System

Abstract

A system is provided.SOLUTION: A system, comprising: means for uploading digital content; identity AI means for analyzing the uploaded digital content; hash generation means for verifying the digital content based on the analysis result; block chain connection means for transmitting the generated hash and the analysis result to a block chain network and generating an identity; and means for storing the identity and the analysis result returned from the block chain network and notifying a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In recent years, technological advances have led to AI-generated images and videos that are indistinguishable from reality. This increases the risk of spreading false rumors and inappropriate content during disasters, potentially causing social unrest. Furthermore, the lack of established methods for verifying the authenticity of digital content makes it difficult for users to identify trustworthy sources of information. Against this backdrop, there is a growing need for new technologies to ensure the reliability of digital content and prevent the spread of false information. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by the following means. The system includes a means for uploading digital content, an identification AI means for analyzing the uploaded digital content, a hash generation means for verifying the authenticity of the digital content based on the analysis results, a blockchain connection means for transmitting the generated hash value and the analysis results to a blockchain network and generating an identifier, and a means for storing the identifier and analysis results returned from the blockchain network and notifying the user. Furthermore, by providing a means for user authentication when uploading digital content, content can be handled more safely. Furthermore, by using a database as a means for storing the analysis results, efficient data management is possible.

[0006] "Digital content" refers to information such as images, video, audio, and text that is stored, displayed, or distributed in electronic form.

[0007] "Upload" refers to the act of a user transferring digital content from a device to a server.

[0008] "Discrimination AI" refers to a system that uses artificial intelligence technology to determine whether digital content is AI-generated or live-action.

[0009] "Analysis" refers to the process by which identification AI analyzes the content of digital content and determines its nature and characteristics.

[0010] "Hash generation" refers to the process of converting digital content data into a fixed-length string that is used to verify the data's authenticity.

[0011] A "blockchain network" is a system that uses distributed ledger technology to record hash values ​​and identification results of digital content and generate unique identifiers.

[0012] "Blockchain connection" refers to the process by which a server accesses a blockchain network to send or receive information.

[0013] "Identifier" refers to a code or string of characters that is uniquely generated by a blockchain network and serves to prove the authenticity or origin of a particular piece of digital content.

[0014] "User authentication" refers to the process of verifying the identity of users accessing a system and ensuring that they have legitimate access rights.

[0015] "Database" refers to a system for storing and managing analysis results and identifiers of digital content. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] The present invention is a system for verifying the authenticity of digital content, and includes the following main components: means for uploading digital content, identification AI means for analyzing the uploaded digital content, hash generation means for verifying the authenticity of the digital content based on the analysis results, blockchain connection means for transmitting the generated hash value and analysis results to a blockchain network and generating an identifier, and means for storing the identifier and analysis results returned from the blockchain network and notifying the user.

[0038] Program processing

[0039] Uploading Digital Content

[0040] A user uses a terminal to upload digital content (e.g., images or videos) to the system. Files are selected through the upload interface and sent to the server, which temporarily stores the received content and begins processing it.

[0041] Content Analysis

[0042] The server sends the stored digital content to an AI that analyzes the received content and determines whether it is AI-generated or live-action. The analysis algorithms include image and video metadata checks, pattern recognition, and deep learning-based analysis techniques.

[0043] Saving the identification results

[0044] The server receives the analysis results from the identification AI and temporarily stores them, including information on whether the digital content is AI-generated or live-action.

[0045] Generating a hash value

[0046] The server generates a hash value of the digital content. The hash generation module calculates the hash value of the content using a hash algorithm such as SHA-256.

[0047] Send to blockchain

[0048] The server sends the generated hash value and analysis results to the blockchain network, which then creates a transaction based on the received information and generates a unique identifier for the digital content, which is then recorded on the blockchain.

[0049] Notifying users of the results

[0050] The server receives the identifier and analysis results returned from the blockchain network and stores them in the user's database. The server then notifies the user, who can then view the results of the content's authenticity verification.

[0051] Specific examples

[0052] 1. Upload and analyze

[0053] Users upload videos they have taken from the scene of a natural disaster.

[0054] The server sends the video to an identification AI, which then analyzes the video.

[0055] The identification AI determines that the video is live action.

[0056] 2. Identifier Generation

[0057] The server generates a hash value for the video.

[0058] The hash value and analysis results are sent to the blockchain network.

[0059] The blockchain network generates a unique identifier and records authenticity information.

[0060] 3. Notice and Confirmation

[0061] The server notifies the user of the identifier and the AI's judgment result.

[0062] The user confirms the notification and confirms that the video is live and its authenticity is verified by the blockchain.

[0063] This invention allows users to easily verify the authenticity of digital content, preventing the spread of false information and avoiding social unrest. Furthermore, by utilizing blockchain technology, it becomes possible to prove the authenticity of content with a high degree of reliability.

[0064] The processing flow will be explained below.

[0065] Step 1:

[0066] The user accesses the system's website or application using a terminal and logs in. They enter their authentication information to authenticate the user, and if authentication is successful, they are taken to the upload screen.

[0067] Step 2:

[0068] The user clicks the "Select File" button on the upload screen to select the digital content (image or video) file. After selecting the file, the user clicks the "Upload" button to send the file to the server.

[0069] Step 3:

[0070] The server receives the digital content sent by the user and temporarily stores it in storage. A message indicating successful storage is logged and a notification of upload completion is displayed to the user.

[0071] Step 4:

[0072] The server sends the stored digital content to the identification AI module, which loads the digital content and runs image or video analysis algorithms to determine whether it is AI-generated or live-action.

[0073] Step 5:

[0074] The identification AI completes the analysis and returns the results to the server, which associates the analysis results with the corresponding digital content entry and temporarily stores them.

[0075] Step 6:

[0076] The server generates a hash value for the digital content. The file path is passed to the hash generation module, which calculates the hash value using a hash algorithm such as SHA-256. The generated hash value is then sent back to the server.

[0077] Step 7:

[0078] The server compiles the hash value and the results of the AI's analysis and sends it to the blockchain network, where a smart contract receives the data, creates a transaction that generates a unique identifier, and records the information on the blockchain.

[0079] Step 8:

[0080] The blockchain network sends the generated identifier back to the server, which associates and stores this identifier and the analysis results in the user's database.

[0081] Step 9:

[0082] The server sends a notification to the user, which includes the identification result and details of the content certified on the blockchain, and is displayed on the user's device.

[0083] Step 10:

[0084] Users can check the notification and confirm that the digital content is authentic and its authenticity is verified by the blockchain, allowing them to use the content with confidence.

[0085] Example 1

[0086] 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."

[0087] In modern society, counterfeiting and tampering of digital content is on the rise, making it important to verify its authenticity. However, conventional methods lack the means to verify the authenticity of digital content with high accuracy. Another problem is the lack of a system that allows users to easily verify the authenticity of content. To solve these issues, it is necessary to analyze digital content and provide the results to users in a reliable manner.

[0088] 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.

[0089] In this invention, the server includes: means for a user to select and upload information; identification AI means for temporarily storing and analyzing the uploaded information; means for storing analysis results received from the identification AI; means for generating a hash value of the digital content; blockchain connection means for transmitting the generated hash value and analysis results to a blockchain network and generating a unique identifier; and means for storing the identifier and analysis results returned from the blockchain network and notifying the user. This makes it possible to verify the authenticity of digital content with high accuracy and reliability and quickly provide the results to the user.

[0090] "User" means a person or entity that accesses the system to upload digital content and receive analysis results thereof.

[0091] "Information" refers to digital content (e.g., images, videos) uploaded by users in this system.

[0092] "Uploading" is the act of a user using a terminal to send digital content to a server.

[0093] "Server" means a central computer system that receives and stores digital content sent by users, analyzes it, generates hashes, connects it to the blockchain, and notifies users.

[0094] "Identification AI means" refers to an artificial intelligence system that analyzes uploaded digital content and determines its authenticity (e.g., whether it is AI-generated or live-action).

[0095] A "hash value" is a fixed-length string that indicates the uniqueness of digital content and is generated using a hash algorithm such as SHA-256.

[0096] A "blockchain network" is a distributed network consisting of multiple computers, and is a system that records transactions and generates identifiers for digital content.

[0097] "Identifier" means a unique ID generated by the blockchain network to indicate the authenticity of digital content.

[0098] "Notification to user" refers to the act of the server communicating the identifier and analysis results received from the blockchain network to the user.

[0099] A "database" is an information system for structuring, storing, and managing information such as analysis results and identifiers.

[0100] The present invention relates to a system for verifying the authenticity of digital content, which allows users to upload information (e.g., images or videos), analyzes its authenticity, and provides the results in a reliable manner.

[0101] The user uploads the digital content to the system using a terminal, accesses the upload interface of the system from the terminal's browser, and sends the selected digital content to the server, which temporarily stores the received content and prepares it for analysis by the identification AI means.

[0102] The server sends the stored digital content to the identification AI means, which analyzes the content using metadata verification, pattern recognition, and deep learning techniques to determine whether it is AI-generated or live-action. The analysis result is sent back to the server as either "AI-generated" or "live-action."

[0103] The server receives the analysis results sent from the identification AI means and temporarily stores them. The server then generates a hash value for the digital content. This hash value is generated using a hash algorithm such as SHA-256.

[0104] The generated hash value and analysis result are sent from the server to the blockchain network, which creates a transaction based on the received information and generates a unique identifier, which is recorded on the blockchain and sent back to the server.

[0105] Finally, the server receives the identifier and analysis results returned from the blockchain network and stores them in the user's database. The server then notifies the user, who can then check the results within the system, enabling high-precision and high-reliability verification of the authenticity of digital content.

[0106] Specific examples

[0107] Upload and analyze

[0108] 1. Users upload videos taken from the scene of a natural disaster.

[0109] 2. The server sends the video to the identification AI means, which analyzes the video.

[0110] 3. The identification AI method determines that the video is live action.

[0111] Identifier Generation

[0112] 1. The server generates a hash value for the video.

[0113] 2. Send the hash value and analysis results to the blockchain network.

[0114] 3. The blockchain network generates a unique identifier and records the authenticity information.

[0115] Notification and confirmation

[0116] 1. The server notifies the user of the identifier and the AI's judgment result.

[0117] 2. The user checks the notification and confirms that the video is live and its authenticity is verified by the blockchain.

[0118] Prompt Sentence Examples

[0119] "Please determine whether the following digital content is AI-generated or live-action, and register the result and its hash value on the blockchain. After the hash value and the result of your determination are returned, please display the final confirmation result."

[0120] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0121] Step 1: User selects and uploads digital content

[0122] The user accesses the system's upload interface from the browser on their device. Here, they select the target digital content (e.g., images or videos). When the user clicks the upload button, the selected digital content is sent from the device to the server. The input is the digital content selected by the user, and the output is the digital content received by the server.

[0123] Step 2: The server stores the digital content

[0124] The server temporarily stores the received digital content. Specifically, it is stored as data in the server's storage. This temporary storage is a preparatory step for subsequent analysis processing. The input is the digital content sent to the server, and the output is the digital content stored in the server's storage.

[0125] Step 3: The server sends the digital content to the identification AI means

[0126] The server transmits the stored digital content to the identification AI means, which often runs on a separate server or cloud service. This transmission process passes the digital content information required for analysis to the identification AI means. The input is the temporarily stored digital content, and the output is the digital content transmitted to the identification AI means.

[0127] Step 4: Identification AI methods analyze the digital content

[0128] The identification AI means analyzes the received digital content. This analysis includes checking metadata, pattern recognition, and analysis using deep learning techniques. The analysis results in information that identifies whether the digital content is AI-generated or live-action. The input is the digital content sent to the identification AI means, and the output is the analysis result, such as "AI-generated" or "live-action."

[0129] Step 5: The server receives and stores the analysis results

[0130] The server receives the analysis results sent from the identification AI means. These results are temporarily stored. The analysis results are used for subsequent hash value generation and blockchain transmission processing. The input is the analysis results sent from the identification AI means, and the output is the analysis results stored in the server's storage.

[0131] Step 6: The server generates a hash value for the digital content.

[0132] The server generates a hash value based on the stored digital content. It uses a hash algorithm such as SHA-256 to calculate a unique hash value for the digital content. This hash value is used to detect data tampering. The input is the digital content stored in the server's storage, and the output is the generated hash value.

[0133] Step 7: The server sends the hash value and analysis result to the blockchain network.

[0134] The server sends the generated hash value and the analysis result to the blockchain network, which then creates a transaction and generates a unique identifier. The input is the generated hash value and the analysis result, and the output is the identifier generated by the blockchain network.

[0135] Step 8: The server receives and stores the identifier and analysis results.

[0136] The server receives the identifier and analysis results returned from the blockchain network. This information is stored in the user's database. The input is the identifier and analysis results returned from the blockchain network, and the output is the identifier and analysis results stored in the user's database.

[0137] Step 9: Server notifies the user

[0138] The server notifies the user. This notification includes the analysis result of the identification AI method and the identifier generated by the blockchain network. The user receives the notification and checks the result within the system. The input is the identifier stored in the user's database and the analysis result, and the output is a notification to the user.

[0139] (Application example 1)

[0140] 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."

[0141] The circulation of digital data in modern society is rapidly increasing, but the inclusion of false information and falsified content within this data is causing problems, resulting in a loss of user trust. Furthermore, there is a lack of means to quickly and reliably verify the authenticity of digital data and prove its reliability using distributed ledger technology. Given this background, there is a need for a system that increases the reliability of digital data and allows users to share and use information with peace of mind.

[0142] 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.

[0143] In this invention, the server includes means for transmitting digital data, identification artificial intelligence means for analyzing the transmitted digital data, hash value generation means for verifying the authenticity of the digital data based on the analysis results, distributed ledger connection means for transmitting the generated hash value and analysis results to the distributed ledger network and generating an identifier, and means for storing the identifier and analysis results returned from the distributed ledger network and notifying the user. This enables the authenticity of digital data to be verified quickly and with high reliability, and enables the provision of reliable information to users.

[0144] "Digital data" is a general term for information and content handled on computers and digital devices, and includes images, videos, audio, text data, etc.

[0145] "Transmission" is the act of transferring digital data from one place to another, often over a network or communication line.

[0146] "Identification artificial intelligence means" refers to artificial intelligence technology trained to analyze the characteristics of digital data and determine its generation method and authenticity.

[0147] A "hash value generation method" is a technology that generates a fixed-length string (hash value) from digital data, and is used to verify data integrity and prevent tampering.

[0148] A "distributed ledger network" is a ledger technology in which data blocks are stored in a linked chain format, and is characterized by being managed in a decentralized manner across multiple nodes.

[0149] "Distributed ledger connection means" refers to technology for transmitting hash values ​​and analysis results of digital data to a distributed ledger network and generating records and identifiers on that network.

[0150] An "identifier" refers to a unique string of characters or code generated by a distributed ledger network to prove the uniqueness and authenticity of digital data.

[0151] "Means for notifying users" is a general term for technologies and methods for informing users of the authenticity and identification results of digital data, and includes push notifications, emails, messaging services, etc.

[0152] "User authentication means" refers to a technology or method for verifying a user's identity and authority before sending or receiving digital data.

[0153] "Data storage" refers to a storage device or location for storing analysis results, hash values, identifiers, etc.

[0154] To implement this invention, a user-operated terminal (such as a smartphone or computer) and an associated server are required. The server operates as a system including the following main components:

[0155] 1. Digital Data Transmission

[0156] A user uses a device to send digital data (e.g., videos, images) to a server. At this time, user authentication is performed on the device. A general authentication service is used for user authentication. For example, Firebase Authentication is used for login and authentication.

[0157] 2. Analysis of transmitted digital data

[0158] The server temporarily stores the received digital data and sends it to an AI identification means (e.g., TensorFlow or OpenCV-based AI identification), which analyzes the received data and determines whether it was actually photographed or generated by AI.

[0159] 3. Generating a hash value

[0160] The server generates a hash value of the analyzed digital data using the SHA-256 algorithm, which provides a unique identifier for the digital data.

[0161] 4. Sending to the distributed ledger network

[0162] The server sends the generated hash value and analysis results to a distributed ledger network (e.g., the Ethereum blockchain), which creates a transaction based on the received information and generates a unique identifier, which is then recorded in the distributed ledger.

[0163] 5. Notice to Users

[0164] The server receives the identifier and analysis results returned from the distributed ledger network and stores them in the user's data storage (e.g., a database). The server then notifies the user of the results using a notification service such as Firebase Cloud Messaging.

[0165] Specific examples

[0166] For example, consider the case where a user uploads a video taken at the site of a natural disaster. The user shoots the video using a smartphone and sends it to a server through an application. The sent video is analyzed by an identification AI and determined to be live footage. The server generates a hash value for the video and sends it to the distributed ledger network. The blockchain generates a unique identifier based on the hash value and sends it back to the server. Finally, the server notifies the user of the identifier and the analysis results, allowing the user to verify the authenticity of the uploaded video.

[0167] Example prompts for generative AI models

[0168] I uploaded a video taken at the scene of a natural disaster and want to verify its authenticity.

[0169] These steps allow users to ensure the authenticity of digital data and build a system that can prove its reliability using distributed ledger technology.

[0170] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0171] Step 1: User authentication and digital data transmission

[0172] Subject: User

[0173] Specific operation: A user logs in using an application on their device. Firebase Authentication can be used for login. If authentication is successful, the user selects digital data (e.g., a video) and clicks the send button. At that time, the device makes a request to send the selected file to the server.

[0174] Input: User login information, digital data (file)

[0175] Output: Authentication token, digital data transmission to server

[0176] Step 2: Temporarily store digital data and analyze it using AI

[0177] Subject: Server

[0178] How it works: The server receives the digital data and temporarily stores it in storage. The stored data is then sent to a classification AI (using TensorFlow and OpenCV). The classification AI analyzes the received data and determines whether it is real or AI-generated.

[0179] Input: Digital data

[0180] Output: Analysis results (real-life or AI-generated)

[0181] Step 3: Generate a hash value

[0182] Subject: Server

[0183] How it works: The server generates a hash value of the uploaded digital data based on the analysis results. The hash value is generated using the SHA-256 algorithm. The generated hash value is used as a unique identifier.

[0184] Input: Digital data

[0185] Output: hash value

[0186] Step 4: Send the hash value and analysis results to the distributed ledger network

[0187] Subject: Server

[0188] How it works: The server sends the generated hash value and analysis results to the distributed ledger network, which uses the received information to create a transaction and generate a unique identifier.

[0189] Input: hash value, analysis result

[0190] Output: A unique identifier

[0191] Step 5: Storing identifiers and analysis results and notifying the user

[0192] Subject: Server

[0193] Specific operation: The server receives the ID and analysis results from the distributed ledger network, stores them in the user's database, and then notifies the user of the identification results using Firebase Cloud Messaging or similar.

[0194] Input: unique identifier, analysis result

[0195] Output: Saved data, notification message

[0196] Through the above processing steps, the user can quickly and reliably confirm the authenticity of the digital data and receive highly reliable information.

[0197] 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.

[0198] The present invention is a system for verifying the authenticity of digital content, which further combines an emotion engine that recognizes user emotions and evaluates the reliability of the content using the analysis results. It includes the following main components: a means for uploading digital content, an identification AI means for analyzing the uploaded digital content, a hash generation means for verifying the authenticity of the digital content based on the analysis results, a blockchain connection means for transmitting the generated hash value and analysis results to a blockchain network and generating an identifier, a means for storing the identifier and analysis results returned from the blockchain network and notifying the user, and an emotion engine means for recognizing user emotions.

[0199] Program processing

[0200] Uploading Digital Content

[0201] A user logs in to the system using a terminal and uploads digital content (e.g., images or videos). The server temporarily stores the received content in storage and begins processing.

