and rapid analysis and correction of data, allowing it to be immediately utilized in advertising campaigns.

[1293] "Photo or video data" refers to digital information in the form of still images and videos taken or generated by a user.

[1294] "User terminal" refers to an electronic device such as a computer, smartphone, or tablet that is used to upload photo or video data to a server.

[1295] A "server" is a central control device that receives data from user terminals and processes it, such as storing, analyzing, and modifying.

[1296] A "generative AI model" is a system that uses machine learning algorithms to analyze images and videos and automatically check and correct copyright and portrait rights.

[1297] "Means for uploading" refers to the functions and processes that allow a user to send photo or video data from their own device to the server.

[1298] "Means of analysis" refers to the process of processing data using a generative AI model to perform copyright and image rights checks.

[1299] "Means for automatic correction" refers to a function that automatically applies necessary corrections to data based on the analysis results.

[1300] "Final verification means" refers to the process of re-analyzing the automatically corrected data using a regenerative AI model to finally verify the accuracy and legality of the data.

[1301] "Means for notifying" refers to a communication function for notifying the user of the final confirmation result, and may include notification via an interface.

[1302] "API integration and sharing" refers to standardized protocols and interfaces for connecting and exchanging data with other computer systems and platforms.

[1303] "Advertising creator" refers to an individual or organization that produces photographs and videos for advertising and carries out marketing activities.

[1304] "Marketer" refers to an individual or organization whose role is to plan, execute, and manage marketing activities.

[1305] "Means that can be used in advertising campaigns in real time" refers to the ability to immediately incorporate data that has been analyzed and corrected into advertising strategies.

[1306] This system enables the uploading of photo and video data, analysis, automatic correction, final confirmation, notification, and API integration. This system is designed specifically to enable advertising creators and marketers to effectively and quickly manage data and utilize it in real-time advertising campaigns.

[1307] System Overview

[1308] The system consists of the following main components:

[1309] 1. User device (PC, smartphone, tablet, etc.)

[1310] 2. Server

[1311] 3. Generative AI Models

[1312] 4. Storage services (e.g., Firebase, Amazon S3)

[1313] 5. Message queue systems (e.g. RabbitMQ)

[1314] 6. Image processing libraries (e.g. OpenCV, dlib, Pillow)

[1315] 7. Notification systems (e.g., Twilio API, Firebase Cloud Messaging)

[1316] 8. API integration platform (e.g. RESTful API)

[1317] Natural language explanation of program processing

[1318] 1. Data upload and storage:

[1319] Photo or video data is uploaded from the user's device to the server, which receives it and stores it securely in a storage service such as Firebase or Amazon S3.

[1320] 2. Analysis preparation and queue management:

[1321] The server adds the uploaded data to a message queue system such as RabbitMQ and prepares it for analysis by the generative AI model.

[1322] 3. Analysis by generative AI model:

[1323] The server uses the generative AI model to perform the following analysis:

[1324] Facial Recognition: Use OpenCV and dlib to recognize faces in photos and videos and verify portrait rights.

[1325] Text Recognition: Use Tesseract OCR to recognize text in images and perform copyright verification.

[1326] 4. Autocorrect:

[1327] Based on the analysis results of the generative AI model, the server uses an image processing library such as Pillow to automatically make any necessary corrections (e.g., blurring faces, trimming unnecessary parts, etc.).

[1328] 5. Double check:

[1329] The corrected data is analyzed again by the generated AI model for final confirmation.

[1330] 6. Result notification:

[1331] The server uses the Twilio API or Firebase Cloud Messaging to notify the user of the final validation result, including whether the data was approved or if further corrections are required.

[1332] 7. API Integration:

[1333] Approved data is shared in real time with advertising platforms (e.g., Facebook Ads, Google Ads) via RESTful APIs.

[1334] Examples of concrete examples and prompts

[1335] For example, let's consider the case where an advertising creator shoots a promotional video for a product and uploads it to the system. The server receives the uploaded data and analyzes portrait rights and copyright using a generative AI model. Based on the analysis results, necessary corrections are automatically made, and after final confirmation, the results are notified to the user. This data is connected to the API in real time and immediately used in advertising campaigns.

[1336] Prompt Sentence Examples

[1337] User: Upload a product photo.

[1338] System: Initiate facial recognition and portrait rights checks.

