system

Abstract

Provide a system. A method for receiving a user input request includes: means for invoking a generative model based on the input request to generate a product; means for collecting metadata including the training data and the version of the generative model used during the generation process; means for hashing the generated product and generating new transaction data based on the hash value and metadata of the generation process; means for adding the transaction data to a blockchain; A means for providing the generated product and information on the blockchain to a user; A system including:

Description

[Technical Field]

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

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

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

[0004] In recent years, technological advances in generative AI have enabled the automatic generation of a wide variety of digital content, but doubts about the authenticity and reliability of these products are increasing. In particular, there are concerns about copyright infringement and unauthorized use of the products, so it is important to record the process of generating the products and ensure traceability. There is also a need for a mechanism that allows users to easily verify the authenticity and reliability of the products. Therefore, the present invention aims to provide a system that solves these problems. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for receiving a user's input request, activating a generative model based on the request, and creating a product. It also includes a means for collecting metadata, including the training data and the version of the generative model, used during the generation process, and a means for hashing the generated product and generating new transaction data based on the hash value and the metadata of the generation process. It also provides a system that includes a means for adding this transaction data to a blockchain and providing the generated product and information on the blockchain (hash value and transaction ID) to the user. This ensures the traceability and reliability of the product, allowing the user to easily verify the authenticity of the product.

[0006] "User" means any person or entity that makes a generation request to the System.

[0007] An "input request" refers to information including the type of content the user wants to generate and specific generation instructions.

[0008] "Generative model" refers to a machine learning model that creates a product using specified initial data and an internal algorithm.

[0009] "Product" refers to the digital content (e.g., images, text) created by a generative model.

[0010] "Metadata" refers to supplementary information collected during the generation process, such as the version of the generative model and the training data used.

[0011] "Hashing" refers to the process of converting the product into a string of a fixed length.

[0012] "Hash value" refers to the unique string obtained by hashing a product.

[0013] "Transaction Data" refers to data including hash values ​​of generated artifacts and collected metadata.

[0014] "Blockchain" refers to a distributed ledger that is created by adding transaction data to a series of blocks.

[0015] "Transaction ID" refers to a unique identifier that identifies a specific transaction on a blockchain. [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 illustrating 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] This invention is a system that combines the AI ​​product generation process and its tracing function. This system consists of the following steps:

[0038] First, a user makes a request to create a product using a terminal. The user inputs the type of product (e.g., image, text) and specific instructions for creation via the interface on the terminal. For example, the user inputs a specific request such as "Please create an image of a dog." This request is then sent from the terminal to the server.

[0039] The server then receives the user's request and analyzes its contents. Based on the request, the server selects and activates an appropriate generative model. This generative model uses its internal algorithms and the specified initial data to create the desired product. This process results in the creation of a specific product (e.g., an image of a dog).

[0040] During the generation process, the server collects metadata such as the training data used, the version of the generative model, the time required for generation, etc. This metadata is necessary to ensure transparency of the generation process.

[0041] Once the creation process is complete, the server hashes the created product, resulting in a unique hash value for the product. This hash value, along with the collected metadata, is used to create new transaction data.

[0042] The server then adds this transaction data to the blockchain, and a new block is generated and integrated into the existing blockchain, recording the entire process of creating the product on a distributed ledger, ensuring the authenticity and traceability of the product.

[0043] Finally, the server returns the product, the hash value, and the transaction ID on the blockchain to the terminal. The user receives this information via the terminal and checks the product and verifies its authenticity. By checking the provided hash value and transaction ID on the blockchain, the user can be sure that the product is an AI-generated product.

[0044] As a concrete example, let's consider the case where a user makes a request to generate an "image of a dog." The user sends the request "Please generate an image of a dog" from their device to the server. The server receives and analyzes this request, and launches the appropriate image generation model. The model generates an image of a dog using the specified initial data and internal algorithm. During the generation process, the server collects metadata such as the training data used and the model version, and hashes the generated image of the dog. Based on this hash value and the collected metadata, new transaction data is generated and added to the blockchain. Finally, the image of the dog, its hash value, and the transaction ID are provided to the user's device. This allows the user to verify that the generated image of the dog is a trustworthy AI product.

[0045] The above is an embodiment of the present invention. This system ensures the traceability and reliability of products, improving trust in AI products.

[0046] The processing flow will be explained below.

[0047] Step 1:

[0048] The user uses a terminal to create a request to create a product. The user selects the type of product (e.g., image or text) on the interface and enters creation instructions (e.g., "Please create an image of a dog"). Furthermore, the user sets creation options (e.g., image style and resolution) as needed. This input data is sent as a request to the server.

[0049] Step 2:

[0050] The server receives a request sent by a user, analyzes the request, extracts the type of product and its parameters, and then refers to an internal database to select an appropriate generative model based on the extracted data.

[0051] Step 3:

[0052] The server launches the selected generative model, which incorporates the specified initial data and algorithms. The server uses this data to create a product (e.g., an image of a dog). During the generation process, the server allocates the necessary computing resources and manages the process to ensure smooth progress.

[0053] Step 4:

[0054] As the generation process progresses, the server collects metadata such as the training data used, the version of the generative model, and the time it took to generate the model, which is necessary for later traceability.

[0055] Step 5:

[0056] Once the generation process is complete, the server hashes the generated product, resulting in a unique hash value for the product, which is important for verifying the product's identity.

[0057] Step 6:

[0058] The server combines the generated hash value with the collected metadata to generate new transaction data, including the hash value of the product, the training data used, the version of the generative model, and the generation time.

[0059] Step 7:

[0060] The server adds new transaction data to the blockchain, generates a new block based on the transaction data, and integrates this block into the existing blockchain, recording the entire process of product creation on a distributed ledger.

[0061] Step 8:

[0062] The server returns the generated product, its hash value, and the transaction ID on the blockchain to the terminal, allowing the user to check the product and details of its generation process.

[0063] Step 9:

[0064] The terminal displays the product, hash value, and transaction ID received from the server to the user, who can then verify the product and use the provided hash value and transaction ID to verify the product's authenticity and traceability on the blockchain.

[0065] Through these steps, the system generates the AI ​​product specified by the user, ensures traceability based on the metadata obtained during the process, and provides a mechanism for users to easily verify the authenticity of the product.

[0066] Example 1

[0067] 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."

[0068] Ensuring transparency and traceability in the generation process of AI products is a challenge for existing systems. There is a need for a means for users to verify the authenticity of the generated data. There is also a need to improve the reliability of products by collecting detailed information about the generation process and providing it to users.

[0069] 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.

[0070] In this invention, the server includes means for receiving a user's input request, means for activating a generative model based on the input request and creating a product, means for collecting metadata including the training data and the version of the generative model used in the generation process, means for hashing the generated product and generating new transaction data based on the hash value and the metadata of the generation process, means for adding the transaction data to a distributed ledger, and means for providing the generated product and information on the distributed ledger to a user. This ensures transparency and traceability of the generation process and enables verification of the authenticity and reliability of the product.

[0071] A "user" is a person or entity that utilizes the system to make a request for a product.

[0072] An "input request" is a request that a user sends information including the type of product and detailed instructions via a terminal.

[0073] A "generative model" is an algorithm or machine learning model used to create a product based on an input request.

[0074] A "product" is an output created using a generative model, and can take the form of an image, text, or other information.

[0075] "Metadata" is relevant information such as the training data used during the generation process, the version of the generative model, and the time it took to generate it.

[0076] "Hashing" is the process of generating a unique hash value for a product, which is used to verify the authenticity of the data.

[0077] A "hash value" is a unique identifier for a product, a fixed-length string obtained through a hashing process.

[0078] "Transaction Data" means a data set containing hash values ​​and metadata about the generation process that is recorded on the blockchain.

[0079] A "distributed ledger" is a database in which data is recorded in a decentralized manner using blockchain technology, making it accessible and verifiable.

[0080] A "block" is a unit of data that is added to a blockchain and contains new transaction data.

[0081] A "transaction ID" is a unique identifier used to identify a specific transaction on a blockchain.

[0082] The present invention is a system that combines the generation process of AI products with a tracing function. This system allows users to make requests to create products, generates products based on those requests, and has the ability to record and manage the entire generation process. Specific embodiments of the system are described below.

[0083] First, a user makes a request to create a product using a terminal. The user inputs the type of product (e.g., image, text) and specific instructions for creation via the interface on the terminal. For example, the user inputs a specific request such as "Please create an image of a dog." This request is then sent from the terminal to the server.

[0084] Next, the server receives the user's request and analyzes its contents. The server selects and launches an appropriate generative model based on the request. This generative model is implemented using libraries such as TENSORFLOW (registered trademark) or PyTorch. The generative model uses its internal algorithm and the specified initial data to create the product desired by the user. This process results in the generation of a specific product (for example, an image of a dog).

[0085] During the generation process, the server collects metadata such as the training data used, the version of the generative model, and the time required for generation, ensuring transparency of the generation process. Once the generation process is complete, the server hashes the generated product using the SHA-256 hashing algorithm. This results in a unique hash value for the product. New transaction data is generated based on this hash value and the collected metadata.

[0086] The server then adds this transaction data to the blockchain using Hyperledger Fabric, creating a new block that is integrated into the existing distributed ledger, recording the entire process of creating the product, ensuring its authenticity and traceability.

[0087] Finally, the server returns the product, the hash value, and the transaction ID on the distributed ledger to the terminal. The user receives this information via the terminal and checks the product and verifies its authenticity. By checking the provided hash value and transaction ID on the distributed ledger, the user can be sure that the product is an AI-generated product.

[0088] As a concrete example, consider the case where a user makes a request to generate an "image of a dog." The user sends the request "Please generate an image of a dog" from their device to the server. The server receives and analyzes this request, then launches the appropriate image generation model. The model generates an image of a dog using the specified initial data and its internal algorithm. During the generation process, the server collects metadata such as the training data used and the model version, and hashes the generated image of the dog. Based on this hash value and the collected metadata, new transaction data is generated and added to the distributed ledger. Finally, the image of the dog, its hash value, and the transaction ID are provided to the user's device. This allows the user to verify that the generated image of the dog is a trustworthy AI creation.

[0089] The above is an embodiment of the present invention. This system ensures the traceability and reliability of products, improving trust in AI products.

[0090] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0091] Step 1:

[0092] The user enters a request.

[0093] The user uses the terminal interface to input a product request, including the type of product and specific instructions.

[0094] Input: A request to create (e.g., "Create an image of a dog")

[0095] Output: Request data

[0096] Specific operation: The user enters the words "Please generate an image of a dog" into the input field displayed on the device screen and presses the send button.

[0097] Step 2:

[0098] The device sends a request to the server.

[0099] The terminal transmits the request input by the user to the server.

[0100] Input: Request data

[0101] Output: JSON formatted request data sent to the server

[0102] Specific operation: When the send button is pressed, the terminal converts the request data into JSON format and sends it to the server via the network.

[0103] Step 3:

[0104] The server receives and parses the request.

[0105] The server receives the request and analyzes its contents, extracting the type of product and the creation instructions.

[0106] Input: Request data in JSON format

[0107] Output: Parsed request content (e.g., type of product "Image", creation instruction "Dog")

[0108] What happens: The server parses the JSON data and extracts the instruction "Generate an image of a dog."

[0109] Step 4:

[0110] The server selects and launches a generative model.

[0111] The server selects and launches an appropriate generative model based on the request.

[0112] Input: Parsed request content

[0113] Output: An instance of the selected generative model

[0114] Specific operation: The server selects the GAN, the generative model that is best suited for "dog image generation," and loads the required initial dataset.

[0115] Step 5:

[0116] The server runs the generation process and creates the product.

[0117] The server uses the generative model to create the product desired by the user.

[0118] Input: Selected generative model, initial dataset

[0119] Output: The generated artifact (e.g., a picture of a dog)

[0120] What happens next: The generative model in the server runs an algorithm to process the initial dataset and generate an image of a dog.

[0121] Step 6:

[0122] The server collects the metadata.

[0123] The server collects metadata such as the training data used during the generation process, the model version, and the generation time.

[0124] Input: Generation process information

[0125] Output: Collected metadata (e.g. dataset name used, model version, generation time)

[0126] Specific operation: The server analyzes the process log and collects the name of the training data used, the model version number, the time taken to generate the model, etc.

[0127] Step 7:

[0128] The server hashes the product.

[0129] The server generates a unique hash value for the product.

[0130] Input: Generated product

[0131] Output: The hash value of the result

[0132] What it does: The server hashes the generated dog image using the SHA-256 algorithm to create a unique hash value.

[0133] Step 8:

[0134] The server adds the transaction data to the distributed ledger.

[0135] The server generates transaction data based on the collected metadata and hash values ​​and adds it to the distributed ledger.

[0136] Input: Metadata, hash value of the generated data

[0137] Output: New transaction data is added to the distributed ledger

[0138] How it works: The server composes the hash value and metadata into a single transaction and adds a new block to the distributed ledger using Hyperledger Fabric.

[0139] Step 9:

[0140] The server sends the results back to the terminal.

[0141] The server returns the product, the hash value, and the transaction ID to the terminal.

[0142] Input: Product, hash value, transaction ID

[0143] Output: Result data sent back to the terminal

[0144] Specific operation: The server sends the generated dog image, hash value, and transaction ID to the terminal in a response.

[0145] Step 10:

[0146] The user checks and verifies the product.

[0147] The user checks the product on the terminal and verifies its authenticity.

[0148] Input: Generated image, hash value, transaction ID

[0149] Output: Confirmed artifacts, verification results

[0150] How it works: The user visually checks the image of the dog displayed on the device and verifies its authenticity by matching the provided hash value with the transaction ID on the distributed ledger.

[0151] (Application example 1)

[0152] 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."

[0153] Ensuring the authenticity and traceability of products is becoming increasingly important in modern society. Particularly in the field of security services, there is a need to guarantee the reliability of generated evidence and data. However, current systems lack effective means to prevent tampering of products and ensure transparency of the generation process, which is a challenge.

[0154] 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.

[0155] In this invention, the server includes means for receiving a user's input request, means for activating a generative model to create a product, means for collecting data used in the generation process and the version of the generative model, means for hashing the generated product and generating transaction data, means for adding the transaction data to a blockchain, means for providing the generated product and information on the blockchain to a user, means for recording content as security evidence of the product to the blockchain, and means for providing an interface for verifying the hash value and metadata of the product, thereby ensuring the reliability and traceability of the product and preventing tampering.

[0156] "Means for receiving user input requests" refers to the interface and communication capabilities by which a user sends a request to the system to create a particular product.

[0157] "Means for invoking a generative model and creating a product" refers to the processes and algorithms for running an AI model based on a user's request and producing a specified product.

[0158] "Means for collecting the data and generative model versions used during the generation process" refers to the ability to record and collect detailed information such as the version information of the datasets and models used when creating the product.

[0159] "Means for hashing the generated product and generating transaction data" refers to a technology for converting the generated product into a unique identifier through encryption and then generating new transaction data that includes that identifier.

[0160] "Means for adding transaction data to the blockchain" refers to the process and technology for incorporating newly generated transaction data into the distributed ledger system.

[0161] "Means for providing users with generated products and information on the blockchain" refers to the user interface and communication functions for visualizing and providing users with details and authenticity of the generated content and its generation process.

[0162] "Means for recording the content of a product as security evidence on a blockchain" refers to a technology that records the content of a product, particularly one related to security, on a blockchain and makes it tamper-proof in order to preserve the content as reliable evidence.

[0163] "Means for providing an interface for verifying the product's hash value and metadata" refers to display and operation means that allow a user to confirm and verify the product's unique identifier (hash value) and detailed information about the creation process (metadata).

[0164] This invention relates to a system for ensuring the authenticity and traceability of products in the field of security services. The system is realized using a user's terminal, a server, and blockchain technology.

[0165] First, a user uses a terminal to input a request to create a product through the interface. For example, the user inputs a specific prompt such as "Please generate an image of a dog." The terminal then sends this request to the server.

[0166] The server receives and analyzes the request, then launches the appropriate generative model. This model uses its internal algorithms and the specified initial data to create a product (e.g., an image of a dog). During the generation process, the server collects metadata such as the training data used, the version of the generative model, and the time it took to generate the product.

[0167] Once generation is complete, the server hashes the generated product, which results in a unique hash value. New transaction data is generated based on this hash value and the collected metadata, and the server adds this transaction data to the blockchain. A new block is generated and integrated into the existing blockchain, recording the entire product generation process on the distributed ledger. This ensures the authenticity and traceability of the product.

[0168] Finally, the server returns the generated result, the hash value, and the transaction ID on the blockchain to the terminal. The user can receive this information via the terminal and check the generated result and verify its authenticity. For example, a specific prompt such as "Please generate an image that identifies suspicious activity from security camera footage in the office" can be used for security-related content. The generated content can be recorded on the blockchain and used as tamper-proof evidence.

[0169] The hardware used is primarily the user's smartphone and a server, while the software includes Python, a REST API, an AI model generation service, and a blockchain library. The server receives data, runs a generative model to generate content, collects metadata, hashes the results, adds transaction data to the blockchain, and provides the results and related information to the user.

[0170] As a result, this system can ensure the reliability and traceability of the product and prevent tampering. The present invention is particularly useful in the field of security services, and functions effectively in situations where the authenticity of the product is highly required.

[0171] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0172] Step 1:

[0173] The user inputs a production request through the terminal interface. This input includes the type of production (e.g., image or text) and a specific prompt (e.g., "Please generate an image of a dog"). The input data is sent to the server as the user's request.

[0174] Step 2:

[0175] The server receives a request from the user. At this time, the server analyzes the request content and extracts the request type and prompt. The analyzed data is used in the next processing step.

[0176] Step 3:

[0177] The server selects and launches an appropriate generative model based on the request. The generative model uses the specified initial data and its internal algorithms to create the desired product. The input to this process is the parsed request data, and the output is the generated product (e.g., a picture of a dog).

[0178] Step 4:

[0179] The server collects metadata such as the training data used during the generation process, the version of the generative model, and the time required for generation. In this step, detailed information about the generation process is recorded and saved for later verification and traceability. The collected metadata will be used in the next step.

[0180] Step 5:

[0181] The server hashes the generated product. This process results in a unique hash value for the product. The input is the generated product, and the output is its hash value. The server then generates new transaction data based on this hash value and the collected metadata.

[0182] Step 6:

[0183] The server adds new transaction data to the blockchain, and a new block is generated based on the existing blockchain data and integrated into the blockchain, so that the entire product creation process is recorded on the distributed ledger.

[0184] Step 7:

[0185] The server returns the product, hash value, and transaction ID on the blockchain to the user's device, where the user can receive this information and verify the product and its authenticity.

[0186] Step 8:

[0187] Using the hash value and transaction ID provided by the user, the product is verified on the blockchain to be truly generated by AI and not tampered with, ensuring the authenticity and traceability of the product.

[0188] For example, a terminal sends a prompt to the server: "Please generate an image that identifies suspicious activity from security camera footage in the office." The server analyzes the request, activates the generative model to generate the appropriate image, and records the generation process on the blockchain to ensure that the generated image has not been tampered with.

[0189] 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.

[0190] This invention is a system that features an AI product generation process and tracing function combined with an emotion engine that recognizes user emotions. This system receives user requests, adjusts the type and specifications of the product based on the emotion recognition results, and generates and traces the product. It also uses blockchain technology to ensure the authenticity of the product and allow users to verify the generation process.

