System

Abstract

To provide a piracy check system using a AI.SOLUTION: Means for uploading a work created by a user, means for storing metadata of the work received by a server in a database, means for extracting features from the work by a AI, means for storing the features in the database by the server, means for uploading new content and sending a request for a piracy check by the user, means for receiving the new content and adding the request to a processing queue by the server, means for passing the new content to a AI engine and comparing the new content with an existing work in the database by the AI to analyze a match or a similarity, and means for processing an analysis result from the AI engine by the server; A system comprising: means for generating a potential piracy report; means for sending the report generated by a server to a user's terminal; and means for the user to review the result report and determine the next action.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Creators of copyrighted works and patent holders face the challenge of finding out whether their content infringes the copyrights of others. Furthermore, searching information at the Patent Office is complex and difficult even for beginners, making it time-consuming and laborious to check on one's own. Furthermore, it is difficult to monitor whether one's patents are being used illegally by others. There is a need for a simple method to reduce the risk of copyright infringement and protect the rights of creators and patent holders. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a copyright infringement checking system using AI. Specifically, the system includes a means for users to upload copyrighted works, a means for a server to store the received copyrighted work metadata in a database, a means for the server to pass the copyrighted works to an AI engine and extract features, a means for the server to store the feature data in a database, a means for a user to upload new content and send a request for copyright infringement checking, a means for the server to receive the new content and add the request to a processing queue, a means for the server to pass the new content to the AI ​​engine, which then compares the content with existing works in the database and analyzes the degree of match or similarity, a means for the server to process the analysis results from the AI ​​engine and generate a report on possible copyright infringement, a means for the server to send the report generated by the server to the user's device, and a means for the user to review the resulting report and decide on the next course of action. This system efficiently reduces the risk of copyright infringement and creates an environment in which users can use and publish content with peace of mind.

[0006] "User" means any person or entity that uses the System to upload copyrighted material or request a copyright infringement check.

[0007] "Terminal" refers to a device such as a computer or smartphone that a user operates to upload copyrighted material to the system.

[0008] "Server" means a computer system that processes requests from users and manages, stores, and analyzes uploaded copyrighted materials.

[0009] An "AI engine" refers to a combination of software and hardware that uses artificial intelligence to analyze the characteristics of a work and compare it with existing works in a database.

[0010] "Metadata" refers to basic information associated with a copyrighted work (e.g., title, author, creation date, etc.).

[0011] "Database" refers to a digital storage system for managing stored metadata and feature data extracted by an AI engine.

[0012] "Feature data" refers to important feature information such as melody, rhythm, and harmony extracted from copyrighted works by the AI ​​engine.

[0013] "Content" refers to new copyrighted material (e.g., music files, image files, etc.) that you upload to the system.

[0014] A "request" refers to the act of a user requesting a server to check new content for copyright infringement.

[0015] A "processing queue" refers to a location where a server temporarily stores received requests for processing in order.

[0016] "Comparison" refers to the process by which the AI ​​engine matches new content with existing works in its database and analyzes them for matches or similarities.

[0017] "Analysis Results" refers to the report generated as a result of the comparison made by the AI ​​engine.

[0018] "Report" means a document generated by the Server notifying the User of a possible copyright infringement.

[0019] "Action" refers to the next step a user takes based on the analysis results (e.g., modifying an image, inquiring about permission to use, etc.). [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] This invention is a copyright infringement checking system using AI, which aims to reduce the risk of copyright infringement and easily protect the rights of creators and patent holders. In this system, users upload their copyrighted works, and the server analyzes, stores, and compares them to identify risks of infringement.

[0042] Explaining program processing in natural language

[0043] 1. Copyright Registration Phase

[0044] 1.1. Uploading copyrighted material

[0045] A user uploads copyrighted material (e.g., music files, image files) to the system from their terminal.

[0046] 1.2. Saving to Database

[0047] The server extracts the metadata (title, author, creation date, etc.) of the received copyrighted work and stores it in a database.

[0048] 1.3. Feature extraction using AI

[0049] The server passes the copyrighted work to an AI engine, which analyzes its characteristics (e.g., melody, rhythm, harmony, etc. in the case of music) and generates feature data. The server then stores the generated feature data in a database.

[0050] Examples:

[0051] A user uploads a music file and the server stores the metadata and characteristics of the music file in a database.

[0052] 2. Copyright Infringement Check Request Phase

[0053] 2.1. Sending a Check Request

[0054] The user uploads new content (e.g., a new advertising image) from their device and submits a request for copyright infringement check.

[0055] 2.2. Receiving Content

[0056] The server receives the new content and adds the request to the processing queue.

[0057] Examples:

[0058] A user uploads a new advertising image to the system and requests a copyright infringement check. The server receives the image and adds it to the processing queue.

[0059] 3. Copyright Infringement Check Phase

[0060] Comparison with Databases

[0061] The server processes the request and passes the new content to the AI ​​engine, which compares it with existing copyrighted material in its database and analyzes the matching or similarity of features.

[0062] 3.2. Producing Results

[0063] The server processes the analysis results returned by the AI ​​engine and generates a report of potential copyright infringement.

[0064] Examples:

[0065] The AI ​​engine compares new advertising images with existing images in the database, and the server generates a report based on the results of the similarity analysis.

[0066] 4. Result notification phase

[0067] 4.1. Sending the results

[0068] The server generates a report of potential copyright infringement and sends it to the user's device.

[0069] 4.2. Determining Actions

[0070] The user reviews the results report received and decides on the next action (e.g., image correction, license inquiry, etc.).

[0071] Examples:

[0072] The server notifies the user of the similarity results, saying, "This image has 90% similarity to an existing copyrighted work." The user receives the results and considers modifying the image.

[0073] This system allows users to easily check for copyright infringement risks, enabling them to use and publish content with peace of mind.

[0074] The processing flow will be explained below.

[0075] Step 1:

[0076] A user uploads copyrighted material (e.g., music files, image files) to the system from a terminal. The terminal then transmits the uploaded copyrighted material to the server.

[0077] Step 2:

[0078] The server analyzes the received copyrighted material and extracts metadata (title, author, creation date, etc.), which it then stores in a database.

[0079] Step 3:

[0080] The server passes the copyrighted work to the AI ​​engine, which analyzes the characteristics of the work (e.g., melody, rhythm, harmony, etc. in the case of music) and generates feature data. The server then stores the generated feature data in a database.

[0081] Step 4:

[0082] The user uploads new content (e.g., a new advertising image) from their device and sends a request for copyright infringement check. The device sends this new content and the request to the server.

[0083] Step 5:

[0084] The server receives the new content and adds the request to the processing queue, preparing to pass the new content to the AI ​​engine.

[0085] Step 6:

[0086] The server passes the new content to the AI ​​engine, which compares it with existing copyrighted material in its database to analyze for matches or similarities.

[0087] Step 7:

[0088] The server processes the analysis results returned by the AI ​​engine and generates a report of potential copyright infringement based on the analysis results.

[0089] Step 8:

[0090] The server generates a report of possible copyright infringement and sends it to the user's device, where the user can view the report.

[0091] Step 9:

[0092] Based on the result report received by the user, the next action (e.g., image correction, license inquiry, etc.) is decided. The user can also provide feedback of countermeasures to the server from the terminal.

[0093] Example 1

[0094] 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."

[0095] With the increasing production and distribution of digital content today, the risk of copyright infringement is rising. Therefore, there is a need for a system that can easily and quickly check for copyright infringement while properly protecting the rights of copyrighted works. However, existing systems require a lot of manual operation, are inefficient, and do not perform infringement checks accurately. Furthermore, the complicated process of notifying users of the results and subsequent responses places a heavy burden on users.

[0096] 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.

[0097] In this invention, the server includes: means for uploading copyrighted works created by users; means for storing metadata of the works received by the server in a database; means for the server to pass the copyrighted works to a machine learning engine and for AI to extract features; means for the server to store the feature data in the database; means for the user to upload new data and send a request for a copyright infringement check; means for the server to receive the new data and add the request to a processing queue; means for the server to pass the new data to the machine learning engine and for AI to compare it with existing works in the database and analyze the degree of match or similarity; means for the server to process the analysis results from the AI ​​engine and generate a report of possible copyright infringement; means for sending the report generated by the server to the user's terminal; and means for the user to check the result report and decide on the next action. This allows users to easily check the risk of copyright infringement and quickly take appropriate measures.

[0098] "Works" refers to content such as music files and image files created by users.

[0099] "Server" refers to a computer system that receives, stores, analyzes, generates, and notifies copyrighted material.

[0100] "User" means any person or entity that uses the System to upload copyrighted material and request copyright infringement checks.

[0101] "Metadata" refers to information associated with a work, such as the title, creator, and creation date.

[0102] "Database" refers to a data management system for storing and managing metadata and characteristic data of copyrighted works.

[0103] "Machine learning engine" refers to an artificial intelligence system that analyzes the characteristics of copyrighted works and generates feature data.

[0104] "Characteristics" refers to specific attributes related to the content of a copyrighted work (e.g., melody, rhythm, harmony, etc. in the case of music).

[0105] "Feature data" refers to specific attribute information of a copyrighted work extracted by a machine learning engine.

[0106] "New data" refers to content that users upload for copyright infringement checks.

[0107] A "request" refers to an operation by a user to request a copyright infringement check from the system.

[0108] The term "processing queue" refers to a waiting line for processing received requests in order.

[0109] "Analysis results" refers to the information resulting from the machine learning engine's comparison of new data with existing works in the database.

[0110] "Potential Copyright Infringement Report" refers to a report generated by the server summarizing the analysis results regarding the risk of copyright infringement.

[0111] "Terminal" refers to an electronic device such as a computer or smartphone that a user uses to access and operate the system.

[0112] This invention is a copyright infringement checking system using AI, which aims to reduce the risk of copyright infringement and easily protect the rights of creators and rights holders. In this system, users upload their copyrighted works, and the server analyzes, stores, and compares them to identify the risk of infringement.

[0113] Copyright registration phase

[0114] First, a user uploads a copyrighted work (e.g., a music file or image file) from their device to the system. The server extracts the metadata of the received copyrighted work and stores it in a database. Next, the server passes the copyrighted work to a machine learning engine (AI engine), which analyzes its features and generates feature data. The server then stores this feature data in a database.

[0115] Specific examples

[0116] A user uploads a music file, and the server extracts the metadata of the music file (e.g., title: Example, creator: John Doe, creation date: October 5, 2023) and stores it in a database. The music file is then analyzed by an AI engine to generate feature data (e.g., melody pattern, rhythm, harmony, etc.), which is also stored in the database.

[0117] Copyright Infringement Check Request Phase

[0118] Next, the user uploads new data (e.g., a new advertising image) to the system from their device and sends a request for copyright infringement check. The server receives the new data, temporarily stores it, and then adds the request to the processing queue.

[0119] Specific examples

[0120] A user uploads a new advertising image and requests a copyright infringement check from the system. The server receives the image and adds it to a processing queue.

[0121] Copyright infringement check phase

[0122] The server takes the request from the processing queue and passes the new data back to the machine learning engine, which compares this data with existing copyrighted material in its database to analyze for matches or similarities. The server receives the analysis results from the AI ​​engine and generates a report of potential copyright infringement.

[0123] Specific examples

[0124] The AI ​​engine compares new advertising images with images in the existing database and calculates their similarity, after which the server generates a report indicating whether there is a potential breach.

[0125] Result notification phase

[0126] Finally, the server sends the generated report of possible copyright infringement to the user's device, where the user can review the report and decide on the next action (e.g., modifying the image, inquiring about license rights, etc.).

[0127] Specific examples

[0128] The server notifies the user of the similarity results, such as "This image has 90% similarity to an existing copyrighted work." The user receives the results and considers modifying the image.

[0129] Prompt Sentence Examples

[0130] Examples of prompts include:

[0131] "Upload new music files and store their metadata and characteristics in our database."

[0132] "Upload new advertising images and compare them with existing content in our database to analyze potential copyright infringement."

[0133] "We notify users of similarity results between new and existing content and invite them to submit reports of potential infringement."

[0134] This system allows users to easily check for copyright infringement risks and quickly take appropriate action.

[0135] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0136] Step 1:

[0137] Uploading copyrighted material

[0138] The user selects a work on the terminal.

[0139] Example: A user selects a music file "example.mp3".

[0140] The user clicks the system's upload button.

[0141] Example: A user clicks the "upload" button to send "example.mp3" to the system.

[0142] The terminal transmits the selected file to the server.

[0143] Input: User-selected music file "example.mp3"

[0144] Output: The server receives the music files.

[0145] Step 2:

[0146] Saving to a database

[0147] The server temporarily stores the received file.

[0148] Example: The server saves "example.mp3" to disk.

[0149] Input: Received music file "example.mp3"

[0150] Output: Temporarily saved file

[0151] The server extracts the metadata of the work.

[0152] Example: The server extracts the following metadata for "example.mp3": "Title: Example, Creator: John Doe, Creation Date: October 5, 2023".

[0153] Input: Received music file "example.mp3"

[0154] Output: Extracted metadata

[0155] The server stores the metadata in a database.

[0156] Example: A server records metadata in a "works" table.

[0157] Input: Extracted metadata

[0158] Output: Metadata stored in a database

[0159] Step 3:

[0160] AI-based feature extraction

[0161] The server passes the received copyrighted material to the AI ​​engine.

[0162] Example: The server inputs "example.mp3" into the AI ​​engine.

[0163] Input: Received music file "example.mp3"

[0164] Output: The file passed to the AI ​​engine

[0165] The AI ​​engine analyzes the characteristics of the work.

[0166] Example: The AI ​​engine analyzes the melody pattern, rhythm, harmony, etc. of "example.mp3."

[0167] Input: Received music file "example.mp3"

[0168] Output: Parsed feature data

[0169] The server stores the generated feature data in a database.

[0170] Example: The server stores "feature data" in a "feature" table.

[0171] Input: Parsed feature data

[0172] Output: Feature data stored in a database

[0173] Step 4:

[0174] Submitting a check request

[0175] The user selects new data from the terminal.

[0176] Example: A user selects a new ad image "new_ad.jpg".

[0177] A user submits a request for copyright infringement check to the system.

[0178] Example: A user clicks the "Check Request" button to send "new_ad.jpg" to the server.

[0179] Input: New ad image "new_ad.jpg"

[0180] Output: The server receives the new data.

[0181] The device sends the selected file and the request to the server.

[0182] Input: New ad image "new_ad.jpg"

[0183] Output: The server receives the new data and the request.

[0184] Step 5:

[0185] Receiving content

[0186] The server receives the new data and stores it temporarily.

[0187] Example: The server saves "new_ad.jpg" to disk.

[0188] Input: New ad image "new_ad.jpg"

[0189] Output: Temporarily saved file

[0190] The server adds the request to a processing queue.

[0191] Example: The server adds a "check request" to the queue.

[0192] Input: New ad image "new_ad.jpg"

[0193] Output: Requests added to the processing queue

[0194] Step 6:

[0195] Comparison with database

[0196] The server takes the request from the processing queue and passes the new data back to the AI ​​engine.

[0197] Example: The server inputs "new_ad.jpg" into the AI ​​engine.

[0198] Input: New ad image "new_ad.jpg"

[0199] Output: The file passed to the AI ​​engine

[0200] The AI ​​engine compares the new data with existing works in the database.

[0201] Example: The AI ​​engine compares "new_ad.jpg" with existing image data in the database.

[0202] Input: New ad image "new_ad.jpg", existing data in database

[0203] Output: Match or similarity analysis results

[0204] Step 7:

[0205] Generate results

[0206] The server receives the analysis results from the AI ​​engine and generates a report on potential copyright infringement.

[0207] Example: The server receives the similarity analysis results from the AI ​​engine.

[0208] Input: Analysis results from the AI ​​engine

[0209] Output: Generated potential copyright infringement report

[0210] Step 8:

[0211] Sending the results

[0212] The server generates a report of potential copyright infringement and sends it to the user's device.

[0213] Example: The server sends a "Potential Infringement Report" to the user's device.

[0214] Input: Generated potential copyright infringement report

[0215] Output: Report sent to user's terminal

[0216] Step 9:

[0217] Deciding on an action

[0218] The user checks the results report they received.

[0219] Example: A user opens and reviews a "Potential Compromise Report."

[0220] Input: User action (check result report)

[0221] Output: Determined next action

[0222] The user decides on the next action (e.g., modify the image, inquire about permission, etc.).

[0223] Example: A user sees a 90% similarity and considers modifying the image or inquiring about usage permissions.

[0224] Input: Content of received report

[0225] Output: Decision on next action

[0226] (Application example 1)

[0227] 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."

[0228] Conventional copyright infringement checking systems make it difficult for content creators to check copyright infringement risks in advance. While real-time checks are particularly required for content distribution platforms, the technology to achieve this is insufficient. Furthermore, even when there is a high risk of copyright infringement, response is delayed, which can lead to legal issues after the content is released. This leaves content creators with no peace of mind when distributing their content.

[0229] 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.

[0230] In this invention, the server includes: means for uploading copyrighted works created by users; means for the server to store the received metadata of the copyrighted works in a database; means for the server to pass the copyrighted works to an AI engine and for the AI ​​to extract features; means for the server to store the feature data in the database; means for the user to upload new content and send a request for a copyright infringement check; means for the server to receive the new content and add the request to a processing queue; means for the server to pass the new content to the AI ​​engine and for the AI ​​to compare the new content with existing copyrighted works in the database and analyze the degree of match or similarity; means for the server to process the analysis results from the AI ​​engine and generate a report on possible copyright infringement; means for the server to send the generated report to the user's device; means for the user to check the result report and decide on the next action; means for the user to upload content from their smartphone and undergo a copyright infringement check in real time; means for the AI ​​engine to analyze the features of the content and compare it with existing copyrighted works in real time; and means for sending a notification to the user if a risk of copyright infringement is determined. This enables content creators to conduct copyright infringement checks in real time on a content distribution platform, thereby identifying the risk of copyright infringement in advance and avoiding legal issues.

[0231] "Copyrighted work" refers to content such as music, images, and video created by a creator.

[0232] "Upload" refers to the act of a user sending data from their device to a server.

[0233] "Metadata" refers to information about a work, including attribute information such as the title, creator, and creation date.

[0234] A "database" refers to a system for organizing and storing data and for efficiently managing and searching it.

[0235] An "AI engine" refers to a software system that uses artificial intelligence technology to analyze data and recognize patterns.

[0236] "Feature extraction" refers to the process of extracting important feature data (e.g., audio waveforms, image patterns, etc.) from copyrighted works using an AI engine.

[0237] "Copyright infringement" refers to the act of using someone else's copyrighted work without permission, which can lead to legal issues.

[0238] "Content distribution" refers to the act of providing and sharing digital content such as music, videos, and images through online platforms.

[0239] "Real-time" refers to processing and results occurring immediately, without delay.

[0240] The "Results Report" is a report summarizing the results of the AI ​​engine's analysis, and includes information about the risk of copyright infringement.

[0241] "Notification" refers to the act of a system conveying specific information to a user.

[0242] MODE FOR CARRYING OUT THE INVENTION

[0243] The present invention provides a system for checking the risk of copyright infringement in real time, especially on a content distribution platform, which comprises the following steps:

[0244] 1. System program generation

[0245] In this system, users upload copyrighted material from their smartphones, and an AI engine analyzes the characteristics of the material. The AI ​​engine stores the metadata and characteristic data of the material in a database, and in response to requests for copyright infringement checks, it compares new content with existing copyrighted material. The server also generates analysis results and notifies users of the risk of copyright infringement.

[0246] 2. Hardware and Software Description

[0247] To realize this system, the following hardware and software are used.

[0248] Hardware

[0249] Server: Stores data, extracts and compares features using AI, and generates and notifies analysis results.

[0250] Smartphone: A device through which users upload content and receive results.

[0251] software

[0252] Flask: Used as a web server framework to manage requests.

[0253] AI engine: A custom AI library (tentative name) that performs feature extraction and database comparison processing.

[0254] Database Management System: A system for efficiently storing and managing metadata and feature data.

[0255] 3. Data processing and calculation

[0256] The server processes and calculates data in the following steps:

[0257] 1. Uploading and Analysis of Copyrighted Materials:

[0258] The user uploads content (e.g., videos, images) from their smartphone. The server stores the metadata of the received content in a database, and the AI ​​engine extracts features. The extracted feature data is also stored in the database.

[0259] 2. Copyright Infringement Check Request:

[0260] A user uploads new content and sends a request for copyright infringement check. The server receives the new content and passes it to an AI engine to compare it with existing copyrighted material in the database.

[0261] 3. Results generation and notification:

[0262] The AI ​​engine returns the comparison results to the server, which processes the analysis results and generates a report of possible copyright infringement, which the server notifies the user.

[0263] 4. Example: Uploading a video to YouTube (registered trademark)

[0264] For example, when a user uploads a video to an online video platform, the system can be used to check for copyright infringement risks. If the video contains an existing movie scene, the system will send a notification saying, "This video is 95% identical to an existing movie scene." Based on this, the user can modify or delete the video to avoid future legal issues.

[0265] 5. Examples of prompts

[0266] Your content guardian will analyze user-uploaded videos and images in real time to determine if they infringe existing copyrighted material. Please provide specific steps for users:

[0267] 1. Uploading Content

[0268] 2. Feature extraction using AI

[0269] 3. Comparison with database

[0270] 4. Generating and Communicating Results

[0271] The above is a description of the mode for carrying out the invention, and this system enables content creators to check copyright infringement risks in real time and prevent legal problems before they occur.

[0272] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0273] Step 1:

[0274] Uploading and analyzing copyrighted material

[0275] A user uploads content from their smartphone. Specifically, videos or images are sent to a specified server endpoint. The server extracts the content's metadata (title, creation date, etc.) and stores it in a database. The input at this stage is the content provided by the user, and the output is the metadata stored in the database. The server then passes this content to an AI engine, which extracts feature data (for example, visual patterns and audio waveforms in the case of videos). This feature data is also stored in the database.

[0276] Step 2:

[0277] Submitting a Copyright Infringement Check Request

[0278] A user uploads new content and sends a request for copyright infringement check. Specifically, the user again uses their smartphone to send the content to the server. This input is new content, and its purpose is to check for copyright infringement. The server receives this new content and adds the request to the processing queue. Once the request is added to the queue, it is ready to proceed to the next step.

[0279] Step 3:

[0280] AI-based feature analysis and database comparison

[0281] The server takes new content from the processing queue and passes it to the AI ​​engine. The AI ​​engine extracts the features of the new content and compares it with copyrighted works in the existing database. At this time, it analyzes how much the feature data matches or is similar to the existing data. The input is the new content and the feature data from the existing database, and the output is the analysis result of the degree of match or similarity. As a concrete example, the analysis result of a video may be output in the form of "This footage is 80% identical to existing movie scenes."

[0282] Step 4:

[0283] Generation and notification of analysis results

[0284] The server generates a report of possible copyright infringement based on the analysis results obtained from the AI ​​engine. Specifically, it compiles similarity and match information into a report format. The input is the analysis results returned by the AI ​​engine, and the output is the generated report. This report is sent to the user's smartphone so that the user can view it. This notification allows the user to decide on the next action (e.g., modifying or deleting the content).