[0202] Content Analysis

[0203] The server sends the stored digital content to an identification AI means, which analyzes the digital content and determines whether it is AI-generated or live-action, using metadata checks, pattern recognition, and deep learning techniques.

[0204] Saving the identification results

[0205] The server receives and temporarily stores the analysis results from the identification AI, which include information on whether the digital content is AI-generated or live-action.

[0206] Generating a hash value

[0207] The server generates a hash value for the digital content. The hash generation module calculates the hash value for the content using an algorithm such as SHA-256.

[0208] Send to blockchain

[0209] The server sends the generated hash value and the analysis results of the identification AI to the blockchain network, where the smart contract in the blockchain network receives the information, generates a unique identifier, and records it on the chain.

[0210] Notifying users of the results

[0211] The blockchain network returns the generated identifier to the server, which associates and stores the identifier and analysis results in the user's database and notifies the user, who can then verify the authenticity of the digital content.

[0212] Emotion analysis and evaluation

[0213] An emotion engine is used to analyze a user's emotions toward uploaded digital content. As a user watches or browses digital content, the emotion engine analyzes the user's reactions in real time. The emotion engine utilizes facial recognition, voice analysis, and other biometric data.

[0214] Saving and utilizing sentiment results

[0215] The server receives the user's emotion results generated by the emotion engine and stores them in a database. The stored emotion results are used to evaluate the trustworthiness of digital content, allowing for further analysis and notifications based on the user's emotion results.

[0216] Specific examples

[0217] 1. Upload and analyze

[0218] Users upload videos taken from the scene of a natural disaster.

[0219] The server sends the video to an identification AI, which then analyzes the video.

[0220] The identification AI determines that the video is live action.

[0221] 2. Use of Emotion Engine

[0222] When a user watches an uploaded video, the emotion engine analyzes the user's reaction.

[0223] The analysis results indicate that the user felt a strong interest in and trust in the video.

[0224] 3. Identifier Generation and Notification

[0225] The server generates a hash value for the video and sends the hash value and analysis results to the blockchain network.

[0226] The blockchain network generates and records a unique identifier.

[0227] The server notifies the user of the identifier and the emotion engine analysis results, and the user confirms the authenticity and reliability of the video.

[0228] This invention allows users to easily verify the authenticity of digital content and further increases the trustworthiness of the content through evaluations using an emotion engine, thereby preventing the spread of false information and avoiding social unrest.

[0229] The processing flow will be explained below.

[0230] Step 1:

[0231] The user accesses the system's website or application using a terminal and logs in. They enter their authentication information to authenticate the user, and if authentication is successful, they are taken to the upload screen.

[0232] Step 2:

[0233] The user clicks the "Select File" button on the upload screen to select the digital content (image or video) file. After selecting the file, the user clicks the "Upload" button to send the file to the server.

[0234] Step 3:

[0235] The server receives the digital content sent by the user and temporarily stores it in storage. A message indicating successful storage is logged and a notification of upload completion is displayed to the user.

[0236] Step 4:

[0237] The server sends the stored digital content to the identification AI means, which loads the digital content and runs image or video analysis algorithms to determine whether it is AI-generated or live-action.

[0238] Step 5:

[0239] The identification AI completes the analysis and returns the results to the server, which associates the analysis results with the corresponding digital content entry and temporarily stores them.

[0240] Step 6:

[0241] The server generates a hash value for the digital content. The file path is passed to the hash generation module, which calculates the hash value using a hash algorithm such as SHA-256. The generated hash value is then sent back to the server.

[0242] Step 7:

[0243] The server compiles the hash value and the results of the AI's analysis and sends it to the blockchain network, where a smart contract receives the data, creates a transaction that generates a unique identifier, and records the information on the blockchain.

[0244] Step 8:

[0245] The blockchain network sends the generated identifier back to the server, which associates and stores this identifier and the analysis results in the user's database.

[0246] Step 9:

[0247] The server sends a notification to the user, which includes the identification result and details of the content certified on the blockchain, and is displayed on the user's device.

[0248] Step 10:

[0249] The server calculates the hash value of the digital content and notifies the user of the analysis results, including the identifier. At the same time, if a viewing operation by the user is detected, the emotion engine is activated. The emotion engine uses the user's camera and microphone data to perform facial recognition and voice analysis.

[0250] Step 11:

[0251] The emotion engine analyzes the user's emotions in real time and sends the results to the server, which stores the emotion data in association with the digital content entry.

[0252] Step 12:

[0253] The server sends a notification to the user and provides feedback on the results of the emotion analysis of the digital content they have viewed. By comparing their own emotions with the analysis results of the digital content, users can intuitively evaluate its reliability.

[0254] Step 13:

[0255] After users have finished rating the content they have viewed, the sentiment analysis results are reflected in the system's overall reliability score. The server uses the collected data to improve the accuracy of future analysis of similar content.

[0256] Through these detailed steps, the present invention achieves a comprehensive credibility assessment that takes into account not only the authenticity of digital content but also the user's emotional response, which is expected to enable users to use the content with peace of mind and prevent the spread of false information in society.

[0257] Example 2

[0258] 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."

[0259] There is a need for a way to easily verify the authenticity of digital content and further evaluate its reliability. In particular, to prevent the spread of false information and avoid social unrest, it is important not only to verify the authenticity of digital content, but also to evaluate its reliability based on user sentiment. However, current systems make it difficult to simultaneously verify the authenticity and evaluate the reliability of digital content. Furthermore, technology for analyzing user responses in real time is still in its infancy. Solving these problems is a challenge.

[0260] 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.

[0261] In this invention, the server includes means for uploading digital content, identification AI means for analyzing the uploaded digital content, hash generation means for verifying the authenticity of the digital content based on the analysis results, blockchain connection means for transmitting the generated hash value and the analysis results to the blockchain network and generating an identifier, means for storing the identifier and the analysis results returned from the blockchain network and notifying the user, and emotion engine means for analyzing the user's emotions toward the digital content, thereby enabling the authenticity of digital content to be verified and its reliability to be evaluated simultaneously.

[0262] "Digital content" is a general term for electronically generated or stored information such as images, video, audio, and text.

[0263] An "uploading means" is a mechanism by which a user transmits digital content from his or her own terminal to a server.

[0264] "Identification AI means" refers to artificial intelligence technology that analyzes uploaded digital content and determines its authenticity (e.g., whether it is AI-generated or live-action).

[0265] A "hash generator" is a mechanism that calculates a unique hash value from digital content to prevent tampering and verify the authenticity of the digital content.

[0266] The "blockchain connection means" is a mechanism for transmitting the generated hash value and analysis results to the blockchain network and recording them on the blockchain.

[0267] "Identifier" means a unique ID generated by the blockchain network and used to verify the authenticity of digital content and its analysis results.

[0268] "Emotion engine means" is a technology for analyzing users' emotional reactions to digital content and collecting and evaluating the results.

[0269] "User authentication means" is a mechanism for verifying the identity of a user before using the system and preventing fraudulent use.

[0270] A "database" is a system that structurally stores and manages analysis results, hash values, identifiers, and user emotion data.

[0271] "Authenticity assessment" is the process of assessing the authenticity of digital content and its trustworthiness based on users' emotional responses.

[0272] The present invention provides a system for verifying the authenticity of digital content and evaluating its reliability by recognizing user emotions. The system includes a means for uploading digital content, an AI identification means, a hash generation means, a blockchain connection means, an emotion engine means, and a means for notifying users.

[0273] A user logs in to the system using a terminal and uploads digital content (e.g., images or videos). The server temporarily stores the received content in storage (e.g., AWS S3 or Google Cloud Storage) and begins processing. User authentication is used to prevent fraudulent use.

[0274] The server then sends the stored digital content to an AI identification method. The AI ​​identification method (using TensorFlow or PyTorch, for example) determines whether the digital content is AI-generated or live-action. Specifically, it uses metadata checks, pattern recognition, and deep learning techniques. The analysis results are temporarily stored in a database (e.g., MySQL or PostgreSQL).

[0275] The server then generates a hash value for the digital content, using a library such as the OpenSSL library with the SHA-256 algorithm, and stores the hash value in a database.

[0276] The generated hash value and the analysis results of the identification AI are sent from the server to a blockchain network (e.g., Ethereum or Hyperledger Fabric). A smart contract on the blockchain network receives the information, generates and records a unique identifier. The identifier is sent back to the server and stored in a database along with the analysis results.

[0277] The server then sends a notification to the user, who can then confirm the authenticity of the digital content. Furthermore, as the user watches or reads the digital content, the system uses an emotion engine (e.g., FaceAPI or IBM Watson) to analyze the user's emotions in real time. The emotion results are stored in a database and used for authenticity assessment.

[0278] As a concrete example, consider a scenario in which a user uploads a video taken at the site of a natural disaster. The server sends the video to an identification AI, which analyzes the video. If the identification AI determines that the video is live action, the analysis result and the generated hash value are sent to the blockchain, and a unique identifier is generated. The server notifies the user of the identifier and the emotion engine's analysis results, and the user confirms the authenticity and reliability of the video.

[0279] An example of a prompt might be:

[0280] "Upload videos taken from natural disaster sites and verify their authenticity using our identification AI and emotion engine."

[0281] As described above, this invention allows users to easily verify the authenticity of digital content, and furthermore, through evaluations using an emotion engine, it is possible to increase the trustworthiness of the content. This system is effective as a means of preventing the spread of false information and avoiding social unrest.

[0282] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0283] Step 1: Log in and upload content

[0284] (Specific explanation)

[0285] A user logs in to the system using a terminal. The user accesses the system and enters their username and password in the login form. The server verifies the user's authentication information and starts a session. After successful login, the user clicks the "Upload" button and selects an image or video file. The terminal sends the selected file to the server. The server temporarily stores the received digital content in storage and logs the storage information.

[0286] (input)

[0287] Usernames, passwords, digital content files (images and videos)

[0288] (Data processing / data calculation)

[0289] The user's authentication information is checked against the database and the file is saved to storage.

[0290] (output)

[0291] Authentication result, saved file path

[0292] Step 2: Analyzing the content

[0293] (Specific explanation)

[0294] The server sends the stored digital content to the identification AI means. The identification AI means receives the uploaded digital content and analyzes whether the content is AI-generated or live-action using metadata verification and pattern recognition algorithms. This analysis uses deep learning techniques, such as TensorFlow or PyTorch models. The identification AI then sends the analysis results back to the server.

[0295] (input)

[0296] File path of the stored digital content

[0297] (Data processing / data calculation)

[0298] Content metadata checks, pattern recognition, and deep learning analysis

[0299] (output)

[0300] Analysis results (determination of whether it is AI-generated or live-action)

[0301] Step 3: Save the analysis results

[0302] (Specific explanation)

[0303] The server receives the analysis results from the identification AI and temporarily stores them in a database. The database associates the content file path with the analysis results and stores them in a database such as MySQL or PostgreSQL.

[0304] (input)

[0305] Analysis results, file paths of digital content

[0306] (Data processing / data calculation)

[0307] Store data in a database and associate content with analysis results.

[0308] (output)

[0309] Saved entries in the database

[0310] Step 4: Generate a hash value

[0311] (Specific explanation)

[0312] The server generates a hash value for the digital content, for example, using the SHA-256 algorithm with the OpenSSL library, and stores the hash value in a database.

[0313] (input)

[0314] Digital content file path

[0315] (Data processing / data calculation)

[0316] A hash value is calculated using the SHA-256 algorithm.

[0317] (output)

[0318] Generated hash value

[0319] Step 5: Send to the blockchain

[0320] (Specific explanation)

[0321] The server sends the generated hash value and the analysis result of the identification AI to the blockchain network (for example, using Ethereum or Hyperledger Fabric). The blockchain network's smart contract receives the information, generates a unique identifier, records it on the chain, and sends this identifier back to the server.

[0322] (input)

[0323] Hash value, analysis results

[0324] (Data processing / data calculation)

[0325] A smart contract is invoked to generate an identifier and record it on-chain.

[0326] (output)

[0327] The identifier returned from the blockchain

[0328] Step 6: Notify users of the results

[0329] (Specific explanation)

[0330] The server associates the identifier and the analysis results with the user's database and stores them. The server then sends the user an email or system notification, allowing them to verify the authenticity of the digital content.

[0331] (input)

[0332] Identifiers, analysis results, user information (email address, etc.)

[0333] (Data processing / data calculation)

[0334] The identifier and analysis results are stored in a database and notifications are generated and sent.

[0335] (output)

[0336] Notifications sent, updated entries in the database

[0337] Step 7: Emotion analysis and evaluation

[0338] (Specific explanation)

[0339] When a user watches or browses digital content, an emotion engine is used to analyze the user's emotions in real time based on facial recognition, voice analysis, and other biometric data, for example, using FaceAPI or IBM Watson.

[0340] (input)

[0341] User biometric data (facial expression data, voice data, etc.)

[0342] (Data processing / data calculation)

[0343] It performs real-time sentiment analysis and generates user sentiment results.

[0344] (output)

[0345] Emotion analysis results

[0346] Step 8: Storing and utilizing sentiment results

[0347] (Specific explanation)

[0348] The server receives the user's emotion results generated by the emotion engine and stores them in a database. The stored emotion results are used to evaluate the trustworthiness of digital content. Feedback and notifications are also provided to the user as needed.

[0349] (input)

[0350] Emotion analysis results

[0351] (Data processing / data calculation)

[0352] The sentiment results are stored in a database and used for reliability evaluation.

[0353] (output)

[0354] Save entries in the database, notifications

[0355] (Application example 2)

[0356] 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."

[0357] As digital content rapidly increases, it is becoming more difficult to verify its authenticity and reliability. In particular, the spread of AI-generated content and fake news has become a problem, creating a need to provide an environment where users can safely use content. Furthermore, there are insufficient means to analyze users' emotional reactions and evaluate the reliability of content. To solve these issues, a system is needed that can verify the authenticity of digital content and analyze user emotions to increase its reliability.

[0358] 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 digital content, identification AI means for analyzing the uploaded digital content, hash generation means for verifying the authenticity of the digital content based on the analysis results, blockchain connection means for transmitting the generated hash value and the analysis results to the blockchain network and generating an identifier, means for saving the identifier and analysis results returned from the blockchain network and notifying the user, emotion engine means for recognizing the user's emotions and evaluating the trustworthiness of the content based on the analysis results, and means for saving the emotion results recognized by the emotion engine means in a database and using them for trustworthiness evaluation. This makes it possible to verify the authenticity of digital content and evaluate trustworthiness through user emotion analysis.

[0359] "Digital content" refers to content stored or distributed in electronic form, including images, videos, audio files, and text.

[0360] "Uploading" refers to the act of a user sending digital content from their own device to a server or cloud service.

[0361] "Discrimination AI" refers to a system that uses artificial intelligence technology to analyze digital content and determine whether it is live-action or AI-generated.

[0362] A "hash generator" refers to an algorithm or process for generating a unique hash value based on the content of digital content. A typical algorithm is SHA-256.

[0363] "Blockchain" is a network that uses distributed ledger technology, and refers to a technology that can prevent tampering while ensuring the reliability and transparency of data.

[0364] "Identifier" refers to an ID or code that is uniquely assigned to data recorded on a blockchain network.

[0365] "Emotion Engine Means" refers to technology for recognizing and analyzing a user's emotions, including facial recognition, voice analysis, and other biometric data analysis technologies.

[0366] A "database" refers to a system for efficiently storing, managing, and searching large amounts of data.

[0367] "User authentication" refers to the means used to verify that a user has the appropriate authority to access a system, typically including username, password, and two-factor authentication.

[0368] "Authenticity" refers to the fact that digital content has not been tampered with and that its origin and creation process are legitimate.

[0369] "Credibility" refers to the degree to which users can trust the content and its source.

[0370] This invention is a system for verifying the authenticity of digital content and assessing its trustworthiness by analyzing user sentiment. The system includes the following components:

[0371] 1. Digital Content Upload Method:

[0372] A user logs in to the system using a terminal and uploads digital content (e.g., images or videos). The server temporarily stores the received content in storage and begins processing.

[0373] 2. Content analysis method (identification AI method):

[0374] The server sends the stored digital content to an identification AI means, which analyzes the digital content and determines whether it is AI-generated or live-action, using metadata checks, pattern recognition, and deep learning techniques.

[0375] 3. Hash generation method:

[0376] The server generates a hash value for the digital content. The hash generation module calculates the hash value for the content using an algorithm such as SHA-256.

[0377] 4. Blockchain connection methods:

[0378] The server sends the generated hash value and the analysis results of the identification AI to the blockchain network, where the smart contract in the blockchain network receives the information, generates a unique identifier, and records it on the chain.

[0379] 5. How to notify users of results:

[0380] The blockchain network returns the generated identifier to the server, which associates and stores the identifier and analysis results in the user's database and notifies the user, who can then verify the authenticity of the digital content.

[0381] 6. Emotion Engine Means:

[0382] As users watch or browse digital content, the emotion engine analyzes their reactions in real time, using facial recognition, voice analysis and other biometric data.

[0383] 7. Storing and utilizing emotion results:

[0384] The server receives the user's emotion results generated by the emotion engine and stores them in a database. The stored emotion results are used to evaluate the trustworthiness of digital content, allowing for further analysis and notifications based on the user's emotion results.

[0385] Specific flow of usage example

[0386] A user uploads a video taken at the scene of a natural disaster. The server sends the video to an identification AI, which analyzes it. As a result, the video is determined to be live action. The server then generates a hash value for the video and sends the generated hash value and analysis results to the blockchain network. The blockchain network generates a unique identifier and records it on the chain. The blockchain network then returns the generated identifier to the server, which stores the identifier and analysis results in the user's database and notifies the user. Furthermore, while the user is watching the video, an emotion engine analyzes the user's reaction and generates an emotion result. The server receives the result, stores it in a database, and uses it to evaluate the reliability of content.

[0387] Prompt Sentence Examples

[0388] "Analyze emotions while users watch video news from the scene of a natural disaster."

[0389] The above is a specific embodiment of the present invention.

[0390] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0391] Step 1: Upload your digital content

[0392] A user uploads digital content (e.g., a video file) from their own device to the system. The user selects the content and presses the upload button. The input is the uploaded digital content, and the output is the content file stored on the server. The server temporarily stores the received content in storage and passes the destination path to the next processing step.

[0393] Step 2: Analyzing the content

[0394] The server sends the stored digital content to the identification AI means, which analyzes the content using a deep learning model. It checks metadata and performs image analysis to determine whether the content is live-action or AI-generated. The input is the digital content in the destination path, and the output is the AI ​​determination result (live-action or AI-generated).

[0395] Step 3: Generate a hash value

[0396] The server generates a hash value for the content. It uses the SHA-256 algorithm to calculate a unique hash value for the digital content. The input is the digital content in the destination path, and the output is the generated hash value. The hash value is used in the next step.

[0397] Step 4: Send to the blockchain

[0398] The server sends the generated hash value and the analysis result of the identification AI to the blockchain network. The blockchain network's smart contract receives the information, generates a unique identifier, and records it on the chain. The input is the hash value and the AI's judgment result, and the output is the identifier generated by the blockchain network.

[0399] Step 5: Notify users of the results

[0400] The server stores the identifier returned from the blockchain network in a database and notifies the user. The notification includes the identifier and the AI ​​judgment result, allowing the user to verify the authenticity of the content. The input is the identifier and the AI ​​judgment result, and the output is a notification to the user. The notification is sent as an email or an internal system message.

[0401] Step 6: Emotion engine analysis

[0402] While a user is viewing digital content, the emotion engine analyzes the user's reactions in real time. It uses facial recognition, voice analysis, and biometric data to estimate the user's emotions. The input is the user's reaction data while viewing, and the output is the analyzed emotion results.

