system

Abstract

The system according to this embodiment aims to ensure copyright management of generated content and transparency of transaction history. [Solution] The system according to the embodiment comprises a registration unit, a management unit, an automation unit, and a recording unit. The registration unit assigns a unique ID to the generated content and registers the copyright. The management unit manages the copyright registered by the registration unit. The automation unit automates the management of content usage licenses and royalties based on the copyright managed by the management unit. The recording unit transparently records the transaction history of the content managed by the automation unit using blockchain technology.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the copyright management of the generated content and the transparency of the transaction history are not sufficiently ensured, and there is room for improvement.

[0005] The system according to the embodiment aims to ensure the copyright management of the generated content and the transparency of the transaction history.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a registration unit, a management unit, an automation unit, and a recording unit. The registration unit assigns a unique ID to the generated content and registers the copyright. The management unit manages the copyrights registered by the registration unit. The automation unit automates the management of content usage licenses and royalties based on the copyrights managed by the management unit. The recording unit transparently records the transaction history of the content managed by the automation unit using blockchain technology. [Effects of the Invention]

[0007] The system according to this embodiment can ensure copyright management of generated content and transparency of transaction history. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are 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).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The content management system according to an embodiment of the present invention is a system that grants copyright to content generated by an AI agent and manages and trades that content. This content management system tracks the generation history and uniqueness of each agent and automates the management of content usage licenses and royalties. For example, the content management system assigns a unique ID to content generated by an AI agent and registers the copyright. At this time, it records detailed information about the generated content (generation date and time, generating agent ID, generation process, etc.). For example, for text generated by an AI agent, it assigns the generation date and time and the generating agent ID and registers the copyright. Next, the content management system builds a system for managing the registered copyrights. This system tracks the generation history and uniqueness of each agent and automates the management of content usage licenses and royalties. For example, it manages the usage licenses for images generated by an AI agent and automatically calculates the number of times they have been used and the royalties. Furthermore, the content management system provides a platform for trading the generated content. This platform uses blockchain technology to create transparent transaction records and track the transaction history of the content. For example, the transaction history of music generated by an AI agent can be recorded on the blockchain to ensure transaction transparency. This mechanism allows for robust copyright protection of content generated by AI agents and creates a transparent market. This enhances the value of the entire creative industry and protects originality and ethics. For instance, it becomes easier for companies and creators using generation AI to prove the originality and value of their creations, reducing the risk of copyright infringement and plagiarism. This allows content management systems to grant copyright to content generated by AI agents and manage and trade it.

[0029] The content management system according to this embodiment comprises a registration unit, a management unit, an automation unit, and a recording unit. The registration unit assigns a unique ID to the generated content and registers the copyright. The registration unit records detailed information about the generated content (such as the date and time of generation, the ID of the generation agent, and the generation process). For example, the registration unit assigns the date and time of generation and the ID of the generation agent to text generated by an AI agent and registers the copyright. The management unit manages the copyrights registered by the registration unit. The management unit tracks the generation history and uniqueness of each agent. For example, the management unit manages the usage licenses for images generated by the AI ​​agent and automatically calculates the number of times they have been used and the royalties. The automation unit automates the management of content usage licenses and royalties based on the copyrights managed by the management unit. The automation unit automatically manages content usage licenses and royalties. For example, the automation unit manages the usage licenses for music generated by an AI agent and automatically calculates the number of times it has been used and the royalties. The recording unit transparently records the transaction history of content managed by the automation unit using blockchain technology. For example, the recording unit transparently records the transaction history of content using blockchain technology. For example, the recording unit records the transaction history of music generated by an AI agent on the blockchain to ensure transaction transparency. As a result, the content management system according to the embodiment can grant copyright to generated content and manage and trade it.

[0030] The registration unit assigns a unique ID to the generated content and registers the copyright. Specifically, it records detailed information about the generated content (generation date and time, generation agent ID, generation process, etc.). For example, it assigns the generation date and time and generation agent ID to text generated by an AI agent and registers the copyright. This clarifies the origin and generation process of the generated content, strengthening copyright protection. Furthermore, by recording detailed metadata of the content, the registration unit facilitates later management and searching. For example, it can also record information such as the genre, theme, and technologies and tools used for the generated content. This allows for management tailored to the characteristics and purpose of use of the content. The registration unit also has a function to record quality evaluations and feedback on the generated content, which can be used to evaluate and improve the performance of the generation agent. For example, it can collect evaluations and comments from users and use them as training data for the generation agent. In this way, the registration unit can centrally manage detailed information about the generated content, contributing to copyright protection and content quality improvement.

[0031] The Management Department manages copyrights registered by the Registration Department. Specifically, it tracks the generation history and originality of each agent. For example, it manages the licensing of images generated by AI agents and automatically calculates the number of uses and royalties. The Management Department can monitor the usage of generated content in real time and take measures to reduce the risk of copyright infringement. For example, if content is used without permission, it can immediately issue a warning and take legal action if necessary. The Management Department also manages contract information and conditions regarding content licensing in detail to ensure that appropriate licensing is performed. This promotes the proper use of content and protects the interests of copyright holders. Furthermore, the Management Department can analyze the performance data of generation agents to optimize and improve the generation process. For example, it can evaluate the generation speed and quality of generation agents and make adjustments and improvements as needed. This allows the Management Department to properly manage the copyright of generated content, understand how content is being used, and protect the interests of copyright holders.

[0032] The Automation Department automates the management of content licenses and royalties based on copyrights managed by the Management Department. Specifically, it automatically manages content licenses and royalties. For example, it manages licenses for music generated by an AI agent and automatically calculates the number of uses and royalties. The Automation Department can quickly and accurately grant content licenses based on pre-set rules and conditions. For example, it can automatically issue licenses to users who meet specific conditions, calculate royalties according to usage, and pay them to copyright holders. This streamlines the content licensing process and ensures that copyright holders' profits are reflected quickly. The Automation Department also ensures transparency by meticulously recording the history of licenses and royalty payments. This allows copyright holders and users to easily check content usage and royalty payment status. Furthermore, the Automation Department utilizes AI to optimize the licensing and royalty management process, achieving efficient operation. For example, it can analyze past data and trends in license issuance and royalty calculation to set optimal rules and conditions. This allows the automated unit to efficiently and accurately manage content licensing and royalties, maximizing the interests of copyright holders.