[0191] First, the user uses a device to make a request to create a product. The user inputs the type of product (e.g., image or text) and specific creation instructions (e.g., "Please create an image of a dog") on the device's interface. The emotion engine then analyzes the user's emotion data in real time and automatically adjusts the product's characteristics and settings based on that emotion. This request information is sent from the device to the server.

[0192] The server then receives the request and emotion data sent by the user and analyzes their content. Based on the user's emotion recognition results, the server automatically adjusts the content of the product and selects an appropriate generative model. This generative model creates the product using its internal algorithm and the specified initial data. During the generation process, the server appropriately allocates the necessary computing resources and manages them to ensure smooth generation.

[0193] During the generation process, the server collects metadata such as the training data used, the version of the generative model, and the time required for generation. Additionally, the user's emotional data recognized by the emotion engine is also collected as metadata. This metadata is important for ensuring traceability later.

[0194] Once the generation process is complete, the server hashes the generated product, resulting in a unique hash value for the product, which is used to verify the product's identity.

[0195] The server then combines the generated hash value with the collected metadata to generate new transaction data, including the generated hash value, the training data used, the version of the generative model, the generation time, and the user's emotional data.

[0196] The server adds this new transaction data to the blockchain, and a new block is generated and integrated into the existing blockchain, recording the entire process of creating the product on a distributed ledger, ensuring the authenticity and traceability of the product.

[0197] Finally, the server returns the generated product, its hash value, and the transaction ID on the blockchain to the terminal, allowing the user to check the product and details of its generation process.

[0198] As a concrete example, consider the case where a user requests the generation of an "image of a dog." The user sends a request from their device to the server: "Please generate an image of a dog." If the user is smiling, the emotion engine recognizes this emotion as "positive" and adjusts the generative model to reflect a bright background and a cheerful dog in the generated image. The server receives and analyzes this request and launches the appropriate image generation model. The model generates an image of a dog using the specified initial data and internal algorithm. During the generation process, the server collects metadata such as the training data used and the model version, as well as the user's emotional data, and hashes the generated image of the dog. Based on this hash value and the collected metadata, new transaction data is generated and added to the blockchain. Finally, the image of the dog, its hash value, and the transaction ID are provided to the user's device. This allows the user to verify that the generated image of the dog is a reliable AI product and further confirm that emotions were correctly reflected in the generation process.

[0199] The above is an embodiment of the present invention. This system ensures the traceability and reliability of products, improves trust in AI products, and enables the provision of customized products that reflect the user's emotions.

[0200] The processing flow will be explained below.

[0201] Step 1:

[0202] The user uses the terminal to create a request for a product. The user selects the type of product (e.g., image or text) on the interface and enters a creation instruction (e.g., "Please create an image of a dog"). The camera and microphone also operate to acquire the user's emotional data, sending the user's facial expressions and voice to the emotion engine in real time. The request containing this information is then sent from the terminal to the server.

[0203] Step 2:

[0204] The server receives a request from the user. The request includes the type of product, creation instructions, and the user's emotion data. The server analyzes this information and recognizes the user's emotion using an emotion engine.

[0205] Step 3:

[0206] The server adjusts the selection of a generative model or the characteristics of the product based on the user's emotion results recognized by the emotion engine. For example, if the user is smiling at the time of the request, the server adjusts the settings so that the generated image of the dog reflects a bright background and a cheerful dog.

[0207] Step 4:

[0208] The server starts the appropriate generative model and creates a product using the specified initial data and algorithm. The generative model refers to the built-in learning data and generates a product that best suits the user's request and emotions.

[0209] Step 5:

[0210] During the generation process, the server collects the training data used, the version of the generative model, the generation time, and the user's sentiment data. This metadata is necessary for later traceability of the generation process.

[0211] Step 6:

[0212] Once the generation process is complete, the server hashes the generated product, obtaining a unique hash value for the product, which is important for verifying the product's identity.

[0213] Step 7:

[0214] The server combines the generated hash value with the collected metadata to generate new transaction data, including the product hash value, the training data used, the version of the generative model, the generation time, and the user's emotional data.

[0215] Step 8:

[0216] The server adds new transaction data to the blockchain, and a new block is generated and integrated into the existing blockchain, recording the entire process of product creation on a distributed ledger, ensuring the authenticity and traceability of the product.

[0217] Step 9:

[0218] The server sends the generated product, hash value, and transaction ID on the blockchain back to the terminal, which receives this information and displays it to the user.

[0219] Step 10:

[0220] Users can check the product through their terminal and verify its authenticity and traceability on the blockchain using the provided hash value and transaction ID. Users can also check whether the product adjusted by the emotion engine matches their own emotions.

[0221] As a specific example, when a user requests the generation of an "image of a dog," the user sends a request from their device to the server saying, "Please generate an image of a dog." If the user is smiling at the time of the request, the emotion engine recognizes this emotion as "positive" and adjusts the generative model to reflect a bright background and a cheerful dog in the generated image. The server receives and analyzes the request, launches the appropriate image generation model, and generates the optimal dog image. During the generation process, the training data used, the model version, and the user's emotion data are collected, and the generated dog image is hashed. New transaction data is generated based on this hash value and the collected metadata and added to the blockchain. Finally, the dog image, its hash value, and the transaction ID are provided to the user's device. This allows the user to verify that the generated dog image is a reliable AI product and further confirm that emotions were correctly reflected in the generation process.

[0222] Example 2

[0223] 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."

[0224] Conventional generative AI systems often fail to ensure the authenticity of the product or the traceability of the generation process, leaving users with limited means to verify the reliability of the product. It is also difficult to provide customized products that reflect the user's emotions. To address these issues, the present invention aims to analyze user emotion data in real time, generate products based on that data, and ensure their traceability.

[0225] 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.

[0226] In this invention, the server includes means for receiving a user's input request, means for collecting metadata during the creation process, means for collecting and analyzing user emotion data, means for hashing the created product and adding transaction data to the blockchain, and means for providing information about the creation process to the user. This ensures the authenticity and traceability of the product and makes it possible to provide a customized product that reflects the user's emotion.

[0227] A "user input request" is a specific instruction that a user sends to the system to create a product.

[0228] A "generative model" is a machine learning algorithm for creating a product using specified initial data and an internal algorithm.

[0229] A "generation process" is a series of processes that activates a generative model based on a user's input request and creates a product.

[0230] "Metadata" is additional information related to the product, such as the training data used during the generation process, the version of the generative model, the generation time, and the user's emotional data.

[0231] "Emotion data" is data that represents the emotional state of the user analyzed from facial expressions, gestures, and the like.

[0232] "Hashing" is a data transformation technique used to generate a unique hash value for a product.

[0233] A "hash value" is a unique identifier generated by a hashing process.

[0234] "Transaction data" is a data packet containing a hash value and metadata about the generation process that is added to the blockchain as a new transaction.

[0235] A "blockchain" is a database that uses distributed ledger technology and consists of a series of blocks that track transactions and data.

[0236] A "transaction ID" is an identifier that uniquely identifies a specific transaction on a blockchain.

[0237] "Product" is the product created by the generative model based on the user's input request and emotion data.

[0238] This invention is a system that features an AI product generation process and tracing function combined with an emotion engine that recognizes user emotions. This system analyzes user emotion data in real time and automatically adjusts the product's characteristics and settings to provide customized products to users, and ensures authenticity and traceability by recording the generation process using blockchain technology.

[0239] First, the user uses a device such as a smartphone or PC to input the type of product (e.g., an image or text) and specific generation instructions (prompt text) into the interface. The software used here can be a general web browser or a custom application.

[0240] Next, the device uses sensors such as a camera and microphone to collect the user's emotional data. The emotion engine uses, for example, a general emotion recognition API (Emotion API). This allows for real-time analysis of the emotional data obtained from the user's facial expressions and voice.

[0241] The collected request information and emotion data are sent from the device to the server. The server receives this data and begins analysis. Based on the emotion recognition results, the server selects an appropriate generative AI model (e.g., a sentence generation model or an image generation model) and starts the generation process. Examples of generative models that can be used here include a natural language processing model (GPT-4 (registered trademark)) and an image generation model (DALL-E 2).

[0242] The server collects metadata such as the training data used during the generation process, the version of the generative model, the generation time, and the user's emotional data, which play an important role in ensuring traceability later.

[0243] Once the creation process is complete, the server hashes the created product to generate a unique hash value. This hash value is combined with the collected metadata to generate new transaction data that is added to the blockchain, ensuring the authenticity of the product and a record of the entire creation process on the distributed ledger.

[0244] Finally, the server returns the generated product, its hash value, and the transaction ID on the blockchain to the terminal, allowing the user to verify the authenticity of the product and details of its creation process.

[0245] For example, if a user requests "generate an image of a dog," and the emotion engine recognizes the user's smile as "positive," the server generates an image of a dog against a bright background that reflects this emotional data. Transaction data is then added to the blockchain based on the metadata of the generation process and the hash value of the product. Finally, the generated dog image, hash value, and transaction ID are provided to the user's device.

[0246] Example prompt sentence:

[0247] "Generate an image of a dog"

[0248] "Write a sentence about natural scenery"

[0249] This allows customized products to be created according to the user's emotions, and the creation process is accurately recorded, improving the reliability and traceability of the products.

[0250] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0251] Step 1:

[0252] User issues a request

[0253] The user uses a terminal to make a request to create a product. The user inputs the type of product and specific instructions (prompt) for creation into the terminal interface. The input prompt is sent from the terminal to the server.

[0254] Input: Type of product, specific production instructions (prompt text)

[0255] Output: Sends the request data to the server

[0256] Specific behavior: A user opens the application on their smartphone, enters "Please generate an image of a dog" into the input form, and clicks the submit button.

[0257] Step 2:

[0258] Collecting emotion data from devices

[0259] The device uses a camera and microphone to collect the user's emotional data in real time, which is then analyzed through an emotion engine and added to the request information.

[0260] Input: User's facial expression or voice data

[0261] Output: Analyzes user emotion data and adds it to the request data

[0262] Specific operation: The device camera captures the user's facial expression, and if the user is smiling, the emotion engine analyzes the emotion as positive.

[0263] Step 3:

[0264] Server receives request and emotion data

[0265] The server receives request information and emotion data sent from the device, analyzes the received data, and selects an appropriate generative model.

[0266] Input: Request information and emotion data sent from the device

[0267] Output: Selection of a generative model based on the analysis results

[0268] Specific operation: The server analyzes the type of product ("image of dog") and emotion data ("positive") and selects an appropriate image generation model.

[0269] Step 4:

[0270] Server-based model initiation and generation

[0271] The server invokes the appropriate generative model and uses the specified initial data and internal algorithms to create a product, which is customized to reflect the user's emotional data.

[0272] Input: Analyzed generative model, given initial data

[0273] Output: Creating a customized artifact

[0274] Specific operation: The server launches an image generation model (e.g., DALL-E 2) and generates an image of a dog that reflects a "positive" emotion.

[0275] Step 5:

[0276] Server-based collection and hashing of metadata

[0277] During the generation process, the server collects metadata such as the training data used, the version of the generative model, the generation time, and the user's emotional data. The generated product is then hashed to generate a unique hash value.

[0278] Input: Various data during the generation process

[0279] Output: Collected metadata, hash value of the artifact

[0280] What it does: The server records the metadata of the generation process and hashes the generated dog image using the SHA-256 algorithm.

[0281] Step 6:

[0282] Registration on the blockchain by the server

[0283] The generated hash value is combined with the collected metadata to generate new transaction data and add it to the blockchain.

[0284] Input: hash value, collected metadata

[0285] Output: New transaction data added to the blockchain

[0286] How it works: The server packages the resulting hash value and metadata into a packet and adds the transaction data to the blockchain using distributed ledger technology.

[0287] Step 7:

[0288] Return and check of product

[0289] The server returns the generated product, its hash value, and the transaction ID on the blockchain to the terminal, allowing the user to verify the authenticity of the product and details of the generation process.

[0290] Input: hash value, transaction ID

[0291] Output: The product, hash value, and transaction ID sent to the user terminal

[0292] How it works: The server sends the image of the dog, its hash value, and the transaction ID on the blockchain to the user's device, where the user can view this information.

[0293] (Application example 2)

[0294] 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."

[0295] Conventional generative AI systems are limited to providing products based on user input, and lack customization that takes user emotions into account and transparency in the generation process. In addition, there are insufficient means to guarantee the authenticity of the products, leaving users with little means to verify the reliability of the products.

[0296] The specific processing by the specific 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 receiving a user's input request, means for activating a generative model based on the input request and creating a product, means for collecting metadata including the training data and the version of the generative model used during the generation process, means for hashing the generated product and generating new transaction data based on the hash value and the metadata of the generation process, means for adding the transaction data to the blockchain, means for providing the generated product and information on the blockchain to the user, means for analyzing the user's emotions and adjusting the characteristics of the product based on the emotions, and means for suggesting products based on the user's emotions. This makes it possible to provide customized products that reflect the user's emotions and ensure the transparency and reliability of the generation process.

[0297] The "means for receiving a user input request" is a function on the device that receives a request input by a user to create a product.

[0298] The "means for activating a generative model and creating a product" is a function that runs a pre-set generative algorithm based on a received input request and generates the required product.

[0299] "Means for collecting metadata including training data and generative model versions" refers to the ability to systematically obtain information about the datasets and algorithm versions used during the generation process.

[0300] The "means of hashing the generated product and generating new transaction data based on the hash value and metadata of the generation process" refers to a function that assigns a unique identification code to the generated product, integrates the code with related information, and records it as new data.

[0301] "Means of adding transaction data to the blockchain" refers to the function of permanently storing the generated data using distributed ledger technology.

[0302] "Means for providing users with information about the generated product and the blockchain" is a function that provides users with detailed data about the product and its generation process in a visible form.

[0303] "Means for analyzing the user's emotions and adjusting the characteristics of the generated product based on those emotions" refers to a function that analyzes the user's emotional state in real time and dynamically changes the characteristics of the generated data and objects based on those emotions.

[0304] The "means for proposing a product based on the user's emotions" is a function for selecting an appropriate product based on the analyzed emotion data and proposing it to the user.

[0305] This invention is a system that combines an emotion engine that analyzes user emotions with an AI product generation process and tracing function. This system receives user requests, adjusts the type and specifications of the product based on the emotion recognition results, and generates and traces the product. It also uses blockchain technology to ensure the authenticity of the product and allow users to verify the generation process.

[0306] First, the user uses a device to make a request to create a product. The user inputs the type of product and specific creation instructions on the device's interface. The emotion engine then analyzes the user's emotion data in real time and automatically adjusts the product's characteristics and settings based on that emotion. This request information is sent from the device to the server.

[0307] The server then receives the request and emotion data sent by the user and analyzes their content. Based on the user's emotion recognition results, the server automatically adjusts the content of the product and selects an appropriate generative model. This generative model creates the product using its internal algorithm and the specified initial data. During the generation process, the server appropriately allocates the necessary computing resources and manages them to ensure smooth generation.

[0308] During the generation process, the server collects metadata such as the training data used, the version of the generative model, and the time required for generation. In addition, the user's emotional data recognized by the emotion engine is also collected as metadata. This metadata is important for ensuring traceability later. Once the generation process is complete, the server hashes the generated product. Hashing results in a unique hash value for the product, which is used to verify the identity of the product.

[0309] Next, the server combines the generated hash value with the collected metadata to generate new transaction data. The transaction data includes the product's hash value, the training data used, the version of the generative model, the generation time, and the user's emotional data. The server adds this new transaction data to the blockchain. A new block is generated and integrated into the existing blockchain, recording the entire product generation process on the distributed ledger. This ensures the authenticity and traceability of the product.

[0310] Finally, the server returns the generated product, its hash value, and the transaction ID on the blockchain to the terminal, allowing the user to check the product and details of its generation process.

[0311] As a concrete example, consider a user searching for products on an online shopping site using the "Emo Shopping" app. The user sends a request to the server from their device asking, "Please suggest new fashion items." If the user is smiling, the emotion engine recognizes the emotion as "happy" and adjusts the generative model to reflect vibrant and cheerful fashion items in the suggested products. The server receives and analyzes this request and launches the appropriate product suggestion model. The model generates a list of suggested products using the specified initial data and internal algorithm. During the generation process, the server collects metadata such as the training data used and the model version, as well as the user's emotional data, and hashes the generated list of products. Based on this hash value and the collected metadata, new transaction data is generated and added to the blockchain. Finally, the list of products, its hash value, and the transaction ID are provided to the user's device. This allows the user to verify that the suggested product list is a reliable AI product and further confirm that emotions were correctly reflected in the generation process.

[0312] Examples of prompts are:

[0313] "We'll suggest products that you might want to choose when you're feeling 'happy' right now. For example, how about brightly colored clothes or fun accessories?"

[0314] This makes it possible to provide customized products that reflect the user's emotions, and ensure the transparency and reliability of the creation process.

[0315] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0316] Step 1:

[0317] A user uses a terminal to input a request to create a product. The request includes the type of product and specific instructions for creation. For example, a request such as "Please suggest a new fashion item" is input. This request information is sent from the terminal to the server. The input data includes the type of product and specific instructions, and the output is the request information.

[0318] Step 2:

[0319] The device acquires the user's emotional data in real time and sends it to the emotion engine. The emotion engine uses a camera and microphone to analyze the user's facial expressions and voice and recognize the emotion. For example, if the user is smiling, the emotion engine recognizes the emotion as "happy." The input data is the user's facial expressions and voice acquired in real time, and the output is the recognized emotional data.

[0320] Step 3:

[0321] The server receives the request information and emotion data sent by the user. The server then analyzes this data and adjusts the product's characteristics based on the user's emotion recognition results. For example, if the emotion is "happy," the server will suggest bright and cheerful items. The input data are the request information and emotion data, and the output is the adjusted product's characteristics.

[0322] Step 4:

[0323] The server selects and launches an appropriate generative model. This generative model uses its internal algorithm and the specified initial data to create a product. For example, it generates a brightly colored fashion item based on the adjusted product characteristics. The input data are the adjusted product characteristics, and the output is the generated product.

[0324] Step 5:

[0325] During the generation process, the server collects metadata such as the training data used, the version of the generative model, and the time required for generation. In addition, the user's emotional data recognized by the emotion engine is also collected as metadata. The input data are various data used during the generation process, and the output is the collected metadata.

[0326] Step 6:

[0327] Once the generation process is complete, the server hashes the generated product, resulting in a unique hash value for the product, which is used to verify the product's identity. The input data is the generated product, and the output is the hash value of the product.

[0328] Step 7:

[0329] The server combines the generated hash value with the collected metadata to generate new transaction data, which includes the generated hash value, the training data used, the version of the generative model, the generation time, and the user's emotional data. The input data are the hash value and the metadata, and the output is the new transaction data.

[0330] Step 8:

[0331] The server adds new transaction data to the blockchain. A new block is generated and integrated into the existing blockchain, recording the entire process of product creation on a distributed ledger. The input data is the new transaction data, and the output is an updated blockchain.

[0332] Step 9:

[0333] Finally, the server returns the generated product, hash value, and transaction ID on the blockchain to the terminal, allowing the user to check the product and details of its generation process. The input data are the product, hash value, and transaction ID, and the output is the information provided to the user terminal.