[0285] Step 5:

[0286] Determining User Actions

[0287] The user checks the report received on their smartphone and decides on the next course of action. Specifically, if it is determined that there is a high risk of copyright infringement, they can take action such as modifying the content or inquiring about usage permissions. The input is the analysis result report, and the output is the user's specific action. This step allows the user to avoid the risk of copyright infringement of content in advance.

[0288] 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.

[0289] This invention is a copyright infringement checking system that uses AI to reduce the risk of copyright infringement and easily protect the rights of creators and patent holders, and it also combines an emotion engine that recognizes user emotions and provides feedback. In this system, users upload their copyrighted works, and the server analyzes, stores, and compares them to identify the risk of infringement, and analyzes user emotions in real time and provides appropriate feedback.

[0290] Explaining program processing in natural language

[0291] 1. Copyright Registration Phase

[0292] 1.1. Uploading copyrighted material

[0293] A user uploads copyrighted material (e.g., music files, image files) to the system from a terminal. The terminal then transmits the uploaded copyrighted material to the server.

[0294] 1.2. Saving to Database

[0295] The server analyzes the received copyrighted material and extracts metadata (title, author, creation date, etc.), which it then stores in a database.

[0296] 1.3. Feature extraction using AI

[0297] The server passes the copyrighted work to an AI engine, which analyzes its characteristics (e.g., melody, rhythm, harmony, etc. in the case of music) and generates feature data. The server then stores the generated feature data in a database.

[0298] Examples:

[0299] A user uploads a music file and the server stores the metadata and characteristics of the music file in a database.

[0300] 2. Copyright Infringement Check Request Phase

[0301] 2.1. Sending a Check Request

[0302] The user uploads new content (e.g., a new advertising image) from their device and sends a request for copyright infringement check. The device sends this new content and the request to the server.

[0303] 2.2. Receiving Content

[0304] The server receives the new content and adds the request to the processing queue, preparing to pass the new content to the AI ​​engine.

[0305] Examples:

[0306] A user uploads a new advertising image to the system and requests a copyright infringement check. The server receives the image and adds it to the processing queue.

[0307] 3. Copyright Infringement Check Phase

[0308] Comparison with Databases

[0309] The server processes the request and passes the new content to the AI ​​engine, which compares the new content with existing copyrighted material in its database and analyzes it for matches or similarities.

[0310] 3.2. Producing Results

[0311] The server processes the analysis results returned by the AI ​​engine and generates a report of potential copyright infringement based on the analysis results.

[0312] Examples:

[0313] The AI ​​engine compares new advertising images with existing images in the database, and the server generates a report based on the results of the similarity analysis.

[0314] 4. Result notification phase

[0315] 4.1. Sending the results

[0316] The server generates a report of possible copyright infringement and sends it to the user's device, where the user can view the report.

[0317] 4.2. Determining Actions

[0318] The user checks the result report they receive and decides on the next action (e.g., image correction, license inquiry, etc.). The user can also provide feedback on countermeasures to the server from their device.

[0319] Examples:

[0320] The server notifies the user of the similarity results, saying, "This image has 90% similarity to an existing copyrighted work." The user receives the results and considers modifying the image.

[0321] 5. Implementing the Emotion Engine

[0322] 5.1. Emotion Data Collection

[0323] As the user operates the interface displayed during the copyright infringement check process, emotional data such as the user's facial expressions, voice, and keyboard input is collected from the device.

[0324] 5.2. Emotion Data Analysis

[0325] The server passes the collected emotional data to the emotion engine, where the AI ​​analyzes the user's emotions. The analysis results are converted into numerical values ​​for the user's emotions, such as stress, frustration, and joy.

[0326] Providing Feedback

[0327] Based on the analysis results, the server provides appropriate feedback to the user's device. For example, if high stress or frustration is detected, a relaxation message or a link to contact support will be displayed.

[0328] Examples:

[0329] The emotion engine analyzes whether the user is feeling frustrated while operating the device, and the server displays a relaxation message such as "Would you like to take a short break?"

[0330] This system not only allows users to easily check the risk of copyright infringement, but also allows them to receive support that takes into consideration their emotional state, allowing them to use and publish content with greater peace of mind.

[0331] The processing flow will be explained below.

[0332] Step 1:

[0333] A user uploads copyrighted material (e.g., music files, image files) to the system from a terminal. The terminal then transmits the uploaded copyrighted material to the server.

[0334] Step 2:

[0335] The server analyzes the received copyrighted material and extracts metadata (title, author, creation date, etc.), which it then stores in a database.

[0336] Step 3:

[0337] The server passes the copyrighted work to the AI ​​engine, which analyzes the characteristics of the work (e.g., melody, rhythm, harmony, etc. in the case of music) and generates feature data. The server then stores the generated feature data in a database.

[0338] Step 4:

[0339] The user uploads new content (e.g., a new advertising image) from their device and sends a request for copyright infringement check. The device sends this new content and the request to the server.

[0340] Step 5:

[0341] The server receives the new content and adds the request to the processing queue, preparing to pass the new content to the AI ​​engine.

[0342] Step 6:

[0343] The server passes the new content to the AI ​​engine, which compares it with existing copyrighted material in its database to analyze for matches or similarities.

[0344] Step 7:

[0345] The server processes the analysis results returned by the AI ​​engine and generates a report of potential copyright infringement based on the analysis results.

[0346] Step 8:

[0347] The server generates a report of possible copyright infringement and sends it to the user's device, where the user can view the report.

[0348] Step 9:

[0349] Based on the results report received, the user decides on the next action (e.g., image correction, inquiry for permission to use, etc.). The user can also provide feedback on countermeasures to the server from the terminal.

[0350] Step 10:

[0351] The device collects the user's emotional data (e.g., facial expressions, voice, keyboard input, etc.) while the device is in operation. The emotional data is sent to the server in real time.

[0352] Step 11:

[0353] The server passes the emotion data to the emotion engine, which analyzes the collected data and quantifies the user's emotion (e.g., stress, frustration, joy, etc.).

[0354] Step 12:

[0355] The server generates appropriate feedback based on the analysis results from the emotion engine. For example, the server creates a relaxation message if high stress or frustration is detected.

[0356] Step 13:

[0357] The server generates feedback (e.g., a relaxation message) and sends it to the user's device, where the user can view the feedback.

[0358] Step 14:

[0359] The user decides the next action (e.g., taking a break, further operation, etc.) based on the feedback. The user's actions are fed back to the server via the device.

[0360] Example 2

[0361] 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."

[0362] Currently, systems for checking copyright infringement of digital content are often cumbersome and time-consuming, which can impair user convenience. Furthermore, few systems can analyze users' emotions and provide appropriate feedback in addition to checking for copyright infringement. This can easily lead to stress and frustration, so there is a need for a system that can integrate the evaluation of copyright infringement with the management of one's own emotional state.

[0363] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0364] In this invention, the server includes a means for uploading digital content created by users, a means for storing metadata of the digital content received by the server in a storage device, and a means for the server to pass the digital content to an artificial intelligence engine and for the artificial intelligence to extract features, thereby enabling copyright infringement checks of digital content and user sentiment analysis to be performed in a unified manner.

[0365] "User" means any person who interacts with the system to upload digital content or submit a request for a copyright infringement check.

[0366] A "terminal" is a device operated by a user, and is an apparatus on which operations such as uploading digital content and checking result reports are performed.

[0367] A "server" is a computer system that receives and processes digital content or requests sent by users.

[0368] "Digital content" refers to electronically recorded information such as music files, image files, and video files.

[0369] "Metadata" is additional information associated with digital content, such as title, creator, and creation date.

[0370] A "storage device" is a storage device for storing digital content, metadata, and feature data.

[0371] An "artificial intelligence engine" is a system that includes algorithms and models for analyzing the characteristics of digital content and analyzing matches or similarities.

[0372] "Feature data" refers to the analysis results of the melody, rhythm, harmony, etc. of digital content extracted by an artificial intelligence engine.

[0373] A "processing queue" is a data structure for temporarily storing new requests and processing them sequentially.

[0374] A "copyright infringement check request" is a request sent by a user to a server to check new digital content for existing copyright infringement.

[0375] "Analysis Results" means the results of the comparison and matching or similarity analysis of digital content conducted by the artificial intelligence engine.

[0376] A "report" is a document generated based on the analysis results, which includes information indicating possible copyright infringement.

[0377] "Emotion data" is data that indicates the user's emotional state, such as the user's facial expression, voice, keyboard input, etc.

[0378] An "emotion recognition engine" is a system that includes algorithms and models for analyzing collected emotional data and quantifying a user's emotional state.

[0379] "Feedback" refers to notifications and messages provided to users based on the results of emotion analysis, and includes, for example, relaxation messages and support information.

[0380] This invention is a system for checking copyright infringement of digital content and analyzing user sentiment in an integrated manner. This system is realized by uploading digital content created by users, and the server analyzes, stores, and performs specific processing on the uploaded content.

[0381] Hardware and software used

[0382] The system is implemented using the following hardware and software:

[0383] Terminal: A device operated by a user, including a PC, smartphone, tablet, etc., that has internet connectivity and file upload capabilities.

[0384] Server: A computer system with high-performance processing capabilities that receives digital content, stores it in a database, connects with the AI ​​engine, and provides feedback on the results of sentiment analysis.

[0385] Database software: Use a relational database management system (RDBMS) such as MySQL® or PostgreSQL to store metadata and feature data for digital content.

[0386] AI Engine: Includes generative AI models built using TENSORFLOW® and PyTorch to extract and compare features of digital content and check for potential copyright infringement.

[0387] Emotion recognition engine: Uses emotion analysis services such as Amazon Rekognition and Google® Cloud Vision to collect and analyze user emotion data.

[0388] Example of a system

[0389] Consider a scenario where a user uploads digital content that they have created, for example, a music file called "MySong.mp3."

[0390] 1. User Action:

[0391] The user uses the device to drag and drop the file "MySong.mp3" into the system interface, then clicks the upload button, and the device sends this file to the server via an HTTP request.

[0392] 2. Server-side processing:

[0393] The server receives the file and saves it in a temporary directory. It then extracts metadata such as the title "My Song," the creator "John Doe," and the creation date "2023-10-10" and stores them in a database. The server then passes the file to an AI engine, which analyzes the melody and rhythmic features. The resulting feature data, "Melody: (C, D, E, F), Rhythm: 4 / 4, Tempo: 120 BPM," is saved in the database.

[0394] 3. Copyright Infringement Check:

[0395] When checking new digital content, a user uploads a new advertising image, "AdsImage.jpg," and requests a copyright infringement check. The server receives this request and adds it to the processing queue. The AI ​​engine compares it with existing data and analyzes the similarity. A "95% similarity" is returned as a result, and a report of possible copyright infringement is generated and sent to the user.

[0396] 4. Sentiment Analysis and Feedback:

[0397] As the user operates the system, the device collects the user's emotional data (facial expressions, voice, keyboard input, etc.). The server passes the collected emotional data to an emotion recognition engine for analysis. If high stress or frustration is detected, the server sends the user relaxation messages or support information.

[0398] Prompt Sentence Examples

[0399] When a user uploads a new music file and analyzes its features based on the AI ​​engine, the prompt text is as follows:

[0400] "Upload the music file 'MySong.mp3'. We'll analyze the melody, rhythm, and tempo of the file and store them in our database."

[0401] This system not only checks for copyright infringement of digital content, but also takes into consideration the emotional state of the user, allowing users to use and publish content with greater peace of mind.

[0402] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0403] Step 1:

[0404] The user selects digital content (e.g., music file "MySong.mp3") on the device and clicks the upload button.

[0405] Input: Music file "MySong.mp3".

[0406] Output: The music file "MySong.mp3" is sent from the device to the server.

[0407] Specific behavior: The device sends the file to the server using an HTTP POST request.

[0408] Step 2:

[0409] The server receives the uploaded file and saves it in a temporary directory.

[0410] Input: Music file "MySong.mp3".

[0411] Output: "MySong.mp3" saved in your temporary directory.

[0412] Specific operation: The server saves the received file in a specific directory (e.g., " / tmp / uploads").

[0413] Step 3:

[0414] The server extracts the metadata of the music files.

[0415] Input: A music file "MySong.mp3" saved in the temporary directory.

[0416] Output: Extracted metadata (Title "My Song", Author "John Doe", Creation Date "2023-10-10").

[0417] Specific operation: The server uses a Python library (e.g., mutagen) to extract metadata from music files.

[0418] Step 4:

[0419] The server stores the extracted metadata in a database.

[0420] Input: Extracted metadata (Title "My Song", Author "John Doe", Creation Date "2023-10-10").

[0421] Output: Metadata stored in a database.

[0422] What happens: The server executes an SQL INSERT query to save the metadata into a MySQL database.

[0423] Step 5:

[0424] The server passes the music file to the AI ​​engine, which extracts its features.

[0425] Input: Music file "MySong.mp3".

[0426] Output: Extracted feature data (melody, rhythm, tempo).

[0427] How it works: The server inputs the file into an AI model using TensorFlow, which analyzes features such as melody, rhythm, and tempo.

[0428] Step 6:

[0429] The server stores the feature data in a database.

[0430] Input: Extracted feature data (melody, rhythm, tempo).

[0431] output: Feature data stored in the database.

[0432] What happens: The server executes an SQL INSERT query to save the feature data into a MySQL database.

[0433] Step 7:

[0434] A user uploads new digital content (e.g., an advertisement image "AdsImage.jpg") from their device and submits a request for copyright infringement check.

[0435] Input: Ad image "AdsImage.jpg".

[0436] output: The ad image "AdsImage.jpg" and the check request are sent to the server.

[0437] Specific behavior: The device sends the file and request to the server using an HTTP POST request.

[0438] Step 8:

[0439] The server receives new digital content and adds it to a processing queue.

[0440] Input: Ad image "AdsImage.jpg" and check request.

[0441] output: The ad image "AdsImage.jpg" and check request added to the processing queue.

[0442] Specific behavior: The server saves the received file in a temporary directory and adds it to the processing queue.

[0443] Step 9:

[0444] The server passes new digital content to the AI ​​engine, which compares it with existing data and analyzes it for matches or similarities.

[0445] Input: Ad image "AdsImage.jpg".

[0446] output: The match or similarity analysis result.

[0447] What it does: The server inputs the file into an AI engine, which compares it with digital content in an existing database.

[0448] Step 10:

[0449] The server generates a report of possible copyright infringement based on the analysis results.

[0450] Input: Match or similarity analysis results.

[0451] output: A report of potential copyright infringement.

[0452] Specific operation: The server automatically generates a PDF report based on the analysis results.

[0453] Step 11:

[0454] The server sends the generated report to the user's terminal.

[0455] Enter: Potential Copyright Infringement Report.

[0456] output: The report sent to the user's terminal.

[0457] Specific operation: The server uses an HTTP response to send the report to the user's device.

[0458] Step 12:

[0459] The user reviews the results report and decides on the next action.

[0460] Enter: Potential Copyright Infringement Report.

[0461] output: Next action (e.g., image modification, license inquiry).

[0462] Specific actions: The user views the report, decides what action is required, and takes further action via the device.

[0463] Step 13:

[0464] As the user operates the system, the device collects emotional data.

[0465] Input: User facial expressions, voice, keyboard input, etc.

[0466] output: The collected emotion data.

[0467] What it does: The device uses the camera, microphone, and keyboard to collect user emotional data.

[0468] Step 14:

[0469] The server passes the collected emotion data to an emotion recognition engine for analysis.

[0470] Input: Collected emotion data.

[0471] output: Sentiment analysis results (e.g., stress level or frustration).

[0472] Specific operation: The server passes the data to the emotion recognition engine and receives the analysis results.

[0473] Step 15:

[0474] The server generates feedback based on the emotion analysis results and provides it to the user.

[0475] Input: Sentiment analysis results.

[0476] output: A feedback message to the user (e.g., a relaxation message or support information).

[0477] Specific operation: The server generates a message appropriate for the user based on the analysis results and sends it to the device.

[0478] (Application example 2)

[0479] 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."

[0480] In content creation, it is important to reduce the risk of copyright infringement and protect the rights of creators. Furthermore, it is necessary to reduce the stress and frustration felt by users during the content uploading process and realize a smooth operation. The problem to be solved by this invention is to effectively check the risk of copyright infringement of content created by users, grasp the user's emotional state, and provide appropriate feedback.

[0481] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for uploading a work created by a user; means for the server to store metadata of the work received in a database; means for the server to pass the work to an AI engine and for the AI ​​to extract features; means for the server to store the feature data in a database; means for the user to upload new content and send a request for a copyright infringement check; means for the server to receive the new content and add the request to a processing queue; means for the server to pass the new content to the AI ​​engine and for the AI ​​to compare it with existing works in the database and analyze the degree of match or similarity; means for the server to process the analysis results from the AI ​​engine and generate a report of possible copyright infringement; means for sending the report generated by the server to the user's terminal; means for the user to check the result report and decide on the next action; means for analyzing the user's facial expressions, voice, and keyboard input to understand the user's emotional state; and means for the server to analyze the emotional data and provide appropriate feedback to the user. This makes it possible to effectively check the risk of copyright infringement in user-created content, and also to analyze the user's emotional state in real time and provide appropriate feedback.

[0482] "User" means a person or entity that uses the System to upload copyrighted material and request a copyright infringement check.

[0483] "Works" are content created by users, and examples include music files and image files.

[0484] The "server" is a central processing unit that receives the copyrighted material uploaded by the user, analyzes, stores, compares, notifies the results, and processes the emotion data.

[0485] "Metadata" is information about a work, including the title, creator, creation date, etc.

[0486] "Database" means a storage device containing metadata and characteristic data of existing works stored by the server.

[0487] An "AI engine" is a program device that uses artificial intelligence to extract characteristics of copyrighted works and compare new content with existing works in a database.

[0488] "Feature data" refers to specific attribute information of a copyrighted work, such as its melody, rhythm, and harmony, extracted by the AI ​​engine.

[0489] A "request" refers to a request by a user to submit new content to a server for copyright infringement checking.

[0490] A "processing queue" is a line of tasks that is temporarily placed to await processing after the server receives new content.

[0491] "Analysis results" refers to the data generated after the AI ​​engine compares new content with existing copyrighted works in its database.

[0492] The "Potential Copyright Infringement Report" is a report that indicates the risk of copyright infringement, generated by the server based on the analysis results from the AI ​​engine.

[0493] "Emotion data" is information that indicates the user's emotional state, obtained from the user's facial expression, voice, keyboard input, and the like.

[0494] "Feedback" refers to constructive advice or messages provided to users based on emotional data analyzed by the server.

[0495] MODE FOR CARRYING OUT THE INVENTION

[0496] This invention relates to a system for checking copyright infringement of user-created content and analyzing the user's emotional state in real time. To specifically implement this system, the following hardware and software, as well as data processing and data calculation, are required.

[0497] 1. Hardware and Software Used

[0498] Hardware

[0499] Smartphone: The device where users upload content

[0500] Head-mounted display: A display device equipped with a camera and microphone that collects the user's facial expressions and voice.

[0501] Server: A central processing unit that analyzes and stores data

[0502] software

[0503] Python: A language for data analysis and AI model execution

[0504] TensorFlow / Keras: Building and running AI models

[0505] OpenCV: Facial Expression Recognition Library

[0506] Azure® Face API: Facial recognition and emotion analysis API

[0507] 2. Natural language description of program processing

[0508] User Content Uploads

[0509] Users upload their own creations (e.g., videos, images, and music files) through the interface of their smartphone or head-mounted display. The creations are then sent from the device to the server.

[0510] Extracting and storing metadata

[0511] The server analyzes the received copyrighted material and extracts metadata (title, author, creation date, etc.), which is then stored in a database.

[0512] AI-based feature extraction

[0513] The copyrighted work is passed from the server to the AI ​​engine, which analyzes its characteristic data (melody, rhythm, harmony, etc.) and stores the analyzed characteristic data in a database.

[0514] Copyright Infringement Check Request

[0515] A user uploads new content and requests a copyright infringement check. The server receives this new content and adds it to a processing queue.

[0516] Compare and analyze new content

[0517] The server then passes the new content to the AI ​​engine, which then compares it with existing works in its database and analyzes the matches and similarities.

[0518] Analysis results and report generation

[0519] The server generates a report of potential copyright infringement based on the analysis results returned by the AI ​​engine, and this report is sent to the user's device.

[0520] Emotion data collection and analysis

[0521] The user's facial expressions, voice, and keyboard input are collected through the camera and microphone of the head-mounted display. This emotional data is sent to a server and analyzed by an AI engine.

[0522] Emotional Feedback

[0523] The server uses the emotion analysis results to determine the user's stress and frustration and provides appropriate feedback. For example, if high stress is detected, a relaxation message such as "Would you like to take a break?" will be displayed.

[0524] 3. Examples and prompts

[0525] Specific examples

[0526] A user uploads a new video to the content distribution platform and requests a copyright infringement check. The server then extracts the video's metadata and feature data and compares it with existing videos in the database. At the same time, it analyzes the user's facial expressions and voice and displays relaxation messages if the user's stress level increases.

[0527] Prompt Sentence Examples

[0528] "Upload a new video to the copyright infringement checking system and check its similarity to existing videos. Extract the metadata and feature data of the uploaded video, compare it with existing videos in the database, analyze the similarity, and notify the user of the results. At the same time, analyze the user's facial and vocal emotional data, and display a relaxation message if stress is detected."

[0529] This invention not only allows users to easily check the risk of copyright infringement for the content they create, but also allows them to receive support that takes into consideration their emotional state, allowing them to use and publish content with greater peace of mind.

[0530] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0531] Step 1:

[0532] Users upload content

[0533] Users upload their own copyrighted works (e.g., videos, images, and music files) to the system using their smartphones or head-mounted displays. The devices then send the uploaded works to the server.

[0534] Input: User-created copyrighted material file

[0535] Output: The copyrighted file sent to the server

[0536] Step 2:

[0537] Extracting and storing metadata

[0538] The server analyzes the received copyrighted material and extracts metadata (title, author, creation date, etc.) and stores this metadata in a database.

[0539] Input: User-uploaded copyrighted material file

[0540] Output: Metadata stored in a database

[0541] What it does: A Python program parses copyrighted files and extracts metadata, which is then stored in a database using SQL queries.

[0542] Step 3:

[0543] AI-based feature extraction

[0544] The server passes the copyrighted work to an AI engine (a model using TensorFlow / Keras), which analyzes and generates feature data (attributes such as melody, rhythm, harmony, etc.) The server then stores the generated feature data in a database.

[0545] Input: Copyrighted material file received by the server

[0546] Output: Feature data stored in a database

[0547] Specific operation: The copyrighted file is loaded into the TensorFlow / Keras model, and feature data is generated. The generated feature data is then stored in a database using SQL queries.

[0548] Step 4:

[0549] User submits request

[0550] The user uploads new content (e.g., a new video file) and sends a request for copyright infringement check. The device sends this new content and the request to the server.

[0551] Input: New content files uploaded by users and copyright infringement check requests

[0552] Output: New content and request sent to the server

[0553] Step 5:

[0554] Request Processing

[0555] The server receives the new content and adds the request to the processing queue, preparing to hand the new content off to the AI ​​engine.