[0033] The Records Unit transparently records the transaction history of content managed by the Automation Unit using blockchain technology. Specifically, it transparently records the transaction history of content using blockchain technology. For example, it records the transaction history of music generated by an AI agent on the blockchain to ensure transaction transparency. By utilizing blockchain technology, the Records Unit can prevent tampering and fraud in transaction history and provide highly reliable records. This allows copyright holders and users to confidently check the transaction history. In addition, the Records Unit can record detailed information of the transaction history, which can be used to resolve future troubles and disputes. For example, it can record detailed information such as the date and time of the transaction, the trading partner, the transaction content, and the payment status, and submit it as evidence if necessary. Furthermore, the Records Unit is equipped with transaction history analysis and reporting functions, allowing for the understanding of transaction trends and patterns. This allows copyright holders and administrators to understand the status of transactions and take appropriate measures. For example, if there is a sudden surge in transactions of a particular piece of content, the cause can be analyzed and appropriate action can be taken. This allows the recording unit to record the transaction history of content in a transparent and reliable manner, protecting the interests of copyright holders and users.

[0034] The registration unit can record detailed information about the generated content. For example, the registration unit records detailed information about the generated content (such as the date and time of generation, the ID of the generation agent, and the generation process). For instance, the registration unit assigns the date and time of generation and the ID of the generation agent to text generated by an AI agent and registers the copyright. This ensures transparency of the content by recording detailed information about the generated content.

[0035] The management department can track the generation history and uniqueness of each agent. For example, the management department can track the generation history and uniqueness of each agent. For example, the management department can manage the usage licenses for images generated by AI agents and automatically calculate the number of times they are used and the royalties. This streamlines content management by tracking the generation history and uniqueness of each agent.

[0036] The automation unit can automatically manage content licenses and royalties. For example, the automation unit can automatically manage content licenses and royalties. For instance, the automation unit manages the licenses for music generated by an AI agent and automatically calculates the number of uses and royalties. This improves management efficiency by automating the management of content licenses and royalties.

[0037] The recording unit can transparently record the transaction history of content using blockchain technology. For example, the recording unit can transparently record the transaction history of content using blockchain technology. For example, the recording unit can record the transaction history of music generated by an AI agent on the blockchain, ensuring the transparency of transactions. In this way, transparency of transaction history is ensured by using blockchain technology.

[0038] The registration unit can record the content generation process in detail, ensuring transparency of the generation process. For example, the registration unit can record each step of content generation with a timestamp, ensuring transparency of the generation process. The registration unit can also record the ID of the generation agent and the version of the algorithm used, clearly indicating the details of the generation process. Furthermore, the registration unit can record errors and correction history that occurred during the generation process, guaranteeing the integrity of the generation process. This ensures transparency in the generation process, thereby improving the reliability of the content.

[0039] The registration unit can evaluate the originality of content and preferentially assign IDs to highly original content. For example, the registration unit can analyze the similarity of content and preferentially assign IDs to highly original content. The registration unit can also identify highly original content by referring to the past generation history of the generation agent. Furthermore, the registration unit can use algorithms to evaluate the originality of content and preferentially assign IDs to highly original content. This allows for an increase in the value of content by preferentially assigning IDs to highly original content.

[0040] The registration unit can prioritize registering highly relevant content by considering the user's geographical location information when registering content. For example, the registration unit can prioritize registering content related to the user's current location. The registration unit can also prioritize registering highly relevant content by referring to the user's past location information. Furthermore, the registration unit can prioritize registering region-specific content based on the user's location information. In this way, highly relevant content can be prioritized by considering the user's geographical location information. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the user's location information into a generating AI and have the generating AI identify highly relevant content.

[0041] The registration unit can analyze a user's social media activity and register relevant content when registering content. For example, the registration unit can analyze a user's social media posts and prioritize registering relevant content. The registration unit can also refer to the activities of the user's followers and friends and register highly relevant content. Furthermore, the registration unit can analyze a user's interests on social media and register relevant content. In this way, highly relevant content can be registered by analyzing the user's social media activity. Some or all of the above processing in the registration unit may be performed using AI, for example, or not using AI. For example, the registration unit can input the user's social media activity data into a generating AI and have the generating AI identify highly relevant content.

[0042] The management department can track the generation history of each agent in detail and ensure transparency of the generation history. For example, the management department can record the generation history of each agent with a timestamp to ensure transparency. The management department can also record the ID of the generating agent and the version of the algorithm used to clearly indicate the details of the generation history. Furthermore, the management department can record any errors or corrections that occurred during the generation history to guarantee the integrity of the generation history. This improves the reliability of the content by ensuring transparency of the generation history. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the generation history data of each agent into a generation AI and have the generation AI perform processes to ensure transparency of the generation history.

[0043] The management department can evaluate the originality of content and apply special management methods to highly original content. For example, the management department can analyze the similarity of content and apply special management methods to highly original content. The management department can also identify highly original content by referring to the past generation history of the generation agent. Furthermore, the management department can use algorithms to evaluate the originality of content and apply special management methods to highly original content. This allows the value of content to be increased by applying special management methods to highly original content. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input content originality evaluation data into a generation AI and have the generation AI execute the application of special management methods.

[0044] The management unit can prioritize the management of highly relevant content by considering the user's geographical location information during management. For example, the management unit can prioritize content related to the user's current location. The management unit can also prioritize highly relevant content by referring to the user's past location information. Furthermore, the management unit can prioritize region-specific content based on the user's location information. In this way, highly relevant content can be prioritized by considering the user's geographical location information. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's location information into a generating AI and have the generating AI identify highly relevant content.

[0045] The management department can analyze users' social media activity and manage relevant content during management. For example, the management department can analyze the content of users' social media posts and prioritize the management of relevant content. The management department can also refer to the activities of users' followers and friends and manage highly relevant content. Furthermore, the management department can analyze users' interests on social media and manage relevant content. In this way, highly relevant content can be managed by analyzing users' social media activity. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input user social media activity data into a generating AI and have the generating AI identify highly relevant content.