[1339] System: Begins recognizing text in images.

[1340] System: Blurring face. Cropping image.

[1341] System: Final checks underway.

[1342] System: Analysis and corrections completed. Data is ready for advertising campaigns.

[1343] In this way, the present invention provides advertising creators and marketers with efficient and accurate data analysis and real-time utilization.

[1344] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1345] Step 1:

[1346] Uploading data

[1347] Users upload photos or video data from their devices. They select the desired files from their devices and send them via the system's upload page. The server receives this data and stores it in secure storage such as Firebase or Amazon S3.

[1348] Input: Photo or video data selected by the user from their device

[1349] Output: Uploaded data stored on the server

[1350] Step 2:

[1351] Analysis preparation and queue management

[1352] The server adds the uploaded data to a message queue system such as RabbitMQ, which prepares the data for analysis tasks and sends it to a generative AI model for analysis.

[1353] Input: Upload data stored on the server

[1354] Output: Data added to the analysis queue

[1355] Step 3:

[1356] Analysis using generative AI models

[1357] The server analyzes the uploaded data using a generative AI model. First, it uses OpenCV and dlib for facial recognition to identify areas where image rights are involved. Second, it uses Tesseract OCR to recognize text within the image and verify copyright.

[1358] Input: Data added to the analysis queue

[1359] Output: Face and text recognition analysis results

[1360] Step 4:

[1361] autocorrect

[1362] Based on the results of the generative AI model's analysis, the server uses image processing libraries such as Pillow to make any necessary corrections, such as adding blur to recognized faces or cropping out unwanted parts.

[1363] Input: Face and text recognition analysis results

[1364] Output: Modified photo or video data

[1365] Step 5:

[1366] Reconfirmation

[1367] The server then analyzes the corrected data again using the generated AI model and performs a final check to ensure the corrections were made correctly and that there are no omissions.

[1368] Input: Modified photo or video data

[1369] Output: Final verified data

[1370] Step 6:

[1371] Result notification

[1372] The server notifies the user of the final validation result using the Twilio API or Firebase Cloud Messaging, including whether the data is approved or requires further correction.

[1373] Input: Final verified data

[1374] Output: A message to inform the user

[1375] Step 7:

[1376] API Integration

[1377] Approved data is shared in real time with advertising platforms (e.g., Facebook Ads, Google Ads) using a RESTful API, allowing advertisers and marketers to instantly use the approved data in their campaigns.

[1378] Input: Approved photo or video data

[1379] Output: Data linked to advertising platform

[1380] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1381] This invention relates to a system for efficiently verifying and correcting copyrights and portrait rights in photo or video data. This system analyzes data uploaded by users using a generative AI model and automatically performs necessary corrections. In addition, by combining it with an emotion engine that recognizes the user's emotions, the system appropriately adjusts notifications and feedback of the analysis results, improving the user experience.

[1382] Uploading photos and video data

[1383] Users access the system's upload page from their own devices (e.g., PCs or smartphones), select the photos or video data they have taken, and upload them. After selecting the files, users send the data by clicking the upload button.

[1384] Data storage and analysis

[1385] The server receives the uploaded data, stores it in storage, and adds it to the analysis queue to prepare it for analysis by the generative AI model.

[1386] Data analysis with generative AI models

[1387] The server passes the data from the analysis queue to the generative AI model, which then begins the analysis process. The generative AI model performs facial and text recognition on the photos and video data to identify issues with portrait rights and copyright. This allows it to extract problematic parts related to copyright and portrait rights.

[1388] Auto-correct and double-check

[1389] The server automatically corrects the photo and video data based on instructions from the generative AI model. Specifically, this includes blurring faces and trimming unnecessary parts. The corrected data is then passed back to the generative AI model for final confirmation.

[1390] Emotional Engine Adjustment

[1391] The emotion engine recognizes the user's emotions in real time and adjusts the analysis results and notifications and feedback appropriately. For example, if the user is feeling stressed, the server will send notifications in a gentler tone. If the user gives positive feedback to the system, the emotion engine uses it to improve the content of future notifications and the accuracy of analysis.

[1392] Notification of approval results

[1393] The server aggregates the final analysis and verification results and notifies the user through the emotion engine. For example, when notifying the user that "all of your uploaded data has been approved," the emotion engine adjusts the wording as necessary.