[0403] Step 7: Storing and utilizing sentiment results

[0404] The server stores the emotion results generated by the emotion engine in a database. The stored emotion results are used to evaluate the trustworthiness of digital content. The input is the analyzed emotion results, and the output is the data stored in the database. For security reasons, the stored data is encrypted.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] [Second embodiment]

[0409] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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).

[0415] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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."

[0421] The present invention is a system for verifying the authenticity of digital content, and includes the following main components: means for uploading digital content, identification AI means for analyzing the uploaded digital content, hash generation means for verifying the authenticity of the digital content based on the analysis results, blockchain connection means for transmitting the generated hash value and analysis results to a blockchain network and generating an identifier, and means for storing the identifier and analysis results returned from the blockchain network and notifying the user.

[0422] Program processing

[0423] Uploading Digital Content

[0424] A user uses a terminal to upload digital content (e.g., images or videos) to the system. Files are selected through the upload interface and sent to the server, which temporarily stores the received content and begins processing it.

[0425] Content Analysis

[0426] The server sends the stored digital content to an AI that analyzes the received content and determines whether it is AI-generated or live-action. The analysis algorithms include image and video metadata checks, pattern recognition, and deep learning-based analysis techniques.

[0427] Saving the identification results

[0428] The server receives the analysis results from the identification AI and temporarily stores them, including information on whether the digital content is AI-generated or live-action.

[0429] Generating a hash value

[0430] The server generates a hash value of the digital content. The hash generation module calculates the hash value of the content using a hash algorithm such as SHA-256.

[0431] Send to blockchain

[0432] The server sends the generated hash value and analysis results to the blockchain network, which then creates a transaction based on the received information and generates a unique identifier for the digital content, which is then recorded on the blockchain.

[0433] Notifying users of the results

[0434] The server receives the identifier and analysis results returned from the blockchain network and stores them in the user's database. The server then notifies the user, who can then view the results of the content's authenticity verification.

[0435] Specific examples

[0436] 1. Upload and analyze

[0437] Users upload videos they have taken from the scene of a natural disaster.

[0438] The server sends the video to an identification AI, which then analyzes the video.

[0439] The identification AI determines that the video is live action.

[0440] 2. Identifier Generation

[0441] The server generates a hash value for the video.

[0442] The hash value and analysis results are sent to the blockchain network.

[0443] The blockchain network generates a unique identifier and records authenticity information.

[0444] 3. Notice and Confirmation

[0445] The server notifies the user of the identifier and the AI's judgment result.

[0446] The user confirms the notification and confirms that the video is live and its authenticity is verified by the blockchain.

[0447] This invention allows users to easily verify the authenticity of digital content, preventing the spread of false information and avoiding social unrest. Furthermore, by utilizing blockchain technology, it becomes possible to prove the authenticity of content with a high degree of reliability.

[0448] The processing flow will be explained below.

[0449] Step 1:

[0450] The user accesses the system's website or application using a terminal and logs in. They enter their authentication information to authenticate the user, and if authentication is successful, they are taken to the upload screen.

[0451] Step 2:

[0452] The user clicks the "Select File" button on the upload screen to select the digital content (image or video) file. After selecting the file, the user clicks the "Upload" button to send the file to the server.

[0453] Step 3:

[0454] The server receives the digital content sent by the user and temporarily stores it in storage. A message indicating successful storage is logged and a notification of upload completion is displayed to the user.

[0455] Step 4:

[0456] The server sends the stored digital content to the identification AI module, which loads the digital content and runs image or video analysis algorithms to determine whether it is AI-generated or live-action.

[0457] Step 5:

[0458] The identification AI completes the analysis and returns the results to the server, which associates the analysis results with the corresponding digital content entry and temporarily stores them.

[0459] Step 6:

[0460] The server generates a hash value for the digital content. The file path is passed to the hash generation module, which calculates the hash value using a hash algorithm such as SHA-256. The generated hash value is then sent back to the server.

[0461] Step 7:

[0462] The server compiles the hash value and the results of the AI's analysis and sends it to the blockchain network, where a smart contract receives the data, creates a transaction that generates a unique identifier, and records the information on the blockchain.

[0463] Step 8:

[0464] The blockchain network sends the generated identifier back to the server, which associates and stores this identifier and the analysis results in the user's database.

[0465] Step 9:

[0466] The server sends a notification to the user, which includes the identification result and details of the content certified on the blockchain, and is displayed on the user's device.

[0467] Step 10:

[0468] Users can check the notification and confirm that the digital content is authentic and its authenticity is verified by the blockchain, allowing them to use the content with confidence.

[0469] Example 1

[0470] 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."

[0471] In modern society, counterfeiting and tampering of digital content is on the rise, making it important to verify its authenticity. However, conventional methods lack the means to verify the authenticity of digital content with high accuracy. Another problem is the lack of a system that allows users to easily verify the authenticity of content. To solve these issues, it is necessary to analyze digital content and provide the results to users in a reliable manner.

[0472] 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.

[0473] In this invention, the server includes: means for a user to select and upload information; identification AI means for temporarily storing and analyzing the uploaded information; means for storing analysis results received from the identification AI; means for generating a hash value of the digital content; blockchain connection means for transmitting the generated hash value and analysis results to a blockchain network and generating a unique identifier; and means for storing the identifier and analysis results returned from the blockchain network and notifying the user. This makes it possible to verify the authenticity of digital content with high accuracy and reliability and quickly provide the results to the user.

[0474] "User" means a person or entity that accesses the system to upload digital content and receive analysis results thereof.

[0475] "Information" refers to digital content (e.g., images, videos) uploaded by users in this system.

[0476] "Uploading" is the act of a user using a terminal to send digital content to a server.

[0477] "Server" means a central computer system that receives and stores digital content sent by users, analyzes it, generates hashes, connects it to the blockchain, and notifies users.

[0478] "Identification AI means" refers to an artificial intelligence system that analyzes uploaded digital content and determines its authenticity (e.g., whether it is AI-generated or live-action).

[0479] A "hash value" is a fixed-length string that indicates the uniqueness of digital content and is generated using a hash algorithm such as SHA-256.

[0480] A "blockchain network" is a distributed network consisting of multiple computers, and is a system that records transactions and generates identifiers for digital content.

[0481] "Identifier" means a unique ID generated by the blockchain network to indicate the authenticity of digital content.

[0482] "Notification to user" refers to the act of the server communicating the identifier and analysis results received from the blockchain network to the user.

[0483] A "database" is an information system for structuring, storing, and managing information such as analysis results and identifiers.

[0484] The present invention relates to a system for verifying the authenticity of digital content, which allows users to upload information (e.g., images or videos), analyzes its authenticity, and provides the results in a reliable manner.

[0485] The user uploads the digital content to the system using a terminal, accesses the upload interface of the system from the terminal's browser, and sends the selected digital content to the server, which temporarily stores the received content and prepares it for analysis by the identification AI means.

[0486] The server sends the stored digital content to the identification AI means, which analyzes the content using metadata verification, pattern recognition, and deep learning techniques to determine whether it is AI-generated or live-action. The analysis result is sent back to the server as either "AI-generated" or "live-action."

[0487] The server receives the analysis results sent from the identification AI means and temporarily stores them. The server then generates a hash value for the digital content. This hash value is generated using a hash algorithm such as SHA-256.

[0488] The generated hash value and analysis result are sent from the server to the blockchain network, which creates a transaction based on the received information and generates a unique identifier, which is recorded on the blockchain and sent back to the server.

[0489] Finally, the server receives the identifier and analysis results returned from the blockchain network and stores them in the user's database. The server then notifies the user, who can then check the results within the system, enabling high-precision and high-reliability verification of the authenticity of digital content.

[0490] Specific examples

[0491] Upload and analyze

[0492] 1. Users upload videos taken from the scene of a natural disaster.

[0493] 2. The server sends the video to the identification AI means, which analyzes the video.

[0494] 3. The identification AI method determines that the video is live action.

[0495] Identifier Generation

[0496] 1. The server generates a hash value for the video.

[0497] 2. Send the hash value and analysis results to the blockchain network.

[0498] 3. The blockchain network generates a unique identifier and records the authenticity information.

[0499] Notification and confirmation

[0500] 1. The server notifies the user of the identifier and the AI's judgment result.

[0501] 2. The user checks the notification and confirms that the video is live and its authenticity is verified by the blockchain.

[0502] Prompt Sentence Examples

[0503] "Please determine whether the following digital content is AI-generated or live-action, and register the result and its hash value on the blockchain. After the hash value and the result of your determination are returned, please display the final confirmation result."

[0504] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0505] Step 1: User selects and uploads digital content

[0506] The user accesses the system's upload interface from the browser on their device. Here, they select the target digital content (e.g., images or videos). When the user clicks the upload button, the selected digital content is sent from the device to the server. The input is the digital content selected by the user, and the output is the digital content received by the server.

[0507] Step 2: The server stores the digital content

[0508] The server temporarily stores the received digital content. Specifically, it is stored as data in the server's storage. This temporary storage is a preparatory step for subsequent analysis processing. The input is the digital content sent to the server, and the output is the digital content stored in the server's storage.

[0509] Step 3: The server sends the digital content to the identification AI means

[0510] The server transmits the stored digital content to the identification AI means, which often runs on a separate server or cloud service. This transmission process passes the digital content information required for analysis to the identification AI means. The input is the temporarily stored digital content, and the output is the digital content transmitted to the identification AI means.

[0511] Step 4: Identification AI methods analyze the digital content

[0512] The identification AI means analyzes the received digital content. This analysis includes checking metadata, pattern recognition, and analysis using deep learning techniques. The analysis results in information that identifies whether the digital content is AI-generated or live-action. The input is the digital content sent to the identification AI means, and the output is the analysis result, such as "AI-generated" or "live-action."

[0513] Step 5: The server receives and stores the analysis results

[0514] The server receives the analysis results sent from the identification AI means. These results are temporarily stored. The analysis results are used for subsequent hash value generation and blockchain transmission processing. The input is the analysis results sent from the identification AI means, and the output is the analysis results stored in the server's storage.

[0515] Step 6: The server generates a hash value for the digital content.

[0516] The server generates a hash value based on the stored digital content. It uses a hash algorithm such as SHA-256 to calculate a unique hash value for the digital content. This hash value is used to detect data tampering. The input is the digital content stored in the server's storage, and the output is the generated hash value.

[0517] Step 7: The server sends the hash value and analysis result to the blockchain network.

[0518] The server sends the generated hash value and the analysis result to the blockchain network, which then creates a transaction and generates a unique identifier. The input is the generated hash value and the analysis result, and the output is the identifier generated by the blockchain network.

[0519] Step 8: The server receives and stores the identifier and analysis results.

[0520] The server receives the identifier and analysis results returned from the blockchain network. This information is stored in the user's database. The input is the identifier and analysis results returned from the blockchain network, and the output is the identifier and analysis results stored in the user's database.

[0521] Step 9: Server notifies the user

[0522] The server notifies the user. This notification includes the analysis result of the identification AI method and the identifier generated by the blockchain network. The user receives the notification and checks the result within the system. The input is the identifier stored in the user's database and the analysis result, and the output is a notification to the user.

[0523] (Application example 1)

[0524] 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."

[0525] The circulation of digital data in modern society is rapidly increasing, but the inclusion of false information and falsified content within this data is causing problems, resulting in a loss of user trust. Furthermore, there is a lack of means to quickly and reliably verify the authenticity of digital data and prove its reliability using distributed ledger technology. Given this background, there is a need for a system that increases the reliability of digital data and allows users to share and use information with peace of mind.

[0526] 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.

[0527] In this invention, the server includes means for transmitting digital data, identification artificial intelligence means for analyzing the transmitted digital data, hash value generation means for verifying the authenticity of the digital data based on the analysis results, distributed ledger connection means for transmitting the generated hash value and analysis results to the distributed ledger network and generating an identifier, and means for storing the identifier and analysis results returned from the distributed ledger network and notifying the user. This enables the authenticity of digital data to be verified quickly and with high reliability, and enables the provision of reliable information to users.

[0528] "Digital data" is a general term for information and content handled on computers and digital devices, and includes images, videos, audio, text data, etc.

[0529] "Transmission" is the act of transferring digital data from one place to another, often over a network or communication line.

[0530] "Identification artificial intelligence means" refers to artificial intelligence technology trained to analyze the characteristics of digital data and determine its generation method and authenticity.

[0531] A "hash value generation method" is a technology that generates a fixed-length string (hash value) from digital data, and is used to verify data integrity and prevent tampering.

[0532] A "distributed ledger network" is a ledger technology in which data blocks are stored in a linked chain format, and is characterized by being managed in a decentralized manner across multiple nodes.

[0533] "Distributed ledger connection means" refers to technology for transmitting hash values ​​and analysis results of digital data to a distributed ledger network and generating records and identifiers on that network.

[0534] An "identifier" refers to a unique string of characters or code generated by a distributed ledger network to prove the uniqueness and authenticity of digital data.

[0535] "Means for notifying users" is a general term for technologies and methods for informing users of the authenticity and identification results of digital data, and includes push notifications, emails, messaging services, etc.

[0536] "User authentication means" refers to a technology or method for verifying a user's identity and authority before sending or receiving digital data.

[0537] "Data storage" refers to a storage device or location for storing analysis results, hash values, identifiers, etc.

[0538] To implement this invention, a user-operated terminal (such as a smartphone or computer) and an associated server are required. The server operates as a system including the following main components:

[0539] 1. Digital Data Transmission

[0540] A user uses a device to send digital data (e.g., videos, images) to a server. At this time, user authentication is performed on the device. A general authentication service is used for user authentication. For example, Firebase Authentication is used for login and authentication.

[0541] 2. Analysis of transmitted digital data

[0542] The server temporarily stores the received digital data and sends it to an AI identification means (e.g., TensorFlow or OpenCV-based AI identification), which analyzes the received data and determines whether it was actually photographed or generated by AI.

[0543] 3. Generating a hash value

[0544] The server generates a hash value of the analyzed digital data using the SHA-256 algorithm, which provides a unique identifier for the digital data.

[0545] 4. Sending to the distributed ledger network

[0546] The server sends the generated hash value and analysis results to a distributed ledger network (e.g., the Ethereum blockchain), which creates a transaction based on the received information and generates a unique identifier, which is then recorded in the distributed ledger.

[0547] 5. Notice to Users

[0548] The server receives the identifier and analysis results returned from the distributed ledger network and stores them in the user's data storage (e.g., a database). The server then notifies the user of the results using a notification service such as Firebase Cloud Messaging.

[0549] Specific examples

[0550] For example, consider the case where a user uploads a video taken at the site of a natural disaster. The user shoots the video using a smartphone and sends it to a server through an application. The sent video is analyzed by an identification AI and determined to be live footage. The server generates a hash value for the video and sends it to the distributed ledger network. The blockchain generates a unique identifier based on the hash value and sends it back to the server. Finally, the server notifies the user of the identifier and the analysis results, allowing the user to verify the authenticity of the uploaded video.

[0551] Example prompts for generative AI models

[0552] I uploaded a video taken at the scene of a natural disaster and want to verify its authenticity.

[0553] These steps allow users to ensure the authenticity of digital data and build a system that can prove its reliability using distributed ledger technology.

[0554] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0555] Step 1: User authentication and digital data transmission

[0556] Subject: User

[0557] Specific operation: A user logs in using an application on their device. Firebase Authentication can be used for login. If authentication is successful, the user selects digital data (e.g., a video) and clicks the send button. At that time, the device makes a request to send the selected file to the server.

[0558] Input: User login information, digital data (file)

[0559] Output: Authentication token, digital data transmission to server

[0560] Step 2: Temporarily store digital data and analyze it using AI

[0561] Subject: Server

[0562] How it works: The server receives the digital data and temporarily stores it in storage. The stored data is then sent to a classification AI (using TensorFlow and OpenCV). The classification AI analyzes the received data and determines whether it is real or AI-generated.

[0563] Input: Digital data

[0564] Output: Analysis results (real-life or AI-generated)

[0565] Step 3: Generate a hash value

[0566] Subject: Server

[0567] How it works: The server generates a hash value of the uploaded digital data based on the analysis results. The hash value is generated using the SHA-256 algorithm. The generated hash value is used as a unique identifier.

[0568] Input: Digital data

[0569] Output: hash value

[0570] Step 4: Send the hash value and analysis results to the distributed ledger network

[0571] Subject: Server

[0572] How it works: The server sends the generated hash value and analysis results to the distributed ledger network, which uses the received information to create a transaction and generate a unique identifier.

[0573] Input: hash value, analysis result

[0574] Output: A unique identifier

[0575] Step 5: Storing identifiers and analysis results and notifying the user

[0576] Subject: Server

[0577] Specific operation: The server receives the ID and analysis results from the distributed ledger network, stores them in the user's database, and then notifies the user of the identification results using Firebase Cloud Messaging or similar.

[0578] Input: unique identifier, analysis result

[0579] Output: Saved data, notification message

[0580] Through the above processing steps, the user can quickly and reliably confirm the authenticity of the digital data and receive highly reliable information.

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

[0582] The present invention is a system for verifying the authenticity of digital content, which further combines an emotion engine that recognizes user emotions and evaluates the reliability of the content using the analysis results. It includes the following main components: a means for uploading digital content, an identification AI means for analyzing the uploaded digital content, a hash generation means for verifying the authenticity of the digital content based on the analysis results, a blockchain connection means for transmitting the generated hash value and analysis results to a blockchain network and generating an identifier, a means for storing the identifier and analysis results returned from the blockchain network and notifying the user, and an emotion engine means for recognizing user emotions.

[0583] Program processing

[0584] Uploading Digital Content

[0585] A user logs in to the system using a terminal and uploads digital content (e.g., images or videos). The server temporarily stores the received content in storage and begins processing.

[0586] Content Analysis

[0587] The server sends the stored digital content to an identification AI means, which analyzes the digital content and determines whether it is AI-generated or live-action, using metadata checks, pattern recognition, and deep learning techniques.

[0588] Saving the identification results

[0589] The server receives and temporarily stores the analysis results from the identification AI, which include information on whether the digital content is AI-generated or live-action.

[0590] Generating a hash value

[0591] The server generates a hash value for the digital content. The hash generation module calculates the hash value for the content using an algorithm such as SHA-256.

[0592] Send to blockchain

[0593] The server sends the generated hash value and the analysis results of the identification AI to the blockchain network, where the smart contract in the blockchain network receives the information, generates a unique identifier, and records it on the chain.

[0594] Notifying users of the results

[0595] The blockchain network returns the generated identifier to the server, which associates and stores the identifier and analysis results in the user's database and notifies the user, who can then verify the authenticity of the digital content.

[0596] Emotion analysis and evaluation

[0597] An emotion engine is used to analyze a user's emotions toward uploaded digital content. As a user watches or browses digital content, the emotion engine analyzes the user's reactions in real time. The emotion engine utilizes facial recognition, voice analysis, and other biometric data.

[0598] Saving and utilizing sentiment results

[0599] The server receives the user's emotion results generated by the emotion engine and stores them in a database. The stored emotion results are used to evaluate the trustworthiness of digital content, allowing for further analysis and notifications based on the user's emotion results.

[0600] Specific examples

[0601] 1. Upload and analyze

[0602] Users upload videos taken from the scene of a natural disaster.

[0603] The server sends the video to an identification AI, which then analyzes the video.

[0604] The identification AI determines that the video is live action.

[0605] 2. Use of Emotion Engine

[0606] When a user watches an uploaded video, the emotion engine analyzes the user's reaction.

[0607] The analysis results indicate that the user felt a strong interest in and trust in the video.