[0046] The automation unit can record the history of content licensing in detail, ensuring the transparency of licensing. For example, the automation unit can record each step of content licensing with a timestamp to ensure the transparency of licensing. The automation unit can also record the ID of the agent that granted the license and the version of the algorithm used, clearly indicating the details of the licensing. Furthermore, the automation unit can record any errors or correction history that occurred during licensing to guarantee the integrity of licensing. This improves the reliability of the content by ensuring the transparency of licensing. Some or all of the above processing in the automation unit may be performed using AI, for example, or not using AI. For example, the automation unit can input the history data of content licensing into a generating AI and have the generating AI execute processing to ensure the transparency of licensing.

[0047] The automation unit can optimize the royalty calculation method and achieve accurate royalty management. For example, the automation unit can optimize the royalty calculation algorithm to achieve accurate royalty management. The automation unit can also accurately calculate royalties based on the generation history and licensing history of each agent. Furthermore, the automation unit can record errors and correction history that occur during royalty calculation to ensure the integrity of royalty management. This makes accurate royalty management possible by optimizing the royalty calculation method. Some or all of the above processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input royalty calculation data into a generation AI and have the generation AI perform the royalty calculation.

[0048] The automation unit can prioritize automating highly relevant content by considering the user's geographical location information during the automation process. For example, the automation unit can prioritize automating content related to the user's current location. The automation unit can also prioritize automating highly relevant content by referring to the user's past location information. Furthermore, the automation unit can prioritize automating region-specific content based on the user's location information. This allows for the priority of automating highly relevant content by considering the user's geographical location information. Some or all of the above-described processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the user's location information into a generating AI and have the generating AI identify highly relevant content.

[0049] The automation unit can analyze the user's social media activity and automate relevant content during the automation process. For example, the automation unit can analyze the user's social media posts and prioritize automating relevant content. The automation unit can also refer to the activities of the user's followers and friends and automate highly relevant content. Furthermore, the automation unit can analyze the user's interests on social media and automate relevant content. This allows for the automation of highly relevant content by analyzing the user's social media activity. Some or all of the above processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the user's social media activity data into a generating AI and have the generating AI identify highly relevant content.

[0050] The record-keeping unit can use blockchain technology to ensure the transparency of transaction history and improve the reliability of transactions. For example, the record-keeping unit can record the timestamp of each transaction on the blockchain to ensure the transparency of the transaction history. The record-keeping unit can also record the IDs of agents involved in the transaction and the details of the transaction on the blockchain to clarify the transaction details. Furthermore, the record-keeping unit can record errors and correction history that occurred during the transaction on the blockchain to guarantee the integrity of the transaction. In this way, the transparency and reliability of the transaction history are improved by using blockchain technology. Some or all of the above processing in the record-keeping unit may be performed using AI, for example, or not using AI. For example, the record-keeping unit can input transaction history data into a generating AI and have the generating AI perform processing to ensure the transparency of the transaction history.

[0051] The recording unit can record detailed information about the transaction history and ensure transaction transparency. For example, the recording unit can record detailed information about each transaction (transaction date and time, transaction agent ID, transaction details, etc.) to ensure transaction transparency. The recording unit can also record the IDs of agents involved in the transaction and the details of the transaction in detail, clearly indicating the transaction details. Furthermore, the recording unit can record errors and correction history that occurred during the transaction to guarantee the integrity of the transaction. As a result, by recording detailed information about the transaction history, transaction transparency is improved. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input detailed information about the transaction history into a generating AI and have the generating AI execute processing to ensure transaction transparency.

[0052] The recording unit can prioritize recording highly relevant transactions by considering the user's geographical location information during recording. For example, the recording unit can prioritize recording transactions related to the user's current location. The recording unit can also prioritize recording highly relevant transactions by referring to the user's past location information. Furthermore, the recording unit can prioritize recording region-specific transactions based on the user's location information. In this way, highly relevant transactions can be prioritized by considering the user's geographical location information. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's location information into a generating AI and have the generating AI identify highly relevant transactions.

[0053] The recording unit can analyze the user's social media activity and record relevant transactions at the time of recording. For example, the recording unit can analyze the content of the user's social media posts and prioritize recording relevant transactions. The recording unit can also refer to the activities of the user's followers and friends and record highly relevant transactions. Furthermore, the recording unit can analyze the user's interests on social media and record relevant transactions. In this way, highly relevant transactions can be recorded by analyzing the user's social media activity. Some or all of the above processing in the recording unit may be performed using AI, for example, or not using AI. For example, the recording unit can input the user's social media activity data into a generating AI and have the generating AI identify highly relevant transactions.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The registration unit can record the content generation process in detail, ensuring transparency of the generation process. For example, it can record each step of content generation with a timestamp to ensure transparency. It can also record the ID of the generation agent and the version of the algorithm used to clearly indicate the details of the generation process. Furthermore, it can record errors and correction history that occurred during the generation process to guarantee the integrity of the generation process. This ensures transparency in the generation process, thereby improving the reliability of the content.

[0056] The management department can track the generation history of each agent in detail and ensure transparency of the generation history. For example, the generation history of each agent can be recorded with a timestamp to ensure transparency. It is also possible to record the ID of the generating agent and the version of the algorithm used to clearly indicate the details of the generation history. Furthermore, errors and correction history that occurred during the generation history can be recorded to guarantee the integrity of the generation history. This ensures transparency in the generation history and improves the reliability of the content.

[0057] The automated unit can meticulously record the history of content licensing, ensuring transparency in licensing. For example, it can record each step of content licensing with a timestamp to ensure transparency. It can also record the ID of the agent that granted the license and the version of the algorithm used, clearly indicating the details of the license. Furthermore, it can record errors and correction history that occurred during licensing to guarantee the integrity of the license. This ensures transparency in licensing, thereby improving the reliability of the content.

[0058] The record-keeping unit can use blockchain technology to ensure the transparency of transaction history and improve the reliability of transactions. For example, the timestamp of each transaction can be recorded on the blockchain to ensure the transparency of the transaction history. It is also possible to record the IDs of agents involved in the transaction and the details of the transaction on the blockchain to clearly indicate the transaction details. Furthermore, errors and correction history that occurred during the transaction can be recorded on the blockchain to guarantee the integrity of the transaction. In this way, the transparency and reliability of transaction history are improved by using blockchain technology.