[0334] 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.

[0335] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0336] 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.

[0337] [Second embodiment]

[0338] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0339] 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.

[0340] 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).

[0341] 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.

[0342] 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.

[0343] 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).

[0344] 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.

[0345] 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.

[0346] 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.

[0347] 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.

[0348] 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.

[0349] 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."

[0350] This invention is a system that combines the AI ​​product generation process and its tracing function. This system consists of the following steps:

[0351] First, a user makes a request to create a product using a terminal. The user inputs the type of product (e.g., image, text) and specific instructions for creation via the interface on the terminal. For example, the user inputs a specific request such as "Please create an image of a dog." This request is then sent from the terminal to the server.

[0352] The server then receives the user's request and analyzes its contents. Based on the request, the server selects and activates an appropriate generative model. This generative model uses its internal algorithms and the specified initial data to create the desired product. This process results in the creation of a specific product (e.g., an image of a dog).

[0353] During the generation process, the server collects metadata such as the training data used, the version of the generative model, the time required for generation, etc. This metadata is necessary to ensure transparency of the generation process.

[0354] Once the creation process is complete, the server hashes the created product, resulting in a unique hash value for the product. This hash value, along with the collected metadata, is used to create new transaction data.

[0355] The server then adds this transaction data to the blockchain, and a new block is generated and integrated into the existing blockchain, recording the entire process of creating the product on a distributed ledger, ensuring the authenticity and traceability of the product.

[0356] Finally, the server returns the product, the hash value, and the transaction ID on the blockchain to the terminal. The user receives this information via the terminal and checks the product and verifies its authenticity. By checking the provided hash value and transaction ID on the blockchain, the user can be sure that the product is an AI-generated product.

[0357] As a concrete example, let's consider the case where a user makes a request to generate an "image of a dog." The user sends the request "Please generate an image of a dog" from their device to the server. The server receives and analyzes this request, and launches the appropriate image generation model. The model generates an image of a dog using the specified initial data and internal algorithm. During the generation process, the server collects metadata such as the training data used and the model version, and hashes the generated image of the dog. Based on this hash value and the collected metadata, new transaction data is generated and added to the blockchain. Finally, the image of the dog, its hash value, and the transaction ID are provided to the user's device. This allows the user to verify that the generated image of the dog is a trustworthy AI product.

[0358] The above is an embodiment of the present invention. This system ensures the traceability and reliability of products, improving trust in AI products.

[0359] The processing flow will be explained below.

[0360] Step 1:

[0361] The user uses a terminal to create a request to create a product. The user selects the type of product (e.g., image or text) on the interface and enters creation instructions (e.g., "Please create an image of a dog"). Furthermore, the user sets creation options (e.g., image style and resolution) as needed. This input data is sent as a request to the server.

[0362] Step 2:

[0363] The server receives a request sent by a user, analyzes the request, extracts the type of product and its parameters, and then refers to an internal database to select an appropriate generative model based on the extracted data.

[0364] Step 3:

[0365] The server launches the selected generative model, which incorporates the specified initial data and algorithms. The server uses this data to create a product (e.g., an image of a dog). During the generation process, the server allocates the necessary computing resources and manages the process to ensure smooth progress.

[0366] Step 4:

[0367] As the generation process progresses, the server collects metadata such as the training data used, the version of the generative model, and the time it took to generate the model, which is necessary for later traceability.

[0368] Step 5:

[0369] Once the generation process is complete, the server hashes the generated product, resulting in a unique hash value for the product, which is important for verifying the product's identity.

[0370] Step 6:

[0371] The server combines the generated hash value with the collected metadata to generate new transaction data, including the hash value of the product, the training data used, the version of the generative model, and the generation time.

[0372] Step 7:

[0373] The server adds new transaction data to the blockchain, generates a new block based on the transaction data, and integrates this block into the existing blockchain, recording the entire process of product creation on a distributed ledger.

[0374] Step 8:

[0375] The server returns the generated product, its hash value, and the transaction ID on the blockchain to the terminal, allowing the user to check the product and details of its generation process.

[0376] Step 9:

[0377] The terminal displays the product, hash value, and transaction ID received from the server to the user, who can then verify the product and use the provided hash value and transaction ID to verify the product's authenticity and traceability on the blockchain.

[0378] Through these steps, the system generates the AI ​​product specified by the user, ensures traceability based on the metadata obtained during the process, and provides a mechanism for users to easily verify the authenticity of the product.

[0379] Example 1

[0380] 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."

[0381] Ensuring transparency and traceability in the generation process of AI products is a challenge for existing systems. There is a need for a means for users to verify the authenticity of the generated data. There is also a need to improve the reliability of products by collecting detailed information about the generation process and providing it to users.

[0382] 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.

[0383] In this invention, the server includes means for receiving a user's input request, means for activating a generative model based on the input request and creating a product, means for collecting metadata including the training data and the version of the generative model used in the generation process, means for hashing the generated product and generating new transaction data based on the hash value and the metadata of the generation process, means for adding the transaction data to a distributed ledger, and means for providing the generated product and information on the distributed ledger to a user. This ensures transparency and traceability of the generation process and enables verification of the authenticity and reliability of the product.

[0384] A "user" is a person or entity that utilizes the system to make a request for a product.

[0385] An "input request" is a request that a user sends information including the type of product and detailed instructions via a terminal.

[0386] A "generative model" is an algorithm or machine learning model used to create a product based on an input request.

[0387] A "product" is an output created using a generative model, and can take the form of an image, text, or other information.

[0388] "Metadata" is relevant information such as the training data used during the generation process, the version of the generative model, and the time it took to generate it.

[0389] "Hashing" is the process of generating a unique hash value for a product, which is used to verify the authenticity of the data.

[0390] A "hash value" is a unique identifier for a product, a fixed-length string obtained through a hashing process.

[0391] "Transaction Data" means a data set containing hash values ​​and metadata about the generation process that is recorded on the blockchain.

[0392] A "distributed ledger" is a database in which data is recorded in a decentralized manner using blockchain technology, making it accessible and verifiable.

[0393] A "block" is a unit of data that is added to a blockchain and contains new transaction data.

[0394] A "transaction ID" is a unique identifier used to identify a specific transaction on a blockchain.

[0395] The present invention is a system that combines the generation process of AI products with a tracing function. This system allows users to make requests to create products, generates products based on those requests, and has the ability to record and manage the entire generation process. Specific embodiments of the system are described below.

[0396] First, a user makes a request to create a product using a terminal. The user inputs the type of product (e.g., image, text) and specific instructions for creation via the interface on the terminal. For example, the user inputs a specific request such as "Please create an image of a dog." This request is then sent from the terminal to the server.

[0397] Next, the server receives the user's request and analyzes its contents. The server selects and launches an appropriate generative model based on the request. This generative model is implemented using libraries such as TensorFlow or PyTorch. The generative model uses its internal algorithms and the specified initial data to create the desired product. This process results in the creation of a specific product (for example, an image of a dog).

[0398] During the generation process, the server collects metadata such as the training data used, the version of the generative model, and the time required for generation, ensuring transparency of the generation process. Once the generation process is complete, the server hashes the generated product using the SHA-256 hashing algorithm. This results in a unique hash value for the product. New transaction data is generated based on this hash value and the collected metadata.

[0399] The server then adds this transaction data to the blockchain using Hyperledger Fabric, creating a new block that is integrated into the existing distributed ledger, recording the entire process of creating the product, ensuring its authenticity and traceability.

[0400] Finally, the server returns the product, the hash value, and the transaction ID on the distributed ledger to the terminal. The user receives this information via the terminal and checks the product and verifies its authenticity. By checking the provided hash value and transaction ID on the distributed ledger, the user can be sure that the product is an AI-generated product.

[0401] As a concrete example, consider the case where a user makes a request to generate an "image of a dog." The user sends the request "Please generate an image of a dog" from their device to the server. The server receives and analyzes this request, then launches the appropriate image generation model. The model generates an image of a dog using the specified initial data and its internal algorithm. During the generation process, the server collects metadata such as the training data used and the model version, and hashes the generated image of the dog. Based on this hash value and the collected metadata, new transaction data is generated and added to the distributed ledger. Finally, the image of the dog, its hash value, and the transaction ID are provided to the user's device. This allows the user to verify that the generated image of the dog is a trustworthy AI creation.

[0402] The above is an embodiment of the present invention. This system ensures the traceability and reliability of products, improving trust in AI products.

[0403] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0404] Step 1:

[0405] The user enters a request.

[0406] The user uses the terminal interface to input a product request, including the type of product and specific instructions.

[0407] Input: A request to create (e.g., "Create an image of a dog")

[0408] Output: Request data

[0409] Specific operation: The user enters the words "Please generate an image of a dog" into the input field displayed on the device screen and presses the send button.

[0410] Step 2:

[0411] The device sends a request to the server.

[0412] The terminal transmits the request input by the user to the server.

[0413] Input: Request data

[0414] Output: JSON formatted request data sent to the server

[0415] Specific operation: When the send button is pressed, the terminal converts the request data into JSON format and sends it to the server via the network.

[0416] Step 3:

[0417] The server receives and parses the request.

[0418] The server receives the request and analyzes its contents, extracting the type of product and the creation instructions.

[0419] Input: Request data in JSON format

[0420] Output: Parsed request content (e.g., type of product "Image", creation instruction "Dog")

[0421] What happens: The server parses the JSON data and extracts the instruction "Generate an image of a dog."

[0422] Step 4:

[0423] The server selects and launches a generative model.

[0424] The server selects and launches an appropriate generative model based on the request.

[0425] Input: Parsed request content

[0426] Output: An instance of the selected generative model

[0427] Specific operation: The server selects the GAN, the generative model that is best suited for "dog image generation," and loads the required initial dataset.

[0428] Step 5:

[0429] The server runs the generation process and creates the product.

[0430] The server uses the generative model to create the product desired by the user.

[0431] Input: Selected generative model, initial dataset

[0432] Output: The generated artifact (e.g., a picture of a dog)

[0433] What happens next: The generative model in the server runs an algorithm to process the initial dataset and generate an image of a dog.

[0434] Step 6:

[0435] The server collects the metadata.

[0436] The server collects metadata such as the training data used during the generation process, the model version, and the generation time.

[0437] Input: Generation process information

[0438] Output: Collected metadata (e.g. dataset name used, model version, generation time)

[0439] Specific operation: The server analyzes the process log and collects the name of the training data used, the model version number, the time taken to generate the model, etc.

[0440] Step 7:

[0441] The server hashes the product.

[0442] The server generates a unique hash value for the product.

[0443] Input: Generated product

[0444] Output: The hash value of the result

[0445] What it does: The server hashes the generated dog image using the SHA-256 algorithm to create a unique hash value.

[0446] Step 8:

[0447] The server adds the transaction data to the distributed ledger.

[0448] The server generates transaction data based on the collected metadata and hash values ​​and adds it to the distributed ledger.

[0449] Input: Metadata, hash value of the generated data

[0450] Output: New transaction data is added to the distributed ledger

[0451] How it works: The server composes the hash value and metadata into a single transaction and adds a new block to the distributed ledger using Hyperledger Fabric.

[0452] Step 9:

[0453] The server sends the results back to the terminal.

[0454] The server returns the product, the hash value, and the transaction ID to the terminal.

[0455] Input: Product, hash value, transaction ID

[0456] Output: Result data sent back to the terminal

[0457] Specific operation: The server sends the generated dog image, hash value, and transaction ID to the terminal in a response.

[0458] Step 10:

[0459] The user checks and verifies the product.

[0460] The user checks the product on the terminal and verifies its authenticity.

[0461] Input: Generated image, hash value, transaction ID

[0462] Output: Confirmed artifacts, verification results

[0463] How it works: The user visually checks the image of the dog displayed on the device and verifies its authenticity by matching the provided hash value with the transaction ID on the distributed ledger.

[0464] (Application example 1)

[0465] 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."

[0466] Ensuring the authenticity and traceability of products is becoming increasingly important in modern society. Particularly in the field of security services, there is a need to guarantee the reliability of generated evidence and data. However, current systems lack effective means to prevent tampering of products and ensure transparency of the generation process, which is a challenge.

[0467] 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.

[0468] In this invention, the server includes means for receiving a user's input request, means for activating a generative model to create a product, means for collecting data used in the generation process and the version of the generative model, means for hashing the generated product and generating transaction data, means for adding the transaction data to a blockchain, means for providing the generated product and information on the blockchain to a user, means for recording content as security evidence of the product to the blockchain, and means for providing an interface for verifying the hash value and metadata of the product, thereby ensuring the reliability and traceability of the product and preventing tampering.

[0469] "Means for receiving user input requests" refers to the interface and communication capabilities by which a user sends a request to the system to create a particular product.

[0470] "Means for invoking a generative model and creating a product" refers to the processes and algorithms for running an AI model based on a user's request and producing a specified product.

[0471] "Means for collecting the data and generative model versions used during the generation process" refers to the ability to record and collect detailed information such as the version information of the datasets and models used when creating the product.

[0472] "Means for hashing the generated product and generating transaction data" refers to a technology for converting the generated product into a unique identifier through encryption and then generating new transaction data that includes that identifier.

[0473] "Means for adding transaction data to the blockchain" refers to the process and technology for incorporating newly generated transaction data into the distributed ledger system.

[0474] "Means for providing users with generated products and information on the blockchain" refers to the user interface and communication functions for visualizing and providing users with details and authenticity of the generated content and its generation process.

[0475] "Means for recording the content of a product as security evidence on a blockchain" refers to a technology that records the content of a product, particularly one related to security, on a blockchain and makes it tamper-proof in order to preserve the content as reliable evidence.

[0476] "Means for providing an interface for verifying the product's hash value and metadata" refers to display and operation means that allow a user to confirm and verify the product's unique identifier (hash value) and detailed information about the creation process (metadata).

[0477] This invention relates to a system for ensuring the authenticity and traceability of products in the field of security services. The system is realized using a user's terminal, a server, and blockchain technology.

[0478] First, a user uses a terminal to input a request to create a product through the interface. For example, the user inputs a specific prompt such as "Please generate an image of a dog." The terminal then sends this request to the server.

[0479] The server receives and analyzes the request, then launches the appropriate generative model. This model uses its internal algorithms and the specified initial data to create a product (e.g., an image of a dog). During the generation process, the server collects metadata such as the training data used, the version of the generative model, and the time it took to generate the product.

[0480] Once generation is complete, the server hashes the generated product, which results in a unique hash value. New transaction data is generated based on this hash value and the collected metadata, and the server adds this transaction data to the blockchain. A new block is generated and integrated into the existing blockchain, recording the entire product generation process on the distributed ledger. This ensures the authenticity and traceability of the product.

[0481] Finally, the server returns the generated result, the hash value, and the transaction ID on the blockchain to the terminal. The user can receive this information via the terminal and check the generated result and verify its authenticity. For example, a specific prompt such as "Please generate an image that identifies suspicious activity from security camera footage in the office" can be used for security-related content. The generated content can be recorded on the blockchain and used as tamper-proof evidence.

[0482] The hardware used is primarily the user's smartphone and a server, while the software includes Python, a REST API, an AI model generation service, and a blockchain library. The server receives data, runs a generative model to generate content, collects metadata, hashes the results, adds transaction data to the blockchain, and provides the results and related information to the user.

[0483] As a result, this system can ensure the reliability and traceability of the product and prevent tampering. The present invention is particularly useful in the field of security services, and functions effectively in situations where the authenticity of the product is highly required.

[0484] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0485] Step 1:

[0486] The user inputs a production request through the terminal interface. This input includes the type of production (e.g., image or text) and a specific prompt (e.g., "Please generate an image of a dog"). The input data is sent to the server as the user's request.

[0487] Step 2:

[0488] The server receives a request from the user. At this time, the server analyzes the request content and extracts the request type and prompt. The analyzed data is used in the next processing step.

[0489] Step 3:

[0490] The server selects and launches an appropriate generative model based on the request. The generative model uses the specified initial data and its internal algorithms to create the desired product. The input to this process is the parsed request data, and the output is the generated product (e.g., a picture of a dog).

[0491] Step 4:

[0492] The server collects metadata such as the training data used during the generation process, the version of the generative model, and the time required for generation. In this step, detailed information about the generation process is recorded and saved for later verification and traceability. The collected metadata will be used in the next step.

[0493] Step 5:

[0494] The server hashes the generated product. This process results in a unique hash value for the product. The input is the generated product, and the output is its hash value. The server then generates new transaction data based on this hash value and the collected metadata.

[0495] Step 6:

[0496] The server adds new transaction data to the blockchain, and a new block is generated based on the existing blockchain data and integrated into the blockchain, so that the entire product creation process is recorded on the distributed ledger.

[0497] Step 7:

[0498] The server returns the product, hash value, and transaction ID on the blockchain to the user's device, where the user can receive this information and verify the product and its authenticity.

[0499] Step 8:

[0500] Using the hash value and transaction ID provided by the user, the product is verified on the blockchain to be truly generated by AI and not tampered with, ensuring the authenticity and traceability of the product.

[0501] For example, a terminal sends a prompt to the server: "Please generate an image that identifies suspicious activity from security camera footage in the office." The server analyzes the request, activates the generative model to generate the appropriate image, and records the generation process on the blockchain to ensure that the generated image has not been tampered with.

[0502] 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.

[0503] This invention is a system that features an AI product generation process and tracing function combined with an emotion engine that recognizes user emotions. This system receives user requests, adjusts the type and specifications of the product based on the emotion recognition results, and generates and traces the product. It also uses blockchain technology to ensure the authenticity of the product and allow users to verify the generation process.

[0504] First, the user uses a device to make a request to create a product. The user inputs the type of product (e.g., image or text) and specific creation instructions (e.g., "Please create an image of a dog") on the device's interface. The emotion engine then analyzes the user's emotion data in real time and automatically adjusts the product's characteristics and settings based on that emotion. This request information is sent from the device to the server.

[0505] The server then receives the request and emotion data sent by the user and analyzes their content. Based on the user's emotion recognition results, the server automatically adjusts the content of the product and selects an appropriate generative model. This generative model creates the product using its internal algorithm and the specified initial data. During the generation process, the server appropriately allocates the necessary computing resources and manages them to ensure smooth generation.

[0506] During the generation process, the server collects metadata such as the training data used, the version of the generative model, and the time required for generation. Additionally, the user's emotional data recognized by the emotion engine is also collected as metadata. This metadata is important for ensuring traceability later.

[0507] Once the generation process is complete, the server hashes the generated product, resulting in a unique hash value for the product, which is used to verify the product's identity.

[0508] The server then combines the generated hash value with the collected metadata to generate new transaction data, including the generated hash value, the training data used, the version of the generative model, the generation time, and the user's emotional data.

[0509] The server adds this new transaction data to the blockchain, and a new block is generated and integrated into the existing blockchain, recording the entire process of creating the product on a distributed ledger, ensuring the authenticity and traceability of the product.

[0510] Finally, the server returns the generated product, its hash value, and the transaction ID on the blockchain to the terminal, allowing the user to check the product and details of its generation process.