[0608] 3. Identifier Generation and Notification

[0609] The server generates a hash value for the video and sends the hash value and analysis results to the blockchain network.

[0610] The blockchain network generates and records a unique identifier.

[0611] The server notifies the user of the identifier and the emotion engine analysis results, and the user confirms the authenticity and reliability of the video.

[0612] This invention allows users to easily verify the authenticity of digital content and further increases the trustworthiness of the content through evaluations using an emotion engine, thereby preventing the spread of false information and avoiding social unrest.

[0613] The processing flow will be explained below.

[0614] Step 1:

[0615] The user accesses the system's website or application using a terminal and logs in. They enter their authentication information to authenticate the user, and if authentication is successful, they are taken to the upload screen.

[0616] Step 2:

[0617] The user clicks the "Select File" button on the upload screen to select the digital content (image or video) file. After selecting the file, the user clicks the "Upload" button to send the file to the server.

[0618] Step 3:

[0619] The server receives the digital content sent by the user and temporarily stores it in storage. A message indicating successful storage is logged and a notification of upload completion is displayed to the user.

[0620] Step 4:

[0621] The server sends the stored digital content to the identification AI means, which loads the digital content and runs image or video analysis algorithms to determine whether it is AI-generated or live-action.

[0622] Step 5:

[0623] The identification AI completes the analysis and returns the results to the server, which associates the analysis results with the corresponding digital content entry and temporarily stores them.

[0624] Step 6:

[0625] The server generates a hash value for the digital content. The file path is passed to the hash generation module, which calculates the hash value using a hash algorithm such as SHA-256. The generated hash value is then sent back to the server.

[0626] Step 7:

[0627] The server compiles the hash value and the results of the AI's analysis and sends it to the blockchain network, where a smart contract receives the data, creates a transaction that generates a unique identifier, and records the information on the blockchain.

[0628] Step 8:

[0629] The blockchain network sends the generated identifier back to the server, which associates and stores this identifier and the analysis results in the user's database.

[0630] Step 9:

[0631] The server sends a notification to the user, which includes the identification result and details of the content certified on the blockchain, and is displayed on the user's device.

[0632] Step 10:

[0633] The server calculates the hash value of the digital content and notifies the user of the analysis results, including the identifier. At the same time, if a viewing operation by the user is detected, the emotion engine is activated. The emotion engine uses the user's camera and microphone data to perform facial recognition and voice analysis.

[0634] Step 11:

[0635] The emotion engine analyzes the user's emotions in real time and sends the results to the server, which stores the emotion data in association with the digital content entry.

[0636] Step 12:

[0637] The server sends a notification to the user and provides feedback on the results of the emotion analysis of the digital content they have viewed. By comparing their own emotions with the analysis results of the digital content, users can intuitively evaluate its reliability.

[0638] Step 13:

[0639] After users have finished rating the content they have viewed, the sentiment analysis results are reflected in the system's overall reliability score. The server uses the collected data to improve the accuracy of future analysis of similar content.

[0640] Through these detailed steps, the present invention achieves a comprehensive credibility assessment that takes into account not only the authenticity of digital content but also the user's emotional response, which is expected to enable users to use the content with peace of mind and prevent the spread of false information in society.

[0641] Example 2

[0642] 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."

[0643] There is a need for a way to easily verify the authenticity of digital content and further evaluate its reliability. In particular, to prevent the spread of false information and avoid social unrest, it is important not only to verify the authenticity of digital content, but also to evaluate its reliability based on user sentiment. However, current systems make it difficult to simultaneously verify the authenticity and evaluate the reliability of digital content. Furthermore, technology for analyzing user responses in real time is still in its infancy. Solving these problems is a challenge.

[0644] 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.

[0645] In this invention, the server includes means for uploading digital content, identification AI means for analyzing the uploaded digital content, hash generation means for verifying the authenticity of the digital content based on the analysis results, blockchain connection means for transmitting the generated hash value and the analysis results to the blockchain network and generating an identifier, means for storing the identifier and the analysis results returned from the blockchain network and notifying the user, and emotion engine means for analyzing the user's emotions toward the digital content, thereby enabling the authenticity of digital content to be verified and its reliability to be evaluated simultaneously.

[0646] "Digital content" is a general term for electronically generated or stored information such as images, video, audio, and text.

[0647] An "uploading means" is a mechanism by which a user transmits digital content from his or her own terminal to a server.

[0648] "Identification AI means" refers to artificial intelligence technology that analyzes uploaded digital content and determines its authenticity (e.g., whether it is AI-generated or live-action).

[0649] A "hash generator" is a mechanism that calculates a unique hash value from digital content to prevent tampering and verify the authenticity of the digital content.

[0650] The "blockchain connection means" is a mechanism for transmitting the generated hash value and analysis results to the blockchain network and recording them on the blockchain.

[0651] "Identifier" means a unique ID generated by the blockchain network and used to verify the authenticity of digital content and its analysis results.

[0652] "Emotion engine means" is a technology for analyzing users' emotional reactions to digital content and collecting and evaluating the results.

[0653] "User authentication means" is a mechanism for verifying the identity of a user before using the system and preventing fraudulent use.

[0654] A "database" is a system that structurally stores and manages analysis results, hash values, identifiers, and user emotion data.

[0655] "Authenticity assessment" is the process of assessing the authenticity of digital content and its trustworthiness based on users' emotional responses.

[0656] The present invention provides a system for verifying the authenticity of digital content and evaluating its reliability by recognizing user emotions. The system includes a means for uploading digital content, an AI identification means, a hash generation means, a blockchain connection means, an emotion engine means, and a means for notifying users.

[0657] A user logs in to the system using a terminal and uploads digital content (e.g., images or videos). The server temporarily stores the received content in storage (e.g., AWS S3 or Google Cloud Storage) and begins processing. User authentication is used to prevent fraudulent use.

[0658] The server then sends the stored digital content to an AI identification method. The AI ​​identification method (using TensorFlow or PyTorch, for example) determines whether the digital content is AI-generated or live-action. Specifically, it uses metadata checks, pattern recognition, and deep learning techniques. The analysis results are temporarily stored in a database (e.g., MySQL or PostgreSQL).

[0659] The server then generates a hash value for the digital content, using a library such as the OpenSSL library with the SHA-256 algorithm, and stores the hash value in a database.

[0660] The generated hash value and the analysis results of the identification AI are sent from the server to a blockchain network (e.g., Ethereum or Hyperledger Fabric). A smart contract on the blockchain network receives the information, generates and records a unique identifier. The identifier is sent back to the server and stored in a database along with the analysis results.

[0661] The server then sends a notification to the user, who can then confirm the authenticity of the digital content. Furthermore, as the user watches or reads the digital content, the system uses an emotion engine (e.g., FaceAPI or IBM Watson) to analyze the user's emotions in real time. The emotion results are stored in a database and used for authenticity assessment.

[0662] As a concrete example, consider a scenario in which a user uploads a video taken at the site of a natural disaster. The server sends the video to an identification AI, which analyzes the video. If the identification AI determines that the video is live action, the analysis result and the generated hash value are sent to the blockchain, and a unique identifier is generated. The server notifies the user of the identifier and the emotion engine's analysis results, and the user confirms the authenticity and reliability of the video.

[0663] An example of a prompt might be:

[0664] "Upload videos taken from natural disaster sites and verify their authenticity using our identification AI and emotion engine."

[0665] As described above, this invention allows users to easily verify the authenticity of digital content, and furthermore, through evaluations using an emotion engine, it is possible to increase the trustworthiness of the content. This system is effective as a means of preventing the spread of false information and avoiding social unrest.

[0666] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0667] Step 1: Log in and upload content

[0668] (Specific explanation)

[0669] A user logs in to the system using a terminal. The user accesses the system and enters their username and password in the login form. The server verifies the user's authentication information and starts a session. After successful login, the user clicks the "Upload" button and selects an image or video file. The terminal sends the selected file to the server. The server temporarily stores the received digital content in storage and logs the storage information.

[0670] (input)

[0671] Usernames, passwords, digital content files (images and videos)

[0672] (Data processing / data calculation)

[0673] The user's authentication information is checked against the database and the file is saved to storage.

[0674] (output)

[0675] Authentication result, saved file path

[0676] Step 2: Analyzing the content

[0677] (Specific explanation)

[0678] The server sends the stored digital content to the identification AI means. The identification AI means receives the uploaded digital content and analyzes whether the content is AI-generated or live-action using metadata verification and pattern recognition algorithms. This analysis uses deep learning techniques, such as TensorFlow or PyTorch models. The identification AI then sends the analysis results back to the server.

[0679] (input)

[0680] File path of the stored digital content

[0681] (Data processing / data calculation)

[0682] Content metadata checks, pattern recognition, and deep learning analysis

[0683] (output)

[0684] Analysis results (determination of whether it is AI-generated or live-action)

[0685] Step 3: Save the analysis results

[0686] (Specific explanation)

[0687] The server receives the analysis results from the identification AI and temporarily stores them in a database. The database associates the content file path with the analysis results and stores them in a database such as MySQL or PostgreSQL.

[0688] (input)

[0689] Analysis results, file paths of digital content

[0690] (Data processing / data calculation)

[0691] Store data in a database and associate content with analysis results.

[0692] (output)

[0693] Saved entries in the database

[0694] Step 4: Generate a hash value

[0695] (Specific explanation)

[0696] The server generates a hash value for the digital content, for example, using the SHA-256 algorithm with the OpenSSL library, and stores the hash value in a database.

[0697] (input)

[0698] Digital content file path

[0699] (Data processing / data calculation)

[0700] A hash value is calculated using the SHA-256 algorithm.

[0701] (output)

[0702] Generated hash value

[0703] Step 5: Send to the blockchain

[0704] (Specific explanation)

[0705] The server sends the generated hash value and the analysis result of the identification AI to the blockchain network (for example, using Ethereum or Hyperledger Fabric). The blockchain network's smart contract receives the information, generates a unique identifier, records it on the chain, and sends this identifier back to the server.

[0706] (input)

[0707] Hash value, analysis results

[0708] (Data processing / data calculation)

[0709] A smart contract is invoked to generate an identifier and record it on-chain.

[0710] (output)

[0711] The identifier returned from the blockchain

[0712] Step 6: Notify users of the results

[0713] (Specific explanation)

[0714] The server associates the identifier and the analysis results with the user's database and stores them. The server then sends the user an email or system notification, allowing them to verify the authenticity of the digital content.

[0715] (input)

[0716] Identifiers, analysis results, user information (email address, etc.)

[0717] (Data processing / data calculation)

[0718] The identifier and analysis results are stored in a database and notifications are generated and sent.

[0719] (output)

[0720] Notifications sent, updated entries in the database

[0721] Step 7: Emotion analysis and evaluation

[0722] (Specific explanation)

[0723] When a user watches or browses digital content, an emotion engine is used to analyze the user's emotions in real time based on facial recognition, voice analysis, and other biometric data, for example, using FaceAPI or IBM Watson.

[0724] (input)

[0725] User biometric data (facial expression data, voice data, etc.)

[0726] (Data processing / data calculation)

[0727] It performs real-time sentiment analysis and generates user sentiment results.

[0728] (output)

[0729] Emotion analysis results

[0730] Step 8: Storing and utilizing sentiment results

[0731] (Specific explanation)

[0732] The server receives the user's emotion results generated by the emotion engine and stores them in a database. The stored emotion results are used to evaluate the trustworthiness of digital content. Feedback and notifications are also provided to the user as needed.

[0733] (input)

[0734] Emotion analysis results

[0735] (Data processing / data calculation)

[0736] The sentiment results are stored in a database and used for reliability evaluation.

[0737] (output)

[0738] Save entries in the database, notifications

[0739] (Application example 2)

[0740] 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."

[0741] As digital content rapidly increases, it is becoming more difficult to verify its authenticity and reliability. In particular, the spread of AI-generated content and fake news has become a problem, creating a need to provide an environment where users can safely use content. Furthermore, there are insufficient means to analyze users' emotional reactions and evaluate the reliability of content. To solve these issues, a system is needed that can verify the authenticity of digital content and analyze user emotions to increase its reliability.

[0742] 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 digital content, identification AI means for analyzing the uploaded digital content, hash generation means for verifying the authenticity of the digital content based on the analysis results, blockchain connection means for transmitting the generated hash value and the analysis results to the blockchain network and generating an identifier, means for saving the identifier and analysis results returned from the blockchain network and notifying the user, emotion engine means for recognizing the user's emotions and evaluating the trustworthiness of the content based on the analysis results, and means for saving the emotion results recognized by the emotion engine means in a database and using them for trustworthiness evaluation. This makes it possible to verify the authenticity of digital content and evaluate trustworthiness through user emotion analysis.

[0743] "Digital content" refers to content stored or distributed in electronic form, including images, videos, audio files, and text.

[0744] "Uploading" refers to the act of a user sending digital content from their own device to a server or cloud service.

[0745] "Discrimination AI" refers to a system that uses artificial intelligence technology to analyze digital content and determine whether it is live-action or AI-generated.

[0746] A "hash generator" refers to an algorithm or process for generating a unique hash value based on the content of digital content. A typical algorithm is SHA-256.

[0747] "Blockchain" is a network that uses distributed ledger technology, and refers to a technology that can prevent tampering while ensuring the reliability and transparency of data.

[0748] "Identifier" refers to an ID or code that is uniquely assigned to data recorded on a blockchain network.

[0749] "Emotion Engine Means" refers to technology for recognizing and analyzing a user's emotions, including facial recognition, voice analysis, and other biometric data analysis technologies.

[0750] A "database" refers to a system for efficiently storing, managing, and searching large amounts of data.

[0751] "User authentication" refers to the means used to verify that a user has the appropriate authority to access a system, typically including username, password, and two-factor authentication.

[0752] "Authenticity" refers to the fact that digital content has not been tampered with and that its origin and creation process are legitimate.

[0753] "Credibility" refers to the degree to which users can trust the content and its source.

[0754] This invention is a system for verifying the authenticity of digital content and assessing its trustworthiness by analyzing user sentiment. The system includes the following components:

[0755] 1. Digital Content Upload Method:

[0756] A user logs in to the system using a terminal and uploads digital content (e.g., images or videos). The server temporarily stores the received content in storage and begins processing.

[0757] 2. Content analysis method (identification AI method):

[0758] The server sends the stored digital content to an identification AI means, which analyzes the digital content and determines whether it is AI-generated or live-action, using metadata checks, pattern recognition, and deep learning techniques.

[0759] 3. Hash generation method:

[0760] The server generates a hash value for the digital content. The hash generation module calculates the hash value for the content using an algorithm such as SHA-256.

[0761] 4. Blockchain connection methods:

[0762] The server sends the generated hash value and the analysis results of the identification AI to the blockchain network, where the smart contract in the blockchain network receives the information, generates a unique identifier, and records it on the chain.

[0763] 5. How to notify users of results:

[0764] The blockchain network returns the generated identifier to the server, which associates and stores the identifier and analysis results in the user's database and notifies the user, who can then verify the authenticity of the digital content.

[0765] 6. Emotion Engine Means:

[0766] As users watch or browse digital content, the emotion engine analyzes their reactions in real time, using facial recognition, voice analysis and other biometric data.

[0767] 7. Storing and utilizing emotion results:

[0768] The server receives the user's emotion results generated by the emotion engine and stores them in a database. The stored emotion results are used to evaluate the trustworthiness of digital content, allowing for further analysis and notifications based on the user's emotion results.

[0769] Specific flow of usage example

[0770] A user uploads a video taken at the scene of a natural disaster. The server sends the video to an identification AI, which analyzes it. As a result, the video is determined to be live action. The server then generates a hash value for the video and sends the generated hash value and analysis results to the blockchain network. The blockchain network generates a unique identifier and records it on the chain. The blockchain network then returns the generated identifier to the server, which stores the identifier and analysis results in the user's database and notifies the user. Furthermore, while the user is watching the video, an emotion engine analyzes the user's reaction and generates an emotion result. The server receives the result, stores it in a database, and uses it to evaluate the reliability of content.

[0771] Prompt Sentence Examples

[0772] "Analyze emotions while users watch video news from the scene of a natural disaster."

[0773] The above is a specific embodiment of the present invention.

[0774] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0775] Step 1: Upload your digital content

[0776] A user uploads digital content (e.g., a video file) from their own device to the system. The user selects the content and presses the upload button. The input is the uploaded digital content, and the output is the content file stored on the server. The server temporarily stores the received content in storage and passes the destination path to the next processing step.

[0777] Step 2: Analyzing the content

[0778] The server sends the stored digital content to the identification AI means, which analyzes the content using a deep learning model. It checks metadata and performs image analysis to determine whether the content is live-action or AI-generated. The input is the digital content in the destination path, and the output is the AI ​​determination result (live-action or AI-generated).

[0779] Step 3: Generate a hash value

[0780] The server generates a hash value for the content. It uses the SHA-256 algorithm to calculate a unique hash value for the digital content. The input is the digital content in the destination path, and the output is the generated hash value. The hash value is used in the next step.

[0781] Step 4: Send to the blockchain

[0782] The server sends the generated hash value and the analysis result of the identification AI to the blockchain network. The blockchain network's smart contract receives the information, generates a unique identifier, and records it on the chain. The input is the hash value and the AI's judgment result, and the output is the identifier generated by the blockchain network.

[0783] Step 5: Notify users of the results

[0784] The server stores the identifier returned from the blockchain network in a database and notifies the user. The notification includes the identifier and the AI ​​judgment result, allowing the user to verify the authenticity of the content. The input is the identifier and the AI ​​judgment result, and the output is a notification to the user. The notification is sent as an email or an internal system message.

[0785] Step 6: Emotion engine analysis

[0786] While a user is viewing digital content, the emotion engine analyzes the user's reactions in real time. It uses facial recognition, voice analysis, and biometric data to estimate the user's emotions. The input is the user's reaction data while viewing, and the output is the analyzed emotion results.

[0787] Step 7: Storing and utilizing sentiment results

[0788] The server stores the emotion results generated by the emotion engine in a database. The stored emotion results are used to evaluate the trustworthiness of digital content. The input is the analyzed emotion results, and the output is the data stored in the database. For security reasons, the stored data is encrypted.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] [Third embodiment]

[0793] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0794] 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.

[0795] 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).

[0796] 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.

[0797] 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.

[0798] 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).

[0799] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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."

[0805] The present invention is a system for verifying the authenticity of digital content, and includes the following main components: means for uploading digital content, identification AI means for analyzing the uploaded digital content, hash generation means for verifying the authenticity of the digital content based on the analysis results, blockchain connection means for transmitting the generated hash value and analysis results to a blockchain network and generating an identifier, and means for storing the identifier and analysis results returned from the blockchain network and notifying the user.

[0806] Program processing

[0807] Uploading Digital Content

[0808] A user uses a terminal to upload digital content (e.g., images or videos) to the system. Files are selected through the upload interface and sent to the server, which temporarily stores the received content and begins processing it.

[0809] Content Analysis

[0810] The server sends the stored digital content to an AI that analyzes the received content and determines whether it is AI-generated or live-action. The analysis algorithms include image and video metadata checks, pattern recognition, and deep learning-based analysis techniques.

[0811] Saving the identification results

[0812] The server receives the analysis results from the identification AI and temporarily stores them, including information on whether the digital content is AI-generated or live-action.

[0813] Generating a hash value

[0814] The server generates a hash value of the digital content. The hash generation module calculates the hash value of the content using a hash algorithm such as SHA-256.

[0815] Send to blockchain

[0816] The server sends the generated hash value and analysis results to the blockchain network, which then creates a transaction based on the received information and generates a unique identifier for the digital content, which is then recorded on the blockchain.