[0059] The automation unit can optimize royalty calculation methods and achieve accurate royalty management. For example, it can optimize the royalty calculation algorithm to ensure accurate royalty management. It can also accurately calculate royalties based on each agent's generation history and licensing history. Furthermore, it can record errors and correction histories that occur during royalty calculation to guarantee the integrity of royalty management. In this way, by optimizing the royalty calculation method, accurate royalty management becomes possible.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The registration unit assigns a unique ID to the generated content and registers the copyright. For example, it records detailed information about the generated content (generation date and time, generation agent ID, generation process, etc.). Specifically, it assigns the generation date and time and the generation agent ID to the text generated by the AI ​​agent and registers the copyright. Step 2: The management department manages the copyrights registered by the registration department. For example, it tracks the generation history and originality of each agent. Specifically, it manages the usage licenses for images generated by AI agents and automatically calculates the number of times they are used and the royalties. Step 3: The automation unit automates the management of content licenses and royalties based on copyrights managed by the management unit. For example, it automatically manages content licenses and royalties. Specifically, it manages the licenses for music generated by the AI ​​agent and automatically calculates the number of times it has been used and the royalties. Step 4: The recording unit transparently records the transaction history of content managed by the automation unit using blockchain technology. For example, the transaction history of content is transparently recorded using blockchain technology. Specifically, the transaction history of music generated by the AI ​​agent is recorded on the blockchain to ensure transaction transparency.

[0062] (Example of form 2) The content management system according to an embodiment of the present invention is a system that grants copyright to content generated by an AI agent and manages and trades that content. This content management system tracks the generation history and uniqueness of each agent and automates the management of content usage licenses and royalties. For example, the content management system assigns a unique ID to content generated by an AI agent and registers the copyright. At this time, it records detailed information about the generated content (generation date and time, generating agent ID, generation process, etc.). For example, for text generated by an AI agent, it assigns the generation date and time and the generating agent ID and registers the copyright. Next, the content management system builds a system for managing the registered copyrights. This system tracks the generation history and uniqueness of each agent and automates the management of content usage licenses and royalties. For example, it manages the usage licenses for images generated by an AI agent and automatically calculates the number of times they have been used and the royalties. Furthermore, the content management system provides a platform for trading the generated content. This platform uses blockchain technology to create transparent transaction records and track the transaction history of the content. For example, the transaction history of music generated by an AI agent can be recorded on the blockchain to ensure transaction transparency. This mechanism allows for robust copyright protection of content generated by AI agents and creates a transparent market. This enhances the value of the entire creative industry and protects originality and ethics. For instance, it becomes easier for companies and creators using generation AI to prove the originality and value of their creations, reducing the risk of copyright infringement and plagiarism. This allows content management systems to grant copyright to content generated by AI agents and manage and trade it.

[0063] The content management system according to this embodiment comprises a registration unit, a management unit, an automation unit, and a recording unit. The registration unit assigns a unique ID to the generated content and registers the copyright. The registration unit records detailed information about the generated content (such as the date and time of generation, the ID of the generation agent, and the generation process). For example, the registration unit assigns the date and time of generation and the ID of the generation agent to text generated by an AI agent and registers the copyright. The management unit manages the copyrights registered by the registration unit. The management unit tracks the generation history and uniqueness of each agent. For example, the management unit manages the usage licenses for images generated by the AI ​​agent and automatically calculates the number of times they have been used and the royalties. The automation unit automates the management of content usage licenses and royalties based on the copyrights managed by the management unit. The automation unit automatically manages content usage licenses and royalties. For example, the automation unit manages the usage licenses for music generated by an AI agent and automatically calculates the number of times it has been used and the royalties. The recording unit transparently records the transaction history of content managed by the automation unit using blockchain technology. For example, the recording unit transparently records the transaction history of content using blockchain technology. For example, the recording unit records the transaction history of music generated by an AI agent on the blockchain to ensure transaction transparency. As a result, the content management system according to the embodiment can grant copyright to generated content and manage and trade it.

[0064] The registration unit assigns a unique ID to the generated content and registers the copyright. Specifically, it records detailed information about the generated content (generation date and time, generation agent ID, generation process, etc.). For example, it assigns the generation date and time and generation agent ID to text generated by an AI agent and registers the copyright. This clarifies the origin and generation process of the generated content, strengthening copyright protection. Furthermore, by recording detailed metadata of the content, the registration unit facilitates later management and searching. For example, it can also record information such as the genre, theme, and technologies and tools used for the generated content. This allows for management tailored to the characteristics and purpose of use of the content. The registration unit also has a function to record quality evaluations and feedback on the generated content, which can be used to evaluate and improve the performance of the generation agent. For example, it can collect evaluations and comments from users and use them as training data for the generation agent. In this way, the registration unit can centrally manage detailed information about the generated content, contributing to copyright protection and content quality improvement.

[0065] The Management Department manages copyrights registered by the Registration Department. Specifically, it tracks the generation history and originality of each agent. For example, it manages the licensing of images generated by AI agents and automatically calculates the number of uses and royalties. The Management Department can monitor the usage of generated content in real time and take measures to reduce the risk of copyright infringement. For example, if content is used without permission, it can immediately issue a warning and take legal action if necessary. The Management Department also manages contract information and conditions regarding content licensing in detail to ensure that appropriate licensing is performed. This promotes the proper use of content and protects the interests of copyright holders. Furthermore, the Management Department can analyze the performance data of generation agents to optimize and improve the generation process. For example, it can evaluate the generation speed and quality of generation agents and make adjustments and improvements as needed. This allows the Management Department to properly manage the copyright of generated content, understand how content is being used, and protect the interests of copyright holders.