[1394] Utilizing data through API integration

[1395] The approved data is then linked to other systems via API by the server. This allows the verified data to be immediately used on platforms such as e-commerce sites and social media. For example, verified photos can be displayed on product description pages in online shops or used as material for advertising campaigns.

[1396] Specific examples

[1397] For example, a freelance photographer user can upload photos taken at an event to the system. The server then analyzes the uploaded photos using a generative AI model to check for portrait rights and copyright issues. Based on the analysis results, any necessary face blurring or cropping is automatically performed. The emotion engine then analyzes the user's reaction and provides optimal feedback. Finally, the verified data is linked to the e-commerce site and used as material for product pages.

[1398] In this manner, the present invention provides users with an efficient and flexible photo and video data review, correction and approval process.

[1399] The processing flow will be explained below.

[1400] Step 1:

[1401] Users access the system's upload page from their own device (e.g., PC, smartphone), select the photos or video data they have taken, and then click the upload button to send the data to the system.

[1402] Step 2:

[1403] The terminal generates a request to upload the selected data to the server and transmits the data to the server.

[1404] Step 3:

[1405] The server receives the uploaded photo or video data and stores it in secure storage, checking for duplicate or corrupted data during the process.

[1406] Step 4:

[1407] The server adds the saved data to the analysis queue, preparing it for analysis by the generative AI model, and logs that the data has been successfully added to the analysis queue.

[1408] Step 5:

[1409] The server passes the data from the analysis queue to the generative AI model, which performs facial and text recognition on the photos and video data to identify any issues with portrait rights or copyright.

[1410] Step 6:

[1411] The generative AI model automatically corrects photos and video data based on the analysis results. This correction includes blurring faces and trimming unwanted parts. The correction process is performed by an image processing tool on the server side.

[1412] Step 7:

[1413] The server passes the corrected data back to the generative AI model for final validation, which then re-analyzes the corrected data to ensure all issues have been resolved.

[1414] Step 8:

[1415] The emotion engine analyzes the user's emotional state and adjusts the content and method of notification of the analysis results. The emotion engine analyzes the user's real-time emotional data (e.g., facial expressions, tone of voice).

[1416] Step 9:

[1417] The server aggregates the final confirmation results and notifies the user through the emotion engine. For example, the message "All your uploaded data has been approved" can be softened depending on the emotion, improving the user experience.

[1418] Step 10:

[1419] The server then connects the final verified data to other systems via API. This connection makes the verified data instantly available on platforms such as e-commerce sites and social media. For example, photos can be automatically uploaded to product description pages on e-commerce sites.

[1420] Step 11:

[1421] The notification system sends notifications to users after API integration to let them know how their data is being used. For example, it notifies users by sending a notification that "your customer photo has been uploaded to the product description page."

[1422] Through this process flow, the present invention provides users with an efficient and flexible process for reviewing, correcting, and approving photo and video data.

[1423] Example 2

[1424] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1425] Conventional image data confirmation and correction systems have the problem of requiring a lot of manual work, which is time-consuming and labor-intensive. Furthermore, the accuracy of confirmation and correction tends to vary, making it difficult to improve the user experience. Furthermore, since the final confirmed data is not linked to other systems in real time, it is difficult to immediately utilize the data. There is a demand for a system that solves these issues.

[1426] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for uploading image data from a user terminal to the server; means for analyzing the image data stored in the server using a generative AI model and checking for issues with intellectual property rights and personal rights; means for automatically correcting the image data based on the analysis results of the generative AI model; means for finally checking the corrected data again using the generative AI model; means for notifying the user of the final check results; means for sharing the finally checked data with other information processing systems through API collaboration; and means including an emotion engine that recognizes user emotions in real time and adjusts notification of the analysis results. This enables the efficiency and accuracy of checking and correcting image data to be improved, and further enables an improved user experience and rapid use of the finally checked data.

[1427] "User terminal" refers to a computing device used by an individual, including a PC or smartphone.

[1428] A "server" is a computing system that provides services over a network and stores and processes data.

[1429] "Image data" is digital data of photographs or videos, and is composed of a series of pixel information.

[1430] A "generative AI model" is a data analysis model that uses artificial intelligence technology and analyzes image data using machine learning algorithms.

[1431] "Analysis" refers to the process of examining the contents of data in detail to find specific information or problems.