[0817] Notifying users of the results

[0818] The server receives the identifier and analysis results returned from the blockchain network and stores them in the user's database. The server then notifies the user, who can then view the results of the content's authenticity verification.

[0819] Specific examples

[0820] 1. Upload and analyze

[0821] Users upload videos they have taken from the scene of a natural disaster.

[0822] The server sends the video to an identification AI, which then analyzes the video.

[0823] The identification AI determines that the video is live action.

[0824] 2. Identifier Generation

[0825] The server generates a hash value for the video.

[0826] The hash value and analysis results are sent to the blockchain network.

[0827] The blockchain network generates a unique identifier and records authenticity information.

[0828] 3. Notice and Confirmation

[0829] The server notifies the user of the identifier and the AI's judgment result.

[0830] The user confirms the notification and confirms that the video is live and its authenticity is verified by the blockchain.

[0831] This invention allows users to easily verify the authenticity of digital content, preventing the spread of false information and avoiding social unrest. Furthermore, by utilizing blockchain technology, it becomes possible to prove the authenticity of content with a high degree of reliability.

[0832] The processing flow will be explained below.

[0833] Step 1:

[0834] The user accesses the system's website or application using a terminal and logs in. They enter their authentication information to authenticate the user, and if authentication is successful, they are taken to the upload screen.

[0835] Step 2:

[0836] The user clicks the "Select File" button on the upload screen to select the digital content (image or video) file. After selecting the file, the user clicks the "Upload" button to send the file to the server.

[0837] Step 3:

[0838] The server receives the digital content sent by the user and temporarily stores it in storage. A message indicating successful storage is logged and a notification of upload completion is displayed to the user.

[0839] Step 4:

[0840] The server sends the stored digital content to the identification AI module, which loads the digital content and runs image or video analysis algorithms to determine whether it is AI-generated or live-action.

[0841] Step 5:

[0842] The identification AI completes the analysis and returns the results to the server, which associates the analysis results with the corresponding digital content entry and temporarily stores them.

[0843] Step 6:

[0844] The server generates a hash value for the digital content. The file path is passed to the hash generation module, which calculates the hash value using a hash algorithm such as SHA-256. The generated hash value is then sent back to the server.

[0845] Step 7:

[0846] The server compiles the hash value and the results of the AI's analysis and sends it to the blockchain network, where a smart contract receives the data, creates a transaction that generates a unique identifier, and records the information on the blockchain.

[0847] Step 8:

[0848] The blockchain network sends the generated identifier back to the server, which associates and stores this identifier and the analysis results in the user's database.

[0849] Step 9:

[0850] The server sends a notification to the user, which includes the identification result and details of the content certified on the blockchain, and is displayed on the user's device.

[0851] Step 10:

[0852] Users can check the notification and confirm that the digital content is authentic and its authenticity is verified by the blockchain, allowing them to use the content with confidence.

[0853] Example 1

[0854] 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."

[0855] In modern society, counterfeiting and tampering of digital content is on the rise, making it important to verify its authenticity. However, conventional methods lack the means to verify the authenticity of digital content with high accuracy. Another problem is the lack of a system that allows users to easily verify the authenticity of content. To solve these issues, it is necessary to analyze digital content and provide the results to users in a reliable manner.

[0856] 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.

[0857] In this invention, the server includes: means for a user to select and upload information; identification AI means for temporarily storing and analyzing the uploaded information; means for storing analysis results received from the identification AI; means for generating a hash value of the digital content; blockchain connection means for transmitting the generated hash value and analysis results to a blockchain network and generating a unique identifier; and means for storing the identifier and analysis results returned from the blockchain network and notifying the user. This makes it possible to verify the authenticity of digital content with high accuracy and reliability and quickly provide the results to the user.

[0858] "User" means a person or entity that accesses the system to upload digital content and receive analysis results thereof.

[0859] "Information" refers to digital content (e.g., images, videos) uploaded by users in this system.

[0860] "Uploading" is the act of a user using a terminal to send digital content to a server.

[0861] "Server" means a central computer system that receives and stores digital content sent by users, analyzes it, generates hashes, connects it to the blockchain, and notifies users.

[0862] "Identification AI means" refers to an artificial intelligence system that analyzes uploaded digital content and determines its authenticity (e.g., whether it is AI-generated or live-action).

[0863] A "hash value" is a fixed-length string that indicates the uniqueness of digital content and is generated using a hash algorithm such as SHA-256.

[0864] A "blockchain network" is a distributed network consisting of multiple computers, and is a system that records transactions and generates identifiers for digital content.

[0865] "Identifier" means a unique ID generated by the blockchain network to indicate the authenticity of digital content.

[0866] "Notification to user" refers to the act of the server communicating the identifier and analysis results received from the blockchain network to the user.

[0867] A "database" is an information system for structuring, storing, and managing information such as analysis results and identifiers.

[0868] The present invention relates to a system for verifying the authenticity of digital content, which allows users to upload information (e.g., images or videos), analyzes its authenticity, and provides the results in a reliable manner.

[0869] The user uploads the digital content to the system using a terminal, accesses the upload interface of the system from the terminal's browser, and sends the selected digital content to the server, which temporarily stores the received content and prepares it for analysis by the identification AI means.

[0870] The server sends the stored digital content to the identification AI means, which analyzes the content using metadata verification, pattern recognition, and deep learning techniques to determine whether it is AI-generated or live-action. The analysis result is sent back to the server as either "AI-generated" or "live-action."

[0871] The server receives the analysis results sent from the identification AI means and temporarily stores them. The server then generates a hash value for the digital content. This hash value is generated using a hash algorithm such as SHA-256.

[0872] The generated hash value and analysis result are sent from the server to the blockchain network, which creates a transaction based on the received information and generates a unique identifier, which is recorded on the blockchain and sent back to the server.

[0873] Finally, the server receives the identifier and analysis results returned from the blockchain network and stores them in the user's database. The server then notifies the user, who can then check the results within the system, enabling high-precision and high-reliability verification of the authenticity of digital content.

[0874] Specific examples

[0875] Upload and analyze

[0876] 1. Users upload videos taken from the scene of a natural disaster.

[0877] 2. The server sends the video to the identification AI means, which analyzes the video.

[0878] 3. The identification AI method determines that the video is live action.

[0879] Identifier Generation

[0880] 1. The server generates a hash value for the video.

[0881] 2. Send the hash value and analysis results to the blockchain network.

[0882] 3. The blockchain network generates a unique identifier and records the authenticity information.

[0883] Notification and confirmation

[0884] 1. The server notifies the user of the identifier and the AI's judgment result.

[0885] 2. The user checks the notification and confirms that the video is live and its authenticity is verified by the blockchain.

[0886] Prompt Sentence Examples

[0887] "Please determine whether the following digital content is AI-generated or live-action, and register the result and its hash value on the blockchain. After the hash value and the result of your determination are returned, please display the final confirmation result."

[0888] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0889] Step 1: User selects and uploads digital content

[0890] The user accesses the system's upload interface from the browser on their device. Here, they select the target digital content (e.g., images or videos). When the user clicks the upload button, the selected digital content is sent from the device to the server. The input is the digital content selected by the user, and the output is the digital content received by the server.

[0891] Step 2: The server stores the digital content

[0892] The server temporarily stores the received digital content. Specifically, it is stored as data in the server's storage. This temporary storage is a preparatory step for subsequent analysis processing. The input is the digital content sent to the server, and the output is the digital content stored in the server's storage.

[0893] Step 3: The server sends the digital content to the identification AI means

[0894] The server transmits the stored digital content to the identification AI means, which often runs on a separate server or cloud service. This transmission process passes the digital content information required for analysis to the identification AI means. The input is the temporarily stored digital content, and the output is the digital content transmitted to the identification AI means.

[0895] Step 4: Identification AI methods analyze the digital content

[0896] The identification AI means analyzes the received digital content. This analysis includes checking metadata, pattern recognition, and analysis using deep learning techniques. The analysis results in information that identifies whether the digital content is AI-generated or live-action. The input is the digital content sent to the identification AI means, and the output is the analysis result, such as "AI-generated" or "live-action."

[0897] Step 5: The server receives and stores the analysis results

[0898] The server receives the analysis results sent from the identification AI means. These results are temporarily stored. The analysis results are used for subsequent hash value generation and blockchain transmission processing. The input is the analysis results sent from the identification AI means, and the output is the analysis results stored in the server's storage.

[0899] Step 6: The server generates a hash value for the digital content.

[0900] The server generates a hash value based on the stored digital content. It uses a hash algorithm such as SHA-256 to calculate a unique hash value for the digital content. This hash value is used to detect data tampering. The input is the digital content stored in the server's storage, and the output is the generated hash value.

[0901] Step 7: The server sends the hash value and analysis result to the blockchain network.

[0902] The server sends the generated hash value and the analysis result to the blockchain network, which then creates a transaction and generates a unique identifier. The input is the generated hash value and the analysis result, and the output is the identifier generated by the blockchain network.

[0903] Step 8: The server receives and stores the identifier and analysis results.

[0904] The server receives the identifier and analysis results returned from the blockchain network. This information is stored in the user's database. The input is the identifier and analysis results returned from the blockchain network, and the output is the identifier and analysis results stored in the user's database.

[0905] Step 9: Server notifies the user

[0906] The server notifies the user. This notification includes the analysis result of the identification AI method and the identifier generated by the blockchain network. The user receives the notification and checks the result within the system. The input is the identifier stored in the user's database and the analysis result, and the output is a notification to the user.

[0907] (Application example 1)

[0908] 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."

[0909] The circulation of digital data in modern society is rapidly increasing, but the inclusion of false information and falsified content within this data is causing problems, resulting in a loss of user trust. Furthermore, there is a lack of means to quickly and reliably verify the authenticity of digital data and prove its reliability using distributed ledger technology. Given this background, there is a need for a system that increases the reliability of digital data and allows users to share and use information with peace of mind.

[0910] 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.

[0911] In this invention, the server includes means for transmitting digital data, identification artificial intelligence means for analyzing the transmitted digital data, hash value generation means for verifying the authenticity of the digital data based on the analysis results, distributed ledger connection means for transmitting the generated hash value and analysis results to the distributed ledger network and generating an identifier, and means for storing the identifier and analysis results returned from the distributed ledger network and notifying the user. This enables the authenticity of digital data to be verified quickly and with high reliability, and enables the provision of reliable information to users.

[0912] "Digital data" is a general term for information and content handled on computers and digital devices, and includes images, videos, audio, text data, etc.

[0913] "Transmission" is the act of transferring digital data from one place to another, often over a network or communication line.

[0914] "Identification artificial intelligence means" refers to artificial intelligence technology trained to analyze the characteristics of digital data and determine its generation method and authenticity.

[0915] A "hash value generation method" is a technology that generates a fixed-length string (hash value) from digital data, and is used to verify data integrity and prevent tampering.

[0916] A "distributed ledger network" is a ledger technology in which data blocks are stored in a linked chain format, and is characterized by being managed in a decentralized manner across multiple nodes.

[0917] "Distributed ledger connection means" refers to technology for transmitting hash values ​​and analysis results of digital data to a distributed ledger network and generating records and identifiers on that network.

[0918] An "identifier" refers to a unique string of characters or code generated by a distributed ledger network to prove the uniqueness and authenticity of digital data.

[0919] "Means for notifying users" is a general term for technologies and methods for informing users of the authenticity and identification results of digital data, and includes push notifications, emails, messaging services, etc.

[0920] "User authentication means" refers to a technology or method for verifying a user's identity and authority before sending or receiving digital data.

[0921] "Data storage" refers to a storage device or location for storing analysis results, hash values, identifiers, etc.

[0922] To implement this invention, a user-operated terminal (such as a smartphone or computer) and an associated server are required. The server operates as a system including the following main components:

[0923] 1. Digital Data Transmission

[0924] A user uses a device to send digital data (e.g., videos, images) to a server. At this time, user authentication is performed on the device. A general authentication service is used for user authentication. For example, Firebase Authentication is used for login and authentication.

[0925] 2. Analysis of transmitted digital data

[0926] The server temporarily stores the received digital data and sends it to an AI identification means (e.g., TensorFlow or OpenCV-based AI identification), which analyzes the received data and determines whether it was actually photographed or generated by AI.

[0927] 3. Generating a hash value

[0928] The server generates a hash value of the analyzed digital data using the SHA-256 algorithm, which provides a unique identifier for the digital data.

[0929] 4. Sending to the distributed ledger network

[0930] The server sends the generated hash value and analysis results to a distributed ledger network (e.g., the Ethereum blockchain), which creates a transaction based on the received information and generates a unique identifier, which is then recorded in the distributed ledger.

[0931] 5. Notice to Users

[0932] The server receives the identifier and analysis results returned from the distributed ledger network and stores them in the user's data storage (e.g., a database). The server then notifies the user of the results using a notification service such as Firebase Cloud Messaging.

[0933] Specific examples

[0934] For example, consider the case where a user uploads a video taken at the site of a natural disaster. The user shoots the video using a smartphone and sends it to a server through an application. The sent video is analyzed by an identification AI and determined to be live footage. The server generates a hash value for the video and sends it to the distributed ledger network. The blockchain generates a unique identifier based on the hash value and sends it back to the server. Finally, the server notifies the user of the identifier and the analysis results, allowing the user to verify the authenticity of the uploaded video.

[0935] Example prompts for generative AI models

[0936] I uploaded a video taken at the scene of a natural disaster and want to verify its authenticity.

[0937] These steps allow users to ensure the authenticity of digital data and build a system that can prove its reliability using distributed ledger technology.

[0938] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0939] Step 1: User authentication and digital data transmission

[0940] Subject: User

[0941] Specific operation: A user logs in using an application on their device. Firebase Authentication can be used for login. If authentication is successful, the user selects digital data (e.g., a video) and clicks the send button. At that time, the device makes a request to send the selected file to the server.

[0942] Input: User login information, digital data (file)

[0943] Output: Authentication token, digital data transmission to server

[0944] Step 2: Temporarily store digital data and analyze it using AI

[0945] Subject: Server

[0946] How it works: The server receives the digital data and temporarily stores it in storage. The stored data is then sent to a classification AI (using TensorFlow and OpenCV). The classification AI analyzes the received data and determines whether it is real or AI-generated.

[0947] Input: Digital data

[0948] Output: Analysis results (real-life or AI-generated)

[0949] Step 3: Generate a hash value

[0950] Subject: Server

[0951] How it works: The server generates a hash value of the uploaded digital data based on the analysis results. The hash value is generated using the SHA-256 algorithm. The generated hash value is used as a unique identifier.

[0952] Input: Digital data

[0953] Output: hash value

[0954] Step 4: Send the hash value and analysis results to the distributed ledger network

[0955] Subject: Server

[0956] How it works: The server sends the generated hash value and analysis results to the distributed ledger network, which uses the received information to create a transaction and generate a unique identifier.

[0957] Input: hash value, analysis result

[0958] Output: A unique identifier

[0959] Step 5: Storing identifiers and analysis results and notifying the user

[0960] Subject: Server

[0961] Specific operation: The server receives the ID and analysis results from the distributed ledger network, stores them in the user's database, and then notifies the user of the identification results using Firebase Cloud Messaging or similar.

[0962] Input: unique identifier, analysis result

[0963] Output: Saved data, notification message

[0964] Through the above processing steps, the user can quickly and reliably confirm the authenticity of the digital data and receive highly reliable information.

[0965] 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.

[0966] The present invention is a system for verifying the authenticity of digital content, which further combines an emotion engine that recognizes user emotions and evaluates the reliability of the content using the analysis results. It includes the following main components: a means for uploading digital content, an identification AI means for analyzing the uploaded digital content, a hash generation means for verifying the authenticity of the digital content based on the analysis results, a blockchain connection means for transmitting the generated hash value and analysis results to a blockchain network and generating an identifier, a means for storing the identifier and analysis results returned from the blockchain network and notifying the user, and an emotion engine means for recognizing user emotions.

[0967] Program processing

[0968] Uploading Digital Content

[0969] A user logs in to the system using a terminal and uploads digital content (e.g., images or videos). The server temporarily stores the received content in storage and begins processing.

[0970] Content Analysis

[0971] The server sends the stored digital content to an identification AI means, which analyzes the digital content and determines whether it is AI-generated or live-action, using metadata checks, pattern recognition, and deep learning techniques.

[0972] Saving the identification results

[0973] The server receives and temporarily stores the analysis results from the identification AI, which include information on whether the digital content is AI-generated or live-action.

[0974] Generating a hash value

[0975] The server generates a hash value for the digital content. The hash generation module calculates the hash value for the content using an algorithm such as SHA-256.

[0976] Send to blockchain

[0977] The server sends the generated hash value and the analysis results of the identification AI to the blockchain network, where the smart contract in the blockchain network receives the information, generates a unique identifier, and records it on the chain.

[0978] Notifying users of the results

[0979] The blockchain network returns the generated identifier to the server, which associates and stores the identifier and analysis results in the user's database and notifies the user, who can then verify the authenticity of the digital content.

[0980] Emotion analysis and evaluation

[0981] An emotion engine is used to analyze a user's emotions toward uploaded digital content. As a user watches or browses digital content, the emotion engine analyzes the user's reactions in real time. The emotion engine utilizes facial recognition, voice analysis, and other biometric data.

[0982] Saving and utilizing sentiment results

[0983] The server receives the user's emotion results generated by the emotion engine and stores them in a database. The stored emotion results are used to evaluate the trustworthiness of digital content, allowing for further analysis and notifications based on the user's emotion results.

[0984] Specific examples

[0985] 1. Upload and analyze

[0986] Users upload videos taken from the scene of a natural disaster.

[0987] The server sends the video to an identification AI, which then analyzes the video.

[0988] The identification AI determines that the video is live action.

[0989] 2. Use of Emotion Engine

[0990] When a user watches an uploaded video, the emotion engine analyzes the user's reaction.

[0991] The analysis results indicate that the user felt a strong interest in and trust in the video.

[0992] 3. Identifier Generation and Notification

[0993] The server generates a hash value for the video and sends the hash value and analysis results to the blockchain network.

[0994] The blockchain network generates and records a unique identifier.

[0995] The server notifies the user of the identifier and the emotion engine analysis results, and the user confirms the authenticity and reliability of the video.

[0996] This invention allows users to easily verify the authenticity of digital content and further increases the trustworthiness of the content through evaluations using an emotion engine, thereby preventing the spread of false information and avoiding social unrest.

[0997] The processing flow will be explained below.

[0998] Step 1:

[0999] The user accesses the system's website or application using a terminal and logs in. They enter their authentication information to authenticate the user, and if authentication is successful, they are taken to the upload screen.

[1000] Step 2:

[1001] The user clicks the "Select File" button on the upload screen to select the digital content (image or video) file. After selecting the file, the user clicks the "Upload" button to send the file to the server.

[1002] Step 3:

[1003] The server receives the digital content sent by the user and temporarily stores it in storage. A message indicating successful storage is logged and a notification of upload completion is displayed to the user.

[1004] Step 4:

[1005] The server sends the stored digital content to the identification AI means, which loads the digital content and runs image or video analysis algorithms to determine whether it is AI-generated or live-action.

[1006] Step 5:

[1007] The identification AI completes the analysis and returns the results to the server, which associates the analysis results with the corresponding digital content entry and temporarily stores them.

[1008] Step 6:

[1009] The server generates a hash value for the digital content. The file path is passed to the hash generation module, which calculates the hash value using a hash algorithm such as SHA-256. The generated hash value is then sent back to the server.

[1010] Step 7:

[1011] The server compiles the hash value and the results of the AI's analysis and sends it to the blockchain network, where a smart contract receives the data, creates a transaction that generates a unique identifier, and records the information on the blockchain.