[0066] The Automation Department automates the management of content licenses and royalties based on copyrights managed by the Management Department. Specifically, it automatically manages content licenses and royalties. For example, it manages licenses for music generated by an AI agent and automatically calculates the number of uses and royalties. The Automation Department can quickly and accurately grant content licenses based on pre-set rules and conditions. For example, it can automatically issue licenses to users who meet specific conditions, calculate royalties according to usage, and pay them to copyright holders. This streamlines the content licensing process and ensures that copyright holders' profits are reflected quickly. The Automation Department also ensures transparency by meticulously recording the history of licenses and royalty payments. This allows copyright holders and users to easily check content usage and royalty payment status. Furthermore, the Automation Department utilizes AI to optimize the licensing and royalty management process, achieving efficient operation. For example, it can analyze past data and trends in license issuance and royalty calculation to set optimal rules and conditions. This allows the automated unit to efficiently and accurately manage content licensing and royalties, maximizing the interests of copyright holders.

[0067] The Records Unit transparently records the transaction history of content managed by the Automation Unit using blockchain technology. Specifically, it transparently records the transaction history of content using blockchain technology. For example, it records the transaction history of music generated by an AI agent on the blockchain to ensure transaction transparency. By utilizing blockchain technology, the Records Unit can prevent tampering and fraud in transaction history and provide highly reliable records. This allows copyright holders and users to confidently check the transaction history. In addition, the Records Unit can record detailed information of the transaction history, which can be used to resolve future troubles and disputes. For example, it can record detailed information such as the date and time of the transaction, the trading partner, the transaction content, and the payment status, and submit it as evidence if necessary. Furthermore, the Records Unit is equipped with transaction history analysis and reporting functions, allowing for the understanding of transaction trends and patterns. This allows copyright holders and administrators to understand the status of transactions and take appropriate measures. For example, if there is a sudden surge in transactions of a particular piece of content, the cause can be analyzed and appropriate action can be taken. This allows the recording unit to record the transaction history of content in a transparent and reliable manner, protecting the interests of copyright holders and users.

[0068] The registration unit can record detailed information about the generated content. For example, the registration unit records detailed information about the generated content (such as the date and time of generation, the ID of the generation agent, and the generation process). For instance, the registration unit assigns the date and time of generation and the ID of the generation agent to text generated by an AI agent and registers the copyright. This ensures transparency of the content by recording detailed information about the generated content.

[0069] The management department can track the generation history and uniqueness of each agent. For example, the management department can track the generation history and uniqueness of each agent. For example, the management department can manage the usage licenses for images generated by AI agents and automatically calculate the number of times they are used and the royalties. This streamlines content management by tracking the generation history and uniqueness of each agent.

[0070] The automation unit can automatically manage content licenses and royalties. For example, the automation unit can automatically manage content licenses and royalties. For instance, the automation unit manages the licenses for music generated by an AI agent and automatically calculates the number of uses and royalties. This improves management efficiency by automating the management of content licenses and royalties.

[0071] The recording unit can transparently record the transaction history of content using blockchain technology. For example, the recording unit can transparently record the transaction history of content using blockchain technology. For example, the recording unit can record the transaction history of music generated by an AI agent on the blockchain, ensuring the transparency of transactions. In this way, transparency of transaction history is ensured by using blockchain technology.

[0072] The registration unit can estimate the user's emotions and adjust the timing of content registration based on those emotions. For example, if the user is stressed, the registration unit can simplify the registration process and allow for faster registration. If the user is relaxed, the registration unit can also provide detailed registration options and suggest a customizable registration method. Furthermore, if the user is in a hurry, the registration unit can prioritize voice input and allow for faster content registration. This allows for content registration at a more appropriate time by adjusting the timing of content registration according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The registration unit can record the content generation process in detail, ensuring transparency of the generation process. For example, the registration unit can record each step of content generation with a timestamp, ensuring transparency of the generation process. The registration unit can also record the ID of the generation agent and the version of the algorithm used, clearly indicating the details of the generation process. Furthermore, the registration unit can record errors and correction history that occurred during the generation process, guaranteeing the integrity of the generation process. This ensures transparency in the generation process, thereby improving the reliability of the content.

[0074] The registration unit can evaluate the originality of content and preferentially assign IDs to highly original content. For example, the registration unit can analyze the similarity of content and preferentially assign IDs to highly original content. The registration unit can also identify highly original content by referring to the past generation history of the generation agent. Furthermore, the registration unit can use algorithms to evaluate the originality of content and preferentially assign IDs to highly original content. This allows for an increase in the value of content by preferentially assigning IDs to highly original content.

[0075] The registration unit can estimate the user's emotions and determine the priority of content to register based on the estimated emotions. For example, if the user is excited, the registration unit will prioritize registering important content. If the user is relaxed, the registration unit can also register content with normal priority. Furthermore, if the user is stressed, the registration unit can prioritize registering urgent content. This allows for the priority registration of important content by determining content priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The registration unit can prioritize registering highly relevant content by considering the user's geographical location information when registering content. For example, the registration unit can prioritize registering content related to the user's current location. The registration unit can also prioritize registering highly relevant content by referring to the user's past location information. Furthermore, the registration unit can prioritize registering region-specific content based on the user's location information. In this way, highly relevant content can be prioritized by considering the user's geographical location information. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the user's location information into a generating AI and have the generating AI identify highly relevant content.

[0077] The registration unit can analyze a user's social media activity and register relevant content when registering content. For example, the registration unit can analyze a user's social media posts and prioritize registering relevant content. The registration unit can also refer to the activities of the user's followers and friends and register highly relevant content. Furthermore, the registration unit can analyze a user's interests on social media and register relevant content. In this way, highly relevant content can be registered by analyzing the user's social media activity. Some or all of the above processing in the registration unit may be performed using AI, for example, or not using AI. For example, the registration unit can input the user's social media activity data into a generating AI and have the generating AI identify highly relevant content.

[0078] The management unit can estimate the user's emotions and adjust management methods based on those estimates. For example, if the user is stressed, the management unit can simplify the management process and enable faster management. If the user is relaxed, the management unit can also provide detailed management options and suggest customizable management methods. Furthermore, if the user is in a hurry, the management unit can prioritize voice input and enable faster management. This allows for more appropriate management by adjusting management methods according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The management department can track the generation history of each agent in detail and ensure transparency of the generation history. For example, the management department can record the generation history of each agent with a timestamp to ensure transparency. The management department can also record the ID of the generating agent and the version of the algorithm used to clearly indicate the details of the generation history. Furthermore, the management department can record any errors or corrections that occurred during the generation history to guarantee the integrity of the generation history. This improves the reliability of the content by ensuring transparency of the generation history. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the generation history data of each agent into a generation AI and have the generation AI perform processes to ensure transparency of the generation history.