[0511] As a concrete example, consider the case where a user requests the generation of an "image of a dog." The user sends a request from their device to the server: "Please generate an image of a dog." If the user is smiling, the emotion engine recognizes this emotion as "positive" and adjusts the generative model to reflect a bright background and a cheerful dog in the generated image. The server receives and analyzes this request and launches the appropriate image generation model. The model generates an image of a dog using the specified initial data and internal algorithm. During the generation process, the server collects metadata such as the training data used and the model version, as well as the user's emotional data, and hashes the generated image of the dog. Based on this hash value and the collected metadata, new transaction data is generated and added to the blockchain. Finally, the image of the dog, its hash value, and the transaction ID are provided to the user's device. This allows the user to verify that the generated image of the dog is a reliable AI product and further confirm that emotions were correctly reflected in the generation process.

[0512] The above is an embodiment of the present invention. This system ensures the traceability and reliability of products, improves trust in AI products, and enables the provision of customized products that reflect the user's emotions.

[0513] The processing flow will be explained below.

[0514] Step 1:

[0515] The user uses the terminal to create a request for a product. The user selects the type of product (e.g., image or text) on the interface and enters a creation instruction (e.g., "Please create an image of a dog"). The camera and microphone also operate to acquire the user's emotional data, sending the user's facial expressions and voice to the emotion engine in real time. The request containing this information is then sent from the terminal to the server.

[0516] Step 2:

[0517] The server receives a request from the user. The request includes the type of product, creation instructions, and the user's emotion data. The server analyzes this information and recognizes the user's emotion using an emotion engine.

[0518] Step 3:

[0519] The server adjusts the selection of a generative model or the characteristics of the product based on the user's emotion results recognized by the emotion engine. For example, if the user is smiling at the time of the request, the server adjusts the settings so that the generated image of the dog reflects a bright background and a cheerful dog.

[0520] Step 4:

[0521] The server starts the appropriate generative model and creates a product using the specified initial data and algorithm. The generative model refers to the built-in learning data and generates a product that best suits the user's request and emotions.

[0522] Step 5:

[0523] During the generation process, the server collects the training data used, the version of the generative model, the generation time, and the user's sentiment data. This metadata is necessary for later traceability of the generation process.

[0524] Step 6:

[0525] Once the generation process is complete, the server hashes the generated product, obtaining a unique hash value for the product, which is important for verifying the product's identity.

[0526] Step 7:

[0527] The server combines the generated hash value with the collected metadata to generate new transaction data, including the product hash value, the training data used, the version of the generative model, the generation time, and the user's emotional data.

[0528] Step 8:

[0529] The server adds new transaction data to the blockchain, and a new block is generated and integrated into the existing blockchain, recording the entire process of product creation on a distributed ledger, ensuring the authenticity and traceability of the product.

[0530] Step 9:

[0531] The server sends the generated product, hash value, and transaction ID on the blockchain back to the terminal, which receives this information and displays it to the user.

[0532] Step 10:

[0533] Users can check the product through their terminal and verify its authenticity and traceability on the blockchain using the provided hash value and transaction ID. Users can also check whether the product adjusted by the emotion engine matches their own emotions.

[0534] As a specific example, when a user requests the generation of an "image of a dog," the user sends a request from their device to the server saying, "Please generate an image of a dog." If the user is smiling at the time of the request, the emotion engine recognizes this emotion as "positive" and adjusts the generative model to reflect a bright background and a cheerful dog in the generated image. The server receives and analyzes the request, launches the appropriate image generation model, and generates the optimal dog image. During the generation process, the training data used, the model version, and the user's emotion data are collected, and the generated dog image is hashed. New transaction data is generated based on this hash value and the collected metadata and added to the blockchain. Finally, the dog image, its hash value, and the transaction ID are provided to the user's device. This allows the user to verify that the generated dog image is a reliable AI product and further confirm that emotions were correctly reflected in the generation process.

[0535] Example 2

[0536] 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."

[0537] Conventional generative AI systems often fail to ensure the authenticity of the product or the traceability of the generation process, leaving users with limited means to verify the reliability of the product. It is also difficult to provide customized products that reflect the user's emotions. To address these issues, the present invention aims to analyze user emotion data in real time, generate products based on that data, and ensure their traceability.

[0538] 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.

[0539] In this invention, the server includes means for receiving a user's input request, means for collecting metadata during the creation process, means for collecting and analyzing user emotion data, means for hashing the created product and adding transaction data to the blockchain, and means for providing information about the creation process to the user. This ensures the authenticity and traceability of the product and makes it possible to provide a customized product that reflects the user's emotion.

[0540] A "user input request" is a specific instruction that a user sends to the system to create a product.

[0541] A "generative model" is a machine learning algorithm for creating a product using specified initial data and an internal algorithm.

[0542] A "generation process" is a series of processes that activates a generative model based on a user's input request and creates a product.

[0543] "Metadata" is additional information related to the product, such as the training data used during the generation process, the version of the generative model, the generation time, and the user's emotional data.

[0544] "Emotion data" is data that represents the emotional state of the user analyzed from facial expressions, gestures, and the like.

[0545] "Hashing" is a data transformation technique used to generate a unique hash value for a product.

[0546] A "hash value" is a unique identifier generated by a hashing process.

[0547] "Transaction data" is a data packet containing a hash value and metadata about the generation process that is added to the blockchain as a new transaction.

[0548] A "blockchain" is a database that uses distributed ledger technology and consists of a series of blocks that track transactions and data.

[0549] A "transaction ID" is an identifier that uniquely identifies a specific transaction on a blockchain.

[0550] "Product" is the product created by the generative model based on the user's input request and emotion data.

[0551] This invention is a system that features an AI product generation process and tracing function combined with an emotion engine that recognizes user emotions. This system analyzes user emotion data in real time and automatically adjusts the product's characteristics and settings to provide customized products to users, and ensures authenticity and traceability by recording the generation process using blockchain technology.

[0552] First, the user uses a device such as a smartphone or PC to input the type of product (e.g., an image or text) and specific generation instructions (prompt text) into the interface. The software used here can be a general web browser or a custom application.

[0553] Next, the device uses sensors such as a camera and microphone to collect the user's emotional data. The emotion engine uses, for example, a general emotion recognition API (Emotion API). This allows for real-time analysis of the emotional data obtained from the user's facial expressions and voice.

[0554] The collected request information and emotion data are sent from the device to the server. The server receives this data and begins analysis. Based on the emotion recognition results, the server selects an appropriate generative AI model (e.g., a sentence generation model or an image generation model) and begins the generation process. The generative model used here could be, for example, a natural language processing model (GPT-4) or an image generation model (DALL-E 2).

[0555] The server collects metadata such as the training data used during the generation process, the version of the generative model, the generation time, and the user's emotional data, which play an important role in ensuring traceability later.

[0556] Once the creation process is complete, the server hashes the created product to generate a unique hash value. This hash value is combined with the collected metadata to generate new transaction data that is added to the blockchain, ensuring the authenticity of the product and a record of the entire creation process on the distributed ledger.

[0557] Finally, the server returns the generated product, its hash value, and the transaction ID on the blockchain to the terminal, allowing the user to verify the authenticity of the product and details of its creation process.

[0558] For example, if a user requests "generate an image of a dog," and the emotion engine recognizes the user's smile as "positive," the server generates an image of a dog against a bright background that reflects this emotional data. Transaction data is then added to the blockchain based on the metadata of the generation process and the hash value of the product. Finally, the generated dog image, hash value, and transaction ID are provided to the user's device.

[0559] Example prompt sentence:

[0560] "Generate an image of a dog"

[0561] "Write a sentence about natural scenery"

[0562] This allows customized products to be created according to the user's emotions, and the creation process is accurately recorded, improving the reliability and traceability of the products.

[0563] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0564] Step 1:

[0565] User issues a request

[0566] The user uses a terminal to make a request to create a product. The user inputs the type of product and specific instructions (prompt) for creation into the terminal interface. The input prompt is sent from the terminal to the server.

[0567] Input: Type of product, specific production instructions (prompt text)

[0568] Output: Sends the request data to the server

[0569] Specific behavior: A user opens the application on their smartphone, enters "Please generate an image of a dog" into the input form, and clicks the submit button.

[0570] Step 2:

[0571] Collecting emotion data from devices

[0572] The device uses a camera and microphone to collect the user's emotional data in real time, which is then analyzed through an emotion engine and added to the request information.

[0573] Input: User's facial expression or voice data

[0574] Output: Analyzes user emotion data and adds it to the request data

[0575] Specific operation: The device camera captures the user's facial expression, and if the user is smiling, the emotion engine analyzes the emotion as positive.

[0576] Step 3:

[0577] Server receives request and emotion data

[0578] The server receives request information and emotion data sent from the device, analyzes the received data, and selects an appropriate generative model.

[0579] Input: Request information and emotion data sent from the device

[0580] Output: Selection of a generative model based on the analysis results

[0581] Specific operation: The server analyzes the type of product ("image of dog") and emotion data ("positive") and selects an appropriate image generation model.

[0582] Step 4:

[0583] Server-based model initiation and generation

[0584] The server invokes the appropriate generative model and uses the specified initial data and internal algorithms to create a product, which is customized to reflect the user's emotional data.

[0585] Input: Analyzed generative model, given initial data

[0586] Output: Creating a customized artifact

[0587] Specific operation: The server launches an image generation model (e.g., DALL-E 2) and generates an image of a dog that reflects a "positive" emotion.

[0588] Step 5:

[0589] Server-based collection and hashing of metadata

[0590] During the generation process, the server collects metadata such as the training data used, the version of the generative model, the generation time, and the user's emotional data. The generated product is then hashed to generate a unique hash value.

[0591] Input: Various data during the generation process

[0592] Output: Collected metadata, hash value of the artifact

[0593] What it does: The server records the metadata of the generation process and hashes the generated dog image using the SHA-256 algorithm.

[0594] Step 6:

[0595] Registration on the blockchain by the server

[0596] The generated hash value is combined with the collected metadata to generate new transaction data and add it to the blockchain.

[0597] Input: hash value, collected metadata

[0598] Output: New transaction data added to the blockchain

[0599] How it works: The server packages the resulting hash value and metadata into a packet and adds the transaction data to the blockchain using distributed ledger technology.

[0600] Step 7:

[0601] Return and check of product

[0602] The server returns the generated product, its hash value, and the transaction ID on the blockchain to the terminal, allowing the user to verify the authenticity of the product and details of the generation process.

[0603] Input: hash value, transaction ID

[0604] Output: The product, hash value, and transaction ID sent to the user terminal

[0605] How it works: The server sends the image of the dog, its hash value, and the transaction ID on the blockchain to the user's device, where the user can view this information.

[0606] (Application example 2)

[0607] 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."

[0608] Conventional generative AI systems are limited to providing products based on user input, and lack customization that takes user emotions into account and transparency in the generation process. In addition, there are insufficient means to guarantee the authenticity of the products, leaving users with little means to verify the reliability of the products.

[0609] The specific processing by the specific 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 receiving a user's input request, means for activating a generative model based on the input request and creating a product, means for collecting metadata including the training data and the version of the generative model used during the generation process, means for hashing the generated product and generating new transaction data based on the hash value and the metadata of the generation process, means for adding the transaction data to the blockchain, means for providing the generated product and information on the blockchain to the user, means for analyzing the user's emotions and adjusting the characteristics of the product based on the emotions, and means for suggesting products based on the user's emotions. This makes it possible to provide customized products that reflect the user's emotions and ensure the transparency and reliability of the generation process.

[0610] The "means for receiving a user input request" is a function on the device that receives a request input by a user to create a product.

[0611] The "means for activating a generative model and creating a product" is a function that runs a pre-set generative algorithm based on a received input request and generates the required product.

[0612] "Means for collecting metadata including training data and generative model versions" refers to the ability to systematically obtain information about the datasets and algorithm versions used during the generation process.

[0613] The "means of hashing the generated product and generating new transaction data based on the hash value and metadata of the generation process" refers to a function that assigns a unique identification code to the generated product, integrates the code with related information, and records it as new data.

[0614] "Means of adding transaction data to the blockchain" refers to the function of permanently storing the generated data using distributed ledger technology.

[0615] "Means for providing users with information about the generated product and the blockchain" is a function that provides users with detailed data about the product and its generation process in a visible form.

[0616] "Means for analyzing the user's emotions and adjusting the characteristics of the generated product based on those emotions" refers to a function that analyzes the user's emotional state in real time and dynamically changes the characteristics of the generated data and objects based on those emotions.

[0617] The "means for proposing a product based on the user's emotions" is a function for selecting an appropriate product based on the analyzed emotion data and proposing it to the user.

[0618] This invention is a system that combines an emotion engine that analyzes user emotions with an AI product generation process and tracing function. This system receives user requests, adjusts the type and specifications of the product based on the emotion recognition results, and generates and traces the product. It also uses blockchain technology to ensure the authenticity of the product and allow users to verify the generation process.

[0619] First, the user uses a device to make a request to create a product. The user inputs the type of product and specific creation instructions on the device's interface. The emotion engine then analyzes the user's emotion data in real time and automatically adjusts the product's characteristics and settings based on that emotion. This request information is sent from the device to the server.

[0620] The server then receives the request and emotion data sent by the user and analyzes their content. Based on the user's emotion recognition results, the server automatically adjusts the content of the product and selects an appropriate generative model. This generative model creates the product using its internal algorithm and the specified initial data. During the generation process, the server appropriately allocates the necessary computing resources and manages them to ensure smooth generation.

[0621] During the generation process, the server collects metadata such as the training data used, the version of the generative model, and the time required for generation. In addition, the user's emotional data recognized by the emotion engine is also collected as metadata. This metadata is important for ensuring traceability later. Once the generation process is complete, the server hashes the generated product. Hashing results in a unique hash value for the product, which is used to verify the identity of the product.

[0622] Next, the server combines the generated hash value with the collected metadata to generate new transaction data. The transaction data includes the product's hash value, the training data used, the version of the generative model, the generation time, and the user's emotional data. The server adds this new transaction data to the blockchain. A new block is generated and integrated into the existing blockchain, recording the entire product generation process on the distributed ledger. This ensures the authenticity and traceability of the product.

[0623] Finally, the server returns the generated product, its hash value, and the transaction ID on the blockchain to the terminal, allowing the user to check the product and details of its generation process.

[0624] As a concrete example, consider a user searching for products on an online shopping site using the "Emo Shopping" app. The user sends a request to the server from their device asking, "Please suggest new fashion items." If the user is smiling, the emotion engine recognizes the emotion as "happy" and adjusts the generative model to reflect vibrant and cheerful fashion items in the suggested products. The server receives and analyzes this request and launches the appropriate product suggestion model. The model generates a list of suggested products using the specified initial data and internal algorithm. During the generation process, the server collects metadata such as the training data used and the model version, as well as the user's emotional data, and hashes the generated list of products. Based on this hash value and the collected metadata, new transaction data is generated and added to the blockchain. Finally, the list of products, its hash value, and the transaction ID are provided to the user's device. This allows the user to verify that the suggested product list is a reliable AI product and further confirm that emotions were correctly reflected in the generation process.

[0625] Examples of prompts are:

[0626] "We'll suggest products that you might want to choose when you're feeling 'happy' right now. For example, how about brightly colored clothes or fun accessories?"

[0627] This makes it possible to provide customized products that reflect the user's emotions, and ensure the transparency and reliability of the creation process.

[0628] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0629] Step 1:

[0630] A user uses a terminal to input a request to create a product. The request includes the type of product and specific instructions for creation. For example, a request such as "Please suggest a new fashion item" is input. This request information is sent from the terminal to the server. The input data includes the type of product and specific instructions, and the output is the request information.

[0631] Step 2:

[0632] The device acquires the user's emotional data in real time and sends it to the emotion engine. The emotion engine uses a camera and microphone to analyze the user's facial expressions and voice and recognize the emotion. For example, if the user is smiling, the emotion engine recognizes the emotion as "happy." The input data is the user's facial expressions and voice acquired in real time, and the output is the recognized emotional data.

[0633] Step 3:

[0634] The server receives the request information and emotion data sent by the user. The server then analyzes this data and adjusts the product's characteristics based on the user's emotion recognition results. For example, if the emotion is "happy," the server will suggest bright and cheerful items. The input data are the request information and emotion data, and the output is the adjusted product's characteristics.

[0635] Step 4:

[0636] The server selects and launches an appropriate generative model. This generative model uses its internal algorithm and the specified initial data to create a product. For example, it generates a brightly colored fashion item based on the adjusted product characteristics. The input data are the adjusted product characteristics, and the output is the generated product.

[0637] Step 5:

[0638] During the generation process, the server collects metadata such as the training data used, the version of the generative model, and the time required for generation. In addition, the user's emotional data recognized by the emotion engine is also collected as metadata. The input data are various data used during the generation process, and the output is the collected metadata.

[0639] Step 6:

[0640] Once the generation process is complete, the server hashes the generated product, resulting in a unique hash value for the product, which is used to verify the product's identity. The input data is the generated product, and the output is the hash value of the product.

[0641] Step 7:

[0642] The server combines the generated hash value with the collected metadata to generate new transaction data, which includes the generated hash value, the training data used, the version of the generative model, the generation time, and the user's emotional data. The input data are the hash value and the metadata, and the output is the new transaction data.

[0643] Step 8:

[0644] The server adds new transaction data to the blockchain. A new block is generated and integrated into the existing blockchain, recording the entire process of product creation on a distributed ledger. The input data is the new transaction data, and the output is an updated blockchain.

[0645] Step 9:

[0646] Finally, the server returns the generated product, hash value, and transaction ID on the blockchain to the terminal, allowing the user to check the product and details of its generation process. The input data are the product, hash value, and transaction ID, and the output is the information provided to the user terminal.

[0647] 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.

[0648] 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.

[0649] 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.

[0650] [Third embodiment]

[0651] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0652] 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.

[0653] 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).

[0654] 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.

[0655] 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.

[0656] 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).

[0657] 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.

[0658] 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.

[0659] 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.

[0660] 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.

[0661] 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.

[0662] 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."

[0663] This invention is a system that combines the AI ​​product generation process and its tracing function. This system consists of the following steps:

[0664] First, a user makes a request to create a product using a terminal. The user inputs the type of product (e.g., image, text) and specific instructions for creation via the interface on the terminal. For example, the user inputs a specific request such as "Please create an image of a dog." This request is then sent from the terminal to the server.

[0665] The server then receives the user's request and analyzes its contents. Based on the request, the server selects and activates an appropriate generative model. This generative model uses its internal algorithms and the specified initial data to create the desired product. This process results in the creation of a specific product (e.g., an image of a dog).

[0666] During the generation process, the server collects metadata such as the training data used, the version of the generative model, the time required for generation, etc. This metadata is necessary to ensure transparency of the generation process.

[0667] Once the creation process is complete, the server hashes the created product, resulting in a unique hash value for the product. This hash value, along with the collected metadata, is used to create new transaction data.

[0668] The server then adds this transaction data to the blockchain, and a new block is generated and integrated into the existing blockchain, recording the entire process of creating the product on a distributed ledger, ensuring the authenticity and traceability of the product.

[0669] Finally, the server returns the product, the hash value, and the transaction ID on the blockchain to the terminal. The user receives this information via the terminal and checks the product and verifies its authenticity. By checking the provided hash value and transaction ID on the blockchain, the user can be sure that the product is an AI-generated product.