[0556] Input: New content and requests uploaded by users

[0557] Output: Request added to processing queue

[0558] What happens: The Python program receives new content and requests and adds them to the processing queue.

[0559] Step 6:

[0560] Copyright Infringement Check

[0561] The server processes the request and passes the new content to an AI engine, which compares the new content with existing copyrighted material in its database and analyzes it for matches or similarities.

[0562] Input: Requests added to the processing queue, new content

[0563] Output: Match or similarity analysis results

[0564] What it does: A TensorFlow / Keras model is used to compare new content with existing works in the database.

[0565] Step 7:

[0566] Processing analysis results and generating reports

[0567] The server generates a report on possible copyright infringement based on the analysis results returned by the AI ​​engine, and sends this report to the user's device.

[0568] Input: Analysis results from the AI ​​engine

[0569] Output: Report sent to user terminal

[0570] Specific operation: Based on the analysis results, a Python program generates a report and sends it to the relevant user's device as an email or notification.

[0571] Step 8:

[0572] Emotion data collection and analysis

[0573] The user's facial expressions, voice, and keyboard input are collected through the camera and microphone of the head-mounted display. This emotional data is sent to a server and analyzed by an AI engine.

[0574] Input: User facial, voice, and keyboard input data

[0575] Output: Parsed emotion data

[0576] Specific operation: Analyzes emotion data in real time using OpenCV and Azure Face API.

[0577] Step 9:

[0578] Emotional Feedback

[0579] The server uses the emotion analysis results to determine the user's stress and frustration and provides appropriate feedback. For example, if high stress is detected, a relaxation message such as "Would you like to take a break?" will be displayed.

[0580] Input: Parsed emotion data

[0581] Output: Feedback message provided to the user

[0582] Specific operation: Based on the results of emotion analysis, a feedback message corresponding to the user's situation is generated and displayed on the user's device.

[0583] 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.

[0584] 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.

[0585] 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.

[0586] [Second embodiment]

[0587] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0588] 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.

[0589] 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).

[0590] 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.

[0591] 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.

[0592] 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).

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0598] 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."

[0599] This invention is a copyright infringement checking system using AI, which aims to reduce the risk of copyright infringement and easily protect the rights of creators and patent holders. In this system, users upload their copyrighted works, and the server analyzes, stores, and compares them to identify risks of infringement.

[0600] Explaining program processing in natural language

[0601] 1. Copyright Registration Phase

[0602] 1.1. Uploading copyrighted material

[0603] A user uploads copyrighted material (e.g., music files, image files) to the system from their terminal.

[0604] 1.2. Saving to Database

[0605] The server extracts the metadata (title, author, creation date, etc.) of the received copyrighted work and stores it in a database.

[0606] 1.3. Feature extraction using AI

[0607] The server passes the copyrighted work to an AI engine, which analyzes its characteristics (e.g., melody, rhythm, harmony, etc. in the case of music) and generates feature data. The server then stores the generated feature data in a database.

[0608] Examples:

[0609] A user uploads a music file and the server stores the metadata and characteristics of the music file in a database.

[0610] 2. Copyright Infringement Check Request Phase

[0611] 2.1. Sending a Check Request

[0612] The user uploads new content (e.g., a new advertising image) from their device and submits a request for copyright infringement check.

[0613] 2.2. Receiving Content

[0614] The server receives the new content and adds the request to the processing queue.

[0615] Examples:

[0616] A user uploads a new advertising image to the system and requests a copyright infringement check. The server receives the image and adds it to the processing queue.

[0617] 3. Copyright Infringement Check Phase

[0618] Comparison with Databases

[0619] The server processes the request and passes the new content to the AI ​​engine, which compares it with existing copyrighted material in its database and analyzes the matching or similarity of features.

[0620] 3.2. Producing Results

[0621] The server processes the analysis results returned by the AI ​​engine and generates a report of potential copyright infringement.

[0622] Examples:

[0623] The AI ​​engine compares new advertising images with existing images in the database, and the server generates a report based on the results of the similarity analysis.

[0624] 4. Result notification phase

[0625] 4.1. Sending the results

[0626] The server generates a report of potential copyright infringement and sends it to the user's device.

[0627] 4.2. Determining Actions

[0628] The user reviews the results report received and decides on the next action (e.g., image correction, license inquiry, etc.).

[0629] Examples:

[0630] The server notifies the user of the similarity results, saying, "This image has 90% similarity to an existing copyrighted work." The user receives the results and considers modifying the image.

[0631] This system allows users to easily check for copyright infringement risks, enabling them to use and publish content with peace of mind.

[0632] The processing flow will be explained below.

[0633] Step 1:

[0634] A user uploads copyrighted material (e.g., music files, image files) to the system from a terminal. The terminal then transmits the uploaded copyrighted material to the server.

[0635] Step 2:

[0636] The server analyzes the received copyrighted material and extracts metadata (title, author, creation date, etc.), which it then stores in a database.

[0637] Step 3:

[0638] The server passes the copyrighted work to the AI ​​engine, which analyzes the characteristics of the work (e.g., melody, rhythm, harmony, etc. in the case of music) and generates feature data. The server then stores the generated feature data in a database.

[0639] Step 4:

[0640] The user uploads new content (e.g., a new advertising image) from their device and sends a request for copyright infringement check. The device sends this new content and the request to the server.

[0641] Step 5:

[0642] The server receives the new content and adds the request to the processing queue, preparing to pass the new content to the AI ​​engine.

[0643] Step 6:

[0644] The server passes the new content to the AI ​​engine, which compares it with existing copyrighted material in its database to analyze for matches or similarities.

[0645] Step 7:

[0646] The server processes the analysis results returned by the AI ​​engine and generates a report of potential copyright infringement based on the analysis results.

[0647] Step 8:

[0648] The server generates a report of possible copyright infringement and sends it to the user's device, where the user can view the report.

[0649] Step 9:

[0650] Based on the result report received by the user, the next action (e.g., image correction, license inquiry, etc.) is decided. The user can also provide feedback of countermeasures to the server from the terminal.

[0651] Example 1

[0652] 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."

[0653] With the increasing production and distribution of digital content today, the risk of copyright infringement is rising. Therefore, there is a need for a system that can easily and quickly check for copyright infringement while properly protecting the rights of copyrighted works. However, existing systems require a lot of manual operation, are inefficient, and do not perform infringement checks accurately. Furthermore, the complicated process of notifying users of the results and subsequent responses places a heavy burden on users.

[0654] 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.

[0655] In this invention, the server includes: means for uploading copyrighted works created by users; means for storing metadata of the works received by the server in a database; means for the server to pass the copyrighted works to a machine learning engine and for AI to extract features; means for the server to store the feature data in the database; means for the user to upload new data and send a request for a copyright infringement check; means for the server to receive the new data and add the request to a processing queue; means for the server to pass the new data to the machine learning engine and for AI to compare it with existing works in the database and analyze the degree of match or similarity; means for the server to process the analysis results from the AI ​​engine and generate a report of possible copyright infringement; means for sending the report generated by the server to the user's terminal; and means for the user to check the result report and decide on the next action. This allows users to easily check the risk of copyright infringement and quickly take appropriate measures.

[0656] "Works" refers to content such as music files and image files created by users.

[0657] "Server" refers to a computer system that receives, stores, analyzes, generates, and notifies copyrighted material.

[0658] "User" means any person or entity that uses the System to upload copyrighted material and request copyright infringement checks.

[0659] "Metadata" refers to information associated with a work, such as the title, creator, and creation date.

[0660] "Database" refers to a data management system for storing and managing metadata and characteristic data of copyrighted works.

[0661] "Machine learning engine" refers to an artificial intelligence system that analyzes the characteristics of copyrighted works and generates feature data.

[0662] "Characteristics" refers to specific attributes related to the content of a copyrighted work (e.g., melody, rhythm, harmony, etc. in the case of music).

[0663] "Feature data" refers to specific attribute information of a copyrighted work extracted by a machine learning engine.

[0664] "New data" refers to content that users upload for copyright infringement checks.

[0665] A "request" refers to an operation by a user to request a copyright infringement check from the system.

[0666] The term "processing queue" refers to a waiting line for processing received requests in order.

[0667] "Analysis results" refers to the information resulting from the machine learning engine's comparison of new data with existing works in the database.

[0668] "Potential Copyright Infringement Report" refers to a report generated by the server summarizing the analysis results regarding the risk of copyright infringement.

[0669] "Terminal" refers to an electronic device such as a computer or smartphone that a user uses to access and operate the system.

[0670] This invention is a copyright infringement checking system using AI, which aims to reduce the risk of copyright infringement and easily protect the rights of creators and rights holders. In this system, users upload their copyrighted works, and the server analyzes, stores, and compares them to identify the risk of infringement.

[0671] Copyright registration phase

[0672] First, a user uploads a copyrighted work (e.g., a music file or image file) from their device to the system. The server extracts the metadata of the received copyrighted work and stores it in a database. Next, the server passes the copyrighted work to a machine learning engine (AI engine), which analyzes its features and generates feature data. The server then stores this feature data in a database.

[0673] Specific examples

[0674] A user uploads a music file, and the server extracts the metadata of the music file (e.g., title: Example, creator: John Doe, creation date: October 5, 2023) and stores it in a database. The music file is then analyzed by an AI engine to generate feature data (e.g., melody pattern, rhythm, harmony, etc.), which is also stored in the database.

[0675] Copyright Infringement Check Request Phase

[0676] Next, the user uploads new data (e.g., a new advertising image) to the system from their device and sends a request for copyright infringement check. The server receives the new data, temporarily stores it, and then adds the request to the processing queue.

[0677] Specific examples

[0678] A user uploads a new advertising image and requests a copyright infringement check from the system. The server receives the image and adds it to a processing queue.

[0679] Copyright infringement check phase

[0680] The server takes the request from the processing queue and passes the new data back to the machine learning engine, which compares this data with existing copyrighted material in its database to analyze for matches or similarities. The server receives the analysis results from the AI ​​engine and generates a report of potential copyright infringement.

[0681] Specific examples

[0682] The AI ​​engine compares new advertising images with images in the existing database and calculates their similarity, after which the server generates a report indicating whether there is a potential breach.

[0683] Result notification phase

[0684] Finally, the server sends the generated report of possible copyright infringement to the user's device, where the user can review the report and decide on the next action (e.g., modifying the image, inquiring about license rights, etc.).

[0685] Specific examples

[0686] The server notifies the user of the similarity results, such as "This image has 90% similarity to an existing copyrighted work." The user receives the results and considers modifying the image.

[0687] Prompt Sentence Examples

[0688] Examples of prompts include:

[0689] "Upload new music files and store their metadata and characteristics in our database."

[0690] "Upload new advertising images and compare them with existing content in our database to analyze potential copyright infringement."

[0691] "We notify users of similarity results between new and existing content and invite them to submit reports of potential infringement."

[0692] This system allows users to easily check for copyright infringement risks and quickly take appropriate action.

[0693] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0694] Step 1:

[0695] Uploading copyrighted material

[0696] The user selects a work on the terminal.

[0697] Example: A user selects a music file "example.mp3".

[0698] The user clicks the system's upload button.

[0699] Example: A user clicks the "upload" button to send "example.mp3" to the system.

[0700] The terminal transmits the selected file to the server.

[0701] Input: User-selected music file "example.mp3"

[0702] Output: The server receives the music files.

[0703] Step 2:

[0704] Saving to a database

[0705] The server temporarily stores the received file.

[0706] Example: The server saves "example.mp3" to disk.

[0707] Input: Received music file "example.mp3"

[0708] Output: Temporarily saved file

[0709] The server extracts the metadata of the work.

[0710] Example: The server extracts the following metadata for "example.mp3": "Title: Example, Creator: John Doe, Creation Date: October 5, 2023".

[0711] Input: Received music file "example.mp3"

[0712] Output: Extracted metadata

[0713] The server stores the metadata in a database.

[0714] Example: A server records metadata in a "works" table.

[0715] Input: Extracted metadata

[0716] Output: Metadata stored in a database

[0717] Step 3:

[0718] AI-based feature extraction

[0719] The server passes the received copyrighted material to the AI ​​engine.

[0720] Example: The server inputs "example.mp3" into the AI ​​engine.

[0721] Input: Received music file "example.mp3"

[0722] Output: The file passed to the AI ​​engine

[0723] The AI ​​engine analyzes the characteristics of the work.

[0724] Example: The AI ​​engine analyzes the melody pattern, rhythm, harmony, etc. of "example.mp3."

[0725] Input: Received music file "example.mp3"

[0726] Output: Parsed feature data

[0727] The server stores the generated feature data in a database.

[0728] Example: The server stores "feature data" in a "feature" table.

[0729] Input: Parsed feature data

[0730] Output: Feature data stored in a database

[0731] Step 4:

[0732] Submitting a check request

[0733] The user selects new data from the terminal.

[0734] Example: A user selects a new ad image "new_ad.jpg".

[0735] A user submits a request for copyright infringement check to the system.

[0736] Example: A user clicks the "Check Request" button to send "new_ad.jpg" to the server.

[0737] Input: New ad image "new_ad.jpg"

[0738] Output: The server receives the new data.

[0739] The device sends the selected file and the request to the server.

[0740] Input: New ad image "new_ad.jpg"

[0741] Output: The server receives the new data and the request.

[0742] Step 5:

[0743] Receiving content

[0744] The server receives the new data and stores it temporarily.

[0745] Example: The server saves "new_ad.jpg" to disk.

[0746] Input: New ad image "new_ad.jpg"

[0747] Output: Temporarily saved file

[0748] The server adds the request to a processing queue.

[0749] Example: The server adds a "check request" to the queue.

[0750] Input: New ad image "new_ad.jpg"

[0751] Output: Requests added to the processing queue

[0752] Step 6:

[0753] Comparison with database

[0754] The server takes the request from the processing queue and passes the new data back to the AI ​​engine.

[0755] Example: The server inputs "new_ad.jpg" into the AI ​​engine.

[0756] Input: New ad image "new_ad.jpg"

[0757] Output: The file passed to the AI ​​engine

[0758] The AI ​​engine compares the new data with existing works in the database.

[0759] Example: The AI ​​engine compares "new_ad.jpg" with existing image data in the database.

[0760] Input: New ad image "new_ad.jpg", existing data in database

[0761] Output: Match or similarity analysis results

[0762] Step 7:

[0763] Generate results

[0764] The server receives the analysis results from the AI ​​engine and generates a report on potential copyright infringement.

[0765] Example: The server receives the similarity analysis results from the AI ​​engine.

[0766] Input: Analysis results from the AI ​​engine

[0767] Output: Generated potential copyright infringement report

[0768] Step 8:

[0769] Sending the results

[0770] The server generates a report of potential copyright infringement and sends it to the user's device.

[0771] Example: The server sends a "Potential Infringement Report" to the user's device.

[0772] Input: Generated potential copyright infringement report

[0773] Output: Report sent to user's terminal

[0774] Step 9:

[0775] Deciding on an action

[0776] The user checks the results report they received.

[0777] Example: A user opens and reviews a "Potential Compromise Report."

[0778] Input: User action (check result report)

[0779] Output: Determined next action

[0780] The user decides on the next action (e.g., modify the image, inquire about permission, etc.).

[0781] Example: A user sees a 90% similarity and considers modifying the image or inquiring about usage permissions.

[0782] Input: Content of received report

[0783] Output: Decision on next action

[0784] (Application example 1)

[0785] 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."

[0786] Conventional copyright infringement checking systems make it difficult for content creators to check copyright infringement risks in advance. While real-time checks are particularly required for content distribution platforms, the technology to achieve this is insufficient. Furthermore, even when there is a high risk of copyright infringement, response is delayed, which can lead to legal issues after the content is released. This leaves content creators with no peace of mind when distributing their content.

[0787] 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.

[0788] In this invention, the server includes: means for uploading copyrighted works created by users; means for the server to store the received metadata of the copyrighted works in a database; means for the server to pass the copyrighted works to an AI engine and for the AI ​​to extract features; means for the server to store the feature data in the database; means for the user to upload new content and send a request for a copyright infringement check; means for the server to receive the new content and add the request to a processing queue; means for the server to pass the new content to the AI ​​engine and for the AI ​​to compare the new content with existing copyrighted works in the database and analyze the degree of match or similarity; means for the server to process the analysis results from the AI ​​engine and generate a report on possible copyright infringement; means for the server to send the generated report to the user's device; means for the user to check the result report and decide on the next action; means for the user to upload content from their smartphone and undergo a copyright infringement check in real time; means for the AI ​​engine to analyze the features of the content and compare it with existing copyrighted works in real time; and means for sending a notification to the user if a risk of copyright infringement is determined. This enables content creators to conduct copyright infringement checks in real time on a content distribution platform, thereby identifying the risk of copyright infringement in advance and avoiding legal issues.

[0789] "Copyrighted work" refers to content such as music, images, and video created by a creator.

[0790] "Upload" refers to the act of a user sending data from their device to a server.

[0791] "Metadata" refers to information about a work, including attribute information such as the title, creator, and creation date.

[0792] A "database" refers to a system for organizing and storing data and for efficiently managing and searching it.

[0793] An "AI engine" refers to a software system that uses artificial intelligence technology to analyze data and recognize patterns.

[0794] "Feature extraction" refers to the process of extracting important feature data (e.g., audio waveforms, image patterns, etc.) from copyrighted works using an AI engine.

[0795] "Copyright infringement" refers to the act of using someone else's copyrighted work without permission, which can lead to legal issues.

[0796] "Content distribution" refers to the act of providing and sharing digital content such as music, videos, and images through online platforms.

[0797] "Real-time" refers to processing and results occurring immediately, without delay.

[0798] The "Results Report" is a report summarizing the results of the AI ​​engine's analysis, and includes information about the risk of copyright infringement.

[0799] "Notification" refers to the act of a system conveying specific information to a user.

[0800] MODE FOR CARRYING OUT THE INVENTION

[0801] The present invention provides a system for checking the risk of copyright infringement in real time, especially on a content distribution platform, which comprises the following steps:

[0802] 1. System program generation

[0803] In this system, users upload copyrighted material from their smartphones, and an AI engine analyzes the characteristics of the material. The AI ​​engine stores the metadata and characteristic data of the material in a database, and in response to requests for copyright infringement checks, it compares new content with existing copyrighted material. The server also generates analysis results and notifies users of the risk of copyright infringement.

[0804] 2. Hardware and Software Description

[0805] To realize this system, the following hardware and software are used.

[0806] Hardware

[0807] Server: Stores data, extracts and compares features using AI, and generates and notifies analysis results.

[0808] Smartphone: A device through which users upload content and receive results.

[0809] software

[0810] Flask: Used as a web server framework to manage requests.

[0811] AI engine: A custom AI library (tentative name) that performs feature extraction and database comparison processing.

[0812] Database Management System: A system for efficiently storing and managing metadata and feature data.

[0813] 3. Data processing and calculation

[0814] The server processes and calculates data in the following steps:

[0815] 1. Uploading and Analysis of Copyrighted Materials:

[0816] The user uploads content (e.g., videos, images) from their smartphone. The server stores the metadata of the received content in a database, and the AI ​​engine extracts features. The extracted feature data is also stored in the database.

[0817] 2. Copyright Infringement Check Request:

[0818] A user uploads new content and sends a request for copyright infringement check. The server receives the new content and passes it to an AI engine to compare it with existing copyrighted material in the database.

[0819] 3. Results generation and notification:

[0820] The AI ​​engine returns the comparison results to the server, which processes the analysis results and generates a report of possible copyright infringement, which the server notifies the user.

[0821] 4. Example: Uploading a video to YouTube

[0822] For example, when a user uploads a video to an online video platform, the system can be used to check for copyright infringement risks. If the video contains an existing movie scene, the system will send a notification saying, "This video is 95% identical to an existing movie scene." Based on this, the user can modify or delete the video to avoid future legal issues.

[0823] 5. Examples of prompts

[0824] Your content guardian will analyze user-uploaded videos and images in real time to determine if they infringe existing copyrighted material. Please provide specific steps for users:

[0825] 1. Uploading Content

[0826] 2. Feature extraction using AI

[0827] 3. Comparison with database

[0828] 4. Generating and Communicating Results

[0829] The above is a description of the mode for carrying out the invention, and this system enables content creators to check copyright infringement risks in real time and prevent legal problems before they occur.

[0830] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0831] Step 1:

[0832] Uploading and analyzing copyrighted material

[0833] A user uploads content from their smartphone. Specifically, videos or images are sent to a specified server endpoint. The server extracts the content's metadata (title, creation date, etc.) and stores it in a database. The input at this stage is the content provided by the user, and the output is the metadata stored in the database. The server then passes this content to an AI engine, which extracts feature data (for example, visual patterns and audio waveforms in the case of videos). This feature data is also stored in the database.

[0834] Step 2:

[0835] Submitting a Copyright Infringement Check Request

[0836] A user uploads new content and sends a request for copyright infringement check. Specifically, the user again uses their smartphone to send the content to the server. This input is new content, and its purpose is to check for copyright infringement. The server receives this new content and adds the request to the processing queue. Once the request is added to the queue, it is ready to proceed to the next step.

[0837] Step 3:

[0838] AI-based feature analysis and database comparison

[0839] The server takes new content from the processing queue and passes it to the AI ​​engine. The AI ​​engine extracts the features of the new content and compares it with copyrighted works in the existing database. At this time, it analyzes how much the feature data matches or is similar to the existing data. The input is the new content and the feature data from the existing database, and the output is the analysis result of the degree of match or similarity. As a concrete example, the analysis result of a video may be output in the form of "This footage is 80% identical to existing movie scenes."

[0840] Step 4:

[0841] Generation and notification of analysis results

[0842] The server generates a report of possible copyright infringement based on the analysis results obtained from the AI ​​engine. Specifically, it compiles similarity and match information into a report format. The input is the analysis results returned by the AI ​​engine, and the output is the generated report. This report is sent to the user's smartphone so that the user can view it. This notification allows the user to decide on the next action (e.g., modifying or deleting the content).

[0843] Step 5:

[0844] Determining User Actions

[0845] The user checks the report received on their smartphone and decides on the next course of action. Specifically, if it is determined that there is a high risk of copyright infringement, they can take action such as modifying the content or inquiring about usage permissions. The input is the analysis result report, and the output is the user's specific action. This step allows the user to avoid the risk of copyright infringement of content in advance.

[0846] 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.

[0847] This invention is a copyright infringement checking system that uses AI to reduce the risk of copyright infringement and easily protect the rights of creators and patent holders, and it also combines an emotion engine that recognizes user emotions and provides feedback. In this system, users upload their copyrighted works, and the server analyzes, stores, and compares them to identify the risk of infringement, and analyzes user emotions in real time and provides appropriate feedback.

[0848] Explaining program processing in natural language

[0849] 1. Copyright Registration Phase

[0850] 1.1. Uploading copyrighted material

[0851] A user uploads copyrighted material (e.g., music files, image files) to the system from a terminal. The terminal then transmits the uploaded copyrighted material to the server.