[0080] The management department can evaluate the originality of content and apply special management methods to highly original content. For example, the management department can analyze the similarity of content and apply special management methods to highly original content. The management department can also identify highly original content by referring to the past generation history of the generation agent. Furthermore, the management department can use algorithms to evaluate the originality of content and apply special management methods to highly original content. This allows the value of content to be increased by applying special management methods to highly original content. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input content originality evaluation data into a generation AI and have the generation AI execute the application of special management methods.

[0081] The management unit can estimate the user's emotions and determine the priority of content to manage based on the estimated emotions. For example, if the user is excited, the management unit will prioritize important content. If the user is relaxed, the management unit can prioritize content with normal priority. Furthermore, if the user is stressed, the management unit can prioritize urgent content. This allows for the prioritization of important content by determining content priority according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The management unit can prioritize the management of highly relevant content by considering the user's geographical location information during management. For example, the management unit can prioritize content related to the user's current location. The management unit can also prioritize highly relevant content by referring to the user's past location information. Furthermore, the management unit can prioritize region-specific content based on the user's location information. In this way, highly relevant content can be prioritized by considering the user's geographical location information. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's location information into a generating AI and have the generating AI identify highly relevant content.

[0083] The management department can analyze users' social media activity and manage relevant content during management. For example, the management department can analyze the content of users' social media posts and prioritize the management of relevant content. The management department can also refer to the activities of users' followers and friends and manage highly relevant content. Furthermore, the management department can analyze users' interests on social media and manage relevant content. In this way, highly relevant content can be managed by analyzing users' social media activity. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input user social media activity data into a generating AI and have the generating AI identify highly relevant content.

[0084] The automation unit can estimate the user's emotions and adjust the automation method based on the estimated emotions. For example, if the user is stressed, the automation unit can simplify the automation process and enable faster automation. If the user is relaxed, the automation unit can also provide detailed automation options and suggest a customizable automation method. Furthermore, if the user is in a hurry, the automation unit can prioritize voice input and enable faster automation. This allows for more appropriate automation by adjusting the automation method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The automation unit can record the history of content licensing in detail, ensuring the transparency of licensing. For example, the automation unit can record each step of content licensing with a timestamp to ensure the transparency of licensing. The automation unit can also record the ID of the agent that granted the license and the version of the algorithm used, clearly indicating the details of the licensing. Furthermore, the automation unit can record any errors or correction history that occurred during licensing to guarantee the integrity of licensing. This improves the reliability of the content by ensuring the transparency of licensing. Some or all of the above processing in the automation unit may be performed using AI, for example, or not using AI. For example, the automation unit can input the history data of content licensing into a generating AI and have the generating AI execute processing to ensure the transparency of licensing.

[0086] The automation unit can optimize the royalty calculation method and achieve accurate royalty management. For example, the automation unit can optimize the royalty calculation algorithm to achieve accurate royalty management. The automation unit can also accurately calculate royalties based on the generation history and licensing history of each agent. Furthermore, the automation unit can record errors and correction history that occur during royalty calculation to ensure the integrity of royalty management. This makes accurate royalty management possible by optimizing the royalty calculation method. Some or all of the above processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input royalty calculation data into a generation AI and have the generation AI perform the royalty calculation.

[0087] The automation unit can estimate the user's emotions and determine the priority of content to automate based on the estimated emotions. For example, if the user is excited, the automation unit will prioritize automating important content. If the user is relaxed, the automation unit can also automate content with normal priority. Furthermore, if the user is stressed, the automation unit can prioritize automating urgent content. This allows for the priority of automating important content by determining content priority according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The automation unit can prioritize automating highly relevant content by considering the user's geographical location information during the automation process. For example, the automation unit can prioritize automating content related to the user's current location. The automation unit can also prioritize automating highly relevant content by referring to the user's past location information. Furthermore, the automation unit can prioritize automating region-specific content based on the user's location information. This allows for the priority of automating highly relevant content by considering the user's geographical location information. Some or all of the above-described processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the user's location information into a generating AI and have the generating AI identify highly relevant content.

[0089] The automation unit can analyze the user's social media activity and automate relevant content during the automation process. For example, the automation unit can analyze the user's social media posts and prioritize automating relevant content. The automation unit can also refer to the activities of the user's followers and friends and automate highly relevant content. Furthermore, the automation unit can analyze the user's interests on social media and automate relevant content. This allows for the automation of highly relevant content by analyzing the user's social media activity. Some or all of the above processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the user's social media activity data into a generating AI and have the generating AI identify highly relevant content.

[0090] The recording unit can estimate the user's emotions and adjust the recording method based on the estimated emotions. For example, if the user is stressed, the recording unit can simplify the recording process and enable faster recording. If the user is relaxed, the recording unit can also provide detailed recording options and suggest a customizable recording method. Furthermore, if the user is in a hurry, the recording unit can prioritize voice input and enable faster recording. This allows for more appropriate recording by adjusting the recording method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The record-keeping unit can use blockchain technology to ensure the transparency of transaction history and improve the reliability of transactions. For example, the record-keeping unit can record the timestamp of each transaction on the blockchain to ensure the transparency of the transaction history. The record-keeping unit can also record the IDs of agents involved in the transaction and the details of the transaction on the blockchain to clarify the transaction details. Furthermore, the record-keeping unit can record errors and correction history that occurred during the transaction on the blockchain to guarantee the integrity of the transaction. In this way, the transparency and reliability of the transaction history are improved by using blockchain technology. Some or all of the above processing in the record-keeping unit may be performed using AI, for example, or not using AI. For example, the record-keeping unit can input transaction history data into a generating AI and have the generating AI perform processing to ensure the transparency of the transaction history.

[0092] The recording unit can record detailed information about the transaction history and ensure transaction transparency. For example, the recording unit can record detailed information about each transaction (transaction date and time, transaction agent ID, transaction details, etc.) to ensure transaction transparency. The recording unit can also record the IDs of agents involved in the transaction and the details of the transaction in detail, clearly indicating the transaction details. Furthermore, the recording unit can record errors and correction history that occurred during the transaction to guarantee the integrity of the transaction. As a result, by recording detailed information about the transaction history, transaction transparency is improved. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input detailed information about the transaction history into a generating AI and have the generating AI execute processing to ensure transaction transparency.