[0670] As a concrete example, let's consider the case where a user makes a request to generate an "image of a dog." The user sends the request "Please generate an image of a dog" from their device to the server. The server receives and analyzes this request, and launches the appropriate image generation model. The model generates an image of a dog using the specified initial data and internal algorithm. During the generation process, the server collects metadata such as the training data used and the model version, and hashes the generated image of the dog. Based on this hash value and the collected metadata, new transaction data is generated and added to the blockchain. Finally, the image of the dog, its hash value, and the transaction ID are provided to the user's device. This allows the user to verify that the generated image of the dog is a trustworthy AI product.

[0671] The above is an embodiment of the present invention. This system ensures the traceability and reliability of products, improving trust in AI products.

[0672] The processing flow will be explained below.

[0673] Step 1:

[0674] The user uses a terminal to create a request to create a product. The user selects the type of product (e.g., image or text) on the interface and enters creation instructions (e.g., "Please create an image of a dog"). Furthermore, the user sets creation options (e.g., image style and resolution) as needed. This input data is sent as a request to the server.

[0675] Step 2:

[0676] The server receives a request sent by a user, analyzes the request, extracts the type of product and its parameters, and then refers to an internal database to select an appropriate generative model based on the extracted data.

[0677] Step 3:

[0678] The server launches the selected generative model, which incorporates the specified initial data and algorithms. The server uses this data to create a product (e.g., an image of a dog). During the generation process, the server allocates the necessary computing resources and manages the process to ensure smooth progress.

[0679] Step 4:

[0680] As the generation process progresses, the server collects metadata such as the training data used, the version of the generative model, and the time it took to generate the model, which is necessary for later traceability.

[0681] Step 5:

[0682] Once the generation process is complete, the server hashes the generated product, resulting in a unique hash value for the product, which is important for verifying the product's identity.

[0683] Step 6:

[0684] The server combines the generated hash value with the collected metadata to generate new transaction data, including the hash value of the product, the training data used, the version of the generative model, and the generation time.

[0685] Step 7:

[0686] The server adds new transaction data to the blockchain, generates a new block based on the transaction data, and integrates this block into the existing blockchain, recording the entire process of product creation on a distributed ledger.

[0687] Step 8:

[0688] The server returns the generated product, its hash value, and the transaction ID on the blockchain to the terminal, allowing the user to check the product and details of its generation process.

[0689] Step 9:

[0690] The terminal displays the product, hash value, and transaction ID received from the server to the user, who can then verify the product and use the provided hash value and transaction ID to verify the product's authenticity and traceability on the blockchain.

[0691] Through these steps, the system generates the AI ​​product specified by the user, ensures traceability based on the metadata obtained during the process, and provides a mechanism for users to easily verify the authenticity of the product.

[0692] Example 1

[0693] 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."

[0694] Ensuring transparency and traceability in the generation process of AI products is a challenge for existing systems. There is a need for a means for users to verify the authenticity of the generated data. There is also a need to improve the reliability of products by collecting detailed information about the generation process and providing it to users.

[0695] 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.

[0696] In this invention, the server includes means for receiving a user's input request, means for activating a generative model based on the input request and creating a product, means for collecting metadata including the training data and the version of the generative model used in the generation process, means for hashing the generated product and generating new transaction data based on the hash value and the metadata of the generation process, means for adding the transaction data to a distributed ledger, and means for providing the generated product and information on the distributed ledger to a user. This ensures transparency and traceability of the generation process and enables verification of the authenticity and reliability of the product.

[0697] A "user" is a person or entity that utilizes the system to make a request for a product.

[0698] An "input request" is a request that a user sends information including the type of product and detailed instructions via a terminal.

[0699] A "generative model" is an algorithm or machine learning model used to create a product based on an input request.

[0700] A "product" is an output created using a generative model, and can take the form of an image, text, or other information.

[0701] "Metadata" is relevant information such as the training data used during the generation process, the version of the generative model, and the time it took to generate it.

[0702] "Hashing" is the process of generating a unique hash value for a product, which is used to verify the authenticity of the data.

[0703] A "hash value" is a unique identifier for a product, a fixed-length string obtained through a hashing process.

[0704] "Transaction Data" means a data set containing hash values ​​and metadata about the generation process that is recorded on the blockchain.

[0705] A "distributed ledger" is a database in which data is recorded in a decentralized manner using blockchain technology, making it accessible and verifiable.

[0706] A "block" is a unit of data that is added to a blockchain and contains new transaction data.

[0707] A "transaction ID" is a unique identifier used to identify a specific transaction on a blockchain.

[0708] The present invention is a system that combines the generation process of AI products with a tracing function. This system allows users to make requests to create products, generates products based on those requests, and has the ability to record and manage the entire generation process. Specific embodiments of the system are described below.

[0709] First, a user makes a request to create a product using a terminal. The user inputs the type of product (e.g., image, text) and specific instructions for creation via the interface on the terminal. For example, the user inputs a specific request such as "Please create an image of a dog." This request is then sent from the terminal to the server.

[0710] Next, the server receives the user's request and analyzes its contents. The server selects and launches an appropriate generative model based on the request. This generative model is implemented using libraries such as TensorFlow or PyTorch. The generative model uses its internal algorithms and the specified initial data to create the desired product. This process results in the creation of a specific product (for example, an image of a dog).

[0711] During the generation process, the server collects metadata such as the training data used, the version of the generative model, and the time required for generation, ensuring transparency of the generation process. Once the generation process is complete, the server hashes the generated product using the SHA-256 hashing algorithm. This results in a unique hash value for the product. New transaction data is generated based on this hash value and the collected metadata.

[0712] The server then adds this transaction data to the blockchain using Hyperledger Fabric, creating a new block that is integrated into the existing distributed ledger, recording the entire process of creating the product, ensuring its authenticity and traceability.

[0713] Finally, the server returns the product, the hash value, and the transaction ID on the distributed ledger to the terminal. The user receives this information via the terminal and checks the product and verifies its authenticity. By checking the provided hash value and transaction ID on the distributed ledger, the user can be sure that the product is an AI-generated product.

[0714] As a concrete example, consider the case where a user makes a request to generate an "image of a dog." The user sends the request "Please generate an image of a dog" from their device to the server. The server receives and analyzes this request, then launches the appropriate image generation model. The model generates an image of a dog using the specified initial data and its internal algorithm. During the generation process, the server collects metadata such as the training data used and the model version, and hashes the generated image of the dog. Based on this hash value and the collected metadata, new transaction data is generated and added to the distributed ledger. Finally, the image of the dog, its hash value, and the transaction ID are provided to the user's device. This allows the user to verify that the generated image of the dog is a trustworthy AI creation.

[0715] The above is an embodiment of the present invention. This system ensures the traceability and reliability of products, improving trust in AI products.

[0716] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0717] Step 1:

[0718] The user enters a request.

[0719] The user uses the terminal interface to input a product request, including the type of product and specific instructions.

[0720] Input: A request to create (e.g., "Create an image of a dog")

[0721] Output: Request data

[0722] Specific operation: The user enters the words "Please generate an image of a dog" into the input field displayed on the device screen and presses the send button.

[0723] Step 2:

[0724] The device sends a request to the server.

[0725] The terminal transmits the request input by the user to the server.

[0726] Input: Request data

[0727] Output: JSON formatted request data sent to the server

[0728] Specific operation: When the send button is pressed, the terminal converts the request data into JSON format and sends it to the server via the network.

[0729] Step 3:

[0730] The server receives and parses the request.

[0731] The server receives the request and analyzes its contents, extracting the type of product and the creation instructions.

[0732] Input: Request data in JSON format

[0733] Output: Parsed request content (e.g., type of product "Image", creation instruction "Dog")

[0734] What happens: The server parses the JSON data and extracts the instruction "Generate an image of a dog."

[0735] Step 4:

[0736] The server selects and launches a generative model.

[0737] The server selects and launches an appropriate generative model based on the request.

[0738] Input: Parsed request content

[0739] Output: An instance of the selected generative model

[0740] Specific operation: The server selects the GAN, the generative model that is best suited for "dog image generation," and loads the required initial dataset.

[0741] Step 5:

[0742] The server runs the generation process and creates the product.

[0743] The server uses the generative model to create the product desired by the user.

[0744] Input: Selected generative model, initial dataset

[0745] Output: The generated artifact (e.g., a picture of a dog)

[0746] What happens next: The generative model in the server runs an algorithm to process the initial dataset and generate an image of a dog.

[0747] Step 6:

[0748] The server collects the metadata.

[0749] The server collects metadata such as the training data used during the generation process, the model version, and the generation time.

[0750] Input: Generation process information

[0751] Output: Collected metadata (e.g. dataset name used, model version, generation time)

[0752] Specific operation: The server analyzes the process log and collects the name of the training data used, the model version number, the time taken to generate the model, etc.

[0753] Step 7:

[0754] The server hashes the product.

[0755] The server generates a unique hash value for the product.

[0756] Input: Generated product

[0757] Output: The hash value of the result

[0758] What it does: The server hashes the generated dog image using the SHA-256 algorithm to create a unique hash value.

[0759] Step 8:

[0760] The server adds the transaction data to the distributed ledger.

[0761] The server generates transaction data based on the collected metadata and hash values ​​and adds it to the distributed ledger.

[0762] Input: Metadata, hash value of the generated data

[0763] Output: New transaction data is added to the distributed ledger

[0764] How it works: The server composes the hash value and metadata into a single transaction and adds a new block to the distributed ledger using Hyperledger Fabric.

[0765] Step 9:

[0766] The server sends the results back to the terminal.

[0767] The server returns the product, the hash value, and the transaction ID to the terminal.

[0768] Input: Product, hash value, transaction ID

[0769] Output: Result data sent back to the terminal

[0770] Specific operation: The server sends the generated dog image, hash value, and transaction ID to the terminal in a response.

[0771] Step 10:

[0772] The user checks and verifies the product.

[0773] The user checks the product on the terminal and verifies its authenticity.

[0774] Input: Generated image, hash value, transaction ID

[0775] Output: Confirmed artifacts, verification results

[0776] How it works: The user visually checks the image of the dog displayed on the device and verifies its authenticity by matching the provided hash value with the transaction ID on the distributed ledger.

[0777] (Application example 1)

[0778] 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."

[0779] Ensuring the authenticity and traceability of products is becoming increasingly important in modern society. Particularly in the field of security services, there is a need to guarantee the reliability of generated evidence and data. However, current systems lack effective means to prevent tampering of products and ensure transparency of the generation process, which is a challenge.

[0780] 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.

[0781] In this invention, the server includes means for receiving a user's input request, means for activating a generative model to create a product, means for collecting data used in the generation process and the version of the generative model, means for hashing the generated product and generating transaction data, means for adding the transaction data to a blockchain, means for providing the generated product and information on the blockchain to a user, means for recording content as security evidence of the product to the blockchain, and means for providing an interface for verifying the hash value and metadata of the product, thereby ensuring the reliability and traceability of the product and preventing tampering.

[0782] "Means for receiving user input requests" refers to the interface and communication capabilities by which a user sends a request to the system to create a particular product.

[0783] "Means for invoking a generative model and creating a product" refers to the processes and algorithms for running an AI model based on a user's request and producing a specified product.

[0784] "Means for collecting the data and generative model versions used during the generation process" refers to the ability to record and collect detailed information such as the version information of the datasets and models used when creating the product.

[0785] "Means for hashing the generated product and generating transaction data" refers to a technology for converting the generated product into a unique identifier through encryption and then generating new transaction data that includes that identifier.

[0786] "Means for adding transaction data to the blockchain" refers to the process and technology for incorporating newly generated transaction data into the distributed ledger system.

[0787] "Means for providing users with generated products and information on the blockchain" refers to the user interface and communication functions for visualizing and providing users with details and authenticity of the generated content and its generation process.

[0788] "Means for recording the content of a product as security evidence on a blockchain" refers to a technology that records the content of a product, particularly one related to security, on a blockchain and makes it tamper-proof in order to preserve the content as reliable evidence.

[0789] "Means for providing an interface for verifying the product's hash value and metadata" refers to display and operation means that allow a user to confirm and verify the product's unique identifier (hash value) and detailed information about the creation process (metadata).

[0790] This invention relates to a system for ensuring the authenticity and traceability of products in the field of security services. The system is realized using a user's terminal, a server, and blockchain technology.

[0791] First, a user uses a terminal to input a request to create a product through the interface. For example, the user inputs a specific prompt such as "Please generate an image of a dog." The terminal then sends this request to the server.

[0792] The server receives and analyzes the request, then launches the appropriate generative model. This model uses its internal algorithms and the specified initial data to create a product (e.g., an image of a dog). During the generation process, the server collects metadata such as the training data used, the version of the generative model, and the time it took to generate the product.

[0793] Once generation is complete, the server hashes the generated product, which results in a unique hash value. New transaction data is generated based on this hash value and the collected metadata, and the server adds this transaction data to the blockchain. A new block is generated and integrated into the existing blockchain, recording the entire product generation process on the distributed ledger. This ensures the authenticity and traceability of the product.

[0794] Finally, the server returns the generated result, the hash value, and the transaction ID on the blockchain to the terminal. The user can receive this information via the terminal and check the generated result and verify its authenticity. For example, a specific prompt such as "Please generate an image that identifies suspicious activity from security camera footage in the office" can be used for security-related content. The generated content can be recorded on the blockchain and used as tamper-proof evidence.

[0795] The hardware used is primarily the user's smartphone and a server, while the software includes Python, a REST API, an AI model generation service, and a blockchain library. The server receives data, runs a generative model to generate content, collects metadata, hashes the results, adds transaction data to the blockchain, and provides the results and related information to the user.

[0796] As a result, this system can ensure the reliability and traceability of the product and prevent tampering. The present invention is particularly useful in the field of security services, and functions effectively in situations where the authenticity of the product is highly required.

[0797] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0798] Step 1:

[0799] The user inputs a production request through the terminal interface. This input includes the type of production (e.g., image or text) and a specific prompt (e.g., "Please generate an image of a dog"). The input data is sent to the server as the user's request.

[0800] Step 2:

[0801] The server receives a request from the user. At this time, the server analyzes the request content and extracts the request type and prompt. The analyzed data is used in the next processing step.

[0802] Step 3:

[0803] The server selects and launches an appropriate generative model based on the request. The generative model uses the specified initial data and its internal algorithms to create the desired product. The input to this process is the parsed request data, and the output is the generated product (e.g., a picture of a dog).

[0804] Step 4:

[0805] The server collects metadata such as the training data used during the generation process, the version of the generative model, and the time required for generation. In this step, detailed information about the generation process is recorded and saved for later verification and traceability. The collected metadata will be used in the next step.

[0806] Step 5:

[0807] The server hashes the generated product. This process results in a unique hash value for the product. The input is the generated product, and the output is its hash value. The server then generates new transaction data based on this hash value and the collected metadata.

[0808] Step 6:

[0809] The server adds new transaction data to the blockchain, and a new block is generated based on the existing blockchain data and integrated into the blockchain, so that the entire product creation process is recorded on the distributed ledger.

[0810] Step 7:

[0811] The server returns the product, hash value, and transaction ID on the blockchain to the user's device, where the user can receive this information and verify the product and its authenticity.

[0812] Step 8:

[0813] Using the hash value and transaction ID provided by the user, the product is verified on the blockchain to be truly generated by AI and not tampered with, ensuring the authenticity and traceability of the product.

[0814] For example, a terminal sends a prompt to the server: "Please generate an image that identifies suspicious activity from security camera footage in the office." The server analyzes the request, activates the generative model to generate the appropriate image, and records the generation process on the blockchain to ensure that the generated image has not been tampered with.

[0815] 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.

[0816] This invention is a system that features an AI product generation process and tracing function combined with an emotion engine that recognizes user emotions. This system receives user requests, adjusts the type and specifications of the product based on the emotion recognition results, and generates and traces the product. It also uses blockchain technology to ensure the authenticity of the product and allow users to verify the generation process.

[0817] First, the user uses a device to make a request to create a product. The user inputs the type of product (e.g., image or text) and specific creation instructions (e.g., "Please create an image of a dog") on the device's interface. The emotion engine then analyzes the user's emotion data in real time and automatically adjusts the product's characteristics and settings based on that emotion. This request information is sent from the device to the server.

[0818] The server then receives the request and emotion data sent by the user and analyzes their content. Based on the user's emotion recognition results, the server automatically adjusts the content of the product and selects an appropriate generative model. This generative model creates the product using its internal algorithm and the specified initial data. During the generation process, the server appropriately allocates the necessary computing resources and manages them to ensure smooth generation.

[0819] During the generation process, the server collects metadata such as the training data used, the version of the generative model, and the time required for generation. Additionally, the user's emotional data recognized by the emotion engine is also collected as metadata. This metadata is important for ensuring traceability later.

[0820] Once the generation process is complete, the server hashes the generated product, resulting in a unique hash value for the product, which is used to verify the product's identity.

[0821] The server then combines the generated hash value with the collected metadata to generate new transaction data, including the generated hash value, the training data used, the version of the generative model, the generation time, and the user's emotional data.

[0822] The server adds this new transaction data to the blockchain, and a new block is generated and integrated into the existing blockchain, recording the entire process of creating the product on a distributed ledger, ensuring the authenticity and traceability of the product.

[0823] Finally, the server returns the generated product, its hash value, and the transaction ID on the blockchain to the terminal, allowing the user to check the product and details of its generation process.

[0824] As a concrete example, consider the case where a user requests the generation of an "image of a dog." The user sends a request from their device to the server: "Please generate an image of a dog." If the user is smiling, the emotion engine recognizes this emotion as "positive" and adjusts the generative model to reflect a bright background and a cheerful dog in the generated image. The server receives and analyzes this request and launches the appropriate image generation model. The model generates an image of a dog using the specified initial data and internal algorithm. During the generation process, the server collects metadata such as the training data used and the model version, as well as the user's emotional data, and hashes the generated image of the dog. Based on this hash value and the collected metadata, new transaction data is generated and added to the blockchain. Finally, the image of the dog, its hash value, and the transaction ID are provided to the user's device. This allows the user to verify that the generated image of the dog is a reliable AI product and further confirm that emotions were correctly reflected in the generation process.

[0825] The above is an embodiment of the present invention. This system ensures the traceability and reliability of products, improves trust in AI products, and enables the provision of customized products that reflect the user's emotions.

[0826] The processing flow will be explained below.

[0827] Step 1:

[0828] The user uses the terminal to create a request for a product. The user selects the type of product (e.g., image or text) on the interface and enters a creation instruction (e.g., "Please create an image of a dog"). The camera and microphone also operate to acquire the user's emotional data, sending the user's facial expressions and voice to the emotion engine in real time. The request containing this information is then sent from the terminal to the server.

[0829] Step 2:

[0830] The server receives a request from the user. The request includes the type of product, creation instructions, and the user's emotion data. The server analyzes this information and recognizes the user's emotion using an emotion engine.

[0831] Step 3:

[0832] The server adjusts the selection of a generative model or the characteristics of the product based on the user's emotion results recognized by the emotion engine. For example, if the user is smiling at the time of the request, the server adjusts the settings so that the generated image of the dog reflects a bright background and a cheerful dog.

[0833] Step 4:

[0834] The server starts the appropriate generative model and creates a product using the specified initial data and algorithm. The generative model refers to the built-in learning data and generates a product that best suits the user's request and emotions.