[1432] "Intellectual property rights" means ownership rights in creative ideas and information that are protected by law, including copyrights and patents.

[1433] "Personal rights" refer to rights that belong to individuals, such as privacy and portrait rights.

[1434] "Automatic correction" means that the system autonomously corrects errors or problems in the data, including blurring or cropping images.

[1435] "Final confirmation" is the process of finally confirming that the analyzed and corrected data is accurate and has no problems.

[1436] "Notification" refers to the act of transmitting information from the system to the user, and is done in the form of email, pop-up, etc.

[1437] "API integration" refers to integration using an application programming interface to share data and functions between different information processing systems.

[1438] The "emotion engine" is a component that analyzes the user's emotional state in real time and adjusts the system's response.

[1439] "Real-time" refers to data processing and communication occurring instantly, with almost no delay.

[1440] The present invention relates to a system for efficiently verifying and correcting intellectual property and personal rights in image data. The system allows users to upload image data from their devices to a server, which then uses a generative AI model to analyze, correct, and finally verify the image data.

[1441] Specifically, the system uses the following hardware and software:

[1442] 1. User terminal: A device such as a desktop computer, laptop, or smartphone that uploads image data.

[1443] 2. Server: A high-performance computing system that stores, analyzes, modifies, and validates data. The server can be a cloud service such as Amazon Web Services (AWS).

[1444] 3. Generative AI models: These are machine learning models built using frameworks such as TensorFlow to analyze image data, perform facial recognition, text recognition, and identify intellectual property and personal rights issues.

[1445] 4. Image processing software: Use libraries such as OpenCV to automatically correct problem areas.

[1446] 5. Emotion Engine: Recognizes the user's emotions in real time and adjusts the notification content based on the analysis results. Specifically, this is achieved by using facial expression recognition APIs and voice analysis software.

[1447] As a concrete example of how the system works, consider a scenario in which a freelance photographer uploads photos taken at an event to the system. The photos uploaded from the user's device are received by the server, stored in storage, and added to the analysis queue. The server then analyzes the photos using a generative AI model, performing facial and text recognition and checking for intellectual property and personal rights issues. Based on the analysis results, the server automatically blurs the face and crops out unnecessary parts. The corrected data is then passed back to the generative AI model for final review.

[1448] The emotion engine recognizes the user's real-time emotions, and if the user is feeling stressed, for example, the server adjusts the tone of the notification to be gentler. The final confirmation result is notified to the user via the emotion engine, and the approved data is shared in real time by the server with other information processing systems via API.

[1449] An example of a prompt might be, "Please explain the process by which a freelance photographer takes photos at an event, uploads them to a system, and automatically checks and corrects copyright and portrait rights."

[1450] In this manner, the present invention provides users with an efficient and flexible image data review, correction and approval process.

[1451] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1452] Step 1:

[1453] The user accesses the upload page of the system using their own terminal, selects image data, and uploads it. When the user selects a file and clicks the "Upload" button, the terminal sends the image data to the server via an HTTP request.

[1454] Input: Image data file path

[1455] Output: HTTP request to the server

[1456] Step 2:

[1457] The server receives the HTTP request and saves the uploaded image data to storage. At the same time, it adds the data to the analysis queue and prepares it for analysis. The server extracts the image data from the request and saves it in the specified directory.

[1458] Input: Image data via HTTP request

[1459] Output: Updates the path of the image data stored in storage and the analysis queue.

[1460] Step 3:

[1461] The server retrieves the next image data from the analysis queue and passes it to the generative AI model, which performs facial and text recognition and identifies intellectual property and personal rights issues.

[1462] Input: Path to the image data saved in storage

[1463] Output: Analysis results by the generative AI model (location and content of the problem area)

[1464] Step 4:

[1465] Based on the analysis results of the generative AI model, the server uses image processing software to automatically correct the image, specifically by blurring problem areas and trimming unnecessary parts.

[1466] Input: Analysis results of the generative AI model

[1467] Output: Corrected image data

[1468] Step 5:

[1469] The server then passes the corrected image data back to the generative AI model for final confirmation, which then rechecks the corrections and confirms that the problem has been resolved.

[1470] Input: Corrected image data

[1471] Output: Final confirmation result

[1472] Step 6:

[1473] The emotion engine recognizes the user's emotions in real time and adjusts the content of notifications sent from the server. For example, if the user is feeling stressed, the notification will be softened.