[0852] 1.2. Saving to Database

[0853] The server analyzes the received copyrighted material and extracts metadata (title, author, creation date, etc.), which it then stores in a database.

[0854] 1.3. Feature extraction using AI

[0855] The server passes the copyrighted work to an AI engine, which analyzes its characteristics (e.g., melody, rhythm, harmony, etc. in the case of music) and generates feature data. The server then stores the generated feature data in a database.

[0856] Examples:

[0857] A user uploads a music file and the server stores the metadata and characteristics of the music file in a database.

[0858] 2. Copyright Infringement Check Request Phase

[0859] 2.1. Sending a Check Request

[0860] The user uploads new content (e.g., a new advertising image) from their device and sends a request for copyright infringement check. The device sends this new content and the request to the server.

[0861] 2.2. Receiving Content

[0862] The server receives the new content and adds the request to the processing queue, preparing to pass the new content to the AI ​​engine.

[0863] Examples:

[0864] A user uploads a new advertising image to the system and requests a copyright infringement check. The server receives the image and adds it to the processing queue.

[0865] 3. Copyright Infringement Check Phase

[0866] Comparison with Databases

[0867] The server processes the request and passes the new content to the AI ​​engine, which compares the new content with existing copyrighted material in its database and analyzes it for matches or similarities.

[0868] 3.2. Producing Results

[0869] The server processes the analysis results returned by the AI ​​engine and generates a report of potential copyright infringement based on the analysis results.

[0870] Examples:

[0871] The AI ​​engine compares new advertising images with existing images in the database, and the server generates a report based on the results of the similarity analysis.

[0872] 4. Result notification phase

[0873] 4.1. Sending the results

[0874] The server generates a report of possible copyright infringement and sends it to the user's device, where the user can view the report.

[0875] 4.2. Determining Actions

[0876] The user checks the result report they receive and decides on the next action (e.g., image correction, license inquiry, etc.). The user can also provide feedback on countermeasures to the server from their device.

[0877] Examples:

[0878] The server notifies the user of the similarity results, saying, "This image has 90% similarity to an existing copyrighted work." The user receives the results and considers modifying the image.

[0879] 5. Implementing the Emotion Engine

[0880] 5.1. Emotion Data Collection

[0881] As the user operates the interface displayed during the copyright infringement check process, emotional data such as the user's facial expressions, voice, and keyboard input is collected from the device.

[0882] 5.2. Emotion Data Analysis

[0883] The server passes the collected emotional data to the emotion engine, where the AI ​​analyzes the user's emotions. The analysis results are converted into numerical values ​​for the user's emotions, such as stress, frustration, and joy.

[0884] Providing Feedback

[0885] Based on the analysis results, the server provides appropriate feedback to the user's device. For example, if high stress or frustration is detected, a relaxation message or a link to contact support will be displayed.

[0886] Examples:

[0887] The emotion engine analyzes whether the user is feeling frustrated while operating the device, and the server displays a relaxation message such as "Would you like to take a short break?"

[0888] This system not only allows users to easily check the risk of copyright infringement, but also allows them to receive support that takes into consideration their emotional state, allowing them to use and publish content with greater peace of mind.

[0889] The processing flow will be explained below.

[0890] Step 1:

[0891] A user uploads copyrighted material (e.g., music files, image files) to the system from a terminal. The terminal then transmits the uploaded copyrighted material to the server.

[0892] Step 2:

[0893] The server analyzes the received copyrighted material and extracts metadata (title, author, creation date, etc.), which it then stores in a database.

[0894] Step 3:

[0895] The server passes the copyrighted work to the AI ​​engine, which analyzes the characteristics of the work (e.g., melody, rhythm, harmony, etc. in the case of music) and generates feature data. The server then stores the generated feature data in a database.

[0896] Step 4:

[0897] The user uploads new content (e.g., a new advertising image) from their device and sends a request for copyright infringement check. The device sends this new content and the request to the server.

[0898] Step 5:

[0899] The server receives the new content and adds the request to the processing queue, preparing to pass the new content to the AI ​​engine.

[0900] Step 6:

[0901] The server passes the new content to the AI ​​engine, which compares it with existing copyrighted material in its database to analyze for matches or similarities.

[0902] Step 7:

[0903] The server processes the analysis results returned by the AI ​​engine and generates a report of potential copyright infringement based on the analysis results.

[0904] Step 8:

[0905] The server generates a report of possible copyright infringement and sends it to the user's device, where the user can view the report.

[0906] Step 9:

[0907] Based on the results report received, the user decides on the next action (e.g., image correction, inquiry for permission to use, etc.). The user can also provide feedback on countermeasures to the server from the terminal.

[0908] Step 10:

[0909] The device collects the user's emotional data (e.g., facial expressions, voice, keyboard input, etc.) while the device is in operation. The emotional data is sent to the server in real time.

[0910] Step 11:

[0911] The server passes the emotion data to the emotion engine, which analyzes the collected data and quantifies the user's emotion (e.g., stress, frustration, joy, etc.).

[0912] Step 12:

[0913] The server generates appropriate feedback based on the analysis results from the emotion engine. For example, the server creates a relaxation message if high stress or frustration is detected.

[0914] Step 13:

[0915] The server generates feedback (e.g., a relaxation message) and sends it to the user's device, where the user can view the feedback.

[0916] Step 14:

[0917] The user decides the next action (e.g., taking a break, further operation, etc.) based on the feedback. The user's actions are fed back to the server via the device.

[0918] Example 2

[0919] 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."

[0920] Currently, systems for checking copyright infringement of digital content are often cumbersome and time-consuming, which can impair user convenience. Furthermore, few systems can analyze users' emotions and provide appropriate feedback in addition to checking for copyright infringement. This can easily lead to stress and frustration, so there is a need for a system that can integrate the evaluation of copyright infringement with the management of one's own emotional state.

[0921] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0922] In this invention, the server includes a means for uploading digital content created by users, a means for storing metadata of the digital content received by the server in a storage device, and a means for the server to pass the digital content to an artificial intelligence engine and for the artificial intelligence to extract features, thereby enabling copyright infringement checks of digital content and user sentiment analysis to be performed in a unified manner.

[0923] "User" means any person who interacts with the system to upload digital content or submit a request for a copyright infringement check.

[0924] A "terminal" is a device operated by a user, and is an apparatus on which operations such as uploading digital content and checking result reports are performed.

[0925] A "server" is a computer system that receives and processes digital content or requests sent by users.

[0926] "Digital content" refers to electronically recorded information such as music files, image files, and video files.

[0927] "Metadata" is additional information associated with digital content, such as title, creator, and creation date.

[0928] A "storage device" is a storage device for storing digital content, metadata, and feature data.

[0929] An "artificial intelligence engine" is a system that includes algorithms and models for analyzing the characteristics of digital content and analyzing matches or similarities.

[0930] "Feature data" refers to the analysis results of the melody, rhythm, harmony, etc. of digital content extracted by an artificial intelligence engine.

[0931] A "processing queue" is a data structure for temporarily storing new requests and processing them sequentially.

[0932] A "copyright infringement check request" is a request sent by a user to a server to check new digital content for existing copyright infringement.

[0933] "Analysis Results" means the results of the comparison and matching or similarity analysis of digital content conducted by the artificial intelligence engine.

[0934] A "report" is a document generated based on the analysis results, which includes information indicating possible copyright infringement.

[0935] "Emotion data" is data that indicates the user's emotional state, such as the user's facial expression, voice, keyboard input, etc.

[0936] An "emotion recognition engine" is a system that includes algorithms and models for analyzing collected emotional data and quantifying a user's emotional state.

[0937] "Feedback" refers to notifications and messages provided to users based on the results of emotion analysis, and includes, for example, relaxation messages and support information.

[0938] This invention is a system for checking copyright infringement of digital content and analyzing user sentiment in an integrated manner. This system is realized by uploading digital content created by users, and the server analyzes, stores, and performs specific processing on the uploaded content.

[0939] Hardware and software used

[0940] The system is implemented using the following hardware and software:

[0941] Terminal: A device operated by a user, including a PC, smartphone, tablet, etc., that has internet connectivity and file upload capabilities.

[0942] Server: A computer system with high-performance processing capabilities that receives digital content, stores it in a database, connects with the AI ​​engine, and provides feedback on the results of sentiment analysis.

[0943] Database software: A relational database management system (RDBMS) such as MySQL or PostgreSQL is used to store metadata and feature data for digital content.

[0944] AI Engine: Contains generative AI models built using TensorFlow and PyTorch to extract features from digital content, compare them, and check for potential copyright infringement.

[0945] Emotion recognition engine: Uses emotion analysis services such as Amazon Rekognition and Google Cloud Vision to collect and analyze user emotion data.

[0946] Example of a system

[0947] Consider a scenario where a user uploads digital content that they have created, for example, a music file called "MySong.mp3."

[0948] 1. User Action:

[0949] The user uses the device to drag and drop the file "MySong.mp3" into the system interface, then clicks the upload button, and the device sends this file to the server via an HTTP request.

[0950] 2. Server-side processing:

[0951] The server receives the file and saves it in a temporary directory. It then extracts metadata such as the title "My Song," the creator "John Doe," and the creation date "2023-10-10" and stores them in a database. The server then passes the file to an AI engine, which analyzes the melody and rhythmic features. The resulting feature data, "Melody: (C, D, E, F), Rhythm: 4 / 4, Tempo: 120 BPM," is saved in the database.

[0952] 3. Copyright Infringement Check:

[0953] When checking new digital content, a user uploads a new advertising image, "AdsImage.jpg," and requests a copyright infringement check. The server receives this request and adds it to the processing queue. The AI ​​engine compares it with existing data and analyzes the similarity. A "95% similarity" is returned as a result, and a report of possible copyright infringement is generated and sent to the user.

[0954] 4. Sentiment Analysis and Feedback:

[0955] As the user operates the system, the device collects the user's emotional data (facial expressions, voice, keyboard input, etc.). The server passes the collected emotional data to an emotion recognition engine for analysis. If high stress or frustration is detected, the server sends the user relaxation messages or support information.

[0956] Prompt Sentence Examples

[0957] When a user uploads a new music file and analyzes its features based on the AI ​​engine, the prompt text is as follows:

[0958] "Upload the music file 'MySong.mp3'. We'll analyze the melody, rhythm, and tempo of the file and store them in our database."

[0959] This system not only checks for copyright infringement of digital content, but also takes into consideration the emotional state of the user, allowing users to use and publish content with greater peace of mind.

[0960] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0961] Step 1:

[0962] The user selects digital content (e.g., music file "MySong.mp3") on the device and clicks the upload button.

[0963] Input: Music file "MySong.mp3".

[0964] Output: The music file "MySong.mp3" is sent from the device to the server.

[0965] Specific behavior: The device sends the file to the server using an HTTP POST request.

[0966] Step 2:

[0967] The server receives the uploaded file and saves it in a temporary directory.

[0968] Input: Music file "MySong.mp3".

[0969] Output: "MySong.mp3" saved in your temporary directory.

[0970] Specific operation: The server saves the received file in a specific directory (e.g., " / tmp / uploads").

[0971] Step 3:

[0972] The server extracts the metadata of the music files.

[0973] Input: A music file "MySong.mp3" saved in the temporary directory.

[0974] Output: Extracted metadata (Title "My Song", Author "John Doe", Creation Date "2023-10-10").

[0975] Specific operation: The server uses a Python library (e.g., mutagen) to extract metadata from music files.

[0976] Step 4:

[0977] The server stores the extracted metadata in a database.

[0978] Input: Extracted metadata (Title "My Song", Author "John Doe", Creation Date "2023-10-10").

[0979] Output: Metadata stored in a database.

[0980] What happens: The server executes an SQL INSERT query to save the metadata into a MySQL database.

[0981] Step 5:

[0982] The server passes the music file to the AI ​​engine, which extracts its features.

[0983] Input: Music file "MySong.mp3".

[0984] Output: Extracted feature data (melody, rhythm, tempo).

[0985] How it works: The server inputs the file into an AI model using TensorFlow, which analyzes features such as melody, rhythm, and tempo.

[0986] Step 6:

[0987] The server stores the feature data in a database.

[0988] Input: Extracted feature data (melody, rhythm, tempo).

[0989] output: Feature data stored in the database.

[0990] What happens: The server executes an SQL INSERT query to save the feature data into a MySQL database.

[0991] Step 7:

[0992] A user uploads new digital content (e.g., an advertisement image "AdsImage.jpg") from their device and submits a request for copyright infringement check.

[0993] Input: Ad image "AdsImage.jpg".

[0994] output: The ad image "AdsImage.jpg" and the check request are sent to the server.

[0995] Specific behavior: The device sends the file and request to the server using an HTTP POST request.

[0996] Step 8:

[0997] The server receives new digital content and adds it to a processing queue.

[0998] Input: Ad image "AdsImage.jpg" and check request.

[0999] output: The ad image "AdsImage.jpg" and check request added to the processing queue.

[1000] Specific behavior: The server saves the received file in a temporary directory and adds it to the processing queue.

[1001] Step 9:

[1002] The server passes new digital content to the AI ​​engine, which compares it with existing data and analyzes it for matches or similarities.

[1003] Input: Ad image "AdsImage.jpg".

[1004] output: The match or similarity analysis result.

[1005] What it does: The server inputs the file into an AI engine, which compares it with digital content in an existing database.

[1006] Step 10:

[1007] The server generates a report of possible copyright infringement based on the analysis results.

[1008] Input: Match or similarity analysis results.

[1009] output: A report of potential copyright infringement.

[1010] Specific operation: The server automatically generates a PDF report based on the analysis results.

[1011] Step 11:

[1012] The server sends the generated report to the user's terminal.

[1013] Enter: Potential Copyright Infringement Report.

[1014] output: The report sent to the user's terminal.

[1015] Specific operation: The server uses an HTTP response to send the report to the user's device.

[1016] Step 12:

[1017] The user reviews the results report and decides on the next action.

[1018] Enter: Potential Copyright Infringement Report.

[1019] output: Next action (e.g., image modification, license inquiry).

[1020] Specific actions: The user views the report, decides what action is required, and takes further action via the device.

[1021] Step 13:

[1022] As the user operates the system, the device collects emotional data.

[1023] Input: User facial expressions, voice, keyboard input, etc.

[1024] output: The collected emotion data.

[1025] What it does: The device uses the camera, microphone, and keyboard to collect user emotional data.

[1026] Step 14:

[1027] The server passes the collected emotion data to an emotion recognition engine for analysis.

[1028] Input: Collected emotion data.

[1029] output: Sentiment analysis results (e.g., stress level or frustration).

[1030] Specific operation: The server passes the data to the emotion recognition engine and receives the analysis results.

[1031] Step 15:

[1032] The server generates feedback based on the emotion analysis results and provides it to the user.

[1033] Input: Sentiment analysis results.

[1034] output: A feedback message to the user (e.g., a relaxation message or support information).

[1035] Specific operation: The server generates a message appropriate for the user based on the analysis results and sends it to the device.

[1036] (Application example 2)

[1037] 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."

[1038] In content creation, it is important to reduce the risk of copyright infringement and protect the rights of creators. Furthermore, it is necessary to reduce the stress and frustration felt by users during the content uploading process and realize a smooth operation. The problem to be solved by this invention is to effectively check the risk of copyright infringement of content created by users, grasp the user's emotional state, and provide appropriate feedback.

[1039] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for uploading a work created by a user; means for the server to store metadata of the work received in a database; means for the server to pass the work to an AI engine and for the AI ​​to extract features; means for the server to store the feature data in a database; means for the user to upload new content and send a request for a copyright infringement check; means for the server to receive the new content and add the request to a processing queue; means for the server to pass the new content to the AI ​​engine and for the AI ​​to compare it with existing works in the database and analyze the degree of match or similarity; means for the server to process the analysis results from the AI ​​engine and generate a report of possible copyright infringement; means for sending the report generated by the server to the user's terminal; means for the user to check the result report and decide on the next action; means for analyzing the user's facial expressions, voice, and keyboard input to understand the user's emotional state; and means for the server to analyze the emotional data and provide appropriate feedback to the user. This makes it possible to effectively check the risk of copyright infringement in user-created content, and also to analyze the user's emotional state in real time and provide appropriate feedback.

[1040] "User" means a person or entity that uses the System to upload copyrighted material and request a copyright infringement check.

[1041] "Works" are content created by users, and examples include music files and image files.

[1042] The "server" is a central processing unit that receives the copyrighted material uploaded by the user, analyzes, stores, compares, notifies the results, and processes the emotion data.

[1043] "Metadata" is information about a work, including the title, creator, creation date, etc.

[1044] "Database" means a storage device containing metadata and characteristic data of existing works stored by the server.

[1045] An "AI engine" is a program device that uses artificial intelligence to extract characteristics of copyrighted works and compare new content with existing works in a database.

[1046] "Feature data" refers to specific attribute information of a copyrighted work, such as its melody, rhythm, and harmony, extracted by the AI ​​engine.

[1047] A "request" refers to a request by a user to submit new content to a server for copyright infringement checking.

[1048] A "processing queue" is a line of tasks that is temporarily placed to await processing after the server receives new content.

[1049] "Analysis results" refers to the data generated after the AI ​​engine compares new content with existing copyrighted works in its database.

[1050] The "Potential Copyright Infringement Report" is a report that indicates the risk of copyright infringement, generated by the server based on the analysis results from the AI ​​engine.

[1051] "Emotion data" is information that indicates the user's emotional state, obtained from the user's facial expression, voice, keyboard input, and the like.

[1052] "Feedback" refers to constructive advice or messages provided to users based on emotional data analyzed by the server.

[1053] MODE FOR CARRYING OUT THE INVENTION

[1054] This invention relates to a system for checking copyright infringement of user-created content and analyzing the user's emotional state in real time. To specifically implement this system, the following hardware and software, as well as data processing and data calculation, are required.

[1055] 1. Hardware and Software Used

[1056] Hardware

[1057] Smartphone: The device where users upload content

[1058] Head-mounted display: A display device equipped with a camera and microphone that collects the user's facial expressions and voice.

[1059] Server: A central processing unit that analyzes and stores data

[1060] software

[1061] Python: A language for data analysis and AI model execution

[1062] TensorFlow / Keras: Building and running AI models

[1063] OpenCV: Facial Expression Recognition Library

[1064] Azure Face API: Facial recognition and emotion analysis API

[1065] 2. Natural language description of program processing

[1066] User Content Uploads

[1067] Users upload their own creations (e.g., videos, images, and music files) through the interface of their smartphone or head-mounted display. The creations are then sent from the device to the server.

[1068] Extracting and storing metadata

[1069] The server analyzes the received copyrighted material and extracts metadata (title, author, creation date, etc.), which is then stored in a database.

[1070] AI-based feature extraction

[1071] The copyrighted work is passed from the server to the AI ​​engine, which analyzes its characteristic data (melody, rhythm, harmony, etc.) and stores the analyzed characteristic data in a database.

[1072] Copyright Infringement Check Request

[1073] A user uploads new content and requests a copyright infringement check. The server receives this new content and adds it to a processing queue.

[1074] Compare and analyze new content

[1075] The server then passes the new content to the AI ​​engine, which then compares it with existing works in its database and analyzes the matches and similarities.

[1076] Analysis results and report generation

[1077] The server generates a report of potential copyright infringement based on the analysis results returned by the AI ​​engine, and this report is sent to the user's device.

[1078] Emotion data collection and analysis

[1079] The user's facial expressions, voice, and keyboard input are collected through the camera and microphone of the head-mounted display. This emotional data is sent to a server and analyzed by an AI engine.

[1080] Emotional Feedback

[1081] The server uses the emotion analysis results to determine the user's stress and frustration and provides appropriate feedback. For example, if high stress is detected, a relaxation message such as "Would you like to take a break?" will be displayed.

[1082] 3. Examples and prompts

[1083] Specific examples

[1084] A user uploads a new video to the content distribution platform and requests a copyright infringement check. The server then extracts the video's metadata and feature data and compares it with existing videos in the database. At the same time, it analyzes the user's facial expressions and voice and displays relaxation messages if the user's stress level increases.

[1085] Prompt Sentence Examples

[1086] "Upload a new video to the copyright infringement checking system and check its similarity to existing videos. Extract the metadata and feature data of the uploaded video, compare it with existing videos in the database, analyze the similarity, and notify the user of the results. At the same time, analyze the user's facial and vocal emotional data, and display a relaxation message if stress is detected."

[1087] This invention not only allows users to easily check the risk of copyright infringement for the content they create, but also allows them to receive support that takes into consideration their emotional state, allowing them to use and publish content with greater peace of mind.

[1088] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1089] Step 1:

[1090] Users upload content

[1091] Users upload their own copyrighted works (e.g., videos, images, and music files) to the system using their smartphones or head-mounted displays. The devices then send the uploaded works to the server.

[1092] Input: User-created copyrighted material file

[1093] Output: The copyrighted file sent to the server

[1094] Step 2:

[1095] Extracting and storing metadata

[1096] The server analyzes the received copyrighted material and extracts metadata (title, author, creation date, etc.) and stores this metadata in a database.

[1097] Input: User-uploaded copyrighted material file

[1098] Output: Metadata stored in a database

[1099] What it does: A Python program parses copyrighted files and extracts metadata, which is then stored in a database using SQL queries.

[1100] Step 3:

[1101] AI-based feature extraction

[1102] The server passes the copyrighted work to an AI engine (a model using TensorFlow / Keras), which analyzes and generates feature data (attributes such as melody, rhythm, harmony, etc.) The server then stores the generated feature data in a database.

[1103] Input: Copyrighted material file received by the server

[1104] Output: Feature data stored in a database

[1105] Specific operation: The copyrighted file is loaded into the TensorFlow / Keras model, and feature data is generated. The generated feature data is then stored in a database using SQL queries.

[1106] Step 4:

[1107] User submits request

[1108] The user uploads new content (e.g., a new video file) and sends a request for copyright infringement check. The device sends this new content and the request to the server.

[1109] Input: New content files uploaded by users and copyright infringement check requests

[1110] Output: New content and request sent to the server

[1111] Step 5:

[1112] Request Processing

[1113] The server receives the new content and adds the request to the processing queue, preparing to hand the new content off to the AI ​​engine.

[1114] Input: New content and requests uploaded by users

[1115] Output: Request added to processing queue

[1116] What happens: The Python program receives new content and requests and adds them to the processing queue.

[1117] Step 6:

[1118] Copyright Infringement Check

[1119] The server processes the request and passes the new content to an AI engine, which compares the new content with existing copyrighted material in its database and analyzes it for matches or similarities.

[1120] Input: Requests added to the processing queue, new content

[1121] Output: Match or similarity analysis results

[1122] What it does: A TensorFlow / Keras model is used to compare new content with existing works in the database.

[1123] Step 7:

[1124] Processing analysis results and generating reports

[1125] The server generates a report on possible copyright infringement based on the analysis results returned by the AI ​​engine, and sends this report to the user's device.

[1126] Input: Analysis results from the AI ​​engine

[1127] Output: Report sent to user terminal

[1128] Specific operation: Based on the analysis results, a Python program generates a report and sends it to the relevant user's device as an email or notification.