[0835] Step 5:

[0836] During the generation process, the server collects the training data used, the version of the generative model, the generation time, and the user's sentiment data. This metadata is necessary for later traceability of the generation process.

[0837] Step 6:

[0838] Once the generation process is complete, the server hashes the generated product, obtaining a unique hash value for the product, which is important for verifying the product's identity.

[0839] Step 7:

[0840] The server combines the generated hash value with the collected metadata to generate new transaction data, including the product hash value, the training data used, the version of the generative model, the generation time, and the user's emotional data.

[0841] Step 8:

[0842] The server adds new transaction data to the blockchain, and a new block is generated and integrated into the existing blockchain, recording the entire process of product creation on a distributed ledger, ensuring the authenticity and traceability of the product.

[0843] Step 9:

[0844] The server sends the generated product, hash value, and transaction ID on the blockchain back to the terminal, which receives this information and displays it to the user.

[0845] Step 10:

[0846] Users can check the product through their terminal and verify its authenticity and traceability on the blockchain using the provided hash value and transaction ID. Users can also check whether the product adjusted by the emotion engine matches their own emotions.

[0847] As a specific example, when a user requests the generation of an "image of a dog," the user sends a request from their device to the server saying, "Please generate an image of a dog." If the user is smiling at the time of the request, the emotion engine recognizes this emotion as "positive" and adjusts the generative model to reflect a bright background and a cheerful dog in the generated image. The server receives and analyzes the request, launches the appropriate image generation model, and generates the optimal dog image. During the generation process, the training data used, the model version, and the user's emotion data are collected, and the generated dog image is hashed. New transaction data is generated based on this hash value and the collected metadata and added to the blockchain. Finally, the dog image, its hash value, and the transaction ID are provided to the user's device. This allows the user to verify that the generated dog image is a reliable AI product and further confirm that emotions were correctly reflected in the generation process.

[0848] Example 2

[0849] 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."

[0850] Conventional generative AI systems often fail to ensure the authenticity of the product or the traceability of the generation process, leaving users with limited means to verify the reliability of the product. It is also difficult to provide customized products that reflect the user's emotions. To address these issues, the present invention aims to analyze user emotion data in real time, generate products based on that data, and ensure their traceability.

[0851] 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.

[0852] In this invention, the server includes means for receiving a user's input request, means for collecting metadata during the creation process, means for collecting and analyzing user emotion data, means for hashing the created product and adding transaction data to the blockchain, and means for providing information about the creation process to the user. This ensures the authenticity and traceability of the product and makes it possible to provide a customized product that reflects the user's emotion.

[0853] A "user input request" is a specific instruction that a user sends to the system to create a product.

[0854] A "generative model" is a machine learning algorithm for creating a product using specified initial data and an internal algorithm.

[0855] A "generation process" is a series of processes that activates a generative model based on a user's input request and creates a product.

[0856] "Metadata" is additional information related to the product, such as the training data used during the generation process, the version of the generative model, the generation time, and the user's emotional data.

[0857] "Emotion data" is data that represents the emotional state of the user analyzed from facial expressions, gestures, and the like.

[0858] "Hashing" is a data transformation technique used to generate a unique hash value for a product.

[0859] A "hash value" is a unique identifier generated by a hashing process.

[0860] "Transaction data" is a data packet containing a hash value and metadata about the generation process that is added to the blockchain as a new transaction.

[0861] A "blockchain" is a database that uses distributed ledger technology and consists of a series of blocks that track transactions and data.

[0862] A "transaction ID" is an identifier that uniquely identifies a specific transaction on a blockchain.

[0863] "Product" is the product created by the generative model based on the user's input request and emotion data.

[0864] This invention is a system that features an AI product generation process and tracing function combined with an emotion engine that recognizes user emotions. This system analyzes user emotion data in real time and automatically adjusts the product's characteristics and settings to provide customized products to users, and ensures authenticity and traceability by recording the generation process using blockchain technology.

[0865] First, the user uses a device such as a smartphone or PC to input the type of product (e.g., an image or text) and specific generation instructions (prompt text) into the interface. The software used here can be a general web browser or a custom application.

[0866] Next, the device uses sensors such as a camera and microphone to collect the user's emotional data. The emotion engine uses, for example, a general emotion recognition API (Emotion API). This allows for real-time analysis of the emotional data obtained from the user's facial expressions and voice.

[0867] The collected request information and emotion data are sent from the device to the server. The server receives this data and begins analysis. Based on the emotion recognition results, the server selects an appropriate generative AI model (e.g., a sentence generation model or an image generation model) and begins the generation process. The generative model used here could be, for example, a natural language processing model (GPT-4) or an image generation model (DALL-E 2).

[0868] The server collects metadata such as the training data used during the generation process, the version of the generative model, the generation time, and the user's emotional data, which play an important role in ensuring traceability later.

[0869] Once the creation process is complete, the server hashes the created product to generate a unique hash value. This hash value is combined with the collected metadata to generate new transaction data that is added to the blockchain, ensuring the authenticity of the product and a record of the entire creation process on the distributed ledger.

[0870] Finally, the server returns the generated product, its hash value, and the transaction ID on the blockchain to the terminal, allowing the user to verify the authenticity of the product and details of its creation process.

[0871] For example, if a user requests "generate an image of a dog," and the emotion engine recognizes the user's smile as "positive," the server generates an image of a dog against a bright background that reflects this emotional data. Transaction data is then added to the blockchain based on the metadata of the generation process and the hash value of the product. Finally, the generated dog image, hash value, and transaction ID are provided to the user's device.

[0872] Example prompt sentence:

[0873] "Generate an image of a dog"

[0874] "Write a sentence about natural scenery"

[0875] This allows customized products to be created according to the user's emotions, and the creation process is accurately recorded, improving the reliability and traceability of the products.

[0876] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0877] Step 1:

[0878] User issues a request

[0879] The user uses a terminal to make a request to create a product. The user inputs the type of product and specific instructions (prompt) for creation into the terminal interface. The input prompt is sent from the terminal to the server.

[0880] Input: Type of product, specific production instructions (prompt text)

[0881] Output: Sends the request data to the server

[0882] Specific behavior: A user opens the application on their smartphone, enters "Please generate an image of a dog" into the input form, and clicks the submit button.

[0883] Step 2:

[0884] Collecting emotion data from devices

[0885] The device uses a camera and microphone to collect the user's emotional data in real time, which is then analyzed through an emotion engine and added to the request information.

[0886] Input: User's facial expression or voice data

[0887] Output: Analyzes user emotion data and adds it to the request data

[0888] Specific operation: The device camera captures the user's facial expression, and if the user is smiling, the emotion engine analyzes the emotion as positive.

[0889] Step 3:

[0890] Server receives request and emotion data

[0891] The server receives request information and emotion data sent from the device, analyzes the received data, and selects an appropriate generative model.

[0892] Input: Request information and emotion data sent from the device

[0893] Output: Selection of a generative model based on the analysis results

[0894] Specific operation: The server analyzes the type of product ("image of dog") and emotion data ("positive") and selects an appropriate image generation model.

[0895] Step 4:

[0896] Server-based model initiation and generation

[0897] The server invokes the appropriate generative model and uses the specified initial data and internal algorithms to create a product, which is customized to reflect the user's emotional data.

[0898] Input: Analyzed generative model, given initial data

[0899] Output: Creating a customized artifact

[0900] Specific operation: The server launches an image generation model (e.g., DALL-E 2) and generates an image of a dog that reflects a "positive" emotion.

[0901] Step 5:

[0902] Server-based collection and hashing of metadata

[0903] During the generation process, the server collects metadata such as the training data used, the version of the generative model, the generation time, and the user's emotional data. The generated product is then hashed to generate a unique hash value.

[0904] Input: Various data during the generation process

[0905] Output: Collected metadata, hash value of the artifact

[0906] What it does: The server records the metadata of the generation process and hashes the generated dog image using the SHA-256 algorithm.

[0907] Step 6:

[0908] Registration on the blockchain by the server

[0909] The generated hash value is combined with the collected metadata to generate new transaction data and add it to the blockchain.

[0910] Input: hash value, collected metadata

[0911] Output: New transaction data added to the blockchain

[0912] How it works: The server packages the resulting hash value and metadata into a packet and adds the transaction data to the blockchain using distributed ledger technology.

[0913] Step 7:

[0914] Return and check of product

[0915] The server returns the generated product, its hash value, and the transaction ID on the blockchain to the terminal, allowing the user to verify the authenticity of the product and details of the generation process.

[0916] Input: hash value, transaction ID

[0917] Output: The product, hash value, and transaction ID sent to the user terminal

[0918] How it works: The server sends the image of the dog, its hash value, and the transaction ID on the blockchain to the user's device, where the user can view this information.

[0919] (Application example 2)

[0920] 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."

[0921] Conventional generative AI systems are limited to providing products based on user input, and lack customization that takes user emotions into account and transparency in the generation process. In addition, there are insufficient means to guarantee the authenticity of the products, leaving users with little means to verify the reliability of the products.

[0922] The specific processing by the specific 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 receiving a user's input request, means for activating a generative model based on the input request and creating a product, means for collecting metadata including the training data and the version of the generative model used during the generation process, means for hashing the generated product and generating new transaction data based on the hash value and the metadata of the generation process, means for adding the transaction data to the blockchain, means for providing the generated product and information on the blockchain to the user, means for analyzing the user's emotions and adjusting the characteristics of the product based on the emotions, and means for suggesting products based on the user's emotions. This makes it possible to provide customized products that reflect the user's emotions and ensure the transparency and reliability of the generation process.

[0923] The "means for receiving a user input request" is a function on the device that receives a request input by a user to create a product.

[0924] The "means for activating a generative model and creating a product" is a function that runs a pre-set generative algorithm based on a received input request and generates the required product.

[0925] "Means for collecting metadata including training data and generative model versions" refers to the ability to systematically obtain information about the datasets and algorithm versions used during the generation process.

[0926] The "means of hashing the generated product and generating new transaction data based on the hash value and metadata of the generation process" refers to a function that assigns a unique identification code to the generated product, integrates the code with related information, and records it as new data.

[0927] "Means of adding transaction data to the blockchain" refers to the function of permanently storing the generated data using distributed ledger technology.

[0928] "Means for providing users with information about the generated product and the blockchain" is a function that provides users with detailed data about the product and its generation process in a visible form.

[0929] "Means for analyzing the user's emotions and adjusting the characteristics of the generated product based on those emotions" refers to a function that analyzes the user's emotional state in real time and dynamically changes the characteristics of the generated data and objects based on those emotions.

[0930] The "means for proposing a product based on the user's emotions" is a function for selecting an appropriate product based on the analyzed emotion data and proposing it to the user.

[0931] This invention is a system that combines an emotion engine that analyzes user emotions with an AI product generation process and tracing function. This system receives user requests, adjusts the type and specifications of the product based on the emotion recognition results, and generates and traces the product. It also uses blockchain technology to ensure the authenticity of the product and allow users to verify the generation process.

[0932] First, the user uses a device to make a request to create a product. The user inputs the type of product and specific creation instructions on the device's interface. The emotion engine then analyzes the user's emotion data in real time and automatically adjusts the product's characteristics and settings based on that emotion. This request information is sent from the device to the server.

[0933] The server then receives the request and emotion data sent by the user and analyzes their content. Based on the user's emotion recognition results, the server automatically adjusts the content of the product and selects an appropriate generative model. This generative model creates the product using its internal algorithm and the specified initial data. During the generation process, the server appropriately allocates the necessary computing resources and manages them to ensure smooth generation.

[0934] During the generation process, the server collects metadata such as the training data used, the version of the generative model, and the time required for generation. In addition, the user's emotional data recognized by the emotion engine is also collected as metadata. This metadata is important for ensuring traceability later. Once the generation process is complete, the server hashes the generated product. Hashing results in a unique hash value for the product, which is used to verify the identity of the product.

[0935] Next, the server combines the generated hash value with the collected metadata to generate new transaction data. The transaction data includes the product's hash value, the training data used, the version of the generative model, the generation time, and the user's emotional data. The server adds this new transaction data to the blockchain. A new block is generated and integrated into the existing blockchain, recording the entire product generation process on the distributed ledger. This ensures the authenticity and traceability of the product.

[0936] Finally, the server returns the generated product, its hash value, and the transaction ID on the blockchain to the terminal, allowing the user to check the product and details of its generation process.

[0937] As a concrete example, consider a user searching for products on an online shopping site using the "Emo Shopping" app. The user sends a request to the server from their device asking, "Please suggest new fashion items." If the user is smiling, the emotion engine recognizes the emotion as "happy" and adjusts the generative model to reflect vibrant and cheerful fashion items in the suggested products. The server receives and analyzes this request and launches the appropriate product suggestion model. The model generates a list of suggested products using the specified initial data and internal algorithm. During the generation process, the server collects metadata such as the training data used and the model version, as well as the user's emotional data, and hashes the generated list of products. Based on this hash value and the collected metadata, new transaction data is generated and added to the blockchain. Finally, the list of products, its hash value, and the transaction ID are provided to the user's device. This allows the user to verify that the suggested product list is a reliable AI product and further confirm that emotions were correctly reflected in the generation process.

[0938] Examples of prompts are:

[0939] "We'll suggest products that you might want to choose when you're feeling 'happy' right now. For example, how about brightly colored clothes or fun accessories?"

[0940] This makes it possible to provide customized products that reflect the user's emotions, and ensure the transparency and reliability of the creation process.

[0941] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0942] Step 1:

[0943] A user uses a terminal to input a request to create a product. The request includes the type of product and specific instructions for creation. For example, a request such as "Please suggest a new fashion item" is input. This request information is sent from the terminal to the server. The input data includes the type of product and specific instructions, and the output is the request information.

[0944] Step 2:

[0945] The device acquires the user's emotional data in real time and sends it to the emotion engine. The emotion engine uses a camera and microphone to analyze the user's facial expressions and voice and recognize the emotion. For example, if the user is smiling, the emotion engine recognizes the emotion as "happy." The input data is the user's facial expressions and voice acquired in real time, and the output is the recognized emotional data.

[0946] Step 3:

[0947] The server receives the request information and emotion data sent by the user. The server then analyzes this data and adjusts the product's characteristics based on the user's emotion recognition results. For example, if the emotion is "happy," the server will suggest bright and cheerful items. The input data are the request information and emotion data, and the output is the adjusted product's characteristics.

[0948] Step 4:

[0949] The server selects and launches an appropriate generative model. This generative model uses its internal algorithm and the specified initial data to create a product. For example, it generates a brightly colored fashion item based on the adjusted product characteristics. The input data are the adjusted product characteristics, and the output is the generated product.

[0950] Step 5:

[0951] During the generation process, the server collects metadata such as the training data used, the version of the generative model, and the time required for generation. In addition, the user's emotional data recognized by the emotion engine is also collected as metadata. The input data are various data used during the generation process, and the output is the collected metadata.

[0952] Step 6:

[0953] Once the generation process is complete, the server hashes the generated product, resulting in a unique hash value for the product, which is used to verify the product's identity. The input data is the generated product, and the output is the hash value of the product.

[0954] Step 7:

[0955] The server combines the generated hash value with the collected metadata to generate new transaction data, which includes the generated hash value, the training data used, the version of the generative model, the generation time, and the user's emotional data. The input data are the hash value and the metadata, and the output is the new transaction data.

[0956] Step 8:

[0957] The server adds new transaction data to the blockchain. A new block is generated and integrated into the existing blockchain, recording the entire process of product creation on a distributed ledger. The input data is the new transaction data, and the output is an updated blockchain.

[0958] Step 9:

[0959] Finally, the server returns the generated product, hash value, and transaction ID on the blockchain to the terminal, allowing the user to check the product and details of its generation process. The input data are the product, hash value, and transaction ID, and the output is the information provided to the user terminal.

[0960] 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.

[0961] 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.

[0962] 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.

[0963] [Fourth embodiment]

[0964] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0965] 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.

[0966] 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).

[0967] 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.

[0968] 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.

[0969] 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).

[0970] 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.

[0971] 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.

[0972] 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.

[0973] 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.

[0974] 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.

[0975] 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.

[0976] 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."

[0977] This invention is a system that combines the AI ​​product generation process and its tracing function. This system consists of the following steps:

[0978] First, a user makes a request to create a product using a terminal. The user inputs the type of product (e.g., image, text) and specific instructions for creation via the interface on the terminal. For example, the user inputs a specific request such as "Please create an image of a dog." This request is then sent from the terminal to the server.

[0979] The server then receives the user's request and analyzes its contents. Based on the request, the server selects and activates an appropriate generative model. This generative model uses its internal algorithms and the specified initial data to create the desired product. This process results in the creation of a specific product (e.g., an image of a dog).

[0980] During the generation process, the server collects metadata such as the training data used, the version of the generative model, the time required for generation, etc. This metadata is necessary to ensure transparency of the generation process.

[0981] Once the creation process is complete, the server hashes the created product, resulting in a unique hash value for the product. This hash value, along with the collected metadata, is used to create new transaction data.

[0982] The server then adds this transaction data to the blockchain, and a new block is generated and integrated into the existing blockchain, recording the entire process of creating the product on a distributed ledger, ensuring the authenticity and traceability of the product.

[0983] Finally, the server returns the product, the hash value, and the transaction ID on the blockchain to the terminal. The user receives this information via the terminal and checks the product and verifies its authenticity. By checking the provided hash value and transaction ID on the blockchain, the user can be sure that the product is an AI-generated product.

[0984] As a concrete example, let's consider the case where a user makes a request to generate an "image of a dog." The user sends the request "Please generate an image of a dog" from their device to the server. The server receives and analyzes this request, and launches the appropriate image generation model. The model generates an image of a dog using the specified initial data and internal algorithm. During the generation process, the server collects metadata such as the training data used and the model version, and hashes the generated image of the dog. Based on this hash value and the collected metadata, new transaction data is generated and added to the blockchain. Finally, the image of the dog, its hash value, and the transaction ID are provided to the user's device. This allows the user to verify that the generated image of the dog is a trustworthy AI product.

[0985] The above is an embodiment of the present invention. This system ensures the traceability and reliability of products, improving trust in AI products.

[0986] The processing flow will be explained below.

[0987] Step 1:

[0988] The user uses a terminal to create a request to create a product. The user selects the type of product (e.g., image or text) on the interface and enters creation instructions (e.g., "Please create an image of a dog"). Furthermore, the user sets creation options (e.g., image style and resolution) as needed. This input data is sent as a request to the server.

[0989] Step 2:

[0990] The server receives a request sent by a user, analyzes the request, extracts the type of product and its parameters, and then refers to an internal database to select an appropriate generative model based on the extracted data.

[0991] Step 3:

[0992] The server launches the selected generative model, which incorporates the specified initial data and algorithms. The server uses this data to create a product (e.g., an image of a dog). During the generation process, the server allocates the necessary computing resources and manages the process to ensure smooth progress.

[0993] Step 4:

[0994] As the generation process progresses, the server collects metadata such as the training data used, the version of the generative model, and the time it took to generate the model, which is necessary for later traceability.

[0995] Step 5:

[0996] Once the generation process is complete, the server hashes the generated product, resulting in a unique hash value for the product, which is important for verifying the product's identity.