[1474] Input: Real-time emotional state of the user (facial expression data, voice data)

[1475] Output: Adjusted notification text

[1476] Step 7:

[1477] The server aggregates the final confirmation results and notifies the user through the emotion engine, for example, by sending a notification such as "All of your uploaded data has been approved."

[1478] Input: Final confirmation results and emotion engine input

[1479] Output: User notification

[1480] Step 8:

[1481] The server shares the final confirmed data with other information processing systems via API integration, allowing the confirmed data to be used in real time on e-commerce sites and social media.

[1482] Input: Final confirmed data

[1483] Output: Data transferred to other systems via API

[1484] In this way, the system efficiently and accurately reviews, corrects, and approves image data.

[1485] (Application example 2)

[1486] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1487] The advertising industry requires the ability to quickly and efficiently check and correct copyright and portrait rights issues in photo and video data. However, doing this manually is time-consuming, labor-intensive, and prone to errors. Furthermore, providing feedback without considering user emotions can lead to a poor user experience and lower satisfaction with the service. To solve these issues, a system is needed that combines automatic data analysis and correction with feedback based on user emotions.

[1488] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1489] In this invention, the server includes: means for uploading photo or video data from a user terminal to the server; means for analyzing the photo or video data stored on the server using a generative AI model to check for copyright and portrait rights issues; means for automatically correcting the photo or video data based on the analysis results of the generative AI model; means for performing a final check of the corrected data using the generative AI model again; means including an emotion engine for recognizing user emotions in real time and appropriately adjusting notifications and feedback of the analysis results; means for notifying the user of the results of the final check; and means for sharing the final checked data with other systems via API integration. This makes it possible to quickly and accurately resolve copyright and portrait rights issues in advertising materials and improve the user experience.

[1490] "Photo or video data" refers to digital files in the form of still images or video images taken by a user using a terminal.

[1491] A "user terminal" is an electronic device that a user can operate by connecting to the Internet, such as a personal computer, smartphone, or tablet.

[1492] A "server" is a computer system connected to a network that stores, processes, and distributes data.

[1493] A "generative AI model" is an algorithm or system that uses artificial intelligence techniques to analyze and generate data.

[1494] "Copyright" is the right of the creator or copyright holder of a work to exclusively exploit that work.

[1495] "Portrait rights" are an individual's right to prevent images of their face or figure from being published or used without their permission.

[1496] An "emotion engine" is a system that recognizes and analyzes a user's emotional state in real time and adjusts responses accordingly.

[1497] "API integration" is a method of using an application programming interface (API) to exchange data between different software and systems and integrate functions.

[1498] "Feedback" refers to response information such as analysis results, notifications, and evaluations that the system provides to the user.

[1499] "Analysis" is the process of analyzing data and clarifying its content and characteristics.

[1500] The system of this invention is composed of a user terminal, a server, a generative AI model, an emotion engine, and API integration.

[1501] User terminal

[1502] The user terminal is an electronic device such as a personal computer, smartphone, or tablet, which the user can use to take photos or video data and upload them to the server. The user selects the photos or video data they have taken and accesses a dedicated upload page to send the data.

[1503] server

[1504] The server is connected to the network and provides many functions such as storing, analyzing, modifying, notifying, and API integration of photo or video data. The server uses the following specific hardware and software:

[1505] Server storage: Amazon S3

[1506] Data analysis: OpenCV, TensorFlow

[1507] Emotion analysis: Microsoft Azure Emotion API

[1508] The data received by the server is stored in storage and added to the analysis queue.

[1509] Generative AI Models

[1510] The generative AI model analyzes photo and video data stored on a server, performs facial and text recognition within the data, and identifies copyright and portrait rights issues. Based on the analysis results, automatic corrections are performed, such as blurring faces and cropping unwanted parts.

[1511] Emotion Engine

[1512] The emotion engine recognizes the user's emotional state in real time and adjusts the analysis results and notifications and feedback. This improves the user experience by sending notifications in a gentler tone if the user is feeling stressed. In addition, if the user gives positive feedback, the emotion engine uses it to improve the content of future notifications and the accuracy of analysis.