[1129] Step 8:

[1130] Emotion data collection and analysis

[1131] The user's facial expressions, voice, and keyboard input are collected through the camera and microphone of the head-mounted display. This emotional data is sent to a server and analyzed by an AI engine.

[1132] Input: User facial, voice, and keyboard input data

[1133] Output: Parsed emotion data

[1134] Specific operation: Analyzes emotion data in real time using OpenCV and Azure Face API.

[1135] Step 9:

[1136] Emotional Feedback

[1137] The server uses the emotion analysis results to determine the user's stress and frustration and provides appropriate feedback. For example, if high stress is detected, a relaxation message such as "Would you like to take a break?" will be displayed.

[1138] Input: Parsed emotion data

[1139] Output: Feedback message provided to the user

[1140] Specific operation: Based on the results of emotion analysis, a feedback message corresponding to the user's situation is generated and displayed on the user's device.

[1141] 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.

[1142] 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.

[1143] 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.

[1144] [Third embodiment]

[1145] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1146] 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.

[1147] 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).

[1148] 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.

[1149] 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.

[1150] 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).

[1151] 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.

[1152] 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.

[1153] 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.

[1154] 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.

[1155] 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.

[1156] 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."

[1157] This invention is a copyright infringement checking system using AI, which aims to reduce the risk of copyright infringement and easily protect the rights of creators and patent holders. In this system, users upload their copyrighted works, and the server analyzes, stores, and compares them to identify risks of infringement.

[1158] Explaining program processing in natural language

[1159] 1. Copyright Registration Phase

[1160] 1.1. Uploading copyrighted material

[1161] A user uploads copyrighted material (e.g., music files, image files) to the system from their terminal.

[1162] 1.2. Saving to Database

[1163] The server extracts the metadata (title, author, creation date, etc.) of the received copyrighted work and stores it in a database.

[1164] 1.3. Feature extraction using AI

[1165] The server passes the copyrighted work to an AI engine, which analyzes its characteristics (e.g., melody, rhythm, harmony, etc. in the case of music) and generates feature data. The server then stores the generated feature data in a database.

[1166] Examples:

[1167] A user uploads a music file and the server stores the metadata and characteristics of the music file in a database.

[1168] 2. Copyright Infringement Check Request Phase

[1169] 2.1. Sending a Check Request

[1170] The user uploads new content (e.g., a new advertising image) from their device and submits a request for copyright infringement check.

[1171] 2.2. Receiving Content

[1172] The server receives the new content and adds the request to the processing queue.

[1173] Examples:

[1174] A user uploads a new advertising image to the system and requests a copyright infringement check. The server receives the image and adds it to the processing queue.

[1175] 3. Copyright Infringement Check Phase

[1176] Comparison with Databases

[1177] The server processes the request and passes the new content to the AI ​​engine, which compares it with existing copyrighted material in its database and analyzes the matching or similarity of features.

[1178] 3.2. Producing Results

[1179] The server processes the analysis results returned by the AI ​​engine and generates a report of potential copyright infringement.

[1180] Examples:

[1181] The AI ​​engine compares new advertising images with existing images in the database, and the server generates a report based on the results of the similarity analysis.

[1182] 4. Result notification phase

[1183] 4.1. Sending the results

[1184] The server generates a report of potential copyright infringement and sends it to the user's device.

[1185] 4.2. Determining Actions

[1186] The user reviews the results report received and decides on the next action (e.g., image correction, license inquiry, etc.).

[1187] Examples:

[1188] The server notifies the user of the similarity results, saying, "This image has 90% similarity to an existing copyrighted work." The user receives the results and considers modifying the image.

[1189] This system allows users to easily check for copyright infringement risks, enabling them to use and publish content with peace of mind.

[1190] The processing flow will be explained below.

[1191] Step 1:

[1192] A user uploads copyrighted material (e.g., music files, image files) to the system from a terminal. The terminal then transmits the uploaded copyrighted material to the server.

[1193] Step 2:

[1194] The server analyzes the received copyrighted material and extracts metadata (title, author, creation date, etc.), which it then stores in a database.

[1195] Step 3:

[1196] The server passes the copyrighted work to the AI ​​engine, which analyzes the characteristics of the work (e.g., melody, rhythm, harmony, etc. in the case of music) and generates feature data. The server then stores the generated feature data in a database.

[1197] Step 4:

[1198] The user uploads new content (e.g., a new advertising image) from their device and sends a request for copyright infringement check. The device sends this new content and the request to the server.

[1199] Step 5:

[1200] The server receives the new content and adds the request to the processing queue, preparing to pass the new content to the AI ​​engine.

[1201] Step 6:

[1202] The server passes the new content to the AI ​​engine, which compares it with existing copyrighted material in its database to analyze for matches or similarities.

[1203] Step 7:

[1204] The server processes the analysis results returned by the AI ​​engine and generates a report of potential copyright infringement based on the analysis results.

[1205] Step 8:

[1206] The server generates a report of possible copyright infringement and sends it to the user's device, where the user can view the report.

[1207] Step 9:

[1208] Based on the result report received by the user, the next action (e.g., image correction, license inquiry, etc.) is decided. The user can also provide feedback of countermeasures to the server from the terminal.

[1209] Example 1

[1210] 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."

[1211] With the increasing production and distribution of digital content today, the risk of copyright infringement is rising. Therefore, there is a need for a system that can easily and quickly check for copyright infringement while properly protecting the rights of copyrighted works. However, existing systems require a lot of manual operation, are inefficient, and do not perform infringement checks accurately. Furthermore, the complicated process of notifying users of the results and subsequent responses places a heavy burden on users.

[1212] 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.

[1213] In this invention, the server includes: means for uploading copyrighted works created by users; means for storing metadata of the works received by the server in a database; means for the server to pass the copyrighted works to a machine learning engine and for AI to extract features; means for the server to store the feature data in the database; means for the user to upload new data and send a request for a copyright infringement check; means for the server to receive the new data and add the request to a processing queue; means for the server to pass the new data to the machine learning engine and for AI to compare it with existing works in the database and analyze the degree of match or similarity; means for the server to process the analysis results from the AI ​​engine and generate a report of possible copyright infringement; means for sending the report generated by the server to the user's terminal; and means for the user to check the result report and decide on the next action. This allows users to easily check the risk of copyright infringement and quickly take appropriate measures.

[1214] "Works" refers to content such as music files and image files created by users.

[1215] "Server" refers to a computer system that receives, stores, analyzes, generates, and notifies copyrighted material.

[1216] "User" means any person or entity that uses the System to upload copyrighted material and request copyright infringement checks.

[1217] "Metadata" refers to information associated with a work, such as the title, creator, and creation date.

[1218] "Database" refers to a data management system for storing and managing metadata and characteristic data of copyrighted works.

[1219] "Machine learning engine" refers to an artificial intelligence system that analyzes the characteristics of copyrighted works and generates feature data.

[1220] "Characteristics" refers to specific attributes related to the content of a copyrighted work (e.g., melody, rhythm, harmony, etc. in the case of music).

[1221] "Feature data" refers to specific attribute information of a copyrighted work extracted by a machine learning engine.

[1222] "New data" refers to content that users upload for copyright infringement checks.

[1223] A "request" refers to an operation by a user to request a copyright infringement check from the system.

[1224] The term "processing queue" refers to a waiting line for processing received requests in order.

[1225] "Analysis results" refers to the information resulting from the machine learning engine's comparison of new data with existing works in the database.

[1226] "Potential Copyright Infringement Report" refers to a report generated by the server summarizing the analysis results regarding the risk of copyright infringement.

[1227] "Terminal" refers to an electronic device such as a computer or smartphone that a user uses to access and operate the system.

[1228] This invention is a copyright infringement checking system using AI, which aims to reduce the risk of copyright infringement and easily protect the rights of creators and rights holders. In this system, users upload their copyrighted works, and the server analyzes, stores, and compares them to identify the risk of infringement.

[1229] Copyright registration phase

[1230] First, a user uploads a copyrighted work (e.g., a music file or image file) from their device to the system. The server extracts the metadata of the received copyrighted work and stores it in a database. Next, the server passes the copyrighted work to a machine learning engine (AI engine), which analyzes its features and generates feature data. The server then stores this feature data in a database.

[1231] Specific examples

[1232] A user uploads a music file, and the server extracts the metadata of the music file (e.g., title: Example, creator: John Doe, creation date: October 5, 2023) and stores it in a database. The music file is then analyzed by an AI engine to generate feature data (e.g., melody pattern, rhythm, harmony, etc.), which is also stored in the database.

[1233] Copyright Infringement Check Request Phase

[1234] Next, the user uploads new data (e.g., a new advertising image) to the system from their device and sends a request for copyright infringement check. The server receives the new data, temporarily stores it, and then adds the request to the processing queue.

[1235] Specific examples

[1236] A user uploads a new advertising image and requests a copyright infringement check from the system. The server receives the image and adds it to a processing queue.

[1237] Copyright infringement check phase

[1238] The server takes the request from the processing queue and passes the new data back to the machine learning engine, which compares this data with existing copyrighted material in its database to analyze for matches or similarities. The server receives the analysis results from the AI ​​engine and generates a report of potential copyright infringement.

[1239] Specific examples

[1240] The AI ​​engine compares new advertising images with images in the existing database and calculates their similarity, after which the server generates a report indicating whether there is a potential breach.

[1241] Result notification phase

[1242] Finally, the server sends the generated report of possible copyright infringement to the user's device, where the user can review the report and decide on the next action (e.g., modifying the image, inquiring about license rights, etc.).

[1243] Specific examples

[1244] The server notifies the user of the similarity results, such as "This image has 90% similarity to an existing copyrighted work." The user receives the results and considers modifying the image.

[1245] Prompt Sentence Examples

[1246] Examples of prompts include:

[1247] "Upload new music files and store their metadata and characteristics in our database."

[1248] "Upload new advertising images and compare them with existing content in our database to analyze potential copyright infringement."

[1249] "We notify users of similarity results between new and existing content and invite them to submit reports of potential infringement."

[1250] This system allows users to easily check for copyright infringement risks and quickly take appropriate action.

[1251] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1252] Step 1:

[1253] Uploading copyrighted material

[1254] The user selects a work on the terminal.

[1255] Example: A user selects a music file "example.mp3".

[1256] The user clicks the system's upload button.

[1257] Example: A user clicks the "upload" button to send "example.mp3" to the system.

[1258] The terminal transmits the selected file to the server.

[1259] Input: User-selected music file "example.mp3"

[1260] Output: The server receives the music files.

[1261] Step 2:

[1262] Saving to a database

[1263] The server temporarily stores the received file.

[1264] Example: The server saves "example.mp3" to disk.

[1265] Input: Received music file "example.mp3"

[1266] Output: Temporarily saved file

[1267] The server extracts the metadata of the work.

[1268] Example: The server extracts the following metadata for "example.mp3": "Title: Example, Creator: John Doe, Creation Date: October 5, 2023".

[1269] Input: Received music file "example.mp3"

[1270] Output: Extracted metadata

[1271] The server stores the metadata in a database.

[1272] Example: A server records metadata in a "works" table.

[1273] Input: Extracted metadata

[1274] Output: Metadata stored in a database

[1275] Step 3:

[1276] AI-based feature extraction

[1277] The server passes the received copyrighted material to the AI ​​engine.

[1278] Example: The server inputs "example.mp3" into the AI ​​engine.

[1279] Input: Received music file "example.mp3"

[1280] Output: The file passed to the AI ​​engine

[1281] The AI ​​engine analyzes the characteristics of the work.

[1282] Example: The AI ​​engine analyzes the melody pattern, rhythm, harmony, etc. of "example.mp3."

[1283] Input: Received music file "example.mp3"

[1284] Output: Parsed feature data

[1285] The server stores the generated feature data in a database.

[1286] Example: The server stores "feature data" in a "feature" table.

[1287] Input: Parsed feature data

[1288] Output: Feature data stored in a database

[1289] Step 4:

[1290] Submitting a check request

[1291] The user selects new data from the terminal.

[1292] Example: A user selects a new ad image "new_ad.jpg".

[1293] A user submits a request for copyright infringement check to the system.

[1294] Example: A user clicks the "Check Request" button to send "new_ad.jpg" to the server.

[1295] Input: New ad image "new_ad.jpg"

[1296] Output: The server receives the new data.

[1297] The device sends the selected file and the request to the server.

[1298] Input: New ad image "new_ad.jpg"

[1299] Output: The server receives the new data and the request.

[1300] Step 5:

[1301] Receiving content

[1302] The server receives the new data and stores it temporarily.

[1303] Example: The server saves "new_ad.jpg" to disk.

[1304] Input: New ad image "new_ad.jpg"

[1305] Output: Temporarily saved file

[1306] The server adds the request to a processing queue.

[1307] Example: The server adds a "check request" to the queue.

[1308] Input: New ad image "new_ad.jpg"

[1309] Output: Requests added to the processing queue

[1310] Step 6:

[1311] Comparison with database

[1312] The server takes the request from the processing queue and passes the new data back to the AI ​​engine.

[1313] Example: The server inputs "new_ad.jpg" into the AI ​​engine.

[1314] Input: New ad image "new_ad.jpg"

[1315] Output: The file passed to the AI ​​engine

[1316] The AI ​​engine compares the new data with existing works in the database.

[1317] Example: The AI ​​engine compares "new_ad.jpg" with existing image data in the database.

[1318] Input: New ad image "new_ad.jpg", existing data in database

[1319] Output: Match or similarity analysis results

[1320] Step 7:

[1321] Generate results

[1322] The server receives the analysis results from the AI ​​engine and generates a report on potential copyright infringement.

[1323] Example: The server receives the similarity analysis results from the AI ​​engine.

[1324] Input: Analysis results from the AI ​​engine

[1325] Output: Generated potential copyright infringement report

[1326] Step 8:

[1327] Sending the results

[1328] The server generates a report of potential copyright infringement and sends it to the user's device.

[1329] Example: The server sends a "Potential Infringement Report" to the user's device.

[1330] Input: Generated potential copyright infringement report

[1331] Output: Report sent to user's terminal

[1332] Step 9:

[1333] Deciding on an action

[1334] The user checks the results report they received.

[1335] Example: A user opens and reviews a "Potential Compromise Report."

[1336] Input: User action (check result report)

[1337] Output: Determined next action

[1338] The user decides on the next action (e.g., modify the image, inquire about permission, etc.).

[1339] Example: A user sees a 90% similarity and considers modifying the image or inquiring about usage permissions.

[1340] Input: Content of received report

[1341] Output: Decision on next action

[1342] (Application example 1)

[1343] 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."

[1344] Conventional copyright infringement checking systems make it difficult for content creators to check copyright infringement risks in advance. While real-time checks are particularly required for content distribution platforms, the technology to achieve this is insufficient. Furthermore, even when there is a high risk of copyright infringement, response is delayed, which can lead to legal issues after the content is released. This leaves content creators with no peace of mind when distributing their content.

[1345] 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.

[1346] In this invention, the server includes: means for uploading copyrighted works created by users; means for the server to store the received metadata of the copyrighted works in a database; means for the server to pass the copyrighted works to an AI engine and for the AI ​​to extract features; means for the server to store the feature data in the database; means for the user to upload new content and send a request for a copyright infringement check; means for the server to receive the new content and add the request to a processing queue; means for the server to pass the new content to the AI ​​engine and for the AI ​​to compare the new content with existing copyrighted works in the database and analyze the degree of match or similarity; means for the server to process the analysis results from the AI ​​engine and generate a report on possible copyright infringement; means for the server to send the generated report to the user's device; means for the user to check the result report and decide on the next action; means for the user to upload content from their smartphone and undergo a copyright infringement check in real time; means for the AI ​​engine to analyze the features of the content and compare it with existing copyrighted works in real time; and means for sending a notification to the user if a risk of copyright infringement is determined. This enables content creators to conduct copyright infringement checks in real time on a content distribution platform, thereby identifying the risk of copyright infringement in advance and avoiding legal issues.

[1347] "Copyrighted work" refers to content such as music, images, and video created by a creator.

[1348] "Upload" refers to the act of a user sending data from their device to a server.

[1349] "Metadata" refers to information about a work, including attribute information such as the title, creator, and creation date.

[1350] A "database" refers to a system for organizing and storing data and for efficiently managing and searching it.

[1351] An "AI engine" refers to a software system that uses artificial intelligence technology to analyze data and recognize patterns.

[1352] "Feature extraction" refers to the process of extracting important feature data (e.g., audio waveforms, image patterns, etc.) from copyrighted works using an AI engine.

[1353] "Copyright infringement" refers to the act of using someone else's copyrighted work without permission, which can lead to legal issues.

[1354] "Content distribution" refers to the act of providing and sharing digital content such as music, videos, and images through online platforms.

[1355] "Real-time" refers to processing and results occurring immediately, without delay.

[1356] The "Results Report" is a report summarizing the results of the AI ​​engine's analysis, and includes information about the risk of copyright infringement.

[1357] "Notification" refers to the act of a system conveying specific information to a user.

[1358] MODE FOR CARRYING OUT THE INVENTION

[1359] The present invention provides a system for checking the risk of copyright infringement in real time, especially on a content distribution platform, which comprises the following steps:

[1360] 1. System program generation

[1361] In this system, users upload copyrighted material from their smartphones, and an AI engine analyzes the characteristics of the material. The AI ​​engine stores the metadata and characteristic data of the material in a database, and in response to requests for copyright infringement checks, it compares new content with existing copyrighted material. The server also generates analysis results and notifies users of the risk of copyright infringement.

[1362] 2. Hardware and Software Description

[1363] To realize this system, the following hardware and software are used.

[1364] Hardware

[1365] Server: Stores data, extracts and compares features using AI, and generates and notifies analysis results.

[1366] Smartphone: A device through which users upload content and receive results.

[1367] software

[1368] Flask: Used as a web server framework to manage requests.

[1369] AI engine: A custom AI library (tentative name) that performs feature extraction and database comparison processing.

[1370] Database Management System: A system for efficiently storing and managing metadata and feature data.

[1371] 3. Data processing and calculation

[1372] The server processes and calculates data in the following steps:

[1373] 1. Uploading and Analysis of Copyrighted Materials:

[1374] The user uploads content (e.g., videos, images) from their smartphone. The server stores the metadata of the received content in a database, and the AI ​​engine extracts features. The extracted feature data is also stored in the database.

[1375] 2. Copyright Infringement Check Request:

[1376] A user uploads new content and sends a request for copyright infringement check. The server receives the new content and passes it to an AI engine to compare it with existing copyrighted material in the database.

[1377] 3. Results generation and notification:

[1378] The AI ​​engine returns the comparison results to the server, which processes the analysis results and generates a report of possible copyright infringement, which the server notifies the user.

[1379] 4. Example: Uploading a video to YouTube

[1380] For example, when a user uploads a video to an online video platform, the system can be used to check for copyright infringement risks. If the video contains an existing movie scene, the system will send a notification saying, "This video is 95% identical to an existing movie scene." Based on this, the user can modify or delete the video to avoid future legal issues.

[1381] 5. Examples of prompts

[1382] Your content guardian will analyze user-uploaded videos and images in real time to determine if they infringe existing copyrighted material. Please provide specific steps for users:

[1383] 1. Uploading Content

[1384] 2. Feature extraction using AI

[1385] 3. Comparison with database

[1386] 4. Generating and Communicating Results

[1387] The above is a description of the mode for carrying out the invention, and this system enables content creators to check copyright infringement risks in real time and prevent legal problems before they occur.

[1388] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1389] Step 1:

[1390] Uploading and analyzing copyrighted material

[1391] A user uploads content from their smartphone. Specifically, videos or images are sent to a specified server endpoint. The server extracts the content's metadata (title, creation date, etc.) and stores it in a database. The input at this stage is the content provided by the user, and the output is the metadata stored in the database. The server then passes this content to an AI engine, which extracts feature data (for example, visual patterns and audio waveforms in the case of videos). This feature data is also stored in the database.

[1392] Step 2:

[1393] Submitting a Copyright Infringement Check Request

[1394] A user uploads new content and sends a request for copyright infringement check. Specifically, the user again uses their smartphone to send the content to the server. This input is new content, and its purpose is to check for copyright infringement. The server receives this new content and adds the request to the processing queue. Once the request is added to the queue, it is ready to proceed to the next step.

[1395] Step 3:

[1396] AI-based feature analysis and database comparison

[1397] The server takes new content from the processing queue and passes it to the AI ​​engine. The AI ​​engine extracts the features of the new content and compares it with copyrighted works in the existing database. At this time, it analyzes how much the feature data matches or is similar to the existing data. The input is the new content and the feature data from the existing database, and the output is the analysis result of the degree of match or similarity. As a concrete example, the analysis result of a video may be output in the form of "This footage is 80% identical to existing movie scenes."

[1398] Step 4:

[1399] Generation and notification of analysis results

[1400] The server generates a report of possible copyright infringement based on the analysis results obtained from the AI ​​engine. Specifically, it compiles similarity and match information into a report format. The input is the analysis results returned by the AI ​​engine, and the output is the generated report. This report is sent to the user's smartphone so that the user can view it. This notification allows the user to decide on the next action (e.g., modifying or deleting the content).

[1401] Step 5:

[1402] Determining User Actions

[1403] The user checks the report received on their smartphone and decides on the next course of action. Specifically, if it is determined that there is a high risk of copyright infringement, they can take action such as modifying the content or inquiring about usage permissions. The input is the analysis result report, and the output is the user's specific action. This step allows the user to avoid the risk of copyright infringement of content in advance.

[1404] 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.

[1405] This invention is a copyright infringement checking system that uses AI to reduce the risk of copyright infringement and easily protect the rights of creators and patent holders, and it also combines an emotion engine that recognizes user emotions and provides feedback. In this system, users upload their copyrighted works, and the server analyzes, stores, and compares them to identify the risk of infringement, and analyzes user emotions in real time and provides appropriate feedback.

[1406] Explaining program processing in natural language

[1407] 1. Copyright Registration Phase

[1408] 1.1. Uploading copyrighted material

[1409] A user uploads copyrighted material (e.g., music files, image files) to the system from a terminal. The terminal then transmits the uploaded copyrighted material to the server.

[1410] 1.2. Saving to Database

[1411] The server analyzes the received copyrighted material and extracts metadata (title, author, creation date, etc.), which it then stores in a database.

[1412] 1.3. Feature extraction using AI

[1413] The server passes the copyrighted work to an AI engine, which analyzes its characteristics (e.g., melody, rhythm, harmony, etc. in the case of music) and generates feature data. The server then stores the generated feature data in a database.

[1414] Examples:

[1415] A user uploads a music file and the server stores the metadata and characteristics of the music file in a database.

[1416] 2. Copyright Infringement Check Request Phase

[1417] 2.1. Sending a Check Request

[1418] The user uploads new content (e.g., a new advertising image) from their device and sends a request for copyright infringement check. The device sends this new content and the request to the server.

[1419] 2.2. Receiving Content

[1420] The server receives the new content and adds the request to the processing queue, preparing to pass the new content to the AI ​​engine.

[1421] Examples:

[1422] A user uploads a new advertising image to the system and requests a copyright infringement check. The server receives the image and adds it to the processing queue.