[0997] Step 6:

[0998] The server combines the generated hash value with the collected metadata to generate new transaction data, including the hash value of the product, the training data used, the version of the generative model, and the generation time.

[0999] Step 7:

[1000] The server adds new transaction data to the blockchain, generates a new block based on the transaction data, and integrates this block into the existing blockchain, recording the entire process of product creation on a distributed ledger.

[1001] Step 8:

[1002] The server returns the generated product, its hash value, and the transaction ID on the blockchain to the terminal, allowing the user to check the product and details of its generation process.

[1003] Step 9:

[1004] The terminal displays the product, hash value, and transaction ID received from the server to the user, who can then verify the product and use the provided hash value and transaction ID to verify the product's authenticity and traceability on the blockchain.

[1005] Through these steps, the system generates the AI ​​product specified by the user, ensures traceability based on the metadata obtained during the process, and provides a mechanism for users to easily verify the authenticity of the product.

[1006] Example 1

[1007] 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."

[1008] Ensuring transparency and traceability in the generation process of AI products is a challenge for existing systems. There is a need for a means for users to verify the authenticity of the generated data. There is also a need to improve the reliability of products by collecting detailed information about the generation process and providing it to users.

[1009] 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.

[1010] In this invention, the server includes means for receiving a user's input request, means for activating a generative model based on the input request and creating a product, means for collecting metadata including the training data and the version of the generative model used in the generation process, means for hashing the generated product and generating new transaction data based on the hash value and the metadata of the generation process, means for adding the transaction data to a distributed ledger, and means for providing the generated product and information on the distributed ledger to a user. This ensures transparency and traceability of the generation process and enables verification of the authenticity and reliability of the product.

[1011] A "user" is a person or entity that utilizes the system to make a request for a product.

[1012] An "input request" is a request that a user sends information including the type of product and detailed instructions via a terminal.

[1013] A "generative model" is an algorithm or machine learning model used to create a product based on an input request.

[1014] A "product" is an output created using a generative model, and can take the form of an image, text, or other information.

[1015] "Metadata" is relevant information such as the training data used during the generation process, the version of the generative model, and the time it took to generate it.

[1016] "Hashing" is the process of generating a unique hash value for a product, which is used to verify the authenticity of the data.

[1017] A "hash value" is a unique identifier for a product, a fixed-length string obtained through a hashing process.

[1018] "Transaction Data" means a data set containing hash values ​​and metadata about the generation process that is recorded on the blockchain.

[1019] A "distributed ledger" is a database in which data is recorded in a decentralized manner using blockchain technology, making it accessible and verifiable.

[1020] A "block" is a unit of data that is added to a blockchain and contains new transaction data.

[1021] A "transaction ID" is a unique identifier used to identify a specific transaction on a blockchain.

[1022] The present invention is a system that combines the generation process of AI products with a tracing function. This system allows users to make requests to create products, generates products based on those requests, and has the ability to record and manage the entire generation process. Specific embodiments of the system are described below.

[1023] First, a user makes a request to create a product using a terminal. The user inputs the type of product (e.g., image, text) and specific instructions for creation via the interface on the terminal. For example, the user inputs a specific request such as "Please create an image of a dog." This request is then sent from the terminal to the server.

[1024] Next, the server receives the user's request and analyzes its contents. The server selects and launches an appropriate generative model based on the request. This generative model is implemented using libraries such as TensorFlow or PyTorch. The generative model uses its internal algorithms and the specified initial data to create the desired product. This process results in the creation of a specific product (for example, an image of a dog).

[1025] During the generation process, the server collects metadata such as the training data used, the version of the generative model, and the time required for generation, ensuring transparency of the generation process. Once the generation process is complete, the server hashes the generated product using the SHA-256 hashing algorithm. This results in a unique hash value for the product. New transaction data is generated based on this hash value and the collected metadata.

[1026] The server then adds this transaction data to the blockchain using Hyperledger Fabric, creating a new block that is integrated into the existing distributed ledger, recording the entire process of creating the product, ensuring its authenticity and traceability.

[1027] Finally, the server returns the product, the hash value, and the transaction ID on the distributed ledger to the terminal. The user receives this information via the terminal and checks the product and verifies its authenticity. By checking the provided hash value and transaction ID on the distributed ledger, the user can be sure that the product is an AI-generated product.

[1028] As a concrete example, consider the case where a user makes a request to generate an "image of a dog." The user sends the request "Please generate an image of a dog" from their device to the server. The server receives and analyzes this request, then launches the appropriate image generation model. The model generates an image of a dog using the specified initial data and its internal algorithm. During the generation process, the server collects metadata such as the training data used and the model version, and hashes the generated image of the dog. Based on this hash value and the collected metadata, new transaction data is generated and added to the distributed ledger. Finally, the image of the dog, its hash value, and the transaction ID are provided to the user's device. This allows the user to verify that the generated image of the dog is a trustworthy AI creation.

[1029] The above is an embodiment of the present invention. This system ensures the traceability and reliability of products, improving trust in AI products.

[1030] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1031] Step 1:

[1032] The user enters a request.

[1033] The user uses the terminal interface to input a product request, including the type of product and specific instructions.

[1034] Input: A request to create (e.g., "Create an image of a dog")

[1035] Output: Request data

[1036] Specific operation: The user enters the words "Please generate an image of a dog" into the input field displayed on the device screen and presses the send button.

[1037] Step 2:

[1038] The device sends a request to the server.

[1039] The terminal transmits the request input by the user to the server.

[1040] Input: Request data

[1041] Output: JSON formatted request data sent to the server

[1042] Specific operation: When the send button is pressed, the terminal converts the request data into JSON format and sends it to the server via the network.

[1043] Step 3:

[1044] The server receives and parses the request.

[1045] The server receives the request and analyzes its contents, extracting the type of product and the creation instructions.

[1046] Input: Request data in JSON format

[1047] Output: Parsed request content (e.g., type of product "Image", creation instruction "Dog")

[1048] What happens: The server parses the JSON data and extracts the instruction "Generate an image of a dog."

[1049] Step 4:

[1050] The server selects and launches a generative model.

[1051] The server selects and launches an appropriate generative model based on the request.

[1052] Input: Parsed request content

[1053] Output: An instance of the selected generative model

[1054] Specific operation: The server selects the GAN, the generative model that is best suited for "dog image generation," and loads the required initial dataset.

[1055] Step 5:

[1056] The server runs the generation process and creates the product.

[1057] The server uses the generative model to create the product desired by the user.

[1058] Input: Selected generative model, initial dataset

[1059] Output: The generated artifact (e.g., a picture of a dog)

[1060] What happens next: The generative model in the server runs an algorithm to process the initial dataset and generate an image of a dog.

[1061] Step 6:

[1062] The server collects the metadata.

[1063] The server collects metadata such as the training data used during the generation process, the model version, and the generation time.

[1064] Input: Generation process information

[1065] Output: Collected metadata (e.g. dataset name used, model version, generation time)

[1066] Specific operation: The server analyzes the process log and collects the name of the training data used, the model version number, the time taken to generate the model, etc.

[1067] Step 7:

[1068] The server hashes the product.

[1069] The server generates a unique hash value for the product.

[1070] Input: Generated product

[1071] Output: The hash value of the result

[1072] What it does: The server hashes the generated dog image using the SHA-256 algorithm to create a unique hash value.

[1073] Step 8:

[1074] The server adds the transaction data to the distributed ledger.

[1075] The server generates transaction data based on the collected metadata and hash values ​​and adds it to the distributed ledger.

[1076] Input: Metadata, hash value of the generated data

[1077] Output: New transaction data is added to the distributed ledger

[1078] How it works: The server composes the hash value and metadata into a single transaction and adds a new block to the distributed ledger using Hyperledger Fabric.

[1079] Step 9:

[1080] The server sends the results back to the terminal.

[1081] The server returns the product, the hash value, and the transaction ID to the terminal.

[1082] Input: Product, hash value, transaction ID

[1083] Output: Result data sent back to the terminal

[1084] Specific operation: The server sends the generated dog image, hash value, and transaction ID to the terminal in a response.

[1085] Step 10:

[1086] The user checks and verifies the product.

[1087] The user checks the product on the terminal and verifies its authenticity.

[1088] Input: Generated image, hash value, transaction ID

[1089] Output: Confirmed artifacts, verification results

[1090] How it works: The user visually checks the image of the dog displayed on the device and verifies its authenticity by matching the provided hash value with the transaction ID on the distributed ledger.

[1091] (Application example 1)

[1092] 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."

[1093] Ensuring the authenticity and traceability of products is becoming increasingly important in modern society. Particularly in the field of security services, there is a need to guarantee the reliability of generated evidence and data. However, current systems lack effective means to prevent tampering of products and ensure transparency of the generation process, which is a challenge.

[1094] 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.

[1095] In this invention, the server includes means for receiving a user's input request, means for activating a generative model to create a product, means for collecting data used in the generation process and the version of the generative model, means for hashing the generated product and generating transaction data, means for adding the transaction data to a blockchain, means for providing the generated product and information on the blockchain to a user, means for recording content as security evidence of the product to the blockchain, and means for providing an interface for verifying the hash value and metadata of the product, thereby ensuring the reliability and traceability of the product and preventing tampering.

[1096] "Means for receiving user input requests" refers to the interface and communication capabilities by which a user sends a request to the system to create a particular product.

[1097] "Means for invoking a generative model and creating a product" refers to the processes and algorithms for running an AI model based on a user's request and producing a specified product.

[1098] "Means for collecting the data and generative model versions used during the generation process" refers to the ability to record and collect detailed information such as the version information of the datasets and models used when creating the product.

[1099] "Means for hashing the generated product and generating transaction data" refers to a technology for converting the generated product into a unique identifier through encryption and then generating new transaction data that includes that identifier.

[1100] "Means for adding transaction data to the blockchain" refers to the process and technology for incorporating newly generated transaction data into the distributed ledger system.

[1101] "Means for providing users with generated products and information on the blockchain" refers to the user interface and communication functions for visualizing and providing users with details and authenticity of the generated content and its generation process.

[1102] "Means for recording the content of a product as security evidence on a blockchain" refers to a technology that records the content of a product, particularly one related to security, on a blockchain and makes it tamper-proof in order to preserve the content as reliable evidence.

[1103] "Means for providing an interface for verifying the product's hash value and metadata" refers to display and operation means that allow a user to confirm and verify the product's unique identifier (hash value) and detailed information about the creation process (metadata).

[1104] This invention relates to a system for ensuring the authenticity and traceability of products in the field of security services. The system is realized using a user's terminal, a server, and blockchain technology.

[1105] First, a user uses a terminal to input a request to create a product through the interface. For example, the user inputs a specific prompt such as "Please generate an image of a dog." The terminal then sends this request to the server.

[1106] The server receives and analyzes the request, then launches the appropriate generative model. This model uses its internal algorithms and the specified initial data to create a product (e.g., an image of a dog). During the generation process, the server collects metadata such as the training data used, the version of the generative model, and the time it took to generate the product.

[1107] Once generation is complete, the server hashes the generated product, which results in a unique hash value. New transaction data is generated based on this hash value and the collected metadata, and the server adds this transaction data to the blockchain. A new block is generated and integrated into the existing blockchain, recording the entire product generation process on the distributed ledger. This ensures the authenticity and traceability of the product.

[1108] Finally, the server returns the generated result, the hash value, and the transaction ID on the blockchain to the terminal. The user can receive this information via the terminal and check the generated result and verify its authenticity. For example, a specific prompt such as "Please generate an image that identifies suspicious activity from security camera footage in the office" can be used for security-related content. The generated content can be recorded on the blockchain and used as tamper-proof evidence.

[1109] The hardware used is primarily the user's smartphone and a server, while the software includes Python, a REST API, an AI model generation service, and a blockchain library. The server receives data, runs a generative model to generate content, collects metadata, hashes the results, adds transaction data to the blockchain, and provides the results and related information to the user.

[1110] As a result, this system can ensure the reliability and traceability of the product and prevent tampering. The present invention is particularly useful in the field of security services, and functions effectively in situations where the authenticity of the product is highly required.

[1111] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1112] Step 1:

[1113] The user inputs a production request through the terminal interface. This input includes the type of production (e.g., image or text) and a specific prompt (e.g., "Please generate an image of a dog"). The input data is sent to the server as the user's request.

[1114] Step 2:

[1115] The server receives a request from the user. At this time, the server analyzes the request content and extracts the request type and prompt. The analyzed data is used in the next processing step.

[1116] Step 3:

[1117] The server selects and launches an appropriate generative model based on the request. The generative model uses the specified initial data and its internal algorithms to create the desired product. The input to this process is the parsed request data, and the output is the generated product (e.g., a picture of a dog).

[1118] Step 4:

[1119] The server collects metadata such as the training data used during the generation process, the version of the generative model, and the time required for generation. In this step, detailed information about the generation process is recorded and saved for later verification and traceability. The collected metadata will be used in the next step.

[1120] Step 5:

[1121] The server hashes the generated product. This process results in a unique hash value for the product. The input is the generated product, and the output is its hash value. The server then generates new transaction data based on this hash value and the collected metadata.

[1122] Step 6:

[1123] The server adds new transaction data to the blockchain, and a new block is generated based on the existing blockchain data and integrated into the blockchain, so that the entire product creation process is recorded on the distributed ledger.

[1124] Step 7:

[1125] The server returns the product, hash value, and transaction ID on the blockchain to the user's device, where the user can receive this information and verify the product and its authenticity.

[1126] Step 8:

[1127] Using the hash value and transaction ID provided by the user, the product is verified on the blockchain to be truly generated by AI and not tampered with, ensuring the authenticity and traceability of the product.

[1128] For example, a terminal sends a prompt to the server: "Please generate an image that identifies suspicious activity from security camera footage in the office." The server analyzes the request, activates the generative model to generate the appropriate image, and records the generation process on the blockchain to ensure that the generated image has not been tampered with.

[1129] 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.

[1130] This invention is a system that features an AI product generation process and tracing function combined with an emotion engine that recognizes user emotions. This system receives user requests, adjusts the type and specifications of the product based on the emotion recognition results, and generates and traces the product. It also uses blockchain technology to ensure the authenticity of the product and allow users to verify the generation process.

[1131] First, the user uses a device to make a request to create a product. The user inputs the type of product (e.g., image or text) and specific creation instructions (e.g., "Please create an image of a dog") on the device's interface. The emotion engine then analyzes the user's emotion data in real time and automatically adjusts the product's characteristics and settings based on that emotion. This request information is sent from the device to the server.

[1132] The server then receives the request and emotion data sent by the user and analyzes their content. Based on the user's emotion recognition results, the server automatically adjusts the content of the product and selects an appropriate generative model. This generative model creates the product using its internal algorithm and the specified initial data. During the generation process, the server appropriately allocates the necessary computing resources and manages them to ensure smooth generation.

[1133] During the generation process, the server collects metadata such as the training data used, the version of the generative model, and the time required for generation. Additionally, the user's emotional data recognized by the emotion engine is also collected as metadata. This metadata is important for ensuring traceability later.

[1134] Once the generation process is complete, the server hashes the generated product, resulting in a unique hash value for the product, which is used to verify the product's identity.

[1135] The server then combines the generated hash value with the collected metadata to generate new transaction data, including the generated hash value, the training data used, the version of the generative model, the generation time, and the user's emotional data.

[1136] The server adds this new transaction data to the blockchain, and a new block is generated and integrated into the existing blockchain, recording the entire process of creating the product on a distributed ledger, ensuring the authenticity and traceability of the product.

[1137] Finally, the server returns the generated product, its hash value, and the transaction ID on the blockchain to the terminal, allowing the user to check the product and details of its generation process.

[1138] As a concrete example, consider the case where a user requests the generation of an "image of a dog." The user sends a request from their device to the server: "Please generate an image of a dog." If the user is smiling, the emotion engine recognizes this emotion as "positive" and adjusts the generative model to reflect a bright background and a cheerful dog in the generated image. The server receives and analyzes this request and launches the appropriate image generation model. The model generates an image of a dog using the specified initial data and internal algorithm. During the generation process, the server collects metadata such as the training data used and the model version, as well as the user's emotional data, and hashes the generated image of the dog. Based on this hash value and the collected metadata, new transaction data is generated and added to the blockchain. Finally, the image of the dog, its hash value, and the transaction ID are provided to the user's device. This allows the user to verify that the generated image of the dog is a reliable AI product and further confirm that emotions were correctly reflected in the generation process.

[1139] The above is an embodiment of the present invention. This system ensures the traceability and reliability of products, improves trust in AI products, and enables the provision of customized products that reflect the user's emotions.

[1140] The processing flow will be explained below.

[1141] Step 1:

[1142] The user uses the terminal to create a request for a product. The user selects the type of product (e.g., image or text) on the interface and enters a creation instruction (e.g., "Please create an image of a dog"). The camera and microphone also operate to acquire the user's emotional data, sending the user's facial expressions and voice to the emotion engine in real time. The request containing this information is then sent from the terminal to the server.

[1143] Step 2:

[1144] The server receives a request from the user. The request includes the type of product, creation instructions, and the user's emotion data. The server analyzes this information and recognizes the user's emotion using an emotion engine.

[1145] Step 3:

[1146] The server adjusts the selection of a generative model or the characteristics of the product based on the user's emotion results recognized by the emotion engine. For example, if the user is smiling at the time of the request, the server adjusts the settings so that the generated image of the dog reflects a bright background and a cheerful dog.

[1147] Step 4:

[1148] The server starts the appropriate generative model and creates a product using the specified initial data and algorithm. The generative model refers to the built-in learning data and generates a product that best suits the user's request and emotions.

[1149] Step 5:

[1150] During the generation process, the server collects the training data used, the version of the generative model, the generation time, and the user's sentiment data. This metadata is necessary for later traceability of the generation process.

[1151] Step 6:

[1152] Once the generation process is complete, the server hashes the generated product, obtaining a unique hash value for the product, which is important for verifying the product's identity.

[1153] Step 7:

[1154] The server combines the generated hash value with the collected metadata to generate new transaction data, including the product hash value, the training data used, the version of the generative model, the generation time, and the user's emotional data.

[1155] Step 8:

[1156] The server adds new transaction data to the blockchain, and a new block is generated and integrated into the existing blockchain, recording the entire process of product creation on a distributed ledger, ensuring the authenticity and traceability of the product.

[1157] Step 9:

[1158] The server sends the generated product, hash value, and transaction ID on the blockchain back to the terminal, which receives this information and displays it to the user.

[1159] Step 10:

[1160] Users can check the product through their terminal and verify its authenticity and traceability on the blockchain using the provided hash value and transaction ID. Users can also check whether the product adjusted by the emotion engine matches their own emotions.

[1161] As a specific example, when a user requests the generation of an "image of a dog," the user sends a request from their device to the server saying, "Please generate an image of a dog." If the user is smiling at the time of the request, the emotion engine recognizes this emotion as "positive" and adjusts the generative model to reflect a bright background and a cheerful dog in the generated image. The server receives and analyzes the request, launches the appropriate image generation model, and generates the optimal dog image. During the generation process, the training data used, the model version, and the user's emotion data are collected, and the generated dog image is hashed. New transaction data is generated based on this hash value and the collected metadata and added to the blockchain. Finally, the dog image, its hash value, and the transaction ID are provided to the user's device. This allows the user to verify that the generated dog image is a reliable AI product and further confirm that emotions were correctly reflected in the generation process.