[1012] Step 8:

[1013] The blockchain network sends the generated identifier back to the server, which associates and stores this identifier and the analysis results in the user's database.

[1014] Step 9:

[1015] The server sends a notification to the user, which includes the identification result and details of the content certified on the blockchain, and is displayed on the user's device.

[1016] Step 10:

[1017] The server calculates the hash value of the digital content and notifies the user of the analysis results, including the identifier. At the same time, if a viewing operation by the user is detected, the emotion engine is activated. The emotion engine uses the user's camera and microphone data to perform facial recognition and voice analysis.

[1018] Step 11:

[1019] The emotion engine analyzes the user's emotions in real time and sends the results to the server, which stores the emotion data in association with the digital content entry.

[1020] Step 12:

[1021] The server sends a notification to the user and provides feedback on the results of the emotion analysis of the digital content they have viewed. By comparing their own emotions with the analysis results of the digital content, users can intuitively evaluate its reliability.

[1022] Step 13:

[1023] After users have finished rating the content they have viewed, the sentiment analysis results are reflected in the system's overall reliability score. The server uses the collected data to improve the accuracy of future analysis of similar content.

[1024] Through these detailed steps, the present invention achieves a comprehensive credibility assessment that takes into account not only the authenticity of digital content but also the user's emotional response, which is expected to enable users to use the content with peace of mind and prevent the spread of false information in society.

[1025] Example 2

[1026] 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."

[1027] There is a need for a way to easily verify the authenticity of digital content and further evaluate its reliability. In particular, to prevent the spread of false information and avoid social unrest, it is important not only to verify the authenticity of digital content, but also to evaluate its reliability based on user sentiment. However, current systems make it difficult to simultaneously verify the authenticity and evaluate the reliability of digital content. Furthermore, technology for analyzing user responses in real time is still in its infancy. Solving these problems is a challenge.

[1028] 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.

[1029] In this invention, the server includes means for uploading digital content, identification AI means for analyzing the uploaded digital content, hash generation means for verifying the authenticity of the digital content based on the analysis results, blockchain connection means for transmitting the generated hash value and the analysis results to the blockchain network and generating an identifier, means for storing the identifier and the analysis results returned from the blockchain network and notifying the user, and emotion engine means for analyzing the user's emotions toward the digital content, thereby enabling the authenticity of digital content to be verified and its reliability to be evaluated simultaneously.

[1030] "Digital content" is a general term for electronically generated or stored information such as images, video, audio, and text.

[1031] An "uploading means" is a mechanism by which a user transmits digital content from his or her own terminal to a server.

[1032] "Identification AI means" refers to artificial intelligence technology that analyzes uploaded digital content and determines its authenticity (e.g., whether it is AI-generated or live-action).

[1033] A "hash generator" is a mechanism that calculates a unique hash value from digital content to prevent tampering and verify the authenticity of the digital content.

[1034] The "blockchain connection means" is a mechanism for transmitting the generated hash value and analysis results to the blockchain network and recording them on the blockchain.

[1035] "Identifier" means a unique ID generated by the blockchain network and used to verify the authenticity of digital content and its analysis results.

[1036] "Emotion engine means" is a technology for analyzing users' emotional reactions to digital content and collecting and evaluating the results.

[1037] "User authentication means" is a mechanism for verifying the identity of a user before using the system and preventing fraudulent use.

[1038] A "database" is a system that structurally stores and manages analysis results, hash values, identifiers, and user emotion data.

[1039] "Authenticity assessment" is the process of assessing the authenticity of digital content and its trustworthiness based on users' emotional responses.

[1040] The present invention provides a system for verifying the authenticity of digital content and evaluating its reliability by recognizing user emotions. The system includes a means for uploading digital content, an AI identification means, a hash generation means, a blockchain connection means, an emotion engine means, and a means for notifying users.

[1041] A user logs in to the system using a terminal and uploads digital content (e.g., images or videos). The server temporarily stores the received content in storage (e.g., AWS S3 or Google Cloud Storage) and begins processing. User authentication is used to prevent fraudulent use.

[1042] The server then sends the stored digital content to an AI identification method. The AI ​​identification method (using TensorFlow or PyTorch, for example) determines whether the digital content is AI-generated or live-action. Specifically, it uses metadata checks, pattern recognition, and deep learning techniques. The analysis results are temporarily stored in a database (e.g., MySQL or PostgreSQL).

[1043] The server then generates a hash value for the digital content, using a library such as the OpenSSL library with the SHA-256 algorithm, and stores the hash value in a database.

[1044] The generated hash value and the analysis results of the identification AI are sent from the server to a blockchain network (e.g., Ethereum or Hyperledger Fabric). A smart contract on the blockchain network receives the information, generates and records a unique identifier. The identifier is sent back to the server and stored in a database along with the analysis results.

[1045] The server then sends a notification to the user, who can then confirm the authenticity of the digital content. Furthermore, as the user watches or reads the digital content, the system uses an emotion engine (e.g., FaceAPI or IBM Watson) to analyze the user's emotions in real time. The emotion results are stored in a database and used for authenticity assessment.

[1046] As a concrete example, consider a scenario in which a user uploads a video taken at the site of a natural disaster. The server sends the video to an identification AI, which analyzes the video. If the identification AI determines that the video is live action, the analysis result and the generated hash value are sent to the blockchain, and a unique identifier is generated. The server notifies the user of the identifier and the emotion engine's analysis results, and the user confirms the authenticity and reliability of the video.

[1047] An example of a prompt might be:

[1048] "Upload videos taken from natural disaster sites and verify their authenticity using our identification AI and emotion engine."

[1049] As described above, this invention allows users to easily verify the authenticity of digital content, and furthermore, through evaluations using an emotion engine, it is possible to increase the trustworthiness of the content. This system is effective as a means of preventing the spread of false information and avoiding social unrest.

[1050] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1051] Step 1: Log in and upload content

[1052] (Specific explanation)

[1053] A user logs in to the system using a terminal. The user accesses the system and enters their username and password in the login form. The server verifies the user's authentication information and starts a session. After successful login, the user clicks the "Upload" button and selects an image or video file. The terminal sends the selected file to the server. The server temporarily stores the received digital content in storage and logs the storage information.

[1054] (input)

[1055] Usernames, passwords, digital content files (images and videos)

[1056] (Data processing / data calculation)

[1057] The user's authentication information is checked against the database and the file is saved to storage.

[1058] (output)

[1059] Authentication result, saved file path

[1060] Step 2: Analyzing the content

[1061] (Specific explanation)

[1062] The server sends the stored digital content to the identification AI means. The identification AI means receives the uploaded digital content and analyzes whether the content is AI-generated or live-action using metadata verification and pattern recognition algorithms. This analysis uses deep learning techniques, such as TensorFlow or PyTorch models. The identification AI then sends the analysis results back to the server.

[1063] (input)

[1064] File path of the stored digital content

[1065] (Data processing / data calculation)

[1066] Content metadata checks, pattern recognition, and deep learning analysis

[1067] (output)

[1068] Analysis results (determination of whether it is AI-generated or live-action)

[1069] Step 3: Save the analysis results

[1070] (Specific explanation)

[1071] The server receives the analysis results from the identification AI and temporarily stores them in a database. The database associates the content file path with the analysis results and stores them in a database such as MySQL or PostgreSQL.

[1072] (input)

[1073] Analysis results, file paths of digital content

[1074] (Data processing / data calculation)

[1075] Store data in a database and associate content with analysis results.

[1076] (output)

[1077] Saved entries in the database

[1078] Step 4: Generate a hash value

[1079] (Specific explanation)

[1080] The server generates a hash value for the digital content, for example, using the SHA-256 algorithm with the OpenSSL library, and stores the hash value in a database.

[1081] (input)

[1082] Digital content file path

[1083] (Data processing / data calculation)

[1084] A hash value is calculated using the SHA-256 algorithm.

[1085] (output)

[1086] Generated hash value

[1087] Step 5: Send to the blockchain

[1088] (Specific explanation)

[1089] The server sends the generated hash value and the analysis result of the identification AI to the blockchain network (for example, using Ethereum or Hyperledger Fabric). The blockchain network's smart contract receives the information, generates a unique identifier, records it on the chain, and sends this identifier back to the server.

[1090] (input)

[1091] Hash value, analysis results

[1092] (Data processing / data calculation)

[1093] A smart contract is invoked to generate an identifier and record it on-chain.

[1094] (output)

[1095] The identifier returned from the blockchain

[1096] Step 6: Notify users of the results

[1097] (Specific explanation)

[1098] The server associates the identifier and the analysis results with the user's database and stores them. The server then sends the user an email or system notification, allowing them to verify the authenticity of the digital content.

[1099] (input)

[1100] Identifiers, analysis results, user information (email address, etc.)

[1101] (Data processing / data calculation)

[1102] The identifier and analysis results are stored in a database and notifications are generated and sent.

[1103] (output)

[1104] Notifications sent, updated entries in the database

[1105] Step 7: Emotion analysis and evaluation

[1106] (Specific explanation)

[1107] When a user watches or browses digital content, an emotion engine is used to analyze the user's emotions in real time based on facial recognition, voice analysis, and other biometric data, for example, using FaceAPI or IBM Watson.

[1108] (input)

[1109] User biometric data (facial expression data, voice data, etc.)

[1110] (Data processing / data calculation)

[1111] It performs real-time sentiment analysis and generates user sentiment results.

[1112] (output)

[1113] Emotion analysis results

[1114] Step 8: Storing and utilizing sentiment results

[1115] (Specific explanation)

[1116] The server receives the user's emotion results generated by the emotion engine and stores them in a database. The stored emotion results are used to evaluate the trustworthiness of digital content. Feedback and notifications are also provided to the user as needed.

[1117] (input)

[1118] Emotion analysis results

[1119] (Data processing / data calculation)

[1120] The sentiment results are stored in a database and used for reliability evaluation.

[1121] (output)

[1122] Save entries in the database, notifications

[1123] (Application example 2)

[1124] 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."

[1125] As digital content rapidly increases, it is becoming more difficult to verify its authenticity and reliability. In particular, the spread of AI-generated content and fake news has become a problem, creating a need to provide an environment where users can safely use content. Furthermore, there are insufficient means to analyze users' emotional reactions and evaluate the reliability of content. To solve these issues, a system is needed that can verify the authenticity of digital content and analyze user emotions to increase its reliability.

[1126] 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 digital content, identification AI means for analyzing the uploaded digital content, hash generation means for verifying the authenticity of the digital content based on the analysis results, blockchain connection means for transmitting the generated hash value and the analysis results to the blockchain network and generating an identifier, means for saving the identifier and analysis results returned from the blockchain network and notifying the user, emotion engine means for recognizing the user's emotions and evaluating the trustworthiness of the content based on the analysis results, and means for saving the emotion results recognized by the emotion engine means in a database and using them for trustworthiness evaluation. This makes it possible to verify the authenticity of digital content and evaluate trustworthiness through user emotion analysis.

[1127] "Digital content" refers to content stored or distributed in electronic form, including images, videos, audio files, and text.

[1128] "Uploading" refers to the act of a user sending digital content from their own device to a server or cloud service.

[1129] "Discrimination AI" refers to a system that uses artificial intelligence technology to analyze digital content and determine whether it is live-action or AI-generated.

[1130] A "hash generator" refers to an algorithm or process for generating a unique hash value based on the content of digital content. A typical algorithm is SHA-256.

[1131] "Blockchain" is a network that uses distributed ledger technology, and refers to a technology that can prevent tampering while ensuring the reliability and transparency of data.

[1132] "Identifier" refers to an ID or code that is uniquely assigned to data recorded on a blockchain network.

[1133] "Emotion Engine Means" refers to technology for recognizing and analyzing a user's emotions, including facial recognition, voice analysis, and other biometric data analysis technologies.

[1134] A "database" refers to a system for efficiently storing, managing, and searching large amounts of data.

[1135] "User authentication" refers to the means used to verify that a user has the appropriate authority to access a system, typically including username, password, and two-factor authentication.

[1136] "Authenticity" refers to the fact that digital content has not been tampered with and that its origin and creation process are legitimate.

[1137] "Credibility" refers to the degree to which users can trust the content and its source.

[1138] This invention is a system for verifying the authenticity of digital content and assessing its trustworthiness by analyzing user sentiment. The system includes the following components:

[1139] 1. Digital Content Upload Method:

[1140] A user logs in to the system using a terminal and uploads digital content (e.g., images or videos). The server temporarily stores the received content in storage and begins processing.

[1141] 2. Content analysis method (identification AI method):

[1142] The server sends the stored digital content to an identification AI means, which analyzes the digital content and determines whether it is AI-generated or live-action, using metadata checks, pattern recognition, and deep learning techniques.

[1143] 3. Hash generation method:

[1144] The server generates a hash value for the digital content. The hash generation module calculates the hash value for the content using an algorithm such as SHA-256.

[1145] 4. Blockchain connection methods:

[1146] The server sends the generated hash value and the analysis results of the identification AI to the blockchain network, where the smart contract in the blockchain network receives the information, generates a unique identifier, and records it on the chain.

[1147] 5. How to notify users of results:

[1148] The blockchain network returns the generated identifier to the server, which associates and stores the identifier and analysis results in the user's database and notifies the user, who can then verify the authenticity of the digital content.

[1149] 6. Emotion Engine Means:

[1150] As users watch or browse digital content, the emotion engine analyzes their reactions in real time, using facial recognition, voice analysis and other biometric data.

[1151] 7. Storing and utilizing emotion results:

[1152] The server receives the user's emotion results generated by the emotion engine and stores them in a database. The stored emotion results are used to evaluate the trustworthiness of digital content, allowing for further analysis and notifications based on the user's emotion results.

[1153] Specific flow of usage example

[1154] A user uploads a video taken at the scene of a natural disaster. The server sends the video to an identification AI, which analyzes it. As a result, the video is determined to be live action. The server then generates a hash value for the video and sends the generated hash value and analysis results to the blockchain network. The blockchain network generates a unique identifier and records it on the chain. The blockchain network then returns the generated identifier to the server, which stores the identifier and analysis results in the user's database and notifies the user. Furthermore, while the user is watching the video, an emotion engine analyzes the user's reaction and generates an emotion result. The server receives the result, stores it in a database, and uses it to evaluate the reliability of content.

[1155] Prompt Sentence Examples

[1156] "Analyze emotions while users watch video news from the scene of a natural disaster."

[1157] The above is a specific embodiment of the present invention.

[1158] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1159] Step 1: Upload your digital content

[1160] A user uploads digital content (e.g., a video file) from their own device to the system. The user selects the content and presses the upload button. The input is the uploaded digital content, and the output is the content file stored on the server. The server temporarily stores the received content in storage and passes the destination path to the next processing step.

[1161] Step 2: Analyzing the content

[1162] The server sends the stored digital content to the identification AI means, which analyzes the content using a deep learning model. It checks metadata and performs image analysis to determine whether the content is live-action or AI-generated. The input is the digital content in the destination path, and the output is the AI ​​determination result (live-action or AI-generated).

[1163] Step 3: Generate a hash value

[1164] The server generates a hash value for the content. It uses the SHA-256 algorithm to calculate a unique hash value for the digital content. The input is the digital content in the destination path, and the output is the generated hash value. The hash value is used in the next step.

[1165] Step 4: Send to the blockchain

[1166] The server sends the generated hash value and the analysis result of the identification AI to the blockchain network. The blockchain network's smart contract receives the information, generates a unique identifier, and records it on the chain. The input is the hash value and the AI's judgment result, and the output is the identifier generated by the blockchain network.

[1167] Step 5: Notify users of the results

[1168] The server stores the identifier returned from the blockchain network in a database and notifies the user. The notification includes the identifier and the AI ​​judgment result, allowing the user to verify the authenticity of the content. The input is the identifier and the AI ​​judgment result, and the output is a notification to the user. The notification is sent as an email or an internal system message.

[1169] Step 6: Emotion engine analysis

[1170] While a user is viewing digital content, the emotion engine analyzes the user's reactions in real time. It uses facial recognition, voice analysis, and biometric data to estimate the user's emotions. The input is the user's reaction data while viewing, and the output is the analyzed emotion results.

[1171] Step 7: Storing and utilizing sentiment results

[1172] The server stores the emotion results generated by the emotion engine in a database. The stored emotion results are used to evaluate the trustworthiness of digital content. The input is the analyzed emotion results, and the output is the data stored in the database. For security reasons, the stored data is encrypted.

[1173] 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.

[1174] 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.

[1175] 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.

[1176] [Fourth embodiment]

[1177] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1178] 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.

[1179] 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).

[1180] 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.

[1181] 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.

[1182] 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).

[1183] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1184] 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.

[1185] 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.

[1186] 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.

[1187] 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.

[1188] 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.

[1189] 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."

[1190] The present invention is a system for verifying the authenticity of digital content, and includes the following main components: means for uploading digital content, identification AI means for analyzing the uploaded digital content, hash generation means for verifying the authenticity of the digital content based on the analysis results, blockchain connection means for transmitting the generated hash value and analysis results to a blockchain network and generating an identifier, and means for storing the identifier and analysis results returned from the blockchain network and notifying the user.

[1191] Program processing

[1192] Uploading Digital Content

[1193] A user uses a terminal to upload digital content (e.g., images or videos) to the system. Files are selected through the upload interface and sent to the server, which temporarily stores the received content and begins processing it.

[1194] Content Analysis

[1195] The server sends the stored digital content to an AI that analyzes the received content and determines whether it is AI-generated or live-action. The analysis algorithms include image and video metadata checks, pattern recognition, and deep learning-based analysis techniques.

[1196] Saving the identification results

[1197] The server receives the analysis results from the identification AI and temporarily stores them, including information on whether the digital content is AI-generated or live-action.

[1198] Generating a hash value

[1199] The server generates a hash value of the digital content. The hash generation module calculates the hash value of the content using a hash algorithm such as SHA-256.

[1200] Send to blockchain

[1201] The server sends the generated hash value and analysis results to the blockchain network, which then creates a transaction based on the received information and generates a unique identifier for the digital content, which is then recorded on the blockchain.

[1202] Notifying users of the results

[1203] The server receives the identifier and analysis results returned from the blockchain network and stores them in the user's database. The server then notifies the user, who can then view the results of the content's authenticity verification.

[1204] Specific examples

[1205] 1. Upload and analyze

[1206] Users upload videos they have taken from the scene of a natural disaster.

[1207] The server sends the video to an identification AI, which then analyzes the video.

[1208] The identification AI determines that the video is live action.

[1209] 2. Identifier Generation

[1210] The server generates a hash value for the video.

[1211] The hash value and analysis results are sent to the blockchain network.

[1212] The blockchain network generates a unique identifier and records authenticity information.

[1213] 3. Notice and Confirmation

[1214] The server notifies the user of the identifier and the AI's judgment result.

[1215] The user confirms the notification and confirms that the video is live and its authenticity is verified by the blockchain.

[1216] This invention allows users to easily verify the authenticity of digital content, preventing the spread of false information and avoiding social unrest. Furthermore, by utilizing blockchain technology, it becomes possible to prove the authenticity of content with a high degree of reliability.

[1217] The processing flow will be explained below.

[1218] Step 1:

[1219] The user accesses the system's website or application using a terminal and logs in. They enter their authentication information to authenticate the user, and if authentication is successful, they are taken to the upload screen.

[1220] Step 2:

[1221] The user clicks the "Select File" button on the upload screen to select the digital content (image or video) file. After selecting the file, the user clicks the "Upload" button to send the file to the server.

[1222] Step 3:

[1223] The server receives the digital content sent by the user and temporarily stores it in storage. A message indicating successful storage is logged and a notification of upload completion is displayed to the user.

[1224] Step 4:

[1225] The server sends the stored digital content to the identification AI module, which loads the digital content and runs image or video analysis algorithms to determine whether it is AI-generated or live-action.