[1423] 3. Copyright Infringement Check Phase

[1424] Comparison with Databases

[1425] The server processes the request and passes the new content to the AI ​​engine, which compares the new content with existing copyrighted material in its database and analyzes it for matches or similarities.

[1426] 3.2. Producing Results

[1427] The server processes the analysis results returned by the AI ​​engine and generates a report of potential copyright infringement based on the analysis results.

[1428] Examples:

[1429] The AI ​​engine compares new advertising images with existing images in the database, and the server generates a report based on the results of the similarity analysis.

[1430] 4. Result notification phase

[1431] 4.1. Sending the results

[1432] The server generates a report of possible copyright infringement and sends it to the user's device, where the user can view the report.

[1433] 4.2. Determining Actions

[1434] The user checks the result report they receive and decides on the next action (e.g., image correction, license inquiry, etc.). The user can also provide feedback on countermeasures to the server from their device.

[1435] Examples:

[1436] The server notifies the user of the similarity results, saying, "This image has 90% similarity to an existing copyrighted work." The user receives the results and considers modifying the image.

[1437] 5. Implementing the Emotion Engine

[1438] 5.1. Emotion Data Collection

[1439] As the user operates the interface displayed during the copyright infringement check process, emotional data such as the user's facial expressions, voice, and keyboard input is collected from the device.

[1440] 5.2. Emotion Data Analysis

[1441] The server passes the collected emotional data to the emotion engine, where the AI ​​analyzes the user's emotions. The analysis results are converted into numerical values ​​for the user's emotions, such as stress, frustration, and joy.

[1442] Providing Feedback

[1443] Based on the analysis results, the server provides appropriate feedback to the user's device. For example, if high stress or frustration is detected, a relaxation message or a link to contact support will be displayed.

[1444] Examples:

[1445] The emotion engine analyzes whether the user is feeling frustrated while operating the device, and the server displays a relaxation message such as "Would you like to take a short break?"

[1446] This system not only allows users to easily check the risk of copyright infringement, but also allows them to receive support that takes into consideration their emotional state, allowing them to use and publish content with greater peace of mind.

[1447] The processing flow will be explained below.

[1448] Step 1:

[1449] A user uploads copyrighted material (e.g., music files, image files) to the system from a terminal. The terminal then transmits the uploaded copyrighted material to the server.

[1450] Step 2:

[1451] The server analyzes the received copyrighted material and extracts metadata (title, author, creation date, etc.), which it then stores in a database.

[1452] Step 3:

[1453] The server passes the copyrighted work to the AI ​​engine, which analyzes the characteristics of the work (e.g., melody, rhythm, harmony, etc. in the case of music) and generates feature data. The server then stores the generated feature data in a database.

[1454] Step 4:

[1455] The user uploads new content (e.g., a new advertising image) from their device and sends a request for copyright infringement check. The device sends this new content and the request to the server.

[1456] Step 5:

[1457] The server receives the new content and adds the request to the processing queue, preparing to pass the new content to the AI ​​engine.

[1458] Step 6:

[1459] The server passes the new content to the AI ​​engine, which compares it with existing copyrighted material in its database to analyze for matches or similarities.

[1460] Step 7:

[1461] The server processes the analysis results returned by the AI ​​engine and generates a report of potential copyright infringement based on the analysis results.

[1462] Step 8:

[1463] The server generates a report of possible copyright infringement and sends it to the user's device, where the user can view the report.

[1464] Step 9:

[1465] Based on the results report received, the user decides on the next action (e.g., image correction, inquiry for permission to use, etc.). The user can also provide feedback on countermeasures to the server from the terminal.

[1466] Step 10:

[1467] The device collects the user's emotional data (e.g., facial expressions, voice, keyboard input, etc.) while the device is in operation. The emotional data is sent to the server in real time.

[1468] Step 11:

[1469] The server passes the emotion data to the emotion engine, which analyzes the collected data and quantifies the user's emotion (e.g., stress, frustration, joy, etc.).

[1470] Step 12:

[1471] The server generates appropriate feedback based on the analysis results from the emotion engine. For example, the server creates a relaxation message if high stress or frustration is detected.

[1472] Step 13:

[1473] The server generates feedback (e.g., a relaxation message) and sends it to the user's device, where the user can view the feedback.

[1474] Step 14:

[1475] The user decides the next action (e.g., taking a break, further operation, etc.) based on the feedback. The user's actions are fed back to the server via the device.

[1476] Example 2

[1477] 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."

[1478] Currently, systems for checking copyright infringement of digital content are often cumbersome and time-consuming, which can impair user convenience. Furthermore, few systems can analyze users' emotions and provide appropriate feedback in addition to checking for copyright infringement. This can easily lead to stress and frustration, so there is a need for a system that can integrate the evaluation of copyright infringement with the management of one's own emotional state.

[1479] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1480] In this invention, the server includes a means for uploading digital content created by users, a means for storing metadata of the digital content received by the server in a storage device, and a means for the server to pass the digital content to an artificial intelligence engine and for the artificial intelligence to extract features, thereby enabling copyright infringement checks of digital content and user sentiment analysis to be performed in a unified manner.

[1481] "User" means any person who interacts with the system to upload digital content or submit a request for a copyright infringement check.

[1482] A "terminal" is a device operated by a user, and is an apparatus on which operations such as uploading digital content and checking result reports are performed.

[1483] A "server" is a computer system that receives and processes digital content or requests sent by users.

[1484] "Digital content" refers to electronically recorded information such as music files, image files, and video files.

[1485] "Metadata" is additional information associated with digital content, such as title, creator, and creation date.

[1486] A "storage device" is a storage device for storing digital content, metadata, and feature data.

[1487] An "artificial intelligence engine" is a system that includes algorithms and models for analyzing the characteristics of digital content and analyzing matches or similarities.

[1488] "Feature data" refers to the analysis results of the melody, rhythm, harmony, etc. of digital content extracted by an artificial intelligence engine.

[1489] A "processing queue" is a data structure for temporarily storing new requests and processing them sequentially.

[1490] A "copyright infringement check request" is a request sent by a user to a server to check new digital content for existing copyright infringement.

[1491] "Analysis Results" means the results of the comparison and matching or similarity analysis of digital content conducted by the artificial intelligence engine.

[1492] A "report" is a document generated based on the analysis results, which includes information indicating possible copyright infringement.

[1493] "Emotion data" is data that indicates the user's emotional state, such as the user's facial expression, voice, keyboard input, etc.

[1494] An "emotion recognition engine" is a system that includes algorithms and models for analyzing collected emotional data and quantifying a user's emotional state.

[1495] "Feedback" refers to notifications and messages provided to users based on the results of emotion analysis, and includes, for example, relaxation messages and support information.

[1496] This invention is a system for checking copyright infringement of digital content and analyzing user sentiment in an integrated manner. This system is realized by uploading digital content created by users, and the server analyzes, stores, and performs specific processing on the uploaded content.

[1497] Hardware and software used

[1498] The system is implemented using the following hardware and software:

[1499] Terminal: A device operated by a user, including a PC, smartphone, tablet, etc., that has internet connectivity and file upload capabilities.

[1500] Server: A computer system with high-performance processing capabilities that receives digital content, stores it in a database, connects with the AI ​​engine, and provides feedback on the results of sentiment analysis.

[1501] Database software: A relational database management system (RDBMS) such as MySQL or PostgreSQL is used to store metadata and feature data for digital content.

[1502] AI Engine: Contains generative AI models built using TensorFlow and PyTorch to extract features from digital content, compare them, and check for potential copyright infringement.

[1503] Emotion recognition engine: Uses emotion analysis services such as Amazon Rekognition and Google Cloud Vision to collect and analyze user emotion data.

[1504] Example of a system

[1505] Consider a scenario where a user uploads digital content that they have created, for example, a music file called "MySong.mp3."

[1506] 1. User Action:

[1507] The user uses the device to drag and drop the file "MySong.mp3" into the system interface, then clicks the upload button, and the device sends this file to the server via an HTTP request.

[1508] 2. Server-side processing:

[1509] The server receives the file and saves it in a temporary directory. It then extracts metadata such as the title "My Song," the creator "John Doe," and the creation date "2023-10-10" and stores them in a database. The server then passes the file to an AI engine, which analyzes the melody and rhythmic features. The resulting feature data, "Melody: (C, D, E, F), Rhythm: 4 / 4, Tempo: 120 BPM," is saved in the database.

[1510] 3. Copyright Infringement Check:

[1511] When checking new digital content, a user uploads a new advertising image, "AdsImage.jpg," and requests a copyright infringement check. The server receives this request and adds it to the processing queue. The AI ​​engine compares it with existing data and analyzes the similarity. A "95% similarity" is returned as a result, and a report of possible copyright infringement is generated and sent to the user.

[1512] 4. Sentiment Analysis and Feedback:

[1513] As the user operates the system, the device collects the user's emotional data (facial expressions, voice, keyboard input, etc.). The server passes the collected emotional data to an emotion recognition engine for analysis. If high stress or frustration is detected, the server sends the user relaxation messages or support information.

[1514] Prompt Sentence Examples

[1515] When a user uploads a new music file and analyzes its features based on the AI ​​engine, the prompt text is as follows:

[1516] "Upload the music file 'MySong.mp3'. We'll analyze the melody, rhythm, and tempo of the file and store them in our database."

[1517] This system not only checks for copyright infringement of digital content, but also takes into consideration the emotional state of the user, allowing users to use and publish content with greater peace of mind.

[1518] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1519] Step 1:

[1520] The user selects digital content (e.g., music file "MySong.mp3") on the device and clicks the upload button.

[1521] Input: Music file "MySong.mp3".

[1522] Output: The music file "MySong.mp3" is sent from the device to the server.

[1523] Specific behavior: The device sends the file to the server using an HTTP POST request.

[1524] Step 2:

[1525] The server receives the uploaded file and saves it in a temporary directory.

[1526] Input: Music file "MySong.mp3".

[1527] Output: "MySong.mp3" saved in your temporary directory.

[1528] Specific operation: The server saves the received file in a specific directory (e.g., " / tmp / uploads").

[1529] Step 3:

[1530] The server extracts the metadata of the music files.

[1531] Input: A music file "MySong.mp3" saved in the temporary directory.

[1532] Output: Extracted metadata (Title "My Song", Author "John Doe", Creation Date "2023-10-10").

[1533] Specific operation: The server uses a Python library (e.g., mutagen) to extract metadata from music files.

[1534] Step 4:

[1535] The server stores the extracted metadata in a database.

[1536] Input: Extracted metadata (Title "My Song", Author "John Doe", Creation Date "2023-10-10").

[1537] Output: Metadata stored in a database.

[1538] What happens: The server executes an SQL INSERT query to save the metadata into a MySQL database.

[1539] Step 5:

[1540] The server passes the music file to the AI ​​engine, which extracts its features.

[1541] Input: Music file "MySong.mp3".

[1542] Output: Extracted feature data (melody, rhythm, tempo).

[1543] How it works: The server inputs the file into an AI model using TensorFlow, which analyzes features such as melody, rhythm, and tempo.

[1544] Step 6:

[1545] The server stores the feature data in a database.

[1546] Input: Extracted feature data (melody, rhythm, tempo).

[1547] output: Feature data stored in the database.

[1548] What happens: The server executes an SQL INSERT query to save the feature data into a MySQL database.

[1549] Step 7:

[1550] A user uploads new digital content (e.g., an advertisement image "AdsImage.jpg") from their device and submits a request for copyright infringement check.

[1551] Input: Ad image "AdsImage.jpg".

[1552] output: The ad image "AdsImage.jpg" and the check request are sent to the server.

[1553] Specific behavior: The device sends the file and request to the server using an HTTP POST request.

[1554] Step 8:

[1555] The server receives new digital content and adds it to a processing queue.

[1556] Input: Ad image "AdsImage.jpg" and check request.

[1557] output: The ad image "AdsImage.jpg" and check request added to the processing queue.

[1558] Specific behavior: The server saves the received file in a temporary directory and adds it to the processing queue.

[1559] Step 9:

[1560] The server passes new digital content to the AI ​​engine, which compares it with existing data and analyzes it for matches or similarities.

[1561] Input: Ad image "AdsImage.jpg".

[1562] output: The match or similarity analysis result.

[1563] What it does: The server inputs the file into an AI engine, which compares it with digital content in an existing database.

[1564] Step 10:

[1565] The server generates a report of possible copyright infringement based on the analysis results.

[1566] Input: Match or similarity analysis results.

[1567] output: A report of potential copyright infringement.

[1568] Specific operation: The server automatically generates a PDF report based on the analysis results.

[1569] Step 11:

[1570] The server sends the generated report to the user's terminal.

[1571] Enter: Potential Copyright Infringement Report.

[1572] output: The report sent to the user's terminal.

[1573] Specific operation: The server uses an HTTP response to send the report to the user's device.

[1574] Step 12:

[1575] The user reviews the results report and decides on the next action.

[1576] Enter: Potential Copyright Infringement Report.

[1577] output: Next action (e.g., image modification, license inquiry).

[1578] Specific actions: The user views the report, decides what action is required, and takes further action via the device.

[1579] Step 13:

[1580] As the user operates the system, the device collects emotional data.

[1581] Input: User facial expressions, voice, keyboard input, etc.

[1582] output: The collected emotion data.

[1583] What it does: The device uses the camera, microphone, and keyboard to collect user emotional data.

[1584] Step 14:

[1585] The server passes the collected emotion data to an emotion recognition engine for analysis.

[1586] Input: Collected emotion data.

[1587] output: Sentiment analysis results (e.g., stress level or frustration).

[1588] Specific operation: The server passes the data to the emotion recognition engine and receives the analysis results.

[1589] Step 15:

[1590] The server generates feedback based on the emotion analysis results and provides it to the user.

[1591] Input: Sentiment analysis results.

[1592] output: A feedback message to the user (e.g., a relaxation message or support information).

[1593] Specific operation: The server generates a message appropriate for the user based on the analysis results and sends it to the device.

[1594] (Application example 2)

[1595] 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."

[1596] In content creation, it is important to reduce the risk of copyright infringement and protect the rights of creators. Furthermore, it is necessary to reduce the stress and frustration felt by users during the content uploading process and realize a smooth operation. The problem to be solved by this invention is to effectively check the risk of copyright infringement of content created by users, grasp the user's emotional state, and provide appropriate feedback.

[1597] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for uploading a work created by a user; means for the server to store metadata of the work received in a database; means for the server to pass the work to an AI engine and for the AI ​​to extract features; means for the server to store the feature data in a database; means for the user to upload new content and send a request for a copyright infringement check; means for the server to receive the new content and add the request to a processing queue; means for the server to pass the new content to the AI ​​engine and for the AI ​​to compare it with existing works in the database and analyze the degree of match or similarity; means for the server to process the analysis results from the AI ​​engine and generate a report of possible copyright infringement; means for sending the report generated by the server to the user's terminal; means for the user to check the result report and decide on the next action; means for analyzing the user's facial expressions, voice, and keyboard input to understand the user's emotional state; and means for the server to analyze the emotional data and provide appropriate feedback to the user. This makes it possible to effectively check the risk of copyright infringement in user-created content, and also to analyze the user's emotional state in real time and provide appropriate feedback.

[1598] "User" means a person or entity that uses the System to upload copyrighted material and request a copyright infringement check.

[1599] "Works" are content created by users, and examples include music files and image files.

[1600] The "server" is a central processing unit that receives the copyrighted material uploaded by the user, analyzes, stores, compares, notifies the results, and processes the emotion data.

[1601] "Metadata" is information about a work, including the title, creator, creation date, etc.

[1602] "Database" means a storage device containing metadata and characteristic data of existing works stored by the server.

[1603] An "AI engine" is a program device that uses artificial intelligence to extract characteristics of copyrighted works and compare new content with existing works in a database.

[1604] "Feature data" refers to specific attribute information of a copyrighted work, such as its melody, rhythm, and harmony, extracted by the AI ​​engine.

[1605] A "request" refers to a request by a user to submit new content to a server for copyright infringement checking.

[1606] A "processing queue" is a line of tasks that is temporarily placed to await processing after the server receives new content.

[1607] "Analysis results" refers to the data generated after the AI ​​engine compares new content with existing copyrighted works in its database.

[1608] The "Potential Copyright Infringement Report" is a report that indicates the risk of copyright infringement, generated by the server based on the analysis results from the AI ​​engine.

[1609] "Emotion data" is information that indicates the user's emotional state, obtained from the user's facial expression, voice, keyboard input, and the like.

[1610] "Feedback" refers to constructive advice or messages provided to users based on emotional data analyzed by the server.

[1611] MODE FOR CARRYING OUT THE INVENTION

[1612] This invention relates to a system for checking copyright infringement of user-created content and analyzing the user's emotional state in real time. To specifically implement this system, the following hardware and software, as well as data processing and data calculation, are required.

[1613] 1. Hardware and Software Used

[1614] Hardware

[1615] Smartphone: The device where users upload content

[1616] Head-mounted display: A display device equipped with a camera and microphone that collects the user's facial expressions and voice.

[1617] Server: A central processing unit that analyzes and stores data

[1618] software

[1619] Python: A language for data analysis and AI model execution

[1620] TensorFlow / Keras: Building and running AI models

[1621] OpenCV: Facial Expression Recognition Library

[1622] Azure Face API: Facial recognition and emotion analysis API

[1623] 2. Natural language description of program processing

[1624] User Content Uploads

[1625] Users upload their own creations (e.g., videos, images, and music files) through the interface of their smartphone or head-mounted display. The creations are then sent from the device to the server.

[1626] Extracting and storing metadata

[1627] The server analyzes the received copyrighted material and extracts metadata (title, author, creation date, etc.), which is then stored in a database.

[1628] AI-based feature extraction

[1629] The copyrighted work is passed from the server to the AI ​​engine, which analyzes its characteristic data (melody, rhythm, harmony, etc.) and stores the analyzed characteristic data in a database.

[1630] Copyright Infringement Check Request

[1631] A user uploads new content and requests a copyright infringement check. The server receives this new content and adds it to a processing queue.

[1632] Compare and analyze new content

[1633] The server then passes the new content to the AI ​​engine, which then compares it with existing works in its database and analyzes the matches and similarities.

[1634] Analysis results and report generation

[1635] The server generates a report of potential copyright infringement based on the analysis results returned by the AI ​​engine, and this report is sent to the user's device.

[1636] Emotion data collection and analysis

[1637] The user's facial expressions, voice, and keyboard input are collected through the camera and microphone of the head-mounted display. This emotional data is sent to a server and analyzed by an AI engine.

[1638] Emotional Feedback

[1639] The server uses the emotion analysis results to determine the user's stress and frustration and provides appropriate feedback. For example, if high stress is detected, a relaxation message such as "Would you like to take a break?" will be displayed.

[1640] 3. Examples and prompts

[1641] Specific examples

[1642] A user uploads a new video to the content distribution platform and requests a copyright infringement check. The server then extracts the video's metadata and feature data and compares it with existing videos in the database. At the same time, it analyzes the user's facial expressions and voice and displays relaxation messages if the user's stress level increases.

[1643] Prompt Sentence Examples

[1644] "Upload a new video to the copyright infringement checking system and check its similarity to existing videos. Extract the metadata and feature data of the uploaded video, compare it with existing videos in the database, analyze the similarity, and notify the user of the results. At the same time, analyze the user's facial and vocal emotional data, and display a relaxation message if stress is detected."

[1645] This invention not only allows users to easily check the risk of copyright infringement for the content they create, but also allows them to receive support that takes into consideration their emotional state, allowing them to use and publish content with greater peace of mind.

[1646] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1647] Step 1:

[1648] Users upload content

[1649] Users upload their own copyrighted works (e.g., videos, images, and music files) to the system using their smartphones or head-mounted displays. The devices then send the uploaded works to the server.

[1650] Input: User-created copyrighted material file

[1651] Output: The copyrighted file sent to the server

[1652] Step 2:

[1653] Extracting and storing metadata

[1654] The server analyzes the received copyrighted material and extracts metadata (title, author, creation date, etc.) and stores this metadata in a database.

[1655] Input: User-uploaded copyrighted material file

[1656] Output: Metadata stored in a database

[1657] What it does: A Python program parses copyrighted files and extracts metadata, which is then stored in a database using SQL queries.

[1658] Step 3:

[1659] AI-based feature extraction

[1660] The server passes the copyrighted work to an AI engine (a model using TensorFlow / Keras), which analyzes and generates feature data (attributes such as melody, rhythm, harmony, etc.) The server then stores the generated feature data in a database.

[1661] Input: Copyrighted material file received by the server

[1662] Output: Feature data stored in a database

[1663] Specific operation: The copyrighted file is loaded into the TensorFlow / Keras model, and feature data is generated. The generated feature data is then stored in a database using SQL queries.

[1664] Step 4:

[1665] User submits request

[1666] The user uploads new content (e.g., a new video file) and sends a request for copyright infringement check. The device sends this new content and the request to the server.

[1667] Input: New content files uploaded by users and copyright infringement check requests

[1668] Output: New content and request sent to the server

[1669] Step 5:

[1670] Request Processing

[1671] The server receives the new content and adds the request to the processing queue, preparing to hand the new content off to the AI ​​engine.

[1672] Input: New content and requests uploaded by users

[1673] Output: Request added to processing queue

[1674] What happens: The Python program receives new content and requests and adds them to the processing queue.

[1675] Step 6:

[1676] Copyright Infringement Check

[1677] The server processes the request and passes the new content to an AI engine, which compares the new content with existing copyrighted material in its database and analyzes it for matches or similarities.

[1678] Input: Requests added to the processing queue, new content

[1679] Output: Match or similarity analysis results

[1680] What it does: A TensorFlow / Keras model is used to compare new content with existing works in the database.

[1681] Step 7:

[1682] Processing analysis results and generating reports

[1683] The server generates a report on possible copyright infringement based on the analysis results returned by the AI ​​engine, and sends this report to the user's device.

[1684] Input: Analysis results from the AI ​​engine

[1685] Output: Report sent to user terminal

[1686] Specific operation: Based on the analysis results, a Python program generates a report and sends it to the relevant user's device as an email or notification.

[1687] Step 8:

[1688] Emotion data collection and analysis

[1689] The user's facial expressions, voice, and keyboard input are collected through the camera and microphone of the head-mounted display. This emotional data is sent to a server and analyzed by an AI engine.

[1690] Input: User facial, voice, and keyboard input data

[1691] Output: Parsed emotion data

[1692] Specific operation: Analyzes emotion data in real time using OpenCV and Azure Face API.

[1693] Step 9:

[1694] Emotional Feedback

[1695] The server uses the emotion analysis results to determine the user's stress and frustration and provides appropriate feedback. For example, if high stress is detected, a relaxation message such as "Would you like to take a break?" will be displayed.

[1696] Input: Parsed emotion data

[1697] Output: Feedback message provided to the user

[1698] Specific operation: Based on the results of emotion analysis, a feedback message corresponding to the user's situation is generated and displayed on the user's device.

[1699] 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.

[1700] 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.

[1701] 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.

[1702] [Fourth embodiment]

[1703] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1704] 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.

[1705] 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).

[1706] 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.

[1707] 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.

[1708] 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).

[1709] 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.

[1710] 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.

[1711] 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.

[1712] 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.