[1162] Example 2

[1163] 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."

[1164] Conventional generative AI systems often fail to ensure the authenticity of the product or the traceability of the generation process, leaving users with limited means to verify the reliability of the product. It is also difficult to provide customized products that reflect the user's emotions. To address these issues, the present invention aims to analyze user emotion data in real time, generate products based on that data, and ensure their traceability.

[1165] 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.

[1166] In this invention, the server includes means for receiving a user's input request, means for collecting metadata during the creation process, means for collecting and analyzing user emotion data, means for hashing the created product and adding transaction data to the blockchain, and means for providing information about the creation process to the user. This ensures the authenticity and traceability of the product and makes it possible to provide a customized product that reflects the user's emotion.

[1167] A "user input request" is a specific instruction that a user sends to the system to create a product.

[1168] A "generative model" is a machine learning algorithm for creating a product using specified initial data and an internal algorithm.

[1169] A "generation process" is a series of processes that activates a generative model based on a user's input request and creates a product.

[1170] "Metadata" is additional information related to the product, such as the training data used during the generation process, the version of the generative model, the generation time, and the user's emotional data.

[1171] "Emotion data" is data that represents the emotional state of the user analyzed from facial expressions, gestures, and the like.

[1172] "Hashing" is a data transformation technique used to generate a unique hash value for a product.

[1173] A "hash value" is a unique identifier generated by a hashing process.

[1174] "Transaction data" is a data packet containing a hash value and metadata about the generation process that is added to the blockchain as a new transaction.

[1175] A "blockchain" is a database that uses distributed ledger technology and consists of a series of blocks that track transactions and data.

[1176] A "transaction ID" is an identifier that uniquely identifies a specific transaction on a blockchain.

[1177] "Product" is the product created by the generative model based on the user's input request and emotion data.

[1178] This invention is a system that features an AI product generation process and tracing function combined with an emotion engine that recognizes user emotions. This system analyzes user emotion data in real time and automatically adjusts the product's characteristics and settings to provide customized products to users, and ensures authenticity and traceability by recording the generation process using blockchain technology.

[1179] First, the user uses a device such as a smartphone or PC to input the type of product (e.g., an image or text) and specific generation instructions (prompt text) into the interface. The software used here can be a general web browser or a custom application.

[1180] Next, the device uses sensors such as a camera and microphone to collect the user's emotional data. The emotion engine uses, for example, a general emotion recognition API (Emotion API). This allows for real-time analysis of the emotional data obtained from the user's facial expressions and voice.

[1181] The collected request information and emotion data are sent from the device to the server. The server receives this data and begins analysis. Based on the emotion recognition results, the server selects an appropriate generative AI model (e.g., a sentence generation model or an image generation model) and begins the generation process. The generative model used here could be, for example, a natural language processing model (GPT-4) or an image generation model (DALL-E 2).

[1182] The server collects metadata such as the training data used during the generation process, the version of the generative model, the generation time, and the user's emotional data, which play an important role in ensuring traceability later.

[1183] Once the creation process is complete, the server hashes the created product to generate a unique hash value. This hash value is combined with the collected metadata to generate new transaction data that is added to the blockchain, ensuring the authenticity of the product and a record of the entire creation process on the distributed ledger.

[1184] Finally, the server returns the generated product, its hash value, and the transaction ID on the blockchain to the terminal, allowing the user to verify the authenticity of the product and details of its creation process.

[1185] For example, if a user requests "generate an image of a dog," and the emotion engine recognizes the user's smile as "positive," the server generates an image of a dog against a bright background that reflects this emotional data. Transaction data is then added to the blockchain based on the metadata of the generation process and the hash value of the product. Finally, the generated dog image, hash value, and transaction ID are provided to the user's device.

[1186] Example prompt sentence:

[1187] "Generate an image of a dog"

[1188] "Write a sentence about natural scenery"

[1189] This allows customized products to be created according to the user's emotions, and the creation process is accurately recorded, improving the reliability and traceability of the products.

[1190] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1191] Step 1:

[1192] User issues a request

[1193] The user uses a terminal to make a request to create a product. The user inputs the type of product and specific instructions (prompt) for creation into the terminal interface. The input prompt is sent from the terminal to the server.

[1194] Input: Type of product, specific production instructions (prompt text)

[1195] Output: Sends the request data to the server

[1196] Specific behavior: A user opens the application on their smartphone, enters "Please generate an image of a dog" into the input form, and clicks the submit button.

[1197] Step 2:

[1198] Collecting emotion data from devices

[1199] The device uses a camera and microphone to collect the user's emotional data in real time, which is then analyzed through an emotion engine and added to the request information.

[1200] Input: User's facial expression or voice data

[1201] Output: Analyzes user emotion data and adds it to the request data

[1202] Specific operation: The device camera captures the user's facial expression, and if the user is smiling, the emotion engine analyzes the emotion as positive.

[1203] Step 3:

[1204] Server receives request and emotion data

[1205] The server receives request information and emotion data sent from the device, analyzes the received data, and selects an appropriate generative model.

[1206] Input: Request information and emotion data sent from the device

[1207] Output: Selection of a generative model based on the analysis results

[1208] Specific operation: The server analyzes the type of product ("image of dog") and emotion data ("positive") and selects an appropriate image generation model.

[1209] Step 4:

[1210] Server-based model initiation and generation

[1211] The server invokes the appropriate generative model and uses the specified initial data and internal algorithms to create a product, which is customized to reflect the user's emotional data.

[1212] Input: Analyzed generative model, given initial data

[1213] Output: Creating a customized artifact

[1214] Specific operation: The server launches an image generation model (e.g., DALL-E 2) and generates an image of a dog that reflects a "positive" emotion.

[1215] Step 5:

[1216] Server-based collection and hashing of metadata

[1217] During the generation process, the server collects metadata such as the training data used, the version of the generative model, the generation time, and the user's emotional data. The generated product is then hashed to generate a unique hash value.

[1218] Input: Various data during the generation process

[1219] Output: Collected metadata, hash value of the artifact

[1220] What it does: The server records the metadata of the generation process and hashes the generated dog image using the SHA-256 algorithm.

[1221] Step 6:

[1222] Registration on the blockchain by the server

[1223] The generated hash value is combined with the collected metadata to generate new transaction data and add it to the blockchain.

[1224] Input: hash value, collected metadata

[1225] Output: New transaction data added to the blockchain

[1226] How it works: The server packages the resulting hash value and metadata into a packet and adds the transaction data to the blockchain using distributed ledger technology.

[1227] Step 7:

[1228] Return and check of product

[1229] The server returns the generated product, its hash value, and the transaction ID on the blockchain to the terminal, allowing the user to verify the authenticity of the product and details of the generation process.

[1230] Input: hash value, transaction ID

[1231] Output: The product, hash value, and transaction ID sent to the user terminal

[1232] How it works: The server sends the image of the dog, its hash value, and the transaction ID on the blockchain to the user's device, where the user can view this information.

[1233] (Application example 2)

[1234] 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."

[1235] Conventional generative AI systems are limited to providing products based on user input, and lack customization that takes user emotions into account and transparency in the generation process. In addition, there are insufficient means to guarantee the authenticity of the products, leaving users with little means to verify the reliability of the products.

[1236] The specific processing by the specific 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 receiving a user's input request, means for activating a generative model based on the input request and creating a product, means for collecting metadata including the training data and the version of the generative model used during the generation process, means for hashing the generated product and generating new transaction data based on the hash value and the metadata of the generation process, means for adding the transaction data to the blockchain, means for providing the generated product and information on the blockchain to the user, means for analyzing the user's emotions and adjusting the characteristics of the product based on the emotions, and means for suggesting products based on the user's emotions. This makes it possible to provide customized products that reflect the user's emotions and ensure the transparency and reliability of the generation process.

[1237] The "means for receiving a user input request" is a function on the device that receives a request input by a user to create a product.

[1238] The "means for activating a generative model and creating a product" is a function that runs a pre-set generative algorithm based on a received input request and generates the required product.

[1239] "Means for collecting metadata including training data and generative model versions" refers to the ability to systematically obtain information about the datasets and algorithm versions used during the generation process.

[1240] The "means of hashing the generated product and generating new transaction data based on the hash value and metadata of the generation process" refers to a function that assigns a unique identification code to the generated product, integrates the code with related information, and records it as new data.

[1241] "Means of adding transaction data to the blockchain" refers to the function of permanently storing the generated data using distributed ledger technology.

[1242] "Means for providing users with information about the generated product and the blockchain" is a function that provides users with detailed data about the product and its generation process in a visible form.

[1243] "Means for analyzing the user's emotions and adjusting the characteristics of the generated product based on those emotions" refers to a function that analyzes the user's emotional state in real time and dynamically changes the characteristics of the generated data and objects based on those emotions.

[1244] The "means for proposing a product based on the user's emotions" is a function for selecting an appropriate product based on the analyzed emotion data and proposing it to the user.

[1245] This invention is a system that combines an emotion engine that analyzes user emotions with an AI product generation process and tracing function. This system receives user requests, adjusts the type and specifications of the product based on the emotion recognition results, and generates and traces the product. It also uses blockchain technology to ensure the authenticity of the product and allow users to verify the generation process.

[1246] First, the user uses a device to make a request to create a product. The user inputs the type of product and specific creation instructions on the device's interface. The emotion engine then analyzes the user's emotion data in real time and automatically adjusts the product's characteristics and settings based on that emotion. This request information is sent from the device to the server.

[1247] The server then receives the request and emotion data sent by the user and analyzes their content. Based on the user's emotion recognition results, the server automatically adjusts the content of the product and selects an appropriate generative model. This generative model creates the product using its internal algorithm and the specified initial data. During the generation process, the server appropriately allocates the necessary computing resources and manages them to ensure smooth generation.

[1248] During the generation process, the server collects metadata such as the training data used, the version of the generative model, and the time required for generation. In addition, the user's emotional data recognized by the emotion engine is also collected as metadata. This metadata is important for ensuring traceability later. Once the generation process is complete, the server hashes the generated product. Hashing results in a unique hash value for the product, which is used to verify the identity of the product.

[1249] Next, the server combines the generated hash value with the collected metadata to generate new transaction data. The transaction data includes the product's hash value, the training data used, the version of the generative model, the generation time, and the user's emotional data. The server adds this new transaction data to the blockchain. A new block is generated and integrated into the existing blockchain, recording the entire product generation process on the distributed ledger. This ensures the authenticity and traceability of the product.

[1250] Finally, the server returns the generated product, its hash value, and the transaction ID on the blockchain to the terminal, allowing the user to check the product and details of its generation process.

[1251] As a concrete example, consider a user searching for products on an online shopping site using the "Emo Shopping" app. The user sends a request to the server from their device asking, "Please suggest new fashion items." If the user is smiling, the emotion engine recognizes the emotion as "happy" and adjusts the generative model to reflect vibrant and cheerful fashion items in the suggested products. The server receives and analyzes this request and launches the appropriate product suggestion model. The model generates a list of suggested products using the specified initial data and internal algorithm. During the generation process, the server collects metadata such as the training data used and the model version, as well as the user's emotional data, and hashes the generated list of products. Based on this hash value and the collected metadata, new transaction data is generated and added to the blockchain. Finally, the list of products, its hash value, and the transaction ID are provided to the user's device. This allows the user to verify that the suggested product list is a reliable AI product and further confirm that emotions were correctly reflected in the generation process.

[1252] Examples of prompts are:

[1253] "We'll suggest products that you might want to choose when you're feeling 'happy' right now. For example, how about brightly colored clothes or fun accessories?"

[1254] This makes it possible to provide customized products that reflect the user's emotions, and ensure the transparency and reliability of the creation process.

[1255] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1256] Step 1:

[1257] A user uses a terminal to input a request to create a product. The request includes the type of product and specific instructions for creation. For example, a request such as "Please suggest a new fashion item" is input. This request information is sent from the terminal to the server. The input data includes the type of product and specific instructions, and the output is the request information.

[1258] Step 2:

[1259] The device acquires the user's emotional data in real time and sends it to the emotion engine. The emotion engine uses a camera and microphone to analyze the user's facial expressions and voice and recognize the emotion. For example, if the user is smiling, the emotion engine recognizes the emotion as "happy." The input data is the user's facial expressions and voice acquired in real time, and the output is the recognized emotional data.

[1260] Step 3:

[1261] The server receives the request information and emotion data sent by the user. The server then analyzes this data and adjusts the product's characteristics based on the user's emotion recognition results. For example, if the emotion is "happy," the server will suggest bright and cheerful items. The input data are the request information and emotion data, and the output is the adjusted product's characteristics.

[1262] Step 4:

[1263] The server selects and launches an appropriate generative model. This generative model uses its internal algorithm and the specified initial data to create a product. For example, it generates a brightly colored fashion item based on the adjusted product characteristics. The input data are the adjusted product characteristics, and the output is the generated product.

[1264] Step 5:

[1265] During the generation process, the server collects metadata such as the training data used, the version of the generative model, and the time required for generation. In addition, the user's emotional data recognized by the emotion engine is also collected as metadata. The input data are various data used during the generation process, and the output is the collected metadata.

[1266] Step 6:

[1267] Once the generation process is complete, the server hashes the generated product, resulting in a unique hash value for the product, which is used to verify the product's identity. The input data is the generated product, and the output is the hash value of the product.

[1268] Step 7:

[1269] The server combines the generated hash value with the collected metadata to generate new transaction data, which includes the generated hash value, the training data used, the version of the generative model, the generation time, and the user's emotional data. The input data are the hash value and the metadata, and the output is the new transaction data.

[1270] Step 8:

[1271] The server adds new transaction data to the blockchain. A new block is generated and integrated into the existing blockchain, recording the entire process of product creation on a distributed ledger. The input data is the new transaction data, and the output is an updated blockchain.

[1272] Step 9:

[1273] Finally, the server returns the generated product, hash value, and transaction ID on the blockchain to the terminal, allowing the user to check the product and details of its generation process. The input data are the product, hash value, and transaction ID, and the output is the information provided to the user terminal.

[1274] 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.

[1275] 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.

[1276] 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.

[1277] 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.

[1278] 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.

[1279] 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.

[1280] 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).

[1281] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1282] 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."

[1283] 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.

[1284] 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).

[1285] 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.

[1286] 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.

[1287] 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.

[1288] 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.

[1289] 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.

[1290] 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.

[1291] 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.

[1292] 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.

[1293] 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.

[1294] 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.

[1295] The following is further disclosed regarding the above embodiment.

[1296] (Claim 1)

[1297] means for receiving a user input request;

[1298] means for invoking a generative model based on the input request to generate a product;

[1299] means for collecting metadata including the training data and the version of the generative model used during the generation process;

[1300] means for hashing the generated product and generating new transaction data based on the hash value and metadata of the generation process;

[1301] means for adding the transaction data to a blockchain;

[1302] A means for providing the generated product and information on the blockchain to a user;

[1303] A system including:

[1304] (Claim 2)

[1305] When adding new transaction data to the blockchain, the method further includes generating a new block based on existing blockchain data.

[1306] 10. The system of claim 1.

[1307] (Claim 3)

[1308] means for providing the user with a hash value of the product and a transaction ID;

[1309] 10. The system of claim 1.

[1310] "Example 1"

[1311] (Claim 1)

[1312] means for receiving a user input request;

[1313] means for invoking a generative model based on the input request to generate a product;

[1314] a means for collecting metadata including the training data and the version of the generative model used during the generation process;

[1315] a means for hashing the generated product and generating new transaction data based on the hash value and metadata of the generation process;

[1316] means for adding the transaction data to a distributed ledger;

[1317] A means for providing the generated products and information on the distributed ledger to users;

[1318] A system including:

[1319] (Claim 2)

[1320] and means for generating a new block based on existing distributed ledger data when adding new transaction data to the distributed ledger.

[1321] 10. The system of claim 1.

[1322] (Claim 3)

[1323] means for providing the user with a hash value of the product and a transaction ID;

[1324] 10. The system of claim 1.

[1325] "Application Example 1"

[1326] (Claim 1)

[1327] means for receiving a user input request;

[1328] means for invoking a generative model based on the input request to generate a product;

[1329] means for collecting metadata including the training data and the version of the generative model used during the generation process;

[1330] means for hashing the generated product and generating new transaction data based on the hash value and metadata of the generation process;

[1331] means for adding the transaction data to a blockchain;

[1332] A means for providing the generated product and information on the blockchain to a user;

[1333] A means for recording the content of the product as security evidence on the blockchain;

[1334] means for providing an interface for verifying the hash value and metadata of the product;

[1335] A system including:

[1336] (Claim 2)

[1337] When adding new transaction data to the blockchain, the method further includes generating a new block based on existing blockchain data.

[1338] 10. The system of claim 1.

[1339] (Claim 3)

[1340] means for providing the user with a hash value of the product and a transaction ID;

[1341] 10. The system of claim 1.

[1342] "Example 2: Combining Emotion Engines"

[1343] (Claim 1)

[1344] means for receiving a user input request;

[1345] means for invoking a generative model based on the input request to generate a product;

[1346] means for collecting metadata including the training data and the version of the generative model used during the generation process;

[1347] means for collecting and analyzing user emotion data;

[1348] means for hashing the generated product and generating new transaction data based on the hash value and metadata of the generation process;

[1349] means for adding the transaction data to a blockchain;

[1350] A means for providing the generated product and information on the blockchain to a user;

[1351] A system including:

[1352] (Claim 2)

[1353] When adding new transaction data to the blockchain, the method further includes generating a new block based on existing blockchain data.

[1354] 10. The system of claim 1.

[1355] (Claim 3)

[1356] means for providing the user with a hash value of the product and a transaction ID;

[1357] 10. The system of claim 1.

[1358] "Application example 2 when combining emotion engines"

[1359] (Claim 1)

[1360] means for receiving a user input request;

[1361] means for invoking a generative model based on the input request to generate a product;

[1362] means for collecting metadata including the training data and the version of the generative model used during the generation process;

[1363] means for hashing the generated product and generating new transaction data based on the hash value and metadata of the generation process;

[1364] means for adding the transaction data to a blockchain;

[1365] A means for providing the generated product and information on the blockchain to a user;

[1366] means for analyzing a user's emotion and adjusting the characteristics of the product based on the emotion;

[1367] means for suggesting products based on the user's emotions;

[1368] A system including:

[1369] (Claim 2)

[1370] When adding new transaction data to the blockchain, the method further includes generating a new block based on existing blockchain data.

[1371] 10. The system of claim 1.

[1372] (Claim 3)

[1373] means for providing the user with a hash value of the product and a transaction ID;

[1374] 10. The system of claim 1. [Explanation of symbols]

[1375] 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 receiving a user input request; means for invoking a generative model based on the input request to generate a product; means for collecting metadata including the training data and the version of the generative model used during the generation process; means for hashing the generated product and generating new transaction data based on the hash value and metadata of the generation process; means for adding the transaction data to a blockchain; A means for providing the generated product and information on the blockchain to a user; A system including:

2. When adding new transaction data to the blockchain, the method further includes generating a new block based on existing blockchain data. The system of claim 1 .

3. means for providing the user with a hash value of the product and a transaction ID; The system of claim 1 .

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