[1226] Step 5:

[1227] The identification AI completes the analysis and returns the results to the server, which associates the analysis results with the corresponding digital content entry and temporarily stores them.

[1228] Step 6:

[1229] The server generates a hash value for the digital content. The file path is passed to the hash generation module, which calculates the hash value using a hash algorithm such as SHA-256. The generated hash value is then sent back to the server.

[1230] Step 7:

[1231] The server compiles the hash value and the results of the AI's analysis and sends it to the blockchain network, where a smart contract receives the data, creates a transaction that generates a unique identifier, and records the information on the blockchain.

[1232] Step 8:

[1233] The blockchain network sends the generated identifier back to the server, which associates and stores this identifier and the analysis results in the user's database.

[1234] Step 9:

[1235] The server sends a notification to the user, which includes the identification result and details of the content certified on the blockchain, and is displayed on the user's device.

[1236] Step 10:

[1237] Users can check the notification and confirm that the digital content is authentic and its authenticity is verified by the blockchain, allowing them to use the content with confidence.

[1238] Example 1

[1239] 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."

[1240] In modern society, counterfeiting and tampering of digital content is on the rise, making it important to verify its authenticity. However, conventional methods lack the means to verify the authenticity of digital content with high accuracy. Another problem is the lack of a system that allows users to easily verify the authenticity of content. To solve these issues, it is necessary to analyze digital content and provide the results to users in a reliable manner.

[1241] 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.

[1242] In this invention, the server includes: means for a user to select and upload information; identification AI means for temporarily storing and analyzing the uploaded information; means for storing analysis results received from the identification AI; means for generating a hash value of the digital content; blockchain connection means for transmitting the generated hash value and analysis results to a blockchain network and generating a unique identifier; and means for storing the identifier and analysis results returned from the blockchain network and notifying the user. This makes it possible to verify the authenticity of digital content with high accuracy and reliability and quickly provide the results to the user.

[1243] "User" means a person or entity that accesses the system to upload digital content and receive analysis results thereof.

[1244] "Information" refers to digital content (e.g., images, videos) uploaded by users in this system.

[1245] "Uploading" is the act of a user using a terminal to send digital content to a server.

[1246] "Server" means a central computer system that receives and stores digital content sent by users, analyzes it, generates hashes, connects it to the blockchain, and notifies users.

[1247] "Identification AI means" refers to an artificial intelligence system that analyzes uploaded digital content and determines its authenticity (e.g., whether it is AI-generated or live-action).

[1248] A "hash value" is a fixed-length string that indicates the uniqueness of digital content and is generated using a hash algorithm such as SHA-256.

[1249] A "blockchain network" is a distributed network consisting of multiple computers, and is a system that records transactions and generates identifiers for digital content.

[1250] "Identifier" means a unique ID generated by the blockchain network to indicate the authenticity of digital content.

[1251] "Notification to user" refers to the act of the server communicating the identifier and analysis results received from the blockchain network to the user.

[1252] A "database" is an information system for structuring, storing, and managing information such as analysis results and identifiers.

[1253] The present invention relates to a system for verifying the authenticity of digital content, which allows users to upload information (e.g., images or videos), analyzes its authenticity, and provides the results in a reliable manner.

[1254] The user uploads the digital content to the system using a terminal, accesses the upload interface of the system from the terminal's browser, and sends the selected digital content to the server, which temporarily stores the received content and prepares it for analysis by the identification AI means.

[1255] The server sends the stored digital content to the identification AI means, which analyzes the content using metadata verification, pattern recognition, and deep learning techniques to determine whether it is AI-generated or live-action. The analysis result is sent back to the server as either "AI-generated" or "live-action."

[1256] The server receives the analysis results sent from the identification AI means and temporarily stores them. The server then generates a hash value for the digital content. This hash value is generated using a hash algorithm such as SHA-256.

[1257] The generated hash value and analysis result are sent from the server to the blockchain network, which creates a transaction based on the received information and generates a unique identifier, which is recorded on the blockchain and sent back to the server.

[1258] Finally, the server receives the identifier and analysis results returned from the blockchain network and stores them in the user's database. The server then notifies the user, who can then check the results within the system, enabling high-precision and high-reliability verification of the authenticity of digital content.

[1259] Specific examples

[1260] Upload and analyze

[1261] 1. Users upload videos taken from the scene of a natural disaster.

[1262] 2. The server sends the video to the identification AI means, which analyzes the video.

[1263] 3. The identification AI method determines that the video is live action.

[1264] Identifier Generation

[1265] 1. The server generates a hash value for the video.

[1266] 2. Send the hash value and analysis results to the blockchain network.

[1267] 3. The blockchain network generates a unique identifier and records the authenticity information.

[1268] Notification and confirmation

[1269] 1. The server notifies the user of the identifier and the AI's judgment result.

[1270] 2. The user checks the notification and confirms that the video is live and its authenticity is verified by the blockchain.

[1271] Prompt Sentence Examples

[1272] "Please determine whether the following digital content is AI-generated or live-action, and register the result and its hash value on the blockchain. After the hash value and the result of your determination are returned, please display the final confirmation result."

[1273] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1274] Step 1: User selects and uploads digital content

[1275] The user accesses the system's upload interface from the browser on their device. Here, they select the target digital content (e.g., images or videos). When the user clicks the upload button, the selected digital content is sent from the device to the server. The input is the digital content selected by the user, and the output is the digital content received by the server.

[1276] Step 2: The server stores the digital content

[1277] The server temporarily stores the received digital content. Specifically, it is stored as data in the server's storage. This temporary storage is a preparatory step for subsequent analysis processing. The input is the digital content sent to the server, and the output is the digital content stored in the server's storage.

[1278] Step 3: The server sends the digital content to the identification AI means

[1279] The server transmits the stored digital content to the identification AI means, which often runs on a separate server or cloud service. This transmission process passes the digital content information required for analysis to the identification AI means. The input is the temporarily stored digital content, and the output is the digital content transmitted to the identification AI means.

[1280] Step 4: Identification AI methods analyze the digital content

[1281] The identification AI means analyzes the received digital content. This analysis includes checking metadata, pattern recognition, and analysis using deep learning techniques. The analysis results in information that identifies whether the digital content is AI-generated or live-action. The input is the digital content sent to the identification AI means, and the output is the analysis result, such as "AI-generated" or "live-action."

[1282] Step 5: The server receives and stores the analysis results

[1283] The server receives the analysis results sent from the identification AI means. These results are temporarily stored. The analysis results are used for subsequent hash value generation and blockchain transmission processing. The input is the analysis results sent from the identification AI means, and the output is the analysis results stored in the server's storage.

[1284] Step 6: The server generates a hash value for the digital content.

[1285] The server generates a hash value based on the stored digital content. It uses a hash algorithm such as SHA-256 to calculate a unique hash value for the digital content. This hash value is used to detect data tampering. The input is the digital content stored in the server's storage, and the output is the generated hash value.

[1286] Step 7: The server sends the hash value and analysis result to the blockchain network.

[1287] The server sends the generated hash value and the analysis result to the blockchain network, which then creates a transaction and generates a unique identifier. The input is the generated hash value and the analysis result, and the output is the identifier generated by the blockchain network.

[1288] Step 8: The server receives and stores the identifier and analysis results.

[1289] The server receives the identifier and analysis results returned from the blockchain network. This information is stored in the user's database. The input is the identifier and analysis results returned from the blockchain network, and the output is the identifier and analysis results stored in the user's database.

[1290] Step 9: Server notifies the user

[1291] The server notifies the user. This notification includes the analysis result of the identification AI method and the identifier generated by the blockchain network. The user receives the notification and checks the result within the system. The input is the identifier stored in the user's database and the analysis result, and the output is a notification to the user.

[1292] (Application example 1)

[1293] 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."

[1294] The circulation of digital data in modern society is rapidly increasing, but the inclusion of false information and falsified content within this data is causing problems, resulting in a loss of user trust. Furthermore, there is a lack of means to quickly and reliably verify the authenticity of digital data and prove its reliability using distributed ledger technology. Given this background, there is a need for a system that increases the reliability of digital data and allows users to share and use information with peace of mind.

[1295] 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.

[1296] In this invention, the server includes means for transmitting digital data, identification artificial intelligence means for analyzing the transmitted digital data, hash value generation means for verifying the authenticity of the digital data based on the analysis results, distributed ledger connection means for transmitting the generated hash value and analysis results to the distributed ledger network and generating an identifier, and means for storing the identifier and analysis results returned from the distributed ledger network and notifying the user. This enables the authenticity of digital data to be verified quickly and with high reliability, and enables the provision of reliable information to users.

[1297] "Digital data" is a general term for information and content handled on computers and digital devices, and includes images, videos, audio, text data, etc.

[1298] "Transmission" is the act of transferring digital data from one place to another, often over a network or communication line.

[1299] "Identification artificial intelligence means" refers to artificial intelligence technology trained to analyze the characteristics of digital data and determine its generation method and authenticity.

[1300] A "hash value generation method" is a technology that generates a fixed-length string (hash value) from digital data, and is used to verify data integrity and prevent tampering.

[1301] A "distributed ledger network" is a ledger technology in which data blocks are stored in a linked chain format, and is characterized by being managed in a decentralized manner across multiple nodes.

[1302] "Distributed ledger connection means" refers to technology for transmitting hash values ​​and analysis results of digital data to a distributed ledger network and generating records and identifiers on that network.

[1303] An "identifier" refers to a unique string of characters or code generated by a distributed ledger network to prove the uniqueness and authenticity of digital data.

[1304] "Means for notifying users" is a general term for technologies and methods for informing users of the authenticity and identification results of digital data, and includes push notifications, emails, messaging services, etc.

[1305] "User authentication means" refers to a technology or method for verifying a user's identity and authority before sending or receiving digital data.

[1306] "Data storage" refers to a storage device or location for storing analysis results, hash values, identifiers, etc.

[1307] To implement this invention, a user-operated terminal (such as a smartphone or computer) and an associated server are required. The server operates as a system including the following main components:

[1308] 1. Digital Data Transmission

[1309] A user uses a device to send digital data (e.g., videos, images) to a server. At this time, user authentication is performed on the device. A general authentication service is used for user authentication. For example, Firebase Authentication is used for login and authentication.

[1310] 2. Analysis of transmitted digital data

[1311] The server temporarily stores the received digital data and sends it to an AI identification means (e.g., TensorFlow or OpenCV-based AI identification), which analyzes the received data and determines whether it was actually photographed or generated by AI.

[1312] 3. Generating a hash value

[1313] The server generates a hash value of the analyzed digital data using the SHA-256 algorithm, which provides a unique identifier for the digital data.

[1314] 4. Sending to the distributed ledger network

[1315] The server sends the generated hash value and analysis results to a distributed ledger network (e.g., the Ethereum blockchain), which creates a transaction based on the received information and generates a unique identifier, which is then recorded in the distributed ledger.

[1316] 5. Notice to Users

[1317] The server receives the identifier and analysis results returned from the distributed ledger network and stores them in the user's data storage (e.g., a database). The server then notifies the user of the results using a notification service such as Firebase Cloud Messaging.

[1318] Specific examples

[1319] For example, consider the case where a user uploads a video taken at the site of a natural disaster. The user shoots the video using a smartphone and sends it to a server through an application. The sent video is analyzed by an identification AI and determined to be live footage. The server generates a hash value for the video and sends it to the distributed ledger network. The blockchain generates a unique identifier based on the hash value and sends it back to the server. Finally, the server notifies the user of the identifier and the analysis results, allowing the user to verify the authenticity of the uploaded video.

[1320] Example prompts for generative AI models

[1321] I uploaded a video taken at the scene of a natural disaster and want to verify its authenticity.

[1322] These steps allow users to ensure the authenticity of digital data and build a system that can prove its reliability using distributed ledger technology.

[1323] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1324] Step 1: User authentication and digital data transmission

[1325] Subject: User

[1326] Specific operation: A user logs in using an application on their device. Firebase Authentication can be used for login. If authentication is successful, the user selects digital data (e.g., a video) and clicks the send button. At that time, the device makes a request to send the selected file to the server.

[1327] Input: User login information, digital data (file)

[1328] Output: Authentication token, digital data transmission to server

[1329] Step 2: Temporarily store digital data and analyze it using AI

[1330] Subject: Server

[1331] How it works: The server receives the digital data and temporarily stores it in storage. The stored data is then sent to a classification AI (using TensorFlow and OpenCV). The classification AI analyzes the received data and determines whether it is real or AI-generated.

[1332] Input: Digital data

[1333] Output: Analysis results (real-life or AI-generated)

[1334] Step 3: Generate a hash value

[1335] Subject: Server

[1336] How it works: The server generates a hash value of the uploaded digital data based on the analysis results. The hash value is generated using the SHA-256 algorithm. The generated hash value is used as a unique identifier.

[1337] Input: Digital data

[1338] Output: hash value

[1339] Step 4: Send the hash value and analysis results to the distributed ledger network

[1340] Subject: Server

[1341] How it works: The server sends the generated hash value and analysis results to the distributed ledger network, which uses the received information to create a transaction and generate a unique identifier.

[1342] Input: hash value, analysis result

[1343] Output: A unique identifier

[1344] Step 5: Storing identifiers and analysis results and notifying the user

[1345] Subject: Server

[1346] Specific operation: The server receives the ID and analysis results from the distributed ledger network, stores them in the user's database, and then notifies the user of the identification results using Firebase Cloud Messaging or similar.

[1347] Input: unique identifier, analysis result

[1348] Output: Saved data, notification message

[1349] Through the above processing steps, the user can quickly and reliably confirm the authenticity of the digital data and receive highly reliable information.

[1350] 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.

[1351] The present invention is a system for verifying the authenticity of digital content, which further combines an emotion engine that recognizes user emotions and evaluates the reliability of the content using the analysis results. It includes the following main components: a means for uploading digital content, an identification AI means for analyzing the uploaded digital content, a hash generation means for verifying the authenticity of the digital content based on the analysis results, a blockchain connection means for transmitting the generated hash value and analysis results to a blockchain network and generating an identifier, a means for storing the identifier and analysis results returned from the blockchain network and notifying the user, and an emotion engine means for recognizing user emotions.

[1352] Program processing

[1353] Uploading Digital Content

[1354] A user logs in to the system using a terminal and uploads digital content (e.g., images or videos). The server temporarily stores the received content in storage and begins processing.

[1355] Content Analysis

[1356] The server sends the stored digital content to an identification AI means, which analyzes the digital content and determines whether it is AI-generated or live-action, using metadata checks, pattern recognition, and deep learning techniques.

[1357] Saving the identification results

[1358] The server receives and temporarily stores the analysis results from the identification AI, which include information on whether the digital content is AI-generated or live-action.

[1359] Generating a hash value

[1360] The server generates a hash value for the digital content. The hash generation module calculates the hash value for the content using an algorithm such as SHA-256.

[1361] Send to blockchain

[1362] The server sends the generated hash value and the analysis results of the identification AI to the blockchain network, where the smart contract in the blockchain network receives the information, generates a unique identifier, and records it on the chain.

[1363] Notifying users of the results

[1364] The blockchain network returns the generated identifier to the server, which associates and stores the identifier and analysis results in the user's database and notifies the user, who can then verify the authenticity of the digital content.

[1365] Emotion analysis and evaluation

[1366] An emotion engine is used to analyze a user's emotions toward uploaded digital content. As a user watches or browses digital content, the emotion engine analyzes the user's reactions in real time. The emotion engine utilizes facial recognition, voice analysis, and other biometric data.

[1367] Saving and utilizing sentiment results

[1368] The server receives the user's emotion results generated by the emotion engine and stores them in a database. The stored emotion results are used to evaluate the trustworthiness of digital content, allowing for further analysis and notifications based on the user's emotion results.

[1369] Specific examples

[1370] 1. Upload and analyze

[1371] Users upload videos taken from the scene of a natural disaster.

[1372] The server sends the video to an identification AI, which then analyzes the video.

[1373] The identification AI determines that the video is live action.

[1374] 2. Use of Emotion Engine

[1375] When a user watches an uploaded video, the emotion engine analyzes the user's reaction.

[1376] The analysis results indicate that the user felt a strong interest in and trust in the video.

[1377] 3. Identifier Generation and Notification

[1378] The server generates a hash value for the video and sends the hash value and analysis results to the blockchain network.

[1379] The blockchain network generates and records a unique identifier.

[1380] The server notifies the user of the identifier and the emotion engine analysis results, and the user confirms the authenticity and reliability of the video.

[1381] This invention allows users to easily verify the authenticity of digital content and further increases the trustworthiness of the content through evaluations using an emotion engine, thereby preventing the spread of false information and avoiding social unrest.

[1382] The processing flow will be explained below.

[1383] Step 1:

[1384] The user accesses the system's website or application using a terminal and logs in. They enter their authentication information to authenticate the user, and if authentication is successful, they are taken to the upload screen.

[1385] Step 2:

[1386] The user clicks the "Select File" button on the upload screen to select the digital content (image or video) file. After selecting the file, the user clicks the "Upload" button to send the file to the server.

[1387] Step 3:

[1388] The server receives the digital content sent by the user and temporarily stores it in storage. A message indicating successful storage is logged and a notification of upload completion is displayed to the user.

[1389] Step 4:

[1390] The server sends the stored digital content to the identification AI means, which loads the digital content and runs image or video analysis algorithms to determine whether it is AI-generated or live-action.

[1391] Step 5:

[1392] The identification AI completes the analysis and returns the results to the server, which associates the analysis results with the corresponding digital content entry and temporarily stores them.

[1393] Step 6:

[1394] The server generates a hash value for the digital content. The file path is passed to the hash generation module, which calculates the hash value using a hash algorithm such as SHA-256. The generated hash value is then sent back to the server.

[1395] Step 7:

[1396] The server compiles the hash value and the results of the AI's analysis and sends it to the blockchain network, where a smart contract receives the data, creates a transaction that generates a unique identifier, and records the information on the blockchain.

[1397] Step 8:

[1398] The blockchain network sends the generated identifier back to the server, which associates and stores this identifier and the analysis results in the user's database.

[1399] Step 9:

[1400] The server sends a notification to the user, which includes the identification result and details of the content certified on the blockchain, and is displayed on the user's device.

[1401] Step 10:

[1402] The server calculates the hash value of the digital content and notifies the user of the analysis results, including the identifier. At the same time, if a viewing operation by the user is detected, the emotion engine is activated. The emotion engine uses the user's camera and microphone data to perform facial recognition and voice analysis.

[1403] Step 11:

[1404] The emotion engine analyzes the user's emotions in real time and sends the results to the server, which stores the emotion data in association with the digital content entry.

[1405] Step 12:

[1406] The server sends a notification to the user and provides feedback on the results of the emotion analysis of the digital content they have viewed. By comparing their own emotions with the analysis results of the digital content, users can intuitively evaluate its reliability.

[1407] Step 13:

[1408] After users have finished rating the content they have viewed, the sentiment analysis results are reflected in the system's overall reliability score. The server uses the collected data to improve the accuracy of future analysis of similar content.

[1409] Through these detailed steps, the present invention achieves a comprehensive credibility assessment that takes into account not only the authenticity of digital content but also the user's emotional response, which is expected to enable users to use the content with peace of mind and prevent the spread of false information in society.

[1410] Example 2

[1411] 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."

[1412] There is a need for a way to easily verify the authenticity of digital content and further evaluate its reliability. In particular, to prevent the spread of false information and avoid social unrest, it is important not only to verify the authenticity of digital content, but also to evaluate its reliability based on user sentiment. However, current systems make it difficult to simultaneously verify the authenticity and evaluate the reliability of digital content. Furthermore, technology for analyzing user responses in real time is still in its infancy. Solving these problems is a challenge.