[1713] 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.

[1714] 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.

[1715] 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."

[1716] This invention is a copyright infringement checking system using AI, which aims to reduce the risk of copyright infringement and easily protect the rights of creators and patent holders. In this system, users upload their copyrighted works, and the server analyzes, stores, and compares them to identify risks of infringement.

[1717] Explaining program processing in natural language

[1718] 1. Copyright Registration Phase

[1719] 1.1. Uploading copyrighted material

[1720] A user uploads copyrighted material (e.g., music files, image files) to the system from their terminal.

[1721] 1.2. Saving to Database

[1722] The server extracts the metadata (title, author, creation date, etc.) of the received copyrighted work and stores it in a database.

[1723] 1.3. Feature extraction using AI

[1724] The server passes the copyrighted work to an AI engine, which analyzes its characteristics (e.g., melody, rhythm, harmony, etc. in the case of music) and generates feature data. The server then stores the generated feature data in a database.

[1725] Examples:

[1726] A user uploads a music file and the server stores the metadata and characteristics of the music file in a database.

[1727] 2. Copyright Infringement Check Request Phase

[1728] 2.1. Sending a Check Request

[1729] The user uploads new content (e.g., a new advertising image) from their device and submits a request for copyright infringement check.

[1730] 2.2. Receiving Content

[1731] The server receives the new content and adds the request to the processing queue.

[1732] Examples:

[1733] A user uploads a new advertising image to the system and requests a copyright infringement check. The server receives the image and adds it to the processing queue.

[1734] 3. Copyright Infringement Check Phase

[1735] Comparison with Databases

[1736] The server processes the request and passes the new content to the AI ​​engine, which compares it with existing copyrighted material in its database and analyzes the matching or similarity of features.

[1737] 3.2. Producing Results

[1738] The server processes the analysis results returned by the AI ​​engine and generates a report of potential copyright infringement.

[1739] Examples:

[1740] The AI ​​engine compares new advertising images with existing images in the database, and the server generates a report based on the results of the similarity analysis.

[1741] 4. Result notification phase

[1742] 4.1. Sending the results

[1743] The server generates a report of potential copyright infringement and sends it to the user's device.

[1744] 4.2. Determining Actions

[1745] The user reviews the results report received and decides on the next action (e.g., image correction, license inquiry, etc.).

[1746] Examples:

[1747] The server notifies the user of the similarity results, saying, "This image has 90% similarity to an existing copyrighted work." The user receives the results and considers modifying the image.

[1748] This system allows users to easily check for copyright infringement risks, enabling them to use and publish content with peace of mind.

[1749] The processing flow will be explained below.

[1750] Step 1:

[1751] A user uploads copyrighted material (e.g., music files, image files) to the system from a terminal. The terminal then transmits the uploaded copyrighted material to the server.

[1752] Step 2:

[1753] The server analyzes the received copyrighted material and extracts metadata (title, author, creation date, etc.), which it then stores in a database.

[1754] Step 3:

[1755] The server passes the copyrighted work to the AI ​​engine, which analyzes the characteristics of the work (e.g., melody, rhythm, harmony, etc. in the case of music) and generates feature data. The server then stores the generated feature data in a database.

[1756] Step 4:

[1757] The user uploads new content (e.g., a new advertising image) from their device and sends a request for copyright infringement check. The device sends this new content and the request to the server.

[1758] Step 5:

[1759] The server receives the new content and adds the request to the processing queue, preparing to pass the new content to the AI ​​engine.

[1760] Step 6:

[1761] The server passes the new content to the AI ​​engine, which compares it with existing copyrighted material in its database to analyze for matches or similarities.

[1762] Step 7:

[1763] The server processes the analysis results returned by the AI ​​engine and generates a report of potential copyright infringement based on the analysis results.

[1764] Step 8:

[1765] The server generates a report of possible copyright infringement and sends it to the user's device, where the user can view the report.

[1766] Step 9:

[1767] Based on the results report received, the user decides on the next action (e.g., image correction, license inquiry, etc.). The user can also provide feedback on countermeasures to the server from their device.

[1768] Example 1

[1769] 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."

[1770] With the increasing production and distribution of digital content today, the risk of copyright infringement is rising. Therefore, there is a need for a system that can easily and quickly check for copyright infringement while properly protecting the rights of copyrighted works. However, existing systems require a lot of manual operation, are inefficient, and do not perform infringement checks accurately. Furthermore, the complicated process of notifying users of the results and subsequent responses places a heavy burden on users.

[1771] 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.

[1772] In this invention, the server includes: means for uploading copyrighted works created by users; means for storing metadata of the works received by the server in a database; means for the server to pass the copyrighted works to a machine learning engine and for AI to extract features; means for the server to store the feature data in the database; means for the user to upload new data and send a request for a copyright infringement check; means for the server to receive the new data and add the request to a processing queue; means for the server to pass the new data to the machine learning engine and for AI to compare it with existing works in the database and analyze the degree of match or similarity; means for the server to process the analysis results from the AI ​​engine and generate a report of possible copyright infringement; means for sending the report generated by the server to the user's terminal; and means for the user to check the result report and decide on the next action. This allows users to easily check the risk of copyright infringement and quickly take appropriate measures.

[1773] "Works" refers to content such as music files and image files created by users.

[1774] "Server" refers to a computer system that receives, stores, analyzes, generates, and notifies copyrighted material.

[1775] "User" means any person or entity that uses the System to upload copyrighted material and request copyright infringement checks.

[1776] "Metadata" refers to information associated with a work, such as the title, creator, and creation date.

[1777] "Database" refers to a data management system for storing and managing metadata and characteristic data of copyrighted works.

[1778] "Machine learning engine" refers to an artificial intelligence system that analyzes the characteristics of copyrighted works and generates feature data.

[1779] "Characteristics" refers to specific attributes related to the content of a copyrighted work (e.g., melody, rhythm, harmony, etc. in the case of music).

[1780] "Feature data" refers to specific attribute information of a copyrighted work extracted by a machine learning engine.

[1781] "New data" refers to content that users upload for copyright infringement checks.

[1782] A "request" refers to an operation by a user to request a copyright infringement check from the system.

[1783] The term "processing queue" refers to a waiting line for processing received requests in order.

[1784] "Analysis results" refers to the information resulting from the machine learning engine's comparison of new data with existing works in the database.

[1785] "Potential Copyright Infringement Report" refers to a report generated by the server summarizing the analysis results regarding the risk of copyright infringement.

[1786] "Terminal" refers to an electronic device such as a computer or smartphone that a user uses to access and operate the system.

[1787] This invention is a copyright infringement checking system using AI, which aims to reduce the risk of copyright infringement and easily protect the rights of creators and rights holders. In this system, users upload their copyrighted works, and the server analyzes, stores, and compares them to identify the risk of infringement.

[1788] Copyright registration phase

[1789] First, a user uploads a copyrighted work (e.g., a music file or image file) from their device to the system. The server extracts the metadata of the received copyrighted work and stores it in a database. Next, the server passes the copyrighted work to a machine learning engine (AI engine), which analyzes its features and generates feature data. The server then stores this feature data in a database.

[1790] Specific examples

[1791] A user uploads a music file, and the server extracts the metadata of the music file (e.g., title: Example, creator: John Doe, creation date: October 5, 2023) and stores it in a database. The music file is then analyzed by an AI engine to generate feature data (e.g., melody pattern, rhythm, harmony, etc.), which is also stored in the database.

[1792] Copyright Infringement Check Request Phase

[1793] Next, the user uploads new data (e.g., a new advertising image) to the system from their device and sends a request for copyright infringement check. The server receives the new data, temporarily stores it, and then adds the request to the processing queue.

[1794] Specific examples

[1795] A user uploads a new advertising image and requests a copyright infringement check from the system. The server receives the image and adds it to a processing queue.

[1796] Copyright infringement check phase

[1797] The server takes the request from the processing queue and passes the new data back to the machine learning engine, which compares this data with existing copyrighted material in its database to analyze for matches or similarities. The server receives the analysis results from the AI ​​engine and generates a report of potential copyright infringement.

[1798] Specific examples

[1799] The AI ​​engine compares new advertising images with images in the existing database and calculates their similarity, after which the server generates a report indicating whether there is a potential breach.

[1800] Result notification phase

[1801] Finally, the server sends the generated report of possible copyright infringement to the user's device, where the user can review the report and decide on the next action (e.g., modifying the image, inquiring about license rights, etc.).

[1802] Specific examples

[1803] The server notifies the user of the similarity results, such as "This image has 90% similarity to an existing copyrighted work." The user receives the results and considers modifying the image.

[1804] Prompt Sentence Examples

[1805] Examples of prompts include:

[1806] "Upload new music files and store their metadata and characteristics in our database."

[1807] "Upload new advertising images and compare them with existing content in our database to analyze potential copyright infringement."

[1808] "We notify users of similarity results between new and existing content and invite them to submit reports of potential infringement."

[1809] This system allows users to easily check for copyright infringement risks and quickly take appropriate action.

[1810] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1811] Step 1:

[1812] Uploading copyrighted material

[1813] The user selects a work on the terminal.

[1814] Example: A user selects a music file "example.mp3".

[1815] The user clicks the system's upload button.

[1816] Example: A user clicks the "upload" button to send "example.mp3" to the system.

[1817] The terminal transmits the selected file to the server.

[1818] Input: User-selected music file "example.mp3"

[1819] Output: The server receives the music files.

[1820] Step 2:

[1821] Saving to a database

[1822] The server temporarily stores the received file.

[1823] Example: The server saves "example.mp3" to disk.

[1824] Input: Received music file "example.mp3"

[1825] Output: Temporarily saved file

[1826] The server extracts the metadata of the work.

[1827] Example: The server extracts the following metadata for "example.mp3": "Title: Example, Creator: John Doe, Creation Date: October 5, 2023".

[1828] Input: Received music file "example.mp3"

[1829] Output: Extracted metadata

[1830] The server stores the metadata in a database.

[1831] Example: A server records metadata in a "works" table.

[1832] Input: Extracted metadata

[1833] Output: Metadata stored in a database

[1834] Step 3:

[1835] AI-based feature extraction

[1836] The server passes the received copyrighted material to the AI ​​engine.

[1837] Example: The server inputs "example.mp3" into the AI ​​engine.

[1838] Input: Received music file "example.mp3"

[1839] Output: The file passed to the AI ​​engine

[1840] The AI ​​engine analyzes the characteristics of the work.

[1841] Example: The AI ​​engine analyzes the melody pattern, rhythm, harmony, etc. of "example.mp3."

[1842] Input: Received music file "example.mp3"

[1843] Output: Parsed feature data

[1844] The server stores the generated feature data in a database.

[1845] Example: The server stores "feature data" in a "feature" table.

[1846] Input: Parsed feature data

[1847] Output: Feature data stored in a database

[1848] Step 4:

[1849] Submitting a check request

[1850] The user selects new data from the terminal.

[1851] Example: A user selects a new ad image "new_ad.jpg".

[1852] A user submits a request for copyright infringement check to the system.

[1853] Example: A user clicks the "Check Request" button to send "new_ad.jpg" to the server.

[1854] Input: New ad image "new_ad.jpg"

[1855] Output: The server receives the new data.

[1856] The device sends the selected file and the request to the server.

[1857] Input: New ad image "new_ad.jpg"

[1858] Output: The server receives the new data and the request.

[1859] Step 5:

[1860] Receiving content

[1861] The server receives the new data and stores it temporarily.

[1862] Example: The server saves "new_ad.jpg" to disk.

[1863] Input: New ad image "new_ad.jpg"

[1864] Output: Temporarily saved file

[1865] The server adds the request to a processing queue.

[1866] Example: The server adds a "check request" to the queue.

[1867] Input: New ad image "new_ad.jpg"

[1868] Output: Requests added to the processing queue

[1869] Step 6:

[1870] Comparison with database

[1871] The server takes the request from the processing queue and passes the new data back to the AI ​​engine.

[1872] Example: The server inputs "new_ad.jpg" into the AI ​​engine.

[1873] Input: New ad image "new_ad.jpg"

[1874] Output: The file passed to the AI ​​engine

[1875] The AI ​​engine compares the new data with existing works in the database.

[1876] Example: The AI ​​engine compares "new_ad.jpg" with existing image data in the database.

[1877] Input: New ad image "new_ad.jpg", existing data in database

[1878] Output: Match or similarity analysis results

[1879] Step 7:

[1880] Generate results

[1881] The server receives the analysis results from the AI ​​engine and generates a report on potential copyright infringement.

[1882] Example: The server receives the similarity analysis results from the AI ​​engine.

[1883] Input: Analysis results from the AI ​​engine

[1884] Output: Generated potential copyright infringement report

[1885] Step 8:

[1886] Sending the results

[1887] The server generates a report of potential copyright infringement and sends it to the user's device.

[1888] Example: The server sends a "Potential Infringement Report" to the user's device.

[1889] Input: Generated potential copyright infringement report

[1890] Output: Report sent to user's terminal

[1891] Step 9:

[1892] Deciding on an action

[1893] The user checks the results report they received.

[1894] Example: A user opens and reviews a "Potential Compromise Report."

[1895] Input: User action (check result report)

[1896] Output: Determined next action

[1897] The user decides on the next action (e.g., modify the image, inquire about permission, etc.).

[1898] Example: A user sees a 90% similarity and considers modifying the image or inquiring about usage permissions.

[1899] Input: Content of received report

[1900] Output: Decision on next action

[1901] (Application example 1)

[1902] 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."

[1903] Conventional copyright infringement checking systems make it difficult for content creators to check copyright infringement risks in advance. While real-time checks are particularly required for content distribution platforms, the technology to achieve this is insufficient. Furthermore, even when there is a high risk of copyright infringement, response is delayed, which can lead to legal issues after the content is released. This leaves content creators with no peace of mind when distributing their content.

[1904] 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.

[1905] In this invention, the server includes: means for uploading copyrighted works created by users; means for the server to store the received metadata of the copyrighted works in a database; means for the server to pass the copyrighted works to an AI engine and for the AI ​​to extract features; means for the server to store the feature data in the database; means for the user to upload new content and send a request for a copyright infringement check; means for the server to receive the new content and add the request to a processing queue; means for the server to pass the new content to the AI ​​engine and for the AI ​​to compare the new content with existing copyrighted works in the database and analyze the degree of match or similarity; means for the server to process the analysis results from the AI ​​engine and generate a report on possible copyright infringement; means for the server to send the generated report to the user's device; means for the user to check the result report and decide on the next action; means for the user to upload content from their smartphone and undergo a copyright infringement check in real time; means for the AI ​​engine to analyze the features of the content and compare it with existing copyrighted works in real time; and means for sending a notification to the user if a risk of copyright infringement is determined. This enables content creators to conduct copyright infringement checks in real time on a content distribution platform, thereby identifying the risk of copyright infringement in advance and avoiding legal issues.

[1906] "Copyrighted work" refers to content such as music, images, and video created by a creator.

[1907] "Upload" refers to the act of a user sending data from their device to a server.

[1908] "Metadata" refers to information about a work, including attribute information such as the title, creator, and creation date.

[1909] A "database" refers to a system for organizing and storing data and for efficiently managing and searching it.

[1910] An "AI engine" refers to a software system that uses artificial intelligence technology to analyze data and recognize patterns.

[1911] "Feature extraction" refers to the process of extracting important feature data (e.g., audio waveforms, image patterns, etc.) from copyrighted works using an AI engine.

[1912] "Copyright infringement" refers to the act of using someone else's copyrighted work without permission, which can lead to legal issues.

[1913] "Content distribution" refers to the act of providing and sharing digital content such as music, videos, and images through online platforms.

[1914] "Real-time" refers to processing and results occurring immediately, without delay.

[1915] The "Results Report" is a report summarizing the results of the AI ​​engine's analysis, and includes information about the risk of copyright infringement.

[1916] "Notification" refers to the act of a system conveying specific information to a user.

[1917] MODE FOR CARRYING OUT THE INVENTION

[1918] The present invention provides a system for checking the risk of copyright infringement in real time, especially on a content distribution platform, which comprises the following steps:

[1919] 1. System program generation

[1920] In this system, users upload copyrighted material from their smartphones, and an AI engine analyzes the characteristics of the material. The AI ​​engine stores the metadata and characteristic data of the material in a database, and in response to requests for copyright infringement checks, it compares new content with existing copyrighted material. The server also generates analysis results and notifies users of the risk of copyright infringement.

[1921] 2. Hardware and Software Description

[1922] To realize this system, the following hardware and software are used.

[1923] Hardware

[1924] Server: Stores data, extracts and compares features using AI, and generates and notifies analysis results.

[1925] Smartphone: A device through which users upload content and receive results.

[1926] software

[1927] Flask: Used as a web server framework to manage requests.

[1928] AI engine: A custom AI library (tentative name) that performs feature extraction and database comparison processing.

[1929] Database Management System: A system for efficiently storing and managing metadata and feature data.

[1930] 3. Data processing and calculation

[1931] The server processes and calculates data in the following steps:

[1932] 1. Uploading and Analysis of Copyrighted Materials:

[1933] The user uploads content (e.g., videos, images) from their smartphone. The server stores the metadata of the received content in a database, and the AI ​​engine extracts features. The extracted feature data is also stored in the database.

[1934] 2. Copyright Infringement Check Request:

[1935] A user uploads new content and sends a request for copyright infringement check. The server receives the new content and passes it to an AI engine to compare it with existing copyrighted material in the database.

[1936] 3. Results generation and notification:

[1937] The AI ​​engine returns the comparison results to the server, which processes the analysis results and generates a report of possible copyright infringement, which the server notifies the user.

[1938] 4. Example: Uploading a video to YouTube

[1939] For example, when a user uploads a video to an online video platform, the system can be used to check for copyright infringement risks. If the video contains an existing movie scene, the system will send a notification saying, "This video is 95% identical to an existing movie scene." Based on this, the user can modify or delete the video to avoid future legal issues.

[1940] 5. Examples of prompts

[1941] Your content guardian will analyze user-uploaded videos and images in real time to determine if they infringe existing copyrighted material. Please provide specific steps for users:

[1942] 1. Uploading Content

[1943] 2. Feature extraction using AI

[1944] 3. Comparison with database

[1945] 4. Generating and Communicating Results

[1946] The above is a description of the mode for carrying out the invention, and this system enables content creators to check copyright infringement risks in real time and prevent legal problems before they occur.

[1947] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1948] Step 1:

[1949] Uploading and analyzing copyrighted material

[1950] A user uploads content from their smartphone. Specifically, videos or images are sent to a specified server endpoint. The server extracts the content's metadata (title, creation date, etc.) and stores it in a database. The input at this stage is the content provided by the user, and the output is the metadata stored in the database. The server then passes this content to an AI engine, which extracts feature data (for example, visual patterns and audio waveforms in the case of videos). This feature data is also stored in the database.

[1951] Step 2:

[1952] Submitting a Copyright Infringement Check Request

[1953] A user uploads new content and sends a request for copyright infringement check. Specifically, the user again uses their smartphone to send the content to the server. This input is new content, and its purpose is to check for copyright infringement. The server receives this new content and adds the request to the processing queue. Once the request is added to the queue, it is ready to proceed to the next step.

[1954] Step 3:

[1955] AI-based feature analysis and database comparison

[1956] The server takes new content from the processing queue and passes it to the AI ​​engine. The AI ​​engine extracts the features of the new content and compares it with copyrighted works in the existing database. At this time, it analyzes how much the feature data matches or is similar to the existing data. The input is the new content and the feature data from the existing database, and the output is the analysis result of the degree of match or similarity. As a concrete example, the analysis result of a video may be output in the form of "This footage is 80% identical to existing movie scenes."

[1957] Step 4:

[1958] Generation and notification of analysis results

[1959] The server generates a report of possible copyright infringement based on the analysis results obtained from the AI ​​engine. Specifically, it compiles similarity and match information into a report format. The input is the analysis results returned by the AI ​​engine, and the output is the generated report. This report is sent to the user's smartphone so that the user can view it. This notification allows the user to decide on the next action (e.g., modifying or deleting the content).

[1960] Step 5:

[1961] Determining User Actions

[1962] The user checks the report received on their smartphone and decides on the next course of action. Specifically, if it is determined that there is a high risk of copyright infringement, they can take action such as modifying the content or inquiring about usage permissions. The input is the analysis result report, and the output is the user's specific action. This step allows the user to avoid the risk of copyright infringement of content in advance.

[1963] 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.

[1964] This invention is a copyright infringement checking system that uses AI to reduce the risk of copyright infringement and easily protect the rights of creators and patent holders, and it also combines an emotion engine that recognizes user emotions and provides feedback. In this system, users upload their copyrighted works, and the server analyzes, stores, and compares them to identify the risk of infringement, and analyzes user emotions in real time and provides appropriate feedback.

[1965] Explaining program processing in natural language

[1966] 1. Copyright Registration Phase

[1967] 1.1. Uploading copyrighted material

[1968] A user uploads copyrighted material (e.g., music files, image files) to the system from a terminal. The terminal then transmits the uploaded copyrighted material to the server.

[1969] 1.2. Saving to Database

[1970] The server analyzes the received copyrighted material and extracts metadata (title, author, creation date, etc.), which it then stores in a database.

[1971] 1.3. Feature extraction using AI

[1972] The server passes the copyrighted work to an AI engine, which analyzes its characteristics (e.g., melody, rhythm, harmony, etc. in the case of music) and generates feature data. The server then stores the generated feature data in a database.

[1973] Examples:

[1974] A user uploads a music file and the server stores the metadata and characteristics of the music file in a database.

[1975] 2. Copyright Infringement Check Request Phase

[1976] 2.1. Sending a Check Request

[1977] The user uploads new content (e.g., a new advertising image) from their device and sends a request for copyright infringement check. The device sends this new content and the request to the server.

[1978] 2.2. Receiving Content

[1979] The server receives the new content and adds the request to the processing queue, preparing to pass the new content to the AI ​​engine.

[1980] Examples:

[1981] A user uploads a new advertising image to the system and requests a copyright infringement check. The server receives the image and adds it to the processing queue.

[1982] 3. Copyright Infringement Check Phase

[1983] Comparison with Databases

[1984] The server processes the request and passes the new content to the AI ​​engine, which compares the new content with existing copyrighted material in its database and analyzes it for matches or similarities.

[1985] 3.2. Producing Results

[1986] The server processes the analysis results returned by the AI ​​engine and generates a report of potential copyright infringement based on the analysis results.

[1987] Examples:

[1988] The AI ​​engine compares new advertising images with existing images in the database, and the server generates a report based on the results of the similarity analysis.

[1989] 4. Result notification phase

[1990] 4.1. Sending the results

[1991] The server generates a report of possible copyright infringement and sends it to the user's device, where the user can view the report.

[1992] 4.2. Determining Actions

[1993] The user checks the result report they receive and decides on the next action (e.g., image correction, license inquiry, etc.). The user can also provide feedback on countermeasures to the server from their device.

[1994] Examples:

[1995] The server notifies the user of the similarity results, saying, "This image has 90% similarity to an existing copyrighted work." The user receives the results and considers modifying the image.