[1513] API Integration

[1514] Approved data can be linked to other systems via APIs, allowing it to be immediately used on e-commerce sites and social media platforms. For example, verified photos can be used on product pages in online shops or as material for advertising campaigns.

[1515] Specific examples

[1516] As a concrete example, consider the case where an advertising creator uploads a photo taken at a local tourist spot to this system. The server analyzes the uploaded photo using a generative AI model to check for portrait rights and copyright issues. Based on the analysis results, faces are automatically blurred or cropped. The emotion engine then analyzes the user's reaction and provides appropriate feedback. For example, a prompt such as "Please recognize all faces in this image, check for portrait rights issues, and blur them if necessary. Also, check for objects that may be copyrighted and crop them if necessary" could be input into the generative AI model.

[1517] In this way, the present invention can quickly and accurately resolve copyright and portrait rights issues of advertising materials and improve user experience.

[1518] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1519] Step 1:

[1520] The user takes a photo or video data and uploads it to the server from the user's device. The user selects the file specified and sends it to the server via the upload page. The photo or video data file is required as input, and the data is saved on the server as output. The specific operation is when the user clicks the upload button.

[1521] Step 2:

[1522] The server saves the received data in storage and adds it to the analysis queue at the same time. It takes the received photo or video data as input, stores it in the destination storage as output, and adds a task to the analysis queue. Specifically, it saves the data in Amazon S3 and adds an analysis task to the queue.

[1523] Step 3:

[1524] The server retrieves data from the analysis queue and passes it to the generative AI model to begin analysis. The input is the photo or video data retrieved from the analysis queue, and the output is the analysis results. Specific operations include facial recognition and text recognition using OpenCV and TensorFlow.

[1525] Step 4:

[1526] The server automatically corrects the photo or video data based on the analysis results of the generative AI model. The analysis results are taken as input, and corrected data is generated as output. Specific operations include blurring the face area and trimming unnecessary parts.

[1527] Step 5:

[1528] The server then passes the corrected data back to the generative AI model for final confirmation. The corrected photo or video data is the input, and the final confirmation result is generated as the output. Specifically, final face and text recognition is performed again.

[1529] Step 6:

[1530] The server uses an emotion engine to recognize the user's emotional state in real time and adjusts the analysis results and feedback accordingly. The input is the user's emotional data and the final confirmation result, and the output is an adjusted feedback message. Specifically, emotion analysis is performed using the Microsoft Azure Emotion API.

[1531] Step 7:

[1532] The server notifies the user of the final confirmation result. The input is the adjusted feedback message, and the output is a notification sent to the user. The specific operation is to send the notification message to the user's device via push notification or email.

[1533] Step 8:

[1534] The server shares the final confirmed data with other systems via API. The confirmed data is input, and the output is immediately available on platforms such as e-commerce sites and social media. Specifically, the confirmed data is sent to other systems via API.

[1535] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1536] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1537] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1538] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1539] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1540] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1541] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1542] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1543] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1544] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1545] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1546] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1547] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1548] 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.

[1549] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1550] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1551] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1552] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1553] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1554] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1555] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1556] The following is further disclosed regarding the above embodiment.

[1557] (Claim 1)

[1558] means for uploading photo or video data from a user terminal to a server;

[1559] A means for analyzing the photo or video data stored in the server using a generative AI model to check for copyright and portrait rights issues;

[1560] A means for automatically correcting photo or video data based on the analysis results of the generative AI model;

[1561] A means to perform a final check of the corrected data using a regenerative AI model, and

[1562] means for notifying a user of the final confirmation result;

[1563] A means for sharing the final confirmed data with other systems through API linkage;

[1564] A system including:

[1565] (Claim 2)

[1566] 2. The system according to claim 1, further comprising means for notifying the user of the specific reason why the corrected data is not acceptable and requesting additional information or correction.

[1567] (Claim 3)

[1568] The system according to claim 1, further comprising means for enabling the confirmed data to be utilized in other platforms in real time through said API integration.

[1569] "Example 1"

[1570] (Claim 1)

[1571] means for uploading photo or video data from a user terminal to a server;

[1572] A means for analyzing the photo or video data stored in the server using a generative AI model to check for copyright and portrait rights issues;

[1573] A means for automatically correcting photo or video data based on the analysis results of the generative AI model;

[1574] A means to perform a final check of the corrected data using a regenerative AI model, and

[1575] means for notifying a user of the final confirmation result;

[1576] A means for sharing the final confirmed data with other systems through API linkage;

[1577] a means of storing the data in secure storage;

[1578] a means for using analytical cues in a generative AI model;

[1579] means for performing facial recognition and text recognition;

[1580] A system including:

[1581] (Claim 2)

[1582] 2. The system according to claim 1, further comprising means for notifying the user of the specific reason why the corrected data is not acceptable and requesting additional information or correction.