[1413] 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.

[1414] In this invention, the server includes means for uploading digital content, identification AI means for analyzing the uploaded digital content, hash generation means for verifying the authenticity of the digital content based on the analysis results, blockchain connection means for transmitting the generated hash value and the analysis results to the blockchain network and generating an identifier, means for storing the identifier and the analysis results returned from the blockchain network and notifying the user, and emotion engine means for analyzing the user's emotions toward the digital content, thereby enabling the authenticity of digital content to be verified and its reliability to be evaluated simultaneously.

[1415] "Digital content" is a general term for electronically generated or stored information such as images, video, audio, and text.

[1416] An "uploading means" is a mechanism by which a user transmits digital content from his or her own terminal to a server.

[1417] "Identification AI means" refers to artificial intelligence technology that analyzes uploaded digital content and determines its authenticity (e.g., whether it is AI-generated or live-action).

[1418] A "hash generator" is a mechanism that calculates a unique hash value from digital content to prevent tampering and verify the authenticity of the digital content.

[1419] The "blockchain connection means" is a mechanism for transmitting the generated hash value and analysis results to the blockchain network and recording them on the blockchain.

[1420] "Identifier" means a unique ID generated by the blockchain network and used to verify the authenticity of digital content and its analysis results.

[1421] "Emotion engine means" is a technology for analyzing users' emotional reactions to digital content and collecting and evaluating the results.

[1422] "User authentication means" is a mechanism for verifying the identity of a user before using the system and preventing fraudulent use.

[1423] A "database" is a system that structurally stores and manages analysis results, hash values, identifiers, and user emotion data.

[1424] "Authenticity assessment" is the process of assessing the authenticity of digital content and its trustworthiness based on users' emotional responses.

[1425] The present invention provides a system for verifying the authenticity of digital content and evaluating its reliability by recognizing user emotions. The system includes a means for uploading digital content, an AI identification means, a hash generation means, a blockchain connection means, an emotion engine means, and a means for notifying users.

[1426] A user logs in to the system using a terminal and uploads digital content (e.g., images or videos). The server temporarily stores the received content in storage (e.g., AWS S3 or Google Cloud Storage) and begins processing. User authentication is used to prevent fraudulent use.

[1427] The server then sends the stored digital content to an AI identification method. The AI ​​identification method (using TensorFlow or PyTorch, for example) determines whether the digital content is AI-generated or live-action. Specifically, it uses metadata checks, pattern recognition, and deep learning techniques. The analysis results are temporarily stored in a database (e.g., MySQL or PostgreSQL).

[1428] The server then generates a hash value for the digital content, using a library such as the OpenSSL library with the SHA-256 algorithm, and stores the hash value in a database.

[1429] The generated hash value and the analysis results of the identification AI are sent from the server to a blockchain network (e.g., Ethereum or Hyperledger Fabric). A smart contract on the blockchain network receives the information, generates and records a unique identifier. The identifier is sent back to the server and stored in a database along with the analysis results.

[1430] The server then sends a notification to the user, who can then confirm the authenticity of the digital content. Furthermore, as the user watches or reads the digital content, the system uses an emotion engine (e.g., FaceAPI or IBM Watson) to analyze the user's emotions in real time. The emotion results are stored in a database and used for authenticity assessment.

[1431] As a concrete example, consider a scenario in which a user uploads a video taken at the site of a natural disaster. The server sends the video to an identification AI, which analyzes the video. If the identification AI determines that the video is live action, the analysis result and the generated hash value are sent to the blockchain, and a unique identifier is generated. The server notifies the user of the identifier and the emotion engine's analysis results, and the user confirms the authenticity and reliability of the video.

[1432] An example of a prompt might be:

[1433] "Upload videos taken from natural disaster sites and verify their authenticity using our identification AI and emotion engine."

[1434] As described above, this invention allows users to easily verify the authenticity of digital content, and furthermore, through evaluations using an emotion engine, it is possible to increase the trustworthiness of the content. This system is effective as a means of preventing the spread of false information and avoiding social unrest.

[1435] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1436] Step 1: Log in and upload content

[1437] (Specific explanation)

[1438] A user logs in to the system using a terminal. The user accesses the system and enters their username and password in the login form. The server verifies the user's authentication information and starts a session. After successful login, the user clicks the "Upload" button and selects an image or video file. The terminal sends the selected file to the server. The server temporarily stores the received digital content in storage and logs the storage information.

[1439] (input)

[1440] Usernames, passwords, digital content files (images and videos)

[1441] (Data processing / data calculation)

[1442] The user's authentication information is checked against the database and the file is saved to storage.

[1443] (output)

[1444] Authentication result, saved file path

[1445] Step 2: Analyzing the content

[1446] (Specific explanation)

[1447] The server sends the stored digital content to the identification AI means. The identification AI means receives the uploaded digital content and analyzes whether the content is AI-generated or live-action using metadata verification and pattern recognition algorithms. This analysis uses deep learning techniques, such as TensorFlow or PyTorch models. The identification AI then sends the analysis results back to the server.

[1448] (input)

[1449] File path of the stored digital content

[1450] (Data processing / data calculation)

[1451] Content metadata checks, pattern recognition, and deep learning analysis

[1452] (output)

[1453] Analysis results (determination of whether it is AI-generated or live-action)

[1454] Step 3: Save the analysis results

[1455] (Specific explanation)

[1456] The server receives the analysis results from the identification AI and temporarily stores them in a database. The database associates the content file path with the analysis results and stores them in a database such as MySQL or PostgreSQL.

[1457] (input)

[1458] Analysis results, file paths of digital content

[1459] (Data processing / data calculation)

[1460] Store data in a database and associate content with analysis results.

[1461] (output)

[1462] Saved entries in the database

[1463] Step 4: Generate a hash value

[1464] (Specific explanation)

[1465] The server generates a hash value for the digital content, for example, using the SHA-256 algorithm with the OpenSSL library, and stores the hash value in a database.

[1466] (input)

[1467] Digital content file path

[1468] (Data processing / data calculation)

[1469] A hash value is calculated using the SHA-256 algorithm.

[1470] (output)

[1471] Generated hash value

[1472] Step 5: Send to the blockchain

[1473] (Specific explanation)

[1474] The server sends the generated hash value and the analysis result of the identification AI to the blockchain network (for example, using Ethereum or Hyperledger Fabric). The blockchain network's smart contract receives the information, generates a unique identifier, records it on the chain, and sends this identifier back to the server.

[1475] (input)

[1476] Hash value, analysis results

[1477] (Data processing / data calculation)

[1478] A smart contract is invoked to generate an identifier and record it on-chain.

[1479] (output)

[1480] The identifier returned from the blockchain

[1481] Step 6: Notify users of the results

[1482] (Specific explanation)

[1483] The server associates the identifier and the analysis results with the user's database and stores them. The server then sends the user an email or system notification, allowing them to verify the authenticity of the digital content.

[1484] (input)

[1485] Identifiers, analysis results, user information (email address, etc.)

[1486] (Data processing / data calculation)

[1487] The identifier and analysis results are stored in a database and notifications are generated and sent.

[1488] (output)

[1489] Notifications sent, updated entries in the database

[1490] Step 7: Emotion analysis and evaluation

[1491] (Specific explanation)

[1492] When a user watches or browses digital content, an emotion engine is used to analyze the user's emotions in real time based on facial recognition, voice analysis, and other biometric data, for example, using FaceAPI or IBM Watson.

[1493] (input)

[1494] User biometric data (facial expression data, voice data, etc.)

[1495] (Data processing / data calculation)

[1496] It performs real-time sentiment analysis and generates user sentiment results.

[1497] (output)

[1498] Emotion analysis results

[1499] Step 8: Storing and utilizing sentiment results

[1500] (Specific explanation)

[1501] The server receives the user's emotion results generated by the emotion engine and stores them in a database. The stored emotion results are used to evaluate the trustworthiness of digital content. Feedback and notifications are also provided to the user as needed.

[1502] (input)

[1503] Emotion analysis results

[1504] (Data processing / data calculation)

[1505] The sentiment results are stored in a database and used for reliability evaluation.

[1506] (output)

[1507] Save entries in the database, notifications

[1508] (Application example 2)

[1509] 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."

[1510] As digital content rapidly increases, it is becoming more difficult to verify its authenticity and reliability. In particular, the spread of AI-generated content and fake news has become a problem, creating a need to provide an environment where users can safely use content. Furthermore, there are insufficient means to analyze users' emotional reactions and evaluate the reliability of content. To solve these issues, a system is needed that can verify the authenticity of digital content and analyze user emotions to increase its reliability.

[1511] 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 digital content, identification AI means for analyzing the uploaded digital content, hash generation means for verifying the authenticity of the digital content based on the analysis results, blockchain connection means for transmitting the generated hash value and the analysis results to the blockchain network and generating an identifier, means for saving the identifier and analysis results returned from the blockchain network and notifying the user, emotion engine means for recognizing the user's emotions and evaluating the trustworthiness of the content based on the analysis results, and means for saving the emotion results recognized by the emotion engine means in a database and using them for trustworthiness evaluation. This makes it possible to verify the authenticity of digital content and evaluate trustworthiness through user emotion analysis.

[1512] "Digital content" refers to content stored or distributed in electronic form, including images, videos, audio files, and text.

[1513] "Uploading" refers to the act of a user sending digital content from their own device to a server or cloud service.

[1514] "Discrimination AI" refers to a system that uses artificial intelligence technology to analyze digital content and determine whether it is live-action or AI-generated.

[1515] A "hash generator" refers to an algorithm or process for generating a unique hash value based on the content of digital content. A typical algorithm is SHA-256.

[1516] "Blockchain" is a network that uses distributed ledger technology, and refers to a technology that can prevent tampering while ensuring the reliability and transparency of data.

[1517] "Identifier" refers to an ID or code that is uniquely assigned to data recorded on a blockchain network.

[1518] "Emotion Engine Means" refers to technology for recognizing and analyzing a user's emotions, including facial recognition, voice analysis, and other biometric data analysis technologies.

[1519] A "database" refers to a system for efficiently storing, managing, and searching large amounts of data.

[1520] "User authentication" refers to the means used to verify that a user has the appropriate authority to access a system, typically including username, password, and two-factor authentication.

[1521] "Authenticity" refers to the fact that digital content has not been tampered with and that its origin and creation process are legitimate.

[1522] "Credibility" refers to the degree to which users can trust the content and its source.

[1523] This invention is a system for verifying the authenticity of digital content and assessing its trustworthiness by analyzing user sentiment. The system includes the following components:

[1524] 1. Digital Content Upload Method:

[1525] A user logs in to the system using a terminal and uploads digital content (e.g., images or videos). The server temporarily stores the received content in storage and begins processing.

[1526] 2. Content analysis method (identification AI method):

[1527] The server sends the stored digital content to an identification AI means, which analyzes the digital content and determines whether it is AI-generated or live-action, using metadata checks, pattern recognition, and deep learning techniques.

[1528] 3. Hash generation method:

[1529] The server generates a hash value for the digital content. The hash generation module calculates the hash value for the content using an algorithm such as SHA-256.

[1530] 4. Blockchain connection methods:

[1531] The server sends the generated hash value and the analysis results of the identification AI to the blockchain network, where the smart contract in the blockchain network receives the information, generates a unique identifier, and records it on the chain.

[1532] 5. How to notify users of results:

[1533] The blockchain network returns the generated identifier to the server, which associates and stores the identifier and analysis results in the user's database and notifies the user, who can then verify the authenticity of the digital content.

[1534] 6. Emotion Engine Means:

[1535] As users watch or browse digital content, the emotion engine analyzes their reactions in real time, using facial recognition, voice analysis and other biometric data.

[1536] 7. Storing and utilizing emotion results:

[1537] The server receives the user's emotion results generated by the emotion engine and stores them in a database. The stored emotion results are used to evaluate the trustworthiness of digital content, allowing for further analysis and notifications based on the user's emotion results.

[1538] Specific flow of usage example

[1539] A user uploads a video taken at the scene of a natural disaster. The server sends the video to an identification AI, which analyzes it. As a result, the video is determined to be live action. The server then generates a hash value for the video and sends the generated hash value and analysis results to the blockchain network. The blockchain network generates a unique identifier and records it on the chain. The blockchain network then returns the generated identifier to the server, which stores the identifier and analysis results in the user's database and notifies the user. Furthermore, while the user is watching the video, an emotion engine analyzes the user's reaction and generates an emotion result. The server receives the result, stores it in a database, and uses it to evaluate the reliability of content.

[1540] Prompt Sentence Examples

[1541] "Analyze emotions while users watch video news from the scene of a natural disaster."

[1542] The above is a specific embodiment of the present invention.

[1543] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1544] Step 1: Upload your digital content

[1545] A user uploads digital content (e.g., a video file) from their own device to the system. The user selects the content and presses the upload button. The input is the uploaded digital content, and the output is the content file stored on the server. The server temporarily stores the received content in storage and passes the destination path to the next processing step.

[1546] Step 2: Analyzing the content

[1547] The server sends the stored digital content to the identification AI means, which analyzes the content using a deep learning model. It checks metadata and performs image analysis to determine whether the content is live-action or AI-generated. The input is the digital content in the destination path, and the output is the AI ​​determination result (live-action or AI-generated).

[1548] Step 3: Generate a hash value

[1549] The server generates a hash value for the content. It uses the SHA-256 algorithm to calculate a unique hash value for the digital content. The input is the digital content in the destination path, and the output is the generated hash value. The hash value is used in the next step.

[1550] Step 4: Send to the blockchain

[1551] The server sends the generated hash value and the analysis result of the identification AI to the blockchain network. The blockchain network's smart contract receives the information, generates a unique identifier, and records it on the chain. The input is the hash value and the AI's judgment result, and the output is the identifier generated by the blockchain network.

[1552] Step 5: Notify users of the results

[1553] The server stores the identifier returned from the blockchain network in a database and notifies the user. The notification includes the identifier and the AI ​​judgment result, allowing the user to verify the authenticity of the content. The input is the identifier and the AI ​​judgment result, and the output is a notification to the user. The notification is sent as an email or an internal system message.

[1554] Step 6: Emotion engine analysis

[1555] While a user is viewing digital content, the emotion engine analyzes the user's reactions in real time. It uses facial recognition, voice analysis, and biometric data to estimate the user's emotions. The input is the user's reaction data while viewing, and the output is the analyzed emotion results.

[1556] Step 7: Storing and utilizing sentiment results

[1557] The server stores the emotion results generated by the emotion engine in a database. The stored emotion results are used to evaluate the trustworthiness of digital content. The input is the analyzed emotion results, and the output is the data stored in the database. For security reasons, the stored data is encrypted.

[1558] 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.

[1559] 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.

[1560] 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.

[1561] 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.

[1562] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1563] 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.

[1564] 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).

[1565] 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, motorcycles, and other devices, 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.

[1566] 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."

[1567] 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.

[1568] 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).

[1569] 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.

[1570] 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.

[1571] 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.

[1572] 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.

[1573] 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.

[1574] 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.

[1575] 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.

[1576] 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.

[1577] 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.

[1578] 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.

[1579] The following is further disclosed regarding the above embodiment.

[1580] (Claim 1)

[1581] means for uploading digital content;

[1582] an identification AI means for analyzing the uploaded digital content;

[1583] a hash generation means for verifying the authenticity of the digital content based on the analysis results;

[1584] a blockchain connection means for transmitting the generated hash value and the analysis result to a blockchain network and generating an identifier;

[1585] A system including a means for storing the identifier and analysis results returned from the blockchain network and notifying the user.

[1586] (Claim 2)

[1587] 10. The system of claim 1, further comprising means for performing user authentication when uploading digital content.

[1588] (Claim 3)

[1589] 2. The system according to claim 1, wherein a database is used as a means for storing the analyzed results.

[1590] "Example 1"

[1591] (Claim 1)

[1592] a means for a user to select and upload information;

[1593] Identification AI means for temporarily storing and analyzing the uploaded information;

[1594] A means for storing the analysis results received from the identification AI;

[1595] means for generating a hash value of the digital content;

[1596] a blockchain connection means for transmitting the generated hash value and the analysis result to a blockchain network and generating a unique identifier;

[1597] A system including a means for storing the identifier and analysis results returned from the blockchain network and notifying the user.

[1598] (Claim 2)

[1599] 10. The system according to claim 1, further comprising means for performing user authentication when uploading digital content.

[1600] (Claim 3)

[1601] 2. The system according to claim 1, wherein a database is used as a means for storing the analyzed results.

[1602] "Application Example 1"

[1603] (Claim 1)

[1604] means for transmitting digital data;

[1605] a discriminatory artificial intelligence means for analyzing the transmitted digital data;

[1606] a hash value generating means for verifying the authenticity of the digital data based on the analysis result;

[1607] a distributed ledger connection means for transmitting the generated hash value and the analysis result to the distributed ledger network and generating an identifier;

[1608] A system that includes a means for storing the identifiers and analysis results returned from the distributed ledger network and notifying users.

[1609] (Claim 2)

[1610] 2. The system according to claim 1, further comprising means for performing user authentication when transmitting digital data.

[1611] (Claim 3)

[1612] 10. The system of claim 1, wherein data storage is used as a means for storing the analyzed results.

[1613] "Example 2: Combining Emotion Engines"

[1614] (Claim 1)

[1615] means for uploading digital content;

[1616] an identification AI means for analyzing the uploaded digital content;

[1617] a hash generation means for verifying the authenticity of the digital content based on the analysis results;

[1618] a blockchain connection means for transmitting the generated hash value and the analysis result to a blockchain network and generating an identifier;

[1619] A means for storing the identifier and analysis results returned from the blockchain network and notifying the user; and

[1620] A system including an emotion engine means for analyzing a user's emotion towards digital content.

[1621] (Claim 2)

[1622] 10. The system of claim 1, further comprising means for performing user authentication when uploading digital content.

[1623] (Claim 3)

[1624] 2. The system according to claim 1, wherein a database is used as a means for storing the analyzed results.

[1625] (Claim 4)

[1626] 2. The system according to claim 1, further comprising means for storing user sentiment results for digital content in a database and utilizing the results for reliability evaluation.

[1627] "Application example 2 when combining emotion engines"

[1628] (Claim 1)

[1629] means for uploading digital content;

[1630] an identification AI means for analyzing the uploaded digital content;

[1631] a hash generation means for verifying the authenticity of the digital content based on the analysis results;

[1632] a blockchain connection means for transmitting the generated hash value and the analysis result to a blockchain network and generating an identifier;

[1633] A means for storing the identifier and analysis results returned from the blockchain network and notifying the user; and

[1634] an emotion engine means for recognizing a user's emotion and evaluating the reliability of the content through the analysis result;

[1635] The system includes a means for storing the emotion results recognized by the emotion engine means in a database and utilizing the results for reliability evaluation.

[1636] (Claim 2)

[1637] 10. The system of claim 1, further comprising means for performing user authentication when uploading digital content.

[1638] (Claim 3)

[1639] 2. The system according to claim 1, further comprising means for storing the analyzed results in a database and for analyzing the reaction video being viewed by the user in real time. [Explanation of symbols]

[1640] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for uploading digital content; an identification AI means for analyzing the uploaded digital content; a hash generation means for verifying the authenticity of the digital content based on the analysis results; a blockchain connection means for transmitting the generated hash value and the analysis result to a blockchain network and generating an identifier; A system including a means for storing the identifier and analysis results returned from the blockchain network and notifying the user.

2. 10. The system according to claim 1, further comprising means for performing user authentication when uploading digital content.

3. 2. The system according to claim 1, wherein a database is used as a means for storing the analyzed results.

Images