[1996] 5. Implementing the Emotion Engine

[1997] 5.1. Emotion Data Collection

[1998] As the user operates the interface displayed during the copyright infringement check process, emotional data such as the user's facial expressions, voice, and keyboard input is collected from the device.

[1999] 5.2. Emotion Data Analysis

[2000] The server passes the collected emotional data to the emotion engine, where the AI ​​analyzes the user's emotions. The analysis results are converted into numerical values ​​for the user's emotions, such as stress, frustration, and joy.

[2001] Providing Feedback

[2002] Based on the analysis results, the server provides appropriate feedback to the user's device. For example, if high stress or frustration is detected, a relaxation message or a link to contact support will be displayed.

[2003] Examples:

[2004] The emotion engine analyzes whether the user is feeling frustrated while operating the device, and the server displays a relaxation message such as "Would you like to take a short break?"

[2005] This system not only allows users to easily check the risk of copyright infringement, but also allows them to receive support that takes into consideration their emotional state, allowing them to use and publish content with greater peace of mind.

[2006] The processing flow will be explained below.

[2007] Step 1:

[2008] A user uploads copyrighted material (e.g., music files, image files) to the system from a terminal. The terminal then transmits the uploaded copyrighted material to the server.

[2009] Step 2:

[2010] The server analyzes the received copyrighted material and extracts metadata (title, author, creation date, etc.), which it then stores in a database.

[2011] Step 3:

[2012] The server passes the copyrighted work to the AI ​​engine, which analyzes the characteristics of the work (e.g., melody, rhythm, harmony, etc. in the case of music) and generates feature data. The server then stores the generated feature data in a database.

[2013] Step 4:

[2014] The user uploads new content (e.g., a new advertising image) from their device and sends a request for copyright infringement check. The device sends this new content and the request to the server.

[2015] Step 5:

[2016] The server receives the new content and adds the request to the processing queue, preparing to pass the new content to the AI ​​engine.

[2017] Step 6:

[2018] The server passes the new content to the AI ​​engine, which compares it with existing copyrighted material in its database to analyze for matches or similarities.

[2019] Step 7:

[2020] The server processes the analysis results returned by the AI ​​engine and generates a report of potential copyright infringement based on the analysis results.

[2021] Step 8:

[2022] The server generates a report of possible copyright infringement and sends it to the user's device, where the user can view the report.

[2023] Step 9:

[2024] Based on the results report received, the user decides on the next action (e.g., image correction, inquiry for permission to use, etc.). The user can also provide feedback on countermeasures to the server from the terminal.

[2025] Step 10:

[2026] The device collects the user's emotional data (e.g., facial expressions, voice, keyboard input, etc.) while the device is in operation. The emotional data is sent to the server in real time.

[2027] Step 11:

[2028] The server passes the emotion data to the emotion engine, which analyzes the collected data and quantifies the user's emotion (e.g., stress, frustration, joy, etc.).

[2029] Step 12:

[2030] The server generates appropriate feedback based on the analysis results from the emotion engine. For example, the server creates a relaxation message if high stress or frustration is detected.

[2031] Step 13:

[2032] The server generates feedback (e.g., a relaxation message) and sends it to the user's device, where the user can view the feedback.

[2033] Step 14:

[2034] The user decides the next action (e.g., taking a break, further operation, etc.) based on the feedback. The user's actions are fed back to the server via the device.

[2035] Example 2

[2036] 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."

[2037] Currently, systems for checking copyright infringement of digital content are often cumbersome and time-consuming, which can impair user convenience. Furthermore, few systems can analyze users' emotions and provide appropriate feedback in addition to checking for copyright infringement. This can easily lead to stress and frustration, so there is a need for a system that can integrate the evaluation of copyright infringement with the management of one's own emotional state.

[2038] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2039] In this invention, the server includes a means for uploading digital content created by users, a means for storing metadata of the digital content received by the server in a storage device, and a means for the server to pass the digital content to an artificial intelligence engine and for the artificial intelligence to extract features, thereby enabling copyright infringement checks of digital content and user sentiment analysis to be performed in a unified manner.

[2040] "User" means any person who interacts with the system to upload digital content or submit a request for a copyright infringement check.

[2041] A "terminal" is a device operated by a user, and is an apparatus on which operations such as uploading digital content and checking result reports are performed.

[2042] A "server" is a computer system that receives and processes digital content or requests sent by users.

[2043] "Digital content" refers to electronically recorded information such as music files, image files, and video files.

[2044] "Metadata" is additional information associated with digital content, such as title, creator, and creation date.

[2045] A "storage device" is a storage device for storing digital content, metadata, and feature data.

[2046] An "artificial intelligence engine" is a system that includes algorithms and models for analyzing the characteristics of digital content and analyzing matches or similarities.

[2047] "Feature data" refers to the analysis results of the melody, rhythm, harmony, etc. of digital content extracted by an artificial intelligence engine.

[2048] A "processing queue" is a data structure for temporarily storing new requests and processing them sequentially.

[2049] A "copyright infringement check request" is a request sent by a user to a server to check new digital content for existing copyright infringement.

[2050] "Analysis Results" means the results of the comparison and matching or similarity analysis of digital content conducted by the artificial intelligence engine.

[2051] A "report" is a document generated based on the analysis results, which includes information indicating possible copyright infringement.

[2052] "Emotion data" is data that indicates the user's emotional state, such as the user's facial expression, voice, keyboard input, etc.

[2053] An "emotion recognition engine" is a system that includes algorithms and models for analyzing collected emotional data and quantifying a user's emotional state.

[2054] "Feedback" refers to notifications and messages provided to users based on the results of emotion analysis, and includes, for example, relaxation messages and support information.

[2055] This invention is a system for checking copyright infringement of digital content and analyzing user sentiment in an integrated manner. This system is realized by uploading digital content created by users, and the server analyzes, stores, and performs specific processing on the uploaded content.

[2056] Hardware and software used

[2057] The system is implemented using the following hardware and software:

[2058] Terminal: A device operated by a user, including a PC, smartphone, tablet, etc., that has internet connectivity and file upload capabilities.

[2059] Server: A computer system with high-performance processing capabilities that receives digital content, stores it in a database, connects with the AI ​​engine, and provides feedback on the results of sentiment analysis.

[2060] Database software: A relational database management system (RDBMS) such as MySQL or PostgreSQL is used to store metadata and feature data for digital content.

[2061] AI Engine: Contains generative AI models built using TensorFlow and PyTorch to extract features from digital content, compare them, and check for potential copyright infringement.

[2062] Emotion recognition engine: Uses emotion analysis services such as Amazon Rekognition and Google Cloud Vision to collect and analyze user emotion data.

[2063] Example of a system

[2064] Consider a scenario where a user uploads digital content that they have created, for example, a music file called "MySong.mp3."

[2065] 1. User Action:

[2066] The user uses the device to drag and drop the file "MySong.mp3" into the system interface, then clicks the upload button, and the device sends this file to the server via an HTTP request.

[2067] 2. Server-side processing:

[2068] The server receives the file and saves it in a temporary directory. It then extracts metadata such as the title "My Song," the creator "John Doe," and the creation date "2023-10-10" and stores them in a database. The server then passes the file to an AI engine, which analyzes the melody and rhythmic features. The resulting feature data, "Melody: (C, D, E, F), Rhythm: 4 / 4, Tempo: 120 BPM," is saved in the database.

[2069] 3. Copyright Infringement Check:

[2070] When checking new digital content, a user uploads a new advertising image, "AdsImage.jpg," and requests a copyright infringement check. The server receives this request and adds it to the processing queue. The AI ​​engine compares it with existing data and analyzes the similarity. A "95% similarity" is returned as a result, and a report of possible copyright infringement is generated and sent to the user.

[2071] 4. Sentiment Analysis and Feedback:

[2072] As the user operates the system, the device collects the user's emotional data (facial expressions, voice, keyboard input, etc.). The server passes the collected emotional data to an emotion recognition engine for analysis. If high stress or frustration is detected, the server sends the user relaxation messages or support information.

[2073] Prompt Sentence Examples

[2074] When a user uploads a new music file and analyzes its features based on the AI ​​engine, the prompt text is as follows:

[2075] "Upload the music file 'MySong.mp3'. We'll analyze the melody, rhythm, and tempo of the file and store them in our database."

[2076] This system not only checks for copyright infringement of digital content, but also takes into consideration the emotional state of the user, allowing users to use and publish content with greater peace of mind.

[2077] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2078] Step 1:

[2079] The user selects digital content (e.g., music file "MySong.mp3") on the device and clicks the upload button.

[2080] Input: Music file "MySong.mp3".

[2081] Output: The music file "MySong.mp3" is sent from the device to the server.

[2082] Specific behavior: The device sends the file to the server using an HTTP POST request.

[2083] Step 2:

[2084] The server receives the uploaded file and saves it in a temporary directory.

[2085] Input: Music file "MySong.mp3".

[2086] Output: "MySong.mp3" saved in your temporary directory.

[2087] Specific operation: The server saves the received file in a specific directory (e.g., " / tmp / uploads").

[2088] Step 3:

[2089] The server extracts the metadata of the music files.

[2090] Input: A music file "MySong.mp3" saved in the temporary directory.

[2091] Output: Extracted metadata (Title "My Song", Author "John Doe", Creation Date "2023-10-10").

[2092] Specific operation: The server uses a Python library (e.g., mutagen) to extract metadata from music files.

[2093] Step 4:

[2094] The server stores the extracted metadata in a database.

[2095] Input: Extracted metadata (Title "My Song", Author "John Doe", Creation Date "2023-10-10").

[2096] Output: Metadata stored in a database.

[2097] What happens: The server executes an SQL INSERT query to save the metadata into a MySQL database.

[2098] Step 5:

[2099] The server passes the music file to the AI ​​engine, which extracts its features.

[2100] Input: Music file "MySong.mp3".

[2101] Output: Extracted feature data (melody, rhythm, tempo).

[2102] How it works: The server inputs the file into an AI model using TensorFlow, which analyzes features such as melody, rhythm, and tempo.

[2103] Step 6:

[2104] The server stores the feature data in a database.

[2105] Input: Extracted feature data (melody, rhythm, tempo).

[2106] output: Feature data stored in the database.

[2107] What happens: The server executes an SQL INSERT query to save the feature data into a MySQL database.

[2108] Step 7:

[2109] A user uploads new digital content (e.g., an advertisement image "AdsImage.jpg") from their device and submits a request for copyright infringement check.

[2110] Input: Ad image "AdsImage.jpg".

[2111] output: The ad image "AdsImage.jpg" and the check request are sent to the server.

[2112] Specific behavior: The device sends the file and request to the server using an HTTP POST request.

[2113] Step 8:

[2114] The server receives new digital content and adds it to a processing queue.

[2115] Input: Ad image "AdsImage.jpg" and check request.

[2116] output: The ad image "AdsImage.jpg" and check request added to the processing queue.

[2117] Specific behavior: The server saves the received file in a temporary directory and adds it to the processing queue.

[2118] Step 9:

[2119] The server passes new digital content to the AI ​​engine, which compares it with existing data and analyzes it for matches or similarities.

[2120] Input: Ad image "AdsImage.jpg".

[2121] output: The match or similarity analysis result.

[2122] What it does: The server inputs the file into an AI engine, which compares it with digital content in an existing database.

[2123] Step 10:

[2124] The server generates a report of possible copyright infringement based on the analysis results.

[2125] Input: Match or similarity analysis results.

[2126] output: A report of potential copyright infringement.

[2127] Specific operation: The server automatically generates a PDF report based on the analysis results.

[2128] Step 11:

[2129] The server sends the generated report to the user's terminal.

[2130] Enter: Potential Copyright Infringement Report.

[2131] output: The report sent to the user's terminal.

[2132] Specific operation: The server uses an HTTP response to send the report to the user's device.

[2133] Step 12:

[2134] The user reviews the results report and decides on the next action.

[2135] Enter: Potential Copyright Infringement Report.

[2136] output: Next action (e.g., image modification, license inquiry).

[2137] Specific actions: The user views the report, decides what action is required, and takes further action via the device.

[2138] Step 13:

[2139] As the user operates the system, the device collects emotional data.

[2140] Input: User facial expressions, voice, keyboard input, etc.

[2141] output: The collected emotion data.

[2142] What it does: The device uses the camera, microphone, and keyboard to collect user emotional data.

[2143] Step 14:

[2144] The server passes the collected emotion data to an emotion recognition engine for analysis.

[2145] Input: Collected emotion data.

[2146] output: Sentiment analysis results (e.g., stress level or frustration).

[2147] Specific operation: The server passes the data to the emotion recognition engine and receives the analysis results.

[2148] Step 15:

[2149] The server generates feedback based on the emotion analysis results and provides it to the user.

[2150] Input: Sentiment analysis results.

[2151] output: A feedback message to the user (e.g., a relaxation message or support information).

[2152] Specific operation: The server generates a message appropriate for the user based on the analysis results and sends it to the device.

[2153] (Application example 2)

[2154] 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."

[2155] In content creation, it is important to reduce the risk of copyright infringement and protect the rights of creators. Furthermore, it is necessary to reduce the stress and frustration felt by users during the content uploading process and realize a smooth operation. The problem to be solved by this invention is to effectively check the risk of copyright infringement of content created by users, grasp the user's emotional state, and provide appropriate feedback.

[2156] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for uploading a work created by a user; means for the server to store metadata of the work received in a database; means for the server to pass the work to an AI engine and for the AI ​​to extract features; means for the server to store the feature data in a database; means for the user to upload new content and send a request for a copyright infringement check; means for the server to receive the new content and add the request to a processing queue; means for the server to pass the new content to the AI ​​engine and for the AI ​​to compare it with existing works in the database and analyze the degree of match or similarity; means for the server to process the analysis results from the AI ​​engine and generate a report of possible copyright infringement; means for sending the report generated by the server to the user's terminal; means for the user to check the result report and decide on the next action; means for analyzing the user's facial expressions, voice, and keyboard input to understand the user's emotional state; and means for the server to analyze the emotional data and provide appropriate feedback to the user. This makes it possible to effectively check the risk of copyright infringement in user-created content, and also to analyze the user's emotional state in real time and provide appropriate feedback.

[2157] "User" means a person or entity that uses the System to upload copyrighted material and request a copyright infringement check.

[2158] "Works" are content created by users, and examples include music files and image files.

[2159] The "server" is a central processing unit that receives the copyrighted material uploaded by the user, analyzes, stores, compares, notifies the results, and processes the emotion data.

[2160] "Metadata" is information about a work, including the title, creator, creation date, etc.

[2161] "Database" means a storage device containing metadata and characteristic data of existing works stored by the server.

[2162] An "AI engine" is a program device that uses artificial intelligence to extract characteristics of copyrighted works and compare new content with existing works in a database.

[2163] "Feature data" refers to specific attribute information of a copyrighted work, such as its melody, rhythm, and harmony, extracted by the AI ​​engine.

[2164] A "request" refers to a request by a user to submit new content to a server for copyright infringement checking.

[2165] A "processing queue" is a line of tasks that is temporarily placed to await processing after the server receives new content.

[2166] "Analysis results" refers to the data generated after the AI ​​engine compares new content with existing copyrighted works in its database.

[2167] The "Potential Copyright Infringement Report" is a report that indicates the risk of copyright infringement, generated by the server based on the analysis results from the AI ​​engine.

[2168] "Emotion data" is information that indicates the user's emotional state, obtained from the user's facial expression, voice, keyboard input, and the like.

[2169] "Feedback" refers to constructive advice or messages provided to users based on emotional data analyzed by the server.

[2170] MODE FOR CARRYING OUT THE INVENTION

[2171] This invention relates to a system for checking copyright infringement of user-created content and analyzing the user's emotional state in real time. To specifically implement this system, the following hardware and software, as well as data processing and data calculation, are required.

[2172] 1. Hardware and Software Used

[2173] Hardware

[2174] Smartphone: The device where users upload content

[2175] Head-mounted display: A display device equipped with a camera and microphone that collects the user's facial expressions and voice.

[2176] Server: A central processing unit that analyzes and stores data

[2177] software

[2178] Python: A language for data analysis and AI model execution

[2179] TensorFlow / Keras: Building and running AI models

[2180] OpenCV: Facial Expression Recognition Library

[2181] Azure Face API: Facial recognition and emotion analysis API

[2182] 2. Natural language description of program processing

[2183] User Content Uploads

[2184] Users upload their own creations (e.g., videos, images, and music files) through the interface of their smartphone or head-mounted display. The creations are then sent from the device to the server.

[2185] Extracting and storing metadata

[2186] The server analyzes the received copyrighted material and extracts metadata (title, author, creation date, etc.), which is then stored in a database.

[2187] AI-based feature extraction

[2188] The copyrighted work is passed from the server to the AI ​​engine, which analyzes its characteristic data (melody, rhythm, harmony, etc.) and stores the analyzed characteristic data in a database.

[2189] Copyright Infringement Check Request

[2190] A user uploads new content and requests a copyright infringement check. The server receives this new content and adds it to a processing queue.

[2191] Compare and analyze new content

[2192] The server then passes the new content to the AI ​​engine, which then compares it with existing works in its database and analyzes the matches and similarities.

[2193] Analysis results and report generation

[2194] The server generates a report of potential copyright infringement based on the analysis results returned by the AI ​​engine, and this report is sent to the user's device.

[2195] Emotion data collection and analysis

[2196] The user's facial expressions, voice, and keyboard input are collected through the camera and microphone of the head-mounted display. This emotional data is sent to a server and analyzed by an AI engine.

[2197] Emotional Feedback

[2198] The server uses the emotion analysis results to determine the user's stress and frustration and provides appropriate feedback. For example, if high stress is detected, a relaxation message such as "Would you like to take a break?" will be displayed.

[2199] 3. Examples and prompts

[2200] Specific examples

[2201] A user uploads a new video to the content distribution platform and requests a copyright infringement check. The server then extracts the video's metadata and feature data and compares it with existing videos in the database. At the same time, it analyzes the user's facial expressions and voice and displays relaxation messages if the user's stress level increases.

[2202] Prompt Sentence Examples

[2203] "Upload a new video to the copyright infringement checking system and check its similarity to existing videos. Extract the metadata and feature data of the uploaded video, compare it with existing videos in the database, analyze the similarity, and notify the user of the results. At the same time, analyze the user's facial and vocal emotional data, and display a relaxation message if stress is detected."

[2204] This invention not only allows users to easily check the risk of copyright infringement for the content they create, but also allows them to receive support that takes into consideration their emotional state, allowing them to use and publish content with greater peace of mind.

[2205] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2206] Step 1:

[2207] Users upload content

[2208] Users upload their own copyrighted works (e.g., videos, images, and music files) to the system using their smartphones or head-mounted displays. The devices then send the uploaded works to the server.

[2209] Input: User-created copyrighted material file

[2210] Output: The copyrighted file sent to the server

[2211] Step 2:

[2212] Extracting and storing metadata

[2213] The server analyzes the received copyrighted material and extracts metadata (title, author, creation date, etc.) and stores this metadata in a database.

[2214] Input: User-uploaded copyrighted material file

[2215] Output: Metadata stored in a database

[2216] What it does: A Python program parses copyrighted files and extracts metadata, which is then stored in a database using SQL queries.

[2217] Step 3:

[2218] AI-based feature extraction

[2219] The server passes the copyrighted work to an AI engine (a model using TensorFlow / Keras), which analyzes and generates feature data (attributes such as melody, rhythm, harmony, etc.) The server then stores the generated feature data in a database.

[2220] Input: Copyrighted material file received by the server

[2221] Output: Feature data stored in a database

[2222] Specific operation: The copyrighted file is loaded into the TensorFlow / Keras model, and feature data is generated. The generated feature data is then stored in a database using SQL queries.

[2223] Step 4:

[2224] User submits request

[2225] The user uploads new content (e.g., a new video file) and sends a request for copyright infringement check. The device sends this new content and the request to the server.

[2226] Input: New content files uploaded by users and copyright infringement check requests

[2227] Output: New content and request sent to the server

[2228] Step 5:

[2229] Request Processing

[2230] The server receives the new content and adds the request to the processing queue, preparing to hand the new content off to the AI ​​engine.

[2231] Input: New content and requests uploaded by users

[2232] Output: Request added to processing queue

[2233] What happens: The Python program receives new content and requests and adds them to the processing queue.

[2234] Step 6:

[2235] Copyright Infringement Check

[2236] The server processes the request and passes the new content to an AI engine, which compares the new content with existing copyrighted material in its database and analyzes it for matches or similarities.

[2237] Input: Requests added to the processing queue, new content

[2238] Output: Match or similarity analysis results

[2239] What it does: A TensorFlow / Keras model is used to compare new content with existing works in the database.

[2240] Step 7:

[2241] Processing analysis results and generating reports

[2242] The server generates a report on possible copyright infringement based on the analysis results returned by the AI ​​engine, and sends this report to the user's device.

[2243] Input: Analysis results from the AI ​​engine

[2244] Output: Report sent to user terminal

[2245] Specific operation: Based on the analysis results, a Python program generates a report and sends it to the relevant user's device as an email or notification.

[2246] Step 8:

[2247] Emotion data collection and analysis

[2248] The user's facial expressions, voice, and keyboard input are collected through the camera and microphone of the head-mounted display. This emotional data is sent to a server and analyzed by an AI engine.

[2249] Input: User facial, voice, and keyboard input data

[2250] Output: Parsed emotion data

[2251] Specific operation: Analyzes emotion data in real time using OpenCV and Azure Face API.

[2252] Step 9:

[2253] Emotional Feedback

[2254] The server uses the emotion analysis results to determine the user's stress and frustration and provides appropriate feedback. For example, if high stress is detected, a relaxation message such as "Would you like to take a break?" will be displayed.

[2255] Input: Parsed emotion data

[2256] Output: Feedback message provided to the user

[2257] Specific operation: Based on the results of emotion analysis, a feedback message corresponding to the user's situation is generated and displayed on the user's device.

[2258] 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.

[2259] 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.

[2260] 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.

[2261] 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.

[2262] FIG. 9 illustrates 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 behaviors 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.

[2263] 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.

[2264] 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).

[2265] 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.

[2266] 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."

[2267] 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.

[2268] 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).

[2269] 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.

[2270] 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.

[2271] 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.

[2272] 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.

[2273] 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.

[2274] 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.

[2275] 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.

[2276] 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.

[2277] 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.

[2278] 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.

[2279] The following is...

Claims

1. a means for uploading user-created works; means for storing the metadata of the works received by the server in a database; The server passes the copyrighted work to the AI ​​engine, and the AI ​​extracts features from it. a means for the server to store the feature data in a database; A means for users to upload new content and submit requests for copyright infringement checks; a means for the server to receive new content and add requests to a processing queue; a means by which the server passes new content to the AI ​​engine, which compares it with existing copyrighted material in the database and analyzes for matches or similarities; a means for the server to process the analysis results from the AI ​​engine and generate a report of potential copyright infringement; means for transmitting the server-generated report to the user's terminal; A means for the user to review the results report and decide on next actions; A system including:

2. 2. The system of claim 1, further comprising means for the server to issue a warning to the user in the event of a likely copyright infringement.

3. 10. The system of claim 1, further comprising means for the server to notify the user when the risk of copyright infringement is low.

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