[0093] The recording unit can estimate the user's emotions and determine the priority of transactions to record based on the estimated emotions. For example, if the user is excited, the recording unit will prioritize recording important transactions. If the user is relaxed, the recording unit can also record transactions with normal priority. Furthermore, if the user is stressed, the recording unit can prioritize recording urgent transactions. This allows for the priority recording of important transactions by determining transaction priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The recording unit can prioritize recording highly relevant transactions by considering the user's geographical location information during recording. For example, the recording unit can prioritize recording transactions related to the user's current location. The recording unit can also prioritize recording highly relevant transactions by referring to the user's past location information. Furthermore, the recording unit can prioritize recording region-specific transactions based on the user's location information. In this way, highly relevant transactions can be prioritized by considering the user's geographical location information. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's location information into a generating AI and have the generating AI identify highly relevant transactions.

[0095] The recording unit can analyze the user's social media activity and record relevant transactions at the time of recording. For example, the recording unit can analyze the content of the user's social media posts and prioritize recording relevant transactions. The recording unit can also refer to the activities of the user's followers and friends and record highly relevant transactions. Furthermore, the recording unit can analyze the user's interests on social media and record relevant transactions. In this way, highly relevant transactions can be recorded by analyzing the user's social media activity. Some or all of the above processing in the recording unit may be performed using AI, for example, or not using AI. For example, the recording unit can input the user's social media activity data into a generating AI and have the generating AI identify highly relevant transactions.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The registration unit can estimate the user's emotions and adjust the timing of content registration based on those emotions. For example, if the user is stressed, the registration process can be simplified to allow for quicker registration. If the user is relaxed, detailed registration options can be provided, and a customizable registration method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow for quicker content registration. This allows for content registration at a more appropriate time by adjusting the timing of content registration according to the user's emotions.

[0098] The management department can estimate the user's emotions and adjust management methods based on those estimates. For example, if the user is stressed, the management process can be simplified to allow for quicker management. If the user is relaxed, detailed management options can be provided, and customizable management methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow for quicker management. This allows for more appropriate management by adjusting management methods according to the user's emotions.

[0099] The automation unit can estimate the user's emotions and adjust the automation method based on those emotions. For example, if the user is stressed, the automation process can be simplified to allow for faster automation. If the user is relaxed, it can offer detailed automation options and suggest a customizable automation method. Furthermore, if the user is in a hurry, voice input can be prioritized to allow for faster automation. This allows for more appropriate automation by adjusting the automation method according to the user's emotions.

[0100] The recording unit can estimate the user's emotions and adjust the recording method based on those emotions. For example, if the user is stressed, the recording process can be simplified to allow for faster recording. If the user is relaxed, detailed recording options can be provided, and a customizable recording method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow for faster recording. This allows for more appropriate recording by adjusting the recording method according to the user's emotions.

[0101] The registration unit can estimate the user's emotions and determine the priority of content to register based on those emotions. For example, if the user is excited, important content will be registered first. If the user is relaxed, content can be registered with normal priority. Furthermore, if the user is stressed, urgent content can be registered first. In this way, by determining content priority according to the user's emotions, important content can be registered first.

[0102] The registration unit can record the content generation process in detail, ensuring transparency of the generation process. For example, it can record each step of content generation with a timestamp to ensure transparency. It can also record the ID of the generation agent and the version of the algorithm used to clearly indicate the details of the generation process. Furthermore, it can record errors and correction history that occurred during the generation process to guarantee the integrity of the generation process. This ensures transparency in the generation process, thereby improving the reliability of the content.

[0103] The management department can track the generation history of each agent in detail and ensure transparency of the generation history. For example, the generation history of each agent can be recorded with a timestamp to ensure transparency. It is also possible to record the ID of the generating agent and the version of the algorithm used to clearly indicate the details of the generation history. Furthermore, errors and correction history that occurred during the generation history can be recorded to guarantee the integrity of the generation history. This ensures transparency in the generation history and improves the reliability of the content.

[0104] The automated unit can meticulously record the history of content licensing, ensuring transparency in licensing. For example, it can record each step of content licensing with a timestamp to ensure transparency. It can also record the ID of the agent that granted the license and the version of the algorithm used, clearly indicating the details of the license. Furthermore, it can record errors and correction history that occurred during licensing to guarantee the integrity of the license. This ensures transparency in licensing, thereby improving the reliability of the content.

[0105] The record-keeping unit can use blockchain technology to ensure the transparency of transaction history and improve the reliability of transactions. For example, the timestamp of each transaction can be recorded on the blockchain to ensure the transparency of the transaction history. It is also possible to record the IDs of agents involved in the transaction and the details of the transaction on the blockchain to clearly indicate the transaction details. Furthermore, errors and correction history that occurred during the transaction can be recorded on the blockchain to guarantee the integrity of the transaction. In this way, the transparency and reliability of transaction history are improved by using blockchain technology.