[1583] (Claim 3)

[1584] The system according to claim 1, further comprising means for enabling the confirmed data to be utilized in other platforms in real time through said API integration.

[1585] "Application Example 1"

[1586] (Claim 1)

[1587] means for uploading photo or video data from a user terminal to a server;

[1588] A means for analyzing the photo or video data stored in the server using a generative AI model to check for copyright and portrait rights issues;

[1589] A means for automatically correcting photo or video data based on the analysis results of the generative AI model;

[1590] A means to perform a final check of the corrected data using a regenerative AI model, and

[1591] means for notifying a user of the final confirmation result;

[1592] A means for sharing the final confirmed data with other systems through API linkage;

[1593] A means to analyze data taken by advertising creators and marketers, and to confirm and correct portrait rights and copyrights,

[1594] A way to use corrected data in real time for advertising campaigns,

[1595] A system including:

[1596] (Claim 2)

[1597] 2. The system according to claim 1, further comprising means for notifying the user of the specific reason why the corrected data is not acceptable and requesting additional information or correction.

[1598] (Claim 3)

[1599] The system according to claim 1, further comprising means for enabling the confirmed data to be utilized in other platforms in real time through said API integration.

[1600] "Example 2: Combining Emotion Engines"

[1601] (Claim 1)

[1602] means for uploading image data from a user terminal to a server;

[1603] A means for analyzing the image data stored in the server using a generative AI model to check for intellectual property rights and personal rights issues;

[1604] A means for automatically correcting image data based on the analysis results of the generating AI model;

[1605] A means to perform a final check of the corrected data using a regenerative AI model, and

[1606] means for notifying a user of the final confirmation result;

[1607] a means for sharing the final confirmed data with other information processing systems through API linkage;

[1608] means including an emotion engine for recognizing user emotions in real time and adjusting notification of analysis results;

[1609] A system including:

[1610] (Claim 2)

[1611] 2. The system according to claim 1, further comprising means for notifying the user of the specific reason why the corrected data is not acceptable and requesting additional information or correction.

[1612] (Claim 3)

[1613] The system according to claim 1, further comprising means for utilizing the confirmed data in other information processing systems in real time through said API linkage.

[1614] "Application example 2 when combining emotion engines"

[1615] (Claim 1)

[1616] means for uploading photo or video data from a user terminal to a server;

[1617] A means for analyzing the photo or video data stored in the server using a generative AI model to check for copyright and portrait rights issues;

[1618] A means for automatically correcting photo or video data based on the analysis results of the generative AI model;

[1619] A means to perform a final check of the corrected data using a regenerative AI model, and

[1620] A means including an emotion engine that recognizes the user's emotions in real time and appropriately adjusts notification of analysis results and feedback;

[1621] means for notifying a user of the final confirmation result;

[1622] A means for sharing the final confirmed data with other systems through API linkage;

[1623] A system including:

[1624] (Claim 2)

[1625] 2. The system according to claim 1, further comprising means for notifying the user of the specific reason why the corrected data is not acceptable and requesting additional information or correction.

[1626] (Claim 3)

[1627] The system according to claim 1, further comprising means for enabling the confirmed data to be utilized in other platforms in real time through said API integration. [Explanation of symbols]

[1628] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for uploading photo or video data from a user terminal to a server; A means for analyzing the photo or video data stored in the server using a generative AI model to check for copyright and portrait rights issues; A means for automatically correcting photo or video data based on the analysis results of the generative AI model; A means to perform a final check of the corrected data using a regenerative AI model, and means for notifying a user of the final confirmation result; A means for sharing the final confirmed data with other systems through API linkage; A system including:

2. 2. The system according to claim 1, further comprising means for notifying the user of the specific reason why the corrected data is not acceptable and requesting additional information or correction.

3. The system according to claim 1, further comprising means for enabling the confirmed data to be utilized in other platforms in real time through said API linkage.

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