[0106] The automation unit can optimize royalty calculation methods and achieve accurate royalty management. For example, it can optimize the royalty calculation algorithm to ensure accurate royalty management. It can also accurately calculate royalties based on each agent's generation history and licensing history. Furthermore, it can record errors and correction histories that occur during royalty calculation to guarantee the integrity of royalty management. In this way, by optimizing the royalty calculation method, accurate royalty management becomes possible.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The registration unit assigns a unique ID to the generated content and registers the copyright. For example, it records detailed information about the generated content (generation date and time, generation agent ID, generation process, etc.). Specifically, it assigns the generation date and time and the generation agent ID to the text generated by the AI ​​agent and registers the copyright. Step 2: The management department manages the copyrights registered by the registration department. For example, it tracks the generation history and originality of each agent. Specifically, it manages the usage licenses for images generated by AI agents and automatically calculates the number of times they are used and the royalties. Step 3: The automation unit automates the management of content licenses and royalties based on copyrights managed by the management unit. For example, it automatically manages content licenses and royalties. Specifically, it manages the licenses for music generated by the AI ​​agent and automatically calculates the number of times it has been used and the royalties. Step 4: The recording unit transparently records the transaction history of content managed by the automation unit using blockchain technology. For example, the transaction history of content is transparently recorded using blockchain technology. Specifically, the transaction history of music generated by the AI ​​agent is recorded on the blockchain to ensure transaction transparency.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the registration unit, management unit, automation unit, and recording unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart device 14, which assigns a unique ID to the generated content and registers the copyright. The management unit is implemented by the identification processing unit 290 of the data processing unit 12, which tracks the generation history and uniqueness of each agent. The automation unit is implemented by the identification processing unit 290 of the data processing unit 12, which automates the management of content usage licenses and royalties. The recording unit is implemented by the control unit 46A of the smart device 14, which transparently records the transaction history of content using blockchain technology. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the registration unit, management unit, automation unit, and recording unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart glasses 214, which assigns a unique ID to the generated content and registers the copyright. The management unit is implemented by the identification processing unit 290 of the data processing unit 12, which tracks the generation history and uniqueness of each agent. The automation unit is implemented by the identification processing unit 290 of the data processing unit 12, which automates the management of content usage licenses and royalties. The recording unit is implemented by the control unit 46A of the smart glasses 214, which transparently records the transaction history of content using blockchain technology. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the registration unit, management unit, automation unit, and recording unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the headset terminal 314, which assigns a unique ID to the generated content and registers the copyright. The management unit is implemented by the identification processing unit 290 of the data processing unit 12, which tracks the generation history and uniqueness of each agent. The automation unit is implemented by the identification processing unit 290 of the data processing unit 12, which automates the management of content usage licenses and royalties. The recording unit is implemented by the control unit 46A of the headset terminal 314, which transparently records the transaction history of content using blockchain technology. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the registration unit, management unit, automation unit, and recording unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the robot 414, which assigns a unique ID to the generated content and registers the copyright. The management unit is implemented by the identification processing unit 290 of the data processing unit 12, which tracks the generation history and uniqueness of each agent. The automation unit is implemented by the identification processing unit 290 of the data processing unit 12, which automates the management of content usage licenses and royalties. The recording unit is implemented by the control unit 46A of the robot 414, which transparently records the transaction history of content using blockchain technology. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) The generated content is assigned a unique ID and the copyright is registered in the registration section, The management department manages the copyrights registered by the aforementioned registration department, An automation unit that automates content usage licenses and royalty management based on copyrights managed by the aforementioned management unit, The system includes a recording unit that transparently records the transaction history of content managed by the aforementioned automation unit using blockchain technology. A system characterized by the following features. (Note 2) The aforementioned registration unit is Record detailed information about the generated content. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned management department, Track the generation history and uniqueness of each agent. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned automation unit, Automate content licensing and royalty management. The system described in Appendix 1, characterized by the features described herein. (Note 5) The recording unit is, Using blockchain technology to transparently record the transaction history of content. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned registration unit is It estimates user sentiment and adjusts the timing of content publication based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned registration unit is The content creation process is documented in detail to ensure transparency in the creation process. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned registration unit is The system evaluates the originality of content and prioritizes assigning IDs to highly original content. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned registration unit is It estimates user sentiment and determines the priority of content to register based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned registration unit is When registering content, the system prioritizes registering highly relevant content by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned registration unit is When registering content, the system analyzes the user's social media activity and registers relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned management department, We estimate the user's emotions and adjust management methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned management department, Detailed tracking of each agent's generation history ensures transparency in the generation process. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned management department, We evaluate the originality of the content and apply special management methods to highly original content. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned management department, It estimates user sentiment and determines the priority of content to manage based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned management department, During management, the system prioritizes the management of highly relevant content, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned management department, During management, analyze users' social media activity and manage relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned automation unit, It estimates the user's emotions and adjusts the automation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned automation unit, We will keep a detailed record of content usage license history and ensure transparency in licensing. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned automation unit, Optimize royalty calculation methods to achieve accurate royalty management. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned automation unit, It estimates user sentiment and prioritizes content to be automated based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned automation unit, During automation, the system prioritizes automating the creation of highly relevant content by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned automation unit, During automation, the system analyzes users' social media activity and automates the creation of relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 24) The recording unit is, The system estimates the user's emotions and adjusts the recording method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The recording unit is, Blockchain technology is used to ensure transparency in transaction history and improve the reliability of transactions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The recording unit is, Record detailed transaction history information to ensure transaction transparency. The system described in Appendix 1, characterized by the features described herein. (Note 27) The recording unit is, It estimates the user's emotions and determines the priority of transactions to record based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The recording unit is, When recording transactions, the system prioritizes recording those that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The recording unit is, During recording, the system analyzes the user's social media activity and records related transactions. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The generated content is assigned a unique ID and the copyright is registered in the registration section, The management department manages the copyrights registered by the aforementioned registration department, An automation unit that automates content usage licenses and royalty management based on copyrights managed by the aforementioned management unit, The system includes a recording unit that transparently records the transaction history of content managed by the aforementioned automation unit using blockchain technology. A system characterized by the following features.

2. The aforementioned registration unit is Record detailed information about the generated content. The system according to feature 1.

3. The aforementioned management department, Track the generation history and uniqueness of each agent. The system according to feature 1.

4. The aforementioned automation unit, Automate content licensing and royalty management. The system according to feature 1.

5. The recording unit is, Using blockchain technology to transparently record the transaction history of content. The system according to feature 1.

6. The aforementioned registration unit is It estimates user sentiment and adjusts the timing of content publication based on the estimated user sentiment. The system according to feature 1.

7. The aforementioned registration unit is The content creation process is documented in detail to ensure transparency in the creation process. The system according to feature 1.

8. The aforementioned registration unit is The system evaluates the originality of content and prioritizes assigning IDs to highly original content. The system according to feature 1.

9. The aforementioned registration unit is It estimates user sentiment and determines the priority of content to register based on the estimated user sentiment. The system according to feature 1.

10. The aforementioned registration unit is When registering content, the system prioritizes registering highly relevant content by considering the user's geographical location. The system according to feature 1.

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